Open AccessIdentify the contribution of elevated industrial plume to ground air quality by optical and machine learning methods
Author(s) -
Limin Feng,
Ting Yang,
Dawei Wang,
Zifa Wang,
Yuepeng Pan,
Ichiro Matsui,
Yong Chen,
Jinyuan Xin,
Huili Huang
Publication year - 2020
Publication title -
environmental research communications
Language(s) - English
Resource type - Journals
ISSN - 2515-7620
DOI - 10.1088/2515-7620/ab7634
Subject(s) - plume , polygon (computer graphics) , stack (abstract data type) , particle (ecology) , decision tree , environmental science , air quality index , meteorology , atmospheric sciences , geography , computer science , artificial intelligence , geology , telecommunications , oceanography , frame (networking) , programming language
Regional severe haze caused by atmospheric particle explosion is one of the biggest environmental problems in China that has yet to be fully understood. This research managed to find the linkage between diversified shapes of heavy industrial stack plume (HISP) and local ground particle concentration. We used two optical methods: LIDAR and auto-shoot camera, to catch the HISP’s vertical shape, and two machine leaning models: binary classification and decision tree, to find the quantitative relationship between the HISP’s shape and PM 2.5 concentration. The PM 2.5 concentration correlated to the polygon length (PL) of HISP’s shape with a logistic function. With a plume length more than twice the height of stack, the spread of HISP’s shape accompanied with PM 2.5 concentration decreasing to 20 h) under uniform offshore dispersion than that in heterogeneous wind field, when the footprint of HISP was estimated to be > 7 km. We acquired a decision tree model to yield an exact prediction of PM 2.5 concentration, in which the HISP’s length played a statistically significant role. Though the plume shape is just one of the easy-to-use indicators of complex meteorological condition, it is still practical for policy makers to identify the particle pollution caused by the elevated sources in the fastest way.