基于GEOS-Chem V12.6.3的全球CO_(2)浓度同化系统的构建

为了研究同化OCO-2卫星柱浓度(XCO_(2))数据对于全球CO_(2)浓度模拟的影响,本文基于GEOS-Chem V12.6.3,采用四维变分(four dimensional variational,4D-Var)的方法,构建了一个同化OCO-2卫星XCO_(2)数据的全球大气CO_(2)浓度同化系统。首先,采用有限差分法验证了观测算子、积云对流、行星边界层和平流4个伴随模块计算结果的正确性。然后,以2018年为例,设计了模拟和同化两个实验,并利用TCCON、地面和航飞3种独立的观测数据进行对比验证。结果显示,同化实验结果与TCCON、地面和航飞观测数据之间的平均误差分别为0.37 mL/m^(3)、0.41 mL/m^(3)和0.51 mL/m^(3),相比于模拟实验,分别改善了40.32%、42.25%和45.15%,表明了同化OCO-2卫星的XCO_(2)数据能显著提高对全球大气CO_(2)浓度模拟的准确性。

不同陆地生态系统碳通量对GEOS-Chem模型模拟全球CO2浓度的影响

大气中CO2含量的增加速率已经超过了自然界所能吸收的速度,并逐步影响到全球气候变暖。利用模型模拟分析已经成为一个重要的工具用以深入对碳循环的理解。本文使用2008~2010年的生物模型SiB3(Simple Biosphere version 3)与优化后的CT2016(Carbon Tracker 2016)陆地生态系统碳通量驱动GEOS-Chem大气化学传输模型模拟全球CO2浓度。通过分析模拟CO2浓度的空间分布与季节变化,加深对全球碳源汇分布特点的理解,探究陆地生态系统碳通量不确定性对模拟结果的影响,进而认识陆地生态系统碳通量反演精度提升的重要性。SiB3与优化后的CT2016陆地生态系统碳通量都具有明显的季节变化,但在欧洲地区碳源汇的表现相反,其全球总量与空间分布也存在极大的不确定性。模拟CO2浓度结果表明:在人为活动较少地区,陆地生态系统碳通量对近地面CO2浓度空间分布起主导作用,尤其在南半球和欧洲地区模拟浓度有明显差异,且两种模拟结果的季节差异依赖于陆地生态系统碳通量的季节变化。将模拟结果与9个观测站点资料进行对比,以期选用合适的陆地生态系统碳通量来提升GEOS-Chem模拟CO2浓度的精度。实验结果表明:两种模拟结果均能较好的模拟CO2浓度的季节变化及其峰谷值,但CT2016模拟的CO2浓度在多数站点处更接近观测资料,模拟准确性更高。

Verifying Fossil-Fuel Carbon Dioxide Emissions Forecasted by an Artificial Neural Network with the GEOS-Chem Model

In this study, the authors developed an en- semble of Elman neural networks to forecast the spatial and temporal distribution of fossil-fuel emissions （ff） in 2009. The authors built and trained 29 Elman neural net- works based on the monthly average grid emission data （1979-2008） from different geographical regions. A three-dimensional global chemical transport model, God- dard Earth Observing System （GEOS）-Chem, was applied to verify the effectiveness of the networks. The results showed that the networks captured the annual increasing trend and interannual variation of ff well. The difference between the simulations with the original and predicted ff ranged from -1 ppmv to 1 ppmv globally. Meanwhile, the authors evaluated the observed and simulated north-south gradient of the atmospheric CO2 concentrations near the surface. The two simulated gradients appeared to have a similar changing pattern to the observations, with a slightly higher background CO2 concentration, - 1 ppmv. The results indicate that the Elman neural network is a useful tool for better understanding the spatial and tem- poral distribution of the atmospheric C02 concentration and ft.

