Characterization of Spatial-Temporal Distribution and Microenvironment Source Contribution of Pm2.5 Concentrations Using a Low-Cost Sensor Network with Artificial Neural Network/Kriging Techniques

Low-cost sensors (LCS) network is widely used to improve the resolution of spatial-temporal distribution of air pollutant concentrations in urban areas. However, studies on air pollution sources contribution in microenvironment especially in industrial and mix-used housing areas are still limited. This study aimed at investigating the spatial-temporal distribution and source contributions of PM2.5 in the urban area based on 6 months LCS network datasets. The Artificial Neural Network (ANN) was used to calibrate the measured PM2.5 values by LCS. The calibrated values were shown to agree with reference values measured by BAM-1020 with R2 of 0.85, MNE of 30.91%, and RMSE of 3.73 μg/m3, which meet the criteria for hotspot identification and personal exposure study purposes. The Kriging method was further used to establish the spatial-temporal distribution of PM2.5 concentrations in the urban area. Results showed that the highest average PM2.5 concentration occurred during winter due to monsoon and topographic effects. From diurnal perspective, the highest level of PM2.5 concentration was observed during daytime due to heavy traffic emissions and industrial production. Based on the present ANN-based microenvironment source contribution assessment model, temples, fried chicken shops, traffic flow, and industrial activities such as machinery processing were identified as the main sources of PM2.5. The numerical algorithm coupled with LCS network presented in this study is an effective framework for PM2.5 hotspots and source identification, aiding decision-makers in reducing atmospheric PM2.5 concentrations and formulating regional air pollution control strategies