College of Environmental Sciences and Engineering, China West Normal University, Nanchong, Sichuan, China; Key Laboratory of Nanchong City of Ecological Environment Protection and Pollution Prevention in Jialing River Basin, China West Normal University, Nanchong, China; College of Biology and Environmental Sciences, Jishou University, Jishou, Hunan, China.
College of Architecture & Environment, Sichuan University, Chengdu, China.
Sci Total Environ. 2023 Feb 1;858(Pt 3):160136. doi: 10.1016/j.scitotenv.2022.160136. Epub 2022 Nov 12.
Properties of PM that can change aerosol chemistry and photolysis rates have great impacts on O sensitivity regime, further affecting the production rate of surface O. However, responses of O sensitivity regime to changes in PM levels are difficult to be accurately determined, due to the complexity and nonlinearity of atmospheric chemistry. Here, based on long-term time series (2016-2020) of air quality variables in north and south Taiwan, fractal analysis along with Pearson correlation analysis are used to directly reveal the impacts of PM on O sensitivity regime in real atmosphere, by capturing the nonlinear dynamic relations among air pollutants. Great regional and seasonal difference in impacts of PM on O sensitivity regime may be ascribed to meteorological factors, PM components and levels of SO, NO, NO, etc. For north Taiwan, increased PM level can enhance the sensitivity of O formation to VOC in spring and summer, whereas the opposite effect can be observed in winter. But for south Taiwan, the influence of PM on O sensitivity regime is not statistically significant, excluding spring. Furthermore, feasibility and availability of fractal analysis is tested by simulations with Empirical Kinetics Modeling Approach (EKMA). The results demonstrate the capability of fractal analysis to identify the impacts of PM on O sensitivity regime in real atmosphere, which can provide suggestions for PM-O coordinated control strategies in regions suffering combined air pollution.
PM 的特性会改变气溶胶化学和光解速率，从而对 O 敏感性区间产生重大影响，进一步影响地表 O 的生成速率。然而，由于大气化学的复杂性和非线性，PM 水平变化对 O 敏感性区间的响应很难准确确定。在此，基于台湾北部和南部的空气质量变量的长期时间序列（2016-2020 年），通过捕捉污染物之间的非线性动态关系，分形分析和皮尔逊相关分析被用于直接揭示实际大气中 PM 对 O 敏感性区间的影响。PM 对 O 敏感性区间的影响在区域和季节上存在显著差异，这可能归因于气象因素、PM 成分以及 SO、NO、NO 等的水平。对于台湾北部，PM 水平的升高会增强春季和夏季 O 形成对 VOC 的敏感性，而在冬季则会出现相反的效果。但对于台湾南部，PM 对 O 敏感性区间的影响并不显著，春季除外。此外，通过经验动力学建模方法（EKMA）的模拟测试了分形分析的可行性和有效性。结果表明，分形分析能够识别实际大气中 PM 对 O 敏感性区间的影响，这可为遭受复合空气污染的地区提供 PM-O 协同控制策略的建议。
