  Formaldehyde (HCHO) is one one of the most important trace gas in the
atmosphere, as it is a pollutant causing respiratory and other diseases. It is
also a precursor of tropospheric ozone which damages crops and deteriorates
human health. Study of HCHO chemistry and long-term monitoring using satellite
data is important from the perspective of human health, food security and air
pollution. Dynamic atmospheric chemistry models struggle to simulate
atmospheric formaldehyde and often overestimate by up to two times relative to
satellite observations and reanalysis. Spatial distribution of modelled HCHO
also fail to match satellite observations. Here, we present deep learning
approach using a simple super-resolution based convolutional neural network
towards simulating fast and reliable atmospheric HCHO. Our approach is an
indirect method of HCHO estimation without the need to chemical equations. We
find that deep learning outperforms dynamical model simulations which involves
complicated atmospheric chemistry representation. Causality establishing the
nonlinear relationships of different variables to target formaldehyde is
established in our approach by using a variety of precursors from meteorology
and chemical reanalysis to target OMI AURA satellite based HCHO predictions. We
choose South Asia for testing our implementation as it doesnt have in situ
measurements of formaldehyde and there is a need for improved quality data over
the region. Moreover, there are spatial and temporal data gaps in the satellite
product which can be removed by trustworthy modelling of atmospheric
formaldehyde. This study is a novel attempt using computer vision for
trustworthy modelling of formaldehyde from remote sensing can lead to cascading
societal benefits.
