Source apportionment of particulate matter (PM₁₀) measurements taken in Delhi, India between January 2013 and June 2014 was carried out using two receptor models, principal component analysis with absolute principal component scores (PCA/APCS) and UNMIX. The results were compared with previous estimates generated using the positive matrix factorization (PMF) receptor model to investigate each model’s source-apportioning capability. All models used the PM₁₀ chemical composition (organic carbon (OC), elemental carbon (EC), water soluble inorganic ions (WSIC), and trace elements) for source apportionment. The average PM₁₀ concentration during the study period was 249.7 ± 103.9 μg/m³ (range: 61.4–584.8 μg/m³). The UNMIX model resolved five sources (soil dust (SD), vehicular emissions (VE), secondary aerosols (SA), a mixed source of biomass burning (BB) and sea salt (SS), and industrial emissions (IE)). The PCA/APCS model also resolved five sources, two of which also included mixed sources (SD, VE, SD+SS, (SA+BB+SS) and IE). The PMF analysis differentiated seven individual sources (SD, VE, SA, BB, SS, IE, and fossil fuel combustion (FFC)). All models identified the main sources contributing to PM₁₀ emissions and reconfirmed that VE, SA, BB, and SD were the dominant contributors in Delhi.

2013年1月至2014年6月在印度德里进行的颗粒物（PM ₁₀）物源分摊是使用两种受体模型进行的，即具有绝对主成分评分（PCA / APCS）的主成分分析和UNMIX。将结果与使用正矩阵分解（PMF）受体模型生成的先前估计值进行比较，以研究每个模型的源分配能力。所有模型都使用PM _10的化学成分（有机碳（OC），元素碳（EC），水溶性无机离子（WSIC）和微量元素）进行源分配。在研究期间，PM ₁₀的平均浓度为249.7± 103.9μg / m ³（范围：61.4–584.8μg/ m ³）。UNMIX模型解析了五个来源（土壤粉尘（SD），车辆排放物（VE），二次气溶胶（SA），生物质燃烧（BB）和海盐（SS）的混合源以及工业排放物（IE））。PCA / APCS模型还解析了五个来源，其中两个还包括混合来源（SD，VE，SD + SS，（SA + BB + SS）和IE）。PMF分析区分了七个单独的来源（SD，VE，SA，BB，SS，IE和化石燃料燃烧（FFC））。所有模型都确定了造成PM ₁₀排放的主要来源，并再次确认VE，SA，BB和SD是德里的主要贡献者。