Spatiotemporal characteristics of PM2.5 concentration in the Yangtze River Delta urban agglomeration, China on the application of big data and wavelet analysis

Jiajia Wang; Xiaoman Lu; Yingting Yan; Liguo Zhou; Weichun Ma

doi:10.1016/j.scitotenv.2020.138134

Spatiotemporal characteristics of PM_2.5 concentration in the Yangtze River Delta urban agglomeration, China on the application of big data and wavelet analysis

Sci Total Environ. 2020 Jul 1:724:138134. doi: 10.1016/j.scitotenv.2020.138134. Epub 2020 Mar 23.

Authors

Jiajia Wang¹, Xiaoman Lu¹, Yingting Yan¹, Liguo Zhou², Weichun Ma³

Affiliations

¹ Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China.
² Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China; Institute of Eco-Chongming (IEC), No. 3663 Northern Zhongshan Road, Shanghai 200062, China. Electronic address: Lgzhou@fudan.edu.cn.
³ Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China; Institute of Eco-Chongming (IEC), No. 3663 Northern Zhongshan Road, Shanghai 200062, China. Electronic address: Wcma@fudan.edu.cn.

PMID: 32408437
DOI: 10.1016/j.scitotenv.2020.138134

Abstract

PM_2.5 pollution has been one of the main environmental issues of concern for the Yangtze River Delta Urban Agglomeration (YRDUA) during the recent decade. In this paper, allied with big data and wavelet analysis, spatiotemporal variations of PM_2.5 and its influencing factors (air pollutants and meteorological factors) are studied based on hourly concentrations of PM_2.5 from 2015 to 2018 in the YRDUA. Results showed that PM_2.5 presented a step-shaped decline from northwest to southeast in space and significant multi-scale temporal variations in time. On the macroscopic level, PM_2.5 concentrations decreased from 2015 to 2018, showing a U-shaped pattern within a year. On the microscopic level, it had a four-stage annual variation (January to March, April to June, July to September, October to December) and the mutation events mainly occurred in winter. There were two dominant periods of PM_2.5, an annual cycle on the time scale of 250-480 d and a semi-annual cycle on the time scale of 130-220 d. In addition, PM_2.5 showed time scale-dependent correlations with air pollutants and meteorological factors. Among air pollutants, the correlation between PM_2.5 and CO was the most consistent, and the correlation between PM_2.5 and SO₂/NO₂ improved with the increase of time scale, while the correlation between PM_2.5 and O₃ was positive at shorter time scales but negative at broader time scales. Among meteorological factors, the correlations between PM_2.5 and wind speed, precipitation, temperature, air pressure and relative humidity were mainly reflected at broader time scales. These findings would be helpful to improve the accuracy of prediction model and provide references for the ongoing joint prevention and control.

Keywords: Air pollution; Big data; PM(2.5); Wavelet analysis; YRDUA.