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Regression analysis of the effect of meteorological parameters on air quality in three neighboring cities located on the Mediterranean coast of Turkey

机译:气象参数对土耳其地中海海岸三个邻城空气质量影响的回归分析

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For the past couple of decades, the increasing health problems caused by air pollution have caught more and more attention. As a result, analysis and prediction of the air quality were widely studied. Several methodologies, both deterministic and statistical, have been proposed. In this study, we use the linear model to detect the relationship between the concentration of an air pollutant (PM10, i.e. particulate matter with an aerodynamic diameter of less than 10 μm) at a specific site and meteorological variables in three neighboring cities, namely Antalya, Isparta and Burdur, located on the Mediterranean region of Turkey. Daily average PM10 concentration values for the years 2014 and 2015 were used as the dependent variable. The PM10 concentrations in each city is measured at air quality stations operated by Ministry of Civilization and Environment. The PM10 concentrations were tried to be explained by using different meteorological parameters as independent variables. The meteorological parameters used in this study were hours of sunshine, wind speed, difference between maximum and minimum temperature, average daily temperature, actual pressure, precipitation and relative humidity. The results showed that in all three cities, increase in hours of sun shine and in wind speed, decreases the PM10 concentration in the ambient atmosphere. Furthermore, increase in pressure and in difference between maximum and minimum temperature, increases the PM10 levels. The R values for the cities varies from 0.62 and 0.68, which were statistically significant in 95% confidence level.
机译:过去几十年来,空气污染造成的健康问题越来越多地关注。结果,广泛研究了空气质量的分析和预测。已经提出了几种方法,包括确定性和统计数据。在这项研究中,我们使用线性模型在特定位点和三个邻城区的特定部位和气象变量下检测空气污染物(PM10,即空气动力直径小于10μm的颗粒物质的颗粒物质)之间的关系。 ,Isparta和Burdur,位于土耳其地中海地区。 2014年和2015年的每日平均PM10浓度值被用作从属变量。每个城市的PM10浓度在文明和环境部运营的空气质量站点测量。尝试通过使用不同的气象参数作为独立变量来解释PM10浓度。本研究中使用的气象参数是阳光,风速,最大和最小温度之间的差异,平均每日温度,实际压力,沉淀和相对湿度之间的差异。结果表明,在所有三个城市中,阳光亮起和风速的数小时,降低了环境大气中的PM10浓度。此外,压力和最大温度之间的压力和差异增加,增加了PM10水平。城市的R值不同于0.62和0.68,在95%的置信水平中具有统计学意义。

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