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EXPERIENCE IN OPTIMIZING PROGRAMS FOR MONITORING AMBIENT AIR QUALITY IN A LARGE INDUSTRIAL CENTER USING HEALTH RISK ASSESSMENT AND GEOINFORMATION TECHNOLOGIES

S.V. Kleyn, Н.В. Зайцева, I.V. May, Alena M. Andrishunas

Abstract

The present work dwells on testing methodical approaches to optimization of programs for monitoring over ambient air quality. The key issue here was to determine how many control points are required and make up a list of substances for monitoring in a large industrial center with its area exceeding 350 square kilometers and population more than 1 million people. Ambient air quality in the examined city is influenced by more than 150 industrial enterprises operating in metallurgy, civil engineering, chemical industry and some other brunches; intense transport flows (up to 9,000 vehicles per hour); autonomous heating supply sources (more than 1,300 units); frequent unfavorable meteorological conditions (35-60 days per year). Overall, more than 240 substances are annually emitted into ambient air in the city (more than 190 thousand tons annually) from stationary sources (more than 6,000 of them emitting more than 116 thousand tons per year) and mobile ones (more than 360 thousand of them emitting more than 76 thousand tons per year). An implemented algorithm aimed at optimizing monitoring over ambient air quality included several sequential stages; they were as follows: creating an electronic database on stationary and mobile sources that emitted pollutants; binding detected sources to a geoinformation system; calculating dispersion and performing analysis with combining calculation results and data obtained via field observations at existing posts for monitoring over ambient air quality; building up concentrations fields; calculating health risk parameters (hazard quotients for acute and chronic non-carcinogenic effects and individual carcinogenic risks); performing cluster analysis of risk fields; determining optimal locations for placing monitoring posts taking into account population density and risk parameters in outlined zones (clusters); making up a list of ambient air quality parameters that were subject to control at each specific monitoring post. Due to available databases on parameters of emission sources the algorithm aimed at optimizing programs for monitoring over ambient air quality was successfully tested. It allowed dividing the examined area into 10 clusters with homogenous sets of health risk parameters; determining 10 optimal locations for monitoring posts; and creating observation programs that included 23 unique substances overall for the city that caused the highest inhalation risks. As a result, monitoring will cover 99% of the city residential territory and it will allow obtaining reliable data on actual exposure and making adequate managerial decision.

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DOI resolver: doi.org/10.5593/sgem2020/5.1/s20.044

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