A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth
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paper_title: Comparison of Four Ground-Level PM2.5 Estimation Models Using PARASOL Aerosol Optical Depth Data from China

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Satellite remote sensing is of considerable importance for estimating ground-level PM2.5 concentrations to support environmental agencies monitoring air quality. However, most current studies have focused mainly on the application of MODIS aerosol optical depth (AOD) to predict PM2.5 concentrations, while PARASOL AOD, which is sensitive to fine-mode aerosols over land surfaces, has received little attention. In this study, we compared a linear regression model, a quadratic regression model, a power regression model and a logarithmic regression model, which were developed using PARASOL level 2 AOD collected in China from 18 January 2013 to 10 October 2013. We obtained R (correlation coefficient) values of 0.64, 0.63, 0.62, and 0.57 for the four models when they were cross validated with the observed values. Furthermore, after all the data were classified into six levels according to the Air Quality Index (AQI), a low level of statistical significance between the four empirical models was found when the ground-level PM2.5 concentrations were greater than 75 μg/m3. The maximum R value was 0.44 (for the logarithmic regression model and the power model), and the minimum R value was 0.28 (for the logarithmic regression model and the power model) when the PM2.5 concentrations were less than 75 μg/m3. We also discussed uncertainty sources and possible improvements.
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paper_title: Using High-Resolution Satellite Aerosol Optical Depth To Estimate Daily PM2.5 Geographical Distribution in Mexico City.

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Recent advances in estimating fine particle (PM2.5) ambient concentrations use daily satellite measurements of aerosol optical depth (AOD) for spatially and temporally resolved exposure estimates. Mexico City is a dense megacity that differs from other previously modeled regions in several ways: it has bright land surfaces, a distinctive climatological cycle, and an elevated semi-enclosed air basin with a unique planetary boundary layer dynamic. We extend our previous satellite methodology to the Mexico City area, a region with higher PM2.5 than most U.S. and European urban areas. Using a novel 1 km resolution AOD product from the MODIS instrument, we constructed daily predictions across the greater Mexico City area for 2004-2014. We calibrated the association of AOD to PM2.5 daily using municipal ground monitors, land use, and meteorological features. Predictions used spatial and temporal smoothing to estimate AOD when satellite data were missing. Our model performed well, resulting in an out-of-sample cross-validation R(2) of 0.724. Cross-validated root-mean-squared prediction error (RMSPE) of the model was 5.55 μg/m(3). This novel model reconstructs long- and short-term spatially resolved exposure to PM2.5 for epidemiological studies in Mexico City.
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paper_title: Long-Term Fine Particulate Matter Exposure and Mortality From Diabetes in Canada

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OBJECTIVE Recent studies suggest that chronic exposure to air pollution can promote the development of diabetes. However, whether this relationship actually translates into an increased risk of mortality attributable to diabetes is uncertain. RESEARCH DESIGN AND METHODS We evaluated the association between long-term exposure to ambient fine particulate matter (PM 2.5 ) and diabetes-related mortality in a prospective cohort analysis of 2.1 million adults from the 1991 Canadian census mortality follow-up study. Mortality information, including ∼5,200 deaths coded as diabetes being the underlying cause, was ascertained by linkage to the Canadian Mortality Database from 1991 to 2001. Subject-level estimates of long-term exposure to PM 2.5 were derived from satellite observations. The hazard ratios (HRs) for diabetes-related mortality were related to PM 2.5 and adjusted for individual-level and contextual variables using Cox proportional hazards survival models. RESULTS Mean PM 2.5 exposure levels for the entire population were low (8.7 µg/m 3 ; SD, 3.9 µg/m 3 ; interquartile range, 6.2 µg/m 3 ). In fully adjusted models, a 10-µg/m 3 elevation in PM 2.5 exposure was associated with an increase in risk for diabetes-related mortality (HR, 1.49; 95% CI, 1.37–1.62). The monotonic change in risk to the population persisted to PM 2.5 concentration 3 . CONCLUSIONS Long-term exposure to PM 2.5 , even at low levels, is related to an increased risk of mortality attributable to diabetes. These findings have considerable public health importance given the billions of people exposed to air pollution and the worldwide growing epidemic of diabetes.
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paper_title: A New Hybrid Spatio-Temporal Model For Estimating Daily Multi-Year PM2.5 Concentrations Across Northeastern USA Using High Resolution Aerosol Optical Depth Data.

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BACKGROUND ::: The use of satellite-based aerosol optical depth (AOD) to estimate fine particulate matter (PM2.5) for epidemiology studies has increased substantially over the past few years. These recent studies often report moderate predictive power, which can generate downward bias in effect estimates. In addition, AOD measurements have only moderate spatial resolution, and have substantial missing data. ::: ::: ::: METHODS ::: We make use of recent advances in MODIS satellite data processing algorithms (Multi-Angle Implementation of Atmospheric Correction (MAIAC), which allow us to use 1 km (versus currently available 10 km) resolution AOD data. We developed and cross validated models to predict daily PM2.5 at a 1×1km resolution across the northeastern USA (New England, New York and New Jersey) for the years 2003-2011, allowing us to better differentiate daily and long term exposure between urban, suburban, and rural areas. Additionally, we developed an approach that allows us to generate daily high-resolution 200 m localized predictions representing deviations from the area 1×1 km grid predictions. We used mixed models regressing PM2.5 measurements against day-specific random intercepts, and fixed and random AOD and temperature slopes. We then use generalized additive mixed models with spatial smoothing to generate grid cell predictions when AOD was missing. Finally, to get 200 m localized predictions, we regressed the residuals from the final model for each monitor against the local spatial and temporal variables at each monitoring site. ::: ::: ::: RESULTS ::: Our model performance was excellent (mean out-of-sample R2=0.88). The spatial and temporal components of the out-of-sample results also presented very good fits to the withheld data (R2=0.87, R2=0.87). In addition, our results revealed very little bias in the predicted concentrations (Slope of predictions versus withheld observations = 0.99). ::: ::: ::: CONCLUSION ::: Our daily model results show high predictive accuracy at high spatial resolutions and will be useful in reconstructing exposure histories for epidemiological studies across this region.
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paper_title: Satellite-derived high resolution PM2.5 concentrations in Yangtze River Delta Region of China using improved linear mixed effects model

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Abstract Satellite remotely sensed aerosol optical depth (AOD) provides an effective way to fill the spatial and temporal gaps left by ground PM2.5 monitoring network. Previous studies have established robust advanced statistical models to estimate PM2.5 using AOD data in China. However, their coarse resolutions (∼10 km or greater) of PM2.5 estimations are not enough to support the health effect studies at urban scales. In this study, 3 km AOD data from Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 products were used to estimate the high resolution PM2.5 concentrations in Yangtze Delta Region of China. We proposed a nested linear mixed effects (LME) model including nested month-, week-, and day-specific random effects of PM2.5-AOD relationships. Validation results show that the LME model only with day-specific random effects (non-nested model) used in previous studies has poor performance in the days without PM2.5-AOD matchups (the R2 of day-of-year-based cross validation (DOY-based CV) is 0.148). The results also show that our nested model cannot improve the performance of non-nested model in the days with PM2.5-AOD matchups (sample-based CV R2 = 0.671 for nested model vs. 0.661 for non-nested model), but can greatly improve the model performance beyond those days (DOY-based CV R2 = 0.339 for nested model vs. 0.148 for non-nested model). To further improve the model performance, we applied the “buffer models” (i.e., models fitted from datasets which ground PM2.5 were matched with the average AOD values within certain radius buffer zones of gridded PM2.5 data) on the 3 km AOD data since the “buffer models” has more days with PM2.5-AOD matchups and can provide more day-specific relationships. The results of this study show that 3 km MODIS C6 AOD data can be used to estimate PM2.5 concentrations and can provide more detailed spatial information for urban scale studies. The application of our nested LME model can greatly improve the accuracy of 3 km PM2.5 predictions.
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paper_title: Impacts of elevated-aerosol-layer and aerosol type on the correlation of AOD and particulate matter with ground-based and satellite measurements in Nanjing, southeast China.

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Assessment of the correlation between aerosol optical depth (AOD) and particulate matter (PM) is critical to satellite remote sensing of air quality, e.g. ground PM10 and ground PM2.5. This study evaluates the impacts of aloft-aerosol-plume and aerosol-type on the correlation of AOD-PM by using synergistic measurement of a polarization-sensitive Raman-Mie lidar, CIMEL sunphotometer (SP) and TEOM PM samplers, as well as the satellite MODIS and CALIPSO, during April to July 2011 in Nanjing city (32.05(○)N/118.77(○)E), southeast China. Aloft-aerosol-layer and aerosol types (e.g. dust and non-dust or urban aerosol) are identified with the range-resolved polarization lidar and SP measurements. The results indicate that the correlations for AOD-PM10 and AOD-PM2.5 can be much improved when screening out the aloft-aerosol-layer. The linear regression slopes show significant differences for the dust and non-dust dominant aerosols in the planetary boundary layer (PBL). In addition, we evaluate the recent released MODIS-AOD product (Collection 6) from the "dark-target" (DT) and "deep-blue" (DB) algorithms and their correlation with the PM in Nanjing urban area. The results verify that the MODIS-DT AODs show a good correlation (R = 0.89) with the SP-AOD but with a systematic overestimate. In contrast, the MODIS-DB AOD shows a moderate correlation (R = 0.66) with the SP-AOD but with a smaller regression intercept (0.07). Furthermore, the moderately high correlations between the MODIS-AOD and PM10 (PM2.5) are indicated, which suggests the feasibility of PM estimate using the MODIS-AOD in Nanjing city.
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paper_title: Monitoring of ambient fine particulate matter concentrations from space: application to European and African cities

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Air pollution is a major issue for global environment as well as human health and well-being. Recently, satellites ::: which are equipped with relevant air quality instruments have been placed into orbit. In this paper, we first ::: present a review on satellite remote sensing of particulate pollution. We then present new results for Europe and ::: on African cities particulate air pollutants using POLDER satellite data. Based on satellite AOD observations, ::: we show that the number of days exceeding the 15.4 μg/m 3 threshold is twice frequent in Ouagadougou, Burkina- ::: Faso than in Paris, France. At the regional scale, we observe that the northern coast of the golf of Guinea is ::: dramatically impacted by poor air quality.
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paper_title: A semi-empirical model for predicting hourly ground-level fine particulate matter (PM2.5) concentration in southern Ontario from satellite remote sensing and ground-based meteorological measurements

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Abstract A semi-empirical model is developed to predict the hourly concentration of ground-level fine particulate matter (PM 2.5 ) coincident to satellite overpass, at a regional scale. The model corrects the aerosol optical depth (AOD) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) by the assimilated parameters characterizing the boundary layer and further adjusts the corrected value according to meteorological conditions near the ground. The model was built and validated using the data collected for southern Ontario, Canada for 2004. Overall, the model is able to explain 65% of the variability in ground-level PM 2.5 concentration. The model-predicted values of PM 2.5 mass concentration are highly correlated with the actual observations. The root-mean-square error of the model is 6.1 µg/m³. The incorporation of ground-level temperature and relative humidity is found to be significant in improving the model predictability. The coarse resolution of the assimilated meteorological fields limits their value in the AOD correction. Although MODIS AOD data is acquired on a daily basis and the valid data coverage can sometimes be very limited due to unfavourable weather conditions, the model provides a cost-effective approach for obtaining supplemental PM 2.5 concentration information in addition to the ground-based monitoring station measurement.
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paper_title: Improving the accuracy of daily satellite-derived ground-level fine aerosol concentration estimates for North America.

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We improve the accuracy of daily ground-level fine particulate matter concentrations (PM(2.5)) derived from satellite observations (MODIS and MISR) of aerosol optical depth (AOD) and chemical transport model (GEOS-Chem) calculations of the relationship between AOD and PM(2.5). This improvement is achieved by (1) applying climatological ground-based regional bias-correction factors based upon comparison with in situ PM(2.5), and (2) applying spatial smoothing to reduce random uncertainty and extend coverage. Initial daily 1-σ mean uncertainties are reduced across the United States and southern Canada from ± (1 μg/m(3) + 67%) to ± (1 μg/m(3) + 54%) by applying the climatological ground-based regional scaling factors. Spatial interpolation increases the coverage of satellite-derived PM(2.5) estimates without increased uncertainty when in close proximity to direct AOD retrievals. Spatial smoothing further reduces the daily 1-σ uncertainty to ±(1 μg/m(3) + 42%) by limiting the random component of uncertainty. We additionally find similar performance for climatological relationships of AOD to PM(2.5) as compared to day-specific relationships.
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paper_title: Chronic disease prevalence in women and air pollution--A 30-year longitudinal cohort study.

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BACKGROUND ::: Air pollution, such as fine particulate matter (PM2.5), can increase risk of adverse health events among people with heart disease, diabetes, asthma and chronic obstructive pulmonary disease (COPD) by aggravating these conditions. Identifying the influence of PM2.5 on prevalence of these conditions may help target interventions to reduce disease morbidity among high-risk populations. ::: ::: ::: OBJECTIVES ::: The objective of this study is to measure the association of exposure of PM2.5 with prevalence risk of various chronic diseases among a longitudinal cohort of women. ::: ::: ::: METHODS ::: Women from Ontario who enrolled in the Canadian National Breast Screening Study (CNBSS) from 1980 to 1985 (n = 29,549) were linked to provincial health administrative data from April 1, 1992 to March 31, 2013 to determine the prevalence of major chronic disease and conditions (heart disease, diabetes, asthma, COPD, acute myocardial infarction, angina, stroke and cancers). Exposure to PM2.5 was measured using satellite data collected from January 1, 1998 to December 31, 2006 and assigned to resident postal-code at time of entry into study. Poisson regression models were used to describe the relationship between exposure to ambient PM2.5 and chronic disease prevalence. Prevalence rate ratios (PRs) were estimated while adjusting for potential confounders: baseline age, smoking, BMI, marital status, education and occupation. Separate models were run for each chronic disease and condition. ::: ::: ::: RESULTS ::: Congestive heart failure (PR = 1.31, 95% CI: 1.13, 1.51), diabetes (PR = 1.28, 95% CI: 1.16, 1.41), ischemic heart disease (PR = 1.22, 95% CI: 1.14, 1.30), and stroke (PR = 1.21, 95% CI: 1.09, 1.35) showed over a 20% increase in PRs per 10 μg/m(3) increase in PM2.5 after adjusting for risk factors. Risks were elevated in smokers and those with BMI greater than 30. ::: ::: ::: CONCLUSIONS ::: This study estimated significant elevated prevalent rate ratios per unit increase in PM2.5 in nine of the ten chronic diseases studied.
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paper_title: MODIS multiannual observations in support of air quality monitoring in Northern Italy

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Aerosol satellite remote sensing can be profitably exploited in Air Quality monitoring in order to estimate the particulate matter concentration (PM) at surface level. Actually, the satellite sensor synoptic view enables monitoring of PM concentration spatial distribution fostering the analysis of the compliance with the new European Commission (EC) Directive on ambient air quality and cleaner air for Europe. In this work MODIS level 2 aerosol data are employed in a semi-empirical approach to estimate daily PM2.5 concentration at the ground over Northern Italy from 2004 to 2008. A further analysis, aimed at providing MODIS-based maps of yearly percentage of days exceeding the PM2.5 daily concentration limit prescribed by EC directive is then presented giving very promising results.
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paper_title: Empirical relationship between particulate matter and aerosol optical depth over Northern Tien-Shan, Central Asia

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Measurements were obtained at two sites in northern Tien-Shan in Central Asia during a 1-year period beginning July 2008 to examine the statistical relationship between aerosol optical depth (AOD) and of fine [PM2.5, particles less than 2.5 μm aerodynamic diameter (AD)] and coarse (PMCoarse, particles between 2.5 and 10 μm AD) mass concentrations and composition. The measurements represent the first extended particulate matter measurements in the northern Tien-Shan region of Central Asia. A sun photometer (Microtops II) was used to measure AOD from the surface, which is a widely used aerosol monitoring technique that is used in the AERONET network. In parallel, less routine measurements of continuous hourly PM2.5 data were obtained with the TEOM/FDMS whereas daily average PM2.5 and PM10 were obtained using URG-3000ABC samplers. Daily samples were collected on an every-other-day basis throughout the year. Since clouds interfere with the AOD measurement, a cloud screening procedure based on LIDAR measurements was applied to the AOD data and cloud impacted days were removed from the AOD data set. Depending on the season, the correlation coefficient (r) varied from 0.56 up to 0.87. Higher correlation coefficients between PM2.5 mass and AOD were observed during the spring and autumn periods and appeared to result from the transport of Asian dust (desert crustal material) particles from outside the area. One of the main source areas was the Taklimakan desert located in northwestern China. Linear regression results between AOD and PM2.5 are presented that allow for an estimate of PM2.5 mass concentrations at the surface based on the AOD data, which can be used to help interpret AOD measurements made in Central Asia and potentially other regions of the world.
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paper_title: Prenatal Particulate Air Pollution and Asthma Onset in Urban Children. Identifying Sensitive Windows and Sex Differences

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Rationale: The influence of particulate air pollution on respiratory health starts in utero. Fetal lung growth and structural development occurs in stages; thus, effects on postnatal respiratory disorders may differ based on timing of exposure.Objectives: We implemented an innovative method to identify sensitive windows for effects of prenatal exposure to particulate matter with a diameter less than or equal to 2.5 μm (PM2.5) on children's asthma development in an urban pregnancy cohort.Methods: Analyses included 736 full-term (≥37 wk) children. Each mother’s daily PM2.5 exposure was estimated over gestation using a validated satellite-based spatiotemporal resolved model. Using distributed lag models, we examined associations between weekly averaged PM2.5 levels over pregnancy and physician-diagnosed asthma in children by age 6 years. Effect modification by sex was also examined.Measurements and Main Results: Most mothers were ethnic minorities (54% Hispanic, 30% black), had 12 or fewer years of education...
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paper_title: Associations between cigarette smoking, obesity, sociodemographic characteristics and remote-sensing-derived estimates of ambient PM2.5: results from a Canadian population-based survey

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Objectives Long-term exposure to ambient fine particles (PM 2.5 ) has been shown to increase mortality. Variables measured on the same spatial scales of air pollution may confound associations, and so the authors9 objectives were to evaluate the associations between PM 2.5 and individual-level measures of smoking, obesity and sociodemographic status. The authors present an approach to evaluate the impact that uncontrolled confounding from smoking may have on associations between PM 2.5 and mortality. Methods Individual-level behavioural and sociodemographic data were obtained from a 2003 national survey of 122 548 Canadians. Estimates of ground-level PM 2.5 at a resolution of 10×10 km between 2001 and 2006 were derived from satellite remote sensing. Exposures were assigned to the residence of the participants at the time of the survey. Differences in the prevalence of smoking across concentrations of PM 2.5 and RRs drawn from the literature were used to model the bias on rate ratios. Results Participants in areas with higher concentrations of PM 2.5 had a higher income and educational attainment, smoked less and were more likely immigrants. Smoking had a negative confounding effect on the associations between PM 2.5 and mortality. To compensate for this bias, for a 10 μg/m 3 increase in PM 2.5 , mortality from lung cancer and heart disease in the referent exposure group needed to be increased by 6.9% and 3.2%, respectively. Conclusions Associations were found between sociodemographic and lifestyle characteristics and PM 2.5 at a resolution of 10×10 km. The authors present a model to adjust for uncontrolled confounding of smoking that can be readily adapted to exposures measured at different spatial resolutions.
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paper_title: The Relation between Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth and PM2.5over the United States: A Geographical Comparison by U.S. Environmental Protection Agency Regions

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Abstract Aerosol optical depth (AOD) acquired from satellite measurements demonstrates good correlation with particulate matter with diameters less than 2.5 µm (PM2.5) in some regions of the United States and has been used for monitoring and nowcasting air quality over the United States. This work investigates the relation between Moderate Resolution Imaging Spectroradiometer (MODIS) AOD and PM2.5 over the 10 U.S. Environmental Protection Agency (EPA)-defined geographic regions in the United States on the basis of a 2-yr (2005–2006) match-up dataset of MODIS AOD and hourly PM2.5 measurements. The AOD retrievals demonstrate a geographical and seasonal variation in their relation with PM2.5. Good correlations are mostly observed over the eastern United States in summer and fall. The southeastern United States has the highest correlation coefficients at more than 0.6. The southwestern United States has the lowest correlation coefficient of approximately 0.2. The seasonal regression relations derived for each...
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paper_title: Estimating national-scale ground-level PM25 concentration in China using geographically weighted regression based on MODIS and MISR AOD

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Taking advantage of the continuous spatial coverage, satellite-derived aerosol optical depth (AOD) products have been widely used to assess the spatial and temporal characteristics of fine particulate matter (PM2.5) on the ground and their effects on human health. However, the national-scale ground-level PM2.5 estimation is still very limited because the lack of ground PM2.5 measurements to calibrate the model in China. In this study, a national-scale geographically weighted regression (GWR) model was developed to estimate ground-level PM2.5 concentration based on satellite AODs, newly released national-wide hourly PM2.5 concentrations, and meteorological parameters. The results showed good agreements between satellite-retrieved and ground-observed PM2.5 concentration at 943 stations in China. The overall cross-validation (CV) R (2) is 0.76 and root mean squared prediction error (RMSE) is 22.26 μg/m(3) for MODIS-derived AOD. The MISR-derived AOD also exhibits comparable performance with a CV R (2) and RMSE are 0.81 and 27.46 μg/m(3), respectively. Annual PM2.5 concentrations retrieved either by MODIS or MISR AOD indicated that most of the residential community areas exceeded the new annual Chinese PM2.5 National Standard level 2. These results suggest that this approach is useful for estimating large-scale ground-level PM2.5 distributions especially for the regions without PMs monitoring sites.
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paper_title: Incorporating Local Land Use Regression And Satellite Aerosol Optical Depth In A Hybrid Model Of Spatiotemporal PM2.5Exposures In The Mid-Atlantic States

