Zhang Ke-Ke, Hu Dong-Mei, Yan Yu-Long, Peng Lin, Duan Xiao-Lin, Yin Hao, Wang Kai, Deng Meng-Jie
College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China.
Huan Jing Ke Xue. 2022 Mar 8;43(3):1226-1234. doi: 10.13227/j.hjkx.202107122.
Based on the daily average concentration of PM, social influencing factor data, and meteorological data of 11 cities in Shanxi Province from 2015 to 2019, the concentration period of PM was determined using wavelet transform. The correlation between PM 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 PM were determined. The results showed that the concentration of PM 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 PM showed a "U" distribution, with the highest value in January (95 μg·m) and the lowest in August (34 μg·m); the average value in winter was approximately twice that in summer. The (PM) in southern cities such as Linfen was 62 μg·m, and the average value in Datong and other northern cities was 45 μg·m, which was high in the south and low in the north. There were significant periodic changes in PM 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 PM 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 PM and the long-term improvement of air quality in Shanxi Province. The differential impact of different urban meteorological factors on PM should be considered when carrying out short-term regional joint prevention and control.
基于山西省11个城市2015 - 2019年的PM日均浓度、社会影响因素数据和气象数据，利用小波变换确定了PM的浓度周期。分别通过Spearman相关性和小波相干谱探讨了PM与社会影响因素及气象因素之间的相关性，确定了PM长期和短期管控的主要影响因素。结果表明，山西省PM浓度在2015 - 2017年呈上升趋势，年均增长率为4.3%，2018 - 2019年呈下降趋势，年均下降率为4.2%。PM平均浓度呈“U”型分布，1月最高（95μg·m），8月最低（34μg·m）；冬季平均值约为夏季的两倍。临汾等南部城市的（PM）为62μg·m，大同及其他北部城市平均值为45μg·m，呈现南高北低的特点。11个城市的PM浓度存在显著的周期性变化，包括约293天的长周期和约27天的短周期。其中，能源消费水平和产业结构是影响山西省PM浓度长期变化的强驱动因素。短期内，受大气环流变化影响较大，不同城市受典型气象因素影响。临汾、运城、大同、朔州和忻州易受风速影响；晋中、吕梁易受温度影响；太原、晋城、阳泉和长治受相对湿度的独特且显著影响。因此，产业结构调整和能源结构调整是山西省大气PM长期管控和空气质量长期改善的关键。在进行短期区域联防联控时，应考虑不同城市气象因素对PM的差异影响。
