Source apportionment of particulates by receptor models over Bay of Bengal during ICARB campaign

Developmental Biology

Sachchidanand Singh¹  Trailokya Saud²  Sudhir Kumar Sharma¹  Tuhin Kumar Mandal¹  Mohit Saxena¹ 

[1] CSIR–National Physical Laboratory, Dr. K S Krishnan Road, New Delhi–110 012, India$$;CSIR–National Physical Laboratory, Dr. K S Krishnan Road, New Delhi–110 012, India$$Indian Institutes of Technology, Delhi–110016, India$$

关键词: Source apportionment;    positive matrix factorization;    principal component analysis;    particulate matter;    Bay of Bengal;   

DOI  :  10.5094/APR.2014.082

学科分类：农业科学（综合）

来源: Dokuz Eylul Universitesi * Department of Environmental Engineering

【 摘 要 】

Source apportionment study of aerosols over Bay of Bengal (BOB) were investigated during Integrated Campaign on Aerosol Radiation Budget (ICARB) in the pre–monsoon (March–April 2006) and winter (December–January 2008–09) seasons. Positive matrix factorization (PMF) was applied to identify sources of ambient particulate matter using daily chemical composition data collected in the pre–monsoon (total suspended particles, TSP) and winter season (particles with a diameter < 10 μm, PM₁₀). Sea salt (SS), secondary aerosol (SA), Si–dust, fossil fuel combustion (FFC), biomass burning (BB) sources have been identified in both seasons, however their relative contributions were different. The combined contribution of Si–dust, secondary aerosol and fossil fuel combustion, constitute ~67% of particulate matter in pre–monsoon, whereas, secondary aerosols and biomass burning were the major contributors (63.2%) to particulate matter in winter. The identified sources effectively predict the measured particulate concentration in the pre–monsoon (r² = 0.74) and winter season (r² = 0.82). Another receptor model, principal component analysis (PCA) was done to increase the plausibility of the results obtained by PMF. PCA resulted in the identification of the sources that were comparable to the PMF outputs. PCA of TSP in the pre–monsoon season resulted in the extraction of three components (crustal dust + secondary aerosol, biomass burning, fossil fuel combustion + industrial emissions) that explained the 83% of the variance in the data. Similarly, in winter season, PCA resulted in the extraction of four components (biomass burning + secondary aerosol, industrial emission, crustal dust, sea salt) that explained the 86% of the variance of the data.

【 授权许可】

Unknown   

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