Sulfur dioxide and primary carbonaceous aerosol emissions in China and India,1996–2010
Atmos.Chem.Phys.,11,9839–9864,2011 https://www.wendangku.net/doc/6416532074.html,/11/9839/2011/ doi:10.5194/acp-11-9839-2011
?Author(s)https://www.wendangku.net/doc/6416532074.html, Attribution3.0
License.Atmospheric Chemistry and Physics
Sulfur dioxide and primary carbonaceous aerosol emissions in China and India,1996–2010
Z.Lu1,Q.Zhang2,and D.G.Streets1
1Decision and Information Sciences Division,Argonne National Laboratory,Argonne,IL60439,USA
2Center for Earth System Science,Tsinghua University,Beijing100084,China
Received:30June2011–Published in Atmos.Chem.Phys.Discuss.:18July2011
Revised:15September2011–Accepted:20September2011–Published:23September2011
Abstract.China and India are the two largest anthropogenic
aerosol generating countries in the world.In this study,we
develop a new inventory of sulfur dioxide(SO2)and primary
carbonaceous aerosol(i.e.,black and organic carbon,BC
and OC)emissions from these two countries for the period
1996–2010,using a technology-based methodology.Emis-
sions from major anthropogenic sources and open biomass
burning are included,and time-dependent trends in activity
rates and emission factors are incorporated in the calcula-
tion.Year-speci?c monthly temporal distributions for major
sectors and gridded emissions at a resolution of0.1?×0.1?
distributed by multiple year-by-year spatial proxies are also
developed.In China,the interaction between economic de-
velopment and environmental protection causes large tem-
poral variations in the emission trends.From1996to2000,
emissions of all three species showed a decreasing trend(by
9%–17%)due to a slowdown in economic growth,a decline
in coal use in non-power sectors,and the implementation of
air pollution control measures.With the economic boom af-
ter2000,emissions from China changed dramatically.BC
and OC emissions increased by46%and33%to1.85Tg
and4.03Tg in2010.SO2emissions?rst increased by61%
to34.0Tg in2006,and then decreased by9.2%to30.8Tg in
2010due to the wide application of?ue-gas desulfurization
(FGD)equipment in power plants.Driven by the remark-
able energy consumption growth and relatively lax emission
controls,emissions from India increased by70%,41%,and
35%to8.81Tg,1.02Tg,and2.74Tg in2010for SO2,BC,
and OC,respectively.Monte Carlo simulations are used to
quantify the emission uncertainties.The average95%con?-
dence intervals(CIs)of SO2,BC,and OC emissions are esti-
mated to be?16%–17%,?43%–93%,and?43%–80
%
Correspondence to:Z.Lu (zlu@https://www.wendangku.net/doc/6416532074.html,)for China,and?15%–16%,?41%–87%,and?44%–92%for India,respectively.Sulfur content,fuel use,and sulfur retention of hard coal and the actual FGD removal ef-?ciency are the main contributors to the uncertainties of SO2 emissions.Biofuel combustion related parameters(i.e.,tech-nology divisions,fuel use,and emission factor determinants) are the largest source of OC uncertainties,whereas BC emis-sions are also sensitive to the parameters of coal combustion in the residential and industrial sectors and the coke-making https://www.wendangku.net/doc/6416532074.html,paring our results with satellite observations, we?nd that the trends of estimated emissions in both China and India are in good agreement with the trends of aerosol optical depth(AOD)and SO2retrievals obtained from dif-ferent satellites.
1Introduction
Atmospheric aerosols affect Earth’s energy budget by scat-tering and absorbing solar radiation and by altering cloud properties and lifetimes.They also exert large in?uences on public health,air quality,weather,atmospheric chemistry, hydrological cycles,and ecosystems(e.g.,Ramanathan and Carmichael,2008;Streets et al.,2006,2009).China and India are the two largest anthropogenic aerosol generating countries in the world.In the past decade,they have been identi?ed as the two hot spots in terms of high aerosol opti-cal depth(AOD)observed from space(Kharol et al.,2011; Prasad and Singh,2007;van Donkelaar et al.,2008).The major active components of aerosols in these two countries are sulfate(of which the precursor is sulfur dioxide,SO2) and the primary carbonaceous aerosols black carbon(BC) and organic carbon(OC),together accounting for more than 60%of the AOD(Chin et al.,2009;Streets et al.,2009). From a global perspective,anthropogenic SO2,BC,and OC
Published by Copernicus Publications on behalf of the European Geosciences Union.
emissions from China and India contribute30%–40%of current global emissions(Bond et al.,2004,2007;JRC/PBL, 2010;Smith et al.,2011),and have received the greatest at-tention from compilers of emission inventories.
Trends in anthropogenic emissions are closely tied to eco-nomic growth and technology development.Over the past two decades,China and India have undergone signi?cant economic reform and have emerged as two of the world’s fastest developing economies.Even during2008–2009, China and India were the two nations that were least affected by the global economic recession,maintaining GDP growth rates of9%and6%,respectively(IEA,2010).In response to this economic growth and the rapid expansion in industrial production,energy consumption has increased accordingly. The share of energy use in China and India to the total world energy consumption increased from about10%in1990to 21%in2008(IEA,2010).Meanwhile,environmental leg-islation in both countries has promoted the introduction of new emission control and production technologies into the market,causing major changes in technology distributions as well as emission factors in relevant sectors.As a result, emissions of aerosols(and their precursors)have changed dramatically since the1990s.Although some previous stud-ies have reported SO2,BC,and OC emissions from China and India,none of them have presented year-by-year trends with up-to-date activity rates and new technology penetration rates,especially for the period after2005(see Sect.3.3.1). Therefore,the main purpose of this study is to use a consis-tent methodology to develop a comprehensive inventory of SO2,BC,and OC emissions from China and India for the period1996–2010on the basis of time-dependent activity rates,technology penetration,emission factors,spatial prox-ies,monthly temporal distributions,etc.
There are sometimes disagreements between observations and model simulations(which make use of bottom-up emis-sion databases),especially for carbonaceous aerosols,imply-ing potentially large uncertainties in emission inventories. For example,Tan et al.(2004)suggested that increases in the TRACE-P emission inventory of particulate carbon by 60–90%would bring modeled results into agreement with observations in China.Top-down estimates based on in-situ measurements of BC and CO during the INDOEX campaign yielded BC emissions of2–3Tg yr?1for the South Asia con-tinent(Dickerson et al.,2002),higher than bottom-up inven-tories(<1Tg yr?1).Ramanathan and Carmichael(2008)es-timated a global BC forcing of0.9W m?2based on observa-tion,three times higher than the average values of0.3W m?2 computed by bottom-up inventories-based general circula-tion models.Therefore,quanti?cation of emission uncertain-ties is as important as estimating central values.Streets et al.(2003)estimated the uncertainty for each emitting sub-sector in the TRACE-P inventory by combining the coef-?cients of variation(CV,the standard deviation divided by the mean)of the contributing factors.The uncertainties were then added linearly or in quadrature based on the judgments of dependent or independent correlations between different emitting subsectors.The con?dence intervals(CIs)of this method are symmetric about the mean because all the un-derlying parameters are assumed to be normally distributed. However,the true probabilities of some parameters are asym-metric.Bond et al.(2004)reviewed the emission charac-teristics of various combustion sources,and found that the lognormal distribution is more appropriate for emission fac-tors of carbonaceous aerosols.To obtain an asymmetric CI of each emitting source,they calculated the upper and lower CIs separately by treating the one-sided CI in the underlying distributions as uncertainties in a lognormal distribution and combining them in quadrature.In the past two decades,the Monte Carlo approach has been introduced into the emission inventory community to quantify the uncertainties of bottom-up emission estimates.It has been gradually extended from an individual sector to multiple sectors in a country(Zhao et al.,2011,and references therein).Taking advantage of combining uncertainties of numerous parameters simultane-ously and identifying the contribution of each parameter to the output’s variance,we choose the Monte Carlo approach to evaluate the uncertainties of emissions estimated in this work.
The prime motivation of this study is to support the modeling work of the National Aeronautics and Space Ad-ministration’s Goddard Space Flight Center(NASA/GSFC). NASA/GSFC is tasked to conduct a hindcast investiga-tion of multi-decadal changes of atmospheric aerosols and their effects on surface radiation using the Goddard Chem-istry Aerosol Radiation and Transport(GOCART)model in combination with aerosol data from satellite observations, ground-based measurements,and?eld experiments.The study is focused on the time period of the satellite era from 1980to2010.In our previous study,we have compiled a time-varying,comprehensive global emission dataset of aerosols and their precursors for the GOCART model for the period1980–2006(Chin et al.,2009;Streets et al.,2006, 2009).This dataset is considered reliable from1980to the mid-1990s,but thereafter updating is necessary to re?ect new statistical data availability and the transformation of technol-ogy.The current work reported here addresses updated and extended emission datasets for China and India,two of the most important regions in the world.Subsequently,the work will be extended to all world regions.
In this study,we estimate the SO2and primary carbona-ceous aerosol(i.e.,BC and OC)emissions from China and India for1996–2010using a detailed technology-based ap-proach.Section2documents the methodology and data sets used in this work.The results,including estimated emissions,uncertainty analysis,comparison with other stud-ies,gridded datasets,and seasonality of emissions are pre-sented in Sect.3.We also use satellite observations to verify our emission trends,the discussion of which is included in Sect.4.Summary and conclusions are in Sect.5.
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2Methodology and data sets 2.1Estimation of SO 2and carbonaceous aerosol emissions
2.1.1
General methodology
A technology-speci?c methodology is more appropriate and accurate for estimating emissions from anthropogenic sources because of the wide variation in emission rates for different types of processes and control technologies.In our previous work,we reported the development of detailed inventories of primary carbonaceous aerosol emissions for China,Asia,and the world (Bond et al.,2004;Streets et al.,2001,2004).In particular,a detailed technology-based global inventory of primary BC and OC emissions was re-ported for the year 1996(Bond et al.,2004).Using the an-nual fuel-use trends by world region and economic growth parameters included in the IMAGE model (National Institute for Public Health and the Environment,2001),which was de-veloped for the Intergovernmental Panel on Climate Change (IPCC),we further extended the 1996inventory to an annual trend for the period 1980–2000and adapted the methodol-ogy to calculate annual SO 2emissions over the same period (Streets et al.,2006,2008,2009).
