The delay between changes in exposure and changes in health is an empirical challenge in estimating potential health effects associated with air pollution exposure. For example, if health impacts of high pollutant exposures have a long latency, risk estimates attributing to lower pollutant concentrations experienced more recently may be biased away from the null. However, the 2019 PM ISA states that "new evidence from recent studies continues to support the previous conclusion that health benefits from reducing air pollution could be expected with a few years of intervention" (U.S. EPA, 2019b). ADDIN EN.CITE <EndNote><Author>U.S. EPA</Author><Year>2019</Year><RecNum>320</RecNum><DisplayText>U.S. EPA, 2019c</DisplayText><record><rec-number>320</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1573071075">320</key></foreign-keys><ref-type name="Government Document">46</ref-type><contributors><authors><author>U.S. EPA,</author></authors><secondary-authors><author>U.S. EPA </author></secondary-authors></contributors><titles><title>Integrated Science Assessment (ISA) for Particulate Matter (Final Report)</title></titles><edition>December 2019</edition><dates><year>2019</year></dates><pub-location>Washington, DC</pub-location><publisher>U.S. Environmental Protection Agency, Office of Research and Development, National Center for Environmental Assessment</publisher><isbn>EPA/600/R-19/188</isbn><urls><related-urls><url>Available at: https://www.epa.gov/naaqs/particulate-matter-pm-standards-integrated-science-assessments-current-review</url></related-urls></urls></record></EndNote> This issue is likely less relevant to O3 exposure-attributable mortality, as those studies often have very similar, if not overlapping, health and air quality data.
Discounting Future Benefit Estimates
Discounting reflects that people prefer benefits presently more than in the future. When appropriately applied, discounting can allow for the direct comparison of future benefits to costs. However, there are potential uncertainties associated with discounting future benefit estimates. EPA bounds discounted benefits and costs using an estimate of the consumption rate of interest and a rate of return on private capital given that the share of capital that is displaced by a regulation is unknown. OMB currently recommends, and EPA uses, a real consumption rate of discount of 3% and a real rate of 7% for the opportunity cost of private capital based on prior empirical estimates (OMB, 2003 ADDIN EN.CITE <EndNote><Author>OMB</Author><Year>2003</Year><RecNum>1398</RecNum><DisplayText>OMB, 2003</DisplayText><record><rec-number>1398</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1611076276">1398</key></foreign-keys><ref-type name="Government Document">46</ref-type><contributors><authors><author>OMB, </author></authors></contributors><titles><title>Circular A-4</title></titles><pages>58366</pages><edition>October 2003</edition><dates><year>2003</year></dates><pub-location>Washinton, DC</pub-location><publisher>Office of Management and Budget</publisher><isbn>68 FR 58366</isbn><urls><related-urls><url>Available at: https://www.federalregister.gov/documents/2003/10/09/03-25606/circular-a-4-regulatory-analysis</url></related-urls></urls><custom1>Office of Management and Budget</custom1><electronic-resource-num>03-25606</electronic-resource-num></record></EndNote>). These values are estimates and therefore introduce uncertainty. Additional detail on discounting can be found in the EPA Guidelines for Preparing Economic Analyses (U.S. EPA, 2014 ADDIN EN.CITE <EndNote><Author>U.S. EPA</Author><Year>2014</Year><RecNum>1300</RecNum><DisplayText>U.S. EPA, 2014</DisplayText><record><rec-number>1300</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1608221942">1300</key></foreign-keys><ref-type name="Generic">13</ref-type><contributors><authors><author>U.S. EPA, </author></authors></contributors><titles><title>Guidelines for Preparing Economic Analyses</title></titles><dates><year>2014</year></dates><publisher>National Center for Environmental Economics. US Environmental Protection Agency Washington, DC</publisher><urls><related-urls><url>https://www.epa.gov/environmental-economics/guidelines-preparing-economic-analyses</url></related-urls></urls></record></EndNote>).
Statistical Estimates of WTP
EPA relies on published peer-reviewed studies to provide statistical estimates of the value of avoided pain and suffering (WTP). While most of these studies provide estimates of the uncertainty due to statistical sampling, there are other important sources of error. First, the statistical models used to produce these estimates may be incorrect, termed modeling error. Second, the statistical samples used to produce these estimates may be selectively chosen and unlike the population of interest, leading to selection error. Third, WTP values are unavailable for many health endpoints of interest. Assigning a value of zero is clearly incorrect, but the EPA has no basis on which to assign other values.
Confounding by Individual Risk Factors
Interindividual variability in both physiological responses and exposures to ambient air pollution can affect the size of reported risk estimates in epidemiologic studies. Well-designed epidemiology studies account for individual risk factors as covariates in their models, and so all else being equal we identify risk estimates adjusted to control for the most covariates that could reasonably impact the risk estimate. However, confounding by individual risk factors remains a potential source of uncertainty as additional relevant covariates may exist that are not included as covariates in epidemiological risk estimates used for health benefits assessment. Unfortunately, we are currently unable to quantitively assess this area of uncertainty but will include qualitative discussions when possible.
Confounding by Other Pollutants
