College of Land Management, Nanjing Agricultural University, Nanjing, 210095, China.
School of Sustainability, Arizona State University, Tempe, 85281, USA.
Sci Rep. 2020 Sep 24;10(1):15639. doi: 10.1038/s41598-020-72722-z.
It remains unclear on how PM interacts with other air pollutants and meteorological factors at different temporal scales, while such knowledge is crucial to address the air pollution issue more effectively. In this study, we explored such interaction at various temporal scales, taking the city of Nanjing, China as a case study. The ensemble empirical mode decomposition (EEMD) method was applied to decompose time series data of PM, five other air pollutants, and six meteorological factors, as well as their correlations were examined at the daily and monthly scales. The study results show that the original PM concentration significantly exhibited non-linear downward trend, while the decomposed time series of PM concentration by EEMD followed daily and monthly cycles. The temporal pattern of PM, SO and NO is synchronous with that of PM. At both daily and monthly scales, PM was positively correlated with CO and negatively correlated with 24-h cumulative precipitation. At the daily scale, PM was positively correlated with O, daily maximum and minimum temperature, and negatively correlated with atmospheric pressure, while the correlation pattern was opposite at the monthly scale.
目前尚不清楚 PM 在不同时间尺度上如何与其他空气污染物和气象因素相互作用,而这种知识对于更有效地解决空气污染问题至关重要。在这项研究中,我们以中国南京市为例,探讨了这种相互作用在不同时间尺度上的情况。我们应用集合经验模态分解(EEMD)方法对 PM、其他五种空气污染物和六种气象因素的时间序列数据进行分解,并在日和月尺度上检查它们的相关性。研究结果表明,原始 PM 浓度表现出显著的非线性下降趋势,而 EEMD 对 PM 浓度的分解时间序列则遵循日和月周期。PM、SO 和 NO 的时间模式与 PM 同步。在日和月尺度上,PM 与 CO 呈正相关,与 24 小时累计降水量呈负相关。在日尺度上,PM 与 O、日最高温和日最低温呈正相关,与大气压呈负相关,而在月尺度上则相反。