Chemical characterization and source apportionment of aerosol over mid Brahmaputra Valley, India

Chemical characterization and source apportionment of aerosol over mid Brahmaputra Valley, India Aerosol samples (as PM10, n = 250) were collected from three rural/remote receptor locations in the mid Brahmaputra plain region and were chemically characterized for metals (Al, Fe, Co, Cu, Cr, Cd, Mn, Ni, Pb), ions (Ca2+, Mg2+, Na+, K+, NH4+, F−, Cl−, NO3−, SO42−), and carbon. Vital ratios like NO3−/SO42−, EC/OC, K+/EC, K+/OC, enrichment factors and inter-species correlations were exploited to appreciate possible sources of aerosol. These empirical analyses pointed towards anthropogenic contributions of aerosol, particularly from biomass burning, vehicular emission, and road dust. The chemically characterized concentration data were subsequently fed into two receptor models viz. Principal Component Analysis-Multiple Linear Regression (PCA-MLR) and Chemical Mass Balance (CMB) for apportionment of sources of aerosol. The PCA-MLR estimates identified that the combustion sources together accounted for ∼42% of aerosol and the contribution of secondary formation to be 24%. Road and crustal dusts have been well apportioned by PCA-MLR, which together accounts for ∼26% of the aerosol. The CMB model estimates explained that the combustion sources taken together contributed ∼47% to the aerosol, which includes biomass burning (27%), vehicular emission (13%), coal (1%), kerosene (4%), and petroleum refining (2%). Other major sources that were apportioned were road dust (15%), crustal dust (26%), and construction dust (6%). There are inherent limitations in the source strength estimations because of uncertainty present in the source emission profiles that have been applied to the remote location of India. However, both the models (PCA-MLR and CMB) estimated the contribution of combustion sources to 42 and 47% respectively, which is comparable. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Environmental Pollution Elsevier

Chemical characterization and source apportionment of aerosol over mid Brahmaputra Valley, India

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Publisher
Elsevier
Copyright
Copyright © 2017 Elsevier Ltd
ISSN
0269-7491
D.O.I.
10.1016/j.envpol.2017.12.009
Publisher site
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Abstract

Aerosol samples (as PM10, n = 250) were collected from three rural/remote receptor locations in the mid Brahmaputra plain region and were chemically characterized for metals (Al, Fe, Co, Cu, Cr, Cd, Mn, Ni, Pb), ions (Ca2+, Mg2+, Na+, K+, NH4+, F−, Cl−, NO3−, SO42−), and carbon. Vital ratios like NO3−/SO42−, EC/OC, K+/EC, K+/OC, enrichment factors and inter-species correlations were exploited to appreciate possible sources of aerosol. These empirical analyses pointed towards anthropogenic contributions of aerosol, particularly from biomass burning, vehicular emission, and road dust. The chemically characterized concentration data were subsequently fed into two receptor models viz. Principal Component Analysis-Multiple Linear Regression (PCA-MLR) and Chemical Mass Balance (CMB) for apportionment of sources of aerosol. The PCA-MLR estimates identified that the combustion sources together accounted for ∼42% of aerosol and the contribution of secondary formation to be 24%. Road and crustal dusts have been well apportioned by PCA-MLR, which together accounts for ∼26% of the aerosol. The CMB model estimates explained that the combustion sources taken together contributed ∼47% to the aerosol, which includes biomass burning (27%), vehicular emission (13%), coal (1%), kerosene (4%), and petroleum refining (2%). Other major sources that were apportioned were road dust (15%), crustal dust (26%), and construction dust (6%). There are inherent limitations in the source strength estimations because of uncertainty present in the source emission profiles that have been applied to the remote location of India. However, both the models (PCA-MLR and CMB) estimated the contribution of combustion sources to 42 and 47% respectively, which is comparable.

Journal

Environmental PollutionElsevier

Published: Mar 1, 2018

References

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