Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China.
Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China; Institute of Eco-Chongming (IEC), No. 3663 Northern Zhongshan Road, Shanghai 200062, China.
Sci Total Environ. 2020 Jul 1;724:138134. doi: 10.1016/j.scitotenv.2020.138134. Epub 2020 Mar 23.
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.
PM 污染一直是长三角城市群（YRDUA）近十年来关注的主要环境问题之一。本研究采用大数据和小波分析相结合的方法，基于 2015-2018 年 YRDUA 逐时 PM 浓度数据，研究了 PM 的时空变化及其影响因素（空气污染物和气象因素）。结果表明，PM 在空间上呈现出从西北向东南的阶梯式下降，在时间上具有显著的多尺度时空变化。在宏观层面上，PM 浓度从 2015 年到 2018 年呈下降趋势，年内呈 U 型分布。在微观层面上，PM 呈四阶段年变化（1 月至 3 月、4 月至 6 月、7 月至 9 月、10 月至 12 月），突变事件主要发生在冬季。PM 存在两个主要周期，时间尺度为 250-480d 的年循环和时间尺度为 130-220d 的半年循环。此外，PM 与空气污染物和气象因素的相关性随时间尺度而变化。在空气污染物中，PM 与 CO 的相关性最一致，而 PM 与 SO/NO 的相关性随着时间尺度的增加而提高，而 PM 与 O 的相关性在较短时间尺度上呈正相关，在较宽时间尺度上呈负相关。在气象因素中，PM 与风速、降水、温度、气压和相对湿度的相关性主要反映在较宽的时间尺度上。这些发现有助于提高预测模型的准确性，并为正在进行的联合防治提供参考。
