Abstract View
Retrieving Low-cost Air Quality Sensor Network by Integrating Fixed and Satellite Monitoring Systems for a Northern China City
HUANG ZHANG, Chun-Ying Chao, Melanie Hammer, Randall Martin, Pratim Biswas, Washington University in St Louis
Abstract Number: 260
Working Group: Satellite-Data and Environmental Health Applications
Abstract
North China Plain (NCP) is one of the most particle matter (PM) polluted area in the world. Integrating measured values from satellite remote sensing, fixed monitoring stations, and a low-cost PM sensor network could enhance the spatial and temporal resolution of ground-level PM2.5 measurements[1]. This study examines the performance of fixed monitoring stations, a low-cost PM sensor network, and satellite aerosol optical depth (AOD) from multiple satellite products (MAIAC, MODIS, and Himawari-8) to improve the spatiotemporal resolution of PM2.5 concentration for a typical city, named Xinxiang city, in the NCP during 2017. First, measured values of low-cost PM sensors are retrieved by fixed monitoring sites. The low-cost sensor datasets demonstrate high linearity against those of fixed monitoring stations with R2 value of 0.84. Second, the retrieval PM2.5 concentrations between the AOD datasets and the fixed monitoring stations demonstrate a moderate correlation coefficient with R2 value of 0.52. Third, to evaluate the impact of human activities on air quality, four traditional Chinese festivals in 2017, Chinese New Year, Tomb Sweeping Day, Ghost Festival, and Moon Festival, are chosen to analyze the spatial and temporal distribution of ground-level PM2.5 concentration by creating heat maps over Xinxiang city. The results show that low-cost PM sensor networks and AOD datasets can be incorporated with traditional measurement methods to substantially improve the resolution of pollution maps.
[1] Li, J., Zhang, H., Chao, C.Y., Chien, C.H., Wu, C.Y., Luo, C.H., Chen, L.J. and Biswas, P., 2020. Integrating low-cost air quality sensor networks with fixed and satellite monitoring systems to study ground-level PM2. 5. Atmospheric Environment, 223, p.117293.