Estimating emissions and concentrations of road dust aerosol over China using the GEOS-Chem model

Paved road dust is one of the most important aerosols in China. The authors estimated road dust emissions using an empirical model （AP-42 model） developed by the U.S. Environmental Protection Agency, and simulated road dust concentrations over China for the years 2006-2011 using the GEOS-Chem model.The annual road dust emissions amount averaged over 2006-2011 is estimated to be 2331.4 kt, with much higher emissions in eastern China than in western China. Because of heavy traffic and a dense road network, emissions are high over Beijing-Tianjin-Tanggu （BTT）, Henan Province, and Shandong Province. Meanwhile, emissions are calculated to be 459.1, 112.0, and 102.7 kt, respectively, over BTT, the Pearl River Delta （PRD） region, and the Yangtze River Delta （YRD）. Due to the monthly variation of precipitation, road dust emissions over China are simulated to be highest in December and lowest in June. The highest annual mean road dust concentration is simulated to be 14.5 tJg m-3 in Beijing. Over 2006-2011, because of the increases in road length and number of vehicles, annual road dust emissions for China as a whole, Bl-r, the PRD, and the YRD, are simulated to increase by 260%, 239%, 266%, and 59%, respectively, leading to 233%, 243%, 273%, and 100% increases in road dust concentrations in these regions, respectively. Our results have important implications for air pollution control in China.

The impacts of modeling global CO2 concentrations with GEOS-Chem using different ocean carbon fluxes

The rise in atmospheric carbon dioxide(C02)concentrations caused by human activities is leading to global climate change,which poses a threat to human development and survival.This study analyzed the distribution of the ocean carbon flux with interannual changes and compared it with the climatological ocean carbon flux to deepen our understanding of carbon sources and sinks.To simulate global CO2 concentrations for the years2008-2010,the ocean carbon flux with interannual changes and the climatological ocean carbon flux were used to drive the GEOS-Chem model,an atmospheric chemical transport model.The simulated values were compared with the CO2 concentrations at nine observation stations to explore the influence of interannual changes in the ocean carbon fluxes on the simulated CO2 concentrations.The authors found that the difference between the two simulation results was greater in the Southern Hemisphere all year,and the difference in autumn was the largest.Compared with the observations,the simulated CO2 concentration of the ocean carbon flux with interannual changes is closer to the observations,indicating that this simulation is more accurate.

An inversion model based on GEOS-Chem for estimating global and China's terrestrial carbon fluxes in 2019

The 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories added the method of assimilating atmospheric CO_(2) concentrations to invert carbon sources and sinks;however,many global carbon inversion models are not publicly available.In addition,our regional assimilation inversion system,CCMVS-R(China Carbon Monitoring,Verification and Supporting for Regional),needs a global carbon inversion model with higher assimilation efficiency to provide boundary conditions.Here,an inversion model based on the global atmospheric chemistry model GEOS-Chem and a more accurate and easier-to-implement ensemble square root Kalman filter(EnSRF)algorithm is constructed and used to infer global and China's carbon fluxes in 2019.Atmospheric CO_(2) concentrations from ObsPack sites and five additional CO_(2) observational sites from China's Greenhouse Gas Observation Network(CGHGNET)were used for data assimilation to improve the estimate.The inverted annual global terrestrial and oceanic carbon uptake is 2.12 and 2.53 Pg C per year,respectively,accounting for 21.1%and 25.1%of global fossil fuel CO_(2) emissions.The remaining 5.41 Pg C per year in the atmosphere is consistent with the global atmospheric CO_(2) growth rates of 5.44 Pg C per year reported by the National Oceanic and Atmospheric Administration(NOAA),showing that the inversion model can provide a reasonable estimate of global-scale natural carbon sinks.The inverted terrestrial carbon sink of China is 0.37 Pg C per year,accounting for approximately 13%of China's fossil CO_(2) emissions.

“大气污染防治行动计划”执行以来我国夏季大气OH浓度变化的数值模拟

OH自由基是对流层中主要的氧化剂,是大气氧化性的重要表征。文章利用GEOS-Chem模式量化了2014~2017年“大气污染防治行动计划”执行以来,人为排放和气象因素变化对中国夏季大气OH浓度变化的贡献。模拟结果表明,2014~2017年间夏季整个中国OH浓度呈现上升趋势,最大上升出现在30°N附近的华南地区。在华北平原地区,OH浓度也呈明显的上升趋势(0.1×10^(6)molecules cm^(-3)a^(-1)),而OH浓度比较高的珠江三角洲地区的OH变化趋势较小。敏感性试验结果表明,气象和人为排放变化都对2014~2017年华北平原OH浓度上升有促进作用,但人为排放的贡献(OH增加10.0%)远大于气象的贡献(OH增加1.5%);OH浓度变化最大的南方地区主要是气象条件控制。进一步对气象因素分析发现,影响全国OH变化最重要的气象要素是太阳短波辐射,决定了2014~2017年中国OH浓度增长趋势最大的区域。但在华北地区,2014~2017年短波辐射略微减少的影响被边界层高度明显降低带来的OH增加所抵消。