In this study,a similar approach is adopted.The emis-sion sources are categorized into ?ve major sectors (i.e.,power generation,industry,residential,transport,and open biomass burning)and more than 120sector/fuel(or prod-uct)/technology combinations,including both fuel combus-tion and non-combustion sources.Total emission (E i,j )for species j and country i is given by the following equation:
E i,j = l
m
A i,l,m n
X i,l,m,n EF i,j,l,m,n
(1)
where l ,m ,and n represent the sector,the fuel/product type,and the technology type for combustion and industrial pro-cesses,respectively.A represents the activity rates,such as fuel consumption and material production,and X represents the fraction of fuel/product for a sector that is consumed by a speci?c technology (i.e.,
X =1for each fuel/product and sector).EF is the net emission factor,and for sub-micrometer carbonaceous aerosols,it is given by:EF BC (or OC )=EF PM ·F 1.0·F BC (or OC )·F control
(2)
where EF PM is the bulk particulate emission factor;F 1.0is the fraction of the emissions with diameters smaller than 1μm;F BC (or F OC )is the fraction of the PM 1.0(particles less than 1.0μm in aerodynamic diameter)that is BC (or OC);and F control is the fraction of PM 1.0that penetrates the con-trol device.For SO 2from fuel combustion sources,EF can be calculated by:
EF SO 2=2·S ·(1?SR )·(1?ηk )
(3)
where ηk is the removal ef?ciency of control technology k ;S
and SR are the sulfur content of fuel and sulfur retention in ash,respectively.Based on this framework,we estimated the SO 2and carbonaceous aerosol emissions in China and India for 1996–2010by incorporating the time-dependent trends in activity rates,technology penetration,emission controls,coal sulfur content,etc.2.1.2
Uncertainty analysis
Due to the various underlying probability distributions of in-put parameters,the uncertainties cannot be combined ana-lytically.In this work,we use a Monte Carlo approach to determine the uncertainties in the emission estimates.The procedure is to generate a set of values of the random vari-ables in accordance with speci?ed probability distributions,so that a series of corresponding solutions is obtained.The methods of statistical estimation and inference can then be applied to such solutions to describe their uncertainties.For Monte Carlo simulations,specifying the probability distri-butions of the input parameters is a fundamental task.For parameters with adequate data and reported distributions,we applied them directly in our model,and for parameters with limited or no observation data,probability distributions were based on the authors’expert judgment.These will be dis-cussed in detail in the following sections.All of the input pa-rameters (e.g.,activity rates,emission factor determinants)and their corresponding probability distributions were then incorporated into a Monte Carlo framework with the Crystal Ball software and at least 6000valid simulations were per-formed.Unless speci?ed otherwise,the term “uncertainty”in this article refers to a 95%CI around the central estimate (i.e.,mean).2.1.3
Activity rates
Energy and fossil fuel consumption data for most of the sector/fuel/technology combinations were from the Interna-tional Energy Agency (IEA,2010),which provides informa-tion on 102?ows (e.g.,imports,exports,and sectoral con-sumption)of 66fuels.We separated and aggregated these activities based on the emission characteristics of each com-bustion process to ?t the source types in our model (see Bond et al.,2004for details).At present,2008is the latest year for which those data are available.Activity rates were therefore extrapolated from 2008to 2010based on national fast-track statistics.If no up-to-date data are available,values from the most recent year are used.Since probability distributions are not provided with of?cial statistics,we applied normal dis-tributions for all of the fossil fuel usage combinations.Gen-erally,the uncertainties were assumed as follows:10%for power generation,20%for industrial and liquid fossil fuels in the residential sector,and 33%for transport and coal use in the residential sector.These values are based on a review of previous studies (Bond et al.,2004;Streets et al.,2003;
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Atmos.Chem.Phys.,11,9839–9864,2011
Smith et al.,2011;Zhao et al.,2011).It was reported that the IEA statistical data(edition2004)of coal consumption in China may be underestimated during1996–2003,and are not recommended for use in emission inventory studies in China during this period(Akimoto et al.,2006).However,IEA re-vised China’s historical coal consumption data in the current edition based on new economic surveys by the China Na-tional Bureau of Statistics(NBS)(IEA,2010).Hence,IEA coal consumption statistics of China are used in this work. For India,there are no of?cial statistics for coal consump-tion in the residential sector,and very little or no coal con-sumption was assumed in several previous Indian emission inventories(Parashar et al.,2005;Reddy and Venkataraman, 2002a).However,IEA reported that the residential sector contributes about20–30%of the non-power-generation coal use(IEA,2010).For this reason,uncertainties of50%were assigned to both industrial and residential coal consumption in India.In India,a high gasoline price leads to the prac-tice of fuel adulteration(i.e.,mixing kerosene into gasoline). The fraction of kerosene in fuel can reach as high as50%, but the actual extent of this practice is unknown(Dickerson et al.,2002;Patra and Mishra,2000).Dickerson et al.(2002) assumed that all spark-ignition engines burn2/3gasoline and 1/https://www.wendangku.net/doc/6416532074.html,ing the same assumption,we multiplied the on-road gasoline consumption in India by1.5,and assigned an uncertainty of50%to it.
Historical biofuel consumption in China and India were obtained from other data sources.For China,the provin-cial consumption of biofuel was derived from the China En-ergy Statistical Yearbook(CESY)(NBS,1998–2010),and the consumption patterns in rural areas were taken from Zhang et al.(2009a).For India,previous estimates of biofuel consumption contained large uncertainties due to the small sample sizes and outdated energy surveys carried out during 1985–1992(e.g.,Bond et al.,2004;Reddy and Venkatara-man,2002b;Streets et al.,2003;Yevich and Logan,2003; Sinha et al.,1998).To address these drawbacks,Habib et al.(2004)developed a new method based on food consump-tion statistics and the speci?c energy requirement for food preparation,and estimated Indian biofuel consumption for cooking in year2000.In this study,we followed a similar methodology,and extended it to an annual trend for the pe-riod1996–2010.The probability distribution of biofuel use is probably not symmetric.Yevich and Logan(2003)examined both the range and the standard deviation of published per capita biofuel usage,and assessed uncertainties of?40%to +95%for biofuel consumption in Asia.Habib et al.(2004) estimated that the95%CI of total biofuel consumption in India is?35%–54%about the mean with lognormal distri-bution.Therefore,we generally assumed lognormal distri-butions for biofuel use for both China and India.The uncer-tainties(upper95%CI about the mean)are46%,74%,and 86%for Indian fuelwood,dung-cake and crop waste,respec-tively(Habib et al.,2004),and80%for biofuel use in China (authors’judgment).
Four types of open biomass burning are included:tropical forests,extra-tropical forests,savanna/grassland,and crop residue burning in?elds.The national dry matter burned of forests and grassland were zonally aggregated accord-ing to the country boundaries of China and India from the Global Fire Emissions Database(GFED)version3.1,which calculates?re emissions based on a revised version of the Carnegie-Ames-Stanford-Approach(CASA)biogeochemi-cal model and improved satellite-derived estimates of area burned,?re activity,and plant productivity(van der Werf et al.,2010).The database provides the?rst global assess-ment of the contributions of different sources to total global ?re emissions at0.5?×0.5?spatial resolution for the1997–2009with a monthly time step.For years1996and2010,we used average values of data between1997and2009.Regard-ing the probability of open burning of each type of?re,nor-mal distributions with year-speci?c uncertainties provided in the GFED v3.1were assumed.Although GFED v3.1con-tains?res from agricultural waste burning,these estimates are likely a lower bound,since the method for measuring burned area can only detect the relatively large?res(van der Werf et al.,2010).Therefore,we adopted a different ap-proach for estimating the burning of crop residue in?elds, using the product of the yield of different crops,the crop-to-residue ratios,and the fraction of crop burnt in the?eld (Cao et al.,2006;Sahai et al.,2010;Venkataraman et al., 2006;Wang and Zhang,2008).Crop production statistics were obtained from the China Agricultural Yearbook(Min-istry of Agriculture of China,1997–2009)and the India Agri-culture Statistics at a Glance(Ministry of Agriculture of In-dia,1996–2010)for China and India,respectively.Crop-to-residue ratios and crop burnt fraction in the?eld were from Cao et al.(2006)and Wang and Zhang(2008)for China,and Sahai et al.(2010)and Venkataraman et al.(2006)for India. Derived from Sahai et al.(2010)and Zhao et al.(2011),nor-mal distributions with uncertainties of40%were assumed for crop waste burned in?elds.
We followed Bond et al.(2004)’s method to estimate open waste burning of the two countries during1996–2010.To-tal open waste combustion was calculated by multiplying per capita waste generation rates,urban populations(assume waste generation in rural areas is low in developing coun-tries because goods are inherently recycled),and fraction of waste burned in urban areas.We acknowledge that our es-timates of open waste burning are quite uncertain,and as-sign uncertainties of200%to these estimates(Bond et al., 2004).For non-combustion emissions,industrial production levels were obtained from various sources,such as the China Industry Economy Statistical Yearbook(NBS,1997–2010a) and the Handbook of Statistics on the Indian Economy(Re-serve Bank of India,2010).We applied normal distributions with uncertainties of20%to these statistics based on expert judgment.
Figure1shows annual energy consumption by sector and fuel type in China and India between1996and2010.
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Fig.1.Energy consumption by sector and fuel type,and biomass burned in(a)China and(b)India during1996–2010. Quantities of fuels were converted into energy equivalents using net calori?c values supplied in IEA Energy Statistics (IEA,2010)and the GAINS model(Klimont et al.,2009). Energy consumption in China remained relatively stable dur-ing1996–2000and then increased dramatically from43.3EJ to105.5EJ during2000–2010,with an annual growth rate of9.3%.On average,the highest sectoral consumption is in industry(39%),followed by power plants(29%),residen-tial(24%),and transportation(8%).Different from China, the energy consumption in India continuously increased from 17.8EJ in1996to29.3EJ in2010,with an annual growth rate of3.6%.Industry,power plants,residential,and trans-portation contribute22%,31%,39%,and8%of the na-tional energy consumption,respectively.Figure1also shows the amount of biomass burned in the two countries.Obvi-ously,open biomass burning has signi?cant interannual vari-ability.The average dry mass burned in China and India dur-ing1996–2010is21.6Mg yr?1and32.0Mg yr?1for forest and grassland,and130.5Mg yr?1and91.2Mg yr?1for agri-cultural waste.
