Hassan Muhammad Azher, Faheem Muhammad, Mehmood Tariq, Yin Yihui, Liu Junjie
Tianjin Key Lab of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin, 300072, China.
Department of Civil Infrastructure and Environmental Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.
Environ Sci Pollut Res Int. 2023 Oct;30(47):104086-104099. doi: 10.1007/s11356-023-29665-5. Epub 2023 Sep 12.
Over the past few years, surface ozone (O) pollution has dominated China's air pollution as particulate matter has decreased. In Beijing, the annual average concentrations of ground-level O from 2015 to 2020 regularly increased from 57.32 to 62.72 μg/m, showing a change of almost 9.4%, with a 1.6% per year increase. The meteorological factors are the primary influencer of elevated O levels; however, their importance and heterogeneity of variables remain rarely understood. In this study, we used 13 meteorological factors and 6 air quality (AQ) parameters to estimate their influencing score using the random forest (RF) algorithm to explain and predict ambient O. Among the meteorological variables and overall, both land surface temperature and temperature at 2 m from the surface emerged as the most influential factors, while NO stood out as the highest influencing factor from the AQ parameters. Indeed, it is crucial and imperative to reduce the temperature caused by climate change in order to effectively control ambient O levels in Beijing. Overall, meteorological factors alone exhibited a higher coefficient of determination (R) value of 0.80, compared with AQ variables of 0.58, for the post-lockdown period. In addition, we calculated the number of days O concentration levels exceeded the WHO standard and newly proposed peak-season maximum daily 8-h average (MDA8) O guideline for Beijing. The exceedance number of days from the WHO standard of MDA8 ambient O was observed to be the highest in June, and each studied year crossed peak season guidelines by almost 2 times margin. This study demonstrates the contributions of meteorological variables and AQ parameters in surging ambient O and highlights the importance of future research toward devising an optimum strategy to combat growing O pollution in urban areas.
在过去几年中，随着颗粒物污染的减少，地表臭氧（O）污染已成为中国空气污染的主要问题。在北京，2015年至2020年期间地面臭氧的年平均浓度从57.32μg/m有规律地升至62.72μg/m，增幅近9.4%，年均增长1.6%。气象因素是臭氧浓度升高的主要影响因素；然而，其重要性以及变量的异质性仍鲜为人知。在本研究中，我们使用13个气象因素和6个空气质量（AQ）参数，通过随机森林（RF）算法估计它们的影响得分，以解释和预测环境臭氧水平。在气象变量和整体因素中，地表温度和距地表2米处的温度均为最具影响力的因素，而在AQ参数中，一氧化氮是影响最大的因素。事实上，为有效控制北京的环境臭氧水平，降低气候变化导致的温度至关重要且刻不容缓。总体而言，在封锁解除后的时期，仅气象因素的决定系数（R）值就高达0.80，相比之下，AQ变量的决定系数为0.58。此外，我们计算了北京臭氧浓度超过世界卫生组织标准的天数以及新提出的旺季日最大8小时平均（MDA8）臭氧指南。MDA8环境臭氧超过世界卫生组织标准的天数在6月最高，且各研究年份超过旺季指南的幅度几乎达两倍。本研究展示了气象变量和AQ参数对环境臭氧激增的影响，并强调了未来研究设计最佳策略以应对城市地区日益严重的臭氧污染的重要性。
