NeSNet: A Deep Network for Estimating Near-Surface Pollutant Concentrations

With the threat of atmospheric pollution on the rise in recent years, round-the-clock monitoring of the concentration of atmospheric gases has become utterly necessary. As opposed to traditional in situ measurement strategies, satellite monitoring offers a convenient alternative for truly global coverage. However, satellite measurements do not provide information about the vertical profile of concentration, and estimation methods must be used to deduce near-surface concentration. Existing works that address this problem often adopt approaches that use auxiliary variables such as meteorological parameters and population density information along with vertical column density (VCD) measurements. In remote areas where such information is not available, these methods are likely to fail. In our work, we propose a near-surface network, a convolutional neural network that has been designed to perform the estimation of near-surface concentrations of atmospheric trace gases using only VCD values. We demonstrate the working of our method for nitrogen dioxide (NO
$_{2}$
), sulfur dioxide (SO
$_{2}$
), and ozone (O
$_{3}$
). The proposed method shows RMSE scores of 6.272, 7.20, and 16.03
$\mu$
g
$/\text{m}^{3}$
for SO
$_{2}$
, NO
$_{2}$
, and O
$_{3}$
, respectively. We also perform a detailed analysis of the impact of various factors on model performance. In the future, this method also use to determine the concentration of additional air pollutants including PM
$_{2.5}$
and PM
$_{10}$
. To possibly improve the effectiveness of the model, other meteorological variables, such as temperature, relative humidity, wind speed, and wind direction can be incorporated.