%0 Journal Article %T 2018年中国城市PM2.5时空分布规律及防治策略
Spatiotemporal Distribution of PM2.5 in Chinese Cities and Prevention Strategies in 2018 %A 马慧 %J Open Journal of Nature Science %P 204-213 %@ 2330-1732 %D 2025 %I Hans Publishing %R 10.12677/ojns.2025.132021 %X 作为雾霾污染的核心构成要素，探究PM2.5的时空分布演变特征及其影响因素，对于有效遏制其浓度增长趋势具有至关重要的意义。本研究依托来自334个地级及以上城市的PM2.5浓度数据指标，并融入GIS空间分析技术，解析PM2.5浓度的时空分布特性。结果表明：1) 时间上，2018年，PM2.5的月均浓度变化趋势呈现出先下降后上升的态势，呈“U”型曲线变化；在季节分布上，2018年PM2.5的季均浓度表现为夏季降至最低，冬季则升至最高，春秋两季则居中分布。2) 空间上，2018年中国城市华北地区、西北地区PM2.5浓度值在全国呈高值状态，其中新疆省PM2.5浓度值最高，PM2.5浓度最高的城市为和田地区(102.56 μg/m3)；空间尺度上，2018年大多数监测站点的PM2.5浓度展现出了清晰的季节性波动趋势。具体而言，每年的PM2.5浓度从冬季开始逐渐降低，直至夏季降至最低点；随后，从夏季到冬季，浓度又逐渐回升，最终在冬季达到峰值。结合2018年334个地级及以上城市的PM2.5浓度数据及社会经济数据，建立多元线性回归模型，得出2018年中国各城市年均人口、地区生产总值、供气总量与2018年PM2.5浓度呈相关性。并基于结果研究分析，从实际情况出发，提出大气污染防治的可行性建议。
As the core component of haze pollution, it is of great significance to explore the spatial and temporal distribution evolution characteristics and influencing factors of PM2.5 to effectively curb its concentration growth trend. In this study, we relied on PM2.5 concentration data from 334 prefecture-level cities and integrated GIS spatial analysis technology to analyze the spatial and temporal distribution characteristics of PM2.5 concentration. The results showed that: 1) In 2018, the monthly average concentration of PM2.5 showed a trend of first decreasing and then rising, showing a “U” curve. In 2018, the seasonal average concentration of PM2.5 decreased to the lowest in summer, rose to the highest in winter, and was in the middle spring and autumn. 2) Spatially, in 2018, the PM2.5 concentration values in North China and Northwest China were high in China, among which Xinjiang Province had the highest PM2.5 concentration and the highest PM2.5 concentration in Hotan (102.56 μg/m3). On a spatial scale, PM2.5 concentrations at most of the monitored sites in 2018 showed a clear seasonal fluctuation trend. Specifically, annual PM2.5 concentrations gradually decrease from winter to summer to their lowest point in summer, and then gradually rise from summer to winter, eventually peaking in winter. Combined with the PM2.5 concentration data and socio-economic data of 334 cities at and above the prefecture level in 2018, a multiple linear regression model was established to show that the average annual population, gross regional %K PM2.5浓度， %K 时空分布， %K 时空变化， %K 空间分析
PM2.5 Concentration %K Spatial and Temporal Distribution %K Spatial and Temporal Variation %K Spatial Analysis %U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=108788