Using remotely sensed data for air pollution assessment
Teresa Bernardino Maria Alexandra Oliveira João Nuno Silva
Air pollution constitutes a global problem of paramount importance that
affects not only human health, but also the environment. The existence of
spatial and temporal data regarding the concentrations of pollutants is crucial
for performing air pollution studies and monitor emissions. However, although
observation data presents great temporal coverage, the number of stations is
very limited and they are usually built in more populated areas.
  The main objective of this work is to create models capable of inferring
pollutant concentrations in locations where no observation data exists. A
machine learning model, more specifically the random forest model, was
developed for predicting concentrations in the Iberian Peninsula in 2019 for
five selected pollutants: $NO_2$, $O_3$ $SO_2$, $PM10$, and $PM2.5$. Model
features include satellite measurements, meteorological variables, land use
classification, temporal variables (month, day of year), and spatial variables
(latitude, longitude, altitude).
  The models were evaluated using various methods, including station 10-fold
cross-validation, in which in each fold observations from 10\% of the stations
are used as testing data and the rest as training data. The $R^2$, RMSE and
mean bias were determined for each model. The $NO_2$ and $O_3$ models presented
good values of $R^2$, 0.5524 and 0.7462, respectively. However, the $SO_2$,
$PM10$, and $PM2.5$ models performed very poorly in this regard, with $R^2$
values of -0.0231, 0.3722, and 0.3303, respectively. All models slightly
overestimated the ground concentrations, except the $O_3$ model. All models
presented acceptable cross-validation RMSE, except the $O_3$ and $PM10$ models
where the mean value was a little higher (12.5934 $\mu g/m^3$ and 10.4737 $\mu
g/m^3$, respectively).