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The Influence of Weather Conditions and Local Climate on Particulate Matter (PM10) Concentration in Metropolitan Area of Iasi, Romania

机译:罗马尼亚雅西都会区的天气条件和当地气候对颗粒物(PM10)浓度的影响

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The aim of this study is to evaluate the role of the weather conditions and local climate on the temporal and spatial variability of particulate matters (PM 10) in Ia?i city which is facing major pollution problems in the recent years. Daily data from 4 monitoring stations of Environmental Protection Agency-Ia?i–for main weather parameters and particulate matters – and the temperature from an inner temperature and relative humidity observation network inside the city were used for a three year study (2013-2015). Linear correlation, composite analysis and multiple regression are the main statistical methods applied in the analysis. In brief, the most important meteorological parameters enhancing air pollution in Ia?i seem to be represented by thermal inversions developing in the region strongly related to local climate conditions. The Pearson correlation coefficient (stronger than -0.40) between PM10 and thermal gradient, the difference in the PM10 concentration exceeding 20 μg/msup3/sup between strong thermal inversions and unstable conditions and the leading role of thermal gradients in multiple regression are the main indicators of the great role of thermal inversion in generating and sustaining pollution conditions in this area. The maximum concentrations of PM10 occur in May and March, gathering more than 30% of the days for the entire year. Complementary studies were taken into account in order to analyse the aerosol optical properties retrieved from Aerosol Robotic Network (AERONET-NASA).
机译:这项研究的目的是评估天气状况和当地气候对近年来面临重大污染问题的伊阿伊市颗粒物(PM 10)的时间和空间变异性的作用。来自美国环境保护局(Ia?i)四个监测站的主要天气参数和颗粒物的每日数据以及城市内部内部温度和相对湿度观测网络的温度用于三年研究(2013-2015年) 。线性相关,综合分析和多元回归是分析中应用的主要统计方法。简而言之,在伊亚西地区增加空气污染的最重要的气象参数似乎表现为与当地气候条件密切相关的区域内发生的热反演。 PM10与热梯度之间的皮尔逊相关系数(大于-0.40),PM10浓度超过20μg/ m 3 的强热反演和不稳定条件之间的差异以及热梯度在铅中的主导作用多元回归是热反演在该地区产生和维持污染状况中发挥重要作用的主要指标。 PM10的最高浓度发生在5月和3月,全年收集的天数超过30%。为了分析从气溶胶机器人网络(AERONET-NASA)获得的气溶胶光学特性,还进行了补充研究。

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