摘要
PM_(2.5)是衡量空气污染程度的重要指标,研究其时空变化特征和影响PM_(2.5)浓度空间分异的关键驱动因素对于治理大气污染,提升区域空气质量具有重要意义.基于黑龙江省2000~2021年PM_(2.5)遥感数据,采用Theil-Sen Median趋势分析、Mann-Kendall显著性检验和空间自相关,分析PM_(2.5)浓度的时空变化特征,利用地理探测器结合多尺度地理加权回归模型,探究影响PM_(2.5)空间分异的关键驱动因子及其影响程度和作用方向.结果表明,①2000~2021年黑龙江省ρ(PM_(2.5))均值在22.01~41µg·m^(-3)之间,2008~2015年PM_(2.5)均值高于《环境空气质量标准》中可吸入颗粒物(粒径≤2.5µm)二级浓度限值(35µg·m^(-3)),2013年为PM_(2.5)浓度变化转折点,总体呈先升后降的变化趋势.冬季是PM_(2.5)污染的高发季.PM_(2.5)浓度空间上呈南高北低的分布格局,高值区常年以哈尔滨市、大庆市及周边地区为主,低值区则分布在大兴安岭和黑河市等北部地区.②因子探测结果表明,年均气温是影响PM_(2.5)空间分异最主要的驱动因子,其余的关键驱动因子按解释力大小依次为:高程、人口密度、年均风速、土地利用、夜间灯光、年降水量、坡度、年均相对湿度和NDVI.交互探测表明各驱动因子在交互作用后对PM_(2.5)分异性的解释力均大于单一因子,说明影响PM_(2.5)空间分异是各驱动因子共同作用的结果,自然因子间的交互作用比社会经济因子间的作用更加明显.③不同影响因子对PM_(2.5)的作用呈现明显的空间差异,年均气温、年均相对湿度、人口密度和夜间灯光对PM_(2.5)污染起促进作用,高程、坡度、年降水量、年均风速、NDVI和土地利用对PM_(2.5)污染起抑制作用;PM_(2.5)与各影响因子的作用尺度具有显著差异,年均气温、年均风速和NDVI的影响尺度最小,变量带宽为43;人口密度和土地利用的作用尺度最大,变量带宽为140.
PM_(2.5) 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_(2.5) 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_(2.5) concentration,and geographical detectors were combined with a multiscale geographical weighted regression model to explore the key driver factor and its influence and direction of the impact and role of PM_(2.5) spatial differences.The results showed that:①The average PM_(2.5) value of Heilongjiang Province was between 22.01 and 41µg·m^(-3) from 2000 to 2021.From 2008 to 2015,the average PM_(2.5) value was higher than the secondary concentration limit(35µg·m^(-3))of the“Environmental Air Quality Standard.”The turning point of the PM_(2.5) 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_(2.5) pollution.The PM_(2.5) 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.②Factor detection results indicated that the average annual temperature was the most important driving factor that affected PM_(2.5) 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_(2.5) points after interaction was higher than a single factor after interaction,indicating that affecting PM_(2.5) spatial difference was the result of the common e
作者
乔璐靖
栾宜通
曾艳丽
琚存勇
陶金涛
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)
出处
《环境科学》
EI
CAS
CSCD
北大核心
2024年第12期6980-6992,共13页
Environmental Science
基金
国家重点研发计划项目(2021YFD2200405)。
关键词
黑龙江省
PM_(2.5)
时空变化
驱动因素
地理探测器
多尺度地理加权回归模型
Heilongjiang Province
PM_(2.5)
spatial-temporal variation
driving factors
geographical detector
multi-scale geographically weighted regression model