2.1.4Technology divisions
As shown in Eq.(1),we used parameter X to divide the sec-tor/fuel(or product)combinations into different technolo-gies.This procedure provides the ability to estimate emis-sions dynamically,because the change of emission factors over time can be represented as a change of technology pen-etration.This is very important for rapidly developing coun-tries like China and India since new technologies are con-tinuously coming into the market,causing dramatic changes in emission factors.For fuel use in the residential,power generation,and industry sectors,the application rates of dif-ferent combustion technologies(or processing technologies for industrial products)and the distribution of emission con-trol devices during1996–2010were compiled from a wide range of literature,such as Lei et al.(2011),Klimont et al.(2009),Lu et al.(2010),Streets et al.(2003),Reddy and Venkataraman(2002a,b),and Zhao et al.(2011).For the transportation sector,technologies refer to different vehi-cle types with different emission standards.In the present work,we classi?ed on-road vehicles into four types,in-cluding light-duty gasoline vehicles,light-duty diesel vehi-cles,heavy-duty diesel vehicles,and motorcycles.Time-dependent distribution of oil consumption between different vehicle/standard types was derived from He et al.(2005)and Wang et al.(2006)for China,and the GAINS-Asia model (Klimont et al.,2009)for India.For off-road vehicles and machinery,we directly tabulated the fuel use of ships,rail-road locomotives,and agricultural vehicles from IEA statis-tics.We also took into account the effect of superemitters of each vehicle type,since they could contribute a large frac-tion of carbonaceous aerosol emissions to the transportation sector(Bond et al.2004,and references therein),and rel-evant information was derived from the Speciated Pollutant Emission Wizard(SPEW)-Trend model(Yan et al.,2011). It is dif?cult to directly quantify the uncertainties of tech-nology divisions because(1)it is almost impossible to obtain the probability distribution and CI of X,and(2)the technol-ogy fractions in each fuel/product sector are highly correlated and should meet the constraint that
X=1.Alternatively, Bond et al.(2004)assigned the uncertainties in technology divisions by increasing the fraction of higher-emitting tech-nologies so that they contribute an additional10%of the total fraction,and decreasing the fraction of lower-emitting technologies by an equal amount.Here,we modi?ed this method to generate random variables of technology frac-tions.For fuel/product with two technology divisions(of which fractions are X1and X2),a uniform distribution was assumed to X1in the range of±0.1about the mean(i.e., [X1,mean?0.1,X1,mean+0.1]),while X2was calculated as1?X1.For fuel/product with three or more divisions(of which fractions are X1?X n),we assumed uniform distributions in the range of±0.1about the mean for both the highest-emitting(i.e.,[X high,mean?0.1,X high,mean+0.1])and lowest-emitting technology(i.e.,[X low,mean?0.1,X low,mean+0.1]), and simply determined the variation ranges of the other tech-nology fractions as±(1-X high?X low?
X other,mean)/(n?2).If negative numbers were generated for any combination, this series of random variables was discarded.In addition,
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when we believe our understanding of a certain technology division is more uncertain,we alter the fraction by0.3in-stead of0.1(e.g.,for coke making with/without controls and superemitter fractions in a vehicle?eet).
2.1.5Emission factors
As shown in Eq.(3),the emission factor of SO2is depen-dent on the fuel sulfur content(S)and the sulfur retention ratio in ash(SR).For China,S values of coal and oil con-sumed by combustion sources were derived from our previ-ous work and recent literature(Klimont et al.,2009;Ohara et al.,2007;Zhao et al.,2008;Streets et al.,2003,2006; Lu et al.,2010).The national average S of coal in China was1.10%in1996,1.08%in2000,and1.02%in2005. We used interpolation values to represent S in each year dur-ing1996–2005,and assumed the sulfur contents did not vary after2005,because no reliable data are available.The S of oil consumed in road transportation in China was determined from the national standards and the GAINS model,and it de-clined from0.05%to0.005%for gasoline and from0.28% to0.02%for diesel during1996–2010.For India,the S val-ues of fossil fuels were based on the data reported by Reddy and Venkataraman(2002a)and the GAINS model(Klimont et al.,2009).The mean S of coal in India was determined to be0.55%,and that of gasoline and diesel for road trans-portation decreased from0.18%to0.08%and from0.47% to0.05%between1996and2010,respectively.Regarding the probability distributions of S,we assumed normal distri-butions with uncertainties of20%for all fossil fuels(Smith et al.,2011;Streets et al.,2003).The SR ratio for coal-?red power plants in China was assumed to be10%with beta distributions(95%CI:7.5%–14%)based on?eld mea-surements by Zhao et al.(2011).For other sectors,SR ra-tios were set at5%–45%,depending on the process type, combustion technology,and coal type.Due to the lack of in-formation on?eld measurements,uniform distributions were assumed in the range of minimum and maximum values re-ported in the literature(Klimont et al.,2009;Ohara et al., 2007;Reddy and Venkataraman,2002a;Smith et al.,2011; Streets et al.,2003;Zhao et al.,2011).The SO2emission factors of biofuel combustion were based on the measure-ments by Habib et al.(2004),and we assumed normal dis-tributions with uncertainties provided in their work.For SO2 emission factors of industrial processes,values in the GAINS model were used and normal distributions with uncertainties of40%were applied based on the authors’judgment(Smith et al.,2011;Streets et al.,2003).
The emission factor of SO2is also strongly dependent on the application rate and the removal ef?ciency of?ue-gas desulfurization(FGD)devices.FGD application rates of power plants in China were estimated by the ratio of aver-age FGD installed capacity to the average capacity of power plants in each year.Relevant data were obtained from the China Ministry of Environmental Protection(MEP)and the China Electric Power Yearbook(State Electricity Regulatory Commission,2000–2009).Ideally,the SO2removal ef?-ciency of FGD can reach95%(Zhao et al.,2011).How-ever,actual operations rarely reach this(Xu et al.,2009).In the present work,symmetrically triangular distributions were assumed for the actual removal rate of FGD.We set the of?-cial data as the most likely value,and the ideal value of95% as the maximum to build the triangular distribution function. For example,the of?cial data from the China MEP reported that73.2%of SO2was removed from coal-?red power plants equipped with FGD in2007(MEP,2009).Thus,the mean value of removal ef?ciency of FGD in2007was73.2%with a triangular distribution in the range of51.4%to95%.For Indian power plants,the application rate of FGD is very low (<2%)(Reddy and Venkataraman,2002a)since Indian coal has a much lower sulfur content.Therefore,the effect of FGD on SO2emissions from India was not considered in this study.
Emission factor determinants of BC and OC for each of the sectors,fuels,and technologies in Eq.(2)were updated in collaboration with Professor Tami Bond on the basis of their previous work(Bond et al.,2004).In addition,we intro-duced minor adjustments after reviewing some new country-speci?c measurements of emission factors for biofuel com-bustion in India(Parashar et al.,2005;Venkataraman et al., 2005;Habib et al.,2008)and China(Cao et al.,2006;Li et al.,2009),and residential coal combustion in China(Chen et al.,2009;Zhi et al.,2008).Regarding the uncertainties of emission factor determinants,Bond et al.(2004)reviewed the BC and OC emission characteristics of various combus-tion sources comprehensively,and incorporated the informa-tion(central estimate,lower and upper bounds,etc.)of each parameter into a program called Speciated Particulate Emis-sions Wizard(SPEW).For the bulk particulate emission fac-tors(EF PM),Bond et al.(2004)found that the lognormal distribution provides a reasonable?t to the measured data. Hence,we assumed EF PM is lognormally distributed with 95%CI at the lower and upper bounds provided in SPEW. For other parameters(F1.0,F BC,F OC,and F control),uniform distributions in the range of lower and upper bounds provided in SPEW were assumed due to the limited data availability. Andreae and Merlet(2001)critically reviewed and eval-uated the emission factors of trace gases and aerosols from open biomass burning.Since not enough data are available, uncertainties of SO2,BC,and OC emission factors for open burning of forests,grasslands and agricultural wastes were characterized by the means and standard deviations of their data,assuming a normal distribution.
2.2Spatial allocation method
We used a“top down”approach to transform country-level emissions to gridded datasets.Sectoral emissions(exclud-ing emissions from power plants and forest and savanna burning)were?rst allocated to each province(or state),
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and then distributed on a0.1?×0.1?grid using appropriate year-by-year spatial proxies.For the?rst step,emission in-formation at provincial(or state)level was obtained from Lu et al.(2010)and Lei et al.(2011)for China,and the GAINS model(Klimont et al.,2009)for India.We gen-erated year-speci?c allocation factors at a resolution of a 0.1?×0.1?by using various types of Geographical Informa-tion System(GIS)datasets:(1)total population data were extracted from the LandScan Global Population Data Set de-veloped by Oak Ridge National Laboratory for the period 2004–2008(ORNL,2009),and from the History Database of the Global Environment(HYDE)developed by the Nether-lands Environmental Assessment Agency for the period be-fore2004(Goldewijk et al.,2011);(2)urban and rural pop-ulation data were developed based on the total population datasets and information from the Global Rural-Urban Map-ping Project(GRUMP)(CIESIN et al.,2004);(3)cropland cover data during1996–2007were obtained from an updated version of the Global Cropland Dataset(Ramankutty and Fo-ley,1999);(4)road networks were extracted from the Digi-tal Chart of the World(DMA,1993);(5)China’s industrial GDP at county level during2000–2008were obtained from the China County Statistical Yearbook(NBS,2001–2009). The allocation rules are:(1)road networks for on-road trans-portation emissions,(2)cropland cover for emissions from agricultural waste burning and off-road tractors,(3)China’s industrial GDP by county for industrial emissions in China, (4)EDGAR4.1(JRC/PBL,2010)industrial gridded emis-sions for industrial emissions in India,(5)rural population for residential biofuel combustion,(6)urban population for emissions from residential coal-?red boilers and open waste burning,and(7)total population for all other area sources. It should be noted that assessing the uncertainty in the spa-tial allocation is beyond the scope of this study and was not considered.
