首页> 外文期刊>Uludag University Journal of The Faculty of Engineering >STATISTICAL ANALYSIS AND EVALUATION OF PM CONCENTRATIONS DURING THE DUST STORMS AT MAY 2020 FOR SEL?UKLU DISTRICT OF KONYA CITY, TURKEY
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STATISTICAL ANALYSIS AND EVALUATION OF PM CONCENTRATIONS DURING THE DUST STORMS AT MAY 2020 FOR SEL?UKLU DISTRICT OF KONYA CITY, TURKEY

机译:统计分析与评估PM浓度在2020年5月2020年SEL的尘埃风暴中的统计分析及评价

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Dust storms are widespread events that occur several times a year and spread over many countries of the world relating the wind direction and speed. Especially particulate matter (PM) is the main pollutant spread over by these storms. Because of the dust storms, PM concentrations increase rapidly in the areas found on the way of dust storm passes In this study, statistical evaluation was made accordingly the PM data measured with personal measurement device in Sel?uklu District of Konya and the meteorological and the air pollution data provided from air quality monitoring station, which is affiliated by the Ministry of Environment and Urbanization, located nearby. Pearson correlation test has been applied to both data sets and a significant relationship has been detected between the measured and provided data. Moreover, multiple linear regression was applied to the data for PM2.5 and PM10 separately. Adjusted R2 of the analysis has been found as 0.573 and 0.559 respectively for PM2.5 and PM10 which explains almost half of the relationship between PM and meteorological variables. The highest positive effect on PM pollution was determined as air temperature. Finally, principal component analysis (PCA) was applied to both data and 4 different principal components were detected. Measured PM2.5 and PM10, air temperature, and relative humidity were clustered at the same component group.
机译:沙尘暴是一年内发生了几次的广泛活动,并在世界上许多国家传播了风向和速度。特别是颗粒物质(PM)是这些风暴蔓延的主要污染物。由于暴风雨,PM浓度在尘埃风暴通过本研究中发现的区域迅速增加,因此根据SEL中的个人测量装置测量的PM数据进行了统计评估?Konya的UKLU区和气象和气象和气象空气质量监测站提供的空气污染数据,该数据由环境和城市化部附近,位于附近。 Pearson相关测试已经应用于数据集,并且在测量和提供的数据之间检测到具有重要关系。此外,将多元线性回归分别应用于PM2.5和PM10的数据。分析的调整后R2分别为PM2.5和PM10分别为0.573和0.559,这解释了PM和气象变量之间几乎一半的关系。 PM污染的最高阳性效果被确定为空气温度。最后,将主成分分析(PCA)应用于数据,检测到4个不同的主成分。在相同的组分基团中聚集测量PM2.5和PM10,空气温度和相对湿度。

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