Optimal Design of Air Quality Monitoring Network for Pollution Detection and Source Identification in Industrial Parks

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 H 2 S 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 (r d ) and source identification efficiency (r b ). 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 r d and r b were discussed. Overall, the proposed method is an effective tool for designing AQMN with optimal SE in industrial parks.

Analysis of the Effectiveness of Air Pollution Control Policies Based on Historical Evaluation and Deep Learning Forecast: A Case Study of Chengdu-Chongqing Region in China

“…In the process of economic development, the improvement in industrial production capacity plays a decisive role, and it is often the only way for a country to achieve modernization [1][2][3]. However, according to historical experience, with the progression of economic development, air pollution brought by industrial production will usually become an important obstacle to the sustainable development [4][5][6].…”