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131.
S. Lehuger B. Gabrielle M. van Oijen D. Makowski J.-C. Germon T. Morvan C. Hnault 《Agriculture, ecosystems & environment》2009,133(3-4):208
Nitrous oxide (N2O) is the main biogenic greenhouse gas contributing to the global warming potential (GWP) of agro-ecosystems. Evaluating the impact of agriculture on climate therefore requires a capacity to predict N2O emissions in relation to environmental conditions and crop management. Biophysical models simulating the dynamics of carbon and nitrogen in agro-ecosystems have a unique potential to explore these relationships, but are fraught with high uncertainties in their parameters due to their variations over time and space. Here, we used a Bayesian approach to calibrate the parameters of the N2O submodel of the agro-ecosystem model CERES-EGC. The submodel simulates N2O emissions from the nitrification and denitrification processes, which are modelled as the product of a potential rate with three dimensionless factors related to soil water content, nitrogen content and temperature. These equations involve a total set of 15 parameters, four of which are site-specific and should be measured on site, while the other 11 are considered global, i.e. invariant over time and space. We first gathered prior information on the model parameters based on the literature review, and assigned them uniform probability distributions. A Bayesian method based on the Metropolis–Hastings algorithm was subsequently developed to update the parameter distributions against a database of seven different field-sites in France. Three parallel Markov chains were run to ensure a convergence of the algorithm. This site-specific calibration significantly reduced the spread in parameter distribution, and the uncertainty in the N2O simulations. The model’s root mean square error (RMSE) was also abated by 73% across the field sites compared to the prior parameterization. The Bayesian calibration was subsequently applied simultaneously to all data sets, to obtain better global estimates for the parameters initially deemed universal. This made it possible to reduce the RMSE by 33% on average, compared to the uncalibrated model. These global parameter values may be used to obtain more realistic estimates of N2O emissions from arable soils at regional or continental scales. 相似文献
132.
133.
基于投入产出法的北京能源消耗温室气体排放清单分析 总被引:2,自引:0,他引:2
城市是一个巨大能源物资消耗体和温室气体排放体,相关研究受到广泛关注.本文以2007年为例基于投入产出法研究北京市能源消耗的温室气体排放量,计算得出CH4和N2O这两种常规温室气体排放量.结果表明,北京市2007年能源消耗温室气体排放量为3531.72万tCO2当量,其中CO2排放量为3514.40万t,CH4排放量为1734.32t,N2O排放量为435.83t.北京市工业部门仍然是主要的温室气体排放部门,其排放的温室气体占CO2总量的98.96%,CH4总量的88.48%和N2O总量的98.99%.不同最终使用部门中,政府部门消费产生的温室气体排放量超过总量的15%,高于城镇消费和农村消费之和;调出和出口部门的碳排放量超过总量的40%,所占比例最大.贸易中,隐含在调出和出口部门中温室气体排放量是隐含在调入和进口部门的十几倍.北京市不同行业的温室气体排放强度略优于全国水平.降低北京市温室气体排放量可从进一步优化产业结构,发挥科技减排的作用,提高不同产业的能源利用率等方面采取措施. 相似文献
134.
面对国家碳达峰、碳中和战略目标,“十四五”时期,北京市提出推进大气污染物和温室气体排放协同控制,因此,开展协同控制效果评估对于持续改善空气质量和减少碳排放具有重要意义 .本研究在减排措施筛选和减排量测算的基础上,分析了主要大气污染物和 CO2的减排潜力,采用协同控制效应坐标系法、协同控制交叉弹性分析法和协同评估指数法,对减排措施主要大气污染物 SO2、NOx、PM10、VOCs 和温室气体CO2的协同控制效果进行评估 . 结果表明,减排措施对于 SO2、NOx的减排潜力均在 20% 以上,对于 CO2的减排潜力约为 7%. 各项措施对 NOx、PM10、VOCs和 CO2排放具有协同控制效果 .从坐标系法和评估指数法分析结果来看,浅山区煤改清洁能源和压减本地火力发电量对 SO2和 CO2的协同控制效果较好... 相似文献
135.
