A Surrogate‐Based Optimization Methodology for the Optimal Design of an Air Quality Monitoring Network

Abstract

A surrogate‐based optimization methodology was proposed for identifying and determining the optimal location and configuration of an air quality monitoring network (AQMN) in an industrial area for different pollutants such as sulfur dioxide (SO2), nitrogen oxide (NOx), and carbon monoxide (CO). Within the framework of the described methodology, an optimal AQMN design was proposed to assess the violation and pattern scores for each pollutant. For this purpose, a criterion for assessing the allocation of monitoring stations was developed by applying a utility function that could describe the spatial coverage of the network and its ability to detect violations of standards for multiple pollutants. An air dispersion model based on the multiple cell approach was used to create monthly spatial distributions for the concentrations of the pollutants emitted from different sources. The data was used to develop the surrogate models. The proposed methodology was applied to a network of existing refinery stacks, and the locations of monitoring stations and their area coverage percentage were obtained. Results clearly indicated that the proposed methodology was successful in designing AQMNs and could be used for as many stations as required.

Optimal Design of Air Quality Monitoring Network and its Application in an Oil Refinery Plant: An Approach to Keep Health Satus of Workers

“… 4 In other words, first four monitoring stations providing network efficiency of 37%, 23%, 15%, and, 11% respectively, were located at points which belong to more number of potential zones at different scenarios. 12 , 13 , 24 …”

“…Therefore, there are two modes in this section: (a) Specifying the number of stations at the start of the algorithm that can be based on budgetary constraints or policies of the environmental managers of industrial plant, (b) Running the program without any constraint for number of stations to reach network efficiency of 100%. 12 , 14 , 24 …”

“…al. 12 proposed a surrogate-based optimization methodology for configuring the AQMN in an industrial area. They used multiple cell approach to create monthly spatial distribu-tions for the concentration of the pollutants.…”

Perspectives of Individual-Worn Sensors Assessing Personal Environmental Exposure

“…Its maxima help to identify and rank the most desirable monitoring locations. The lowest number of optimal locations are characterized by non-overlapping spheres-of-influence (SOI), determined by a cut-off value in the spatial autocorrelation between the pollution level at this site and the neighboring monitoring sites [36] , [37] .…”

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

“…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.…”