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Satellite-derived aerosol optical depth (AOD) measurements have the potential to provide spatiotemporally resolved predictions of both long and short-term exposures, but previous studies have generally shown moderate predictive power and lacked detailed high spatio- temporal resolution predictions across large domains. We aimed at extending our previous work by validating our model in another region with different geographical and metrological characteristics, and incorporating fine scale land use regression and nonrandom missingness to better predict PM2.5 concentrations for days with or without satellite AOD measures. We start by calibrating AOD data for 2000–2008 across the Mid-Atlantic. We used mixed models regressing PM2.5 measurements against day-specific random intercepts, and fixed and random AOD and temperature slopes. We used inverse probability weighting to account for nonrandom missingness of AOD, nested regions within days to capture spatial variation in the daily calibration, and introduced ...
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paper_title: Assessing temporally and spatially resolved PM2.5 exposures for epidemiological studies using satellite aerosol optical depth measurements

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Land use regression (LUR) models provide good estimates of spatially resolved long-term exposures, but are poor at capturing short term exposures. Satellite-derived Aerosol Optical Depth (AOD) measurements have the potential to provide spatio-temporally resolved predictions of both long and short term exposures, but previous studies have generally showed relatively low predictive power. Our objective was to extend our previous work on day-specific calibrations of AOD data using ground PM2.5 measurements by incorporating commonly used LUR variables and meteorological variables, thus benefiting from both the spatial resolution from the LUR models and the spatio-temporal resolution from the satellite models. Later we use spatial smoothing to predict PM2.5 concentrations for day/locations with missing AOD measures. We used mixed models with random slopes for day to calibrate AOD data for 2000–2008 across New-England with monitored PM2.5 measurements. We then used a generalized additive mixed model with spatial smoothing to estimate PM2.5 in location–day pairs with missing AOD, using regional measured PM2.5, AOD values in neighboring cells, and land use. Finally, local (100 m) land use terms were used to model the difference between grid cell prediction and monitored value to capture very local traffic particles. Out-of-sample ten-fold cross-validation was used to quantify the accuracy of our predictions. For days with available AOD data we found high out-of-sample R2 (mean out-of-sample R2 = 0.830, year to year variation 0.725–0.904). For days without AOD values, our model performance was also excellent (mean out-of-sample R2 = 0.810, year to year variation 0.692–0.887). Importantly, these R2 are for daily, rather than monthly or yearly, values. Our model allows one to assess short term and long-term human exposures in order to investigate both the acute and chronic effects of ambient particles, respectively.
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paper_title: Optimal estimation for global ground-level fine particulate matter concentrations

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[1] We develop an optimal estimation (OE) algorithm based on top-of-atmosphere reflectances observed by the MODIS satellite instrument to retrieve near-surface fine particulate matter (PM2.5). The GEOS-Chem chemical transport model is used to provide prior information for the Aerosol Optical Depth (AOD) retrieval and to relate total column AOD to PM2.5. We adjust the shape of the GEOS-Chem relative vertical extinction profiles by comparison with lidar retrievals from the CALIOP satellite instrument. Surface reflectance relationships used in the OE algorithm are indexed by land type. Error quantities needed for this OE algorithm are inferred by comparison with AOD observations taken by a worldwide network of sun photometers (AERONET) and extended globally based upon aerosol speciation and cross correlation for simulated values, and upon land type for observational values. Significant agreement in PM2.5 is found over North America for 2005 (slope = 0.89; r = 0.82; 1-σ error = 1 µg/m3 + 27%), with improved coverage and correlation relative to previous work for the same region and time period, although certain subregions, such as the San Joaquin Valley of California are better represented by previous estimates. Independently derived error estimates of the OE PM2.5 values at in situ locations over North America (of ±(2.5 µg/m3 + 31%) and Europe of ±(3.5 µg/m3 + 30%) are corroborated by comparison with in situ observations, although globally (error estimates of ±(3.0 µg/m3 + 35%), may be underestimated. Global population-weighted PM2.5 at 50% relative humidity is estimated as 27.8 µg/m3 at 0.1° × 0.1° resolution.
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paper_title: Spatiotemporal associations between GOES aerosol optical depth retrievals and ground-level PM2.5.

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We analyze the strength of association between aerosol optical depth (AOD) retrievals from the GOES aerosol/smoke product (GASP) and ground-level fine particulate matter (PM2.5) to assess AOD as a proxy for PM2.5 in the United States. GASP AOD is retrieved from a geostationary platform, giving half-hourly observations every day, in contrast to once per day snapshots from polar-orbiting satellites. However, GASP AOD is based on a less-sophisticated instrument and retrieval algorithm. We find that daily correlations between GASP AOD and PM2.5 over time at fixed locations are reasonably high, except in the winter and in the western U.S. Correlations over space at fixed times are lower. Simple averaging to the month and year actually reduces correlations over space, but statistical calibration allows averaging over time that produces moderately strong correlations. These results and the data density of GASP AOD highlight its potential to help improve exposure estimates for epidemiological analyses. On average 39% of days in a month have a GASP AOD retrieval compared to 11% for MODIS and 5% for MISR. Furthermore, GASP AOD has been retrieved since November 1994, providing a long-term record that predates the availability of most PM2.5 monitoring data and other satellite instruments.
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paper_title: Spatial Analysis of Air Pollution and Mortality in California

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RATIONALE ::: Although substantial scientific evidence suggests that chronic exposure to ambient air pollution contributes to premature mortality, uncertainties exist in the size and consistency of this association. Uncertainty may arise from inaccurate exposure assessment. ::: ::: ::: OBJECTIVES ::: To assess the associations of three types of air pollutants (fine particulate matter, ozone [O3], and nitrogen dioxide [NO2]) with the risk of mortality in a large cohort of California adults using individualized exposure assessments. ::: ::: ::: METHODS ::: For fine particulate matter and NO2, we used land use regression models to derive predicted individualized exposure at the home address. For O3, we estimated exposure with an inverse distance weighting interpolation. Standard and multilevel Cox survival models were used to assess the association between air pollution and mortality. ::: ::: ::: MEASUREMENTS AND MAIN RESULTS ::: Data for 73,711 subjects who resided in California were abstracted from the American Cancer Society Cancer Prevention II Study cohort, with baseline ascertainment of individual characteristics in 1982 and follow-up of vital status through to 2000. Exposure data were derived from government monitors. Exposure to fine particulate matter, O3, and NO2 was positively associated with ischemic heart disease mortality. NO2 (a marker for traffic pollution) and fine particulate matter were also associated with mortality from all causes combined. Only NO2 had significant positive association with lung cancer mortality. ::: ::: ::: CONCLUSIONS ::: Using the first individualized exposure assignments in this important cohort, we found positive associations of fine particulate matter, O3, and NO2 with mortality. The positive associations of NO2 suggest that traffic pollution relates to premature death.
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paper_title: Estimating ground‐level PM2.5 using aerosol optical depth determined from satellite remote sensing

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[1] We assess the relationship of ground-level fine particulate matter (PM 2.5 ) concentrations for 2000-2001 measured as part of the Canadian National Air Pollution Surveillance (NAPS) network and the U.S. Air Quality System (AQS), versus remote-sensed PM 2.5 determined from aerosol optical depths (AOD) measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging Spectroradiometer (MISR) satellite instruments. A global chemical transport model (GEOS-CHEM) is used to simulate the factors affecting the relation between AOD and PM 2.5 . AERONET AOD is used to evaluate the method (r = 0.71, N = 48, slope = 0.69). We find significant spatial variation of the annual mean ground-based measurements with PM 2.5 determined from MODIS (r = 0.69, N = 199, slope = 0.82) and MISR (r = 0.58, N = 199, slope = 0.57). Excluding California significantly increases the respective slopes and correlations. The relative vertical profile of aerosol extinction is the most important factor affecting the spatial relationship between satellite and surface measurements of PM 2.5 ; neglecting this parameter would reduce the spatial correlation to 0.36. In contrast, temporal variation in AOD is the most influential parameter affecting the temporal relationship between satellite and surface measurements of PM 2.5 ; neglecting daily variation in this parameter would decrease the correlation in eastern North America from 0.5-0.8 to less than 0.2. Other simulated aerosol properties, such as effective radius and extinction efficiency have a minor role temporally, but do influence the spatial correlation. Global mapping of PM 2.5 from both MODIS and MISR reveals annual mean concentrations of 40-50 ug/m 3 over northern India and China.
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paper_title: Spatiotemporal prediction of fine particulate matter using high-resolution satellite images in the Southeastern US 2003–2011

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Spatiotemporal prediction of fine particulate matter using high-resolution satellite images in the Southeastern US 2003–2011
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paper_title: Estimating PM2.5 in Xi'an, China using aerosol optical depth: A comparison between the MODIS and MISR retrieval models

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article = 0.56) with an APE of 21%. Further analysis showed that there was a significantimprovement incorrelation coefficient when using the nonlinear multiple regression model compared to using a simple linear regression model of AOD and PM2.5. These results are useful for assessing surface PM2.5 concentration and monitoring regional air quality.
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paper_title: Risk of Nonaccidental and Cardiovascular Mortality in Relation to Long-term Exposure to Low Concentrations of Fine Particulate Matter: A Canadian National-Level Cohort Study

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Few cohort studies have evaluated the risk of mortality associated with long-term exposure to fine particulate matter (≤ 2.5 µm in aerodynamic diameter (PM2.5)). This is the first national-level cohort study to investigate these risks in Canada. The authors investigated the association between long-term exposure to ambient PM2.5 and cardiovascular mortality in nonimmigrant Canadian adults. They assigned estimates of exposure to ambient PM2.5 derived from satellite observations to a cohort of 2.1 million Canadian adults who in 1991 were among the 20% of the population mandated to provide detailed census data. They identified deaths occurring between 1991 and 2001 through record linkage. They calculated hazard ratios (HRs) and 95% confidence intervals (CIs) adjusted for available individual-level and contextual covariates using both standard Cox proportional survival models and nested, spatial random-effects survival models. Using standard Cox models, they calculated HRs of 1.15 (95% CI: 1.13, 1.16) from nonaccidental causes and 1.31 (95% CI: 1.27, 1.35) from ischemic heart disease for each 10-µg/m3 increase in concentrations of PM2.5. Using spatial random-effects models controlling for the same variables, they calculated HRs of 1.10 (95% CI: 1.05, 1.15) and 1.30 (95% CI: 1.18, 1.43), respectively. We found similar associations between nonaccidental mortality and PM2.5 based on satellite-derived estimates and ground-based measurements in a subanalysis of subjects in 11 cities. In this large national cohort of nonimmigrant Canadians, mortality was associated with long-term exposure to PM2.5. Associations were observed with exposures to PM2.5 at concentrations that were predominantly lower (mean, 8.7 µg/m3; interquartile range, 6.2 µg/m3) than those reported previously.
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paper_title: Prediction of daily fine particulate matter concentrations using aerosol optical depth retrievals from the Geostationary Operational Environmental Satellite (GOES)

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UNLABELLED ::: Although ground-level PM2.5 (particulate matter with aerodynamic diameter < 2.5 microm) monitoring sites provide accurate measurements, their spatial coverage within a given region is limited and thus often insufficient for exposure and epidemiological studies. Satellite data expand spatial coverage, enhancing our ability to estimate location- and/or subject-specific exposures to PM2.5. In this study, the authors apply a mixed-effects model approach to aerosol optical depth (AOD) retrievals from the Geostationary Operational Environmental Satellite (GOES) to predict PM2.5 concentrations within the New England area of the United States. With this approach, it is possible to control for the inherent day-to-day variability in the AOD-PM2.5 relationship, which depends on time-varying parameters such as particle optical properties, vertical and diurnal concentration profiles, and ground surface reflectance. The model-predicted PM2.5 mass concentration are highly correlated with the actual observations, R2 = 0.92. Therefore, adjustment for the daily variability in AOD-PM2.5 relationship allows obtaining spatially resolved PM2.5 concentration data that can be of great value to future exposure assessment and epidemiological studies. ::: ::: ::: IMPLICATIONS ::: The authors demonstrated how AOD can be used reliably to predict daily PM2.5 mass concentrations, providing determination of their spatial and temporal variability. Promising results are found by adjusting for daily variability in the AOD-PM2.5 relationship, without the need to account for a wide variety of individual additional parameters. This approach is of a great potential to investigate the associations between subject-specific exposures to PM2.5 and their health effects. Higher 4 x 4-km resolution GOES AOD retrievals comparing with the conventional MODerate resolution Imaging Spectroradiometer (MODIS) 10-km product has the potential to capture PM2.5 variability within the urban domain.
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paper_title: Global Estimates of Ambient Fine Particulate Matter Concentrations from Satellite-Based Aerosol Optical Depth: Development and Application

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Chronic exposure to airborne fine particulate matter with diameter < 2.5 μm (PM2.5) is associated with adverse human health impacts including morbidity and mortality (e.g., Dockery et al. 1993; McDonnell et al. 2000; Pope et al. 2009). Several national environmental agencies in North America and Europe monitor PM2.5 concentrations at numerous sites throughout their jurisdictions, but even these relatively dense networks have limited geographic coverage. Few long-term measurement sites exist elsewhere in the world, particularly in rapidly developing countries where concentrations and estimated health impacts are greatest (Cohen et al. 2004). Point measurements collected at monitoring sites are not necessarily representative of regional concentration, and regional variability is difficult to assess from point measurements alone. In recent years, application of satellite observation to surface air quality has advanced considerably (Hoff and Christopher 2009; Martin 2008). In fact, global aerosol observations from satellite remote sensing could substantially improve estimates of population exposure to PM2.5. ::: ::: Since the mid 2000s, the MODIS (Moderate Resolution Imaging Spectroradiometer) and MISR (Multiangle Imaging Spectroradiometer) instruments onboard the National Aeronautics and Space Administration’s (NASA) Terra satellite has provided global observations of aerosol optical depth (AOD), a measure of light extinction by aerosol in the atmospheric column above the earth’s surface. Terra’s sun-synchronous orbit encircles the earth approximately 15 times each day, with each pass crossing the equator at approximately 1030 hours local solar time. Observations of AOD from Terra provide daily insight into the global distribution of column-integrated aerosol. However, the applicability of AOD to surface air quality depends on several factors, including the vertical structure, composition, size distribution, and water content of atmospheric aerosol. ::: ::: Many studies have investigated the relationship between total-column AOD and surface PM2.5 measurements. Most have developed simple empirical relationships between these two variables (e.g., Engel-Cox et al. 2004a; Wang and Christopher 2003); more recent investigations often have used local meteorological information to better relate AOD and PM2.5 (e.g., Koelemeijer et al. 2006; Liu et al. 2005) or to filter the AOD (e.g., Gupta et al. 2006). Some studies have employed light detection and ranging (LIDAR) instruments to capture the vertical aerosol distribution at specific locations (e.g., Engel-Cox et al. 2006; Schaap et al. 2008). Schaap et al. (2008) noted that locally derived AOD–PM2.5 relationships cannot be extended easily to other regions because of variation in meteorology and aerosol composition. Unique, local, time-dependent AOD–PM2.5 relationships are necessary to infer global estimates of PM2.5. Ground-based measurements of aerosol vertical profiles and properties have insufficient coverage to estimate global AOD–PM2.5 relationships. ::: ::: Global chemical transport models (CTMs) resolve atmospheric composition at a resolution of hundreds of kilometers horizontally by hundreds of meters vertically, with a temporal frequency of tens of minutes. Liu et al. (2004) first estimated surface-level PM2.5 from MISR observations by using CTM output to represent local AOD–PM2.5 conversion factors over the contiguous United States. van Donkelaar et al. (2006) extended the approach used by Liu et al. (2004) to estimate PM2.5 from both MODIS and MISR observations and investigated the factors affecting the agreement between AOD and surface-level PM2.5. Statistical models have also been used to relate AOD to PM2.5. For example, Liu et al. (2007) used MISR-retrieved spherical versus nonspherical particle fraction, in addition to model-derived vertical distribution, to separate mineral dust from other aerosol species. More recently, Paciorek and Liu (2009) probed the limitations of using AOD without accounting for vertical distribution or speciation and concluded that agreement with ground-based monitors based on this approach might depend on factors other than satellite observations. ::: ::: We developed a global satellite-based estimate of surface PM2.5 at a spatial resolution of 0.1° × 0.1°, or approximately 10 km × 10 km at midlatitudes. We developed an approach for combining MODIS and MISR AOD into a single improved estimate of AOD. Using this methodology, we calculated AOD–PM2.5 conversion factors with a global CTM and produced and applied these factors to the AOD. We present a global estimate of PM2.5 concentrations and validate it with ground-based (in situ) observations. We estimate global exposure to outdoor ambient PM2.5 using our satellite-derived product to demonstrate potential application for global health studies. We then examined sources of error.
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paper_title: Estimating urban PM 10 and PM 2.5 concentrations, based on synergistic MERIS/AATSR aerosol observations, land cover and morphology data

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Abstract This study evaluates alternative spatio-temporal approaches for quantitative estimation of daily mean Particulate Matter (PM) concentrations. Both fine (PM2.5) and coarse (PM10) concentrations were estimated over the area of London (UK) for the 2002–2012 time period, using Aerosol Optical Thickness (AOT) derived from MERIS (Medium Resolution Imaging Spectrometer)/AATSR (Advanced Along-Track Scanning Radiometer) synergistic observations at 1 km × 1 km resolution. Relative humidity, temperature and the K-Index obtained from MODIS (Moderate Resolution Imaging Spectroradiometer) sensor were used as additional predictors. High-resolution (100 m × 100 m) local urban land cover and morphology datasets were incorporated in the analysis in order to capture the effects of local scale emissions and sequestration. Spatial (2-D) and spatio-temporal (3-D) kriging were applied to in situ urban PM measurements to investigate their association with satellite-derived AOT while accounting for differences in spatial support. Linear mixed-effects models with day-specific and site-specific random intercepts and slopes were estimated to associate satellite-derived products with kriged PM concentration and their predictive performance was evaluated.
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paper_title: National-Scale Estimates of Ground-Level PM2.5 Concentration in China Using Geographically Weighted Regression Based on 3 km Resolution MODIS AOD

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High spatial resolution estimating of exposure to particulate matter 2.5 (PM2.5) is currently very limited in China. This study uses the newly released nationwide, hourly PM2.5 concentrations to create a nationwide, geographically weighted regression (GWR) model to estimate ground-level PM2.5 concentrations in China. A3 km resolution aerosol optical depth (AOD) product from MODIS is used as the primary predictor. Fire emissions detected by MODIS fire count were considered in the model development process. Additionally, meteorological features were used as covariates in the model to improve the estimation of ground-level PM2.5 concentrations. The model performed well and explained 81% of the daily PM2.5 concentration variations in model predictions, and the cross validations R2 is 0.79. The cross-validated root mean squared error (RMSE) of the model was 18.6 μg/m3.Annual PM2.5 concentrations retrieved by the MODIS 3 km AOD product indicated that most of the residential community areas exceeded the new annual Chinese PM2.5 National Standard level 2. Estimated high-resolution national-scale daily PM2.5 maps are useful to identify severe air pollution episodes and determine health risk assessments. These results suggest that this approach is useful for estimating large-scale ground-level PM2.5 distributions, especially for regions without PM monitoring sites.
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paper_title: Integrating lidar and satellite optical depth with ambient monitoring for 3-dimensional particulate characterization

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A combination of in-situ PM2.5, sunphotometers, upward pointing lidar and satellite aerosol optical depth (AOD) instruments have been employed to better understand variability in the correlation between AOD and PM2.5 at the surface. Previous studies have shown good correlation between these measures, especially in the US east, and encouraged the use of satellite data for spatially interpolating between ground sensors. This work shows that cases of weak correlation can be better understood with knowledge of whether the aerosol is confined to the surface planetary boundary layer (PBL) or aloft. Lidar apportionment of the fraction of aerosol optical depth that is within the PBL can be scaled to give better agreement with surface PM2.5 than does the total column amount. The study has shown that lidar combined with surface and remotely sensed data might be strategically used to improve our understanding of long-range or regionally transported pollutants in multiple dimensions. r 2006 Elsevier Ltd. All rights reserved.
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paper_title: Fifteen-Year Global Time Series of Satellite-Derived Fine Particulate Matter

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Ambient fine particulate matter (PM2.5) is a leading environmental risk factor for premature mortality. We use aerosol optical depth (AOD) retrieved from two satellite instruments, MISR and SeaWiFS, to produce a unified 15-year global time series (1998–2012) of ground-level PM2.5 concentration at a resolution of 1° x 1°. The GEOS-Chem chemical transport model (CTM) is used to relate each individual AOD retrieval to ground-level PM2.5. Four broad areas showing significant, spatially coherent, annual trends are examined in detail: the Eastern U.S. (−0.39 ± 0.10 μg m–3 yr–1), the Arabian Peninsula (0.81 ± 0.21 μg m–3 yr–1), South Asia (0.93 ± 0.22 μg m–3 yr–1) and East Asia (0.79 ± 0.27 μg m–3 yr–1). Over the period of dense in situ observation (1999–2012), the linear tendency for the Eastern U.S. (−0.37 ± 0.13 μg m–3 yr–1) agrees well with that from in situ measurements (−0.38 ± 0.06 μg m–3 yr–1). A GEOS-Chem simulation reveals that secondary inorganic aerosols largely explain the observed PM2.5 trend over ...
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paper_title: High-Resolution Satellite Mapping of Fine Particulates Based on Geographically Weighted Regression

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Satellite-retrieved aerosol optical depth (AOD) has been increasingly utilized for the mapping of fine particulate matter (PM2.5) concentrations. An accurate estimation and mapping of PM2.5 concentrations depends on the high-resolution AOD data and a robust mathematical model that takes into account the spatial nonstationary relationship between PM2.5 and AOD. Take the core portion of the Beijing-Hebei-Tianjin (Jing-Jin-Ji) urban agglomeration as case study (the most seriously polluted region in China). Land use, population, meteorological variables, and simplified aerosol retrieval algorithm-retrieved AOD at 1-km resolution are employed as the predictors for the geographically weighted regression (GWR) and the ordinary least squares (OLS) model to map the spatial distribution of PM2.5 concentrations. The GWR model shows significant spatial variations in PM2.5 concentrations over the region than the traditional OLS model, which reveals relative homogeneous variations. Validation with ground-level PM2.5 concentrations demonstrates that PM2.5 concentrations predicted by the GWR model (R2 = 0.75, RMSE = 10 μg/m3) correlate better than those by the OLS model (R2 = 0.53, RMSE = 16 μg/m3). These results suggest that the GWR model offered a more reliable way for the prediction of spatial distribution of PM2.5 concentrations over urban areas.
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paper_title: Satellite-based PM concentrations and their application to COPD in Cleveland, OH

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A hybrid approach is proposed to estimate exposure to fine particulate matter (PM(2.5)) at a given location and time. This approach builds on satellite-based aerosol optical depth (AOD), air pollution data from sparsely distributed Environmental Protection Agency (EPA) sites and local time-space Kriging, an optimal interpolation technique. Given the daily global coverage of AOD data, we can develop daily estimate of air quality at any given location and time. This can assure unprecedented spatial coverage, needed for air quality surveillance and management and epidemiological studies. In this paper, we developed an empirical relationship between the 2 km AOD and PM(2.5) data from EPA sites. Extrapolating this relationship to the study domain resulted in 2.3 million predictions of PM(2.5) between 2000 and 2009 in Cleveland Metropolitan Statistical Area (MSA). We have developed local time-space Kriging to compute exposure at a given location and time using the predicted PM(2.5). Daily estimates of PM(2.5) were developed for Cleveland MSA between 2000 and 2009 at 2.5 km spatial resolution; 1.7 million (∼79.8%) of 2.13 million predictions required for multiyear and geographic domain were robust. In the epidemiological application of the hybrid approach, admissions for an acute exacerbation of chronic obstructive pulmonary disease (AECOPD) was examined with respect to time-space lagged PM(2.5) exposure. Our analysis suggests that the risk of AECOPD increases 2.3% with a unit increase in PM(2.5) exposure within 9 days and 0.05° (∼5 km) distance lags. In the aggregated analysis, the exposed groups (who experienced exposure to PM(2.5) >15.4 μg/m(3)) were 54% more likely to be admitted for AECOPD than the reference group. The hybrid approach offers greater spatiotemporal coverage and reliable characterization of ambient concentration than conventional in situ monitoring-based approaches. Thus, this approach can potentially reduce exposure misclassification errors in the conventional air pollution epidemiology studies.
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paper_title: Methods for characterizing fine particulate matter using ground observations and remotely sensed data: potential use for environmental public health surveillance.

