2018•Climate Change Research LettersRequires access

Changes of PM2.5/PM10 Concentration and Its Influencing Factors

晓建 纪

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Abstract

本文根据哈密基准气候站2015年环境监测数据和自动站气象数据,采用M-K突变检测、线性倾向估计、皮尔逊(pearson)相关系数计算及相关性t检验等方法,分析了PM2.5/PM10的浓度变化规律及其影响因子。研究表明,PM2.5/PM10浓度1月中旬上升趋势最为显著,达到了P < 0.001显著性检验水平。从季节变化来看,PM2.5浓度峰值出现在冬季1月份,月均浓度值为105.1 ug/m3,PM10浓度峰值出现在春季4月份,月均浓度值为261.5 ug/m3。其日变化特征浓度高值则出现在正午14:00左右。从变化趋势可知,PM2.5/PM10浓度高低和大气能见度、降水量和风速有关。PM浓度高低和能见度好坏、降水量大小呈相反趋势,与风速大小趋势相一致并通过了不同程度的相关性检验。 According to the Hami climate stations 2015 environmental monitoring data and meteorological data of automatic station using M-K mutation detection, linear trend estimation, Pearson (Pear-son) calculated the correlation coefficient and correlation between t test and other methods, and analyzed the concentration changes of PM2.5/PM10 and its influencing factors. The results showed that the trend of PM2.5/PM10 concentration in the middle of January was the most significant, reaching the significant test level of P < 0.001. From the seasonal changes, the peak concentration of PM2.5 appeared in winter in January; the monthly average concentration was 105.1 ug/m3; the peak value of PM10 concentration appeared in spring in April; and the monthly average concentration was 261.5 ug/m3. Its diurnal variation and high concentration appeared at about 14:00 noon. From the changing trend, the concentration of PM2.5/PM10 is related to atmospheric visibility, precipitation and wind speed. The concentration of PM is in the opposite trend with the visibility and the amount of precipitation, which is consistent with the trend of wind speed and the correlation test of different degrees.

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What this paper is about

本文根据哈密基准气候站2015年环境监测数据和自动站气象数据,采用M-K突变检测、线性倾向估计、皮尔逊(pearson)相关系数计算及相关性t检验等方法,分析了PM2.5/PM10的浓度变化规律及其影响因子。研究表明,PM2.5/PM10浓度1月中旬上升趋势最为显著,达到了P < 0.001显著性检验水平。从季节变化来看,PM2.5浓度峰值出现在冬季1月份,月均浓度值为105.1 ug/m3,PM10浓度峰值出现在春季4月份,月均浓度值为261.5 ug/m3。其日变化特征浓度高值则出现在正午14:00左右。从变化趋势可知,PM2.5/PM10浓度高低和大气能见度、降水量和风速有关。PM浓度高低和能见度好坏、降水量大小呈相反趋势,与风速大小趋势相一致并通过了不同程度的相关性检验。 According to the Hami climate stations 2015 environmental monitoring data and meteorological data of automatic station using M-K mutation detection, linear trend estimation, Pearson (Pear-son) calculated the correlation coefficient and correlation between t test and other methods, and analyzed the concentration changes of PM2.5/PM10 and its influencing factors. The results showed that the trend of PM2.5/PM10 concentration in the middle of January was the most significant, reaching the significant test level of P < 0.001. From the seasonal changes, the peak concentration of PM2.5 appeared in winter in January; the monthly average concentration was 105.1 ug/m3; the peak value of PM10 concentration appeared in spring in April; and the monthly average concentration was 261.5 ug/m3. Its diurnal variation and high concentration appeared at about 14:00 noon. From the changing trend, the concentration of PM2.5/PM10 is related to atmospheric visibility, precipitation and wind speed. The concentration of PM is in the opposite trend with the visibility and the amount of precipitation, which is consistent with the trend of wind speed and the correlation test of different degrees.

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Available abstract

本文根据哈密基准气候站2015年环境监测数据和自动站气象数据,采用M-K突变检测、线性倾向估计、皮尔逊(pearson)相关系数计算及相关性t检验等方法,分析了PM2.5/PM10的浓度变化规律及其影响因子。研究表明,PM2.5/PM10浓度1月中旬上升趋势最为显著,达到了P < 0.001显著性检验水平。从季节变化来看,PM2.5浓度峰值出现在冬季1月份,月均浓度值为105.1 ug/m3,PM10浓度峰值出现在春季4月份,月均浓度值为261.5 ug/m3。其日变化特征浓度高值则出现在正午14:00左右。从变化趋势可知,PM2.5/PM10浓度高低和大气能见度、降水量和风速有关。PM浓度高低和能见度好坏、降水量大小呈相反趋势,与风速大小趋势相一致并通过了不同程度的相关性检验。 According to the Hami climate stations 2015 environmental monitoring data and meteorological data of automatic station using M-K mutation detection, linear trend estimation, Pearson (Pear-son) calculated the correlation coefficient and correlation between t test and other methods, and analyzed the concentration changes of PM2.5/PM10 and its influencing factors. The results showed that the trend of PM2.5/PM10 concentration in the middle of January was the most significant, reaching the significant test level of P < 0.001. From the seasonal changes, the peak concentration of PM2.5 appeared in winter in January; the monthly average concentration was 105.1 ug/m3; the peak value of PM10 concentration appeared in spring in April; and the monthly average concentration was 261.5 ug/m3. Its diurnal variation and high concentration appeared at about 14:00 noon. From the changing trend, the concentration of PM2.5/PM10 is related to atmospheric visibility, precipitation and wind speed. The concentration of PM is in the opposite trend with the visibility and the amount of precipitation, which is consistent with the trend of wind speed and the correlation test of different degrees.

Key concepts: Environmental science, Chemistry, Animal science, Biology

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