Fine particulate matter (PM<sub>2.5</sub>) mainly originates from combustion emissions on-road transportation. Exposure to PM<sub>2.5</sub> could be considered one of the primary causes of dise...Fine particulate matter (PM<sub>2.5</sub>) mainly originates from combustion emissions on-road transportation. Exposure to PM<sub>2.5</sub> could be considered one of the primary causes of diseases such as heart attack, stroke, lung cancer, and chronic respiratory, which made it one of the most important co-benefits when evaluating the impact of GHG mitigation measures. This study quantifies the co-benefit of Ha Noi’s modal shift from private to public means of transport, which are reduced air pollution and extended life expectancy, combining AERMOD model and benefit transfer method. Analytical results show that shifting from motorbike to electric train could be the most beneficial option in term of health co-benefit, compared to the usage of standard buses and BRTs.展开更多
We try to enhance the AERMOD industrial pollution dispersion model with remote sensing observations and climatic models based on them. In this paper, we focus on surface parameters (albedo, roughness, Bowen ratio) and...We try to enhance the AERMOD industrial pollution dispersion model with remote sensing observations and climatic models based on them. In this paper, we focus on surface parameters (albedo, roughness, Bowen ratio) and land use classification on which they depend. We model maximum hourly concentrations and the resulting acute health risk and assess the effect on them produced by using remote sensing data for local areas around industrial plants instead of global standard AERMOD parameters. We consider five real multi-source plants for the effect of classification and two of them for the effect of surface parameters. The effect on the critical pollutant is measured in three ways: a) as difference between the yearly maxima of hourly concentrations of a critical pollutant (“absolute”);b) the same limited to daytime workhours and 95% quantile instead of absolute maximum (“regulatory”);c) as maximum hourly difference over a year (“instant”). The measure of effect is divided either by the reference concentration of the pollutant, which yields the impact on health risk, or by the concentration obtained with AERMOD standards, which yields relative measure of impact. For a), the impact of roughness dominates, that of albedo is small and that of the Bowen ratio is almost zero. For b), the impact of roughness is less prominent, and that of albedo and Bowen ratio is noticeable. For c), the impact is considerable for all three parameters. The effect of land use classification is considerable in all three cases a) </span><span style="font-family:Verdana;">-</span><span style="font-family:Verdana;"> c). We provide the figures for different measures of remote sensing data effect and discuss the perspective of using remote sensing data in regulatory context.展开更多
We applied the model of American Meteorological Society-Environmental Protection Agency Regulatory Model（AERMOD） as a tool for the analysis of nitrogen dioxide（NO2） emissions from a cement complex as a part of the...We applied the model of American Meteorological Society-Environmental Protection Agency Regulatory Model（AERMOD） as a tool for the analysis of nitrogen dioxide（NO2） emissions from a cement complex as a part of the environmental impact assessment.The dispersion of NO2 from four cement plants within the selected cement complex were investigated both by measurement and AERMOD simulation in dry and wet seasons.Simulated values of NO2 emissions were compared with those obtained during a 7-day continuous measurement campaign at 12 receptors.It was predicted that NO2 concentration peaks were found more within 1 to 5 km,where the measurement and simulation were in good agreement,than at the receptors 5 km further away from the reference point.The QuantileQuantile plots of NO2 concentrations in dry season were mostly fitted to the middle line compared to those in wet season.This can be attributed to high NO2 wet deposition.The results show that for both the measurement and the simulation using the AERMOD,NO2 concentrations do not exceed the NO2 concentration limit set by the National Ambient Air Quality Standards（NAAQS） of Thailand.This indicates that NO2 emissions from the cement complex have no significant impact on nearby communities.It can be concluded that the AERMOD can provide useful information to identify high pollution impact areas for the EIA guidelines.展开更多
Vehicular pollution is becoming significant in urban areas because of increasing population. This is at ground level, so it gives high population exposure. In this study, Chembur, which is the most polluted area in Mu...Vehicular pollution is becoming significant in urban areas because of increasing population. This is at ground level, so it gives high population exposure. In this study, Chembur, which is the most polluted area in Mumbai city due to industrial and vehicular sources, is selected for vehicular pollution modeling using AMS/EPA Regulatory Model (AERMOD). Meteorological parameters, land use surface characteristics and source emission data are collected as required by AERMOD. The results of modelling depend upon reliability of input data and meteorological data has a vital role in the performance of the model. Generally, temporally and spatially interpolated meteorological data is used in modeling. This is generally collected from nearby meteorological station but this causes inaccuracy of the results. In this paper, the Weather Research and Forecasting (WRF) model has been used to generate onsite data on nine meteorological parameters. The modeling of six roads of Chembur has been performed using above meteorological data. This approach gives good results of traffic modeling. The results of AERMOD are compared with observed air quality which has contribution from all sources in the region and relative contribution of vehicular sources identified.展开更多
文摘Fine particulate matter (PM<sub>2.5</sub>) mainly originates from combustion emissions on-road transportation. Exposure to PM<sub>2.5</sub> could be considered one of the primary causes of diseases such as heart attack, stroke, lung cancer, and chronic respiratory, which made it one of the most important co-benefits when evaluating the impact of GHG mitigation measures. This study quantifies the co-benefit of Ha Noi’s modal shift from private to public means of transport, which are reduced air pollution and extended life expectancy, combining AERMOD model and benefit transfer method. Analytical results show that shifting from motorbike to electric train could be the most beneficial option in term of health co-benefit, compared to the usage of standard buses and BRTs.