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This study describes and demonstrates different techniques for surface fitting daily environmental hazards data of particulate matter with aerodynamic diameter less than or equal to 2.5 microm (PM2.5) for the purpose of integrating respiratory health and environmental data for the Centers for Disease Control and Prevention (CDC) pilot study of Health and Environment Linked for Information Exchange (HELIX)-Atlanta. It presents a methodology for estimating daily spatial surfaces of ground-level PM2.5 concentrations using the B-Spline and inverse distance weighting (IDW) surface-fitting techniques, leveraging National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectrometer (MODIS) data to complement U.S. Environmental Protection Agency (EPA) ground observation data. The study used measurements of ambient PM2.5 from the EPA database for the year 2003 as well as PM2.5 estimates derived from NASA's satellite data. Hazard data have been processed to derive the surrogate PM2.5 exposure estimates. This paper shows that merging MODIS remote sensing data with surface observations of PM,2. not only provides a more complete daily representation of PM,2. than either dataset alone would allow, but it also reduces the errors in the PM2.5-estimated surfaces. The results of this study also show that although the IDW technique can introduce some numerical artifacts that could be due to its interpolating nature, which assumes that the maxima and minima can occur only at the observation points, the daily IDW PM2.5 surfaces had smaller errors in general, with respect to observations, than those of the B-Spline surfaces. Finally, the methods discussed in this paper establish a foundation for environmental public health linkage and association studies for which determining the concentrations of an environmental hazard such as PM2.5 with high accuracy is critical.
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paper_title: Effects of prenatal community violence and ambient air pollution on childhood wheeze in an urban population

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Background Prenatal exposures to stress and physical toxins influence children's respiratory health, although few studies consider these factors together. Objectives We sought to concurrently examine the effects of prenatal community-level psychosocial (exposure to community violence [ECV]) and physical (air pollution) stressors on repeated wheeze in 708 urban children followed to age 2 years. Methods Multi-item ECV reported by mothers in pregnancy was summarized into a continuous score by using Rasch modeling. Prenatal black carbon exposure was estimated by using land-use regression (LUR) modeling; particulate matter with a diameter of less than 2.5 μm (PM 2.5 ) was estimated by using LUR modeling incorporating satellite data. Mothers reported child's wheeze every 3 months. The effects of ECV and air pollutants on repeated wheeze (≥2 episodes) were examined by using logistic regression. Interactions between ECV and pollutants were examined. Results Mothers were primarily black (29%) and Hispanic (55%), with lower education (62% with ≤12 years); 87 (12%) children wheezed repeatedly. In models examining concurrent exposures, ECV (odds ratio [OR], 1.95; 95% CI, 1.13-3.36; highest vs lowest tertile) and black carbon (OR, 1.84; 95% CI, 1.08-3.12; median or greater vs less than median) were independently associated with wheeze adjusting for sex, birth season, maternal atopy, education, race, and cockroach antigen. Associations were similar for PM 2.5 (adjusted OR, 2.02; 95% CI, 1.20-3.40). An interaction between ECV with air pollution levels was suggested. Conclusions These findings suggest that both prenatal community violence and air pollution can contribute to respiratory health in these urban children. Moreover, place-based psychosocial stressors might affect host resistance such that physical pollutants can have adverse effects, even at relatively lower levels.
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paper_title: Consequences of kriging and land use regression for PM2.5 predictions in epidemiologic analyses: insights into spatial variability using high-resolution satellite data

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Many epidemiological studies use predicted air pollution exposures as surrogates for true air pollution levels. These predicted exposures contain exposure measurement error, yet simulation studies have typically found negligible bias in resulting health effect estimates. However, previous studies typically assumed a statistical spatial model for air pollution exposure, which may be oversimplified. We address this shortcoming by assuming a realistic, complex exposure surface derived from fine-scale (1 km × 1 km) remote-sensing satellite data. Using simulation, we evaluate the accuracy of epidemiological health effect estimates in linear and logistic regression when using spatial air pollution predictions from kriging and land use regression models. We examined chronic (long-term) and acute (short-term) exposure to air pollution. Results varied substantially across different scenarios. Exposure models with low out-of-sample R2 yielded severe biases in the health effect estimates of some models, ranging from 60% upward bias to 70% downward bias. One land use regression exposure model with >0.9 out-of-sample R2 yielded upward biases up to 13% for acute health effect estimates. Almost all models drastically underestimated the SEs. Land use regression models performed better in chronic effect simulations. These results can help researchers when interpreting health effect estimates in these types of studies.
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paper_title: Use of satellite-based aerosol optical depth and spatial clustering to predict ambient PM2.5 concentrations.

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Satellite-based PM(2.5) monitoring has the potential to complement ground PM(2.5) monitoring networks, especially for regions with sparsely distributed monitors. Satellite remote sensing provides data on aerosol optical depth (AOD), which reflects particle abundance in the atmospheric column. Thus AOD has been used in statistical models to predict ground-level PM(2.5) concentrations. However, previous studies have shown that AOD may not be a strong predictor of PM(2.5) ground levels. Another shortcoming of remote sensing is the large number of non-retrieval days (i.e., days without satellite data available) due to clouds and snow- and ice-cover. In this paper we propose statistical approaches to overcome these two shortcomings, thereby making satellite imagery a viable method to estimate PM(2.5) concentrations. First, we render AOD a robust predictor of PM(2.5) mass concentration by introducing an AOD daily calibration approach through the use of mixed effects model. Second, we develop models that combine AOD and ground monitoring data to predict PM(2.5) concentrations during non-retrieval days. A key feature of this approach is that we develop these prediction models separately for groups of days defined by the observed amount of spatial heterogeneity in concentrations across the study region. Subsequently, these methodologies were applied to examine the spatial and temporal patterns of daily PM(2.5) concentrations for both retrieval days (i.e., days with satellite data available) and non-retrieval days in the New England region of the United States during the period 2000-2008. Overall, for the years 2000-2008, our statistical models predicted surface PM(2.5) concentrations with reasonably high R(2) (0.83) and low percent mean relative error (3.5%). Also the spatial distribution of the estimated PM(2.5) levels in the study domain clearly exhibited densely populated and high traffic areas. The method we have developed demonstrates that remote sensing can have a tremendous impact on the fields of environmental monitoring and human exposure assessment.
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paper_title: A novel calibration approach of MODIS AOD data to predict PM2.5 concentrations

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Epidemiological studies investigating the human health effects of PM 2.5 are susceptible to exposure measurement errors, a form of bias in exposure estimates, since they rely on data from a limited number of PM 2.5 monitors within their study area. Satellite data can be used to expand spatial coverage, potentially enhancing our ability to estimate location- or subject-specific exposures to PM 2.5 , but some have reported poor predictive power. A new methodology was developed to calibrate aerosol optical depth (AOD) data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). Subsequently, this method was used to predict ground daily PM 2.5 concentrations in the New England region. 2003 MODIS AOD data corresponding to the New England region were retrieved, and PM 2.5 concentrations measured at 26 US Environmental Protection Agency (EPA) PM 2.5 monitoring sites were used to calibrate the AOD data. A mixed effects model which allows day-to-day variability in daily PM 2.5 -AOD relationships was used to predict location-specific PM 2.5 levels. PM 2.5 concentrations measured at the monitoring sites were compared to those predicted for the corresponding grid cells. Both cross-sectional and longitudinal comparisons between the observed and predicted concentrations suggested that the proposed new calibration approach renders MODIS AOD data a potentially useful predictor of PM 2.5 concentrations. Furthermore, the estimated PM 2.5 levels within the study domain were examined in relation to air pollution sources. Our approach made it possible to investigate the spatial patterns of PM 2.5 concentrations within the study domain.
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paper_title: A method to estimate concentrations of surface-level particulate matter using satellite-based aerosol optical thickness

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We propose a new method to estimate surface-level particulate matter (PM) concentrations by using satellite-retrieved Aerosol Optical Thickness (AOT). This method considers the distribution and variation of Planetary Boundary Layer (PBL) height and relative humidity (RH) at the regional scale. The method estimates surface-level particulate matter concentrations using the data simulated by an atmospheric boundary layer model RAMS and satellite-retrieved AOT. By incorporation MODIS AOT, PBL height and RH simulated by RAMS, this method is applied to estimate the surface-level PM2.5 concentrations in North China region. The result is evaluated by using 16 ground-based observations deployed in the research region, and the result shows a good agreement between estimated PM2.5 concentrations and observations, and the coefficient of determination R2 is 0.61 between the estimated PM2.5 concentrations and the observations. In addition, surface-level PM2.5 concentrations are also estimated by using MODIS AOT, ground-based LIDAR observations and RH measurements. A comparison between the two estimated PM2.5 concentrations shows that the new method proposed in this paper is better than the traditional method. The coefficient of determination R2 is improved from 0.32 to 0.62.
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paper_title: Use of Satellite Observations for Long-Term Exposure Assessment of Global Concentrations of Fine Particulate Matter

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BACKGROUND ::: More than a decade of satellite observations offers global information about the trend and magnitude of human exposure to fine particulate matter (PM2.5). ::: ::: ::: OBJECTIVE ::: In this study, we developed improved global exposure estimates of ambient PM2.5 mass and trend using PM2.5 concentrations inferred from multiple satellite instruments. ::: ::: ::: METHODS ::: We combined three satellite-derived PM2.5 sources to produce global PM2.5 estimates at about 10 km × 10 km from 1998 through 2012. For each source, we related total column retrievals of aerosol optical depth to near-ground PM2.5 using the GEOS-Chem chemical transport model to represent local aerosol optical properties and vertical profiles. We collected 210 global ground-based PM2.5 observations from the literature to evaluate our satellite-based estimates with values measured in areas other than North America and Europe. ::: ::: ::: RESULTS ::: We estimated that global population-weighted ambient PM2.5 concentrations increased 0.55 μg/m3/year (95% CI: 0.43, 0.67) (2.1%/year; 95% CI: 1.6, 2.6) from 1998 through 2012. Increasing PM2.5 in some developing regions drove this global change, despite decreasing PM2.5 in some developed regions. The estimated proportion of the population of East Asia living above the World Health Organization (WHO) Interim Target-1 of 35 μg/m3 increased from 51% in 1998-2000 to 70% in 2010-2012. In contrast, the North American proportion above the WHO Air Quality Guideline of 10 μg/m3 fell from 62% in 1998-2000 to 19% in 2010-2012. We found significant agreement between satellite-derived estimates and ground-based measurements outside North America and Europe (r = 0.81; n = 210; slope = 0.68). The low bias in satellite-derived estimates suggests that true global concentrations could be even greater. ::: ::: ::: CONCLUSIONS ::: Satellite observations provide insight into global long-term changes in ambient PM2.5 concentrations. Satellite-derived estimates and ground-based PM2.5 observations from this study are available for public use.
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paper_title: Merging Satellite Measurement with Ground-Based Air Quality Monitoring Data to Assess Health Effects of Fine Particulate Matter Pollution

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Geospatial technologies have been widely used in environmental health research, including air pollution and human health. This chapter demonstrates the potential of integrating satellite air quality measurement with ground-based PM2.5 data to explore health effects of fine particulate air pollution. This study assesses the association of estimated PM2.5 concentration with chronic coronary heart disease (CCHD) mortality. Years 2003 and 2004 daily MODIS (Moderate Resolution Imaging Spectrometer) Level 2 AOD images were collated with US EPA PM2.5 data covering the conterminous USA. Pearson’s correlation analysis and geographically weighted regression (GWR) found that the relationship between PM2.5 and AOD is not spatially consistent across the conterminous states. GWR predicts well in the east and poorly in the west. The GWR model was used to derive a PM2.5 grid surface for the eastern US (RMSE = 1.67 μg/m3). A Bayesian hierarchical model found that areas with higher values of PM2.5 show high rates of CCHD mortality: \(\beta _{{\textrm{PM}}_{2.5} } \)= 0.802, posterior 95% Bayesian credible interval (CI) = (0.386, 1.225). Aerosol remote sensing and GIS spatial analyses and modelling could help fill pervasive data gaps in ground-based air quality monitoring that impede efforts to study air pollution and protect public health.
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paper_title: Satellite remote sensing of particulate matter and air quality assessment over global cities

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Using 1 year of aerosol optical thickness (AOT) retrievals from the MODerate resolution Imaging Spectro-radiometer (MODIS) on board NASA’s Terra and Aqua satellite along with ground measurements of PM2.5 mass concentration, we assess particulate matter air quality over different locations across the global urban areas spread over 26 locations in Sydney, Delhi, Hong Kong, New York City and Switzerland. An empirical relationship between AOT and PM2.5 mass is obtained and results show that there is an excellent correlation between the bin-averaged daily mean satellite and groundbased values with a linear correlation coefficient of 0.96. Using meteorological and other ancillary datasets, we assess the effects of wind speed, cloud cover, and mixing height (MH) on particulate matter (PM) air quality and conclude that these data are necessary to further apply satellite data for air quality research. Our study clearly demonstrates that satellitederived AOT is a good surrogate for monitoring PM air quality over the earth. However, our analysis shows that the PM2.5–AOT relationship strongly depends on aerosol concentrations, ambient relative humidity (RH), fractional cloud cover and height of the mixing layer. Highest correlation between MODIS AOT and PM2.5 mass is found under clear sky conditions with less than 40–50% RH and when atmospheric MH ranges from 100 to 200 m. Future remote sensing sensors such as Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) that have the capability to provide vertical distribution of aerosols will further enhance our ability to monitor and forecast air pollution. This study is among the first to examine the relationship between satellite and ground measurements over several global locations. r 2006 Elsevier Ltd. All rights reserved.
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paper_title: Associations between prenatal traffic-related air pollution exposure and birth weight: Modification by sex and maternal pre-pregnancy body mass index.

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BACKGROUND ::: Prenatal traffic-related air pollution exposure is linked to adverse birth outcomes. However, modifying effects of maternal body mass index (BMI) and infant sex remain virtually unexplored. ::: ::: ::: OBJECTIVES ::: We examined whether associations between prenatal air pollution and birth weight differed by sex and maternal BMI in 670 urban ethnically mixed mother-child pairs. ::: ::: ::: METHODS ::: Black carbon (BC) levels were estimated using a validated spatio-temporal land-use regression (LUR) model; fine particulate matter (PM2.5) was estimated using a hybrid LUR model incorporating satellite-derived Aerosol Optical Depth measures. Using stratified multivariable-adjusted regression analyses, we examined whether associations between prenatal air pollution and calculated birth weight for gestational age (BWGA) z-scores varied by sex and maternal pre-pregnancy BMI. ::: ::: ::: RESULTS ::: Median birth weight was 3.3±0.6kg; 33% of mothers were obese (BMI ≥30kg/m(3)). In stratified analyses, the association between higher PM2.5 and lower birth weight was significant in males of obese mothers (-0.42 unit of BWGA z-score change per IQR increase in PM2.5, 95%CI: -0.79 to -0.06) ( PM2.5×sex×obesity Pinteraction=0.02). Results were similar for BC models (Pinteraction=0.002). ::: ::: ::: CONCLUSIONS ::: Associations of prenatal exposure to traffic-related air pollution and reduced birth weight were most evident in males born to obese mothers.
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paper_title: A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010

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PMC Free-text ::: ::: BACKGROUND ::: ::: Quantification of the disease burden caused by different risks informs prevention by providing an account of health loss different to that provided by a disease-by-disease analysis. No complete revision of global disease burden caused by risk factors has been done since a comparative risk assessment in 2000, and no previous analysis has assessed changes in burden attributable to risk factors over time. ::: ::: METHODS ::: ::: We estimated deaths and disability-adjusted life years (DALYs; sum of years lived with disability [YLD] and years of life lost [YLL]) attributable to the independent effects of 67 risk factors and clusters of risk factors for 21 regions in 1990 and 2010. We estimated exposure distributions for each year, region, sex, and age group, and relative risks per unit of exposure by systematically reviewing and synthesising published and unpublished data. We used these estimates, together with estimates of cause-specific deaths and DALYs from the Global Burden of Disease Study 2010, to calculate the burden attributable to each risk factor exposure compared with the theoretical-minimum-risk exposure. We incorporated uncertainty in disease burden, relative risks, and exposures into our estimates of attributable burden. ::: ::: FINDINGS ::: ::: In 2010, the three leading risk factors for global disease burden were high blood pressure (7·0% [95% uncertainty interval 6·2-7·7] of global DALYs), tobacco smoking including second-hand smoke (6·3% [5·5-7·0]), and alcohol use (5·5% [5·0-5·9]). In 1990, the leading risks were childhood underweight (7·9% [6·8-9·4]), household air pollution from solid fuels (HAP; 7·0% [5·6-8·3]), and tobacco smoking including second-hand smoke (6·1% [5·4-6·8]). Dietary risk factors and physical inactivity collectively accounted for 10·0% (95% UI 9·2-10·8) of global DALYs in 2010, with the most prominent dietary risks being diets low in fruits and those high in sodium. Several risks that primarily affect childhood communicable diseases, including unimproved water and sanitation and childhood micronutrient deficiencies, fell in rank between 1990 and 2010, with unimproved water and sanitation accounting for 0·9% (0·4-1·6) of global DALYs in 2010. However, in most of sub-Saharan Africa childhood underweight, HAP, and non-exclusive and discontinued breastfeeding were the leading risks in 2010, while HAP was the leading risk in south Asia. The leading risk factor in Eastern Europe, most of Latin America, and southern sub-Saharan Africa in 2010 was alcohol use; in most of Asia, North Africa and Middle East, and central Europe it was high blood pressure. Despite declines, tobacco smoking including second-hand smoke remained the leading risk in high-income north America and western Europe. High body-mass index has increased globally and it is the leading risk in Australasia and southern Latin America, and also ranks high in other high-income regions, North Africa and Middle East, and Oceania. ::: ::: INTERPRETATION ::: ::: Worldwide, the contribution of different risk factors to disease burden has changed substantially, with a shift away from risks for communicable diseases in children towards those for non-communicable diseases in adults. These changes are related to the ageing population, decreased mortality among children younger than 5 years, changes in cause-of-death composition, and changes in risk factor exposures. New evidence has led to changes in the magnitude of key risks including unimproved water and sanitation, vitamin A and zinc deficiencies, and ambient particulate matter pollution. The extent to which the epidemiological shift has occurred and what the leading risks currently are varies greatly across regions. In much of sub-Saharan Africa, the leading risks are still those associated with poverty and those that affect children.
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paper_title: High-resolution satellite-based analysis of ground-level PM2.5 for the city of Montreal.

