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首页> 外文期刊>Nature environment and pollution technology >Correlation Analysis Between PM2.5 Concentration and Meteorological, Vegetation and Topographical Factors in the Urbanized Ecosystem in Beijing, China
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Correlation Analysis Between PM2.5 Concentration and Meteorological, Vegetation and Topographical Factors in the Urbanized Ecosystem in Beijing, China

机译:北京市城市化生态系统中PM2.5浓度和气象,植被和地形因素的相关分析

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摘要

With the economic growth and massive industrialization, the air quality of China in general and industrial regions in specific has saturated with different health hazard pollutants. Among the pollutants, PM2.5 is posing some serious threats to the society. In this study we evaluated the correlation between PM2.5concentration and 12 different meteorological, vegetation and topographical factors in Beijing, China. We used the Difference Index (DI) method and dark pixel method to retrieve the PM2.5 concentration of 30m and 1km spatial resolution. Spearman correlation analysis method was used to analyse the correlation between PM2.5 concentration and three types of 12 factors. The results showed that the forest land can play a major role in decreasing the PM2.5 concentration in the air, as in this study a significant drop of (18.78%) was observed in PM2.5 concentration in the regions having coniferous forest. Moreover, the PM2.5 reduction rate was positively correlated with forest vegetation coverage (FVC). Our results demonstrated that relative humidity, air pressure and water vapour pressure were positively correlated with PM2.5, while air temperature and wind speed were negatively correlated. The altitude and slope showed a weak negative correlation with PM2.5 concentration, while, aspect was very weakly correlated with the PM2.5 concentration. The findings of this study could help design the urban green space planning and air pollutioncontrol in the heavily populated urban ecosystems.
机译:随着经济增长和巨大的产业化,中国常规和工业区的空气质量已经饱和了不同的健康危害污染物。在污染物中,PM2.5对社会产生了一些严重的威胁。在这项研究中,我们评估了PM2.5复合与12种不同气象,植被和地形因素之间的相关性。我们使用了差异指数(DI)方法和暗像素方法来检索30M和1KM空间分辨率的PM2.5浓度。 Spearman相关性分析方法用于分析PM2.5浓度与三种类型的12个因素之间的相关性。结果表明,森林土地可以在减少空气中的PM2.5浓度方面发挥重要作用,如本研究中,在具有针叶树林的地区的PM2.5浓度下观察到显着的(18.78%)。此外,PM2.5减少率与森林植被覆盖率呈正相关(FVC)。我们的结果表明,相对湿度,空气压力和水蒸气压力与PM2.5正相关,而空气温度和风速呈负相关。高度和斜率显示出与PM2.5浓度的弱负相关,而方面与PM2.5浓度非常弱。本研究的调查结果可以帮助设计大量人口化的城市生态系统中的城市绿地规划和空气污染康尔策略。

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