College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China.
Institute of Zhejiang University-Quzhou, Quzhou 324000, China.
Int J Environ Res Public Health. 2022 Jun 12;19(12):7201. doi: 10.3390/ijerph19127201.
Air pollution episodes (APEs) caused by excessive emissions from chemical industry parks (CIPs) have resulted in severe environmental damage in recent years. Therefore, it is of great importance to detect APEs timely and effectively using contaminant measurements from the air quality monitoring network (AQMN) in the CIP. Traditionally, APE can be detected by determining whether the contaminant concentration at any ambient monitoring station exceeds the national environmental standard. However, the environmental standards used are unified in various ambient monitoring stations, which ignores the source-receptor relationship in the CIP and challenges the effective detection of excessive emissions in some scenarios. In this paper, an approach based on a multivariate statistical analysis (MSA) method is proposed to detect the APEs caused by excessive emissions from CIPs. Using principal component analysis (PCA), the spatial relationships hidden among the historical environmental monitoring data are extracted, and the high-dimensional data are projected into only two subspaces. Then, two monitoring indices, T2 and , which represent the variability in these subspaces, are utilized to monitor the pollution status and detect the potential APEs in the CIP. In addition, the concept of APE detectability is also defined, and the condition for APE detectability is derived, which explains when the APEs can be detectable. A simulated case for a CIP in Zhejiang province of China is studied to evaluate the performance of this approach. The study indicates that the method can have an almost 100% APE detection rate. The real-world measurements of Total Volatile Organic Compounds (TVOC) at a 10-min time interval from 3 December 2020∼12 December 2020 are also analyzed, and 64 APEs caused by excessive TVOC emissions are detected in a total of 1440 time points.
近年来，化工园区（CIP）过度排放引发的空气污染事件（APEs）造成了严重的环境破坏。因此，利用 CIP 空气质量监测网络（AQMN）中的污染物测量数据及时、有效地检测 APE 非常重要。传统上，可以通过确定任何环境监测站的污染物浓度是否超过国家环境标准来检测 APE。然而，各个环境监测站使用的环境标准是统一的，这忽略了 CIP 中的源-受体关系，并在某些情况下对过量排放的有效检测构成挑战。本文提出了一种基于多元统计分析（MSA）方法的方法，用于检测 CIP 过度排放引起的 APE。利用主成分分析（PCA），提取历史环境监测数据中隐藏的空间关系，并将高维数据投影到仅两个子空间中。然后，利用代表这些子空间变化的两个监测指标 T2 和 ，监测 CIP 的污染状况并检测潜在的 APE。此外，还定义了 APE 可检测性的概念，并推导出 APE 可检测性的条件，解释了何时可以检测到 APE。研究了中国浙江省一个 CIP 的模拟案例，以评估该方法的性能。研究表明，该方法可以实现近 100%的 APE 检测率。还分析了 2020 年 12 月 3 日至 12 月 12 日期间每 10 分钟间隔的总挥发性有机化合物（TVOC）的实时测量值，总共在 1440 个时间点检测到 64 次由 TVOC 过度排放引起的 APE。