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Satellite remote sensing offers the opportunity to determine the spatial distribution of aerosol properties and could fill the gap of ground-level observations. Various algorithms have recently been developed in order to retrieve the aerosol optical depth (AOD) at continental scales. However, they are, to some extent, subject to coarse spatial resolutions which are not appropriate for intraurban scales as usually needed in health studies. This paper presents an improved AOD retrieval algorithm for satellite instrument MODIS at 1-km resolution for intraurban scales. The MODIS-retrieved AODs are used to derive the ground-level PM2.5 concentrations using the aerosol vertical profiles and local scale factors obtained from the GEOS-Chem model simulation. The developed method has been applied to retrieve the AODs and to evaluate the ground-level PM2.5 over the city of Montreal, Canada for 2009 on daily, monthly and annual scales. The daily and monthly results are compared with the monitoring values with correlations R(2) ranging from 0.86 to 0.93. Especially, the annual mean PM2.5 concentrations are in good agreement with the measurement values at all monitoring stations (r=0.96, slope=1.0132 ± 0.0025, intercept=0.5739 ± 0.0013). This illustrates that the developed AOD retrieval algorithm can be used to retrieve AODs at a higher spatial resolution than previous studies to further derive the regional full coverage PM2.5 results at finer spatial and temporal scales. The study results are useful in health risk assessment across this region.
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paper_title: Exploring the relation between aerosol optical depth and PM2.5 at Cabauw, the Netherlands

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To acquire daily estimates of PM2.5 distributions based on satellite data one depends critically on an established relation between AOD and ground level PM2.5. In this study we aimed to experimentally establish the AOD-PM2.5 relationship for the Netherlands. For that purpose an experiment was set-up at the AERONET site Cabauw. The average PM 2.5 concentration during this ten month study was 18 μg/m 3, which confirms that the Netherlands are characterised by a high PM burden. A first inspection of the AERONET level 1.5 (L1.5) AOD and PM2.5 data at Cabauw showed a low correlation between the two properties. However, after screening for cloud contamination in the AERONET L1.5 data, the correlation improved substantially. When also constraining the dataset to data points acquired around noon, the correlation between AOD and PM2.5 amounted to R2=0.6 for situations with fair weather. This indicates that AOD data contain information about the temporal evolution of PM2.5. We had used LIDAR observations to detect residual cloud contamination in the AERONET L1.5 data. Comparison of our cloud-screed L1.5 with AERONET L2 data that became available near the end of the study showed favorable agreement. The final relation found for Cabauw is PM 2.5=124.5*AODĝ€"0.34 (with PM2.5 in μg/m3) and is valid for fair weather conditions. The relationship determined between MODIS AOD and ground level PM2.5 at Cabauw is very similar to that based on the much larger dataset from the sun photometer data, after correcting for a systematic overestimation of the MODIS data of 0.05. We applied the relationship to a MODIS composite map to assess the PM2.5 distribution over the Netherlands. Spatial dependent systematic errors in the MODIS AOD, probably related to variability in surface reflectance, hamper a meaningful analysis of the spatial distribution of PM2.5 using AOD data at the scale of the Netherlands.
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paper_title: Spatiotemporal air pollution exposure assessment for a Canadian population-based lung cancer case-control study

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Few epidemiological studies of air pollution have used residential histories to develop long-term retrospective exposure estimates for multiple ambient air pollutants and vehicle and industrial emissions. We present such an exposure assessment for a Canadian population-based lung cancer case-control study of 8353 individuals using self-reported residential histories from 1975 to 1994. We also examine the implications of disregarding and/or improperly accounting for residential mobility in long-term exposure assessments. National spatial surfaces of ambient air pollution were compiled from recent satellite-based estimates (for PM2.5 and NO2) and a chemical transport model (for O3). The surfaces were adjusted with historical annual air pollution monitoring data, using either spatiotemporal interpolation or linear regression. Model evaluation was conducted using an independent ten percent subset of monitoring data per year. Proximity to major roads, incorporating a temporal weighting factor based on Canadian mobile-source emission estimates, was used to estimate exposure to vehicle emissions. A comprehensive inventory of geocoded industries was used to estimate proximity to major and minor industrial emissions. Calibration of the national PM2.5 surface using annual spatiotemporal interpolation predicted historical PM2.5 measurement data best (R2 = 0.51), while linear regression incorporating the national surfaces, a time-trend and population density best predicted historical concentrations of NO2 (R2 = 0.38) and O3 (R2 = 0.56). Applying the models to study participants residential histories between 1975 and 1994 resulted in mean PM2.5, NO2 and O3 exposures of 11.3 μg/m3 (SD = 2.6), 17.7 ppb (4.1), and 26.4 ppb (3.4) respectively. On average, individuals lived within 300 m of a highway for 2.9 years (15% of exposure-years) and within 3 km of a major industrial emitter for 6.4 years (32% of exposure-years). Approximately 50% of individuals were classified into a different PM2.5, NO2 and O3 exposure quintile when using study entry postal codes and spatial pollution surfaces, in comparison to exposures derived from residential histories and spatiotemporal air pollution models. Recall bias was also present for self-reported residential histories prior to 1975, with cases recalling older residences more often than controls. We demonstrate a flexible exposure assessment approach for estimating historical air pollution concentrations over large geographical areas and time-periods. In addition, we highlight the importance of including residential histories in long-term exposure assessments. For submission to: Environmental Health
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paper_title: A Geographically and Temporally Weighted Regression Model for Ground-Level PM2.5 Estimation from Satellite-Derived 500 m Resolution AOD

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Regional haze episodes have occurred frequently in eastern China over the past decades. As a critical indicator to evaluate air quality, the mass concentration of ambient fine particulate matters smaller than 2.5 μm in aerodynamic diameter (PM2.5) is involved in many studies. To overcome the limitations of ground measurements on PM2.5 concentration, which is featured in disperse representation and coarse coverage, many statistical models were developed to depict the relationship between ground-level PM2.5 and satellite-derived aerosol optical depth (AOD). However, the current satellite-derived AOD products and statistical models on PM2.5–AOD are insufficient to investigate PM2.5 characteristics at the urban scale, in that spatial resolution is crucial to identify the relationship between PM2.5 and anthropogenic activities. This paper presents a geographically and temporally weighted regression (GTWR) model to generate ground-level PM2.5 concentrations from satellite-derived 500 m AOD. The GTWR model incorporates the SARA (simplified high resolution MODIS aerosol retrieval algorithm) AOD product with meteorological variables, including planetary boundary layer height (PBLH), relative humidity (RH), wind speed (WS), and temperature (TEMP) extracted from WRF (weather research and forecasting) assimilation to depict the spatio-temporal dynamics in the PM2.5–AOD relationship. The estimated ground-level PM2.5 concentration has 500 m resolution at the MODIS satellite’s overpass moments twice a day, which can be used for air quality monitoring and haze tracking at the urban and regional scale. To test the performance of the GTWR model, a case study was carried out in a region covering the adjacent parts of Jiangsu, Shandong, Henan, and Anhui provinces in central China. A cross validation was done to evaluate the performance of the GTWR model. Compared with OLS, GWR, and TWR models, the GTWR model obtained the highest value of coefficient of determination (R2) and the lowest values of mean absolute difference (MAD), root mean square error (RMSE), and mean absolute percentage error (MAPE).
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paper_title: Comparison of Geostatistical Interpolation and Remote Sensing Techniques for Estimating Long-Term Exposure to Ambient PM2.5 Concentrations across the Continental United States

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Background: A better understanding of the adverse health effects of chronic exposure to fine particulate matter (PM2.5) requires accurate estimates of PM2.5 variation at fine spatial scales. Remote sensing has emerged as an important means of estimating PM2.5 exposures, but relatively few studies have compared remote-sensing estimates to those derived from monitor-based data. ::: ::: Objective: We evaluated and compared the predictive capabilities of remote sensing and geostatistical interpolation. ::: ::: Methods: We developed a space–time geostatistical kriging model to predict PM2.5 over the continental United States and compared resulting predictions to estimates derived from satellite retrievals. ::: ::: Results: The kriging estimate was more accurate for locations that were about 100 km from a monitoring station, whereas the remote sensing estimate was more accurate for locations that were > 100 km from a monitoring station. Based on this finding, we developed a hybrid map that combines the kriging and satellite-based PM2.5 estimates. ::: ::: Conclusions: We found that for most of the populated areas of the continental United States, geostatistical interpolation produced more accurate estimates than remote sensing. The differences between the estimates resulting from the two methods, however, were relatively small. In areas with extensive monitoring networks, the interpolation may provide more accurate estimates, but in the many areas of the world without such monitoring, remote sensing can provide useful exposure estimates that perform nearly as well.
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paper_title: Validation of multiangle imaging spectroradiometer (MISR) aerosol optical thickness measurements using aerosol robotic network (AERONET) observations over the contiguous United States

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[1] Aerosol optical thickness (AOT) data retrieved by the Multiangle Imaging Spectroradiometer (MISR) in 2001 were compared with AOT measurements from 16 Aerosol Robotic Network (AERONET) sites over the contiguous United States. Overall, MISR and AERONET AOTs were strongly correlated (r = 0.73). Regression analysis showed that the root mean square error (RMSE) of MISR AOT was 0.05. The overall retrieval error of MISR AOT was within ±0.04 ± 0.18 × AOT. This result as well as the regression slope and intercept were comparable with previous results using AOT retrievals from MISR or Moderate Resolution Imaging Spectroradiometer (MODIS). The agreement between MISR and AERONET AOTs was improved (R2 = 0.90, RMSE = 0.04) when data from three western inland sites were excluded. A paired t test indicated that MISR systematically overestimated AOT by 0.02 ± 0.007. It was also shown that this positive bias in MISR AOT was greater during the spring and summer as well as in western United States. Together, these results suggest that MISR AOT measurements may be suitable for quantitative analysis of aerosol abundance. Finally, it is unlikely that the current results will vary when using alternative MISR AOT parameters since our analysis also showed the MISR AOT parameters (best fit, regional mean, and weighted regional mean AOTs) to be highly comparable.
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paper_title: Improving correlations between MODIS aerosol optical thickness and ground-based PM2.5 observations through 3D spatial analyses

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The Center for Space Research (CSR) continues to focus on developing methods to improve correlations between satellite-based aerosol optical thickness (AOT) values and ground-based, air pollution observations made at continuous ambient monitoring sites (CAMS) operated by the Texas commission on environmental quality (TCEQ). Strong correlations and improved understanding of the relationships between satellite and ground observations are needed to formulate reliable real-time predictions of air quality using data accessed from the moderate resolution imaging spectroradiometer (MODIS) at the CSR direct-broadcast ground station. In this paper, improvements in these correlations are demonstrated first as a result of the evolution in the MODIS retrieval algorithms. Further improvement is then shown using procedures that compensate for differences in horizontal spatial scales between the nominal 10-km MODIS AOT products and CAMS point measurements. Finally, airborne light detection and ranging (lidar) observations, collected during the Texas Air Quality Study of 2000, are used to examine aerosol profile concentrations, which may vary greatly between aerosol classes as a result of the sources, chemical composition, and meteorological conditions that govern transport processes. Further improvement in correlations is demonstrated with this limited dataset using insights into aerosol profile information inferred from the vertical motion vectors in a trajectory-based forecast model. Analyses are ongoing to verify these procedures on a variety of aerosol classes using data collected by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite (Calipso) lidar.
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paper_title: Using High-Resolution Satellite Aerosol Optical Depth To Estimate Daily PM2.5 Geographical Distribution in Mexico City.

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Recent advances in estimating fine particle (PM2.5) ambient concentrations use daily satellite measurements of aerosol optical depth (AOD) for spatially and temporally resolved exposure estimates. Mexico City is a dense megacity that differs from other previously modeled regions in several ways: it has bright land surfaces, a distinctive climatological cycle, and an elevated semi-enclosed air basin with a unique planetary boundary layer dynamic. We extend our previous satellite methodology to the Mexico City area, a region with higher PM2.5 than most U.S. and European urban areas. Using a novel 1 km resolution AOD product from the MODIS instrument, we constructed daily predictions across the greater Mexico City area for 2004-2014. We calibrated the association of AOD to PM2.5 daily using municipal ground monitors, land use, and meteorological features. Predictions used spatial and temporal smoothing to estimate AOD when satellite data were missing. Our model performed well, resulting in an out-of-sample cross-validation R(2) of 0.724. Cross-validated root-mean-squared prediction error (RMSPE) of the model was 5.55 μg/m(3). This novel model reconstructs long- and short-term spatially resolved exposure to PM2.5 for epidemiological studies in Mexico City.
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paper_title: A New Hybrid Spatio-Temporal Model For Estimating Daily Multi-Year PM2.5 Concentrations Across Northeastern USA Using High Resolution Aerosol Optical Depth Data.

paper_content:

BACKGROUND ::: The use of satellite-based aerosol optical depth (AOD) to estimate fine particulate matter (PM2.5) for epidemiology studies has increased substantially over the past few years. These recent studies often report moderate predictive power, which can generate downward bias in effect estimates. In addition, AOD measurements have only moderate spatial resolution, and have substantial missing data. ::: ::: ::: METHODS ::: We make use of recent advances in MODIS satellite data processing algorithms (Multi-Angle Implementation of Atmospheric Correction (MAIAC), which allow us to use 1 km (versus currently available 10 km) resolution AOD data. We developed and cross validated models to predict daily PM2.5 at a 1×1km resolution across the northeastern USA (New England, New York and New Jersey) for the years 2003-2011, allowing us to better differentiate daily and long term exposure between urban, suburban, and rural areas. Additionally, we developed an approach that allows us to generate daily high-resolution 200 m localized predictions representing deviations from the area 1×1 km grid predictions. We used mixed models regressing PM2.5 measurements against day-specific random intercepts, and fixed and random AOD and temperature slopes. We then use generalized additive mixed models with spatial smoothing to generate grid cell predictions when AOD was missing. Finally, to get 200 m localized predictions, we regressed the residuals from the final model for each monitor against the local spatial and temporal variables at each monitoring site. ::: ::: ::: RESULTS ::: Our model performance was excellent (mean out-of-sample R2=0.88). The spatial and temporal components of the out-of-sample results also presented very good fits to the withheld data (R2=0.87, R2=0.87). In addition, our results revealed very little bias in the predicted concentrations (Slope of predictions versus withheld observations = 0.99). ::: ::: ::: CONCLUSION ::: Our daily model results show high predictive accuracy at high spatial resolutions and will be useful in reconstructing exposure histories for epidemiological studies across this region.
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paper_title: A New Method to Jointly Estimate the Mortality Risk of Long-Term Exposure to Fine Particulate Matter and its Components

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Most studies on the association between exposure to fine particulate matter (PM2.5) and mortality have considered only total concentration of PM2.5 or individual components of PM2.5, and not the combined effects of concentration and particulate composition. We sought to develop a method to estimate the risk of death from long-term exposure to PM2.5 and the distribution of its components, namely: sulphate, nitrate, ammonium, organic mass, black carbon, and mineral dust. We decomposed PM2.5 exposure into the sum of total concentration and the proportion of each component. We estimated the risk of death due to exposure using a cohort of ~2.4 million Canadians who were followed for vital status over 16 years. Modelling the concentration of PM2.5 with the distribution of the proportions of components together was a superior predictor for mortality than either total PM2.5 concentration alone, or all component concentrations modelled together. Our new approach has the advantage of characterizing the toxicity of the atmosphere in its entirety. This is required to fully understand the health benefits associated with strategies to improve air quality that may result in complex changes not only in PM2.5 concentration, but also in the distribution of particle components.
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paper_title: Exposure Assessment for Estimation of the Global Burden of Disease Attributable to Outdoor Air Pollution

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Ambient air pollution is associated with numerous adverse health impacts. Previous assessments of global attributable disease burden have been limited to urban areas or by coarse spatial resolution of concentration estimates. Recent developments in remote sensing, global chemical-transport models, and improvements in coverage of surface measurements facilitate virtually complete spatially resolved global air pollutant concentration estimates. We combined these data to generate global estimates of long-term average ambient concentrations of fine particles (PM(2.5)) and ozone at 0.1° × 0.1° spatial resolution for 1990 and 2005. In 2005, 89% of the world's population lived in areas where the World Health Organization Air Quality Guideline of 10 μg/m(3) PM(2.5) (annual average) was exceeded. Globally, 32% of the population lived in areas exceeding the WHO Level 1 Interim Target of 35 μg/m(3), driven by high proportions in East (76%) and South (26%) Asia. The highest seasonal ozone levels were found in North and Latin America, Europe, South and East Asia, and parts of Africa. Between 1990 and 2005 a 6% increase in global population-weighted PM(2.5) and a 1% decrease in global population-weighted ozone concentrations was apparent, highlighted by increased concentrations in East, South, and Southeast Asia and decreases in North America and Europe. Combined with spatially resolved population distributions, these estimates expand the evaluation of the global health burden associated with outdoor air pollution.
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paper_title: Spatio-Temporal Variation of PM2.5 Concentrations and Their Relationship with Geographic and Socioeconomic Factors in China

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The air quality in China, particularly the PM2.5 (particles less than 2.5 μm in aerodynamic diameter) level, has become an increasing public concern because of its relation to health risks. The distribution of PM2.5 concentrations has a close relationship with multiple geographic and socioeconomic factors, but the lack of reliable data has been the main obstacle to studying this topic. Based on the newly published Annual Average PM2.5 gridded data, together with land use data, gridded population data and Gross Domestic Product (GDP) data, this paper explored the spatial-temporal characteristics of PM2.5 concentrations and the factors impacting those concentrations in China for the years of 2001-2010. The contributions of urban areas, high population and economic development to PM2.5 concentrations were analyzed using the Geographically Weighted Regression (GWR) model. The results indicated that the spatial pattern of PM2.5 concentrations in China remained stable during the period 2001-2010; high concentrations of PM2.5 are mostly found in regions with high populations and rapid urban expansion, including the Beijing-Tianjin-Hebei region in North China, East China (including the Shandong, Anhui and Jiangsu provinces) and Henan province. Increasing populations, local economic growth and urban expansion are the three main driving forces impacting PM2.5 concentrations.
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paper_title: Estimating PM2.5 Concentrations in Xi'an City Using a Generalized Additive Model with Multi-Source Monitoring Data

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Particulate matter with an aerodynamic diameter <2.5 μm (PM2.5) represents a severe environmental problem and is of negative impact on human health. Xi'an City, with a population of 6.5 million, is among the highest concentrations of PM2.5 in China. In 2013, in total, there were 191 days in Xi’an City on which PM2.5 concentrations were greater than 100 μg/m3. Recently, a few studies have explored the potential causes of high PM2.5 concentration using remote sensing data such as the MODIS aerosol optical thickness (AOT) product. Linear regression is a commonly used method to find statistical relationships among PM2.5 concentrations and other pollutants, including CO, NO2, SO2, and O3, which can be indicative of emission sources. The relationships of these variables, however, are usually complicated and non-linear. Therefore, a generalized additive model (GAM) is used to estimate the statistical relationships between potential variables and PM2.5 concentrations. This model contains linear functions of SO2 and CO, univariate smoothing non-linear functions of NO2, O3, AOT and temperature, and bivariate smoothing non-linear functions of location and wind variables. The model can explain 69.50% of PM2.5 concentrations, with R2 = 0.691, which improves the result of a stepwise linear regression (R2 = 0.582) by 18.73%. The two most significant variables, CO concentration and AOT, represent 20.65% and 19.54% of the deviance, respectively, while the three other gas-phase concentrations, SO2, NO2, and O3 account for 10.88% of the total deviance. These results show that in Xi'an City, the traffic and other industrial emissions are the primary source of PM2.5. Temperature, location, and wind variables also non-linearly related with PM2.5.
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paper_title: Impacts of elevated-aerosol-layer and aerosol type on the correlation of AOD and particulate matter with ground-based and satellite measurements in Nanjing, southeast China.

paper_content:

Assessment of the correlation between aerosol optical depth (AOD) and particulate matter (PM) is critical to satellite remote sensing of air quality, e.g. ground PM10 and ground PM2.5. This study evaluates the impacts of aloft-aerosol-plume and aerosol-type on the correlation of AOD-PM by using synergistic measurement of a polarization-sensitive Raman-Mie lidar, CIMEL sunphotometer (SP) and TEOM PM samplers, as well as the satellite MODIS and CALIPSO, during April to July 2011 in Nanjing city (32.05(○)N/118.77(○)E), southeast China. Aloft-aerosol-layer and aerosol types (e.g. dust and non-dust or urban aerosol) are identified with the range-resolved polarization lidar and SP measurements. The results indicate that the correlations for AOD-PM10 and AOD-PM2.5 can be much improved when screening out the aloft-aerosol-layer. The linear regression slopes show significant differences for the dust and non-dust dominant aerosols in the planetary boundary layer (PBL). In addition, we evaluate the recent released MODIS-AOD product (Collection 6) from the "dark-target" (DT) and "deep-blue" (DB) algorithms and their correlation with the PM in Nanjing urban area. The results verify that the MODIS-DT AODs show a good correlation (R = 0.89) with the SP-AOD but with a systematic overestimate. In contrast, the MODIS-DB AOD shows a moderate correlation (R = 0.66) with the SP-AOD but with a smaller regression intercept (0.07). Furthermore, the moderately high correlations between the MODIS-AOD and PM10 (PM2.5) are indicated, which suggests the feasibility of PM estimate using the MODIS-AOD in Nanjing city.
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paper_title: Associations between cigarette smoking, obesity, sociodemographic characteristics and remote-sensing-derived estimates of ambient PM2.5: results from a Canadian population-based survey

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Objectives Long-term exposure to ambient fine particles (PM 2.5 ) has been shown to increase mortality. Variables measured on the same spatial scales of air pollution may confound associations, and so the authors9 objectives were to evaluate the associations between PM 2.5 and individual-level measures of smoking, obesity and sociodemographic status. The authors present an approach to evaluate the impact that uncontrolled confounding from smoking may have on associations between PM 2.5 and mortality. Methods Individual-level behavioural and sociodemographic data were obtained from a 2003 national survey of 122 548 Canadians. Estimates of ground-level PM 2.5 at a resolution of 10×10 km between 2001 and 2006 were derived from satellite remote sensing. Exposures were assigned to the residence of the participants at the time of the survey. Differences in the prevalence of smoking across concentrations of PM 2.5 and RRs drawn from the literature were used to model the bias on rate ratios. Results Participants in areas with higher concentrations of PM 2.5 had a higher income and educational attainment, smoked less and were more likely immigrants. Smoking had a negative confounding effect on the associations between PM 2.5 and mortality. To compensate for this bias, for a 10 μg/m 3 increase in PM 2.5 , mortality from lung cancer and heart disease in the referent exposure group needed to be increased by 6.9% and 3.2%, respectively. Conclusions Associations were found between sociodemographic and lifestyle characteristics and PM 2.5 at a resolution of 10×10 km. The authors present a model to adjust for uncontrolled confounding of smoking that can be readily adapted to exposures measured at different spatial resolutions.
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paper_title: Estimating national-scale ground-level PM25 concentration in China using geographically weighted regression based on MODIS and MISR AOD

