| 基于小波变换的山西省PM2.5污染特征及影响因素 |
| 摘要点击 2062 全文点击 591 投稿时间：2021-07-14 修订日期：2021-08-22 |
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| 中文关键词 山西省 PM2.5 污染特征 影响因素 小波变换 |
| 英文关键词 Shanxi province PM2.5 pollution characteristics influence factor wavelet transform |
| 作者 | 单位 | E-mail | | 张可可 | 华北电力大学环境科学与工程学院, 资源环境系统优化教育部重点实验室, 北京 102206 | kk1441241825@163.com | | 胡冬梅 | 华北电力大学环境科学与工程学院, 资源环境系统优化教育部重点实验室, 北京 102206 | huhu3057@163.com | | 闫雨龙 | 华北电力大学环境科学与工程学院, 资源环境系统优化教育部重点实验室, 北京 102206 | | | 彭林 | 华北电力大学环境科学与工程学院, 资源环境系统优化教育部重点实验室, 北京 102206 | | | 段小琳 | 华北电力大学环境科学与工程学院, 资源环境系统优化教育部重点实验室, 北京 102206 | | | 尹浩 | 华北电力大学环境科学与工程学院, 资源环境系统优化教育部重点实验室, 北京 102206 | | | 王凯 | 华北电力大学环境科学与工程学院, 资源环境系统优化教育部重点实验室, 北京 102206 | | | 邓萌杰 | 华北电力大学环境科学与工程学院, 资源环境系统优化教育部重点实验室, 北京 102206 | |
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| 中文摘要 |
| 基于山西省11城市2015~2019年PM2.5日均浓度、社会影响因素数据和气象数据，利用小波变换确定PM2.5浓度周期，通过Spearman相关性和小波相干谱分别探究PM2.5与社会影响因素和气象因素的关联，确定PM2.5长短周期管控的主要影响因子.结果表明，2015~2017年山西省PM2.5浓度年均值呈上升趋势，年均上升率为4.3%，2018~2019年呈下降趋势，年均下降率为4.2%；ρ（PM2.5）月均值呈"U"型分布，1月最高（95 μg·m-3），8月最低（34 μg·m-3），冬季均值约为夏季的2倍；临汾等南部城市ρ（PM2.5）均值为62 μg·m-3，大同等北部城市均值为45 μg·m-3，空间上呈南高北低.11城市PM2.5浓度存在显著周期性变化，主要周期包括293 d左右的长周期和27 d左右的短周期.其中，能源消耗水平和产业结构偏重是影响山西省长周期上PM2.5浓度的强驱动因素；短周期上则受大气环流变化影响较大，且不同城市PM2.5的主要气象影响因子不同，临汾、运城、大同、朔州和忻州易受风速影响，晋中和吕梁易受温度影响，太原、晋城、阳泉和长治较为特殊，受相对湿度影响显著.因此，产业结构调整和能源结构调整等是山西省大气PM2.5长期管控和空气质量长效改善的关键；开展短期区域联防联控时需考虑不同城市气象因子对PM2.5的差异化影响. |
| 英文摘要 |
| Based on the daily average concentration of PM2.5, social influencing factor data, and meteorological data of 11 cities in Shanxi Province from 2015 to 2019, the concentration period of PM2.5 was determined using wavelet transform. The correlation between PM2.5 and social influencing factors and meteorological factors was explored respectively through Spearman correlation and the wavelet coherence spectrum, and the main influencing factors of long-term and short-term management and control of PM2.5 were determined. The results showed that the concentration of PM2.5 in Shanxi Province showed an upward trend from 2015 to 2017, with an average annual increase rate of 4.3% and a downward trend from 2018 to 2019, with an average annual decrease rate of 4.2%. The average concentration of PM2.5 showed a "U" distribution, with the highest value in January (95 μg·m-3) and the lowest in August (34 μg·m-3); the average value in winter was approximately twice that in summer. The ρ(PM2.5) in southern cities such as Linfen was 62 μg·m-3, and the average value in Datong and other northern cities was 45 μg·m-3, which was high in the south and low in the north. There were significant periodic changes in PM2.5 concentration in the 11 cities, including a long period of approximately 293 d and a short period of approximately 27 d. Among them, the energy consumption level and industrial structure were the strong driving factors affecting the PM2.5 concentration in the long period of Shanxi Province. In the short period, it was greatly affected by the change in atmospheric circulation, and different cities were affected by typical meteorological factors. Linfen, Yuncheng, Datong, Shuozhou, and Xinzhou were vulnerable to wind speed; Jinzhong and Luliang were vulnerable to temperature; and Taiyuan, Jincheng, Yangquan, and Changzhi were uniquely and significantly affected by relative humidity. Therefore, industrial structure adjustment and energy structure adjustment are key to the long-term control of atmospheric PM2.5 and the long-term improvement of air quality in Shanxi Province. The differential impact of different urban meteorological factors on PM2.5 should be considered when carrying out short-term regional joint prevention and control. |
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