Abstract
Precise source identification for ambient pollution incidents in industrial parks were often difficult due to limited measurements. Source area analysis method was one of the applicable source identification methods, which could provide potential source areas under these circumstances. However, a source area usually covered several sources and the method was unable to identify the real one. This article introduces a case study on the statistical source identification of methyl mercaptan based on the long-term measurements, in 2014, in an industrial park. A procedure for statistical source area analysis was established, which contains independent pollution episode extraction, source area calculation scenario definition, meteorological data selection, and source area statistical analysis. A total of 414 violation records were detected by five monitors inside the park. Three kinds of calculation scenarios were found and, finally, three key source areas were revealed. The typical scenarios of source area calculations were described in detail. The characteristics of the statistical source areas for all pollution episodes were examined. Finally, the applicability of the method, as well as the source of uncertainties, was discussed. This study shows that more concentrated source areas can be identified through the statistical source area method if several excessive emission sources exist in an industrial park.
Highlights
Locating emission sources of an ambient pollution episode is a critical step for air pollution control
Penenko V et al addressed the data from 10 monitors to obtain the probability distribution density function of radionuclide emission sources [5]; Rude et al indicated that at least 4 monitors on the ground were necessary to evaluate source location and source strength of a constant ground source by means of the inverse framework method [6]; Cantelli et al applied 25 monitors to retrieve 3 different pollution sources using the genetic algorithm inverse model (GAIM) successfully [1]
This work presents source identification cases based on long-term abnormal concentration
Summary
Locating emission sources of an ambient pollution episode is a critical step for air pollution control. Back-calculation of source parameters based on ambient concentration monitoring is one of the popular methods for locating emission sources, and has attracted increasing attention from researchers and environmental protection authorities [1,2,3,4]. Measurements from a monitoring network with a proper scale are fundamental for precise back-calculation of source parameters. The required scale for a monitoring network depends on the number of unknown parameters. Increased monitoring sites might improve the obtained result. Penenko V et al addressed the data from 10 monitors to obtain the probability distribution density function of radionuclide emission sources [5]; Rude et al indicated that at least 4 monitors on the ground were necessary to evaluate source location and source strength of a constant ground source by means of the inverse framework method [6]; Cantelli et al applied 25 monitors to retrieve 3 different pollution sources using the genetic algorithm inverse model (GAIM) successfully [1]
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