When considering the relationship between pollutant exposure and health impacts, it can be informative to consider whether risk estimates are changed when other pollutants are included in copollutant models, especially when health impacts of multiple pollutants are being estimated concurrently. Regarding long-term exposures, the 2019 PM ISA concluded that "positive associations observed between long-term PM2.5 exposure and total mortality remain relatively unchanged after adjustment for...NO2 and PM10−2.5" and the 2020 O3 ISA found that "positive associations observed between long term O3 exposure and total mortality remain relatively unchanged after adjustment for...NO2." However, confounding due to the effects of copollutants other than O3 and PM2.5 are a potential source of uncertainty.
Risk Attributable to Long-Term and Short-Term Exposures
Long- and short-term exposures may follow similar or divergent biological pathways. When pathways are similar, estimates of impacts from long-term exposures may include short-term impacts and vice-versa. However, if pathways diverge, long- and short-term impacts may be the sum, or even greater than the sum, of the two exposure durations. As there is little research directly comparing long- and short-term effects, we are currently unable to quantitatively assess this area of uncertainty for either PM2.5- or O3-attributable health effects.
Heterogeneity of Risk Estimates
Epidemiologic studies often differ according to study design, geographic locations, age groups, population attributes, study size, methods for estimating exposure, range of pollutant concentrations, time periods, study sizes, and follow-up durations. These differences in turn influence the magnitude and standard error of study-reported risk estimates. The diversity of identified risk estimates could reflect either the variability across the populations studied or uncertainty around the risk estimates. Importantly, heterogeneous risk estimates are not necessarily indicative of bias, but could also result from variability of the underlying input parameters.
O3 Metrics
O3 exposure metrics used to develop risk estimates can take many forms, though the most widely used metrics are the maximum daily 8-hour average (MDA8), daily average 24-hour (DA24), daily average 8-hr from 10AM to 6PM (DA8), and maximum daily 1-hour average (MDA1) metrics.
Historically, if epidemiologic studies developed risk estimates based on O3 metrics other than MDA8, EPA would adapt the risk estimates based on average conversion ratios to be appropriate for use with air quality surfaces projected in the MDA8 metric (Anderson and Bell, 2010 ADDIN EN.CITE <EndNote><Author>Anderson</Author><Year>2010</Year><RecNum>1378</RecNum><DisplayText>Anderson and Bell, 2010</DisplayText><record><rec-number>1378</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1609275724">1378</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Anderson, G. B.</author><author>Bell, M. L.</author></authors></contributors><auth-address>Environmental Engineering, Yale University, New Haven, CT 06511, USA.</auth-address><titles><title>Does one size fit all? The suitability of standard ozone exposure metric conversion ratios and implications for epidemiology</title><secondary-title>J Expo Sci Environ Epidemiol</secondary-title></titles><periodical><full-title>J Expo Sci Environ Epidemiol</full-title></periodical><pages>2-11</pages><volume>20</volume><number>1</number><edition>2008/11/06</edition><keywords><keyword>Air Pollutants/*analysis/toxicity</keyword><keyword>Environmental Exposure/adverse effects/*analysis/prevention & control/standards</keyword><keyword>Epidemiologic Methods</keyword><keyword>Geography</keyword><keyword>Humans</keyword><keyword>Models, Biological</keyword><keyword>Ozone/*analysis/toxicity</keyword><keyword>Risk Assessment</keyword><keyword>Seasons</keyword><keyword>Time Factors</keyword><keyword>United States/epidemiology</keyword><keyword>Urban Health/statistics & numerical data</keyword><keyword>Weather</keyword></keywords><dates><year>2010</year><pub-dates><date>Jan</date></pub-dates></dates><isbn>1559-064X (Electronic)
1559-0631 (Linking)</isbn><accession-num>18985076</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/18985076</url></related-urls></urls><custom2>PMC3169292</custom2><electronic-resource-num>10.1038/jes.2008.69</electronic-resource-num></record></EndNote>). This approach brings with it uncertainties associated with the simplifying assumptions used to develop the conversion ratios. In most cases, the day to day variation in different metrics (e.g., DA24 vs MDA8) is highly correlated. As such, the relationships between health impacts and different ozone metrics will also be highly correlated. However, when we apply risk estimates derived from time series results to evaluate the impacts of a specific policy scenario, we focus most on the shift in the overall distribution of O3 concentrations over an entire season, instead of on the day to day variation in O3 levels. Because specific policy scenarios might result in different temporal distributions of ozone concentrations than was observed in the monitored ozone data used in the studies, it is important to choose an O3 metric that is best suited to capturing changes in O3 that are likely to occur during hours where populations are likely to be exposed.
Converting O3 Risk Estimate Exposure Metrics
When epidemiologic risk estimates are developed using non-MDA8 O3 exposure metrics, EPA has typically converted the beta risk estimates into MDA8 metrics, which brings in a potential source of uncertainty (Anderson and Bell, 2010 ADDIN EN.CITE <EndNote><Author>Anderson</Author><Year>2010</Year><RecNum>1378</RecNum><DisplayText>Anderson and Bell, 2010</DisplayText><record><rec-number>1378</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1609275724">1378</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Anderson, G. B.</author><author>Bell, M. L.</author></authors></contributors><auth-address>Environmental Engineering, Yale University, New Haven, CT 06511, USA.</auth-address><titles><title>Does one size fit all? The suitability of standard ozone exposure metric conversion ratios and implications for epidemiology</title><secondary-title>J Expo Sci Environ Epidemiol</secondary-title></titles><periodical><full-title>J Expo Sci Environ Epidemiol</full-title></periodical><pages>2-11</pages><volume>20</volume><number>1</number><edition>2008/11/06</edition><keywords><keyword>Air Pollutants/*analysis/toxicity</keyword><keyword>Environmental Exposure/adverse effects/*analysis/prevention & control/standards</keyword><keyword>Epidemiologic Methods</keyword><keyword>Geography</keyword><keyword>Humans</keyword><keyword>Models, Biological</keyword><keyword>Ozone/*analysis/toxicity</keyword><keyword>Risk Assessment</keyword><keyword>Seasons</keyword><keyword>Time Factors</keyword><keyword>United States/epidemiology</keyword><keyword>Urban Health/statistics & numerical data</keyword><keyword>Weather</keyword></keywords><dates><year>2010</year><pub-dates><date>Jan</date></pub-dates></dates><isbn>1559-064X (Electronic)
