During 2005, the filter samples of ambient PM10 from five sites and the source samples of particulate matter were collected in Kaifeng, Henan Province of China. Nineteen elements, water-soluble ions, total carbon （TC...During 2005, the filter samples of ambient PM10 from five sites and the source samples of particulate matter were collected in Kaifeng, Henan Province of China. Nineteen elements, water-soluble ions, total carbon （TC） and organic carbon （OC） contained in samples were analyzed. Seven contributive source types were identified and their contributions to ambient PM10 were estimated by chemical mass balance （CMB） receptor model. Weak associations between the concentrations of organic carbon and element carbon （EC） were observed during the sampling periods, indicating that there was secondary organic aerosol pollution in the urban atmosphere. An indirect method of ＂OC/EC minimum ratio＂ was applied to estimate the concentration of secondary organic carbon （SOC）. The results showed that SOC contributed 26.2%, 32.4% and 18.0% of TC in spring, summer-fall and winter, respectively, and the annual average SOC concentration was 7.07 μg/m^3, accounting for 5.73% of the total mass in ambient PM10. The carbon species concentrations in ambient PM10 were recalculated by subtracting SOC concentrations from measured concentrations of TC and OC to increase the compatibility of source and receptor measurements for CMB model.展开更多
Source apportionment study of PM10 （Particulate Matter） in a critically polluted area of Jhafia coalfield, India has been carried out using Dispersion model, Principle Component Analysis （PCA） and Chemical Mass Ba...Source apportionment study of PM10 （Particulate Matter） in a critically polluted area of Jhafia coalfield, India has been carried out using Dispersion model, Principle Component Analysis （PCA） and Chemical Mass Balance （CMB） techniques. Dispersion model Atmospheric Dispersion Model （AERMOD） was introduced to simplify the complexity of sources in Jharia coalfield. PCA and CMB analysis indicates that monitoring stations near the mining area were mainly affected by the emission from open coal mining and its associated activities such as coal transportation, loading and unloading of coal. Mine fire emission also contributed a considerable amount of particulate matters in monitoring stations. Locations in the city area were mostly affected by vehicular, Liquid Petroleum Gas （LPG） ＆ Diesel Generator （DG） set emissions, residential, and commercial activities. The experimental data sampling and their analysis could aid understanding how dispersion based model technique along with receptor model based concept can be strategically used for quantitative analysis of Natural and Anthropogenic sources of PM10.展开更多
This study investigates the correlation between PM10 and meteorological factors such as wind speed, atmospheric visibility, dew point, relative humidity, and ambient temperature during a brown haze episode. In order t...This study investigates the correlation between PM10 and meteorological factors such as wind speed, atmospheric visibility, dew point, relative humidity, and ambient temperature during a brown haze episode. In order to identify the potential sources of PMlo during brown haze episode, respirable par- ticulate matter （PM10） was collected during both non-haze days and haze days and further analyzed for metallic elements, ionic species, and carbonaceous contents. Among them, ionic species contributed 45-64% to PM10, while metallic elements contributed 7-21% to PM10 which was smaller than the other chemical constituents. The average OC/EC ratio （42） in haze days was about three times of the average OC/EC ratio （14） in non-haze days. By using chemical mass balance （CMB） receptor model, the major sources were apportioned, including traffics, incinerators, coal combustion, steel industry, petrochemical industry, and secondary aerosols, etc. The contribution to PM10 concentration of each source was calcu- lated for all the samples collected. The results showed that coal combustion was the major source of PM10 in non-haze days and secondary aerosols were the major source in haze days, followed by petrochemical industry, incinerators, and traffics, while other sources had negligible effect.展开更多
基金supported by the National Technology Supporting, Kaifeng Environmental Protec-tion Bureau, Henan Province, China
文摘During 2005, the filter samples of ambient PM10 from five sites and the source samples of particulate matter were collected in Kaifeng, Henan Province of China. Nineteen elements, water-soluble ions, total carbon （TC） and organic carbon （OC） contained in samples were analyzed. Seven contributive source types were identified and their contributions to ambient PM10 were estimated by chemical mass balance （CMB） receptor model. Weak associations between the concentrations of organic carbon and element carbon （EC） were observed during the sampling periods, indicating that there was secondary organic aerosol pollution in the urban atmosphere. An indirect method of ＂OC/EC minimum ratio＂ was applied to estimate the concentration of secondary organic carbon （SOC）. The results showed that SOC contributed 26.2%, 32.4% and 18.0% of TC in spring, summer-fall and winter, respectively, and the annual average SOC concentration was 7.07 μg/m^3, accounting for 5.73% of the total mass in ambient PM10. The carbon species concentrations in ambient PM10 were recalculated by subtracting SOC concentrations from measured concentrations of TC and OC to increase the compatibility of source and receptor measurements for CMB model.
基金Dean, Director and Chairman of Marwadi Education Foundation's Group of Institutions for their continuous support
文摘Source apportionment study of PM10 （Particulate Matter） in a critically polluted area of Jhafia coalfield, India has been carried out using Dispersion model, Principle Component Analysis （PCA） and Chemical Mass Balance （CMB） techniques. Dispersion model Atmospheric Dispersion Model （AERMOD） was introduced to simplify the complexity of sources in Jharia coalfield. PCA and CMB analysis indicates that monitoring stations near the mining area were mainly affected by the emission from open coal mining and its associated activities such as coal transportation, loading and unloading of coal. Mine fire emission also contributed a considerable amount of particulate matters in monitoring stations. Locations in the city area were mostly affected by vehicular, Liquid Petroleum Gas （LPG） ＆ Diesel Generator （DG） set emissions, residential, and commercial activities. The experimental data sampling and their analysis could aid understanding how dispersion based model technique along with receptor model based concept can be strategically used for quantitative analysis of Natural and Anthropogenic sources of PM10.
基金supported by Open Project of State Key Laboratory of Urban Water Resources and Environments, Harbin Institute of Technology (No. QA200902)
文摘This study investigates the correlation between PM10 and meteorological factors such as wind speed, atmospheric visibility, dew point, relative humidity, and ambient temperature during a brown haze episode. In order to identify the potential sources of PMlo during brown haze episode, respirable par- ticulate matter （PM10） was collected during both non-haze days and haze days and further analyzed for metallic elements, ionic species, and carbonaceous contents. Among them, ionic species contributed 45-64% to PM10, while metallic elements contributed 7-21% to PM10 which was smaller than the other chemical constituents. The average OC/EC ratio （42） in haze days was about three times of the average OC/EC ratio （14） in non-haze days. By using chemical mass balance （CMB） receptor model, the major sources were apportioned, including traffics, incinerators, coal combustion, steel industry, petrochemical industry, and secondary aerosols, etc. The contribution to PM10 concentration of each source was calcu- lated for all the samples collected. The results showed that coal combustion was the major source of PM10 in non-haze days and secondary aerosols were the major source in haze days, followed by petrochemical industry, incinerators, and traffics, while other sources had negligible effect.
