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首页> 外文期刊>Polish Journal of Environmental Studies. >Spatiotemporal Distribution of PM_(2.5) and Its Correlation with Other Air Pollutants in Winter During 2016~2018 in Xi'an, China
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Spatiotemporal Distribution of PM_(2.5) and Its Correlation with Other Air Pollutants in Winter During 2016~2018 in Xi'an, China

机译:PM_(2.5)的时尚分布及其与冬季其他空气污染物的相关性2016〜2018年西安,中国

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High concentration of PM2.5 has seriously affected people's daily lives in recent years. It is necessary to analyze PM2.5 and the correlations with other pollutants in winter. Data presented in this paper were obtained from monitoring stations from 2016 to 2018. Attention was fixed on PM2.5 and its monthly and daily variations in winter. Furthermore, the correlations between PM2.5 and CO, SO2, NO2, O-3 and PM10 were studied. The results showed concentrating PM2.5 was roughly consistent with the monthly and daily trends. It was JanuaryDecemberFebruaryNovemberMarch. The mass concentration ranges of PM2.5 before and after the adjustment of heating energy structures were 64.5-184.1 mu g/m(3), and 86.4-140.1 mu g/m(3), respectively. The average concentrations of PM2.5 were 135.5 mu g/m(3), and 109.1 mu g/m(3), decreased by 26.4 mu g/m(3). PM2.5/PM10 was changed from 64.6% to 62.6%, reduced by 2%. The linear correlation analysis revealed a strong correlation between PM2.5 and CO, SO2, NO2, and PM10, but a negative correlation between PM2.5 and O-3. Two multiple linear regression models on the pollutants were established, respectively. This study helps understand the concentrating distribution of PM2.5 and other pollutants in winter. It will provide some useful references to control air pollution for some cities, which have a similar type of heating energy structure.
机译:高浓度的PM2.5近年来严重影响了人们的日常生活。有必要在冬季分析PM2.5和与其他污染物的相关性。本文提出的数据是从2016年到2018年的监测站获得。在PM2.5及其在冬季的每月和日常变化中,注意力得到了注意力。此外,研究了PM2.5和CO,SO2,NO2,O-3和PM10之间的相关性。结果表明PM2.5浓缩,大致与月度和日常趋势一致。它是1月≫ 12月≫ 2月≫ 11月≫ 3月。在加热能量结构调节之前和之后PM2.5的质量浓度范围分别为64.5-184.1μg/ m(3)和86.4-140.1μg/ m(3)。 PM2.5的平均浓度为135.5μg/ m(3),109.1μg/ m(3),减少26.4μg/ m(3)。 PM2.5 / PM10从64.6%变为62.6%,减少2%。线性相关性分析显示PM2.5和CO,SO2,NO2和PM10之间的强相关,但PM2.5和O-3之间的负相关性。分别建立了污染物上的两种多元线性回归模型。本研究有助于了解冬季PM2.5和其他污染物的集中分布。它将提供一些有用的参考,以控制某些城市的空气污染,具有类似类型的加热能量结构。

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