Long-term and synchronous monitoring of PMIo and PM2.s was conducted in Chengdu in China from 2007 to 2013. The levels, variations, compositions and size distributions were investigated. The sources were quantified by...Long-term and synchronous monitoring of PMIo and PM2.s was conducted in Chengdu in China from 2007 to 2013. The levels, variations, compositions and size distributions were investigated. The sources were quantified by two-way and three-way receptor models （PMF2, ME2-2way and ME2-3way）, Consistent results were found： the primary source categories contributed 63.4% （PMF2）, 64.8% （ME2-2way） and 66.8% （ME2-Bway） to PMIo, and contributed 60.9% （PMF2）, 65.5% （ME2-2way） and 61.0% （ME2-3way） to PM2.s. Secondary sources contributed 31.8% （PMF2）, 32.9% （ME2-2way） and 31.7% （ME2-3way） to PMIo, and 35.0% （PMF2）, 33.8% （ME2-2way） and 36.0% （ME2-3way） to PM2.s. The size distribution of source categories was estimated better by the ME2-3way method. The three-way model can simultaneously consider chemical species, temporal variability and PM sizes, while a two-way model independently computes datasets of different sizes. A method called source directional apportionment （SDA） was employed to quantify the contributions from various directions for each source category. Crustal dust from east-north-east （ENE） contributed the highest to both PM^o （12.7%） and PMzs （9.7%） in Chengdu, followed by the crustal dust from south-east （SE） for PMao （9.8%） and secondary nitrate ＆ secondary organic carbon from ENE for PMzs （9.6%）. Source contributions from different directions are associated with meteorological conditions, source locations and emission patterns during the sampling period. These findings and methods provide useful tools to better understand PM pollution status and tn dovolon offoctive nolhltion control gtrateMeg.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
基金supported by the Tianjin Natural Science Foundation(No.16JCQNJC08700)the Fundamental Research Funds for the Central Universities+4 种基金National Key Research and Development Program of China(No.2016YFC0208500)the National Natural Science Foundation of China(No.21407174)the Tianjin Research Program of Application Foundation(No.14JCQNJC08100)the Tianjin Science and Technology Project(Nos.16YFZCSF00260,14ZCDGSF00027,14ZCDGSF00029)the Special Funds for Research on Public Welfares of the Ministry of Environmental Protection of China(201309072)
文摘Long-term and synchronous monitoring of PMIo and PM2.s was conducted in Chengdu in China from 2007 to 2013. The levels, variations, compositions and size distributions were investigated. The sources were quantified by two-way and three-way receptor models （PMF2, ME2-2way and ME2-3way）, Consistent results were found： the primary source categories contributed 63.4% （PMF2）, 64.8% （ME2-2way） and 66.8% （ME2-Bway） to PMIo, and contributed 60.9% （PMF2）, 65.5% （ME2-2way） and 61.0% （ME2-3way） to PM2.s. Secondary sources contributed 31.8% （PMF2）, 32.9% （ME2-2way） and 31.7% （ME2-3way） to PMIo, and 35.0% （PMF2）, 33.8% （ME2-2way） and 36.0% （ME2-3way） to PM2.s. The size distribution of source categories was estimated better by the ME2-3way method. The three-way model can simultaneously consider chemical species, temporal variability and PM sizes, while a two-way model independently computes datasets of different sizes. A method called source directional apportionment （SDA） was employed to quantify the contributions from various directions for each source category. Crustal dust from east-north-east （ENE） contributed the highest to both PM^o （12.7%） and PMzs （9.7%） in Chengdu, followed by the crustal dust from south-east （SE） for PMao （9.8%） and secondary nitrate ＆ secondary organic carbon from ENE for PMzs （9.6%）. Source contributions from different directions are associated with meteorological conditions, source locations and emission patterns during the sampling period. These findings and methods provide useful tools to better understand PM pollution status and tn dovolon offoctive nolhltion control gtrateMeg.
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.
