In this work, receptor models were used to identify the PM2.5 sources and its contribution to the air quality in residential, comercial and industrial sampling sites in the Metropolitan Area of Costa Rica. Principal c...In this work, receptor models were used to identify the PM2.5 sources and its contribution to the air quality in residential, comercial and industrial sampling sites in the Metropolitan Area of Costa Rica. Principal component analysis with absolute principal component scores (PCA-APCS), UNIMX and positive matrix factorization (PMF) was applied to analyze the data collected during 1 year of sampling campaign (2010-2011). The PM2.5 samples were characterized through its composition looking for trace elements, inorganic ions and organic and elemental carbon. These three models identified some common sources of PM2.5: marine aerosol, crustal material, traffic, secondary aerosols (secondary sulfate and secondary nitrate resolved by PMF), a mixed source of heavy fuels combustion and biomass burning, and industrial emissions. The three models predicted that the major sources of PM2.5 in the Metropolitan Area of Costa Rica were related to anthropogenic sources (73%, 65% and 69%, respectively, for PCA-APCS, Unmix and PMF) although natural sources also contributed to PM2.5 (21%, 24% and 26%). On average, PCA and PMF methods resolved 94% and 95% of the PM2.5 mass concentrations, respectively. The results were comparable to the estimate using UNMIX.展开更多
The source apportionment of PM2.5 is essential for pollution prevention.In view of the weaknesses of individual models,we proposed an integrated chemical mass balancesource emission inventory(CMB-SEI)model to acquire ...The source apportionment of PM2.5 is essential for pollution prevention.In view of the weaknesses of individual models,we proposed an integrated chemical mass balancesource emission inventory(CMB-SEI)model to acquire more accurate results.First,the SEI of secondary component precursors(SO2,NOx,NH3,and VOCs)was compiled to acquire the emission ratios of these sources for the precursors.Then,a regular CMB simulation was executed to obtain the contributions of primary particle sources and secondary components(SO4^2-,NO3^-3,NH4^+,and SOC).Afterwards,the contributions of secondary components were apportioned into primary sources according to the source emission ratios.The final source apportionment results combined the contributions of primary sources by CMB and SEI.This integrated approach was carried out via a case study of three coastal cities(Zhoushan,Taizhou,and Wenzhou;abbreviated WZ,TZ,and ZS)in Zhejiang Province,China.The regular CMB simulation results showed that PM2.5 pollution was mainly affected by secondary components and mobile sources.The SEI results indicated that electricity,industrial production and mobile sources were the largest contributors to the emission of PM2.5 gaseous precursors.The simulation results of the CMB-SEI model showed that PM2.5 pollution in the coastal areas of Zhejiang Province presented complex pollution characteristics dominated by mobile sources,electricity production sources and industrial production sources.Compared to the results of the CMB and SEI models alone,the CMB-SEI model completely apportioned PM2.5 to primary sources and simultaneously made the results more accurate and reliable in accordance with local industrial characteristics.展开更多
文摘In this work, receptor models were used to identify the PM2.5 sources and its contribution to the air quality in residential, comercial and industrial sampling sites in the Metropolitan Area of Costa Rica. Principal component analysis with absolute principal component scores (PCA-APCS), UNIMX and positive matrix factorization (PMF) was applied to analyze the data collected during 1 year of sampling campaign (2010-2011). The PM2.5 samples were characterized through its composition looking for trace elements, inorganic ions and organic and elemental carbon. These three models identified some common sources of PM2.5: marine aerosol, crustal material, traffic, secondary aerosols (secondary sulfate and secondary nitrate resolved by PMF), a mixed source of heavy fuels combustion and biomass burning, and industrial emissions. The three models predicted that the major sources of PM2.5 in the Metropolitan Area of Costa Rica were related to anthropogenic sources (73%, 65% and 69%, respectively, for PCA-APCS, Unmix and PMF) although natural sources also contributed to PM2.5 (21%, 24% and 26%). On average, PCA and PMF methods resolved 94% and 95% of the PM2.5 mass concentrations, respectively. The results were comparable to the estimate using UNMIX.
基金supported by the National Key Research and Development Program of China(No.2018YFC0214102)。
文摘The source apportionment of PM2.5 is essential for pollution prevention.In view of the weaknesses of individual models,we proposed an integrated chemical mass balancesource emission inventory(CMB-SEI)model to acquire more accurate results.First,the SEI of secondary component precursors(SO2,NOx,NH3,and VOCs)was compiled to acquire the emission ratios of these sources for the precursors.Then,a regular CMB simulation was executed to obtain the contributions of primary particle sources and secondary components(SO4^2-,NO3^-3,NH4^+,and SOC).Afterwards,the contributions of secondary components were apportioned into primary sources according to the source emission ratios.The final source apportionment results combined the contributions of primary sources by CMB and SEI.This integrated approach was carried out via a case study of three coastal cities(Zhoushan,Taizhou,and Wenzhou;abbreviated WZ,TZ,and ZS)in Zhejiang Province,China.The regular CMB simulation results showed that PM2.5 pollution was mainly affected by secondary components and mobile sources.The SEI results indicated that electricity,industrial production and mobile sources were the largest contributors to the emission of PM2.5 gaseous precursors.The simulation results of the CMB-SEI model showed that PM2.5 pollution in the coastal areas of Zhejiang Province presented complex pollution characteristics dominated by mobile sources,electricity production sources and industrial production sources.Compared to the results of the CMB and SEI models alone,the CMB-SEI model completely apportioned PM2.5 to primary sources and simultaneously made the results more accurate and reliable in accordance with local industrial characteristics.
