| 天山北坡城市群PM2.5浓度时空分布特征及影响因素分析 |
| 摘要点击 3186 全文点击 927 投稿时间：2023-03-02 修订日期：2023-05-24 |
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| 中文关键词 地理加权回归(GWR)模型 天山北坡城市群 时空分布特征 变化趋势 影响因素分析 |
| 英文关键词 geographically weighted regression (GWR) model urban agglomerations on the northern slope of Tianshan Mountains temporal and spatial distribution characteristics change trend analysis of influencing factors |
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| 中文摘要 |
| 针对天山北坡城市群开展PM2.5浓度时空分布特征和影响因素分析，对区域经济建设和环境保护具有积极的意义.通过地理加权回归（GWR）模型，利用MCD19A2气溶胶产品结合气象因子，反演得到天山北坡城市群2015~2021年3~11月的PM2.5浓度时空分布，继而实现变化趋势和影响因素分析.结果如下：①研究区PM2.5浓度高值主要分布在天山北麓和古尔班通古特沙漠之间的绿洲城市群地带，呈现“四周低，中间高”和“西低东高”的空间分布特征，2015~2021年研究区的ρ（PM2.5）年均值为16.98 μg·m-3，高值主要聚集在乌鲁木齐市市区部分，并向昌吉市和阜康市延伸递减；ρ（PM2.5）月均值分布规律与年均一致，但存在季节差异，表现为：秋季（20.32 μg·m-3）>春季（18.25 μg·m-3）>夏季（12.47 μg·m-3），春季和冬季聚集现象会更明显；②研究区PM2.5浓度年均值在2015~2021年呈现下降趋势，3~10月均值同样表现为下降趋势，仅11月表现为略有升高；从PM2.5浓度变化趋势空间分布分析，下降集中在主要城市市区部分，尤其是乌鲁木齐市市区部分及其周边地区减少幅度最大，变化最为剧烈；③研究区气温、气压与PM2.5浓度呈现正相关效应，而相对湿度，风速，大气边界层高度，降水量与PM2.5浓度呈现负相关效应；各因子影响程度从高向低排列为：大气边界层高度>相对湿度>气压>气温>风速>降水量. |
| 英文摘要 |
| Analysis of the spatial and temporal distribution characteristics and influencing factors of PM2.5 concentrations for the urban agglomeration on the northern slope of Tianshan Mountain is of positive significance for regional economic construction and environmental protection. The spatial and temporal distributions of PM2.5 concentrations in the Tianshan North Slope urban agglomeration from March to November 2015 to 2021 were obtained through the inversion of the MCD19A2 aerosol product combined with meteorological factors using a geographically weighted regression (GWR) model, followed by the analysis of change trends and influencing factors. The results were as follows:① the high PM2.5 concentrations in the study area were mainly distributed in the oasis city cluster between the northern foot of Tianshan Mountain and the Gurbantunggut Desert, showing the spatial distribution characteristics of being "low around and high in the middle" and "low in the west and high in the east." The annual average value of ρ(PM2.5) in the study area was 16.98 μg·m-3, with high values mainly concentrated in the urban part of Urumqi and decreasing towards Changji and Fukang. The monthly average ρ(PM2.5) distribution pattern was consistent with the annual average, but there were seasonal differences as follows:autumn (20.32 μg·m-3) > spring (18.25 μg·m-3) > summer (12.47 μg·m-3). The accumulation phenomenon was more pronounced in spring and winter. ② The study area's annual average PM2.5 concentration showed a decreasing trend from 2015 to 2021, and the average value from March to October also showed a decreasing trend, with only a slight increase in November. From the analysis of the spatial distribution of PM2.5 concentration trends, the decrease was concentrated in the urban parts of major cities, especially in the urban part of Urumqi and its surrounding areas, where the decrease was the largest and the change was the most drastic. ③ Temperature and air pressure were positively correlated with PM2.5 concentrations, whereas relative humidity, wind speed, atmospheric boundary layer height, and precipitation were negatively correlated with PM2.5 concentrations. The degree of influence of each factor was ranked from high to low as follows:atmospheric boundary layer height > relative humidity > air pressure > air temperature > wind speed > precipitation. |
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