Qiao Lu-Jing, Luan Yi-Tong, Zeng Yan-Li, Ju Cun-Yong, Tao Jin-Tao
Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, College of Forestry, Northeast Forestry University, Harbin 150040, China.
Shandong Huayu Institute of Technology Admissions Office, Dezhou 253034, China.
Huan Jing Ke Xue. 2024 Dec 8;45(12):6980-6992. doi: 10.13227/j.hjkx.202311170.
PM is an important indicator for measuring the degree of air pollution. Studying the space-time variation and the driving factors of spatial heterogeneity is important for controlling air pollution and improving regional air quality. Based on PM remote sensing data from 2000 to 2021, the Theil-Sen Median trend analysis, Mann-Kendall significant inspection, and spatial auto correlation were used to analyze the characteristics of space-time changes in PM concentration, and geographical detectors were combined with a multi-scale geographical weighted regression model to explore the key driver factor and its influence and direction of the impact and role of PM spatial differences. The results showed that： ① The average PM value of Heilongjiang Province was between 22.01 and 41 μg·m from 2000 to 2021. From 2008 to 2015, the average PM value was higher than the secondary concentration limit （35 μg·m） of the "Environmental Air Quality Standard." The turning point of the PM concentration change that occurred in 2013 generally showed the trend of change and then a downward trend. Winter was the high incidence season for PM pollution. The PM concentration space was a distributed pattern in the south and north and the high-value zone was mainly based on Harbin, Daqing City, and the surrounding area. The low-value areas were distributed in the northern regions such as the Great Khingan Mountains Region and Heihe City. ② Factor detection results indicated that the average annual temperature was the most important driving factor that affected PM spatial differences. The remaining key driver factors were in turn： high-end, population density, average annual wind speed, land use, night lights, annual years precipitation, slope, annual relative humidity, and NDVI. Interactive detection showed that the interpretation of PM points after interaction was higher than a single factor after interaction, indicating that affecting PM spatial difference was the result of the common effect of each driver factor. The effect of natural factors was more obvious than that of social and economic factors. ③ The effect of different influence factors on PM had a significant spatial difference. The average annual temperature, average annual relative humidity, population density, and night lighting played a promotion effect on PM pollution and NDVI and land use played an inhibitory effect on PM pollution. PM was significantly different from the action role of various influencing factors and the average annual temperature, annual average wind speed, and NDVI impact scale were the smallest, with a variable bandwidth of 43； population density and land use impact scale were the largest, with a variable bandwidth of 140.
颗粒物（PM）是衡量空气污染程度的重要指标。研究其时空变化及空间异质性的驱动因素对于控制空气污染和改善区域空气质量具有重要意义。基于2000—2021年的PM遥感数据，运用泰尔-森中位数趋势分析、曼-肯德尔显著性检验和空间自相关分析PM浓度的时空变化特征，并结合地理探测器和多尺度地理加权回归模型，探究PM空间差异的关键驱动因素及其影响程度和作用方向。结果表明：①2000—2021年黑龙江省PM平均值在22.01～41μg·m之间。2008—2015年，PM平均值高于《环境空气质量标准》二级浓度限值（35μg·m）。2013年出现的PM浓度变化转折点总体呈先上升后下降趋势。冬季是PM污染高发季节。PM浓度空间呈南北分布格局，高值区主要集中在哈尔滨、大庆市及其周边地区。低值区分布在大兴安岭地区、黑河市等北部区域。②因子探测结果表明，年均气温是影响PM空间差异的最重要驱动因素。其余关键驱动因素依次为：高端产业、人口密度、年均风速、土地利用、夜间灯光、年降水量、坡度、年相对湿度和归一化植被指数（NDVI）。交互探测表明，交互后PM点位的解释力高于单一因子交互后，说明影响PM空间差异是各驱动因素共同作用的结果。自然因素的作用比社会经济因素更明显。③不同影响因素对PM的作用存在显著空间差异。年均气温、年相对湿度、人口密度和夜间灯光对PM污染起促进作用，NDVI和土地利用对PM污染起抑制作用。PM与各影响因素的作用关系显著不同，年均气温、年均风速和NDVI的影响尺度最小，变带宽为43；人口密度和土地利用的影响尺度最大，变带宽为140。
