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Frontiers of Environmental Science & Engineering

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Front Envir Sci Eng Chin    0, Vol. Issue () : 378-390    https://doi.org/10.1007/s11783-011-0357-8
RESEARCH ARTICLE
Impact of energy structure adjustment on air quality: a case study in Beijing, China
Bin ZHAO, Jiayu XU(), Jiming HAO
School of Environment, Tsinghua University, Beijing 100084, China
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Abstract

Energy consumption is a major cause of air pollution in Beijing, and the adjustment of the energy structure is of strategic importance to the reduction of carbon intensity and the improvement of air quality. In this paper, we explored the future trend of energy structure adjustment in Beijing till 2020, designed five energy scenarios focusing on the fuel substitution in power plants and heating sectors, established emission inventories, and utilized the Mesoscale Modeling System Generation 5 (MM5) and the Models-3/Community Multiscale Air Quality Model (CMAQ) to evaluate the impact of these measures on air quality. By implementing this systematic energy structure adjustment, the emissions of PM10, PM2.5, SO2, NOx, and non-methane volatile organic compounds (NMVOCs) will decrease distinctly by 34.0%, 53.2%, 78.3%, 47.0%, and 30.6% respectively in the most coal-intensive scenario of 2020 compared with 2005. Correspondingly, MM5-Models-3/CMAQ simulations indicate significant reduction in the concentrations of major pollutants, implying that energy structure adjustment can play an important role in improving Beijing’s air quality. By fuel substitution for power plants and heating boilers, PM10, PM2.5, SO2, NOx, and NMVOCs will be reduced further, but slightly by 1.7%, 4.5%, 11.4%, 13.5%, and 8.8% respectively in the least coal-intensive scenario. The air quality impacts of different scenarios in 2020 resemble each other, indicating that the potential of air quality improvement due to structure adjustment in power plants and heating sectors is limited. However, the CO2 emission is 10.0% lower in the least coal-intensive scenario than in the most coal-intensive one, contributing to Beijing’s ambition to build a low carbon city. Except for energy structure adjustment, it is necessary to take further measures to ensure the attainment of air quality standards.

Keywords Beijing      energy structure adjustment      air quality      Community Multiscale Air Quality Model (CMAQ)      scenario     
Corresponding Author(s): XU Jiayu,Email:jiayu-xu@mail.tsinghua.edu.cn   
Issue Date: 05 September 2011
 Cite this article:   
Jiming HAO,Bin ZHAO,Jiayu XU. Impact of energy structure adjustment on air quality: a case study in Beijing, China[J]. Front Envir Sci Eng Chin, 0, (): 378-390.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-011-0357-8
https://academic.hep.com.cn/fese/EN/Y0/V/I/378
20052020
GDP/(×109 CNY, 2005 price)6972800
maximum power load/GW10.530
installed capacity of power plants/GW3.615
urban heat supply area/(×106 m2)389600
suburb heat supply area/(×106 m2)116400
Tab.1  Projection of future GDP, electricity generation, and heat supply area
sectorstechnologies2020A2020B2020C2020D2020E
power generation structurenumber of huge gas-fired CHPs a)44320
number of huge coal-fired CHPs00124
other power plantsbesides the 4 huge CHPs, several medium-sized gas-fired power plants and pumped-storage power plants are built to meet the total electricity demand.
heat supply area/(×106 m2)CHPs8080200200220
peak-shaving gas-fired boilers8080200200220
other gas-fired boilers640530350350310
coal-fired boilers b)150260200200200
others c)5050505050
Tab.2  Major assumptions of the energy scenarios
sectorssub-sectorsSO2NOxPM10PM2.5NMVOCs
power plantsHuaneng power plant/(kg·t-1)4.042.720.400.341.17
Guohua power plant/(kg·t-1)4.966.760.100.091.17
Gaojing power plant/(kg·t-1)16.126.044.573.860.81
Jingfeng power plant/(kg·t-1)25.1110.73.853.250.81
Jingneng power plant/(kg·t-1)8.985.631.451.220.81
other coal-fired power plants/(kg·t-1)16.126.044.573.860.81
gas-fired power plants/(g·m-3)0.181.680.070.07-
industrial and heating boilerscoal-fired boilers(>20t·h-1)/(kg·t-1)2.22.372.52.293.11
coal-fired boilers(<20t·h-1)/(kg·t-1)6.182.761.280.725.1
gas-fired boilers/(g·m-3)0.181.280.140.14-
domesticcoal stoves/(kg·t-1)1.470.500.310.101.51
Tab.3  Emission factors for some key sources in 2005
sectorssub-sectorsSO2NOxPM10PM2.5NMVOCs
power plantscoal-fired power plants/(kg·t-1)0.180.900.080.071.17
gas-fired power plants/(g·m-3)0.180.250.070.07-
industrial and heating boilerscoal-fired boilers(>20t·h-1)/(kg·t-1)0.652.370.330.283.11
coal-fired boilers(<20t·h-1)/(kg·t-1)1.602.761.280.725.1
gas-fired boilers/(g·m-3)0.181.020.140.14-
domesticcoal stoves/(kg·t-1)1.470.500.310.101.51
Tab.4  Emission factors for some key sources in 2020
Fig.1  Energy consumption in 2005 and the 5 scenarios in 2020: (a) total energy, (b) coal, and (c) natural gas
Fig.2  Emissions of major air pollutants and CO in 2005 and the 5 scenarios in 2020: (a) PM, (b) PM, (c) SO, (d) NO, (e) NMVOCs, and (f) CO
Fig.3  Comparison of simulated daily PM concentrations and observed data (API) in the urban area of Beijing in (a) January, (b) April, (c) July, and (d) October
Fig.4  Comparison of simulated daytime mean O concentrations and observed data in the urban area of Beijing in July
monthurban/suburbconcentrations of 2005/(μg·m-3)concentrations of 2020A/(μg·m-3)concentrations of 2020E/(μg·m -3)
PM10PM2.5SO2NO2O3PM10PM2.5SO2NO2O3PM10PM2.5SO2NO2O3
Januaryurban123.686.9100.860.865.897.963.531.446.072.798.363.932.145.472.3
suburb40.833.928.520.278.135.529.516.715.079.735.729.716.914.979.7
Aprilurban97.658.832.043.9121.295.153.119.124.7128.695.253.219.325.4127.6
suburb33.626.914.011.2123.032.225.511.87.2125.832.325.611.97.4125.6
Julyurban69.553.026.737.5126.657.942.113.619.6123.858.442.614.021.0127.2
suburb37.132.811.89.5119.232.929.08.75.9110.833.229.38.86.2113.4
Octoberurban104.473.345.051.182.694.463.028.833.191.994.563.229.033.890.6
suburb48.942.221.815.889.345.839.618.810.592.446.039.718.910.792.1
averageurban98.868.051.148.399.186.355.423.230.8104.386.655.723.631.4104.4
suburb40.134.019.014.2102.436.630.914.09.6102.236.831.114.19.8102.7
Tab.5  Urban/suburb average concentrations of major pollutants in 2005 and 2020
Fig.5  
Fig.6  Spatial distributions of the monthly-mean concentrations of major pollutants in January/July in 2005, 2020A and 2020E scenarios (all values are monthly-mean concentrations except for O, which are monthly-mean of daily maximum concentrations; the units of concentrations are μg·m)
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