1559-0631 (Linking)</isbn><accession-num>18985076</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/18985076</url></related-urls></urls><custom2>PMC3169292</custom2><electronic-resource-num>10.1038/jes.2008.69</electronic-resource-num></record></EndNote>). We discuss uncertainties associated with converting various common O3 exposure metrics into the MDA8 metric below.
DA24 to MDA8
Currently, air quality projections using the DA24 metric are unavailable, so a conversion factor from Anderson and Bell, 2010 ADDIN EN.CITE <EndNote><Author>Anderson</Author><Year>2010</Year><RecNum>1378</RecNum><DisplayText>Anderson and Bell, 2010</DisplayText><record><rec-number>1378</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1609275724">1378</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Anderson, G. B.</author><author>Bell, M. L.</author></authors></contributors><auth-address>Environmental Engineering, Yale University, New Haven, CT 06511, USA.</auth-address><titles><title>Does one size fit all? The suitability of standard ozone exposure metric conversion ratios and implications for epidemiology</title><secondary-title>J Expo Sci Environ Epidemiol</secondary-title></titles><periodical><full-title>J Expo Sci Environ Epidemiol</full-title></periodical><pages>2-11</pages><volume>20</volume><number>1</number><edition>2008/11/06</edition><keywords><keyword>Air Pollutants/*analysis/toxicity</keyword><keyword>Environmental Exposure/adverse effects/*analysis/prevention & control/standards</keyword><keyword>Epidemiologic Methods</keyword><keyword>Geography</keyword><keyword>Humans</keyword><keyword>Models, Biological</keyword><keyword>Ozone/*analysis/toxicity</keyword><keyword>Risk Assessment</keyword><keyword>Seasons</keyword><keyword>Time Factors</keyword><keyword>United States/epidemiology</keyword><keyword>Urban Health/statistics & numerical data</keyword><keyword>Weather</keyword></keywords><dates><year>2010</year><pub-dates><date>Jan</date></pub-dates></dates><isbn>1559-064X (Electronic)
1559-0631 (Linking)</isbn><accession-num>18985076</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/18985076</url></related-urls></urls><custom2>PMC3169292</custom2><electronic-resource-num>10.1038/jes.2008.69</electronic-resource-num></record></EndNote> is used in order to apply these risk estimates to MDA8 air quality surface projections. We multiply the beta risk estimate by the inverse of the median summer ratio of MDA8 to DA24 mean O3 concentrations (i.e., 1 / 1.53 = 0.6536) for studies assessing summer O3 exposure or by the inverse of the fixed effects average ratio of MDA8 to DA24 mean O3 concentrations (i.e., 1 / 1.53 = 0.6536) for studies assessing all-year O3 exposure. We note that Anderson and Bell, 2010 ADDIN EN.CITE <EndNote><Author>Anderson</Author><Year>2010</Year><RecNum>1378</RecNum><DisplayText>Anderson and Bell, 2010</DisplayText><record><rec-number>1378</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1609275724">1378</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Anderson, G. B.</author><author>Bell, M. L.</author></authors></contributors><auth-address>Environmental Engineering, Yale University, New Haven, CT 06511, USA.</auth-address><titles><title>Does one size fit all? The suitability of standard ozone exposure metric conversion ratios and implications for epidemiology</title><secondary-title>J Expo Sci Environ Epidemiol</secondary-title></titles><periodical><full-title>J Expo Sci Environ Epidemiol</full-title></periodical><pages>2-11</pages><volume>20</volume><number>1</number><edition>2008/11/06</edition><keywords><keyword>Air Pollutants/*analysis/toxicity</keyword><keyword>Environmental Exposure/adverse effects/*analysis/prevention & control/standards</keyword><keyword>Epidemiologic Methods</keyword><keyword>Geography</keyword><keyword>Humans</keyword><keyword>Models, Biological</keyword><keyword>Ozone/*analysis/toxicity</keyword><keyword>Risk Assessment</keyword><keyword>Seasons</keyword><keyword>Time Factors</keyword><keyword>United States/epidemiology</keyword><keyword>Urban Health/statistics & numerical data</keyword><keyword>Weather</keyword></keywords><dates><year>2010</year><pub-dates><date>Jan</date></pub-dates></dates><isbn>1559-064X (Electronic)
1559-0631 (Linking)</isbn><accession-num>18985076</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/18985076</url></related-urls></urls><custom2>PMC3169292</custom2><electronic-resource-num>10.1038/jes.2008.69</electronic-resource-num></record></EndNote> included a range of ratios from 1.23-1.83.
DA8 to MDA8
A comparison of the MDA8 and DA8 metrics using 20 years of O3 monitoring data (2000-2019) for the entire contiguous U.S. resulted in a very high rate of correlation (Figure 17). The correlation was based on a simple linear regression with zero intercept, meaning that if the MDA8 is 0, then the DA8 mean must also be zero. The green line denotes the regression line and the light gray line represents a 1:1 relationship. Please note, the MDA8 cannot exceed the DA8 and the high density of the ~7 million points shown in the graph cluster near the 1:1 line. In fact, the MDA8 and DA8 metrics are identical approximately 30% of the time and differ by 2 ppb or less about 80% of the time. Based on this comparison, the conversion factor from DA8 to MDA8 is 0.97.
Figure 17. Correlation of MDA8 and DA8 O3 Exposures Between 2000-2019 (R=0.986)