基于模式分析一次沙尘暴过程中沙尘表面非均相化学过程对中国地区污染物浓度的影响

利用戈达德对地观测系统(GEOS)提供的再分析气象场GEOS-5驱动的GEOS-Chem模式,模拟中国地区2009年4月22~29日沙尘暴期间沙尘气溶胶表面非均相化学过程对我国污染物的影响。模拟结果表明,沙尘暴期间,全国平均沙尘硝酸盐和沙尘硫酸盐浓度分别为0.2μg m^(-3)和0.4μg m^(-3),占总硝酸盐(非沙尘硝酸盐与沙尘硝酸盐之和)和总硫酸盐(非沙尘硫酸盐与沙尘硫酸盐之和)的24%和10%。我国西部地区沙尘硝酸盐占比(>80%)要大于其他地区,而西部地区的沙尘硫酸盐占比则要小于下游地区。考虑非均相化学反应后,沙尘暴期间,全国平均的二氧化硫(SO_2)、硝酸(HNO_3)、臭氧(O_3)、非沙尘硫酸盐、总硫酸盐、非沙尘硝酸盐、总硝酸盐、NH_3、总铵盐浓度变化量分别为-7%、-15%、-2%、-8%、3%、-2%、14%、21%、-5%。

Simulated Spatial Distribution and Seasonal Variation of Atmospheric Methane over China:Contributions from Key Sources

We used the global atmospheric chemical transport model,GEOS-Chem,to simulate the spatial distribution and seasonal variation of surface-layer methane （CH4） in 2004,and quantify the impacts of individual domestic sources and foreign transport on CH4 concentrations over China.Simulated surface-layer CH4 concentrations over China exhibit maximum concentrations in summer and minimum concentrations in spring.The annual mean CH4 concentrations range from 1800 ppb over western China to 2300 ppb over the more populated eastern China.Foreign emissions were found to have large impacts on CH4 concentrations over China,contributing to about 85％ of the CH4 concentrations over western China and about 80％ of those over eastern China.The tagged simulation results showed that coal mining,livestock,and waste are the dominant domestic contributors to CH4 concentrations over China,accounting for 36％,18％,and 16％,respectively,of the annual and national mean increase in CH4 concentration from all domestic emissions.Emissions from rice cultivation were found to make the largest contributions to CH4 concentrations over China in the summer,which is the key factor that leads to the maximum seasonal mean CH4 concentrations in summer.

Real-data assimilation experiment with a joint data assimilation system: assimilating carbon dioxide mole fraction measurements from the Greenhouse gases Observing Satellite

The performance of a joint data assimilation system（Tan-Tracker）,which is based on the PODEn4 Dvar assimilation method,in assimilating Greenhouse gases Observing SATellite（GOSAT） carbon dioxide（CO2） data,was evaluated.Atmospheric 3D CO2 concentrations and CO2 surface fluxes（CFs） from2010 were simulated using a global chemistry transport model（GEOS-Chem）.TheTan-Tracker system used the simulated CO2 concentrations and fluxes as a background field and assimilated the GOSAT column average dry-air mole fraction of CO2（X（CO2）） data to optimize CO2 concentrations and CFs in the same assimilation window.Monthly simulated X（CO2）（X（CO2）Sim）） and assimilated X（CO2）（X（CO2）,TT） data retrieved at different satellite scan positions were compared with GOSAT-observed X（CO2）（X（CO2）,obs）data.The average RMSE between the monthly X（CO2）,TT and X（CO2）,Obs data was significantly（30%） lower than the average RMSE between X（CO2）,Sim and X（CO2）,Obs）.Specifically,reductions in error were found for the positions of northern Africa（the Sahara）,the Indian peninsula,southern Africa,southern North America,and western Australia.The difference between the correlation coefficients of the X（CO2）,Sim）and X（CO2）,Obs and those of the X（CO2）Π）,TT and X（CO2）,Obs was only small.In general,the Tan-Tracker system performed very well after assimilating the GOSAT data.