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Taking advantage of the continuous spatial coverage, satellite-derived aerosol optical depth (AOD) products have been widely used to assess the spatial and temporal characteristics of fine particulate matter (PM2.5) on the ground and their effects on human health. However, the national-scale ground-level PM2.5 estimation is still very limited because the lack of ground PM2.5 measurements to calibrate the model in China. In this study, a national-scale geographically weighted regression (GWR) model was developed to estimate ground-level PM2.5 concentration based on satellite AODs, newly released national-wide hourly PM2.5 concentrations, and meteorological parameters. The results showed good agreements between satellite-retrieved and ground-observed PM2.5 concentration at 943 stations in China. The overall cross-validation (CV) R (2) is 0.76 and root mean squared prediction error (RMSE) is 22.26 μg/m(3) for MODIS-derived AOD. The MISR-derived AOD also exhibits comparable performance with a CV R (2) and RMSE are 0.81 and 27.46 μg/m(3), respectively. Annual PM2.5 concentrations retrieved either by MODIS or MISR AOD indicated that most of the residential community areas exceeded the new annual Chinese PM2.5 National Standard level 2. These results suggest that this approach is useful for estimating large-scale ground-level PM2.5 distributions especially for the regions without PMs monitoring sites.
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paper_title: Assessing temporally and spatially resolved PM2.5 exposures for epidemiological studies using satellite aerosol optical depth measurements

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Land use regression (LUR) models provide good estimates of spatially resolved long-term exposures, but are poor at capturing short term exposures. Satellite-derived Aerosol Optical Depth (AOD) measurements have the potential to provide spatio-temporally resolved predictions of both long and short term exposures, but previous studies have generally showed relatively low predictive power. Our objective was to extend our previous work on day-specific calibrations of AOD data using ground PM2.5 measurements by incorporating commonly used LUR variables and meteorological variables, thus benefiting from both the spatial resolution from the LUR models and the spatio-temporal resolution from the satellite models. Later we use spatial smoothing to predict PM2.5 concentrations for day/locations with missing AOD measures. We used mixed models with random slopes for day to calibrate AOD data for 2000–2008 across New-England with monitored PM2.5 measurements. We then used a generalized additive mixed model with spatial smoothing to estimate PM2.5 in location–day pairs with missing AOD, using regional measured PM2.5, AOD values in neighboring cells, and land use. Finally, local (100 m) land use terms were used to model the difference between grid cell prediction and monitored value to capture very local traffic particles. Out-of-sample ten-fold cross-validation was used to quantify the accuracy of our predictions. For days with available AOD data we found high out-of-sample R2 (mean out-of-sample R2 = 0.830, year to year variation 0.725–0.904). For days without AOD values, our model performance was also excellent (mean out-of-sample R2 = 0.810, year to year variation 0.692–0.887). Importantly, these R2 are for daily, rather than monthly or yearly, values. Our model allows one to assess short term and long-term human exposures in order to investigate both the acute and chronic effects of ambient particles, respectively.
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paper_title: Using new satellite based exposure methods to study the association between pregnancy pm2.5 exposure, premature birth and birth weight in Massachusetts

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Adverse birth outcomes such as low birth weight and premature birth have been previously linked with exposure to ambient air pollution. Most studies relied on a limited number of monitors in the region of interest, which can introduce exposure error or restrict the analysis to persons living near a monitor, which reduces sample size and generalizability and may create selection bias. We evaluated the relationship between premature birth and birth weight with exposure to ambient particulate matter (PM2.5) levels during pregnancy in Massachusetts for a 9-year period (2000–2008). Building on a novel method we developed for predicting daily PM2.5 at the spatial resolution of a 10x10km grid across New-England, we estimated the average exposure during 30 and 90 days prior to birth as well as the full pregnancy period for each mother. We used linear and logistic mixed models to estimate the association between PM2.5 exposure and birth weight (among full term births) and PM2.5 exposure and preterm birth adjusting for infant sex, maternal age, maternal race, mean income, maternal education level, prenatal care, gestational age, maternal smoking, percent of open space near mothers residence, average traffic density and mothers health. Birth weight was negatively associated with PM2.5 across all tested periods. For example, a 10 μg/m3 increase of PM2.5 exposure during the entire pregnancy was significantly associated with a decrease of 13.80 g [95% confidence interval (CI) = −21.10, -6.05] in birth weight after controlling for other factors, including traffic exposure. The odds ratio for a premature birth was 1.06 (95% confidence interval (CI) = 1.01–1.13) for each 10 μg/m3 increase of PM2.5 exposure during the entire pregnancy period. The presented study suggests that exposure to PM2.5 during the last month of pregnancy contributes to risks for lower birth weight and preterm birth in infants.
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paper_title: Estimating the global abundance of ground level presence of particulate matter (PM2.5).

paper_content:

With the increasing awareness of the health impacts of particulate matter, there is a growing need to comprehend the spatial and temporal variations of the global abundance of ground level airborne particulate matter with a diameter of 2.5 microns or less (PM2.5). Here we use a suite of remote sensing and meteorological data products together with ground-based observations of particulate matter from 8,329 measurement sites in 55 countries taken 1997-2014 to train a machine-learning algorithm to estimate the daily distributions of PM2.5 from 1997 to the present. In this first paper of a series, we present the methodology and global average results from this period and demonstrate that the new PM2.5 data product can reliably represent global observations of PM2.5 for epidemiological studies.
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paper_title: Estimating ground-level PM2.5 concentrations in the southeastern U.S. using geographically weighted regression $

paper_content:

Abstract Most of currently reported models for predicting PM 2.5 concentrations from satellite retrievals of aerosol optical depth are global methods without considering local variations, which might introduce significant biases into prediction results. In this paper, a geographically weighted regression model was developed to examine the relationship among PM 2.5 , aerosol optical depth, meteorological parameters, and land use information. Additionally, two meteorological datasets, North American Regional Reanalysis and North American Land Data Assimilation System, were fitted into the model separately to compare their performances. The study area is centered at the Atlanta Metro area, and data were collected from various sources for the year 2003. The results showed that the mean local R 2 of the models using North American Regional Reanalysis was 0.60 and those using North American Land Data Assimilation System reached 0.61. The root mean squared prediction error showed that the prediction accuracy was 82.7% and 83.0% for North American Regional Reanalysis and North American Land Data Assimilation System in model fitting, respectively, and 69.7% and 72.1% in cross validation. The results indicated that geographically weighted regression combined with aerosol optical depth, meteorological parameters, and land use information as the predictor variables could generate a better fit and achieve high accuracy in PM 2.5 exposure estimation, and North American Land Data Assimilation System could be used as an alternative of North American Regional Reanalysis to provide some of the meteorological fields.
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paper_title: Spatiotemporal prediction of fine particulate matter using high-resolution satellite images in the Southeastern US 2003–2011

paper_content:

Spatiotemporal prediction of fine particulate matter using high-resolution satellite images in the Southeastern US 2003–2011
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paper_title: Global Estimates of Ambient Fine Particulate Matter Concentrations from Satellite-Based Aerosol Optical Depth: Development and Application

paper_content:

Chronic exposure to airborne fine particulate matter with diameter < 2.5 μm (PM2.5) is associated with adverse human health impacts including morbidity and mortality (e.g., Dockery et al. 1993; McDonnell et al. 2000; Pope et al. 2009). Several national environmental agencies in North America and Europe monitor PM2.5 concentrations at numerous sites throughout their jurisdictions, but even these relatively dense networks have limited geographic coverage. Few long-term measurement sites exist elsewhere in the world, particularly in rapidly developing countries where concentrations and estimated health impacts are greatest (Cohen et al. 2004). Point measurements collected at monitoring sites are not necessarily representative of regional concentration, and regional variability is difficult to assess from point measurements alone. In recent years, application of satellite observation to surface air quality has advanced considerably (Hoff and Christopher 2009; Martin 2008). In fact, global aerosol observations from satellite remote sensing could substantially improve estimates of population exposure to PM2.5. ::: ::: Since the mid 2000s, the MODIS (Moderate Resolution Imaging Spectroradiometer) and MISR (Multiangle Imaging Spectroradiometer) instruments onboard the National Aeronautics and Space Administration’s (NASA) Terra satellite has provided global observations of aerosol optical depth (AOD), a measure of light extinction by aerosol in the atmospheric column above the earth’s surface. Terra’s sun-synchronous orbit encircles the earth approximately 15 times each day, with each pass crossing the equator at approximately 1030 hours local solar time. Observations of AOD from Terra provide daily insight into the global distribution of column-integrated aerosol. However, the applicability of AOD to surface air quality depends on several factors, including the vertical structure, composition, size distribution, and water content of atmospheric aerosol. ::: ::: Many studies have investigated the relationship between total-column AOD and surface PM2.5 measurements. Most have developed simple empirical relationships between these two variables (e.g., Engel-Cox et al. 2004a; Wang and Christopher 2003); more recent investigations often have used local meteorological information to better relate AOD and PM2.5 (e.g., Koelemeijer et al. 2006; Liu et al. 2005) or to filter the AOD (e.g., Gupta et al. 2006). Some studies have employed light detection and ranging (LIDAR) instruments to capture the vertical aerosol distribution at specific locations (e.g., Engel-Cox et al. 2006; Schaap et al. 2008). Schaap et al. (2008) noted that locally derived AOD–PM2.5 relationships cannot be extended easily to other regions because of variation in meteorology and aerosol composition. Unique, local, time-dependent AOD–PM2.5 relationships are necessary to infer global estimates of PM2.5. Ground-based measurements of aerosol vertical profiles and properties have insufficient coverage to estimate global AOD–PM2.5 relationships. ::: ::: Global chemical transport models (CTMs) resolve atmospheric composition at a resolution of hundreds of kilometers horizontally by hundreds of meters vertically, with a temporal frequency of tens of minutes. Liu et al. (2004) first estimated surface-level PM2.5 from MISR observations by using CTM output to represent local AOD–PM2.5 conversion factors over the contiguous United States. van Donkelaar et al. (2006) extended the approach used by Liu et al. (2004) to estimate PM2.5 from both MODIS and MISR observations and investigated the factors affecting the agreement between AOD and surface-level PM2.5. Statistical models have also been used to relate AOD to PM2.5. For example, Liu et al. (2007) used MISR-retrieved spherical versus nonspherical particle fraction, in addition to model-derived vertical distribution, to separate mineral dust from other aerosol species. More recently, Paciorek and Liu (2009) probed the limitations of using AOD without accounting for vertical distribution or speciation and concluded that agreement with ground-based monitors based on this approach might depend on factors other than satellite observations. ::: ::: We developed a global satellite-based estimate of surface PM2.5 at a spatial resolution of 0.1° × 0.1°, or approximately 10 km × 10 km at midlatitudes. We developed an approach for combining MODIS and MISR AOD into a single improved estimate of AOD. Using this methodology, we calculated AOD–PM2.5 conversion factors with a global CTM and produced and applied these factors to the AOD. We present a global estimate of PM2.5 concentrations and validate it with ground-based (in situ) observations. We estimate global exposure to outdoor ambient PM2.5 using our satellite-derived product to demonstrate potential application for global health studies. We then examined sources of error.
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paper_title: National-Scale Estimates of Ground-Level PM2.5 Concentration in China Using Geographically Weighted Regression Based on 3 km Resolution MODIS AOD

paper_content:

High spatial resolution estimating of exposure to particulate matter 2.5 (PM2.5) is currently very limited in China. This study uses the newly released nationwide, hourly PM2.5 concentrations to create a nationwide, geographically weighted regression (GWR) model to estimate ground-level PM2.5 concentrations in China. A3 km resolution aerosol optical depth (AOD) product from MODIS is used as the primary predictor. Fire emissions detected by MODIS fire count were considered in the model development process. Additionally, meteorological features were used as covariates in the model to improve the estimation of ground-level PM2.5 concentrations. The model performed well and explained 81% of the daily PM2.5 concentration variations in model predictions, and the cross validations R2 is 0.79. The cross-validated root mean squared error (RMSE) of the model was 18.6 μg/m3.Annual PM2.5 concentrations retrieved by the MODIS 3 km AOD product indicated that most of the residential community areas exceeded the new annual Chinese PM2.5 National Standard level 2. Estimated high-resolution national-scale daily PM2.5 maps are useful to identify severe air pollution episodes and determine health risk assessments. These results suggest that this approach is useful for estimating large-scale ground-level PM2.5 distributions, especially for regions without PM monitoring sites.
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paper_title: High-Resolution Satellite Mapping of Fine Particulates Based on Geographically Weighted Regression

paper_content:

Satellite-retrieved aerosol optical depth (AOD) has been increasingly utilized for the mapping of fine particulate matter (PM2.5) concentrations. An accurate estimation and mapping of PM2.5 concentrations depends on the high-resolution AOD data and a robust mathematical model that takes into account the spatial nonstationary relationship between PM2.5 and AOD. Take the core portion of the Beijing-Hebei-Tianjin (Jing-Jin-Ji) urban agglomeration as case study (the most seriously polluted region in China). Land use, population, meteorological variables, and simplified aerosol retrieval algorithm-retrieved AOD at 1-km resolution are employed as the predictors for the geographically weighted regression (GWR) and the ordinary least squares (OLS) model to map the spatial distribution of PM2.5 concentrations. The GWR model shows significant spatial variations in PM2.5 concentrations over the region than the traditional OLS model, which reveals relative homogeneous variations. Validation with ground-level PM2.5 concentrations demonstrates that PM2.5 concentrations predicted by the GWR model (R2 = 0.75, RMSE = 10 μg/m3) correlate better than those by the OLS model (R2 = 0.53, RMSE = 16 μg/m3). These results suggest that the GWR model offered a more reliable way for the prediction of spatial distribution of PM2.5 concentrations over urban areas.
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paper_title: Estimating ground-level PM2.5 concentrations over three megalopolises in China using satellite-derived aerosol optical depth measurements

paper_content:

Abstract Numerous previous studies have revealed that statistical models which combine satellite-derived aerosol optical depth (AOD) and PM2.5 measurements acquired at scattered monitoring sites provide an effective method for deriving continuous spatial distributions of ground-level PM2.5 concentrations. Using the national monitoring networks that have recently been established by central and local governments in China, we developed linear mixed-effects (LMEs) models that integrate Moderate Resolution Imaging Spectroradiometer (MODIS) AOD measurements, meteorological parameters, and satellite-derived tropospheric NO2 column density measurements as predictors to estimate PM2.5 concentrations over three major industrialized regions in China, namely, the Beijing–Tianjin–Hebei region (BTH), the Yangtze River Delta region (YRD), and the Pearl River Delta region (PRD). The models developed for these three regions exploited different predictors to account for their varying topographies and meteorological conditions. Considering the importance of unbiased PM2.5 predictions for epidemiological studies, the correction factors calculated from the surface PM2.5 measurements were applied to correct biases in the predicted annual average PM2.5 concentrations introduced by non-stochastic missing AOD measurements. Leave-one-out cross-validation (LOOCV) was used to quantify the accuracy of our models. Cross-validation of the daily predictions yielded R2 values of 0.77, 0.8 and 0.8 and normalized mean error (NME) values of 22.4%, 17.8% and 15.2% for BTH, YRD and PRD, respectively. For the annual average PM2.5 concentrations, the LOOCV R2 values were 0.85, 0.76 and 0.71 for the three regions, respectively, whereas the LOOCV NME values were 8.0%, 6.9% and 8.4%, respectively. We found that the incorporation of satellite-based NO2 column density into the LMEs model contribute to considerable improvements in annual prediction accuracy for both BTH and YRD. The satisfactory performance of our models indicates that constructing LMEs models using various combinations of predictors for different regions would be helpful for predicting PM2.5 concentrations with high accuracy.
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paper_title: Use of Satellite Observations for Long-Term Exposure Assessment of Global Concentrations of Fine Particulate Matter

paper_content:

BACKGROUND ::: More than a decade of satellite observations offers global information about the trend and magnitude of human exposure to fine particulate matter (PM2.5). ::: ::: ::: OBJECTIVE ::: In this study, we developed improved global exposure estimates of ambient PM2.5 mass and trend using PM2.5 concentrations inferred from multiple satellite instruments. ::: ::: ::: METHODS ::: We combined three satellite-derived PM2.5 sources to produce global PM2.5 estimates at about 10 km × 10 km from 1998 through 2012. For each source, we related total column retrievals of aerosol optical depth to near-ground PM2.5 using the GEOS-Chem chemical transport model to represent local aerosol optical properties and vertical profiles. We collected 210 global ground-based PM2.5 observations from the literature to evaluate our satellite-based estimates with values measured in areas other than North America and Europe. ::: ::: ::: RESULTS ::: We estimated that global population-weighted ambient PM2.5 concentrations increased 0.55 μg/m3/year (95% CI: 0.43, 0.67) (2.1%/year; 95% CI: 1.6, 2.6) from 1998 through 2012. Increasing PM2.5 in some developing regions drove this global change, despite decreasing PM2.5 in some developed regions. The estimated proportion of the population of East Asia living above the World Health Organization (WHO) Interim Target-1 of 35 μg/m3 increased from 51% in 1998-2000 to 70% in 2010-2012. In contrast, the North American proportion above the WHO Air Quality Guideline of 10 μg/m3 fell from 62% in 1998-2000 to 19% in 2010-2012. We found significant agreement between satellite-derived estimates and ground-based measurements outside North America and Europe (r = 0.81; n = 210; slope = 0.68). The low bias in satellite-derived estimates suggests that true global concentrations could be even greater. ::: ::: ::: CONCLUSIONS ::: Satellite observations provide insight into global long-term changes in ambient PM2.5 concentrations. Satellite-derived estimates and ground-based PM2.5 observations from this study are available for public use.
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paper_title: A Geographically and Temporally Weighted Regression Model for Ground-Level PM2.5 Estimation from Satellite-Derived 500 m Resolution AOD

paper_content:

Regional haze episodes have occurred frequently in eastern China over the past decades. As a critical indicator to evaluate air quality, the mass concentration of ambient fine particulate matters smaller than 2.5 μm in aerodynamic diameter (PM2.5) is involved in many studies. To overcome the limitations of ground measurements on PM2.5 concentration, which is featured in disperse representation and coarse coverage, many statistical models were developed to depict the relationship between ground-level PM2.5 and satellite-derived aerosol optical depth (AOD). However, the current satellite-derived AOD products and statistical models on PM2.5–AOD are insufficient to investigate PM2.5 characteristics at the urban scale, in that spatial resolution is crucial to identify the relationship between PM2.5 and anthropogenic activities. This paper presents a geographically and temporally weighted regression (GTWR) model to generate ground-level PM2.5 concentrations from satellite-derived 500 m AOD. The GTWR model incorporates the SARA (simplified high resolution MODIS aerosol retrieval algorithm) AOD product with meteorological variables, including planetary boundary layer height (PBLH), relative humidity (RH), wind speed (WS), and temperature (TEMP) extracted from WRF (weather research and forecasting) assimilation to depict the spatio-temporal dynamics in the PM2.5–AOD relationship. The estimated ground-level PM2.5 concentration has 500 m resolution at the MODIS satellite’s overpass moments twice a day, which can be used for air quality monitoring and haze tracking at the urban and regional scale. To test the performance of the GTWR model, a case study was carried out in a region covering the adjacent parts of Jiangsu, Shandong, Henan, and Anhui provinces in central China. A cross validation was done to evaluate the performance of the GTWR model. Compared with OLS, GWR, and TWR models, the GTWR model obtained the highest value of coefficient of determination (R2) and the lowest values of mean absolute difference (MAD), root mean square error (RMSE), and mean absolute percentage error (MAPE).
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paper_title: Predicting regional space–time variation of PM2.5 with land-use regression model and MODIS data

paper_content:

Purpose ::: Existing land-use regression (LUR) models use land use/cover, population, and traffic information to predict long-term intra-urban variation of air pollution. These models are limited to explaining spatial variation of air pollutants, and few of them are capable of addressing temporal variability. This article proposes a space–time LUR model at a regional scale by incorporating aerosol optical depth (AOD) data from the Moderate Resolution Imaging Spectroradiometer (MODIS).
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paper_title: An Improved Method for Monitoring Fine Particulate Matter Mass Concentrations via Satellite Remote Sensing

paper_content:

Ground level monitoring of Particulate Matter (PM) is limited by spatial coverage and resolution, in spite of possessing high temporal resolution and accuracy. Atmospheric Aerosol Optical Depth (AOD), a product of space-borne remote sensing, has shown significant potential for estimating ground level PM concentrations. Several approaches have been used to improve the correlation between AOD-PM by providing corrections for the aerosol vertical profile and ground level humidity. However, the effects of the vertical profile of humidity and aerosol size on the AOD-PM relationship requires further study. In this paper, we propose a method for developing an AOD-PM_(2.5) relationship by retrieving the vertical profile of relative humidity via ground observation data and aerosol size distribution in Beijing. Moreover, a series of Hanel growth coefficients (γ) are applied to determine the specific value, which maximizes the correlation. The results show that applying our proposed method can improve the correlation from R = 0.610 to R = 0.707 for Terra and R = 0.707 to 0.752 for Aqua. The best correlations were obtained for γ = 1.2 and 1.3 for Terra and Aqua, respectively. A good correlation (R = 0.8) between ground based and MODIS based PM_(2.5) measurements, together with employing MODIS to predict true air pollution levels (65% accuracy), suggests that the vertical profile of RH derived via ground level observation and aerosol size should be considered and applied to models in future studies, which utilize satellite data for air pollution monitoring and controlling.
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paper_title: Estimating Regional Spatial and Temporal Variability of PM2.5 Concentrations Using Satellite Data, Meteorology, and Land Use Information

paper_content:

BackgroundStudies of chronic health effects due to exposures to particulate matter with aerodynamic diameters ≤ 2.5 μm (PM2.5) are often limited by sparse measurements. Satellite aerosol remote sen...
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paper_title: Satellite-based estimation of regional particulate matter (PM) in Beijing using vertical-and-RH correcting method

paper_content:

