Abstract：
PM2.5 remote sensing data was applied in this study, and Theil-Sen Median trend analysis and the Mann-Kendall significance test were utilized to analyze the temporal and spatial variation in PM2.5 in the Shandong Province from 2000 to 2021. The influencing power of the influencing factors on the spatial differentiation of PM2.5 concentration in the Shandong Province was detected at the provincial-city-county levels based on Geo-detector data. The results showed that:① on the temporal scale, the mean ρ(PM2.5)in the Shandong Province ranged from 38.15 to 88.63 μg·m-3 from 2000 to 2021, which was slightly higher than the secondary limit of inhalable particulate matter (35 μg·m-3) in the Ambient Air Quality Standards. On the interannual scale, 2013 was the peak year for the variation in ρ(PM2.5) with a value of 83.36 μg·m-3, according to which the trend of PM2.5 concentrations in the Shandong Province was divided into two phases:a continuous increase and a rapid decrease. On the seasonal scale, PM2.5 concentration presented the distribution characteristics of \"low in summer and high in winter and moderate in spring and autumn\" and the U-shaped change rule of first decreasing and then increasing. ② On the spatial scale, the PM2.5 concentration in the Shandong Province presented a spatial distribution pattern of \"high in the west and low in the east.\" The areas with high PM2.5 concentration were distributed in the western area of the Shandong Province, whereas the areas with low PM2.5 concentration were distributed in the eastern peninsula region. The spatial variation in the changing trend of PM2.5 concentration showed significant spatial heterogeneity, and the extremely significant decrease was mainly distributed in the eastern peninsula region. ③ The results of factor detection showed that climate factor was an important factor affecting the spatial differentiation of PM2.5 concentration in the Shandong Province. Mean temperature had the highest influence on the spatial differentiation of PM2.5 concentration in the Shandong Province, with a q value of 0.512. Provincial-city-county multi-scale detection results showed that the influencing factors affecting the spatial differentiation of PM2.5 concentration and their influencing power differed at different spatial scales. At the provincial scale, mean temperature, sunshine duration, and slope were the main factors affecting the spatial differentiation of PM2.5 concentration. At the city level, precipitation, elevation, and relative humidity were the main factors affecting the spatial differentiation of PM2.5. At the county level, precipitation, mean temperature, and sunshine duration were the main factors affecting the spatial variation in PM2.5 concentration.
摘要：
本研究应用PM2.5遥感数据,利用Theil-Sen中值趋势分析和Mann-Kendall显著性检验对2000-2021年山东省PM2.5的时空变化进行了分析。基于地理探测器数据,在省-市-县一级检测了各影响因素对山东省PM2.5浓度空间分异的影响力。结果表明:①在时间尺度上,2000年至2021年，山东省的平均ρ（PM2.5）范围为38.15至88.63μg·m-3，略高于环境空气质量标准中可吸入颗粒物的二级限值（35μg·m-3）。在年际尺度上，2013年是ρ(PM2.5)变化的高峰年,为83.36μg·m-3,山东省PM2.5浓度变化趋势分为持续升高和快速降低两个阶段。在季节性尺度上，PM2.5浓度呈现“夏低冬高、春秋适中”的分布特征和先降低后升高的U型变化规律。②在空间尺度上，山东省PM2.5浓度呈现西高东低的空间分布格局。PM2.5浓度较高的地区分布在山东省西部地区,而PM2.5浓度较低的地区分布在半岛东部地区。PM2.5浓度变化趋势的空间变异表现出显著的空间异质性,极显著的下降主要分布在东部半岛地区。③因子检测结果表明,气候因子是影响山东省PM2.5浓度空间分异的重要因子。平均气温对山东省PM2.5浓度空间分异的影响最大,q值为0.512。省-市-县多尺度检测结果表明,影响PM2.5浓度空间分异的影响因素及其影响力在不同空间尺度上存在差异。在省级范围内，平均温度,日照时间,和坡度是影响PM2.5浓度空间分异的主要因素。在城市层面,降水,高程,相对湿度是影响PM2.5空间分异的主要因素。在县一级,降水,平均温度,和日照时数是影响PM2.5浓度空间变化的主要因素。
