School of Geography, Earth, and Environmental Sciences, University of Birmingham, Birmingham, UK.
Department of Computer Engineering, University of Isfahan, Isfahan, Iran.
Environ Sci Pollut Res Int. 2024 Aug;31(39):51619-51632. doi: 10.1007/s11356-024-34648-1. Epub 2024 Aug 8.
This paper analyses the intertwined impacts of the 2018 US sanctions on Iran and the COVID-19 pandemic (as examples of unplanned international conflicts and global crises) on the source and extent of air pollution in Tehran, the capital of Iran. The impacts are parametrized using the levels of criteria air pollutants (CAP) for 5 years (2015-2020), which were previously deweathered using the promising machine learning technique of Random Forest (RF). The absolute principal component scores-multiple linear regression (APCS-MLR) method and the bivariate polar plot (BPP) technique are used here to analyze the source apportionment profile of the city for the business as usual (BAU; 2015 to 2018), sanctions (2019), and COVID-19 and sanctions (2020) intervals. The results show the severe impact of the 2018 US sanctions on Tehran's air quality (AQ); O, NO, CO, PM, and PM levels increased by 117%, 55%, 20%, 35%, and 10%, respectively, while SO levels decreased by 30%. The sanctions also triggered a number of events, such as the disruption of the high-grade fuel supply chain and the Mazut crisis, which directly or indirectly accelerated the photochemical production of local tropospheric ozone to some extent. Sanctions also disrupted Tehran's AQ response to the pandemic, with CAP levels decreasing by only 2-7% during the pandemic. The ozone and PM BPPs show that the source apportionment profile of the city is dominated by local anthropogenic emission sources, especially urban transport, after the sanctions and the pandemic. Results also show that the impact of soft wars, such as the US sanctions against Iran, on urban air quality degradation is much stronger than that of hard wars, such as the Russia-Ukraine war.
本文分析了 2018 年美国对伊朗制裁和 COVID-19 大流行（作为非计划国际冲突和全球危机的例子）对伊朗首都德黑兰的空气污染来源和程度的交织影响。使用 5 年（2015-2020 年）的标准空气污染物（CAP）水平对影响进行参数化，这些水平之前使用有前途的机器学习技术随机森林（RF）进行了去天气化处理。绝对主成分得分-多元线性回归（APCS-MLR）方法和双变量极图（BPP）技术用于分析城市在正常情况下（BAU；2015 年至 2018 年）、制裁（2019 年）以及 COVID-19 和制裁（2020 年）期间的源分配情况。结果表明，2018 年美国对德黑兰空气质量（AQ）的制裁产生了严重影响；O、NO、CO、PM 和 PM 水平分别增加了 117%、55%、20%、35%和 10%，而 SO 水平下降了 30%。制裁还引发了一些事件，例如高等级燃料供应链的中断和 Mazut 危机，这些事件直接或间接地在一定程度上加速了当地对流层臭氧的光化学产生。制裁还扰乱了德黑兰对大流行的 AQ 应对，大流行期间 CAP 水平仅下降了 2-7%。臭氧和 PM 极图表明，制裁和大流行后，城市的源分配情况主要由当地人为排放源主导，尤其是城市交通。结果还表明，软战争（如美国对伊朗的制裁）对城市空气质量恶化的影响远强于硬战争（如俄乌战争）。
