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

PM 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 and its influencing factors (air pollutants and meteorological factors) are studied based on hourly concentrations of PM from 2015 to 2018 in the YRDUA. Results showed that PM presented a step-shaped decline from northwest to southeast in space and significant multi-scale temporal variations in time. On the macroscopic level, PM 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, 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 showed time scale-dependent correlations with air pollutants and meteorological factors. Among air pollutants, the correlation between PM and CO was the most consistent, and the correlation between PM and SO/NO improved with the increase of time scale, while the correlation between PM and O was positive at shorter time scales but negative at broader time scales. Among meteorological factors, the correlations between PM 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.