珠江三角洲地区重点VOC排放行业的排放清单

根据收集的珠江三角洲(珠三角)重点挥发性有机物(VOC)排放行业的活动水平数据,采用近年来VOC估算方面的研究成果及估算方法,建立了该地区2006年重点挥发性有机物排放行业和分城市的VOC排放清单.结果表明:珠江三角洲地区2006年重点挥发性有机物排放行业VOC排放总量为416.9kt,其不确定性(95%置信区间)为302.5~689.6kt(-31%~58%);家具制造业、建筑涂料使用、制鞋业是珠江三角洲重点VOC排放行业的主要来源,分别占总排放量的23.3%,21.2%和17.5%;东莞是珠江三角洲地区2006年重点挥发性有机物排放行业VOC排放量贡献最大的城市,其次是深圳,两者排放量分别占总排放量的23.6%和21.9%,主要的排放亦来源于家具制造业、建筑涂料使用与制鞋业.缺乏本地排放因子和良好的活动水平数据是本研究VOC排放量估算中主要的不确定性来源.     

广东省人为源BC、OC排放清单建立与校验

根据收集的人为源活动水平数据和最新的排放因子,采用"自下而上"和"自上而下"相结合的排放因子法建立了广东省2012年人为源BC、OC排放清单.结果显示,2012年广东省人为源BC、OC排放量分别为53.5×103、78.8×103t.BC排放主要来自道路移动源和生物质燃烧源,贡献率分别为30.1%和29.4%,生物质燃烧源和餐饮源是主要的OC排放贡献源,贡献率分别为48.5%和16.9%.建立的BC、OC排放源清单仍然具有较大的不确定性,分别为-66%~154%和-63%~126%.其中,道路移动源和生物质燃烧源是主要的不确定贡献源,餐饮源和扬尘源次之,不确定性主要来自由质量分数间接得到的BC和OC排放因子.最后,采用清单结果横向比较法和基于环境监测浓度结果对比法2种方法对本研究的结果进行了校验,结果表明,本研究清单结果基本合理.建议统一不同排放源成分谱的建立方法,加强排放源颗粒物测试,并重视清单结果校验的研究以降低不确定性,从而改进BC、OC排放源清单.     

中国人为源VOC排放清单不确定性研究

针对采用"排放因子法"构建的中国人为源VOC排放清单,开展了不确定性研究工作.基于排放清单输入信息的准确性和可靠性,构建了活动水平和排放因子的不确定度评估系统,获得了清单输入信息概率密度分布函数.利用Monte Carlo模型将输入信息的不确定度传递到清单的输出值,计算了2005年我国人为源VOC排放概率密度分布函数....     

成都市大气污染物排放清单高分辨率的时空分配

传统的污染物时空分配方法由于分辨率较低而常无法满足空气质量模拟的需要.本研究根据各污染源的排放特点,确定可用于高分辨率时空分配的识别因子和建立时空分配权重的估算方法,并以成都市为例,建立了2012年成都地区高分辨率时空分配清单.结果表明,根据清单的时空分配结果,成都市的污染物排放主要集中在成都市区、成都周边的工业区(特别是东部城区)及交通流量大的高速路地区,且排放时间大部分集中在冬季和春季.这与实际的污染排放来源及环境空气质量的实际监测结果较为一致.说明本研究提出的高分辨时空分配方法较为合理可靠,可以有效降低传统方法空间分配的偏差和提高分配结果的精度,可满足后续的空气质量模拟的需要.     

Uncertainties in greenhouse gas emission inventories — evaluation,comparability and implications

This paper reviews quantitative assessments of uncertainty in level and trend in national greenhouse gas inventories. The reported uncertainty in the total emissions of high-quality greenhouse gas inventories ranges from ±5–20% in studies of five industrialised countries. The differences in uncertainty are, in particular, due to different subjective assessment of the uncertainty in emissions of nitrous oxide from agricultural soils. The fraction of CO₂ in the inventory has little effect on the uncertainty. The uncertainties in trends are about ±4–5 percentage points for those countries that have made estimates. High uncertainties of emission levels indicate potential for improvements and, consequently, recalculations. Recalculations will reduce uncertainty, but might also cause practical problems. A high uncertainty in the emission level for large emission sources may be an obstacle for assessing cost-effective reduction strategies as well as for designing effective systems of emission trading. This could imply that the more uncertain emission sources should be excluded from emission trading. Alternatively, subjective uncertainty estimates may be expressed in terms of an economic risk of recalculation. The latter system may allow a market-based encouragement to reduce emission uncertainty. Reductions in uncertainties are anticipated in the future. However, it will be extremely difficult to reduce the trend uncertainty. Trend uncertainties may consequently remain high compared with the emission reduction targets in the Kyoto protocol.     

