| 基于空间尺度效应的山东省PM2.5浓度时空变化及空间分异地理探测 |
| 摘要点击 5225 全文点击 689 投稿时间：2023-06-08 修订日期：2023-07-10 |
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| 中文关键词 山东省 PM2.5浓度 多尺度 时空变化 地理探测器 影响因素 |
| 英文关键词 Shandong Province PM2.5 concentration multi-scale spatial-temporal variation Geo-detector influencing factors |
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| 中文摘要 |
| 基于PM2.5遥感数据,采用Theil-Sen Median趋势分析和Mann-Kendall显著性检验,分析2000~2021年山东省PM2.5浓度时空变化特征,结合地理探测器,在省-市-县三级空间尺度上探测影响山东省PM2.5浓度空间分异的影响因子影响力.结果表明：①时间上,2000~2021年山东省ρ（PM2.5）均值在38.15~88.63 μg·m-3之间,略微高于《环境空气质量标准》中可吸入颗粒物的二级标准限值（35 μg·m-3）.在年际尺度上,2013年是ρ（PM2.5）变化的峰值年,其值为83.36 μg·m-3,据此将山东省PM2.5浓度变化趋势分为两个阶段：持续上升和快速下降阶段.在季节尺度上,PM2.5浓度呈现“夏低冬高,春秋居中”分布特征和先降后升的“U”型变化规律.②空间上,山东省PM2.5浓度呈现出“西高东低”的空间分布格局,PM2.5浓度高值区分布山东省西部地区,低值区则分布在东部半岛地区.PM2.5浓度空间变化趋势呈现显著的空间异质性,极显著下降的区域主要分布在东部半岛地区.③因子探测结果表明,气候因子是影响山东省PM2.5浓度空间分异的重要影响因素,平均气温对山东省PM2.5浓度空间分异的影响最高,q值为0.512.省-市-县多尺度探测结果显示,影响PM2.5浓度空间分异的影响因子及其影响力在不同空间尺度上具有差异性.省级尺度上,平均气温、日照时数和坡度是影响PM2.5浓度空间分异的主要影响因子；市级尺度上,降水、高程和相对湿度是影响PM2.5空间分异的主要影响因子；县级尺度上,降水、平均气温和日照时数是影响PM2.5浓度空间分异的主要影响因子. |
| 英文摘要 |
| 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. |
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