Emissions from biomass burning and coal-?red power plants were treated separately in this study.For open biomass burning from forest and savanna,gridded data from GFED v3.1(van der Werf et al.,2010)were directly used.For China’s coal-?red power sector emissions,year-by-year grid-ded data were obtained from our collaborators at Tsinghua University(Zhang et al.,2009b;Zhao et al.,2008).They were generated from a detailed,unit-based inventory speci?-cally for China’s power sector,and all power generation units with capacity larger than300MW(～400units)were iden-ti?ed as large point sources(LPSs),while other plants were treated as area sources.Similar to China,we also developed a detailed,unit-based inventory for India’s power sector.The unit-level information was derived from various series of the Performance Review of Thermal Power Stations(Central Electricity Authority,2000–2010),and all power generation units with capacity larger than20MW(～500units)were in-cluded.2.3Estimation of seasonal variations
Year-speci?c monthly temporal distributions for SO2,BC, and OC emissions from each major sector during1996–2010 were developed.For the residential sector,we followed the same methodology used in the TRACE-P inventory(Streets et al.,2003),assuming a dependence of stove operation on provincial(or state)monthly mean temperatures,to generate monthly emissions.For the other sectors,monthly temporal distributions were determined from monthly activity data of power generation,industrial GDP(or industrial production index),sulfuric acid and coke production,volume of pas-senger and freight transported by ship,railway,and aviation, etc.(Reserve Bank of India,2010;Central Statistical Orga-nization,2000–2010;NBS,1997–2010b,c).The monthly emissions of open biomass burning from forest and savanna were obtained directly from GFED v3.1(van der Werf et al., 2010),and those from agricultural waste burning were de-termined based on the work of Wang and Zhang(2008)for China and Venkataraman et al.(2006)for India.
2.4Data sets of SO2and AOD
SO2and AOD satellite data are used to compare with our emission estimates.The SO2satellite data are from the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography(SCIAMACHY,aboard the European Space Agency’s ENVISAT satellite,launched in March2002)and the Ozone Monitoring Instrument(OMI,aboard NASA’s EOS/Aura satellite,launched in July2004).Appropri-ate air-mass factors(AMF)are required to convert the re-trieved slant columns of SO2from both instruments into vertical columns.The value of AMF is dependent on the satellite viewing geometry,the SO2vertical distribu-tion,the re?ectivity(albedo)of the earth’s surface,the to-tal column ozone,aerosols,clouds,etc.(Krotkov et al., 2008;Lee et al.,2009).For SCIAMACHY,we used the monthly level-3product with grid cells of0.25?×0.25?from the Support to Aviation Control Service(SACS,http: //sacs.aeronomie.be/index.php),for which the AMF was pre-calculated with the radiative transfer model LIDORT. For OMI,the planetary boundary layer(PBL)SO2data in the OMSO2Level-2G products were used(a?xed global AMF of0.36is applied),and they were acquired from NASA’s Goddard Earth Sciences Data and Informa-tion Services Center(GES-DISC)at http://disc.sci.gsfc.nasa. gov/Aura/data-holdings/OMI/omso2g v003.shtml.Daily re-trievals were?rst?ltered to remove data with large so-lar zenith angle(>70?),or relatively high radiative cloud fraction(>0.3)and terrain height(>1.5km),or anomalous scenes,and then averaged at0.5?×0.5?resolution to reduce the noise(Nickolay Krotkov,personal communication).An-nual mean SO2column amounts were then calculated from the daily data for the years2005–2010.
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19962000200420082010 SO2Power plants9104995915655124866587 Industry114368559118901637020388 Residential32121947204823652931 Transport499614865819857
Coal2273719448276972888127372 Oil709845136312741352 Biofuel8987126129127 Other716699127317561912 Forest&savanna burning1423101414 Agricultural waste burning5451525858 Total2431821153305203211230834
BC Power plants1211141921 Industry527370437510501 Residential790639826888936 Transport92139194259283 Coal849518576636662 Oil141205290396434 Biofuel418417583620619 Other1418212425 Forest&savanna burning1219101312 Agricultural waste burning9086889797
Total15241263156917871850
OC Power plants1210111011 Industry520359405446384 Residential21501893251926702790 Transport85152197241260 Coal1120685740821850 Oil102175229284308 Biofuel15281533213822342257 Other1721252830 Forest&savanna burning12721288126126 Agricultural waste burning427409419467463
Total33223035363839594033
AOD satellite retrievals are from the Moderate Resolu-tion Imaging Spectroradiometer(MODIS)and Multi-angle Imaging Spectroradiometer(MISR).The MODIS sensors are aboard both the NASA EOS/Terra and EOS/Aqua satellites, which were launched in December1999and May2002,re-spectively.The MODIS AOD retrieval is based on scene brightness over dark surfaces,using empirical relationships in the spectral variation in surface re?ectivity(Remer et al., 2005).The AOD data have discontinuities in some mesh grid points,mainly in middle and high latitudes(i.e.,bright land surfaces such as the desert and snow-covered surfaces), which were excluded in the analysis.Besides MODIS,the EOS/Terra satellite also has the MISR instrument on board.It uses observed differences in the re?ective properties of Earth’s surface with nine viewing angles to retrieve AOD (Kahn et al.,2005).In this study,the monthly level-3prod-ucts of Terra-MODIS(v5.1,550nm),Aqua-MODIS(v5.1, 550nm),and MISR(v31,555nm)are used,and they were acquired using the NASA’s GES-DISC Interactive Online Visualization and Analysis Infrastructure(Giovanni)(http: //https://www.wendangku.net/doc/6416532074.html,/giovanni).Global coverage in the ab-sence of clouds is obtained in one to two days for MODIS and in six to nine days for MISR.Horizontal resolutions are 1?×1?and0.5?×0.5?for MODIS and MISR,respectively. For the purpose of identifying the months in which an-thropogenic emissions have the greatest impact on AOD,and
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19962000200420082010 SO2Power plants25503251379147085236 Industry19451973210225442784
Residential374321350543583
Transport263225207192144
Coal33753779455960196730
Oil15331732159316381661
Biofuel8784879999
Other138175210232257
Forest&savanna burning1715171417
Agricultural waste burning3633364244
Total51855819650280448807 BC Power plants34455 Industry155168198217227
Residential402421481563579
Transport808888107111
Coal177172209276295
Oil117126124153159
Biofuel338373426449454
Other810121415
Forest&savanna burning1917191619
Agricultural waste burning6056597174
Total7187538509791015 OC Power plants68101214 Industry155166195208214
Residential13791476172518991946
Transport5261565854
Coal203186226322346
Oil6776707572
Biofuel13131438167617631792
Other911141717
Forest&savanna burning157142158133157
Agricultural waste burning287269285340354
Total20352122242926512739
obtaining the conversion factors between AOD and emission mass(see Sect.4.1in detail),we use results from the GO-CART model updated to version c3.1simulation for2000–2007(Chin et al.,2009)(available on Giovanni).The GO-CART model simulates physical and chemical processes of major tropospheric aerosol components,including sulfate, dust,BC,OC,and sea salt,as well as the precursor gaseous species of SO2and dimethylsul?de(DMS).It uses the assim-ilated meteorological?elds of the Goddard Earth Observing System Data Assimilation System(GEOS DAS)version4, with a spatial resolution of1?latitude by1.25?longitude,and 30vertical sigma layers.The annual anthropogenic emis-sions of SO2,BC,and OC are based on our previous work (Bond et al.,2004;Streets et al.,2006,2009),and time-varying emissions from aircraft and ships,biomass burning, biogenic,oceanic and volcanic sources,wind-blown dust,sea salt,and so on are also included.AOD in the model is deter-mined from the dry mass concentrations and mass extinction ef?ciencies which are calculated from Mie theory on the ba-sis of size distributions,refractive indices,and hygroscopic properties of individual aerosol types.
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Fig.2.SO2emissions by sector and fuel type in(a)China and(b)India during
1996–2010. Fig.3.BC emissions by sector and fuel type in(a)China and(b)India during1996–2010.
3Emissions of SO2,BC,and OC from China and India during1996–2010
3.1Emissions overview
Tables1and2summarize SO2,BC,and OC emissions by major emitting sector and fuel type in China and India,re-spectively,during1996–2010.The net emission factors are shown in Tables S3and S4in the Supplement.
3.1.1SO2
Figure2a shows the annual trend of SO2emissions and its distribution among sectors and fuel types in China.Gener-ally,the trend can be divided into three distinct time periods. During1996–2000,SO2emissions decreased by13%from 24.3Tg to21.2Tg.This is consistent with the estimates of previous work(Ohara et al.,2007;Smith et al.,2011;Streets et al.,2006,2008),and the decline is attributed to the com-bination of a slowdown in economic growth caused by the Asian economic crisis,the fundamental restructuring of the Chinese industrial economy,a decline in coal use in the resi-dential and industrial sectors,and a reduction in the average sulfur content of coal burned(Ohara et al.,2007;Streets et al.,2003).After2000,SO2emissions in China increased dramatically by61%from21.2Tg in2000to34.0Tg in 2006,with an annual growth rate(AGR)of8.2%.This growth rate is slightly higher than our previous estimate of 7.3%(Lu et al.,2010),which was calculated from of?cial Chinese energy statistics,but is still in good agreement with values reported in other bottom-up inventories(6.3%–9.9%) (Klimont et al.,2009;Ohara et al.,2007;Smith et al.,2011; Zhang et al.,2009b)and derived from satellite constraints (6.2%–9.6%)(van Donkelaar et al.,2008).The dramatic change during this period was driven by the rapid increase of energy consumption(87%growth,Fig.1a)due to the eco-nomic boom(99%growth in GDP).Although GDP and en-ergy consumption in China were still increasing after2006, national SO2emissions began to decrease,due to the appli-cation of FGD technology and the phase-out of small,high-emitting power generation units(Lu et al.,2010).During 2000–2010,the average FGD penetration rate in China in-creased from1%to78%,and the net emission factor of coal-?red power plants decreased by76%correspondingly (Table S3in the Supplement).By the end of2010,FGD penetration of power plants had risen to83%,which is esti-mated to eliminate about19.4Tg SO2in that year.In terms of fuel-type and sectoral contribution,coal combustion was the single largest contributor(89%–93%).Emissions from the power sector increased from37%in1996to51%in2004, and later decreased to21%in2010.The contribution of
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industry decreased from47%in1996to38%in2002,but increased to66%in2010.The share of the residential sector slightly varied between6%and13%.The ongoing tension between two forces–economic development and environ-mental protection–causes the emission trends to be decid-edly non-linear,as the government imposes new measures to address one aspect of air pollution or another,and it is im-portant to re?ect these signi?cant changes from year to year in emissions and impacts analysis.