Abstract Theoretical analysis based on the atmospheric radiative transfer indicated a positive correlation between the aerosol optical thickness (AOT) and the surface-level particulate matter (PM) concentrations, and this correlation is improved significantly using vertical-and-RH correcting method. The correlative analysis of the ground-based measurement indicates that, (a) the correlation between AOT and the aerosol extinction coefficient at surface level (ka,0) is improved as a result of the vertical correction, with the coefficient of determination R2 increasing from 0.35 to 0.56; (b) the correlation between ka,0 and PM concentrations can be significantly improved by the RH correction with the R2 increasing from 0.43 to 0.77 for PM10, and from 0.35 to 0.66 for PM2.5. Based on the in-situ measurements in Beijing, two linear correlative models between the ground-based AOT and PMs (e.g. PM10 and PM2.5) concentrations were developed. These models are used to estimate the regional distribution of PM10 and PM2.5 using the satellite-retrieved AOT in Beijing area. Validation against the in-situ measurements in Beijing shows that both of the correlations of the satellite-estimated PM10 and PM2.5 with the measurements are R2 = 0.47, and the biases are 26.33% and 6.49% respectively. When averaged in the urban area of Beijing, the R2 between the estimated PM10 and the measurements increased to 0.66. These results suggest that by using the vertical-and-RH correcting method we can use the MODIS data to monitor the regional air pollution.
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paper_title: Modeling spatial effects of PM(2.5) on term low birth weight in Los Angeles County.

paper_content:

Air pollution epidemiological studies suggest that elevated exposure to fine particulate matter (PM2.5) is associated with higher prevalence of term low birth weight (TLBW). Previous studies have generally assumed the exposure-response of PM2.5 on TLBW to be the same throughout a large geographical area. Health effects related to PM2.5 exposures, however, may not be uniformly distributed spatially, creating a need for studies that explicitly investigate the spatial distribution of the exposure-response relationship between individual-level exposure to PM2.5 and TLBW. Here, we examine the overall and spatially varying exposure-response relationship between PM2.5 and TLBW throughout urban Los Angeles (LA) County, California. We estimated PM2.5 from a combination of land use regression (LUR), aerosol optical depth from remote sensing, and atmospheric modeling techniques. Exposures were assigned to LA County individual pregnancies identified from electronic birth certificates between the years 1995-2006 (N=1,359,284) provided by the California Department of Public Health. We used a single pollutant multivariate logistic regression model, with multilevel spatially structured and unstructured random effects set in a Bayesian framework to estimate global and spatially varying pollutant effects on TLBW at the census tract level. Overall, increased PM2.5 level was associated with higher prevalence of TLBW county-wide. The spatial random effects model, however, demonstrated that the exposure-response for PM2.5 and TLBW was not uniform across urban LA County. Rather, the magnitude and certainty of the exposure-response estimates for PM2.5 on log odds of TLBW were greatest in the urban core of Central and Southern LA County census tracts. These results suggest that the effects may be spatially patterned, and that simply estimating global pollutant effects obscures disparities suggested by spatial patterns of effects. Studies that incorporate spatial multilevel modeling with random coefficients allow us to identify areas where air pollutant effects on adverse birth outcomes may be most severe and policies to further reduce air pollution might be most effective.
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paper_title: Calibrating MODIS aerosol optical depth for predicting daily PM2.5 concentrations via statistical downscaling

paper_content:

Calibrating MODIS aerosol optical depth for predicting daily PM 2.5 concentrations via statistical downscaling
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paper_title: Assessing PM2.5 Exposures with High Spatiotemporal Resolution across the Continental United States

paper_content:

A number of models have been developed to estimate PM2.5 exposure, including satellite-based aerosol optical depth (AOD) models, land-use regression, or chemical transport model simulation, all with both strengths and weaknesses. Variables like normalized difference vegetation index (NDVI), surface reflectance, absorbing aerosol index, and meteoroidal fields are also informative about PM2.5 concentrations. Our objective is to establish a hybrid model which incorporates multiple approaches and input variables to improve model performance. To account for complex atmospheric mechanisms, we used a neural network for its capacity to model nonlinearity and interactions. We used convolutional layers, which aggregate neighboring information, into a neural network to account for spatial and temporal autocorrelation. We trained the neural network for the continental United States from 2000 to 2012 and tested it with left out monitors. Ten-fold cross-validation revealed a good model performance with a total R2 of 0....
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paper_title: Correlating MODIS aerosol optical thickness data with ground-based PM2.5 observations across Texas for use in a real-time air quality prediction system

paper_content:

Abstract Investigations have been conducted at the Center for Space Research (CSR) into approaches to correlate MODIS aerosol optical thickness (AOT) values with ground-based, PM2.5 observations made at continuous air monitoring station locations operated by the Texas Commission on Environmental Quality (TCEQ). These correlations are needed to more fully utilize real-time MODIS AOT analyses generated at CSR in operational air quality forecasts issued by TCEQ using a trajectory-based forecast model developed by NASA. Initial analyses of two data sets collected during 3 months in 2003 and all of 2004 showed linear correlations in the 0.4–0.5 range in the data collected over Texas. Stronger correlations (exceeding 0.9) were obtained by averaging these same data over longer timescales but this approach is considered unsuitable for use in issuing air quality forecasts. Peculiarities in the MODIS AOT analyses, referred to as hot spots, were recognized while attempting to improve these correlations. It is demonstrated that hot spots are possible when pixels that contain surface water are not detected and removed from the AOT retrieval algorithms. An approach to reduce the frequency of hot spots in AOT analyses over Texas is demonstrated by tuning thresholds used to detect inland water surfaces and remove pixels that contain them from the analysis. Finally, the potential impact of hot spots on MODIS AOT-PM2.5 correlations is examined through the analysis of a third data set that contained sufficient levels of aerosols to mask inland water surfaces from the AOT algorithms. In this case, significantly stronger correlations, that exceed the 0.9 value considered suitable for use in a real-time air quality prediction system, were observed between the MODIS AOT observations and ground-based PM2.5 measurements.
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paper_title: Spatial distribution of aerosol hygroscopicity and its effect on PM2.5 retrieval in East China

paper_content:

Abstract The hygroscopic properties of aerosol particles have strong impact on climate as well as visibility in polluted areas. Understanding of the scattering enhancement due to water uptake is of great importance in linking dry aerosol measurements with relevant ambient measurements, especially for satellite retrievals. In this study, an observation-based algorithm combining meteorological data with the particulate matter (PM) measurement was introduced to estimate spatial distribution of indicators describing the integrated humidity effect in East China and the main factors impacting the hygroscopicity were explored. Investigation of 1 year data indicates that the larger mass extinction efficiency α ext values (> 9.0 m 2 /g) located in middle and northern Jiangsu Province, which might be caused by particulate organic material (POM) and sulfate aerosol from industries and human activities. The high level of POM in Jiangsu Province might also be responsible for the lower growth coefficient γ value in this region. For the inland junction provinces of Jiangsu and Anhui, a considerable higher hygroscopic growth region in East China might be attributed to more hygroscopic particles mainly comprised of inorganic salts (e.g., sulfates and nitrates) from several large-scale industrial districts distributed in this region. Validation shows good agreement of calculated PM 2.5 mass concentrations with in situ measurements in most stations with correlative coefficients of over 0.85, even if several defective stations induced by station location or seasonal variation of aerosol properties in this region. This algorithm can be used for more accurate surface level PM 2.5 retrieval from satellite-based aerosol optical depth (AOD) with combination of the vertical correction for aerosol profile.
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paper_title: A hybrid prediction model for PM2.5 mass and components using a chemical transport model and land use regression

paper_content:

Abstract GEOS-Chem, a chemical transport model, provides time-space continuous estimates of atmospheric pollutants including PM 2.5 and its major components, but model predictions are not highly correlated with ground monitoring data. In addition, its spatial resolution is usually too coarse to characterize the spatial pattern in pollutant concentrations in urban environments. Our objective was to calibrate daily GEOS-Chem simulations using ground monitoring data and incorporating meteorological variables, land-use terms and spatial-temporal lagged terms. Major PM 2.5 components of our interest include sulfate, nitrate, organic carbon, elemental carbon, ammonium, sea salt and dust. We used a backward propagation neural network to calibrate GEOS-Chem predictions with a spatial resolution of 0.500° × 0.667° using monitoring data collected during the period from 2001 to 2010 for the Northeastern United States. Subsequently, we made predictions at 1 km × 1 km grid cells. We determined the accuracy of the spatial-temporal predictions using ten-fold cross-validation and “leave-one-day-out” cross-validation techniques. We found a high total R 2 for PM 2.5 mass (all data R 2 0.85, yearly values: 0.80–0.88) and PM 2.5 components (R 2 for individual components were around 0.70–0.80). Our model makes it possible to assess spatially- and temporally-resolved short- and long-term exposures to PM 2.5 mass and components for epidemiological studies.
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paper_title: Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter over Europe

paper_content:

Abstract To mitigate the harmful effect of particulates on human health, EU wide limit values for concentrations have been set. However, particulate matter (PM) measurements suffer from substantial uncertainty because PM is difficult to measure on a routine basis, which is necessary for monitoring compliance. Moreover, different measurement and calibration methods are used in the many air-quality networks in Europe. Consequently, the understanding of PM levels over Europe as a whole is rather limited. We aim to improve this situation by making use of additional information from satellite observations. As a first step, we present here a comparison for Europe of spatio-temporal variations of PM with those of aerosol optical thickness (AOT) measured by the MODIS satellite instrument, for 2003. The MODIS measurements clearly show the major aerosol source regions in Northern Italy, Southern Poland, and the Belgium/Netherlands/Ruhr area, as well as individual large cities and industrialised valleys (Rhone, Danube). The spatial correlation between yearly average PM10 and AOT is 0.6 for rural background stations. The seasonal variation of AOT and PM is distinctly different, however. Throughout most of Europe, the AOT as measured by MODIS has a clear minimum in the winter months. The seasonal variation in PM differs across Europe, and at many locations the seasonal variation is less marked than that of the AOT. Consequently, the correlation between one-year time-series of AOT with PM10/PM2.5 is low (0.3). The correlation between PM and AOT is improved when the AOT is divided by the boundary layer height and, to a lesser extent, when it is corrected for growth of aerosols with relative humidity. In that case, the average correlation is 0.5 (PM10) and 0.6 (PM2.5), averaged over rural and (sub) urban background stations. These results indicate that AOT measurements can be useful to improve the monitoring of PM distributions over Europe.
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paper_title: Spatial scales of pollution from variable resolution satellite imaging.

paper_content:

The Moderate Resolution Imaging Spectroradiometer (MODIS) provides daily global coverage, but the 10 km resolution of its aerosol optical depth (AOD) product is not adequate for studying spatial variability of aerosols in urban areas. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for MODIS which provides AOD at 1 km resolution. Using MAIAC data, the relationship between MAIAC AOD and PM(2.5) as measured by the EPA ground monitoring stations was investigated at varying spatial scales. Our analysis suggested that the correlation between PM(2.5) and AOD decreased significantly as AOD resolution was degraded. This is so despite the intrinsic mismatch between PM(2.5) ground level measurements and AOD vertically integrated measurements. Furthermore, the fine resolution results indicated spatial variability in particle concentration at a sub-10 km scale. Finally, this spatial variability of AOD within the urban domain was shown to depend on PM(2.5) levels and wind speed.
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paper_title: Impact of data quality and surface-to-column representativeness on the PM2.5 / satellite AOD relationship for the contiguous United States

paper_content:

Abstract. Satellite-derived aerosol optical depth (AOD) observations have been used to estimate particulate matter smaller than 2.5 μm (PM2.5). However, such a relationship could be affected by the representativeness of satellite-derived AOD to surface aerosol particle mass concentration and satellite AOD data quality. Using purely measurement-based methods, we have explored the impacts of data quality and representativeness on the AOD-inferred PM2.5 / AOD relationship for the contiguous United States (CONUS). This is done through temporally and spatially collocated data sets of PM2.5 and AOD retrievals from Aqua/Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-angle Imaging Spectroradiometer (MISR), and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). These analyses show that improving data quality of satellite AOD, such as done with data assimilation-grade retrievals, increases their correlation with PM2.5. However, overall correlation is relatively low across the CONUS. Also, integrated extinction observed within 500 m above ground level (a.g.l.), as measured by CALIOP, is not well representative of the total column AOD. Surface aerosol in the eastern CONUS is better correlated with total column AOD than in the western CONUS. The best correlation values are found for estimated dry mass CALIOP extinction at 200–300 m a.g.l. and PM2.5, but additional work is needed to address the ability of using actively sensed AOD as a proxy for PM2.5 concentrations.
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paper_title: Influence of regional biomass burning on the highly elevated organic carbon concentrations observed at Gosan, South Korea during a strong Asian dust period

paper_content:

PM2.5 carbonaceous particles were measured at Gosan, South Korea during 29 March–11 April 2002 which includes a pollution period (30 March–01 April) when the highest concentrations of major anthropogenic species (nss-SO42−, NO3−, and NH4+) were observed and a strong Asian dust (AD) period (08–10 April) when the highest concentrations of mainly dust-originated trace elements (Al, Ca, Mg, and Fe) were seen. The concentrations of elemental carbon (EC) measured in the pollution period were higher than those measured in the strong AD period, whereas an inverse variation in the concentrations of organic carbon (OC) was observed. Based on the OC/EC ratios, the possible source that mainly contributed to the highly elevated OC concentrations measured in the strong AD period was biomass burning. The influence of the long-range transport of smoke plumes emitted from regional biomass burning sources was evaluated by using MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data for fire locations and the potential source contribution function analysis. The most potential source regions of biomass burning were the Primorsky and Amur regions in Far Eastern Russia and southeastern and southwestern Siberia, Russia. Further discussion on the source characteristics suggested that the high OC concentrations measured in the strong AD period were significantly affected by the smoldering phase of biomass burning. In addition to biomass burning, secondary OC (SOC) formed during atmospheric long-range transport should be also considered as an important source of OC concentration measured at Gosan. Although this study dealt with the episodic case of the concurrent increase of dust and biomass burning particles, understanding the characteristics of heterogeneous mixing aerosol is essential in assessing the radiative forcing of aerosol.
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paper_title: Estimating the global abundance of ground level presence of particulate matter (PM2.5).

paper_content:

With the increasing awareness of the health impacts of particulate matter, there is a growing need to comprehend the spatial and temporal variations of the global abundance of ground level airborne particulate matter with a diameter of 2.5 microns or less (PM2.5). Here we use a suite of remote sensing and meteorological data products together with ground-based observations of particulate matter from 8,329 measurement sites in 55 countries taken 1997-2014 to train a machine-learning algorithm to estimate the daily distributions of PM2.5 from 1997 to the present. In this first paper of a series, we present the methodology and global average results from this period and demonstrate that the new PM2.5 data product can reliably represent global observations of PM2.5 for epidemiological studies.
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paper_title: Spatiotemporal associations between GOES aerosol optical depth retrievals and ground-level PM2.5.

paper_content:

We analyze the strength of association between aerosol optical depth (AOD) retrievals from the GOES aerosol/smoke product (GASP) and ground-level fine particulate matter (PM2.5) to assess AOD as a proxy for PM2.5 in the United States. GASP AOD is retrieved from a geostationary platform, giving half-hourly observations every day, in contrast to once per day snapshots from polar-orbiting satellites. However, GASP AOD is based on a less-sophisticated instrument and retrieval algorithm. We find that daily correlations between GASP AOD and PM2.5 over time at fixed locations are reasonably high, except in the winter and in the western U.S. Correlations over space at fixed times are lower. Simple averaging to the month and year actually reduces correlations over space, but statistical calibration allows averaging over time that produces moderately strong correlations. These results and the data density of GASP AOD highlight its potential to help improve exposure estimates for epidemiological analyses. On average 39% of days in a month have a GASP AOD retrieval compared to 11% for MODIS and 5% for MISR. Furthermore, GASP AOD has been retrieved since November 1994, providing a long-term record that predates the availability of most PM2.5 monitoring data and other satellite instruments.
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paper_title: Improving retrievals of regional fine particulate matter concentrations from Moderate Resolution Imaging Spectroradiometer (MODIS) and Ozone Monitoring Instrument (OMI) multisatellite observations

paper_content:

A combination of multiplatform satellite observations and statistical data analysis are used to improve the correlation between estimates of PM2.5 (particulate mass with aerodynamic diameter less that 2.5 µm) retrieved from satellite observations and ground-level measured PM2.5. Accurate measurements of PM2.5 can be used to assess the impact of air pollution levels on human health and the environment and to validate air pollution models. The area under study is California's San Joaquin Valley (SJV) that has a history of poor particulate air quality. Attempts to use simple linear regressions to estimate PM2.5 from satellite-derived aerosol optical depth (AOD) have not yielded good results. The period of study for this project was from October 2004 to July 2008 for six sites in the SJV. A simple linear regression between surface-measured PM2.5 and satellite-observed AOD (from MODIS [Moderate Resolution Imaging Spectroradiometer]) yields a correlation coefficient of about 0.17 in this region. The correlation...
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paper_title: Estimating urban PM 10 and PM 2.5 concentrations, based on synergistic MERIS/AATSR aerosol observations, land cover and morphology data

paper_content:

Abstract This study evaluates alternative spatio-temporal approaches for quantitative estimation of daily mean Particulate Matter (PM) concentrations. Both fine (PM2.5) and coarse (PM10) concentrations were estimated over the area of London (UK) for the 2002–2012 time period, using Aerosol Optical Thickness (AOT) derived from MERIS (Medium Resolution Imaging Spectrometer)/AATSR (Advanced Along-Track Scanning Radiometer) synergistic observations at 1 km × 1 km resolution. Relative humidity, temperature and the K-Index obtained from MODIS (Moderate Resolution Imaging Spectroradiometer) sensor were used as additional predictors. High-resolution (100 m × 100 m) local urban land cover and morphology datasets were incorporated in the analysis in order to capture the effects of local scale emissions and sequestration. Spatial (2-D) and spatio-temporal (3-D) kriging were applied to in situ urban PM measurements to investigate their association with satellite-derived AOT while accounting for differences in spatial support. Linear mixed-effects models with day-specific and site-specific random intercepts and slopes were estimated to associate satellite-derived products with kriged PM concentration and their predictive performance was evaluated.
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paper_title: Particulate matter air quality assessment using integrated surface, satellite, and meteorological products: 2. A neural network approach

paper_content:

[1] In recent years, sparse, surface-based air quality monitoring has been improved by using wide-swath, satellite-derived aerosol products. However, satellites are sensitive to the entire aerosol column, not only the aerosol near the surface that impacts human health. In part 1 of this series, we used multiple regression to demonstrate how inclusion of meteorological analyses can help constrain the surface level proportion of the aerosol profile and improve the estimate of surface PM2.5. Here, instead of multiple regression technique, we describe an artificial neural network (ANN) framework that reduces the uncertainty of surface PM estimation from satellite data. We use 3 years of MODIS aerosol optical thickness data at 0.55 μm and meteorological analyses from the rapid update cycle to estimate surface level PM2.5 over the southeast United States (EPA region 4). As compared to regression coefficients obtained through simple correlation (R = 0.60) or multiple regression (R = 0.68) techniques, the ANN derives hourly PM2.5 data that compare with observations with R = 0.74. For estimating daily mean PM2.5, the ANN techniques results in correlation of R = 0.78. Although the degree of improvement varies over different sites and seasons, this study demonstrates the potential for using ANN for operational air quality monitoring.
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paper_title: Correlating seasonal averaged in situ monitoring of fine PM with satellite remote sensing data using geographic information system (GIS)

paper_content:

Satellite remote sensing data are another source of information to study air quality, supplementing the in situ monitoring networks. Satellite data have primarily been used to study specific events that affect air quality, such as wildfires, biomass burning, dust storms, and volcanoes. In this exploratory analysis we have used the monthly averaged aerosol optical depth (AOD) product of the MODIS sensor data from the Terra satellite platform to study fine particulate matter throughout the contiguous U.S. While most of the previous quantitative work has focused on hourly correlations between in situ monitors and satellite AOD data, we have attempted to quantify monthly, seasonal, and annual correlations. Our analysis of 2001 monthly data found that correlations do exist, but not throughout the entire study period or area. The best correlations were seen in the northeast and industrial Midwest during the summer months.
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paper_title: Application of the deletion/substitution/addition algorithm to selecting land use regression models for interpolating air pollution measurements in California

paper_content:

Abstract Land use regression (LUR) models are widely employed in health studies to characterize chronic exposure to air pollution. The LUR is essentially an interpolation technique that employs the pollutant of interest as the dependent variable with proximate land use, traffic, and physical environmental variables used as independent predictors. Two major limitations with this method have not been addressed: (1) variable selection in the model building process, and (2) dealing with unbalanced repeated measures. In this paper, we address these issues with a modeling framework that implements the deletion/substitution/addition (DSA) machine learning algorithm that uses a generalized linear model to average over unbalanced temporal observations. Models were derived for fine particulate matter with aerodynamic diameter of 2.5 microns or less (PM2.5) and nitrogen dioxide (NO2) using monthly observations. We used 4119 observations at 108 sites and 15,301 observations at 138 sites for PM2.5 and NO2, respectively. We derived models with good predictive capacity (cross-validated-R2 values were 0.65 and 0.71 for PM2.5 and NO2, respectively). By addressing these two shortcomings in current approaches to LUR modeling, we have developed a framework that minimizes arbitrary decisions during the model selection process. We have also demonstrated how to integrate temporally unbalanced data in a theoretically sound manner. These developments could have widespread applicability for future LUR modeling efforts.
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paper_title: Estimating Ground-Level PM(sub 2.5) Concentrations in the Southeastern United States Using MAIAC AOD Retrievals and a Two-Stage Model

paper_content:

Abstract Previous studies showed that fine particulate matter (PM2.5, particles smaller than 2.5 μm in aerodynamic diameter) is associated with various health outcomes. Ground in situ measurements of PM2.5 concentrations are considered to be the gold standard, but are time-consuming and costly. Satellite-retrieved aerosol optical depth (AOD) products have the potential to supplement the ground monitoring networks to provide spatiotemporally-resolved PM2.5 exposure estimates. However, the coarse resolutions (e.g., 10 km) of the satellite AOD products used in previous studies make it very difficult to estimate urban-scale PM2.5 characteristics that are crucial to population-based PM2.5 health effects research. In this paper, a new aerosol product with 1 km spatial resolution derived by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was examined using a two-stage spatial statistical model with meteorological fields (e.g., wind speed) and land use parameters (e.g., forest cover, road length, elevation, and point emissions) as ancillary variables to estimate daily mean PM2.5 concentrations. The study area is the southeastern U.S., and data for 2003 were collected from various sources. A cross validation approach was implemented for model validation. We obtained R2 of 0.83, mean prediction error (MPE) of 1.89 μg/m3, and square root of the mean squared prediction errors (RMSPE) of 2.73 μg/m3 in model fitting, and R2 of 0.67, MPE of 2.54 μg/m3, and RMSPE of 3.88 μg/m3 in cross validation. Both model fitting and cross validation indicate a good fit between the dependent variable and predictor variables. The results showed that 1 km spatial resolution MAIAC AOD can be used to estimate PM2.5 concentrations.
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paper_title: QUALITATIVE AND QUANTITATIVE EVALUATION OF MODIS SATELLITE SENSOR DATA FOR REGIONAL AND URBAN SCALE AIR QUALITY

paper_content:

Advances in satellite sensors have provided new datasets for monitoring air quality at urban and regional scales. Qualitative true color images and quantitative aerosol optical depth data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on the Terra satellite were compared with ground-based particulate matter data from US Environmental Protection Agency (EPA) monitoring networks covering the period from 1 April to 30 September, 2002. Using both imagery and statistical analysis, satellite data enabled the determination of the regional sources of air pollution events, the general type of pollutant (smoke, haze, dust), the intensity of the events, and their motion. Very high and very low aerosol optical depths were found to be eliminated by the algorithm used to calculate the MODIS aerosol optical depth data. Correlations of MODIS aerosol optical depth with ground-based particulate matter were better in the eastern and Midwest portion of the United States (east of 100 � W). Data were patchy and had poorer correlations in the western US, although the correlation was dependent on location. This variability is likely due to a combination of the differences between ground-based and column average datasets, regression artifacts, variability of terrain, and MODIS cloud mask and aerosol optical depth algorithms. Preliminary analysis of the algorithms indicated that aerosol optical depth measurements calculated from the sulfate-rich aerosol model may be more useful in predicting ground-based particulate matter levels, but further analysis would be required to verify the effect of the model on correlations. Overall, the use of satellite sensor data such as from MODIS has significant potential to enhance air quality monitoring over synoptic and regional scales. r 2004 Elsevier Ltd. All rights reserved.
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paper_title: Estimation of long-term population exposure to PM 2.5 for dense urban areas using 1-km MODIS data

paper_content:

Abstract The lack of long-term PM 2.5 measurements in developing countries makes it difficult to quantify the overall PM 2.5 pollution exposures and health impacts in these countries where the PM 2.5 concentrations are often very high. Moreover, it is also difficult for traditional fixed-site monitoring to capture the substantial spatial variability of PM 2.5 over dense urban areas or regions with significant topography. Hence, recent developments in satellite-based remote-sensing allowing the reconstruction of long-term, wide-area and high-resolution estimates of current and historical PM 2.5 concentration is an important step forward, allowing the quantification of the long-term pollution exposure of PM 2.5 in developing cities and in dense urban areas using the satellite-derived PM 2.5 data. In this study, instead of just looking at the spatial average PM 2.5 concentrations, we have studied the long-term population exposure of PM 2.5 by analyzing the population-weighted PM 2.5 concentrations at regional, city and district scales by combining 1 km × 1 km satellite-derived PM 2.5 and population density data sets. The variation of population exposure to PM 2.5 across the urban areas in the Pearl River Delta (PRD) region from 2000 to 2014 was studied. Our result shows that the PM 2.5 concentrations over the PRD increased steadily from 2000 to 2004, remained at quite a high level through 2008 and then started to decline after 2009. More importantly, our analysis also shows that, at regional, city and district levels, the population-weighted mean PM 2.5 concentrations from data with 1 km resolutions are typically the highest, followed by the population-weighted mean PM 2.5 concentrations from data with 10 km resolutions and then the simple spatial PM 2.5 averages. This suggests that the use of simple spatial concentrations can lead to systematic underestimation of the overall population exposure and the associated health impacts. This systematic difference is related to the positive correlation between PM 2.5 pollutant concentration and population density, and shows the usefulness of using high-resolution satellite-retrieved PM 2.5 concentrations to quantify the overall population exposure. The higher population-weighted mean PM 2.5 concentration in comparison with simple spatial average indicates that, for more effective reduction of overall population exposure and protection of public health, control efforts must be further targeted at high-population high-pollution areas, and land-use and city planning should also encourage population to redistribute away from the highly polluted areas.
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paper_title: Synergy of satellite and ground based observations in estimation of particulate matter in eastern China.

paper_content:

Estimating particulate matter (PM) from space is not straightforward and is mainly achieved using the aerosol optical depth (AOD) retrieved from satellite sensors. However, AOD is a columnar measure, whereas PM is a ground observation. Linking AOD and PM remains a challenge for air pollution monitoring. In this study, a back-propagation artificial neural network (BP ANN) algorithm trained with bayesian regularization that benefited from the synergy of satellite- and ground-based observations was developed to estimate PM in eastern China. Correlations between observed and estimated PM (denoted by R) during the period 2007-2008 over seven individual sites were investigated comprehensively in terms of site scale, seasonal scale, particle size, and spatio-temporal scale. With respect to site differences, the Nanning site had the best results with 80.3% of cases having a moderate or strong correlation value. Lushan and Zhengzhou followed with results of 75% and 73.8%, respectively. Furthermore, R exhibited a significant seasonal variation characterized by a maximum (80.2%) during the autumn period, whereas no obvious differences in R for various spatial scales (spatial averaging schemes of MODIS AOD) were observed. Likewise, the ratio value for daily averaging (64.7%) was found to be better than those for the two hourly temporal averaging schemes (i.e., 61.1% for HA1 and 58.3% for HA2). In addition, PM(1) estimated from the ANN algorithm developed in this study had slightly higher R values than did PM(10) and PM(2.5). The planetary boundary layer (PBL) effect on PM estimation was decreasing R with increasing height of the PBL, which is consistent with previous studies. Comparisons of observed versus estimated PM(10) mass time series implied that the ANN algorithm basically reproduced the observed PM concentration. However, PM mass at certain sites may be underestimated under the condition of high observed PM concentrations.
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paper_title: Correlation between PM concentrations and Aerosol Optical Depth in eastern China based on BP neural networks

paper_content:

Two years (2007–2008) of observational particulate matter (PM) data from ten ground-stations over eastern China, as well as collocated Aerosol Optical Depth (hereafter called AOD) derived from Moderate Resolution Imaging Spectroradiometer (MODIS), in conjunction with meteorological parameters, such as planetary boundary layer height (PBLH), relative humid (RH), temperature (TEMP), wind speed (WS), wind direction (WD), were applied to investigate the correlation between PM and AOD with the back propagation (BP) neural networks algorithm, notably the PM-AOD correlation change with seasons. The PM 2.5 concentrations retrieved from AOD/MODIS data based on BP neural networks presents significant change with seasons. Albeit the sensitivity to seasons of PM 2.5 retrieval from satellite, results indicated that the algorithm of BP network could be applied in retrieving long term PM concentrations from satellite.
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paper_title: Limitations of Remotely Sensed Aerosol as a Spatial Proxy for Fine Particulate Matter

paper_content:

BackgroundRecent research highlights the promise of remotely sensed aerosol optical depth (AOD) as a proxy for ground-level particulate matter with aerodynamic diameter ≤ 2.5 μm (PM2.5). Particular...
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paper_title: A Geographically and Temporally Weighted Regression Model for Ground-Level PM2.5 Estimation from Satellite-Derived 500 m Resolution AOD

paper_content:

Regional haze episodes have occurred frequently in eastern China over the past decades. As a critical indicator to evaluate air quality, the mass concentration of ambient fine particulate matters smaller than 2.5 μm in aerodynamic diameter (PM2.5) is involved in many studies. To overcome the limitations of ground measurements on PM2.5 concentration, which is featured in disperse representation and coarse coverage, many statistical models were developed to depict the relationship between ground-level PM2.5 and satellite-derived aerosol optical depth (AOD). However, the current satellite-derived AOD products and statistical models on PM2.5–AOD are insufficient to investigate PM2.5 characteristics at the urban scale, in that spatial resolution is crucial to identify the relationship between PM2.5 and anthropogenic activities. This paper presents a geographically and temporally weighted regression (GTWR) model to generate ground-level PM2.5 concentrations from satellite-derived 500 m AOD. The GTWR model incorporates the SARA (simplified high resolution MODIS aerosol retrieval algorithm) AOD product with meteorological variables, including planetary boundary layer height (PBLH), relative humidity (RH), wind speed (WS), and temperature (TEMP) extracted from WRF (weather research and forecasting) assimilation to depict the spatio-temporal dynamics in the PM2.5–AOD relationship. The estimated ground-level PM2.5 concentration has 500 m resolution at the MODIS satellite’s overpass moments twice a day, which can be used for air quality monitoring and haze tracking at the urban and regional scale. To test the performance of the GTWR model, a case study was carried out in a region covering the adjacent parts of Jiangsu, Shandong, Henan, and Anhui provinces in central China. A cross validation was done to evaluate the performance of the GTWR model. Compared with OLS, GWR, and TWR models, the GTWR model obtained the highest value of coefficient of determination (R2) and the lowest values of mean absolute difference (MAD), root mean square error (RMSE), and mean absolute percentage error (MAPE).
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paper_title: Estimating Regional Spatial and Temporal Variability of PM2.5 Concentrations Using Satellite Data, Meteorology, and Land Use Information

paper_content:

BackgroundStudies of chronic health effects due to exposures to particulate matter with aerodynamic diameters ≤ 2.5 μm (PM2.5) are often limited by sparse measurements. Satellite aerosol remote sen...
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paper_title: Satellite-derived high resolution PM2.5 concentrations in Yangtze River Delta Region of China using improved linear mixed effects model

paper_content:

Abstract Satellite remotely sensed aerosol optical depth (AOD) provides an effective way to fill the spatial and temporal gaps left by ground PM2.5 monitoring network. Previous studies have established robust advanced statistical models to estimate PM2.5 using AOD data in China. However, their coarse resolutions (∼10 km or greater) of PM2.5 estimations are not enough to support the health effect studies at urban scales. In this study, 3 km AOD data from Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 products were used to estimate the high resolution PM2.5 concentrations in Yangtze Delta Region of China. We proposed a nested linear mixed effects (LME) model including nested month-, week-, and day-specific random effects of PM2.5-AOD relationships. Validation results show that the LME model only with day-specific random effects (non-nested model) used in previous studies has poor performance in the days without PM2.5-AOD matchups (the R2 of day-of-year-based cross validation (DOY-based CV) is 0.148). The results also show that our nested model cannot improve the performance of non-nested model in the days with PM2.5-AOD matchups (sample-based CV R2 = 0.671 for nested model vs. 0.661 for non-nested model), but can greatly improve the model performance beyond those days (DOY-based CV R2 = 0.339 for nested model vs. 0.148 for non-nested model). To further improve the model performance, we applied the “buffer models” (i.e., models fitted from datasets which ground PM2.5 were matched with the average AOD values within certain radius buffer zones of gridded PM2.5 data) on the 3 km AOD data since the “buffer models” has more days with PM2.5-AOD matchups and can provide more day-specific relationships. The results of this study show that 3 km MODIS C6 AOD data can be used to estimate PM2.5 concentrations and can provide more detailed spatial information for urban scale studies. The application of our nested LME model can greatly improve the accuracy of 3 km PM2.5 predictions.
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paper_title: Monitoring of ambient fine particulate matter concentrations from space: application to European and African cities

paper_content:

Air pollution is a major issue for global environment as well as human health and well-being. Recently, satellites ::: which are equipped with relevant air quality instruments have been placed into orbit. In this paper, we first ::: present a review on satellite remote sensing of particulate pollution. We then present new results for Europe and ::: on African cities particulate air pollutants using POLDER satellite data. Based on satellite AOD observations, ::: we show that the number of days exceeding the 15.4 μg/m 3 threshold is twice frequent in Ouagadougou, Burkina- ::: Faso than in Paris, France. At the regional scale, we observe that the northern coast of the golf of Guinea is ::: dramatically impacted by poor air quality.
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paper_title: The Relation between Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth and PM2.5over the United States: A Geographical Comparison by U.S. Environmental Protection Agency Regions

paper_content:

Abstract Aerosol optical depth (AOD) acquired from satellite measurements demonstrates good correlation with particulate matter with diameters less than 2.5 µm (PM2.5) in some regions of the United States and has been used for monitoring and nowcasting air quality over the United States. This work investigates the relation between Moderate Resolution Imaging Spectroradiometer (MODIS) AOD and PM2.5 over the 10 U.S. Environmental Protection Agency (EPA)-defined geographic regions in the United States on the basis of a 2-yr (2005–2006) match-up dataset of MODIS AOD and hourly PM2.5 measurements. The AOD retrievals demonstrate a geographical and seasonal variation in their relation with PM2.5. Good correlations are mostly observed over the eastern United States in summer and fall. The southeastern United States has the highest correlation coefficients at more than 0.6. The southwestern United States has the lowest correlation coefficient of approximately 0.2. The seasonal regression relations derived for each...
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paper_title: Estimating national-scale ground-level PM25 concentration in China using geographically weighted regression based on MODIS and MISR AOD

paper_content:

Taking advantage of the continuous spatial coverage, satellite-derived aerosol optical depth (AOD) products have been widely used to assess the spatial and temporal characteristics of fine particulate matter (PM2.5) on the ground and their effects on human health. However, the national-scale ground-level PM2.5 estimation is still very limited because the lack of ground PM2.5 measurements to calibrate the model in China. In this study, a national-scale geographically weighted regression (GWR) model was developed to estimate ground-level PM2.5 concentration based on satellite AODs, newly released national-wide hourly PM2.5 concentrations, and meteorological parameters. The results showed good agreements between satellite-retrieved and ground-observed PM2.5 concentration at 943 stations in China. The overall cross-validation (CV) R (2) is 0.76 and root mean squared prediction error (RMSE) is 22.26 μg/m(3) for MODIS-derived AOD. The MISR-derived AOD also exhibits comparable performance with a CV R (2) and RMSE are 0.81 and 27.46 μg/m(3), respectively. Annual PM2.5 concentrations retrieved either by MODIS or MISR AOD indicated that most of the residential community areas exceeded the new annual Chinese PM2.5 National Standard level 2. These results suggest that this approach is useful for estimating large-scale ground-level PM2.5 distributions especially for the regions without PMs monitoring sites.
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paper_title: Estimating ground-level PM2.5 in eastern China using aerosol optical depth determined from the GOCI satellite instrument

paper_content:

Abstract. We determine and interpret fine particulate matter (PM2.5) concentrations in eastern China for January to December 2013 at a horizontal resolution of 6 km from aerosol optical depth (AOD) retrieved from the Korean geostationary ocean color imager (GOCI) satellite instrument. We implement a set of filters to minimize cloud contamination in GOCI AOD. Evaluation of filtered GOCI AOD with AOD from the Aerosol Robotic Network (AERONET) indicates significant agreement with mean fractional bias (MFB) in Beijing of 6.7 % and northern Taiwan of −1.2 %. We use a global chemical transport model (GEOS-Chem) to relate the total column AOD to the near-surface PM2.5. The simulated PM2.5 / AOD ratio exhibits high consistency with ground-based measurements in Taiwan (MFB = −0.52 %) and Beijing (MFB = −8.0 %). We evaluate the satellite-derived PM2.5 versus the ground-level PM2.5 in 2013 measured by the China Environmental Monitoring Center. Significant agreement is found between GOCI-derived PM2.5 and in situ observations in both annual averages (r2 = 0.66, N = 494) and monthly averages (relative RMSE = 18.3 %), indicating GOCI provides valuable data for air quality studies in Northeast Asia. The GEOS-Chem simulated chemical composition of GOCI-derived PM2.5 reveals that secondary inorganics (SO42-, NO3-, NH4+) and organic matter are the most significant components. Biofuel emissions in northern China for heating increase the concentration of organic matter in winter. The population-weighted GOCI-derived PM2.5 over eastern China for 2013 is 53.8 μg m−3, with 400 million residents in regions that exceed the Interim Target-1 of the World Health Organization.
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paper_title: Using new satellite based exposure methods to study the association between pregnancy pm2.5 exposure, premature birth and birth weight in Massachusetts

paper_content:

Adverse birth outcomes such as low birth weight and premature birth have been previously linked with exposure to ambient air pollution. Most studies relied on a limited number of monitors in the region of interest, which can introduce exposure error or restrict the analysis to persons living near a monitor, which reduces sample size and generalizability and may create selection bias. We evaluated the relationship between premature birth and birth weight with exposure to ambient particulate matter (PM2.5) levels during pregnancy in Massachusetts for a 9-year period (2000–2008). Building on a novel method we developed for predicting daily PM2.5 at the spatial resolution of a 10x10km grid across New-England, we estimated the average exposure during 30 and 90 days prior to birth as well as the full pregnancy period for each mother. We used linear and logistic mixed models to estimate the association between PM2.5 exposure and birth weight (among full term births) and PM2.5 exposure and preterm birth adjusting for infant sex, maternal age, maternal race, mean income, maternal education level, prenatal care, gestational age, maternal smoking, percent of open space near mothers residence, average traffic density and mothers health. Birth weight was negatively associated with PM2.5 across all tested periods. For example, a 10 μg/m3 increase of PM2.5 exposure during the entire pregnancy was significantly associated with a decrease of 13.80 g [95% confidence interval (CI) = −21.10, -6.05] in birth weight after controlling for other factors, including traffic exposure. The odds ratio for a premature birth was 1.06 (95% confidence interval (CI) = 1.01–1.13) for each 10 μg/m3 increase of PM2.5 exposure during the entire pregnancy period. The presented study suggests that exposure to PM2.5 during the last month of pregnancy contributes to risks for lower birth weight and preterm birth in infants.
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paper_title: Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter over Europe

paper_content:

Abstract To mitigate the harmful effect of particulates on human health, EU wide limit values for concentrations have been set. However, particulate matter (PM) measurements suffer from substantial uncertainty because PM is difficult to measure on a routine basis, which is necessary for monitoring compliance. Moreover, different measurement and calibration methods are used in the many air-quality networks in Europe. Consequently, the understanding of PM levels over Europe as a whole is rather limited. We aim to improve this situation by making use of additional information from satellite observations. As a first step, we present here a comparison for Europe of spatio-temporal variations of PM with those of aerosol optical thickness (AOT) measured by the MODIS satellite instrument, for 2003. The MODIS measurements clearly show the major aerosol source regions in Northern Italy, Southern Poland, and the Belgium/Netherlands/Ruhr area, as well as individual large cities and industrialised valleys (Rhone, Danube). The spatial correlation between yearly average PM10 and AOT is 0.6 for rural background stations. The seasonal variation of AOT and PM is distinctly different, however. Throughout most of Europe, the AOT as measured by MODIS has a clear minimum in the winter months. The seasonal variation in PM differs across Europe, and at many locations the seasonal variation is less marked than that of the AOT. Consequently, the correlation between one-year time-series of AOT with PM10/PM2.5 is low (0.3). The correlation between PM and AOT is improved when the AOT is divided by the boundary layer height and, to a lesser extent, when it is corrected for growth of aerosols with relative humidity. In that case, the average correlation is 0.5 (PM10) and 0.6 (PM2.5), averaged over rural and (sub) urban background stations. These results indicate that AOT measurements can be useful to improve the monitoring of PM distributions over Europe.
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paper_title: Short Term Effects of Particle Exposure on Hospital Admissions in the Mid-Atlantic States: A Population Estimate

paper_content:

BACKGROUND ::: Many studies report significant associations between PM(2.5) (particulate matter <2.5 micrometers) and hospital admissions. These studies mostly rely on a limited number of monitors which introduces exposure error, and excludes rural and suburban populations from locations where monitors are not available, reducing generalizability and potentially creating selection bias. ::: ::: ::: METHODS ::: Using prediction models developed by our group, daily PM(2.5) exposure was estimated across the Mid-Atlantic (Washington D.C., and the states of Delaware, Maryland, New Jersey, Pennsylvania, Virginia, New York and West Virginia). We then investigated the short-term effects of PM(2.5)exposures on emergency hospital admissions of the elderly in the Mid-Atlantic region.We performed case-crossover analysis for each admission type, matching on day of the week, month and year and defined the hazard period as lag01 (a moving average of day of admission exposure and previous day exposure). ::: ::: ::: RESULTS ::: We observed associations between short-term exposure to PM(2.5) and hospitalization for all outcomes examined. For example, for every 10-µg/m(3) increase in short-term PM(2.5) there was a 2.2% increase in respiratory diseases admissions (95% CI = 1.9 to 2.6), and a 0.78% increase in cardiovascular disease (CVD) admission rate (95% CI = 0.5 to 1.0). We found differences in risk for CVD admissions between people living in rural and urban areas. For every10-µg/m(3) increase in PM(2.5) exposure in the 'rural' group there was a 1.0% increase (95% CI = 0.6 to 1.5), while for the 'urban' group the increase was 0.7% (95% CI = 0.4 to 1.0). ::: ::: ::: CONCLUSIONS ::: Our findings showed that PM(2.5) exposure was associated with hospital admissions for all respiratory, cardio vascular disease, stroke, ischemic heart disease and chronic obstructive pulmonary disease admissions. In addition, we demonstrate that our AOD (Aerosol Optical Depth) based exposure models can be successfully applied to epidemiological studies investigating the health effects of short-term exposures to PM(2.5).
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paper_title: Spatiotemporal associations between GOES aerosol optical depth retrievals and ground-level PM2.5.

paper_content:

We analyze the strength of association between aerosol optical depth (AOD) retrievals from the GOES aerosol/smoke product (GASP) and ground-level fine particulate matter (PM2.5) to assess AOD as a proxy for PM2.5 in the United States. GASP AOD is retrieved from a geostationary platform, giving half-hourly observations every day, in contrast to once per day snapshots from polar-orbiting satellites. However, GASP AOD is based on a less-sophisticated instrument and retrieval algorithm. We find that daily correlations between GASP AOD and PM2.5 over time at fixed locations are reasonably high, except in the winter and in the western U.S. Correlations over space at fixed times are lower. Simple averaging to the month and year actually reduces correlations over space, but statistical calibration allows averaging over time that produces moderately strong correlations. These results and the data density of GASP AOD highlight its potential to help improve exposure estimates for epidemiological analyses. On average 39% of days in a month have a GASP AOD retrieval compared to 11% for MODIS and 5% for MISR. Furthermore, GASP AOD has been retrieved since November 1994, providing a long-term record that predates the availability of most PM2.5 monitoring data and other satellite instruments.
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paper_title: Estimating ground-level PM2.5 concentrations in the southeastern U.S. using geographically weighted regression $

paper_content:

Abstract Most of currently reported models for predicting PM 2.5 concentrations from satellite retrievals of aerosol optical depth are global methods without considering local variations, which might introduce significant biases into prediction results. In this paper, a geographically weighted regression model was developed to examine the relationship among PM 2.5 , aerosol optical depth, meteorological parameters, and land use information. Additionally, two meteorological datasets, North American Regional Reanalysis and North American Land Data Assimilation System, were fitted into the model separately to compare their performances. The study area is centered at the Atlanta Metro area, and data were collected from various sources for the year 2003. The results showed that the mean local R 2 of the models using North American Regional Reanalysis was 0.60 and those using North American Land Data Assimilation System reached 0.61. The root mean squared prediction error showed that the prediction accuracy was 82.7% and 83.0% for North American Regional Reanalysis and North American Land Data Assimilation System in model fitting, respectively, and 69.7% and 72.1% in cross validation. The results indicated that geographically weighted regression combined with aerosol optical depth, meteorological parameters, and land use information as the predictor variables could generate a better fit and achieve high accuracy in PM 2.5 exposure estimation, and North American Land Data Assimilation System could be used as an alternative of North American Regional Reanalysis to provide some of the meteorological fields.
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paper_title: Spatiotemporal prediction of fine particulate matter using high-resolution satellite images in the Southeastern US 2003–2011

paper_content:

Spatiotemporal prediction of fine particulate matter using high-resolution satellite images in the Southeastern US 2003–2011
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paper_title: Improving retrievals of regional fine particulate matter concentrations from Moderate Resolution Imaging Spectroradiometer (MODIS) and Ozone Monitoring Instrument (OMI) multisatellite observations

paper_content:

A combination of multiplatform satellite observations and statistical data analysis are used to improve the correlation between estimates of PM2.5 (particulate mass with aerodynamic diameter less that 2.5 µm) retrieved from satellite observations and ground-level measured PM2.5. Accurate measurements of PM2.5 can be used to assess the impact of air pollution levels on human health and the environment and to validate air pollution models. The area under study is California's San Joaquin Valley (SJV) that has a history of poor particulate air quality. Attempts to use simple linear regressions to estimate PM2.5 from satellite-derived aerosol optical depth (AOD) have not yielded good results. The period of study for this project was from October 2004 to July 2008 for six sites in the SJV. A simple linear regression between surface-measured PM2.5 and satellite-observed AOD (from MODIS [Moderate Resolution Imaging Spectroradiometer]) yields a correlation coefficient of about 0.17 in this region. The correlation...
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paper_title: Prediction of daily fine particulate matter concentrations using aerosol optical depth retrievals from the Geostationary Operational Environmental Satellite (GOES)

paper_content:

UNLABELLED ::: Although ground-level PM2.5 (particulate matter with aerodynamic diameter < 2.5 microm) monitoring sites provide accurate measurements, their spatial coverage within a given region is limited and thus often insufficient for exposure and epidemiological studies. Satellite data expand spatial coverage, enhancing our ability to estimate location- and/or subject-specific exposures to PM2.5. In this study, the authors apply a mixed-effects model approach to aerosol optical depth (AOD) retrievals from the Geostationary Operational Environmental Satellite (GOES) to predict PM2.5 concentrations within the New England area of the United States. With this approach, it is possible to control for the inherent day-to-day variability in the AOD-PM2.5 relationship, which depends on time-varying parameters such as particle optical properties, vertical and diurnal concentration profiles, and ground surface reflectance. The model-predicted PM2.5 mass concentration are highly correlated with the actual observations, R2 = 0.92. Therefore, adjustment for the daily variability in AOD-PM2.5 relationship allows obtaining spatially resolved PM2.5 concentration data that can be of great value to future exposure assessment and epidemiological studies. ::: ::: ::: IMPLICATIONS ::: The authors demonstrated how AOD can be used reliably to predict daily PM2.5 mass concentrations, providing determination of their spatial and temporal variability. Promising results are found by adjusting for daily variability in the AOD-PM2.5 relationship, without the need to account for a wide variety of individual additional parameters. This approach is of a great potential to investigate the associations between subject-specific exposures to PM2.5 and their health effects. Higher 4 x 4-km resolution GOES AOD retrievals comparing with the conventional MODerate resolution Imaging Spectroradiometer (MODIS) 10-km product has the potential to capture PM2.5 variability within the urban domain.
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paper_title: Global Estimates of Ambient Fine Particulate Matter Concentrations from Satellite-Based Aerosol Optical Depth: Development and Application

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Chronic exposure to airborne fine particulate matter with diameter < 2.5 μm (PM2.5) is associated with adverse human health impacts including morbidity and mortality (e.g., Dockery et al. 1993; McDonnell et al. 2000; Pope et al. 2009). Several national environmental agencies in North America and Europe monitor PM2.5 concentrations at numerous sites throughout their jurisdictions, but even these relatively dense networks have limited geographic coverage. Few long-term measurement sites exist elsewhere in the world, particularly in rapidly developing countries where concentrations and estimated health impacts are greatest (Cohen et al. 2004). Point measurements collected at monitoring sites are not necessarily representative of regional concentration, and regional variability is difficult to assess from point measurements alone. In recent years, application of satellite observation to surface air quality has advanced considerably (Hoff and Christopher 2009; Martin 2008). In fact, global aerosol observations from satellite remote sensing could substantially improve estimates of population exposure to PM2.5. ::: ::: Since the mid 2000s, the MODIS (Moderate Resolution Imaging Spectroradiometer) and MISR (Multiangle Imaging Spectroradiometer) instruments onboard the National Aeronautics and Space Administration’s (NASA) Terra satellite has provided global observations of aerosol optical depth (AOD), a measure of light extinction by aerosol in the atmospheric column above the earth’s surface. Terra’s sun-synchronous orbit encircles the earth approximately 15 times each day, with each pass crossing the equator at approximately 1030 hours local solar time. Observations of AOD from Terra provide daily insight into the global distribution of column-integrated aerosol. However, the applicability of AOD to surface air quality depends on several factors, including the vertical structure, composition, size distribution, and water content of atmospheric aerosol. ::: ::: Many studies have investigated the relationship between total-column AOD and surface PM2.5 measurements. Most have developed simple empirical relationships between these two variables (e.g., Engel-Cox et al. 2004a; Wang and Christopher 2003); more recent investigations often have used local meteorological information to better relate AOD and PM2.5 (e.g., Koelemeijer et al. 2006; Liu et al. 2005) or to filter the AOD (e.g., Gupta et al. 2006). Some studies have employed light detection and ranging (LIDAR) instruments to capture the vertical aerosol distribution at specific locations (e.g., Engel-Cox et al. 2006; Schaap et al. 2008). Schaap et al. (2008) noted that locally derived AOD–PM2.5 relationships cannot be extended easily to other regions because of variation in meteorology and aerosol composition. Unique, local, time-dependent AOD–PM2.5 relationships are necessary to infer global estimates of PM2.5. Ground-based measurements of aerosol vertical profiles and properties have insufficient coverage to estimate global AOD–PM2.5 relationships. ::: ::: Global chemical transport models (CTMs) resolve atmospheric composition at a resolution of hundreds of kilometers horizontally by hundreds of meters vertically, with a temporal frequency of tens of minutes. Liu et al. (2004) first estimated surface-level PM2.5 from MISR observations by using CTM output to represent local AOD–PM2.5 conversion factors over the contiguous United States. van Donkelaar et al. (2006) extended the approach used by Liu et al. (2004) to estimate PM2.5 from both MODIS and MISR observations and investigated the factors affecting the agreement between AOD and surface-level PM2.5. Statistical models have also been used to relate AOD to PM2.5. For example, Liu et al. (2007) used MISR-retrieved spherical versus nonspherical particle fraction, in addition to model-derived vertical distribution, to separate mineral dust from other aerosol species. More recently, Paciorek and Liu (2009) probed the limitations of using AOD without accounting for vertical distribution or speciation and concluded that agreement with ground-based monitors based on this approach might depend on factors other than satellite observations. ::: ::: We developed a global satellite-based estimate of surface PM2.5 at a spatial resolution of 0.1° × 0.1°, or approximately 10 km × 10 km at midlatitudes. We developed an approach for combining MODIS and MISR AOD into a single improved estimate of AOD. Using this methodology, we calculated AOD–PM2.5 conversion factors with a global CTM and produced and applied these factors to the AOD. We present a global estimate of PM2.5 concentrations and validate it with ground-based (in situ) observations. We estimate global exposure to outdoor ambient PM2.5 using our satellite-derived product to demonstrate potential application for global health studies. We then examined sources of error.
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paper_title: National-Scale Estimates of Ground-Level PM2.5 Concentration in China Using Geographically Weighted Regression Based on 3 km Resolution MODIS AOD

paper_content:

High spatial resolution estimating of exposure to particulate matter 2.5 (PM2.5) is currently very limited in China. This study uses the newly released nationwide, hourly PM2.5 concentrations to create a nationwide, geographically weighted regression (GWR) model to estimate ground-level PM2.5 concentrations in China. A3 km resolution aerosol optical depth (AOD) product from MODIS is used as the primary predictor. Fire emissions detected by MODIS fire count were considered in the model development process. Additionally, meteorological features were used as covariates in the model to improve the estimation of ground-level PM2.5 concentrations. The model performed well and explained 81% of the daily PM2.5 concentration variations in model predictions, and the cross validations R2 is 0.79. The cross-validated root mean squared error (RMSE) of the model was 18.6 μg/m3.Annual PM2.5 concentrations retrieved by the MODIS 3 km AOD product indicated that most of the residential community areas exceeded the new annual Chinese PM2.5 National Standard level 2. Estimated high-resolution national-scale daily PM2.5 maps are useful to identify severe air pollution episodes and determine health risk assessments. These results suggest that this approach is useful for estimating large-scale ground-level PM2.5 distributions, especially for regions without PM monitoring sites.
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paper_title: Integrating lidar and satellite optical depth with ambient monitoring for 3-dimensional particulate characterization

paper_content:

A combination of in-situ PM2.5, sunphotometers, upward pointing lidar and satellite aerosol optical depth (AOD) instruments have been employed to better understand variability in the correlation between AOD and PM2.5 at the surface. Previous studies have shown good correlation between these measures, especially in the US east, and encouraged the use of satellite data for spatially interpolating between ground sensors. This work shows that cases of weak correlation can be better understood with knowledge of whether the aerosol is confined to the surface planetary boundary layer (PBL) or aloft. Lidar apportionment of the fraction of aerosol optical depth that is within the PBL can be scaled to give better agreement with surface PM2.5 than does the total column amount. The study has shown that lidar combined with surface and remotely sensed data might be strategically used to improve our understanding of long-range or regionally transported pollutants in multiple dimensions. r 2006 Elsevier Ltd. All rights reserved.
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paper_title: Satellite-based PM concentrations and their application to COPD in Cleveland, OH

paper_content:

A hybrid approach is proposed to estimate exposure to fine particulate matter (PM(2.5)) at a given location and time. This approach builds on satellite-based aerosol optical depth (AOD), air pollution data from sparsely distributed Environmental Protection Agency (EPA) sites and local time-space Kriging, an optimal interpolation technique. Given the daily global coverage of AOD data, we can develop daily estimate of air quality at any given location and time. This can assure unprecedented spatial coverage, needed for air quality surveillance and management and epidemiological studies. In this paper, we developed an empirical relationship between the 2 km AOD and PM(2.5) data from EPA sites. Extrapolating this relationship to the study domain resulted in 2.3 million predictions of PM(2.5) between 2000 and 2009 in Cleveland Metropolitan Statistical Area (MSA). We have developed local time-space Kriging to compute exposure at a given location and time using the predicted PM(2.5). Daily estimates of PM(2.5) were developed for Cleveland MSA between 2000 and 2009 at 2.5 km spatial resolution; 1.7 million (∼79.8%) of 2.13 million predictions required for multiyear and geographic domain were robust. In the epidemiological application of the hybrid approach, admissions for an acute exacerbation of chronic obstructive pulmonary disease (AECOPD) was examined with respect to time-space lagged PM(2.5) exposure. Our analysis suggests that the risk of AECOPD increases 2.3% with a unit increase in PM(2.5) exposure within 9 days and 0.05° (∼5 km) distance lags. In the aggregated analysis, the exposed groups (who experienced exposure to PM(2.5) >15.4 μg/m(3)) were 54% more likely to be admitted for AECOPD than the reference group. The hybrid approach offers greater spatiotemporal coverage and reliable characterization of ambient concentration than conventional in situ monitoring-based approaches. Thus, this approach can potentially reduce exposure misclassification errors in the conventional air pollution epidemiology studies.
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paper_title: Methods for characterizing fine particulate matter using ground observations and remotely sensed data: potential use for environmental public health surveillance.

paper_content:

This study describes and demonstrates different techniques for surface fitting daily environmental hazards data of particulate matter with aerodynamic diameter less than or equal to 2.5 microm (PM2.5) for the purpose of integrating respiratory health and environmental data for the Centers for Disease Control and Prevention (CDC) pilot study of Health and Environment Linked for Information Exchange (HELIX)-Atlanta. It presents a methodology for estimating daily spatial surfaces of ground-level PM2.5 concentrations using the B-Spline and inverse distance weighting (IDW) surface-fitting techniques, leveraging National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectrometer (MODIS) data to complement U.S. Environmental Protection Agency (EPA) ground observation data. The study used measurements of ambient PM2.5 from the EPA database for the year 2003 as well as PM2.5 estimates derived from NASA's satellite data. Hazard data have been processed to derive the surrogate PM2.5 exposure estimates. This paper shows that merging MODIS remote sensing data with surface observations of PM,2. not only provides a more complete daily representation of PM,2. than either dataset alone would allow, but it also reduces the errors in the PM2.5-estimated surfaces. The results of this study also show that although the IDW technique can introduce some numerical artifacts that could be due to its interpolating nature, which assumes that the maxima and minima can occur only at the observation points, the daily IDW PM2.5 surfaces had smaller errors in general, with respect to observations, than those of the B-Spline surfaces. Finally, the methods discussed in this paper establish a foundation for environmental public health linkage and association studies for which determining the concentrations of an environmental hazard such as PM2.5 with high accuracy is critical.
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paper_title: Consequences of kriging and land use regression for PM2.5 predictions in epidemiologic analyses: insights into spatial variability using high-resolution satellite data

paper_content:

Many epidemiological studies use predicted air pollution exposures as surrogates for true air pollution levels. These predicted exposures contain exposure measurement error, yet simulation studies have typically found negligible bias in resulting health effect estimates. However, previous studies typically assumed a statistical spatial model for air pollution exposure, which may be oversimplified. We address this shortcoming by assuming a realistic, complex exposure surface derived from fine-scale (1 km × 1 km) remote-sensing satellite data. Using simulation, we evaluate the accuracy of epidemiological health effect estimates in linear and logistic regression when using spatial air pollution predictions from kriging and land use regression models. We examined chronic (long-term) and acute (short-term) exposure to air pollution. Results varied substantially across different scenarios. Exposure models with low out-of-sample R2 yielded severe biases in the health effect estimates of some models, ranging from 60% upward bias to 70% downward bias. One land use regression exposure model with >0.9 out-of-sample R2 yielded upward biases up to 13% for acute health effect estimates. Almost all models drastically underestimated the SEs. Land use regression models performed better in chronic effect simulations. These results can help researchers when interpreting health effect estimates in these types of studies.
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paper_title: A method to estimate concentrations of surface-level particulate matter using satellite-based aerosol optical thickness

paper_content:

We propose a new method to estimate surface-level particulate matter (PM) concentrations by using satellite-retrieved Aerosol Optical Thickness (AOT). This method considers the distribution and variation of Planetary Boundary Layer (PBL) height and relative humidity (RH) at the regional scale. The method estimates surface-level particulate matter concentrations using the data simulated by an atmospheric boundary layer model RAMS and satellite-retrieved AOT. By incorporation MODIS AOT, PBL height and RH simulated by RAMS, this method is applied to estimate the surface-level PM2.5 concentrations in North China region. The result is evaluated by using 16 ground-based observations deployed in the research region, and the result shows a good agreement between estimated PM2.5 concentrations and observations, and the coefficient of determination R2 is 0.61 between the estimated PM2.5 concentrations and the observations. In addition, surface-level PM2.5 concentrations are also estimated by using MODIS AOT, ground-based LIDAR observations and RH measurements. A comparison between the two estimated PM2.5 concentrations shows that the new method proposed in this paper is better than the traditional method. The coefficient of determination R2 is improved from 0.32 to 0.62.
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paper_title: Satellite remote sensing of particulate matter and air quality assessment over global cities

paper_content:

Using 1 year of aerosol optical thickness (AOT) retrievals from the MODerate resolution Imaging Spectro-radiometer (MODIS) on board NASA’s Terra and Aqua satellite along with ground measurements of PM2.5 mass concentration, we assess particulate matter air quality over different locations across the global urban areas spread over 26 locations in Sydney, Delhi, Hong Kong, New York City and Switzerland. An empirical relationship between AOT and PM2.5 mass is obtained and results show that there is an excellent correlation between the bin-averaged daily mean satellite and groundbased values with a linear correlation coefficient of 0.96. Using meteorological and other ancillary datasets, we assess the effects of wind speed, cloud cover, and mixing height (MH) on particulate matter (PM) air quality and conclude that these data are necessary to further apply satellite data for air quality research. Our study clearly demonstrates that satellitederived AOT is a good surrogate for monitoring PM air quality over the earth. However, our analysis shows that the PM2.5–AOT relationship strongly depends on aerosol concentrations, ambient relative humidity (RH), fractional cloud cover and height of the mixing layer. Highest correlation between MODIS AOT and PM2.5 mass is found under clear sky conditions with less than 40–50% RH and when atmospheric MH ranges from 100 to 200 m. Future remote sensing sensors such as Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) that have the capability to provide vertical distribution of aerosols will further enhance our ability to monitor and forecast air pollution. This study is among the first to examine the relationship between satellite and ground measurements over several global locations. r 2006 Elsevier Ltd. All rights reserved.
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paper_title: Exploring the relation between aerosol optical depth and PM2.5 at Cabauw, the Netherlands

paper_content:

To acquire daily estimates of PM2.5 distributions based on satellite data one depends critically on an established relation between AOD and ground level PM2.5. In this study we aimed to experimentally establish the AOD-PM2.5 relationship for the Netherlands. For that purpose an experiment was set-up at the AERONET site Cabauw. The average PM 2.5 concentration during this ten month study was 18 μg/m 3, which confirms that the Netherlands are characterised by a high PM burden. A first inspection of the AERONET level 1.5 (L1.5) AOD and PM2.5 data at Cabauw showed a low correlation between the two properties. However, after screening for cloud contamination in the AERONET L1.5 data, the correlation improved substantially. When also constraining the dataset to data points acquired around noon, the correlation between AOD and PM2.5 amounted to R2=0.6 for situations with fair weather. This indicates that AOD data contain information about the temporal evolution of PM2.5. We had used LIDAR observations to detect residual cloud contamination in the AERONET L1.5 data. Comparison of our cloud-screed L1.5 with AERONET L2 data that became available near the end of the study showed favorable agreement. The final relation found for Cabauw is PM 2.5=124.5*AODĝ€"0.34 (with PM2.5 in μg/m3) and is valid for fair weather conditions. The relationship determined between MODIS AOD and ground level PM2.5 at Cabauw is very similar to that based on the much larger dataset from the sun photometer data, after correcting for a systematic overestimation of the MODIS data of 0.05. We applied the relationship to a MODIS composite map to assess the PM2.5 distribution over the Netherlands. Spatial dependent systematic errors in the MODIS AOD, probably related to variability in surface reflectance, hamper a meaningful analysis of the spatial distribution of PM2.5 using AOD data at the scale of the Netherlands.
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paper_title: Improving correlations between MODIS aerosol optical thickness and ground-based PM2.5 observations through 3D spatial analyses

paper_content:

The Center for Space Research (CSR) continues to focus on developing methods to improve correlations between satellite-based aerosol optical thickness (AOT) values and ground-based, air pollution observations made at continuous ambient monitoring sites (CAMS) operated by the Texas commission on environmental quality (TCEQ). Strong correlations and improved understanding of the relationships between satellite and ground observations are needed to formulate reliable real-time predictions of air quality using data accessed from the moderate resolution imaging spectroradiometer (MODIS) at the CSR direct-broadcast ground station. In this paper, improvements in these correlations are demonstrated first as a result of the evolution in the MODIS retrieval algorithms. Further improvement is then shown using procedures that compensate for differences in horizontal spatial scales between the nominal 10-km MODIS AOT products and CAMS point measurements. Finally, airborne light detection and ranging (lidar) observations, collected during the Texas Air Quality Study of 2000, are used to examine aerosol profile concentrations, which may vary greatly between aerosol classes as a result of the sources, chemical composition, and meteorological conditions that govern transport processes. Further improvement in correlations is demonstrated with this limited dataset using insights into aerosol profile information inferred from the vertical motion vectors in a trajectory-based forecast model. Analyses are ongoing to verify these procedures on a variety of aerosol classes using data collected by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite (Calipso) lidar.
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Title: A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth

Section 1: Theory Background and Application
Description 1: Overview of different models used to predict PM2.5 from satellite AOD, including their methodology and key findings.

Section 2: Advantages and Disadvantages
Description 2: Discussion of the strengths and weaknesses of different models based on previous studies.

Section 3: Other Models
Description 3: Exploration of additional models used for PM2.5 estimation and their comparative performance.

Section 4: Summary
Description 4: Summarizes the accuracy, adjusting factors, and handling of missing AOD data for different models.

Section 5: Conclusions
Description 5: Concluding remarks on the overall performance of different models and recommendations for future studies.