Impacts of strong El Ninon summertime near-surface ozone over China

The influences of strong El Nino events(1997/98 and 2015/16)on summertime near-surface ozone(O_(3))concentrations over China are investigated using the GEOS-Chem model.The results show that near-surface O_(3) concentrations increased by a maximum of 6 ppb(parts per billion)during the summer of the developing phase of the 1997/98 El Nino in northeastern China,mainly due to the increased chemical production related to the hot and dry conditions.Besides,the O_(3) concentration increased by 3 ppb during the developing summer of both the 1997/98 and 2015/16 El Nino in southern China.It was linked to the weakened prevailing monsoon winds,which led to the accumulation of O_(3) in southern China.In contrast,in the summer of the decaying phase of the two El Nino events,O_(3) concentrations decreased over many regions of China when the El Nino reversed to the cooling phase.This highlights that El Nino plays an important role in modulating near-surface O_(3) concentrations over China.

Model-Simulated Atmospheric Carbon Dioxide: Comparisons with Satellite Retrievals and Ground-Based Observations

Atmospheric CO2 concentrations from January 2010 to December 2010 were simulated using the GEOS-Chem(Goddard Earth Observing System-Chemistry) model and the results were compared to satellite Gases Observing Satellite(GOSAT) and ground-based the Total Carbon Column Observing Network(TCCON) data. It was found that CO2 concentrations based on GOSAT satellite retrievals were generally higher than those simulated by GEOS-Chem. The differences over the land area in January and April ranged from 1 to 2 ppm, and there were major differences in June and August. At high latitudes in the Northern Hemisphere in June, as well as south of the Sahara, the difference was greater than 5 ppm. In the high latitudes of the Northern Hemisphere the model results were higher than the GOSAT retrievals, while in South America the satellite data were higher. The trend of the difference in the high latitudes of the Northern Hemisphere and the Saharan region in August was opposite to June. Maximum correlation coefficients were found in April, reaching 0.72, but were smaller in June and August. In January, the correlation coefficient was only 0.36. The comparisons between GEOS-Chem data and TCCON observations showed better results than the comparison between GEOS and GOSAT. The correlation coefficients ranged between 0.42(Darwin) and 0.92(Izana). Analysis of the results indicated that the inconsistency between satellite observations and model simulations depended on inversion errors caused by data inaccuracies of the model simulation's inputs, as well as the mismatch of satellite retrieval model input parameters.

Response of fine particulate matter to reductions in anthropogenic emissions in Beijing during the 2014 Asia–Pacific Economic Cooperation summit

The nested-grid capability of the global chemical transport model GEOS-Chem, with a horizontal resolution of 1/4°× 5/16° （latitude x longitude）, was used to identify the chemical species whose reductions made the largest contributions to decreases in PM2.s concentrations （fine particulate matter, diameter 〈 2.5μm, defined in this study as the sum of sulfate, nitrate, ammonium, black carbon, and organic carbon aerosols） in Beijing during the 2014 Asia-Pacific Economic Cooperation （APEC） summit. A number of numerical experiments were carried out for the period 15 October-29 November 2014. The model reproduced the observed daily variations of concentrations of PM2.s and gas-phase species （carbon monoxide, nitrogen dioxide, and sulfur dioxide）. Simulated PM2.s concentrations decreased by 55.9%-58.5% during the APEC period, compared to other periods in October and November 2014, which agreed closely with measurements. Sensitivity results showed that emissions control measures regarding nitrogen oxides and organic carbon over North China led to the largest reductions in PM2.s concentrations in Beijing during the APEC summit, which led to overall reductions in the PM2.5 concentration of Beijing by 5.7% and 4.6%, respectively. The control of ammonia emissions was found to be able to greatly reduce PM2.5 concentrations in the whole of North China during the APEC meeting.

