Abstract
Dense air quality monitoring network (AQMN) is one of main ways to surveil industrial air pollution. This paper is concerned with the design of a dense AQMN for H2S for a chemical industrial park in Shanghai, China. An indicator (Surveillance Efficiency, SE) for the long-term performance of AQMN was constructed by averaging pollution detection efficiency (rd) and source identification efficiency (rb). A ranking method was developed by combing Gaussian puff model and Source area analysis for improving calculation efficiency. Candidate combinations with highest score were given priority in the selection of next site. Two existing monitors were suggested to relocate to the west and southwest of this park. SE of optimized AQMN increased quickly with monitor number, and then the growth trend started to flatten when the number reached about 60. The highest SE occurred when the number reached 110. Optimal schemes of AQMNs were suggested which can achieve about 98% of the highest SE, while using only about 60 monitors. Finally, the reason why the highest SE is less than 1 and the variation characteristics of rd and rb were discussed. Overall, the proposed method is an effective tool for designing AQMN with optimal SE in industrial parks.
Highlights
Dense air quality monitoring stations have been considered one of the main ways to surveil air pollution in industrial parks
The results showed that the optimal distribution of the air quality monitoring network (AQMN) was arranged almost at the boundary of this park when the number of added monitors is limited
The results showed that surveillance efficiency (SE) score for each real emission source was improved with an increasing number of added monitors
Summary
Dense air quality monitoring stations have been considered one of the main ways to surveil air pollution in industrial parks. Designing a proper dense air quality monitoring network (AQMN). Optimal design of AQMN has been widely investigated in the literature [1,2,3,4,5,6,7,8,9,10,11]. It can be considered as an optimization problem searching for the best combination of the location and number of candidate monitors. Many heuristic algorithms have been applied to solve the optimization problem for AQMN design at different scales [6,8,11,14,15]
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