A Novel Approach to Mitigating Air Pollution and Climate Change with Graph Convolutional RNN Models

There is a connection between climate change and air pollution due to the fact that weather patterns impact the transportation and degradation of pollutants. Variations in humidity, vertical mixing, and wind speed all have an impact on the transport, dispersion, emission, and deposition of pollutants, which in turn impacts air quality. Because of shifts in precipitation, atmospheric chemistry, and pollutant removal mechanisms, air quality is predicted to deteriorate in highly populated regions as a result of climate change. Both primary and secondary pollutants, such as particulate matter and near-surface ozone, may be formed more quickly as a result of climate change. To enhance air pollution prediction, a comprehensive data preparation stage was used to scale data and errors, identify and eliminate anomalies, and more. It used RMSE, MAE, and R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> measures to assess GCRNN performance with and without DPFS. Using DPFS greatly improved the accuracy of predictions for PM10 concentrations, with RMSE=1.342, MAE=0.264, and R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>=0.532. Air quality predictions seem to be a good fit for DL, according to these results. Air pollution is becoming worse due to climate change, and this study demonstrates how advanced prediction models can assist.