Impacts of Seasonal Fossil and Ocean Emissions on the Seasonal Cycle of Atmospheric CO_2

The seasonal cycle of atmospheric CO2 at surface observation stations in the northern hemisphere is driven primarily by net ecosystem production (NEP) fluxes from terrestrial ecosystems. In addition to NEP from terrestrial ecosystems, surface fluxes from fossil fuel combustion and ocean exchange also contribute to the seasonal cycle of atmospheric CO2. Here the authors use the Goddard Earth Observing System-Chemistry (GEOS-Chem) model (version 8-02-01), with modifications, to assess the impact of these fluxes on the seasonal cycle of atmospheric CO2 in 2005. Modifications include monthly fossil and ocean emission inventories. CO2 simulations with monthly varying and annual emission inventories were carried out separately. The sources and sinks of monthly averaged net surface flux are different from those of annual emission inventories for every month. Results indicate that changes in monthly averaged net surface flux have a greater impact on the average concentration of atmospheric CO2 in the northern hemisphere than on the average concentration for latitudes 30-90°S in July. The concentration values differ little between both emission inventories over the latitudinal range from the equator to 30°S in January and July. The accumulated impacts of the monthly averaged fossil and ocean emissions contribute to an increase of the total global monthly average of CO2 from May to December.An apparent discrepancy for global average CO2 concentration between model results and observation was because the observation stations were not sufficiently representative. More accurate values for monthly varying net surface flux will be necessary in future to run the CO2 simulation.

SO₂Oxidation Efficiency Patterns during an Episode of Plume Transport over Northeast India: Implications to an OH Minimum

Systematic monitoring of the fluctuations in atmospheric SO2 oxidation efficiency—measured as a molar ratio of SO42- to total SOx (SOx=SO2+SO42-), referred as S-ratio—have been performed during a major long range plume transport to northeast India (Shillong: 25.67°N, 91.91°E, 1064 m ASL) in March 2009. Anomalously low S-ratios (median, 0.03) were observed during the episode—associated with a cyclonic circulation—and the SO42- and SO2 exhibited unusual features in the ‘relative phase’ of their peaks. During initial days, when SO2 levels were dictated by the long range influx, the SO42- and SO2 variabilities were in anti-phase—for the differing mobility/loss mechanisms. When SO2 levels were governed by the boundary layer diurnality in the latter days, the anti-phase is explained by a ‘depleted OH level’—major portion being consumed in the initial period by the elevated SO2 and other pollutants. Simulations with a global 3D chemical transport model, GEOS-Chem (v8-03-01), also indicated ‘suppressed oxidation conditions’—with characteristic low S-ratios and poor phase agreements. The modelled OH decreased steadily from the initial days, and OH normalized to SO2—referred as OHspecific—was consistently low during the ‘suppressed S-ratio period’. Further, the geographical distribution of modelled OH showed a pronounced minimum over the region surrounding (20°N, 95°E) spanning parts of northeast India and the adjacent regions to the southeast of it—prevalent throughout the year, though the magnitude and the area of influence have a seasonality to it—with significant implications for reducing the oxidizing power of the regional atmosphere. A second set of measurements during January 2010—when prominent long range transports were absent—exhibited no anomalies, and the S-ratios were well within the acceptable limits (median, 0.32). This work highlights the GEOS-Chem model skill in simulating/detecting the ‘transient fluctuations’ in the oxidation efficiency, down to a regional scale.

Temporal Trends in Ambient SO₂at a High Altitude Site in Semi-Arid Western India: Observations versus Chemical Transport Modeling

Ambient sulphur dioxide (SO2) measurements have been performed at a high altitude site in the semi arid region of western India, Gurushikhar, Mt. Abu (24.6°N, 72.7°E, 1680 m ASL), during different sampling periods span over Sep-Dec 2009 and Feb-Mar 2010. A global three dimensional chemical transport Model, GEOS-Chem, (v8-03-01) is employed to generate the SO2 profile for the entire region for the different sampling months which in turn is used to explain the major features in the measured SO2 spectra via correlating with HYSPLIT generated wind back trajectories. The mean SO2 concentrations recorded at the sampling site varied for the different sampling periods (4.3 ppbv in Sep-Oct 2009, 3.4 ppbv in Nov 2009, 3.5 ppbv in Dec 2009, 7.7 ppbv in Feb 2010 and 9.2 ppbv in Mar 2010) which were found to be strongly influenced by long range transport from a source region surrounding 30°N, 75°E—the one projected with the highest SO2 concentration in the GEOS-Chem generated profiles for the region—lying only a few co-ordinates away. A diurnal cycle of SO2 concentration exists throughout the sampling periods, with the greatest day-night changes observed during Feb and Mar 2010, barely detectable during Sep-Oct 2009, and intermediate values for Nov and Dec 2009 which are systematically studied using the time series PBL height and OH radical values from the GEOS-Chem model. During the sampling period in Nov 2009, a plume transport to the sampling site also was detected when a major fire erupted at an oil depot in Jaipur (26.92°N, 75.82°E), located few co-ordinates away. Separate runs of the model, performed to study the long range transport effects, show a drop in the SO2 levels over the sampling region in the absence of transport, throughout the year with Jan to Apr seen to be influenced the lowest by long range transport while Jul and Dec influenced the highest.

