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Potential Effect of Halogens on Atmospheric Oxidation and Air Quality in China.

ABSTRACT: Air pollution has been a hazard in China over recent decades threatening the health of half a billion people. Much effort has been devoted to mitigating air pollution in China leading to a significant reduction in primary pollutants emissions from 2013 to 2017, while a continuously worsening trend of surface ozone (O₃, a secondary pollutant and greenhouse gas) was observed over the same period. Atmospheric oxidation, dominated by daytime reactions involving hydroxyl radicals (OH), is the critical process to convert freshly-emitted compounds into secondary pollutants, and is underestimated in current models of China's air pollution. Halogens (chlorine, bromine, and iodine) are known to profoundly influence oxidation chemistry in the marine environment; however, their impact on atmospheric oxidation and air pollution in China is unknown. In the present study, we report for the first time that halogens substantially enhance the total atmospheric oxidation capacity in polluted areas of China, typically 10% to 20% (up to 87% in winter) and mainly by significantly increasing OH level. The enhanced oxidation along the coast is driven by oceanic emissions of bromine and iodine, and that over the inland areas by anthropogenic emission of chlorine. The extent and seasonality of halogen impact are largely explained by the dynamics of Asian monsoon, location and intensity of halogen emissions, and O₃ formation regime. The omission of halogen emissions and chemistry may lead to significant errors in historical re-assessments and future projections of the evolution of atmospheric oxidation in polluted regions.

SUBMITTER: Li Q

PROVIDER: S-EPMC7286431 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:With the ongoing challenge of air pollution posing serious health and environmental threats, particularly in rapidly industrializing regions, accurate forecasting and effective pollutant identification are crucial for enhancing public health and ecological stability. This study aimed to optimize air quality management through the prediction of the Air Quality Index (AQI) and identification of air pollutants. Our study spans nine representative cities (Hohhot, Yinchuan, Lanzhou, Beijing, Taiyuan, Xi'an, Shanghai, Nanjing, Wuhan) in China, with data collected from January 1, 2015, to November 30, 2021. We proposed a new model for daily AQI prediction, termed VMD-CSA-CNN-LSTM, which employed advanced machine learning techniques, including convolutional neural networks (CNN) and long short-term memory (LSTM) networks, and leveraged the chameleon swarm algorithm (CSA) for hyperparameter optimization, integrated through a variational mode decomposition approach. The model was developed using data from Lanzhou, with a split ratio of 8:1:1 into training, validation, and test sets, achieving an RMSE of 2.25, MAPE of 0.02, adjusted R-squared of 98.91%, and training efficiency of 5.31%. The model was further externally validated in the other eight cities, yielding comparable results, with an adjusted R-squared above 96%, MAPE below 0.1, and RMSE below 7.5. Additionally, we employed a random forest algorithm to identify the primary pollutants contributing to AQI levels. Our results indicated that PM2.5 was the most significant pollutant in Beijing, Taiyuan, and Xi'an, while PM10 was dominant in Hohhot, Yinchuan, and Lanzhou. In Shanghai, Nanjing, and Wuhan, both PM2.5 and PM10 were critical, with ozone also identified as a major air pollutant. This study not only advances the predictive accuracy of AQI models but also aids policymakers by providing a reliable tool for air quality management and strategic planning aimed at pollution reduction. The integration of these advanced computational techniques into environmental monitoring practices offers a promising avenue for enhancing air quality and mitigating pollution-related risks.

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Potential Effect of Halogens on Atmospheric Oxidation and Air Quality in China.

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