Investigating the seasonal variability in source contribution to PM2.5 and PM10 using different receptor models during 2013–2016 in Delhi, India
2021
Jain, Srishti | Sharma, Sudhir Kumar | Vijayan, Narayanswami | Mandal, Tuhin Kumar
The present work deals with the seasonal variations in the contribution of sources to PM₂.₅ and PM₁₀ in Delhi, India. Samples of PM₂.₅ and PM₁₀ were collected from January 2013 to December 2016 at an urban site of Delhi, India, and analyzed to evaluate their chemical components [organic carbon (OC), elemental carbon (EC), water-soluble inorganic components (WSICs), and major and trace elements]. The average concentrations of PM₂.₅ and PM₁₀ were 131 ± 79 μg m⁻³ and 238 ± 106 μg m⁻³, respectively during the entire sampling period. The analyzed and seasonally segregated data sets of both PM₂.₅ and PM₁₀ were used as input in the three different receptor models, i.e., principal component analysis-absolute principal component score (PCA-APCS), UNMIX, and positive matrix factorization (PMF), to achieve conjointly corroborated results. The present study deals with the implementation and comparison of results of three different multivariate receptor models (PCA-APCS, UNMIX, and PMF) on the same data sets that allowed a better understanding of the probable sources of PM₂.₅ and PM₁₀ as well as the comportment of these sources with respect to different seasons. PCA-APCS, UNMIX, and PMF extracted similar sources but in different contributions to PM₂.₅ and PM₁₀. All the three models extracted 7 similar sources while mutually confirmed the 4 major sources over Delhi, i.e., secondary aerosols, vehicular emissions, biomass burning, and soil dust, although the contribution of these sources varies seasonally. PCA-APCS and UNMIX analysis identified a less number of sources (besides mixed type) as compared to the PMF, which may cause erroneous interpretation of seasonal implications on source contribution to the PM mass concentration.
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