Effect of Asian Dust Storms on the Ambient SO₂Concentration over North-East India: A Case Study

Ambient SO2 concentration at a high rain fall site, Shillong (25.67oN, 91.91oE, 1064 m ASL), located in North-East India, was measured during March 2009 and January 2010 with the aim to understand the effect of long range transport of pollutants from North-East Asia on the ambient SO2 levels at this relatively clean site. The concentrations recorded during the former sampling period were very high (Max: 262.3 ppb)—which decayed down gradually towards the end the sampling period—whereas those during the latter sampling period were well within the acceptable limits (Max: 29.7 ppb). This elevated SO2 concentrations during March 2009 is proposed to have association with a major cold air outbreak and an associated cyclone preceding one of the dust storm events reported in China, and a resultant sudden change in wind trajectory leading to the long range transport of pollutants to the sampling site. The argument is formulated on the basis of the back trajectory analysis performed using HYSPLIT for the month of March 2009—the plots clearly showed a drastic change in wind trajectories between 8th and 15th of March 2009 wherein the winds traveled over some of the highly polluted regions such as the Perm region of Russia—and on the results from model runs performed using the global 3-D model of tropospheric chemistry, GEOS-Chem (v8-03-01)—it clearly showed the tropospheric SO2 over Perm region in Russia peaking during Nov, Dec, Jan, Feb and Mar every year, possibly due to central heating. The observation of long range transport of SO2 from the highly industrialized areas of Perm in Russia to North-East India during dust storm events has important implications to the present understanding on its relative contribution to the Asian pollutant outflow to the Pacific during spring as the GEOS-Chem model runs also showed regions in and around Russia with relatively high concentrations of atmospheric NOx, Peroxyacetyl Nitrate, Lumped Peroxypropionyl Nitrate, HNO3, HNO4,C3H8, C2H6, SO4, NH4, Inorganic Sulphur Nitrates and Lumped Alkyl Nitrate.

基于随机森林的南京市PM_(2.5)和O_(3)对减排的响应

自2013年《大气污染防治行动计划》实施后,南京市大气污染有所改善,但仍面临着细颗粒物(PM_(2.5))和臭氧(O_(3))污染问题.为探究污染物浓度对其前体物减排的响应,获得有效的减排策略,常利用大气化学模式进行多组基于排放扰动的敏感性试验,而这需要消耗大量计算时间和计算资源.应用随机森林算法对2015年大气化学传输模式(GEOS-Chem)模拟结果进行机器学习,高效地预测了南京2019年PM_(2.5)浓度日均值和日最大8 h臭氧(MDA8 O_(3))浓度对不同人为源排放控制情景的响应.随机森林结果表明2019年中国人为排放每减少10%,南京ρ(PM_(2.5))季节平均值下降2~4μg·m^(-3).当2019年中国人为源减排比例高于20%时,南京ρ(PM_(2.5))年均值将低于国家二级限值(35μg·m^(-3)).若仅对中国地区O_(3)前体物氮氧化物(NO_(x))和挥发性有机污染物(VOCs)同比例减排,反而可能导致南京MDA8 O_(3)浓度季节平均值上升.2019年中国地区人为排放同等比例减少10%~50%,南京ρ(MDA8 O_(3))季节平均值在春、秋和冬季分别比基准试验增高约1~3、1~4和3~11μg·m^(-3).而当中国地区NO_(x)减排10%且VOCs减排20%时,南京各季节的ρ(MDA8 O_(3))平均值均有所下降(3~6μg·m^(-3));在此基础上,进一步加大VOCs减排比例(30%),南京ρ(MDA8 O_(3))年均值将减少7μg·m^(-3).若是仅进行南京本地人为源减排,南京O_(3)浓度年均值将出现增加.因此,为有效缓解南京O_(3)污染,中国地区NO_(x)和VOCs减排比需小于1∶2.结合随机森林和GEOS-Chem模式可高效地得到污染物对前体物减排的响应,为大气污染防治策略的制定提供有效的科学支撑.

