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Estimation of ground-level PM2.5 concentrations using MODIS satellite data in Fuzhou, China

  • role： First author第一作者
  • Affiliation：

    College of Environment and Resources, Institute of Remote Sensing Information Engineering, Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion, Fuzhou University, Fuzhou 350116, China

  • Email：subrinarzhong@aliyun.com
  • Introduction：杨立娟(1985—　)，女，博士研究生，研究方向为环境与资源遥感。E-mail：subrinarzhong@aliyun.com
Lijuan YANG1,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Environment and Resources, Institute of Remote Sensing Information Engineering, Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion, Fuzhou University, Fuzhou 350116, China

    Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou 350116, China

  • Email：hxu@fzu.edu.cn
  • Introduction：徐涵秋(1955—　)，男，教授，研究方向为环境与资源遥感。E-mail：hxu@fzu.edu.cn
Hanqiu XU12*,  
  • Affiliation：

    Fuzhou Environmental Monitoring Center, Fuzhou 350001, China

Zhifan JIN3

résumé

Remote sensing techniques offer a unique opportunity to monitor air quality and are thus crucial for the management and surveillance of the air quality of polluted megacities. MODIS Aerosol Optical Depth (AOD) products with a spatial resolution of 10 km have been widely used to monitor ground-level particulate matters. However, the demands of air quality estimation are difficult to meet in local areas due to the coarse resolution of 10 km AOD. Taking Fuzhou city as an example, this study used the newly released AOD with a spatial resolution of 3 km and meteorological data to map ground-level PM2.5 concentrations in the city and ultimately reveal the spatial details of the PM2.5 exposure. Two regression models, namely, the daily calibration model and site daily calibration model, were developed to estimate and map ground-level PM2.5 concentrations in Fuzhou, China. The MODIS 3 km AOD data for 2014—2015, in situ PM2.5 concentration data for the same period, and meteorological data of wind speed and relative humidity were used. A simple linear model was also derived and used for comparison with the two calibration models. Results showed that the PM2.5 concentrations and AOD had an extremely low agreement when a linear fit was applied, with the R2 value being 0.117 and the RMSE being 19.510 μg/m-3. Strong correlations were obtained with the use of the daily calibration model, which yielded an R2 of 0.762 and RMSE of 10.146 μg/m–3. A relatively high degree of agreement was achieved when the site daily calibration model was used; R2 was 0.814, and RMSE was 8.965 μg/m–3. Ten-fold Cross Validation (CV) was conducted to evaluate the performance of the regression models. The CV results showed that the site daily calibration model performed better than the daily calibration model. Correlation coefficients (R2) of the estimated PM2.5 concentrations with the in situ data were 0.781 (RMSE=9.687 μg·m–3) and 0.724 (RMSE=10.993 μg·m–3). In addition, the PM2.5 concentrations estimated by the site daily calibration model had a better agreement with the observed values for all seasons from 2014 to 2015. The R2 of the estimated and observed values of the seasonal average PM2.5 concentrations for the two models were 0.999 and 0.995, respectively, indicating that both models could reflect daily variations in the relationship among AOD, meteorological data, and PM2.5 concentrations. In this study, we proposed a daily calibration model and a site daily calibration model using the newly released MODIS 3 km AOD product and meteorological data to estimate ground-level PM2.5 concentrations in Fuzhou, China. The daily calibration model was used to retrieve the distribution of PM2.5 concentrations in Fuzhou, as the site effect parameters needed for the site daily calibration model is not available for every 3 km grid. Nevertheless, these two models perform similarly in PM2.5 estimation. The spatial distribution of PM2.5 concentrations in Fuzhou derived from the MODIS 3 km AOD exhibits high concentrations over central urban areas and low values over suburban districts. These results clearly reveal the spatial variation of PM2.5 in the area. This study indicated that the satellite-derived model based on the MODIS 3 km AOD product could work effectively in estimating PM2.5 concentrations on a local scale.

mots-clés

MODIS 3 km AOD;PM2.5 concentration;remote sensing estimation;daily calibration model;site-daily calibration model

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