Identify the contribution of elevated industrial plume to ground air quality by optical and machine learning methods

Abstract

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 PM2.5 concentration. The PM2.5 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 PM2.5 concentration decreasing to <100 μg m−3. The residence time of HISP’s particles was longer (>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 PM2.5 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.