超低排放改造推广及NH_(3)减排对京津冀冬季环境效益研究

为量化京津冀(BTH)地区超低排放(ULE)改造推广应用潜在的环境效益,基于GEOS-Chem大气化学模型,设计了2个全国情景和6个区域情景,从区域大气输送、超低排放改造在燃煤电厂(CPPs)、工业燃煤(ICB)推广及控制NH;排放等方面进行研究.结果表明:(1)全国燃煤电厂完成ULE改造,使得京津冀地区2015年1月PM_(2.5)浓度下降3.2%(2.4μg·m^(-3)),如只是京津冀地区燃煤电厂完成ULE改造,可使京津冀地区PM_(2.5)浓度降低1.1%(0.8μg·m^(-3)),可知区域联防联控对雾霾的治理具有重要意义;(2)在京津冀地区燃煤电厂完成ULE改造的基础上,工业燃煤完成ULE改造、NH;排放减少30%和50%,可使得京津冀地区PM_(2.5)浓度分别降低5.4%(3.5μg·m^(-3))、4.7%(4.0μg·m^(-3))和7.7%(5.7μg·m^(-3)),可知工业燃煤的ULE改造和NH;减排,均可显著降低PM_(2.5)的浓度;(3)在京津冀地区燃煤电厂和工业燃煤都完成ULE改造的基础上,NH;排放分别减少30%和50%,可使得PM_(2.5)浓度分别降低8.5%(6.3μg·m^(-3))和11.2%(8.3μg·m^(-3)),可知工业燃煤的ULE改造降低常规污染物或NH;减排控制均能显著降低PM_(2.5)浓度,为更好地降低京津冀地区PM_(2.5)的浓度应综合考虑工业燃煤的ULE改造、NH;减排及区域联防联控,可通过经济代价和环境效益分析确定最佳的雾霾治理方案.

A comparison of atmospheric CO_2 concentration GOSAT-based observations and model simulations

Satellite observations of atmospheric CO2 are able to truly capture the variation of global and regional CO2 concentration.The model simulations based on atmospheric transport models can also assess variations of atmospheric CO2 concentrations in a continuous space and time,which is one of approaches for qualitatively and quantitatively studying the atmospheric transport mechanism and spatio-temporal variation of atmospheric CO2 in a global scale.Satellite observations and model simulations of CO2 offer us two different approaches to understand the atmospheric CO2.However,the difference between them has not been comprehensively compared and assessed for revealing the global and regional features of atmospheric CO2.In this study,we compared and assessed the spatio-temporal variation of atmospheric CO2 using two datasets of the column-averaged dry air mole fractions of atmospheric CO2(XCO2)in a year from June 2009 to May 2010,respectively from GOSAT retrievals(V02.xx)and from Goddard Earth Observing System-Chemistry(GEOS-Chem),which is a global 3-D chemistry transport model.In addition to the global comparison,we further compared and analyzed the difference of CO2 between the China land region and the United States(US)land region from two datasets,and demonstrated the reasonability and uncertainty of satellite observations and model simulations.The results show that the XCO2 retrieved from GOSAT is globally lower than GEOS-Chem model simulation by 2 ppm on average,which is close to the validation conclusion for GOSAT by ground measures.This difference of XCO2 between the two datasets,however,changes with the different regions.In China land region,the difference is large,from 0.6 to 5.6 ppm,whereas it is 1.6 to 3.7 ppm in the global land region and 1.4 to 2.7 ppm in the US land region.The goodness of fit test between the two datasets is 0.81 in the US land region,which is higher than that in the global land region(0.67)and China land region(0.68).The analysis results further indicate that the inconsistency of CO2concentration between satellite observations and model simulations in China is larger than that in the US and the globe.This inconsistency is related to the GOSAT retrieval error of CO2 caused by the interference among input parameters of satellite retrieval algorithm,and the uncertainty of driving parameters in GEOS-Chem model.