MDA1 to MDA8
Due to time and resource limitations, air quality projections using the MDA1 metric are also unavailable for the Revised CSAPR Update final rulemaking, so a conversion factor from Anderson and Bell, 2010 ADDIN EN.CITE <EndNote><Author>Anderson</Author><Year>2010</Year><RecNum>1378</RecNum><DisplayText>Anderson and Bell, 2010</DisplayText><record><rec-number>1378</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1609275724">1378</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Anderson, G. B.</author><author>Bell, M. L.</author></authors></contributors><auth-address>Environmental Engineering, Yale University, New Haven, CT 06511, USA.</auth-address><titles><title>Does one size fit all? The suitability of standard ozone exposure metric conversion ratios and implications for epidemiology</title><secondary-title>J Expo Sci Environ Epidemiol</secondary-title></titles><periodical><full-title>J Expo Sci Environ Epidemiol</full-title></periodical><pages>2-11</pages><volume>20</volume><number>1</number><edition>2008/11/06</edition><keywords><keyword>Air Pollutants/*analysis/toxicity</keyword><keyword>Environmental Exposure/adverse effects/*analysis/prevention & control/standards</keyword><keyword>Epidemiologic Methods</keyword><keyword>Geography</keyword><keyword>Humans</keyword><keyword>Models, Biological</keyword><keyword>Ozone/*analysis/toxicity</keyword><keyword>Risk Assessment</keyword><keyword>Seasons</keyword><keyword>Time Factors</keyword><keyword>United States/epidemiology</keyword><keyword>Urban Health/statistics & numerical data</keyword><keyword>Weather</keyword></keywords><dates><year>2010</year><pub-dates><date>Jan</date></pub-dates></dates><isbn>1559-064X (Electronic)
1559-0631 (Linking)</isbn><accession-num>18985076</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/18985076</url></related-urls></urls><custom2>PMC3169292</custom2><electronic-resource-num>10.1038/jes.2008.69</electronic-resource-num></record></EndNote> is used in order to apply these risk estimates to MDA8 air quality surface projections. We multiply the beta risk estimate by the median summer ratio of MDA1 to MDA8 O3 concentrations (i.e., 1.13) for studies assessing summer O3 exposure or by the fixed effects average ratio of MDA1 to MDA8 O3 concentrations (i.e., 1.14) for studies assessing all-year O3 exposure. We note that Anderson and Bell, 2010 ADDIN EN.CITE <EndNote><Author>Anderson</Author><Year>2010</Year><RecNum>1378</RecNum><DisplayText>Anderson and Bell, 2010</DisplayText><record><rec-number>1378</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1609275724">1378</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Anderson, G. B.</author><author>Bell, M. L.</author></authors></contributors><auth-address>Environmental Engineering, Yale University, New Haven, CT 06511, USA.</auth-address><titles><title>Does one size fit all? The suitability of standard ozone exposure metric conversion ratios and implications for epidemiology</title><secondary-title>J Expo Sci Environ Epidemiol</secondary-title></titles><periodical><full-title>J Expo Sci Environ Epidemiol</full-title></periodical><pages>2-11</pages><volume>20</volume><number>1</number><edition>2008/11/06</edition><keywords><keyword>Air Pollutants/*analysis/toxicity</keyword><keyword>Environmental Exposure/adverse effects/*analysis/prevention & control/standards</keyword><keyword>Epidemiologic Methods</keyword><keyword>Geography</keyword><keyword>Humans</keyword><keyword>Models, Biological</keyword><keyword>Ozone/*analysis/toxicity</keyword><keyword>Risk Assessment</keyword><keyword>Seasons</keyword><keyword>Time Factors</keyword><keyword>United States/epidemiology</keyword><keyword>Urban Health/statistics & numerical data</keyword><keyword>Weather</keyword></keywords><dates><year>2010</year><pub-dates><date>Jan</date></pub-dates></dates><isbn>1559-064X (Electronic)
1559-0631 (Linking)</isbn><accession-num>18985076</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/18985076</url></related-urls></urls><custom2>PMC3169292</custom2><electronic-resource-num>10.1038/jes.2008.69</electronic-resource-num></record></EndNote> included a range of ratios from 1.08-1.26.
O3 Season
Studies of O3 vary with regards to O3 season, limiting analyses to various definitions of summer (e.g., April-September, May-September or June-August) and exposures over the full calendar year. These differences can reflect state-specific, EPA-defined O3 seasons or another seasonal definition chosen by the study author.  O3 exposure estimates are arguably more accurate during the summer when concentrations are typically higher and more monitors are operational. In addition, respiratory effects associated with short-term exposures are commonly limited to the warm season and therefore reflect the incidence that occurs during the 5- or 6-month O3 season (U.S. EPA, 2020e). ADDIN EN.CITE <EndNote><Author>U.S. EPA</Author><Year>2020</Year><RecNum>329</RecNum><DisplayText>U.S. EPA, 2020d</DisplayText><record><rec-number>329</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1573741499">329</key></foreign-keys><ref-type name="Government Document">46</ref-type><contributors><authors><author>U.S. EPA,</author></authors><secondary-authors><author>U.S. EPA </author></secondary-authors></contributors><titles><title>Policy Assessment for the Review of the Ozone National Ambient Air Quality Standards</title></titles><edition>2020 </edition><dates><year>2020</year></dates><pub-location>Research Triangle Park, NC</pub-location><publisher>U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Heath and Environmental Impacts Division</publisher><isbn>EPA-452/R-20-001</isbn><urls><related-urls><url>Available at: https://www.epa.gov/naaqs/ozone-o3-standards-policy-assessments-current-review</url></related-urls></urls></record></EndNote> Recently, there are an increased number of long-term analyses of O3-attributable health impacts over the full calendar year using hybrid modeling techniques and where O3 monitoring data is collected for the entire year. These studies likely represent a more complete estimate of O3-attributable health impacts.
While epidemiologic studies assessing all-year O3 exposures would likely present more comprehensive estimates of health impacts, hybrid O3 surface projections for baseline and policy rulemaking scenarios are not currently available. As such, we identified epidemiologic studies and associated risk estimates that evaluated associations between exposures and warm season effects when available. There was some variability amongst the warm season definitions within the list of studies identified in this update (e.g., April-September and June-August), although only the respiratory emergency department and asthma symptom risk estimate was based on full year O3 exposures (Barry et al., 2019, Lewis et al., 2013). It should be noted that the exposures for asthma symptoms among the identified studies were not evenly distributed across all the seasons (i.e., three in Spring, two in Summer, two in Fall, and one in Winter).