The temporal evolution of SO2emissions from India and its sectoral and fuel-type distribution between1996and2010 is shown in Fig.2b.In contrast to the situation in China, anthropogenic SO2in India shows a continuously increas-ing trend,which re?ects rapid economic and social devel-opment driven by growing fossil-fuel use and relatively lax emission controls.The national emissions increased by70% from5.2Tg in1996to8.8Tg in2010with an AGR of3.9%. Power plants are the main sources of SO2(contributing49% in1996and59%in2010to the total emissions),followed by industry(～34%),residential(～6%),and transportation (～3%).Compared to a35%increase in emissions in other sectors,SO2emission from power plants increased by105%, from2.6Tg in1996to5.2Tg in2010,which can be viewed in the context of a117%increase of total thermal-based electricity generation during the same period.Although the contribution of emissions from coal combustion(～69%)is smaller than that of China(>89%),it dominates the growth of national emission.During1996–2010,SO2emissions from coal combustion increased by3.4Tg,accounting for 93%of the national growth.
3.1.2BC
Figure3displays BC emissions by sector and by fuel type in China and India.Although both SO2and BC are mainly from the process of fuel combustion,and the trends between SO2and BC emissions are similar to some extent in China and India,the emission distributions among sectors and fuel types are quite different.First,BC is produced mostly from incomplete combustion in small,low-temperature facilities and not power plants or large industrial facilities,whereas SO2emissions are closely related to the total coal and oil use.Second,a signi?cant amount of BC is produced from biofuel combustion and open biomass burning,whereas both of these generate little SO2.
In China,the trend of BC emissions is controlled by the balance between decreasing net emission factors for major sources and increasing activity rates.To improve air qual-ity,the Chinese government has issued a series of emission standards for PM emitting sources during1996–2010,and a large number of emission reduction measures have been implemented.These include:replacing cyclones and wet scrubbers on power and industrial boilers with electrostatic precipitators and fabric?lters;increasing the market share of boilers with large capacity;converting residential coal use from raw coal to briquettes,and introducing cleaner fuels like LPG and electricity;phasing out beehive brick kilns and indigenous coke production facilities;implementing vehicle emission standards from Euro I to Euro IV,etc.(Chen et al., 2009;Lei et al.,2011;Zhang et al.,2009b).As a result, these measures caused dramatic changes in the technology distribution as well as the emission factors in the relevant sectors.For example,the mean BC emission factors of coal consumed in the industrial and residential sector decreased by64%and34%,respectively,during1996–2010(Table S3 in the Supplement).The decrease of emission factors for ma-jor BC sources(except for biofuel combustion),along with the decrease of industrial and residential coal consumption, is the main reason for a17%BC emission decline in China during1996–2000(Fig.3a).Although the emission factors for major BC sources were still decreasing after2000,BC emissions in China increased by46%from1.26Tg in2000 to1.85Tg in2010.This was driven by rapidly increasing energy consumption(144%growth,Fig.1a),industrial pro-duction(e.g.,292%growth in coke production),and vehicle population(366%growth).The dramatic increase of activ-ity rates counteracts the effect of technology improvements, and makes the BC emissions continuously grow.Examining the sectoral distributions,the residential sector is the main source of BC(51%±3%).The contribution of the industry sector decreased from35%in1996to27%in2010,whereas that of transportation increased from6%to15%. Although there were some PM reduction measures in In-dia during1996–2010(e.g.,replacing traditional cookstoves with improved cookstoves,implementing new emission stan-dards for vehicles,etc.),the progress was not as fast as in China.As shown in Table S4in the Supplement,the mean emission factors of major BC sources only have small changes over time.Therefore,the trend of BC emissions in India is governed by the trend of energy consumption.Fig-ure3b shows that BC emissions from India increased steadily from0.72Tg in1996to1.02Tg in2010,with an AGR of 2.5%.Biofuel combustion was the dominant contributor in India(45%–52%),followed by coal(22%–29%)and oil(14%–17%).The distribution of BC emissions among different sectors was relatively stable during1996–2010,at about57%,22%,11%,8%,and2%for residential,indus-try,transportation,agricultural waste burning,and forest and savanna burning,respectively.
3.1.3OC
Similar to the BC trend for China,the anthropogenic OC emissions(i.e.,excluding forest and savanna burning)de-creased from3.20Tg in1996to2.82Tg in2000,but then increased to3.91Tg in2010(Fig.4a).The residential sector (69%),especially for biofuel combustion(56%),is the dom-inant contributor of anthropogenic OC emissions in China. With the rapid increase of vehicle population and continuous decrease of emission factor in industrial coal use,the share
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Fig.4.OC emissions by sector and fuel type in(a)China and(b)India during1996–2010.
of transportation increased from3%in1996to7%in2010, and that of industry decreased from16%to10%.Open biomass burning of agricultural waste is another large source, accounting for about13%of the anthropogenic OC emis-sions.For India,anthropogenic OC increased by38%from 1.88Tg to2.58Tg during1996–2010(Fig.4b).The highest sectoral contributor is from the residential sector(75%,in which biofuel combustion accounts for96%),followed by agricultural waste burning(14%),industry(8%),and trans-portation(3%).
We note a signi?cant year-to-year variation in OC emis-sions from open biomass burning of forest and savanna, which is determined by the extent of?res,and is driven largely by precipitation amounts and soil moisture content. OC emissions from this source type accounted for2%–11% and4%–18%of the total emissions in China and India,de-pending on the year.As shown in Fig.4,2003was a year of extensive open biomass burning in China,while1999was such a year for India.This variability is the main cause of the interannual?uctuations in the trend of total OC emissions in China and India.
3.2Uncertainties
Using the Monte Carlo approach,we provide95%CIs for all the model outputs.The uncertainty ranges of SO2,BC, and OC emissions by major sector and fuel type are shown in Tables S1and S2in the Supplement for China and India, respectively.The net emission factor uncertainties by sec-tor and fuel type are listed in Tables S3and S4in the Sup-plement.Since SO2emission is largely dependent on sulfur contents and activity rates of fossil fuels,it has lower uncer-tainty than BC and OC emissions which are strongly in?u-enced by combustion condition.The average uncertainties of SO2,BC,and OC emissions were estimated to be?16% to17%,?43%to93%,and?43%to80%for China,and ?15%to16%,?41%to87%,and?44%to92%for In-dia.The right subgraphs of Figs.5and6display the emis-sion distributions of each species in the year2010,according to Monte Carlo simulations.The distribution of SO2is ap-proximately symmetric since most of the relevant parameters were assumed to have normal or uniform distributions.BC and OC distributions are asymmetric,re?ecting our lognor-mal treatment of emission factors and biofuel consumption. Table3shows the average contribution of each sector to total uncertainties during1996–2010.Power plants and industry contribute more than83%of the SO2emission uncertainty in both China and India.The residential sector is the sin-gle largest contributor to uncertainty of carbonaceous aerosol emissions(>60%for BC and>67%for OC),followed by industry,open biomass burning,transportation,and power plants.Examining the interannual variation of the uncer-tainty for all three species in both countries,95%CIs have no obvious change except for BC emissions in China(Ta-bles S1and S2in the Supplement).The signi?cant decrease of China’s BC uncertainty over time can be explained by the decreasing share of residential and industry emissions,which are highly uncertain.
We also conducted sensitivity analysis of the outputs.As shown in Fig.7,the results are expressed as the contribu-tion of each parameter in the model to the total variance of emission estimates.In this study,more than600input pa-rameters are included in the Monte Carlo framework.We therefore aggregated them into several major parameters or fuel/usage combinations.For example,the combination“oil”in Fig.7a and b includes the contributions of sulfur contents and fuel use of all kinds of oils;the combination“Wood/RE”in Fig.7c–f includes the contributions of technology divi-sions,fuel use,and all emission factor determinants of fuel-wood combustion in the residential sector.
For SO2emissions in China,hard-coal related parameters contributed to more than96%of the variances before2005 (Fig.7a).The proportions of sulfur content,fuel use,and sulfur retention in ash of hard coal were61%,27%,and 10%,respectively.After2005,SO2emissions were sen-sitive to the FGD removal ef?ciency,the shares of which in the variances were in the range of1%to16%,depend-ing on the year.As we mentioned previously,FGD devices were widely installed during China’s11th Five-Year Plan pe-riod(2006–2010).However,the actual operation of FGD
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https://www.wendangku.net/doc/6416532074.html,parison of emission estimates(excluding emissions from open biomass burning)for China:(a)SO2,(b)BC,and(c) OC.The right subgraphs present the distributions of estimated emis-sions in2010.The blue bars are beyond the95%CIs. equipment is unknown.It was reported that China’s of?-cial data overestimated the actual performance of SO2scrub-bers before2007(Xu et al.,2009;Xu,2011).For exam-ple,of?cial data announced that73.2%of SO2was
removed https://www.wendangku.net/doc/6416532074.html,parison of emission estimates(excluding emissions from open biomass burning)for India:(a)SO2,(b)BC,and(c)OC. The right subgraphs present the distributions of estimated emissions in2010.The blue bars are beyond the95%CIs.
from coal-?red power plants that had FGD in2007(MEP, 2009),whereas this rate was found to be only64.1%in the coastal province of Jiangsu,which has a relatively good track record on environmental protection(Xu et al.,2009).Due to
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Fig.7.The contributions of major parameters or fuel/usage combinations to variance in SO2(a,b),BC(c,d),and OC(e,f)emissions from China(a,c,e)and India(b,d,f)during1996–2010.IN,RE,and TR represent industry,residential,and transportation sector,respectively.
