应用MM5(Fifth-Generation NCAR/Penn State Mesoscale Model)-Models-3/CMAQ(Community Multi-Scale Air Quality)空气质量模拟系统对京津冀地区进行了模拟,分别采用Brute Force方法和DDM-3D(Decoupled Direct Method in 3 Dimensions...应用MM5(Fifth-Generation NCAR/Penn State Mesoscale Model)-Models-3/CMAQ(Community Multi-Scale Air Quality)空气质量模拟系统对京津冀地区进行了模拟,分别采用Brute Force方法和DDM-3D(Decoupled Direct Method in 3 Dimensions)技术对两个代表性城市石家庄、北京的PM2.5来源进行了分析计算.结果表明,两种方法的计算结果具有显著相关性,相关系数在0.950～0.989之间;其次,在某一地区浓度贡献较低的情况下,两种方法的计算结果非常接近,但随着浓度贡献的增加,Brute Force方法的计算结果逐渐高于DDM-3D方法,直线拟合的斜率在1.14～2.05之间.以石家庄为例,Brute Force和DDM-3D方法估算的河北南部地区排放的浓度贡献分别为54.7%和64.4%,相差10%左右.浓度贡献空间分布的对比表明,Brute Force方法计算出的浓度影响范围更大,出现某些离散的负值点,或某些负值点与很大的正值点相邻,反映了数值计算带来的计算误差;相比之下,DDM-3D方法的计算结果则更为合理.展开更多
We present comparisons of the NO2 regional Chemical Transport Model （CTM） simulations over North-eastern North America during the time period from May to September, 1998 with hourly surface NO2 observations and the ...We present comparisons of the NO2 regional Chemical Transport Model （CTM） simulations over North-eastern North America during the time period from May to September, 1998 with hourly surface NO2 observations and the NO2 columns retrieved from the GOME （Global Ozone Monitoring Experiment） satellite instrument. The model calculations were performed using the Mesoscale Meteorological Model 5 （MM5）, Sparse Matrix Operator Kernal Emissions （SMOKE）, and Community Multiscale Air Quality （CMAQ） modeling systems, using the emission data from the National Emissions Inventory （NEI） databases of 1996 （U.S.） and 1995 （Canada）. The major objectives were to assess the performance of the CMAQ model and the accuracy of the emissions inventories as they affected the simulations of this important short-lived atmospheric species. The modeled （NcMAQ） and measured （NGOME） NO2 column amounts, as well as their temporal variations, agreed reasonably well. The absolute differences （NcMAQ-NGOME） across the domain were between ±3.0×10^15 molecules cm^-2, but they were less than ±1.0×10^15 molecules cm^-2 over the majority （80%） of the domain studied. The overall correlation coefficient between the measurements and the simulations was 0.75. The differences were mainly ascribed to a combination of inaccurate emission data for the CTM and the uncertainties in the GOME retrievals. Of these, the former were the more easily identifiable.展开更多
京津冀位于华北平原腹地,面临着严重的空气污染问题,尤其是河北省的重点工业城市唐山,长期位于全国空气质量最差的前十名。为改善空气质量,过去的十多年间我国颁布实施了多项污染防治计划,但唐山的PM_(2.5)和夏季O_(3)浓度仍超国家标准...京津冀位于华北平原腹地,面临着严重的空气污染问题,尤其是河北省的重点工业城市唐山,长期位于全国空气质量最差的前十名。为改善空气质量,过去的十多年间我国颁布实施了多项污染防治计划,但唐山的PM_(2.5)和夏季O_(3)浓度仍超国家标准。为此,使用WRF(Weather Research and Forecasting Model)-CMAQ(Community Multiscale Air Quality Model)模型量化了唐山市2020年PM_(2.5)和O_(3)浓度的行业贡献并分析其协同控制可行性。工业源对唐山市PM_(2.5)浓度贡献最大,约占45%,其次是居民源约占16%。冬季能源、居民源和农业源占比为全年最高,分别达17%、19%和11%。O_(3)浓度的背景值约占一半以上,4月占比最高。在非背景值中,唐山O_(3)浓度最大来源为工业源,约占53%,其次是交通源,约占22%。生物源、交通源和能源行业的贡献在7月有所上升,分别约10%、27%和20%。不同污染情景下对唐山市PM_(2.5)和O_(3)的来源比较发现,工业和能源是其最重要的共同来源。展开更多
