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Enhanced source identification of southeast aerosols using temperature-resolved carbon fractions and gas phase components

机译:利用温度分辨碳馏分和气相组分增强东南气溶胶的来源识别

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Four gas components (CO, SO_2, HNO_3 and NO_y) and PM_(2.5) (particulate matter ≤ 2.5 μm in aerodynamic diameter) composition data including eight individual carbon fractions collected at four sites in Georgia and Alabama were analyzed with the positive matrix factorization (PMF) method. Multiple linear regression (MLR) was applied to regress the total PM mass against the estimated source contributions. The regression coefficients were used to scale the factor profiles. Nine factors were resolved at two urban sites (Atlanta, GA (JST) and Birmingham, AL (BHM)) and one rural site (Centerville, AL (CTR)). Eight factors were resolved at the other rural site (Yorkville, GA (YRK)). Six factors we refer to as soil, coal combustion/other, diesel emission, secondary sulfate, secondary nitrate, and wood smoke are common among the four sites. Two industry-related factors are similar at the two sites in the same state, but differ between states. Contrary to previous results using only PM_(2.5) data with non-speciated EC and OC data, diesel and gasoline emission factors were resolved at the two urban sites instead of only one single motor vehicle factor; diesel and gasoline factors were also resolved at the CTR site and a diesel factor was found at YRK instead of no motor vehicle factors at the two rural sites. The inclusion of gas components also improved the identification of the coal combustion/other factor among the four sites. This study shows that inclusion of gas phase data and temperature-resolved fractional carbon data can enhance the resolving power of source apportionment studies, especially for the factors we refer to as gas, diesel, and coal combustion/other.
机译:使用正矩阵因子分解分析了四个气体成分(CO,SO_2,HNO_3和NO_y)和PM_(2.5)(空气动力学直径≤2.5μm的颗粒物)组成数据,包括在佐治亚州和阿拉巴马州四个地点收集的八个单独的碳馏分( PMF)方法。应用多元线性回归(MLR)将总PM质量与估计的源贡献进行回归。回归系数用于定标因子分布。在两个城市地点(佐治亚州亚特兰大(JST)和阿拉巴马州伯明翰(BHM))和一个农村地点(Centerville,AL(CTR))解决了九个因素。在其他农村地区(乔治亚州约克维尔(YRK))解决了八个因素。在这四个地点中,我们称为土壤,煤燃烧/其他,柴油排放,二次硫酸盐,二次硝酸盐和木材烟雾的六个因素很普遍。在同一州的两个地点,两个与行业相关的因素相似,但是州之间有所不同。与仅使用PM_(2.5)数据和未指定EC和OC数据的先前结果相反,在两个城市地点解决了柴油和汽油排放因子,而不是一个机动车因子。在CTR站点上也解决了柴油和汽油因素,在YRK上发现了柴油因素,而不是在两个农村站点发现了机动车因素。气体成分的加入还改善了四个地点中煤燃烧/其他因素的识别。这项研究表明,将气相数据和温度分辨的分数碳数据包括在内可以增强源分配研究的分辨能力,尤其是对于我们称为天然气,柴油和煤炭燃烧/其他的因素而言。

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