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...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 1E）. 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.展开更多
Principal component analysis/absolute principal component scores (PCA/APCS) and positive matrix factorization (PMF2), an advanced factor analysis technique were employed to apportion the sources influencing the PM2.5 ...Principal component analysis/absolute principal component scores (PCA/APCS) and positive matrix factorization (PMF2), an advanced factor analysis technique were employed to apportion the sources influencing the PM2.5 levels measured during 2003 through 2005 at a rural coastal site located within the Corpus Christi urban airshed in South Texas. PCA/APCS identified five sources while PMF2 apportioned an optimal solution of eight sources. Both PCA/APCS and PMF2 quantified secondary sulfates to be the major contributor accounting for 47% and 45% of the apportioned PM2.5 levels. The other common sources apportioned by the models included crustal dust, fresh sea salt and traffic emissions. PMF2 successfully apportioned distinct sources of fresh and aged sea salt along with biomass burns while PCA/APCS was unsuccessful in identifying aged sea salt and biomass burns;however it successfully identified secondary organic aerosols from photochemical oxidations and also emitted by petrochemical refineries. The influence of long range transport was noted for sources such as secondary sulfates, biomass burns and crustal dust affecting the region. Continued collection of speciation data at the rural and urban sites will enhance the understanding of local versus regional source contributions for air quality policy makers and stakeholders.展开更多
文摘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 1E）. 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.
文摘Principal component analysis/absolute principal component scores (PCA/APCS) and positive matrix factorization (PMF2), an advanced factor analysis technique were employed to apportion the sources influencing the PM2.5 levels measured during 2003 through 2005 at a rural coastal site located within the Corpus Christi urban airshed in South Texas. PCA/APCS identified five sources while PMF2 apportioned an optimal solution of eight sources. Both PCA/APCS and PMF2 quantified secondary sulfates to be the major contributor accounting for 47% and 45% of the apportioned PM2.5 levels. The other common sources apportioned by the models included crustal dust, fresh sea salt and traffic emissions. PMF2 successfully apportioned distinct sources of fresh and aged sea salt along with biomass burns while PCA/APCS was unsuccessful in identifying aged sea salt and biomass burns;however it successfully identified secondary organic aerosols from photochemical oxidations and also emitted by petrochemical refineries. The influence of long range transport was noted for sources such as secondary sulfates, biomass burns and crustal dust affecting the region. Continued collection of speciation data at the rural and urban sites will enhance the understanding of local versus regional source contributions for air quality policy makers and stakeholders.
