Using Sensor Network for Tracing and Locating Air Pollution Sources

Atmospheric fine particulate matter (particulate matter with an aerodynamic diameter <= 2.5 mu m in ambient air; PM2.5) is a major pollutant causing regional air pollution and harm to human health. To monitor PM2.5, Chinese industry authorities use data from a small number of fixed stations with uneven distribution. Pollution control is promoted through assessment and enforcement, which can include industry-wide shutdown measures harmful to local economic development. The objective of this study was to establish a fine particulate matter network (FPMN) of sensors based on the Internet of Things with low cost, high spatiotemporal resolution, flexible distribution points, large numbers, and high collection frequency. The FPMN-derived data, together with other multisource environment-related data, could be used to track and locate atmospheric pollutants and selectively identify source. This study adopted Chizhou, China as the research object. Specifically, the work included designing the FPMN, selecting locations for sensor placement on the basis of local weather, terrain, and land use, and using software/hardware collaborative calibration technology to ensure consistency between FPMN-derived data and National Control Station (NCS) data. The analysis revealed that FPMN data effectively reconstructed a reliable regional field of PM2.5 concentration with improved spatiotemporal accuracy. The research results will have great importance regarding the traceability of sources of PM2.5 pollution, analysis of pollution causes and transboundary pollution, optimization and adjustment of industrial layouts, and differentiation of the temporal and spatial control of pollution. Ultimately, the FPMN could directly support management and decision-making processes of local governments in relation to the environment and industry.

Automated summary

This study aimed to establish a fine particulate matter network based on the Internet of Things for monitoring PM2.5 with high spatiotemporal accuracy, using Chizhou, China as the research object. The analysis revealed that FPMN data effectively reconstructed a reliable regional field of PM2.5 concentration, with significant implications for traceability of pollution sources, optimization of industrial layouts, and pollution control strategies.