There is some variability regarding the definition of the warm season amongst epidemiologic studies included in the ISAs and the main risk estimates identified here for O3 benefits estimates. When there is a substantial difference, such as the June-August warm season assessed by Zanobetti and Schwartz, 2008 ADDIN EN.CITE <EndNote><Author>Zanobetti</Author><Year>2008</Year><RecNum>1184</RecNum><DisplayText>Zanobetti and Schwartz, 2008</DisplayText><record><rec-number>1184</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603988156">1184</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Zanobetti, A.</author><author>Schwartz, J.</author></authors></contributors><auth-address>Department of Environmental Health, Exposure Epidemiology and Risk Program, Harvard School of Public Health, 401 Park Drive, Landmark Center, Suite 415, P.O. Box 15698, Boston, MA 02215, USA<EMAIL_ADDRESS>displacement in the association of ozone with mortality: an analysis of 48 cities in the United States</title><secondary-title>Am J Respir Crit Care Med</secondary-title></titles><periodical><full-title>American Journal of Respiratory and Critical Care Medicine</full-title>Am. J. Respir. Crit. Care Med.Am J Respir Crit Care Med</periodical><pages>184-9</pages><volume>177</volume><number>2</number><edition>2007/10/13</edition><keywords><keyword>Cause of Death</keyword><keyword>Cities</keyword><keyword>Confidence Intervals</keyword><keyword>Environmental Exposure/*adverse effects</keyword><keyword>Humans</keyword><keyword>*Mortality</keyword><keyword>Oxidants, Photochemical/*adverse effects</keyword><keyword>Ozone/*adverse effects</keyword><keyword>Seasons</keyword><keyword>United States/epidemiology</keyword></keywords><dates><year>2008</year><pub-dates><date>Jan 15</date></pub-dates></dates><isbn>1535-4970 (Electronic)
1073-449X (Linking)</isbn><accession-num>17932375</accession-num><urls><related-urls><url>https://www.ncbi.nlm.nih.gov/pubmed/17932375</url></related-urls></urls><electronic-resource-num>10.1164/rccm.200706-823OC</electronic-resource-num></record></EndNote>, we develop season-specific air quality projections, when feasible. However, many studies begin the 5-7 month warm season in either April or May and conclude the season in September or October. Since projected full year hybrid O3 surfaces are currently not available, epidemiologic risk estimates will be applied to the air quality projection most closely matching the exposure season in the study (e.g., April-September exposures will be applied to May-September air quality projections). We expect this seasonal mismatch will only have a limited effect on the magnitude of related health incidence.
Shape of the Concentration-Response Relationship
PM2.5
An important consideration when characterizing uncertainty is whether the concentration-response relationship is linear across the full concentration range that is encountered, or if there are concentration ranges where there are departures from linearity. Overall, evidence from the 2019 PM ISA continues to "support a linear, no-threshold concentration-response relationship for long-term exposure to PM2.5 and total (nonaccidental) mortality, especially at lower ambient PM2.5 concentrations, with confidence in some studies in the range of 5−8 μg/m[3]" and "there is less certainty in the shape of the concentration-response curve at mean annual PM2.5 concentrations generally below 8 μg/m[3], although some studies characterize the concentration-response relationship with certainty down to 4 μg/m[3]" (U.S. EPA, 2019b). ADDIN EN.CITE <EndNote><Author>U.S. EPA</Author><Year>2019</Year><RecNum>320</RecNum><DisplayText>U.S. EPA, 2019c</DisplayText><record><rec-number>320</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1573071075">320</key></foreign-keys><ref-type name="Government Document">46</ref-type><contributors><authors><author>U.S. EPA,</author></authors><secondary-authors><author>U.S. EPA </author></secondary-authors></contributors><titles><title>Integrated Science Assessment (ISA) for Particulate Matter (Final Report)</title></titles><edition>December 2019</edition><dates><year>2019</year></dates><pub-location>Washington, DC</pub-location><publisher>U.S. Environmental Protection Agency, Office of Research and Development, National Center for Environmental Assessment</publisher><isbn>EPA/600/R-19/188</isbn><urls><related-urls><url>Available at: https://www.epa.gov/naaqs/particulate-matter-pm-standards-integrated-science-assessments-current-review</url></related-urls></urls></record></EndNote>
Although ten large cohort studies of long-term PM2.5-attributable mortality observed linear, no-threshold concentration-response relationships, three Canadian studies presented evidence of deviations from linearity down to the lowest concentration evaluated. Two studies found evidence of a supralinear relationship at lower concentrations, although only one was statistically significant. And a single study found that the best fit for the long-term PM2.5 mortality relationship was in a threshold model with a threshold at 11 μg/m[3].
There are several potential explanations for these results, one of which is that studies may be unable to adequately evaluate the relationship at low levels without sufficient population exposure at those levels. Consistent with that hypothesis, the single statistically significant study finding evidence of supralinearity did have one of the lowest mean PM2.5 concentrations, at 6.3 μg/m[3]. Another possible explanation with support from the 2019 ISA is that the shape of the concentration-response relationship could differ by health outcome.
Although there were no evaluations of the shape of the long-term PM2.5-attributable respiratory mortality relationship in the 2019 PM ISA, there were several studies of the relationship between long-term PM2.5exposure and cardiovascular disease. When considering long-term PM2.5-attributable cardiovascular mortality, again most results "continue to support a linear, no-threshold relationship...especially at lower ambient concentrations of PM2.5...[with] a number of the concentration-response analyses include concentration ranges <=12 μg/m[3]." As with total mortality, a few studies found that risk was greater at lower concentrations, although the deviation from linearity was not statistically significant. The only evidence of nonlinearity in the long-term PM2.5-attributable cardiovascular mortality relationship came from two studies by the same group, which included exposure from cigarette smoking. They observed that the concentration-response relationship was much steeper at lower PM2.5 concentrations, such as those due to ambient air pollution, than at the higher concentrations associated with cigarette smoking.
There were a small number of studies of the relationship between long-term PM2.5 exposure and cardiovascular morbidity endpoints in the 2019 PM ISA. A study of hypertension and another of ischemic heart disease incidence found no deviations from linearity. Two studies of coronary artery calcification found evidence of deviations from linearity, but the direction of the results was inconsistent. One study found evidence of sublinearity at higher concentrations while the other found evidence of supralinearity at both high and low concentrations.