珠三角地区人为热排放演变趋势及不确定性分析

人类生产生活中产生的大量废热对城市环境中的温度、城市边界层结构、空气质量及人类健康都有着重要的影响.然而目前人为热排放清单的计算方法存在所需基础资料种类繁多、计算过程复杂,空间分布和排放量有较大偏差等不足.本研究采用统计回归法基于CO和NO_x污染源清单计算了2007—2015年珠江三角洲地区的人为热排放清单,对其排放趋势、热排放来源类型变化以及空间格局的演变进行了梳理.结果表明：2007—2015年珠三角地区年平均人为热均大于9 W·m^-2,人为热排放峰值出现在2010年,趋势为先增加后减少,这与燃料消耗总量的变化趋势基本一致,而工业源和道路移动源是影响人为热排放总量变化的最大因素：2007—2015年珠三角地区大部分区域人为热排放处于0~20 W·m^-2,高值区（>20 W·m^-2）主要分布于珠三角地区中心地带的城市群,高值区缩减的面积和速度在2010—2012年期间达到最大.进一步对2012年人为热清单的不确定性分析表明：总人为热的95%置信区间的不确定性为-16%~49%,其中电厂类别排放估算中的不确定性最小,为-13%~16%,而工业源的不确定性最高,为-46%~73%.     

港口自有移动源大气污染物排放清单

基于全面开展大气污染源排放清单编制工作的要求,研究制定了天津市港口自有移动源排放清单.对道路和非道路移动源各源类6种大气污染物建立了分辨率为3 km×3 km的网格化排放清单,并分析其污染物排放时空分布特征,利用蒙特卡罗方法分析了清单的不确定性.结果表明,2020年港口自有移动源共排放PM₁₀ 148.22 t、 PM_2.5 135.34 t、 SO₂ 1 061.04 t、 NO_x 4 027.16 t、 CO 756.60 t和VOCs 237.07 t,其中道路和非道路移动源污染物总排放量占移动源排放量的比例分别为6.66%和93.34%.全港区自有道路移动源机动车污染物排放的主要贡献源是小型、中型、大型载客汽车(汽油)和重型载货汽车(柴油),非道路移动源排放的各污染物的主要贡献源均是船舶和工程机械.不确定性分析结果表明,移动源总体不确定性范围为-13.3%～16.53%.     

天津郊区土壤风蚀源PM10排放清单不确定性分析

运用课题组自主开发的空气颗粒物风蚀源排放清单构建模型软件（PMEI-WES）,估算2016年天津市郊区土壤风蚀源颗粒物排放清单.采用蒙特卡罗模拟,分析了主要气象参数和土壤参数输入不确定性对排放量的影响,量化排放清单的不确定性.结果表明：2016年天津市郊区土壤风蚀源PM₁₀排放总量为22025.1731t.风速是影响排放量的最主要参数,排放量随风速增加呈指数增长,土壤碳酸钙与排放量呈正相关关系,土壤有机质与排放量呈负相关关系.排放总量95%概率范围为（15237.7581t,37434.8873t）,不确定度为（-37.48%,53.60%）;90%概率范围排放量为（16111.8606t,36104.7554t）,不确定度为（-33.89%,48.14%）.各区排放量不确定度大小与风速误差大小最显著.土壤参数对不确定度极值的影响较大.     

Development of a high-resolution spatial inventory of greenhouse gas emissions for Poland from stationary and mobile sources

Greenhouse gas (GHG) inventories at national or provincial levels include the total emissions as well as the emissions for many categories of human activity, but there is a need for spatially explicit GHG emission inventories. Hence, the aim of this research was to outline a methodology for producing a high-resolution spatially explicit emission inventory, demonstrated for Poland. GHG emission sources were classified into point, line, and area types and then combined to calculate the total emissions. We created vector maps of all sources for all categories of economic activity covered by the IPCC guidelines, using official information about companies, the administrative maps, Corine Land Cover, and other available data. We created the algorithms for the disaggregation of these data to the level of elementary objects such as emission sources. The algorithms used depend on the categories of economic activity under investigation. We calculated the emissions of carbon, nitrogen sulfure and other GHG compounds (e.g., CO₂, CH₄, N₂O, SO₂, NMVOC) as well as total emissions in the CO₂-equivalent. Gridded data were only created in the final stage to present the summarized emissions of very diverse sources from all categories. In our approach, information on the administrative assignment of corresponding emission sources is retained, which makes it possible to aggregate the final results to different administrative levels including municipalities, which is not possible using a traditional gridded emission approach. We demonstrate that any grid size can be chosen to match the aim of the spatial inventory, but not less than 100 m in this example, which corresponds to the coarsest resolution of the input datasets. We then considered the uncertainties in the statistical data, the calorific values, and the emission factors, with symmetric and asymmetric (lognormal) distributions. Using the Monte Carlo method, uncertainties, expressed using 95% confidence intervals, were estimated for high point-type emission sources, the provinces, and the subsectors. Such an approach is flexible, provided the data are available, and can be applied to other countries.