文摘应用MM5(Fifth-Generation NCAR/Penn State Mesoscale Model)-Models-3/CMAQ(Community Multi-Scale Air Quality)空气质量模拟系统对京津冀地区进行了模拟,分别采用Brute Force方法和DDM-3D(Decoupled Direct Method in 3 Dimensions)技术对两个代表性城市石家庄、北京的PM2.5来源进行了分析计算.结果表明,两种方法的计算结果具有显著相关性,相关系数在0.950～0.989之间;其次,在某一地区浓度贡献较低的情况下,两种方法的计算结果非常接近,但随着浓度贡献的增加,Brute Force方法的计算结果逐渐高于DDM-3D方法,直线拟合的斜率在1.14～2.05之间.以石家庄为例,Brute Force和DDM-3D方法估算的河北南部地区排放的浓度贡献分别为54.7%和64.4%,相差10%左右.浓度贡献空间分布的对比表明,Brute Force方法计算出的浓度影响范围更大,出现某些离散的负值点,或某些负值点与很大的正值点相邻,反映了数值计算带来的计算误差;相比之下,DDM-3D方法的计算结果则更为合理.
文摘We present comparisons of the NO2 regional Chemical Transport Model （CTM） simulations over North-eastern North America during the time period from May to September, 1998 with hourly surface NO2 observations and the NO2 columns retrieved from the GOME （Global Ozone Monitoring Experiment） satellite instrument. The model calculations were performed using the Mesoscale Meteorological Model 5 （MM5）, Sparse Matrix Operator Kernal Emissions （SMOKE）, and Community Multiscale Air Quality （CMAQ） modeling systems, using the emission data from the National Emissions Inventory （NEI） databases of 1996 （U.S.） and 1995 （Canada）. The major objectives were to assess the performance of the CMAQ model and the accuracy of the emissions inventories as they affected the simulations of this important short-lived atmospheric species. The modeled （NcMAQ） and measured （NGOME） NO2 column amounts, as well as their temporal variations, agreed reasonably well. The absolute differences （NcMAQ-NGOME） across the domain were between ±3.0×10^15 molecules cm^-2, but they were less than ±1.0×10^15 molecules cm^-2 over the majority （80%） of the domain studied. The overall correlation coefficient between the measurements and the simulations was 0.75. The differences were mainly ascribed to a combination of inaccurate emission data for the CTM and the uncertainties in the GOME retrievals. Of these, the former were the more easily identifiable.
文摘京津冀位于华北平原腹地,面临着严重的空气污染问题,尤其是河北省的重点工业城市唐山,长期位于全国空气质量最差的前十名。为改善空气质量,过去的十多年间我国颁布实施了多项污染防治计划,但唐山的PM_(2.5)和夏季O_(3)浓度仍超国家标准。为此,使用WRF(Weather Research and Forecasting Model)-CMAQ(Community Multiscale Air Quality Model)模型量化了唐山市2020年PM_(2.5)和O_(3)浓度的行业贡献并分析其协同控制可行性。工业源对唐山市PM_(2.5)浓度贡献最大,约占45%,其次是居民源约占16%。冬季能源、居民源和农业源占比为全年最高,分别达17%、19%和11%。O_(3)浓度的背景值约占一半以上,4月占比最高。在非背景值中,唐山O_(3)浓度最大来源为工业源,约占53%,其次是交通源,约占22%。生物源、交通源和能源行业的贡献在7月有所上升,分别约10%、27%和20%。不同污染情景下对唐山市PM_(2.5)和O_(3)的来源比较发现,工业和能源是其最重要的共同来源。