The shape of the relationships between PM2.5 exposure and health effects may also include a threshold, or PM2.5 exposure concentration below which human health is not adversely impacted. Although evidence does not currently support the existence of a measurable PM2.5 exposure population-level threshold, prior higher concentration exposures with longer latency periods could make thresholds difficult to detect. However, the 2019 PM ISA states that "new evidence from recent studies continues to support the previous conclusion that health benefits from reducing air pollution could be expected with a few years of intervention," reducing the likelihood of this potential source of uncertainty.
Based on the evidence and lack of nonlinear relationships between long-term PM2.5 exposure and health impacts, we continue to assume a linear, no-threshold relationship and do not quantitatively assess uncertainties related to the shape of the concentration-response relationships
O3
The 2020 final O3 ISA evaluated a number of studies examining the shape of the concentration-response relationship for long term O3 exposure and mortality using various different statistical techniques, including linear models and restricted cubic splines, which we use to inform the long-term O3-attributable respiratory mortality relationship (U.S. EPA, 2020b). ADDIN EN.CITE <EndNote><Author>U.S. EPA</Author><Year>2020</Year><RecNum>343</RecNum><DisplayText>U.S. EPA, 2020a</DisplayText><record><rec-number>343</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1581631219">343</key></foreign-keys><ref-type name="Government Document">46</ref-type><contributors><authors><author>U.S. EPA,</author></authors><secondary-authors><author>Office of Research and Development</author></secondary-authors></contributors><titles><title>Integrated Science Assessment for Ozone and Related Photochemical Oxidants</title></titles><dates><year>2020</year></dates><pub-location>Washington, DC</pub-location><publisher>U.S. Environmental Protection Agency</publisher><isbn>EPA/600/R-20/012</isbn><urls><related-urls><url>Available at: https://www.epa.gov/isa/integrated-science-assessment-isa-ozone-and-related-photochemical-oxidants</url></related-urls></urls></record></EndNote> The ISA concluded that:
Generally linear, no-threshold relationships exist down to 35−40 ppb, although the results were not entirely consistent. Some studies observed a sublinear relationship, indicating larger changes in risk for higher O3 concentrations compared with lower O3 concentrations. Several studies also included threshold analyses and support the possibility of a threshold near 35 to 40 ppb. (3432020a3430xd5s9009txfw1eafstv220i5ds2wvtpwf5d1581631219">343</U.S. EPA, 2020b, section 6.2.7)
The ozone ISA also found that:
Recent multicity studies continue to support a linear [concentration-response] relationship with no evidence of a threshold between short term ozone exposure and mortality over the range of ozone concentrations typically observed in the U.S. Studies that used different statistical approaches and ozone averaging times (i.e., 24 hour avg and 8 hour max) provide evidence of a linear concentration-response relationship, with less certainty in the shape of the curve at lower concentrations [i.e., 40 ppb for 24 hour avg and 30 ppb for 8 hour max]. An examination of whether a threshold exists in the ozone mortality concentration-response relationship provided no evidence of a concentration below which mortality effects do not occur when examining 5 μg/m[3] (~2.55 ppb) increments across the range of 1 hour max concentrations reported in the U.S. and Canadian cities included in [a large cohort]. (3432020a3430xd5s9009txfw1eafstv220i5ds2wvtpwf5d1581631219">343</U.S. EPA, 2020b, section 6.1.8)
Collectively, these results continue to support the conclusion of the 2006 Ozone Air Quality Criteria Document that "if a population threshold level exists in ozone health effects, it is likely near the lower limit of ambient ozone concentrations in the U.S." and this we assume linear, no-threshold relationships exist between ozone and health impacts in the main benefits estimate.
In addition, the studies identified as best characterizing respiratory mortality exposures did not provide threshold models or find evidence supporting a threshold associated with warm-season effects. Turner et al., 2016 ADDIN EN.CITE <EndNote><Author>Turner</Author><Year>2016</Year><RecNum>544</RecNum><DisplayText>Turner et al., 2016</DisplayText><record><rec-number>544</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1603732084">544</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Turner, M. C.</author><author>Jerrett, M.</author><author>Pope, A., III</author><author>Krewski, D.</author><author>Gapstur, S. M.</author><author>Diver, W. R.</author><author>Beckerman, B. S.</author><author>Marshall, J. D.</author><author>Su, J.</author><author>Crouse, D. L.</author><author>Burnett, R. T.</author></authors></contributors><titles><title>Long-term ozone exposure and mortality in a large prospective study</title><secondary-title>American Journal of Respiratory and Critical Care Medicine</secondary-title><alt-title>Am J Respir Crit Care Med</alt-title></titles><periodical><full-title>American Journal of Respiratory and Critical Care Medicine</full-title>Am. J. Respir. Crit. Care Med.Am J Respir Crit Care Med</periodical><alt-periodical><full-title>American Journal of Respiratory and Critical Care Medicine</full-title>Am. J. Respir. Crit. Care Med.Am J Respir Crit Care Med</alt-periodical><pages>1134-1142</pages><volume>193</volume><number>10</number><dates><year>2016</year></dates><isbn>ISSN 1073-449X
EISSN 1535-4970</isbn><accession-num>26680605</accession-num>3060878<urls><related-urls><url>http://dx.doi.org/10.1164/rccm.201508-1633OC</url></related-urls></urls><electronic-resource-num>10.1164/rccm.201508-1633OC</electronic-resource-num><language>English</language></record></EndNote> did find "some evidence that a threshold model improved model fit for respiratory mortality at 35 ppb (P = 0.002) compared with a linear model using year-round but not summertime O3 (HR per 10 ppb using threshold O3 indicator at 35 ppb for respiratory mortality, 1.17; 95% CI, 1.11 - 1.22)." However, as we are currently unable to obtain all year air quality projections, we are unable to quantitatively assess this year-round-specific uncertainty associated with long-term O3 exposures.
Short-Term Lag Structure
Epidemiologic analyses of short-term exposures often present results as health outcomes occurring a certain time period, or lag days, after exposure. Although there are means of aggregating outcomes that do not occur simultaneously, such as distributed or multi-day lags, there is a possibility that the full impact may not be captured by discrete lag periods in short-term study results. Although uncertainty remains as to whether short-term health impacts are fully captured by discrete lag durations, potentially biasing results toward the null, we are currently unable perform quantitative uncertainty analyses regarding this source of uncertainty.
