Determination of the physical domain for air quality monitoring stations using the ANP-OWA method in GIS.

Abstract

Air pollution is a major concern in some megacities of Iran. Specific cities in the country have reached an extremely harmful level of air pollution which poses a serious risk to the daily lives of Iranians. According to news reports, the air quality index of the city of Tehran hovers around 159, which is more than three times the World Health Organization's advised maximum. For the purpose of air pollution abatement, it is necessary to precisely know the air pollution distribution in the area. In order to obtain this figure, it is necessary to properly locate the city's air quality monitoring stations that measure the spatial pollutant distribution. According to various reports, the city must have at least 56 air quality monitoring stations to properly measure Tehran's air quality. However, there are currently only 20 stations within the city. Thus, the main purpose of this study was to identify the most sufficient areas for deploying new air quality monitoring stations. This study provided an integration of hybrid multi-criteria decision-making (MCDM) theories and geographical information system (GIS) processes in order to determine suitable areas to establish air quality monitoring stations. Unlike traditional models, the proposed MCDM method, ANP-OWA, is an efficient decision analysis which considers dependencies between criteria and defines different scenarios between pessimistic and optimistic conditions for decision makers. This method was applied to several parameters such as point, area, and line sources; population density; sensitive receptors; distance from current air quality stations; prediction error; and spatial distribution of CO, NO2, SO2, and PM10 pollutants. The output results specified several suitable locations to establish air pollution monitoring stations within Tehran Province. The stability and reliability of the output results were evaluated with a robust sensitivity analysis method. Moreover, the results demonstrated that the proposed method can produce stable results. Obtaining knowledge regarding population density, distance from current air quality stations, and spatial distribution of CO pollutant criteria is essential when selecting locations for air quality monitoring stations.