湖南省臭氧(O3)污染已成为六种常见常规污染物中最重要的因素(即,NO2,SO2,CO,大气环境中的O3,PM10和PM2.5)。进一步的调查表明,对O3的相关研究不足。因此,对于政府监管机构来说,澄清O3变化的关键驱动因素至关重要。在这项研究中,一种由广义加性模型(GAM)组成的组合方法,经验正交函数(EOF),和绝对主成分得分(APC)模型用于识别和量化气象学和局部光化学生成(局部)以及从外部(非局部)运输对2018-2020年O3变化的影响。同时,分析了长沙市2018-2019年和2019-2020年O3年值的驱动因素。结果表明,当前兆的相对贡献较高时,O3事件通常是由气象学引起的,在短期时间尺度上。总的来说,在时间尺度上,气象学和当地是2018年至2019年O3年度增加的驱动因素。此外,气象学的贡献,当地,2019年至2020年,非本地下降,导致2020年O3浓度水平较低。地理上,东方,北,长沙南部主要受气象影响,当地,和非本地的,分别。在这三年中,非本地表现出持续下降的趋势,而气象学和当地的趋势因年份和地理而异。从2018年到2019年,长沙北部的本地贡献有所增加,这可能归因于生物挥发性有机化合物排放(BVOCs)的增加,由于NOx的消耗增加,南部的水平降低了。从2018年到2019年,气象学对所有站点O3的影响都得到了加强。相比之下,从2019年到2020年,随着BVOC和NOx排放量的下降,北部地区的局部贡献减少,南部地区的贡献增加,当时对整个地区O3的气象影响变弱。
Ozone (O3) pollution in Hunan province has become the most important factor among the six common conventional pollutants (i.e., NO2, SO2, CO, O3, PM10, and PM2.5) in the atmospheric environment. Further investigation has indicated that the relevant studies of O3 are insufficient. Therefore, it is essential to clarify the key driving factors of O3 variations for government regulators. In this study, a combined method consisting of a generalized additive model (GAM), empirical orthogonal function (EOF), and absolute principal component scores (APCs) model was employed to identify and quantify the impacts of meteorology and local photochemical generation (local) and that transported from outside (nonlocal) on O3 variations from 2018-2020. Simultaneously, the driving factors of O3 annual values from 2018 to 2019 and from 2019 to 2020 in Changsha were analyzed. The results showed that O3 episodes were commonly caused by meteorology when the relative contribution from precursors was high, on the short-term time scale. Overall, on the temporal scale, meteorology and local were the driving factors for the increasing annual O3 from 2018 to 2019. Additionally, the contribution from meteorology, local, and nonlocal decreased from 2019 to 2020, leading to a lower level of O3 concentration in 2020. Geographically, the east, north, and south of Changsha were mainly affected by meteorology, local, and nonlocal, respectively. Throughout the three years, nonlocal exhibited a sustained decreasing trend, whereas the tendencies from meteorology and local varied by year and geography. Local contribution in the north of Changsha increased from 2018 to 2019, which was likely attributed to the increasing biogenic volatile organic compound emission (BVOCs), and it became lower in the south owing to the strengthened consumption by NOx. Impacts from meteorology on O3 in all sites were enhanced from 2018 to 2019. By contrast, local contribution decreased in the north and increased in the south with the decline in BVOC and NOx emissions from 2019 to 2020, when the meteorological impacts on O3 in the whole area became weak.