Annual and Periodic Variations of Particulates and Selected Gaseous Pollutants in Astana, Kazakhstan: Source Identification via Conditional Bivariate Probability Function
Aerosol Science and Engineering(2023)
摘要
The degradation of air quality remains one of the most pressing environmental issues as exposure to air pollutants is extensively associated with various health problems including respiratory and cardiovascular diseases. The present study aims to (1) reveal the annual and periodic variations of PM 2.5 , total suspended particles, and selected gaseous pollutants (SO 2 , CO, NO 2 , HF) in Astana, Kazakhstan by analyzing 2-year air pollution monitoring data (October 2018–September 2020) divided into two study cycles (October 2018–September 2019 and October 2019–September 2020, respectively); and to (2) identify potential air pollution sources in the region using conditional bivariate probability function (CBPF). Annual concentrations of PM 2.5 and other gaseous pollutants were generally high, exceeding World Health Organization air quality guidelines and nationally adopted air quality standards, with heating periods (October–April) characterized, on average, by higher ambient concentrations than non-heating periods. Notably, the concentrations of observed pollutants were higher during the 2018–2019 study cycle than in 2019–2020. Obtained results are useful for subsequent estimation of the burden of respiratory and cardiovascular diseases in the region. The CBPF analysis of PM 2.5 data suggested a general contribution of the coal-fired power plants as well as residential heating activities to the air pollution in the city, while a joint contribution of vehicular emissions and power plant activity was identified as the pollution source of SO 2 . Control measures for PM 2.5 and SO 2 emissions specifically arising from the coal-fired power plants need to be urgently implemented.
更多查看译文
关键词
Air pollution,PM2.5,Receptor modeling,Source apportionment,Total suspended particles,Urban pollution
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要