文摘We try to enhance the AERMOD industrial pollution dispersion model with remote sensing observations and climatic models based on them. In this paper, we focus on surface parameters (albedo, roughness, Bowen ratio) and land use classification on which they depend. We model maximum hourly concentrations and the resulting acute health risk and assess the effect on them produced by using remote sensing data for local areas around industrial plants instead of global standard AERMOD parameters. We consider five real multi-source plants for the effect of classification and two of them for the effect of surface parameters. The effect on the critical pollutant is measured in three ways: a) as difference between the yearly maxima of hourly concentrations of a critical pollutant (“absolute”);b) the same limited to daytime workhours and 95% quantile instead of absolute maximum (“regulatory”);c) as maximum hourly difference over a year (“instant”). The measure of effect is divided either by the reference concentration of the pollutant, which yields the impact on health risk, or by the concentration obtained with AERMOD standards, which yields relative measure of impact. For a), the impact of roughness dominates, that of albedo is small and that of the Bowen ratio is almost zero. For b), the impact of roughness is less prominent, and that of albedo and Bowen ratio is noticeable. For c), the impact is considerable for all three parameters. The effect of land use classification is considerable in all three cases a) </span><span style="font-family:Verdana;">-</span><span style="font-family:Verdana;"> c). We provide the figures for different measures of remote sensing data effect and discuss the perspective of using remote sensing data in regulatory context.
基金the Royal Golden Jubilee Ph.D program (IUG50K0021)Thailand Research Fund (TRF) for the financial support
文摘We applied the model of American Meteorological Society-Environmental Protection Agency Regulatory Model（AERMOD） as a tool for the analysis of nitrogen dioxide（NO2） emissions from a cement complex as a part of the environmental impact assessment.The dispersion of NO2 from four cement plants within the selected cement complex were investigated both by measurement and AERMOD simulation in dry and wet seasons.Simulated values of NO2 emissions were compared with those obtained during a 7-day continuous measurement campaign at 12 receptors.It was predicted that NO2 concentration peaks were found more within 1 to 5 km,where the measurement and simulation were in good agreement,than at the receptors 5 km further away from the reference point.The QuantileQuantile plots of NO2 concentrations in dry season were mostly fitted to the middle line compared to those in wet season.This can be attributed to high NO2 wet deposition.The results show that for both the measurement and the simulation using the AERMOD,NO2 concentrations do not exceed the NO2 concentration limit set by the National Ambient Air Quality Standards（NAAQS） of Thailand.This indicates that NO2 emissions from the cement complex have no significant impact on nearby communities.It can be concluded that the AERMOD can provide useful information to identify high pollution impact areas for the EIA guidelines.
文摘Vehicular pollution is becoming significant in urban areas because of increasing population. This is at ground level, so it gives high population exposure. In this study, Chembur, which is the most polluted area in Mumbai city due to industrial and vehicular sources, is selected for vehicular pollution modeling using AMS/EPA Regulatory Model (AERMOD). Meteorological parameters, land use surface characteristics and source emission data are collected as required by AERMOD. The results of modelling depend upon reliability of input data and meteorological data has a vital role in the performance of the model. Generally, temporally and spatially interpolated meteorological data is used in modeling. This is generally collected from nearby meteorological station but this causes inaccuracy of the results. In this paper, the Weather Research and Forecasting (WRF) model has been used to generate onsite data on nine meteorological parameters. The modeling of six roads of Chembur has been performed using above meteorological data. This approach gives good results of traffic modeling. The results of AERMOD are compared with observed air quality which has contribution from all sources in the region and relative contribution of vehicular sources identified.
