Abstract
Urban air pollution is a major concern affecting public health and welfare, especially in industrial megacities. One of the imperative concerns of air quality managers is to maintain a specific level of air quality with respect to the mandates known as ambient air quality standards. This paper utilizes a proper approach for theoretical management of air quality by simulating the distribution of air pollutants via different statistical distributions and estimation of required source emission reduction of each pollutant in order to reach their relevant ambient air quality standard. In addition, the return period of extreme concentrations is computed for each monitoring site. For this purpose, air quality data regarding average concentrations of O3, PM10 and CO were extracted from seven air quality monitoring stations located in multiple sites of Tehran. The distribution of each air pollutant was modeled with a set of probability density functions, and the most appropriate distribution was selected to estimate the probability of exceedance from air quality standards. In most stations, PM10, O3 and CO distributions were generally consistent with 3 parameter log-logistic, beta and lognormal distributions, respectively. In general, the results emphasized the importance of PM10 as the major air pollutant and its estimated emission source reduction ranged from 10.59 to 52.91%, throughout various points of Tehran megacity. The emission reduction of O3 was estimated to range from 3.2 to 27.37%, whereas CO did not violate the primary and secondary ambient air quality standard in all of the monitored locations.
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Acknowledgements
The authors appreciate Tehran air quality control company staff for their valuable effort in gathering and amalgamation of air quality data through their accessible air quality monitoring network.
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Dr. Yousef Rashidi proposed the idea of this research, and Sam Dehhaghi conducted the whole research process (including statistical analysis and writing of the paper).
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Dehhaghi, S., Rashidi, Y. Evaluation of air quality standard compliance by means of statistical distribution modeling: a study in Tehran. Int. J. Environ. Sci. Technol. 19, 12235–12248 (2022). https://doi.org/10.1007/s13762-022-04443-x
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DOI: https://doi.org/10.1007/s13762-022-04443-x