全球气候变化与区域气象及空气质量的关系研究——以成都市为例  

Correlational Analysis of Global Climate Change Versus Regional Meteorology and Air Quality:A Case Study in Chengdu

作　　者：武文琪 张凯山[1] WU Wen-qi;ZHANG Kai-shan(College of Architecture&Environment,Sichuan University,Chengdu 610065,China)

机构地区：[1]四川大学建筑与环境学院,四川成都610065

出　　处：《长江流域资源与环境》2020年第4期985-996,共12页Resources and Environment in the Yangtze Basin

基　　金：环境保护部公益性行业科研专项项目(201409012)。

摘　　要：区域气象条件及空气质量或与全球气候变化关系密切。研究通过分析不同气候条件下成都地区1951~2017年主要气象要素及其2013~2017年大气污染物浓度变化趋势,并结合大数据挖掘技术探究厄尔尼诺/拉尼娜事件与成都地区气象及空气质量的关系。结果表明,全球气候变化对区域气象及空气质量影响明显。异常气候造成成都地区气温、降水、风速、日照时长等气象条件发生明显变化。这些变化通常利于大气扩散条件的改善而使污染物浓度下降,但相应时期的臭氧浓度却有所升高。研究同时利用KNN大数据挖掘算法评估不同气候条件下气象和减排对空气质量改善的贡献。结果显示,在全球厄尔尼诺发生频繁的2015年,成都地区重污染天数明显减少,气象和减排的贡献率分别为27%和73%;而在全球拉尼娜现象频发的2016年,成都地区空气质量也有明显改善,重污染天数的减少有42%归功于气象条件的变化,几乎与大气污染物的减排贡献相当。因此,为实现空气质量的有效改善,空气质量改善管理政策的制定,既要从源头上控制污染物的排放,同时也应考虑全球气候变化的影响。Regional meteorological conditions and air quality may be closely related to global climate changes. Global climate changes were categorized as El Niňo and La Niňa. Based on the large data mining technologies of k-nearest neighbor(KNN), historical meteorological data from 1951 to 2017 and air quality data from 2013 to 2017 of Chengdu were both used for trend analysis and its correlation with global climate changes. The K-nearest neighbor(KNN) was used to evaluate the contribution of both meteorological conditions and emissions reduction to the air quality improvement. Results showed that global climate changes have significant impacts on both regional meteorological conditions and air quality. Both El Niňo and La Niňa have caused regional changes of meteorological conditions such as air temperature, precipitation, wind speed, and sunshine hours. It has facilitated the diffusion and dispersion of atmospheric pollutants. As a result, the air quality has been improved locally except for tropospheric zone. Compared to years with typical meteorological conditions, higher ozone concentrations were observed under El Niňo or La Niňa due to a variety of reasons. For the year of 2015, A KNN model was developed using the observed meteorology factors and heave pollution days. It is found that for the reduced heave pollution days in 2015(El Niňo year), 73% can be attributed to the emission reduction, and 27% was caused by the improvement of meteorological conditions. In 2016(La Niňa year), 42% was caused by the improvement of meteorological conditions. Thus, policy-making regarding air quality improvement and management should take into account both meteorological conditions and emissions sources.

关 键 词：厄尔尼诺 拉尼娜 空气质量 气候变化 K近邻算法(KNN) 

分 类 号：X51[环境科学与工程—环境工程]