Integrating land use and traffic to spatial prediction of particulate matter

Abstract

With degrading air quality and inefficient monitoring systems, spatial prediction of pollutants has become necessary. Air pollution exposure causes significant impacts on human health in urban areas around the world. The current study focuses on developing a deep learning model for spatial prediction of particulate matter at locations where the monitored data is unavailable. Delhi, India, is selected as the study area due to its high air pollution and fewer fixed monitoring stations throughout the city. The parameters used for the prediction modeling included PM2.5 from fixed monitoring stations, PM2.5 from mobile sensors, meteorological parameters (atmospheric temperature and relative humidity), traffic flow data, and land use parameters. The data is taken for six months, from November 2022 to April 2023, at an interval of 15 min. Four buffer sizes are used to find the spatial variability. The ANN model is used for the spatial predictions using the above-mentioned parameters. K-fold cross-validation is used to validate the robustness of the model. The SHAP summary plot is used to identify the feature importance of the features used in the model. It is observed that the integration of the land use data and traffic data increases the prediction capability of the model.