PM2.5
The 2019 final PM ISA states that "a number of recent studies conducted systematic evaluations of the lag structure of associations for the [short-term] PM2.5 [exposure]-mortality relationship by examining either multiday lags or a series of single-day lags, and these studies continue to support an immediate effect (i.e., lag 0-1 days) of short-term PM2.5 exposures on mortality" (U.S. EPA, 2019b). With respect to morbidity effects, the ISA found that "while recent studies provided evidence of associations in the range of 0-5 days for respiratory effects, there was evidence of an immediate effect for cardiovascular effects and mortality (i.e., 0-1 days) with some initial evidence of associations occurring over longer exposure durations (e.g., 0-4 days)."
O3
The 2020 final O3 ISA found that "for respiratory health effects, when examining more overt effects, such as respiratory related hospital admissions and ED visits (i.e., asthma, COPD, and all respiratory outcomes), epidemiologic studies reported strongest associations occurring within the 1st few days of exposure (i.e., in the range of 0 to 3 days)" (U.S. EPA, 2020b).
Statistical Technique/Model Used to Quantify Risks in Epidemiologic Study
Multiple statistical techniques are used in epidemiological analyses, including the Cox proportional hazards model and the Poisson survival analysis.
PM2.5
The 2019 PM ISA compared the use of various statistical techniques, spatial random effects, and fixed effect models (U.S. EPA, 2019b). ADDIN EN.CITE <EndNote><Author>U.S. EPA</Author><Year>2019</Year><RecNum>320</RecNum><DisplayText>U.S. EPA, 2019c</DisplayText><record><rec-number>320</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1573071075">320</key></foreign-keys><ref-type name="Government Document">46</ref-type><contributors><authors><author>U.S. EPA,</author></authors><secondary-authors><author>U.S. EPA </author></secondary-authors></contributors><titles><title>Integrated Science Assessment (ISA) for Particulate Matter (Final Report)</title></titles><edition>December 2019</edition><dates><year>2019</year></dates><pub-location>Washington, DC</pub-location><publisher>U.S. Environmental Protection Agency, Office of Research and Development, National Center for Environmental Assessment</publisher><isbn>EPA/600/R-19/188</isbn><urls><related-urls><url>Available at: https://www.epa.gov/naaqs/particulate-matter-pm-standards-integrated-science-assessments-current-review</url></related-urls></urls></record></EndNote> The ISA found that "results from well-studied, highly regarded cohorts help to reduce uncertainties that the observed associations between long-term PM2.5 exposure and mortality could be due to the statistical techniques employed or model specification."
O3
The 2020 O3 ISA found that "studies used a number of different statistical techniques to evaluate the shape of the [long-term exposure] concentration-response function, including linear models and restricted cubic splines, and generally observed linear, no-threshold relationships down to 35−40 ppb, although the results are not entirely consistent" (3432020a3430xd5s9009txfw1eafstv220i5ds2wvtpwf5d1581631219">343</U.S. EPA, 2020b).
Temperature and Weather
Temperature and weather may impact observed associations between air pollution exposure and health effects in epidemiologic studies, especially in short-term exposure studies. Although a few studies attempt to disentangle the influence of temperature and/or weather, there is insufficient information available to perform quantitative assessments of uncertainty.
PM2.5
The PM ISA included a number of studies that assessed whether statistical models adequately account for temporal trends and weather covariates (U.S. EPA, 2019b). The ISA found that:
Across studies that evaluated model specification, [short-term] PM2.5-mortality, associations remained positive, although in some cases were attenuated, when using different approaches to account for temporal trends or weather covariates. Seasonal analyses continue to provide evidence that associations are larger in magnitude during warmer months, but it remains unclear whether copollutants confound the associations observed. In addition to seasonal analyses, some studies also examined whether temperature modifies the [short-term] PM2.5-mortality relationship. Initial evidence indicates that the PM2.5-mortality association may be larger in magnitude at lower and higher temperatures, but this observation has not been substantiated by studies conducted in the U.S. (U.S. EPA, 2019b, section 11.1.12) ADDIN EN.CITE <EndNote><Author>U.S. EPA</Author><Year>2019</Year><RecNum>320</RecNum><DisplayText>U.S. EPA, 2019c</DisplayText><record><rec-number>320</rec-number><foreign-keys><key app="EN" db-id="0xd5s9009txfw1eafstv220i5ds2wvtpwf5d" timestamp="1573071075">320</key></foreign-keys><ref-type name="Government Document">46</ref-type><contributors><authors><author>U.S. EPA,</author></authors><secondary-authors><author>U.S. EPA </author></secondary-authors></contributors><titles><title>Integrated Science Assessment (ISA) for Particulate Matter (Final Report)</title></titles><edition>December 2019</edition><dates><year>2019</year></dates><pub-location>Washington, DC</pub-location><publisher>U.S. Environmental Protection Agency, Office of Research and Development, National Center for Environmental Assessment</publisher><isbn>EPA/600/R-19/188</isbn><urls><related-urls><url>Available at: https://www.epa.gov/naaqs/particulate-matter-pm-standards-integrated-science-assessments-current-review</url></related-urls></urls></record></EndNote>
O3
Temperature and weather can also impact epidemiologic results, especially in short-term exposure analyses. While there is limited evidence of differential O3 mortality associations by season, the 2020 O3 ISA determined that the most extensive analyses conducted by recent studies examined whether temperature (i.e., long-term average temperatures or the distribution of mean daily temperatures) modifies the O3 mortality association (U.S. EPA, 2020b). Analyses focusing on temperature indicate that locations with lower long-term average temperature have higher O3 mortality risk estimates, which is also reflected by the observed difference in risk estimates between northern and southern U.S. cities in a single study. However, as long term average temperature may be a surrogate for air conditioning prevalence and studies that examined either the joint or stratified effects of O3 and temperature on mortality provided evidence of O3 mortality associations that are larger in magnitude at temperature extremes, we do not plan on including quantitative uncertainty analyses for the effect of temperature on ozone effects.
Unquantified Impacts
As with all estimates of benefits, due to the lack of complete data, not all human health impacts attributable PM2.5 and O3 can be identified and quantified. EPA acknowledges the existence of unquantified impacts, such as subclinical health endpoints (e.g., hypertension, inflammation, changes in lung/heart function, etc.) or pollutant-attributable clinical endpoints not evaluated in epidemiologic studies.

