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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.

Air pollution episodes of unknown origins are often detected by online equipment for air quality monitoring in industrial parks in China. The number of monitors available to provide observation data, as well as the source information, is often very limited. In such case, the identification of a potential source area is more practical than the precise back-calculation of the real source. The potential source area which can be deduced from the observation data from limited monitors was concerned in this paper. In order to do the source area identification, two inverse methods, a direct method and a statistical sampling method, were applied with a Gaussian puff model as the forward modeling method. The characteristic of the potential source area was illustrated by case studies. Both synthetic and real cases were presented. The distribution of the source locations and its variation with the other unknown source parameters were mainly focused in the case study. As a screening method, source area identification can be applied not only when the number of effective monitors is limited but also when an ideal number of monitors are available as long as the source information is almost uncertain. [ABSTRACT FROM AUTHOR]

Abstract: The advent of spatial analysis and geographic information systems (GIS) has led to studies of chronic exposure and health effects based on the rationale that intra-urban variations in ambient air pollution concentrations are as great as inter-urban differences. Such studies typically rely on local spatial covariates (e.g., traffic, land use type) derived from circular areas (buffers) to predict concentrations/exposures at receptor sites, as a means of averaging the annual net effect of meteorological influences (i.e., wind speed, wind direction and insolation). This is the approach taken in the now popular land use regression (LUR) method. However spatial studies of chronic exposures and temporal studies of acute exposures have not been adequately integrated. This paper presents an innovative LUR method implemented in a GIS environment that reflects both temporal and spatial variability and considers the role of meteorology. The new source area LUR integrates wind speed, wind direction and cloud cover/insolation to estimate hourly nitric oxide (NO) and nitrogen dioxide (NO2) concentrations from land use types (i.e., road network, commercial land use) and these concentrations are then used as covariates to regress against NO and NO2 measurements at various receptor sites across the Vancouver region and compared directly with estimates from a regular LUR. The results show that, when variability in seasonal concentration measurements is present, the source area LUR or SA-LUR model is a better option for concentration estimation. [Copyright &y& Elsevier]

Both air pollution detection and source identification for air pollution episodes are highly desirable for detecting and controlling industrial air pollution. Surveillance of air pollution episodes in industrial parks is the focus of this article. The surveillance in this study consists of air pollution detection and subsequent source identification. The Gaussian puff model is applied to simulate the dispersion of air pollution, and the source area analysis method is used to reconstruct unknown source terms. A case study involving hydrogen sulfide emissions in a typical chemical industrial park is presented. The long-term efficiencies of both pollution detection and source identification of a developing planning of boundary-type air quality monitoring network (AQMN) are evaluated. Five typical scenarios are identified for the evaluation. Moreover, several key factors for the surveillance efficiency variation (i.e., meteorological conditions, monitor number and distance between sources) are discussed. The efficiency of pollution detection increases with the number of monitors. The efficiency of source identification increases with the number of monitors and the distance between sources. • The efficiency of industrial air pollution surveillance is evaluated. • The efficiencies of pollution detection and source identification are included. • Key factors for surveillance efficiency variation are discussed. • Surveillance efficiency increases with the number of monitors. • The efficiency of source identification increases with distance between sources. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]