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Chemical characteristics and source apportionment of PM2.5 using PCA/APCS, UNMIX, and PMF at an urban site of Delhi, India

机译:PM2.5使用PCA / APCS,UNICIX和PMF在印度城市地点的PM2.5的化学特征和源分配

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The present study investigated the comprehensive chemical composition [organic carbon (OC), elemental carbon (EC), water-soluble inorganic ionic components (WSICs), and major & trace elements] of particulate matter (PM2.5) and scrutinized their emission sources for urban region of Delhi. The 135 PM2.5 samples were collected from January 2013 to December 2014 and analyzed for chemical constituents for source apportionment study. The average concentration of PM2.5 was recorded as 121.9 +/- 93.2 mu g m(-3) (range 25.1-429.8 mu g m(-3)), whereas the total concentration of trace elements (Na, Ca, Mg, Al, S, Cl, K, Cr, Si, Ti, As, Br, Pb, Fe, Zn, and Mn) was accounted for similar to 17% of PM2.5. Strong seasonal variation was observed in PM2.5 mass concentration and its chemical composition with maxima during winter and minima during monsoon seasons. The chemical composition of the PM2.5 was reconstructed using IMPROVE equation, which was observed to be in good agreement with the gravimetric mass. Source apportionment of PM2.5 was carried out using the following three different receptor models: principal component analysis with absolute principal component scores (PCA/APCS), which identified five major sources; UNMIX which identified four major sources; and positive matrix factorization (PMF), which explored seven major sources. The applied models were able to identify the major sources contributing to the PM2.5 and re-confirmed that secondary aerosols (SAs), soil/road dust (SD), vehicular emissions (VEs), biomass burning (BB), fossil fuel combustion (FFC), and industrial emission (IE) were dominant contributors to PM2.5 in Delhi. The influences of local and regional sources were also explored using 5-day backward air mass trajectory analysis, cluster analysis, and potential source contribution function (PSCF). Cluster and PSCF results indicated that local as well as long-transported PM2.5 from the north-west India and Pakistan were mostly pertinent.
机译:本实验研究的全面的化学组成[有机碳(OC),元素碳(EC),水溶性无机离子成分(WSICs),和主要及微量元素]的颗粒物质(PM2.5)和审查它们的发射源德里的城市区域。 135个PM2.5样品收集从2013年1月2014年12月并为源解析研究化学成分进行分析。 PM2.5的平均浓度记录为121.9 +/- 93.2亩克(-3)(范围25.1-429.8亩克(-3)),而痕量元素的总浓度(钠,钙,镁,铝,硫,氯,K,铬,硅,钛,如,溴,铅,铁,锌,和Mn)为占类似于PM2.5的17%。在季风季节冬季和最小值期间PM2.5质量浓度及其与最大值的化学成分中观察到强烈的季节性变化。使用IMPROVE方程,这是观察到在与重力质量好的协议PM2.5的化学组成被重建。 PM2.5的源解析进行了使用以下三种不同的受体模型:用绝对主成分得分(PCA / APCS),它确定了五个主要来源主成分分析; UNMIX其中确定了四个主要来源;和正矩阵分解(PMF),其中探讨七个主要来源。适用产品型号能够找出导致的PM2.5和主要来源重新确认二次气溶胶(SAS),土壤/道路扬尘(SD),车辆排放(VES),生物质燃烧(BB),化石燃料燃烧(FFC),和工业排放(IE)占主导地位的贡献者德里PM2.5。的本地和区域源的影响还探讨利用5天的向后气团轨迹分析,聚类分析,和潜在的源贡献函数(PSCF)。集群和PSCF结果表明,当地从西北印度和巴基斯坦以及长运PM2.5大多是恰当的。

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