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[Federal Register Volume 84, Number 160 (Monday, August 19, 2019)]
[Notices]
[Pages 42940-42941]
[FR Doc No: 2019-17737]

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DEPARTMENT OF HOMELAND SECURITY

Coast Guard

[Docket No. USCG-2019-0703]

Information Collection Request to Office of Management and
Budget; OMB Control Number: 1625-NEW

AGENCY: Coast Guard, DHS.

ACTION: Sixty-day notice requesting comments.

-----------------------------------------------------------------------

SUMMARY: In compliance with the Paperwork Reduction Act of 1995, the
U.S. Coast Guard intends to submit an Information Collection Request
(ICR) to the Office of Management and Budget (OMB), Office of
Information and Regulatory Affairs (OIRA), requesting approval for the
following collection of information: 1625-NEW, Intermodal Container
Inspection Program. Our ICR describes the information we seek to
collect from the public. Before submitting this ICR to OIRA, the Coast
Guard is inviting comments as described below.

DATES: Comments must reach the Coast Guard on or before October 18,
2019.

ADDRESSES: You may submit comments identified by Coast Guard docket
number [USCG-2019-0703] to the Coast Guard using the Federal
eRulemaking Portal at https://www.regulations.gov. See the ``Public
participation and request for comments'' portion of the SUPPLEMENTARY
INFORMATION section for further instructions on submitting comments.
    A copy of the ICR is available through the docket on the internet
at https://

www.regulations.gov. Additionally, copies are available from:
COMMANDANT (CG-612), ATTN: PAPERWORK REDUCTION ACT MANAGER, U.S. COAST
GUARD, 2703 MARTIN LUTHER KING JR. AVE SE, STOP 7710, WASHINGTON, DC
20593-7710.

FOR FURTHER INFORMATION CONTACT: Mr. Anthony Smith, Office of
Information Management, telephone<PHONE_NUMBER>, or fax<PHONE_NUMBER>,
for questions on these documents.

SUPPLEMENTARY INFORMATION:

Public Participation and Request for Comments

    This notice relies on the authority of the Paperwork Reduction Act
of 1995; 44 U.S.C. chapter 35, as amended. An ICR is an application to
OIRA seeking the approval, extension, or renewal of a Coast Guard
collection of information (Collection). The ICR contains information
describing the Collection's purpose, the Collection's likely burden on
the affected public, an explanation of the necessity of the Collection,
and other important information describing the Collection. There is one
ICR for each Collection.
    The Coast Guard invites comments on whether this ICR should be
granted based on the Collection being necessary for the proper
performance of Departmental functions. In particular, the Coast Guard
would appreciate comments addressing: (1) The practical utility of the
Collection; (2) the accuracy of the estimated burden of the Collection;
(3) ways to enhance the quality, utility, and clarity of information
subject to the Collection; and (4) ways to minimize the burden of the
Collection on respondents, including the use of automated collection
techniques or other forms of information technology. Consistent with
the requirements of Executive Order 13771, Reducing Regulation and
Controlling Regulatory Costs, and Executive Order 13777, Enforcing the
Regulatory Reform Agenda, the Coast Guard is also requesting comments
on the extent to which this request for information could be modified
to reduce the burden on respondents.
    In response to your comments, we may revise the this ICR or decide
not to seek approval for the Collection. We will consider all comments
and material received during the comment period.
    We encourage you to respond to this request by submitting comments
and related materials. Comments must contain the OMB Control Number of
the ICR and the docket number of this request, [USCG-2019-0703], and
must be received by October 18, 2019.

Submitting Comments

    We encourage you to submit comments through the Federal eRulemaking
Portal at https://www.regulations.gov. If your material cannot be
submitted using https://www.regulations.gov, contact the person in the
FOR FURTHER INFORMATION CONTACT section of this document for alternate
instructions. Documents mentioned in this notice, and all public
comments, are in our online docket at https://www.regulations.gov and
can be viewed by following that website's instructions. Additionally,
if you go to the online docket and sign up for email alerts, you will
be notified when comments are posted.
    We accept anonymous comments. All comments received will be posted
without change to https://www.regulations.gov and will include any
personal information you have provided. For more about privacy and the
docket, you may review a Privacy Act notice regarding the Federal
Docket Management System in the March 24, 2005, issue of the Federal
Register (70 FR 15086).

Information Collection Request

    Title: Intermodal Container Inspection Program.
    OMB Control Number: 1625-NEW.
    Summary: The Coast Guard inspects containers and cargo within
containers to ensure compliance with domestic and international
standards. Coast Guard-issued forms provide stakeholders with the
results of container examinations.
    Need: Under the National Container Inspection Program, Coast Guard
personnel inspect intermodal containers and cargo within containers to
ensure compliance with applicable regulations and to promote maritime
safety, security, and stewardship for U.S. ports and waterways.
Specifically, the Coast Guard inspects containers for compliance with
the--
     Federal Hazardous Materials Transportation Law,
     International Safe Container Act, and
     International Maritime Dangerous Goods Code.
    Forms: CG-5577, Intermodal Container Inspection Report; CG-5577A,
Intermodal Container Non-Deficiency Inspection Report; CG-5577B,
Intermodal Container Targeted Inspection Report.
    Respondents: Operators of container facilities.
    Frequency: On occasion.
    Hour Burden Estimate: The estimated burden is 625 hours annually.
    Authority: The Paperwork Reduction Act of 1995; 44 U.S.C. chapter
35, as amended.

    Dated: August 13, 2019.
James D. Roppel,
U.S. Coast Guard, Chief, Office of Information Management.
[FR Doc. 2019-17737 Filed 8-16-19; 8:45 am]
 BILLING CODE 9110-04-P