Table3.Average contribution of each sector to total uncertainties during1996–2010(unit:%).
China India
SO2BC OC SO2BC OC Power plants37204611
Industry47291437238
Residential136067106574 Transport266361
Forest&savanna burning012124
Agricultural waste burning13113512the sharply expanded FGD installation and low FGD opera-tion,the parameter“FGD removal ef?ciency”played an in-creasingly important role in the uncertainty of national emis-sions during2005–2007(Fig.7a).However,the situation has changed since2007.To motivate the use of FGD equip-ment,the Chinese government has taken several measures since2007,including the installation of continuous monitor-ing systems in power plants with FGD,the implementation of a premium/penalty scheme of electricity price that varies with the FGD’s operation,and severe penalties for the non-operation of FGD(Xu,2011).These new policy incentives were reported to be effective.For example,also in Jiangsu province,FGD devices were found to be operating with SO2 removal ef?ciencies of over90%for more than90%of the
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time after July2007(Xu et al.,2009).Most FGD devices in China operate properly in2009based on a series of?eld interviews conducted by Xu(2011).Therefore,the impor-tance of FGD removal ef?ciency to the emission uncertainty is reducing after2007(Fig.7a).Similar to China,hard-coal related parameters are also the largest contributors to the SO2 emission variances in India(>89%,Fig.7b),and their con-tributions are continuously increasing over time due to the increasing share of hard coal in the Indian energy structure (Fig.1b).
The contributions of major fuel/usage combinations to variances of carbonaceous aerosol emissions in China and India are shown in Fig.7c–f.For BC emissions in China, combustion of residential agricultural waste,residential fuel-wood,residential coal,industrial coal,and coke-making pro-cess are the largest contributors,together comprising91%–97%of the variances(Fig.7c).The shares of fuel/usage combinations are changing over time,re?ecting the changes of technology divisions,emission characteristics,and activ-ity rates.For example,we estimate the shares of the mecha-nized and the indigenous(i.e.,traditional)coking facilities by using the coke productions of these two manufacturing tech-nologies,which are reported annually in the China Industry Economy Statistical Yearbook(NBS,1997–2010a).Based on the of?cial statistics,the share of indigenous coking fa-cilities,the emission characteristics of which are highly un-certain,decreased from50%to0%during1996–2010.As a result,the contribution of the coke-making process to the variance has decreased from about15%in1996to2%in 2010.Similarly,the contribution of residential coal combus-tion to variance decreased from36%to13%,and the reason is mainly attributed to the increasing proportion of briquettes used in residential stoves.Different from BC,OC emis-sions in China are much more sensitive to residential biofuel combustion(Fig.7e),which accounts for64%–84%of the variances.The second largest variance is due to residential coal combustion,accounting for4%–20%of the variances, depending on the year.Due to the relatively lax applica-tion of PM emission controls in India,the contributions of major fuel/usage combinations to variances of carbonaceous aerosol emissions were relatively stable during1996–2010 (Fig.7d and f).For BC emissions in India,the largest vari-ance is due to the residential fuelwood combustion(～70%), followed by coal combustion in the industrial(～15%)and residential(～5%)sectors.Residential fuelwood combus-tion accounts for an even higher rate for Indian OC emission variances(>83%).It should be noted that the transportation sector is not a big contributor to variance of either species in China and India(<3%).This is different from the sit-uation in regions like North America and Europe(Bond et al.,2004),because carbonaceous aerosol emissions in China and India are mainly from residential biofuel and coal com-bustion,which have higher uncertainties.For both countries, open biomass burning only contributes1%and4%of the BC and OC emission variances,respectively.This is much lower than the fractions estimated by Bond et al.(2004).The reason is mainly due to the improved methodology in esti-mating the open burning of agricultural waste and the use of GFEDv3.1datasets,the uncertainty of which is relatively well quanti?ed.
One bene?t of Fig.7is that it can point out areas in which additional research could help to reduce uncertainties.For SO2emissions,more information on sulfur contents of coals and precise coal consumption data are essential to get reliable emission estimates.More?eld measurements of PM emis-sion characteristics in residential biofuel combustion,resi-dential/industrial coal combustion,and coke making will be critical to improve the carbonaceous aerosol emission esti-mates in the future.
3.3Comparison with previous studies
3.3.1Bottom-up inventories
Figures5and6compare the emission estimated in this study(excluding emissions from open biomass burning)to other bottom-up inventories,including regional and global inventories,such as GAINS(Klimont et al.,2009),REAS (Ohara et al.,2007),TRACE-P(Streets et al.,2003),INTEX-B(Zhang et al.,2009b),HTAP-EDGAR(http://edgar.jrc.it/ eolo/),EDGAR4.1(JRC/PBL,2010),AEROCOM(http:// dataipsl.ipsl.jussieu.fr/AEROCOM/emissions.html),Bond et al.(2004,2007),and Smith(2011);and country-speci?c emission estimates,such as Lu et al.(2010),Streets et al.(2000,2001),Lei et al.(2011),Cao et al.(2006),and Zhao et al.(2009,2011)for China,and Reddy and Venkatara-man(2002a,b),Venkataraman et al.(2005),Dickerson et al.(2002),Parashar et al.(2005),Mitra and Sharma(2002), Sahu et al.(2008),and Garg et al.(2006)for India.Some of these other estimates are trends,and some are single-year estimates.
As shown in Figs.5a and6a,most previous estimates of SO2emissions from China and India are within the95%CIs of the current study,except for China’s SO2emission esti-mated by the REAS(Ohara et al.,2007)and reported by the China MEP(MEP,2011),and India’s emission estimated by Garg et al.(2006).The discrepancies between different stud-ies are caused by a combined effect of the different amounts and distribution of fuel consumption between sectors and the implied emission factor assumptions(Klimont et al.,2009). China’s SO2emissions in the REAS inventory have been found to be too high by a number of researchers(Aikawa et al.,2010;Klimont et al.,2009;Lu et al.,2010;Smith et al., 2011;Zhang et al.,2009b;Zhao et al.,2011),especially for the period after2000.After examining the emissions care-fully,we attribute the discrepancies mainly to the high emis-sion factors chosen in the REAS inventory.For example,the emission factor for industrial coal combustion in the REAS inventory is934.2g GJ?1in2000,which is70%higher than our value(549.2g GJ?1)and outside our uncertainty range
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(449.9–660.1g GJ?1)(Table S3in the Supplement).Due to this factor alone,SO2emissions in China in REAS are over-estimated by4.2Tg in2000.Although ef?ciencies of SO2 removal by power plants were considered in three REAS sce-narios in China,the main reason for the consistency between the REAS and our estimate in2010is the seriously under-estimated fuel consumption in China.The fuel consumption was projected to be57.9EJ in the REAS reference scenario of2010,which is45%lower than our data(105.6EJ,Fig.1). Comparing with REAS,China’s fuel consumption in2010in the GAINS model(97.1EJ)is much closer to the actual data. However,the net emission factor of power plants for coal in the GAINS model(677g GJ?1and440g GJ?1for old and new power plants,respectively)is much higher than the cur-rent work(mean value of204g GJ?1,see Table S3in the Supplement and Sect.3.1.1).For this reason,the GAINS’s estimation in2010is higher than this study.Our estimates follow the trend of values reported by the China MEP(MEP, 2011)(R=0.83),but are signi?cantly higher,which may be caused by the omission of SO2emissions from rural in-dustries and biofuels in the China MEP inventory(Streets et al.,2003;Zhang et al.,2009b).For India,all of the esti-mates show an increasing SO2emission trend during1996–2010(Fig.6a).The AGR of our estimates is3.9%,which is in line with AGRs of3.5%–5.1%estimated by other re-searchers during this period.The Garg et al.(2006)values are below the lower bounds of the95%CIs calculated in this study.This is mainly due to the lower coal consumption (25%lower than this study)used in their calculation.
In general,the agreement among estimates of BC emis-sions for China is reasonably good(Fig.5b),though these studies rely to a greater or lesser extent on the same original emission factors presented by Streets et al.(2001)and Bond et al.(2004),which,however,have a much larger uncertainty. For India,the data are more scattered(Fig.6b),mainly due to the widely varying emission factors for residential bio-fuel combustion that were applied in the different studies. For example,the BC emission factors of residential biofuel are about1.0–1.3g kg?1in GAINS,REAS,TRACE-P,and Parashar et al.(2005),which are at least twice the values used in Bond et al.(2004,2007),Reddy and Venkataraman (2002b),and Venkataraman et al.(2005)(around0.5g kg?1). In this work,new emission factors for biofuel combustion obtained from?eld tests have been incorporated(Habib et al.,2008;Parashar et al.,2005;Venkataraman et al.,2005). However,it remains the situation that much of the underly-ing analytical approach relies on emission factors extracted from PM measurements in developed countries that may or may not be re?ective of the true nature of Chinese and/or India emitters.Even when emission factors have been mea-sured in?eld tests in developing countries,there is a surpris-ingly high uncertainty,re?ective of the fact that the condi-tions of the stove,air?ow,fuel,and combustion conditions –which vary from household to household–dictate the na-ture of the particles that are generated.The aggregate amount of fuel burned in households must also inevitably be uncer-tain.Besides the lower BC emission factors of residential biofuel used in Venkataraman et al.(2005)and Reddy and Venkataraman(2002a,b),the fact that emissions estimated in these two studies are slightly below the lower bounds of 95%CIs of this work is also attributed to the omission of sources like residential coal combustion and/or biofuel con-sumption for heating.Although Sahu et al.(2008)’s estima-tion(1344Gg)for2001lies within the uncertainty range es-timated here,they used extremely high emission factors for fossil fuels,which are no longer used by the community. Figures5c and6c compare the OC emissions from China and India estimated in this study to other work.For China, the agreement among different estimates is quite good,al-though the emission factors are highly uncertain.OC emis-sion estimates are generally in the range of2.0–3.5Tg yr?1, with the exception of the point estimate of3.8Tg for2000by Cao et al.(2006),whose industrial emission value(1.12Tg) was much higher than other studies(around0.03Tg).The agreement for OC emissions in India is even worse than for BC.The poor agreement is attributed to the enhanced role of biofuel/biomass burning and the dif?culties in obtaining good emission factors and estimating reliable activity levels for these sources.The laboratory-test results of Venkatara-man et al.(2005)and Habib et al.(2008)indicate that OC emission factors of fuelwood are about0.4g kg?1at low burn rates,whereas they rise to2.7g kg?1at high burn rates.Their results also show that the OC emission factor varies in the range of0.6–4.7g kg?1between different types of agricul-tural residue.For dung cake,their measurements give an OC emission factor of about2.4g kg?1;however,Parashar et al.(2005)found it could be as high as12.6g kg?1un-der smoldering conditions during its use as a source of en-ergy in rural areas of India.OC emission factors of biofuel used in other studies in Fig.6c are3.45g kg?1for GAINS, 5.0g kg?1for TRACE-P,and6.28g kg?1for REAS.The large range of emission factors brings high uncertainty to the OC estimates,especially for India,where biofuel combustion is dominant.