温室气体对气候环境的影响预测及其不确定性 总被引:1,自引:1,他引:1
随着温室气体排放量的增加,联系温室气体特有的保温效应,大气平均气温将升高。在采用气候模式对未来近百年的气候变化做出一定预测的基础上,分析未来可能出现的气候变化所造成的对气候环境方面的影响。详细分析了现阶段预测未来气候环境变化存在的不确定性,正是这些不确定性影响了预测的可信度。 相似文献
136.
利用城市垃圾筛上物制备RDF可以实现垃圾处理的减量化和资源化。研究了北京市城市生活垃圾筛上物制备RDF的工艺条件,探讨不同成型压力、含水率以及助燃剂含量对RDF成型特性的影响,在此基础上研究添加CaO对其燃烧特性的影响。结果表明:含水率10%,成型压力1 MPa的RDF在加入1%的KClO4后成型较好,燃烧后残渣少。随着CaO添加量的增加,RDF燃烧SO2的释放量减少;而当CaO添加量大于2%时,HCl的释放量基本保持不变。在RDF中加入CaO可以将其中的氯元素和硫元素有效固定在残渣中,从而有效减少了酸性气体排放。 相似文献
137.
以液体石蜡和水制备乳化液,以制备的乳化液吸收甲苯废气。考察了制备乳化液时油水质量比、乳化剂的质量分数、HLB值(亲水亲油平衡值)、乳化时间、乳化温度的选择及Tween 80、正辛醇、NaCl、Na2SO4对甲苯吸收效果的影响。结果表明,室温下油水质量比为7∶3,乳化剂质量分数为7%,HLB值为10,乳化时间为0.5 h时制备的乳化液稳定性最好。试验条件下乳化液对甲苯的初始吸收率可达96.53%,增加Tween 80的量和添加正辛醇能提高乳化液的吸收效果,添加NaCl和Na2SO4则降低乳化液对甲苯的吸收能力。 相似文献
138.
Large-scale environmental research efforts are conducted under frameworks that provide a common basis for evaluating research
in many diverse fields. Such frameworks should be subjected to critical review to determine if they meet crucial expectations.
In the case of the US Global Change Research Program, we perceive that most research follows a framework that we define as
the predictive model framework. We believe this framework is insufficient for resolving the unprecedented predicaments posed
by global change. We recommend a complementary framework, the system response framework, which directs research toward useful
indicators of change rather than precise predictions. We further argue that, even if research is complementary and effective
under the two frameworks, conclusive results prior to decisions should not be expected. The burden of proof must itself be
a continuing topic of open discourse and inquiry. 相似文献
139.
140.
LU Xuyang CHENG Genwei XIAO Feipeng FAN Jihui 《环境科学学报(英文版)》2008,20(3):339-346
Abies fabric forest in the eastern slope of Gongga mountain is one type of subalpine dark coniferous forests of southwestern China. It is located on the southeastern edge of the Qinghai-Tibet plateau and is sensitive to climatic changes. A process-oriented biogeochemical model, Forest-DNDC, was applied to simulate the effects of climatic factors, temperature and precipitation changes on carbon characteristics, and greenhouse gases (GHGs) emissions in A. fabric forest. Validation indicated that the Forest-DNDC could be used to predict carbon characteristics and GHGs emissions with reasonable accuracy. The model simulated carbon fluxes, soil carbon dynamics, soil CO2, N2O, and NO emissions with the changes of temperature and precipitation conditions. The results showed that with variation in the baseline temperature from -2℃ to +2℃, the gross primary production (GPP) and soil organic carbon (SOC) increased, and the net primary production (NPP) and net ecosystem production (NEP) decreased because of higher respiration rate. With increasing baseline precipitation the GPP and NPP increased slightly, and the NEP and SOC showed decreasing trend. Soil CO2 emissions increased with the increase of temperature, and CO2 emissions changed little with increased baseline precipitation. With increased temperature and decreased baseline temperature, the total annual soil N2O emissions increased. With the variation of baseline temperature from -2℃ to +2℃, the total annual soil NO emissions increased. The total annual N2O and NO emissions showed increasing trends with the increase of precipitation. The biogeochemical simulation of the typical forest indicated that temperature changes strongly affected carbon fluxes, soil carbon dynamics, and soil GHGs emissions. The precipitation was not a principal factor affecting carbon fluxes, soil carbon dynamics, and soil CO2 emissions, but changes in precipitation could exert strong effect on soil N2O and NO emissions. 相似文献