Spatiotemporal Estimation and Analysis of PM2.5 Concentrations in Wuhan Utilizing Multisource Remote Sensing Data and NOx as Inputs for Machine Learning Models

Abstract

Atmospheric fine particulate matter (PM _2.5 ) poses significant risks to both environmental and human health, highlighting the need for regional estimations and spatiotemporal analyses. While most studies have focused on large-scale areas, such as global or national levels, fewer studies addressed PM _2.5 at the urban level. This study analyzed PM _2.5 monitoring data from ground stations in Wuhan, collected between July 2018 and July 2023, integrating 1 km Aerosol Optical Depth (AOD) products, Sentinel-5 NO ₂ column concentration data, nighttime light remote sensing, and ERA5 reanalysis meteorological data. Key innovations included selecting NO ₂ column concentration data, as NO _X primarily exists as NO ₂ , and using novel Sentinel-5P measurements rarely explored in related research. Three PM _2.5 estimation models were developed: multiple linear regression (MLR), extreme gradient boosting (XGBoost), and random forest (RF). Evaluation results showed that all models achieved Pearson correlation coefficients (r) above 0.8, with the segmented RF-XGBoost model performing best, reaching an average relative error of 10.38%. Using this optimal model, monthly spatiotemporal maps of PM _2.5 concentrations in Wuhan were generated. Key findings include: (1) Seasonal PM _2.5 levels in Wuhan were lower in summer and higher in winter. (2) Significant regional disparities in PM _2.5 levels were observed, with persistently high pollution in areas such as Qing Shan. (3) Significant changes in PM _2.5 levels before and after the COVID-19 pandemic, characterized by an overall decrease in concentrations from 2019 to 2020, followed by gradual increases in certain districts post-lockdown. This study provides valuable insights for urban-level PM _2.5 estimation, supporting effective pollution control strategies.