3.3.2Uncertainty range
We have compared our uncertainty ranges with those re-ported in other studies.Our estimated uncertainty ranges of SO2emissions(about±16%)are close to the results of TRACE-P(±13%),INTEX-B(±12%),and Zhao et al.(2011)(±14%)for China,but lower than TRACE-P for India(±26%)and Smith et al.(2011)for both China (±29%)and India(±24%).Estimates of carbonaceous aerosol emissions in this work are signi?cantly improved. The average uncertainty ranges of BC(?43%–93%)and OC(?43%–80%)in China are much lower than the re-sults in TRACE-P(?83%–584%for BC and?83%–595% for OC),INTEX-B(?68%–308%for BC and?72%–358%for OC),Lei et al.(2011)(?65%–287%for BC and
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Fig.8.Emission distributions of SO2,BC,and OC at0.5?×0.5?resolution in2000and2008.International shipping and aviation are not included.
?70%–329%for OC),Bond et al.(2004)(?36%–149% for BC and?44%–103%for OC),and Zhao et al.(2011) (?25%–136%for BC and?40%–121%for OC).For India, our results(?41%–87%for BC and?44%–92%for OC) are also lower than the estimations of TRACE-P(?78%–459%for BC and?84%–644%for OC)and Bond et al.(2004)(?38%–119%for BC and?43%–93%for OC). The following reasons may be attributed to the reduction of uncertainties.First,we applied the Monte Carlo approach to our detailed technology-based emission model,and the “compensation-of-error”mechanism of Monte Carlo simu-lation can reduce random errors signi?cantly(Zhao et al., 2011).Second,in the present work,we obtained more de-tailed information about the technology distribution,activ-ity rate,and emission characteristic for both China and In-dia.Third,some newly developed methodologies or inven-tories were incorporated,e.g.,the GFED3.1inventory,unit-based power-plant emission inventories,newly estimated In-dian biofuel consumption,etc.
3.3.3Constraints from observations and models Bottom-up emission inventories can be evaluated,con-strained,and improved by observations directly(includ-ing ground-,aircraft-,and balloon-based measurements and satellite retrievals)or by the forward or inverse modeling of these observations.In previous work,we compared the SO2emissions in China with a variety of observed sulfur related quantities over East Asia,including SO2and SO2?
4 concentrations,surface solar radiation,and AOD(Lu et al.,
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Fig.9.Average seasonality of SO2,BC,and OC emissions(a)and monthly pro?les of major sectors(b)in China and India during1996–2010.
2010).We found the trends of these observations are gener-ally consistent with the trend of our SO2emission estimates during2000–2008.Van Donkelaar et al.(2008)analyzed AOD data from MISR and MODIS for2000–2006with the GEOS-Chem model.They derived the annual growth in Chi-nese sulfur emissions to be6.2%and9.6%,respectively, which is in good agreement with our current work(8.2%). Aikawa et al.(2010)compared the measured sulfate concen-tration at multiple sites over the East Asia Paci?c Rim region with CMAQ model simulations using both the REAS and the China MEP SO2inventories.They concluded that the REAS inventory overestimates,whereas the China MEP inventory underestimates the SO2emissions from China.Our central estimates as well as uncertainty ranges fall in the middle of these two inventories.During the TRACE-P and the ACE-Asia?eld experiments,intensive measurements were used in conjunction with forward and inverse modeling analysis to evaluate emission estimates for Asia.The results indicated that SO2emissions in the TRACE-P inventory are reason-able(Carmichael et al.,2003;Russo et al.,2003),while BC emissions are qualitatively correct at the national level,but the spatial distributions are questionable(Carmichael et al., 2003;Hakami et al.,2005).Recently,Kondo et al.(2011) estimated the BC emission rate of China by comparing BC concentrations observed at a remote site in the East China Sea and those predicted by3-D chemical transport models. They derived the annually averaged BC emission?ux over China to be1.92Tg with an uncertainty of about40%dur-ing2008–2009.This value is very close to our estimation of 1.79Tg with an uncertainty of?41%–84%in2008.
3.4Gridded emissions
Figures S1–S3in the Supplement show the spatial distribu-tions of SO2,BC,and OC emissions in China and India at a resolution of0.1?×0.1?in1996,2000,2005,and2010.The annual gridded emissions data by sector are available from the corresponding author.To present the emissions from LPSs more clearly(especially for SO2emissions from power plants),we give the emission distributions at a resolution of 0.5?×0.5?in Fig.8.As shown in Fig.8,a signi?cant in-crease of emissions can be seen in both countries between 2000and2008.For SO2,emission?uxes are high at grids with power plants and industrialized city clusters(e.g.,east-ern central China and Sichuan Basin).More SO2hot spots are observed in China than in India during2000–2008be-cause the increase of thermal based electricity generation in China was realized by building new power plants–often in undeveloped parts of the country–whereas that in India was realized by increasing the capacities of existing plants. Compared to SO2,high emission regions of carbonaceous aerosols are not concentrated in hot spots,but spread across eastern and central China and the northern and eastern states of India where rural population densities are high and resi-dential coal and biofuel combustion are prevalent.
3.5Seasonality of emissions
Figure9presents the average seasonality of SO2,BC, and OC emissions,as well as sectors with signi?cant monthly variations(maxima/minima>1.2)in China and In-dia.Biomass burning of forest and savanna occurs usually in February–June for both countries,and that of crop waste burning peaks in July and October for China,and April and September–November for India,corresponding to the major harvest seasons.Residential emissions in China are higher in December,January and February due to residential heat-ing needs in winter.Signi?cant monthly variations are also found in the power and industry sectors of China.Emis-sions are higher in December and lower in February,with maxima-to-minima ratios of1.4and1.5for the power and industry sectors,respectively.Regarding the seasonality of each species,it is a combination of sectoral emissions on the basis of their weight contribution to the total emissions.The ratios of monthly SO2,BC,and OC emissions between max-ima and minima are1.4,2.1,and2.5for China,and1.1,1.2, and1.5for India(Fig.9a).
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Fig.10.AOD from GOCART model simulations and the MODIS(Terra and Aqua)and MISR satellite instruments over eastern central China(latitude<45?N,longitude>100?E).(a)Monthly variation of GOCART AOD and the combined contribution from sulfate,BC,and POM to total AOD during2000–2007.(b)Monthly mean variability of satellite AOD retrievals during September to January2000–2010. Solid and dashed lines represent the linear tendencies before and after2006,respectively.(c)Trend of estimated AOD due to SO2,BC, and OC emissions,and evolutions of satellite AOD averaged between September and January during2000–2010.R values shown are the correlation coef?cients of each satellite AOD with estimated AOD.Error bars express one standard deviation of the monthly mean.
4Comparison of emission estimates and satellite observations
As mentioned in Sect.3.3.3,observations from?eld mea-surements and satellites can be used directly to constrain bottom-up emission https://www.wendangku.net/doc/6416532074.html,paring with ground-, aircraft-,and balloon-based measurements,satellite observa-tions provide better temporal sampling and spatial coverage. In the following section,we will use satellite retrievals of AOD and SO2to verify the emission trends of this study.4.1AOD
AOD is strongly in?uenced by the natural particulate com-ponent(e.g.,dust and sea salt)in China and India(Chin et al.,2009;Streets et al.,2009).To compare the satellite AOD and our emission estimates,the?rst step is to identify the months in which anthropogenic emissions have the great-est impact on AOD.Figures10a and11a show the monthly AOD variations of the major aerosol components over east-ern central China(latitude<45?N,longitude>100?E)and India from GOCART model simulations.For eastern central China,dust(originating mainly from the Taklimakan Desert
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Fig.11.AOD from GOCART model simulations and the MODIS(Terra and Aqua)and MISR satellite instruments over India.(a)Monthly variation of GOCART AOD and the combined contribution from sulfate,BC,and POM to total AOD during2000–2007.(b)Monthly mean variability of satellite AOD retrievals during October to February2000–2010.Solid lines represent the linear tendencies.(c)Trend of estimated AOD due to SO2,BC,and OC emissions,and evolutions of satellite AOD averaged between October and February during 2000–2010.R values shown are the correlation coef?cients of each satellite AOD with estimated AOD.Error bars express one standard deviation of the monthly mean.
and the Gobi Desert)comprises a large fraction of AOD in spring(March–May,33%).The combined contribution from sulfate,BC,and primary organic matter(POM)to total AOD is high during June–January,accounting for82%of total AOD.To minimize the potential effect of biomass burning of forest and savanna in summer(June–August),we select September–January as our study period for China.In India, the monsoon meteorology can be divided into four basic pe-riods:winter(December–February),summer/pre-monsoon (March–June),monsoon(late June–September),and post-monsoon(October–November).Since the transportation of mineral dust from Iran,Afghanistan,and the Thar Desert in western India is pronounced during summer and mon-soon months(Kharol et al.,2011;Prasad and Singh,2007), October–February is selected as our study period for India (the average combined contribution from sulfate,BC,and POM to AOD is79%).
Figures10b and11b show the temporal variation of monthly AOD values averaged over eastern central China and India,respectively,from Terra/MODIS,Aqua/MODIS, and MISR satellite retrievals during the selected study pe-riods.Generally,Terra/MODIS has higher AOD values over China and India,while values of MISR AOD are lower.The correlations between the three datasets are high
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历年托福考试阅读真题汇总含答案
0308 托福试题 阅读( 55minutes ) Question 1-11 seen in putrefying materials .He did (10) this by passing air through guncotton filters, the fibers of which stop solid particles. After the guncotton was dissolved in a mixture of alcohol and ether, the particles that it had trapped fell to the bottom of the liquid and were examined on a microscope slide .Pasteur found that in ordinary air these exists a variety of solid structures ranging in size from 0.01 mm to more than 1.0 mm .Many of these bodies resembled the reproductive (15) structures of common molds, single-celled animals, and various other microbial cells . As many as 20 to 30 of them were found in fifteen If food is allowed to stand for some time, putrefies .When the putrefied material is examined microscopically ,it is teeming with bacteria. Where do these bacteria come from , since they are fresh food? Even until the mid-nineteenth century, many people believed microorganisms originated by spontaneous (5 ) generation ,a hypothetical living organisms develop from nonliving matter. The most powerful spontaneous generation microbiologist Louis showed that structures present in air closely found not that it to be seen in such process by which of the theory of French chemist and opponent was the Pasteur(1822-1895).Pasteur resemble the microorganisms
初二阅读理解及答案
初二阅读理解及答案
一.阅读常识（9分） 1. “煮豆燃豆箕，豆在釜中泣。本是同根生，相煎何太急。”这首诗的作者是时期的。（2分） 2.请根据课外阅读的外国名著，补全下面的名人对联。（２分） 上联：搏命运风浪奏出一支支悲壮的乐曲（贝多芬）下联：炼钢铁意志（奥斯特洛夫斯基）3.《杂文报》上登过一首仿李白诗的诗作：“朝辞白鹿彩云间，千里莲花一日还，考察才经两站路，旅费已花万余元。”（白鹿，指庐山白鹿书院；莲花，指黄山的莲花峰。） ①从上诗中摘录词语填空。（3分） 这首诗揭露和谴责了一些人以“”为名，游山玩水，大肆挥霍国家钱财，糟蹋人民血汗钱的丑恶行径。“”和“”形成鲜明对比，激愤之情，溢于言表。 ②这首诗仿写李白的《早发白帝城》，请写出原诗内容。（2分） ，。 ，。
二．阅读感悟和能力(15分) 1．展开合理想像，用优美流畅的语言(50字左右)，把下面诗句（选一）所表现的画面描述出来。（4分）树树皆秋色，山山唯落晖。（王绩《野望》） 晴川历历汉阳树，芳草萋萋鹦鹉洲。（崔颢《黄鹤楼》） 2．请结合自己的人生感悟，谈谈你对下面诗句的理解。（3分） 会当凌绝顶，一览众山小。（杜甫《望岳》） 3．如果将下面一段描写天气的语言改成天气预报的语言形式，请用恰当的语言表述。（3分） 清晨，天上飘着片片白云，中午，天空渐渐聚集了层层阴云，到了下午，便下起了蒙蒙细雨，轻风拂面，使人略感凉意。 4.目前世界上所鉴定的生物物种有170多万种。上个世纪80年代，世界上平均每天至少有一种生物灭绝，从1990年开始，平均每小时消失一种物种，到2000年，估计有100多万种生物物种从地球上消失。（2分）
2019托福阅读考试真题(3)
2019托福阅读：模拟试题及答案解析（6） 【托福】 Although only 1 person in 20 in the Colonial period lived in a city, the cities had a disproportionate influence on the development of North America. They were at the cutting edge of social change. It was in the cities that the elements that can be associated with modern capitalism first appeared — the use of money and commercial paper in place of barter, open competition in place of social deference and hierarchy, with an attendant rise in social disorder, and the appearance of factories using coat or water power in place of independent craftspeople working with hand tools. "The cities predicted the future," wrote historian Gary. B. Nash, "even though they were but overgrown villages compared to the great urban centers of Europe, the Middle East and China." Except for Boston, whose population stabilized at about 16,000 in 1760, cities grew by exponential leaps through the eighteenth century. In the fifteen years prior to the outbreak of the War for independence in 1775, more than 200,000 immigrants arrived on North American shores. This meant that a population the size of Boston was arriving every year, and most of it flowed into the port cities in the Northeast. Philadelphia's population nearly doubted in those years, reaching about 30,000 in 1774, New York grew at almost the same rate, reaching about 25,000 by 1775. The quality of the hinterland dictated the pace of growth of the cities. The land surrounding Boston had always been poor farm country, and by the mid-eighteenth century it
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(节选自《再说长江》解说词，有删改) 12．文章第三段和第四段介绍了天门山。作为张家界的风景名胜，天门山具有哪些特点?(2 分) 13．文章第一段引用了张良逃难隐居的故事，你认为有必要吗?为什么?(2 分) 14．亲爱的同学，透过解说词，我们感受到张家界秀美的风光。如果你是一位导游，请用生动的语言为张家界的朋友介绍山西的一处风景名胜(例如晋祠、平遥古城、乔家大院、五台山、汾河公园?6?7?6?7)。(不少于80 字)(4 分) [友情提示]晋祠，位于太原西南25 公里的悬瓮山下，晋水源头，是奉祀西周初晋国开国侯唐叔虞的祠堂，距今已有 1500 余年的历史，是集中国古代祭祀建筑、园林、雕塑、壁画、碑刻艺术为一体的历史文化遗产。目前主要景点是：内有 43尊宋代彩色泥塑的圣母殿、鱼沼飞梁、周柏隋槐和难老泉等。 [友情提示]平遥古城在山西太原以南90公里处，始建于西周，距今已有两千多年的历史。古城是由完整的城墙、街道、店铺、寺庙、民居等组成的一组庞大古建筑群。 2.东方宝石——褐马鸡 ①褐马鸡是我国特产珍稀鸟类，被誉为“东方宝石”，山西省将它定为省鸟，中国鸟类学会也以褐马鸡为会标。 ②褐马鸡仅．见于我国山西吕梁山、河北西北部、陕西黄龙山和北京东灵山。1987年 调查野生种群仅有数百只，根据2009年文献报道，我国现存的野生褐马鸡数量在17900。 只左右 ．． ③褐马鸡主要栖息在以华北落叶松、云杉次生林为主的林区和华北落叶松、云杉、杨树、桦树次生针阔混交森林中。它白天多活动于灌草丛中，夜间栖宿在大树枝权上，冬季多活动于1000米-1500米的高山地带，夏秋两季多在1500米-1800米的山谷、山坡和有清泉的山坳里活动。
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一.阅读常识（9分） 1. “煮豆燃豆箕，豆在釜中泣。本是同根生，相煎何太急。”这首诗的作者是时期的。（2分） 2.请根据课外阅读的外国名著，补全下面的名人对联。（２分） 上联：搏命运风浪奏出一支支悲壮的乐曲（贝多芬）下联：炼钢铁意志（奥斯特洛夫斯基）3.《杂文报》上登过一首仿李白诗的诗作：“朝辞白鹿彩云间，千里莲花一日还，考察才经两站路，旅费已花万余元。”（白鹿，指庐山白鹿书院；莲花，指黄山的莲花峰。） ①从上诗中摘录词语填空。（3分） 这首诗揭露和谴责了一些人以“”为名，游山玩水，大肆挥霍国家钱财，糟蹋人民血汗钱的丑恶行径。“”和“”形成鲜明对比，激愤之情，溢于言表。 ②这首诗仿写李白的《早发白帝城》，请写出原诗内容。（2分） ，。 ，。
二．阅读感悟和能力(15分) 1．展开合理想像，用优美流畅的语言(50字左右)，把下面诗句（选一）所表现的画面描述出来。（4分）树树皆秋色，山山唯落晖。（王绩《野望》） 晴川历历汉阳树，芳草萋萋鹦鹉洲。（崔颢《黄鹤楼》） 2．请结合自己的人生感悟，谈谈你对下面诗句的理解。（3分） 会当凌绝顶，一览众山小。（杜甫《望岳》） 3．如果将下面一段描写天气的语言改成天气预报的语言形式，请用恰当的语言表述。（3分）清晨，天上飘着片片白云，中午，天空渐渐聚集了层层阴云，到了下午，便下起了蒙蒙细雨，轻风拂面，使人略感凉意。 4.目前世界上所鉴定的生物物种有170多万种。上个世纪80年代，世界上平均每天至少有一种生物灭绝，从1990年开始，平均每小时消失一种物种，到2000年，估计有100多万种生物物种从地球上消失。（2分）
5.读下面的小故事，联系学习、工作、生活某方面，写出故事给你的感悟。（不超过20字）（3分） 猫女要出嫁了。猫父为她置办了丰厚的嫁妆—许多干鱼和鲜虾，可猫女一条也不要，她只求父亲将祖传的织网绝技教给她。猫父欣然答应了。 三．整体阅读 （新闻阅读：12分） （中新网酒泉10月15日电）本社记者孙自法在酒泉卫星发射中心载人航天发射场现场报道，中国第一艘载人飞船“神舟”五号发射升空十多分钟后，已成功进入预定轨道。中国首位航天员杨利伟由此踏上中国人期待了千年之久的太空之旅。 北京时间十五日上午九时整，“神舟”五号载人飞船由“神箭”-“长征二号F”运载火箭从此间载人航天发射场发射升空，“神箭”升空十多分钟后，中国载人航天工程指挥部总指挥宣布，“神舟”五号载人飞船已成功进入预定轨道，发射取得成功。 这是中国首次进行载人航天飞行，也是全世界第二百四十一次载人飞行和第九百五十二人次进入太空。同时，本次发射是长征系列运载火箭第七十一次飞行，也是一九九六年十月以来，中国航天发射连续
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