     

基于EnKF排放清单反演方法的关键影响参数评估与优化

以中国一氧化碳(CO)排放反演为例,利用敏感性分析手段评估了集合数目、局地化半径､膨胀因子、观测站点密度和观测数据时间分辨率对排放清单反演的影响.结果表明:站点密度是影响排放反演的最重要参数.在不同站点密度下,反演的中国CO排放总量差异可达34%.同时,站点密度还会影响排放反演对其他参数的敏感性.随着站点密度的降低,排放反演对局地化半径、集合数目和膨胀因子参数变得更为敏感,但对观测数据时间分辨率的敏感性则有所下降.因此在站点稀疏地区,局地化半径是排放反演的主要影响参数,集合数目和膨胀因子次之;但在观测站点密集地区,局地化半径和观测数据时间分辨率是主要的影响参数,而膨胀因子和集合数目的影响相对较小.该研究能够为不同尺度的排放反演开展参数优化提供借鉴.在中国CO排放反演案例(站点密度为1.55个/10⁴km²)中,建议反演参数设置为:集合数目为50、局地化半径为100km、最大似然估计膨胀方案(MLE)、日均或小时观测数据.     

四川省大气固定污染源排放清单及特征

根据收集到的四川省电厂、工业及民用部门的活动水平数据,采用合理的估算方法和排放因子,建立了四川省2010年大气固定污染源排放清单.结果表明:12010年四川省固定源共排放SO₂ 84.1万t、NO_x 44.9万t、CO 318.8万t、PM₁₀ 44.1万t、PM_2.5 25.5万t、VOC 17.9万t;2电厂和工业过程是固定源排放的主要贡献源;3燃煤是固定燃烧排放的主要贡献源,煤矸石、焦炭、天然气对污染物的贡献也不容忽视,水泥、钢铁、轻工业制造是本地区主要的工业过程排放源;4宜宾、成都及攀枝花是固定源污染物的主要贡献城市,约占四川省总排放量的20%~40%;5电厂、能源工业燃烧清单的不确定性主要来自排放因子,而工业过程涉及排放源种类繁多且复杂,排放测试研究较少,不确定性较高.     

Use of IPCC GHG key sources analysis to Mexico’s environmental policy

Intergovernmental Panel on Climate Change (IPCC) Tier 1 key sources level 1 assessment was applied to the 1994–1994 National Greenhouse Gases (GHG) emission inventory for Mexico in order to identify and analyze the key sources within it. Top key sources were from land use change and energy combustion contributing to about 60% of total national emissions. In addition, a Tier 1 trend assessment revealed some changes with respect to Tier 1 level assessment: Top key sources according to this analysis are waste disposal and delayed emissions from land clearing. Important insight for cost effective preventive mitigation actions can be extracted from this analysis. A comparison with other countries was carried out to find similarities in the GHG national emissions inventories related to common features on economic development.     

Uncertainty analysis of emission estimates in the RAINS integrated assessment model

Uncertainty is a critical issue for all models that attempt to quantify the necessary emission reductions that are required to meet environmental quality targets. This paper discusses a methodology specifically developed to analyse the uncertainties in the emission estimates with the regional air pollution information and simulation (RAINS) integrated assessment model, considering the uncertainties in the model parameters themselves. Overall, it was found that a typical range of uncertainties for modeled national emissions of sulfur dioxide, nitrogen oxides and ammonia in Europe lies between 10 and 30%. In general, the uncertainties are strongly dependent on the potential for error compensation. This compensation potential is larger (and uncertainties are smaller) if calculated emissions are composed of a larger number of equal-sized source categories, where the errors in input parameters are not correlated with each other. Thus, estimates of the national total emissions are generally more certain than estimates of sectoral emissions. A sensitivity analysis with respect to the uncertainty in input parameters showed that the actual uncertainties are critically influenced by the specific situation (pollutant, year, country). However, the emission factor is an important contributor to the uncertainty in estimates of historical emissions, while uncertainty in the activity data dominates the future estimates.     

大气污染物排放源清单不确定性定量分析方法及案例研究

大气污染物排放源清单由于在数据收集过程中存在的不可避免的监测误差、随机误差、关键数据缺乏以及数据代表性不足等因素而具有不确定性,而排放源清单的不确定性指的是人们对排放清单的真实值缺乏认识和了解.介绍了目前大气排放源清单定量不确定性方法框架,并使用电厂NO_x在线监测数据,通过实际案例量化排放源清单中的不确定性.结果表明:即使对被认为具有较高准确性的火电厂点源排放清单,案例中NO_x的排放源清单来自随机误差的不确定性在±15%左右.对排放源清单的不确定性量化有助于决策者确定污染物排放削减目标的可达性和科学制定大气污染物控制策略,指导排放源清单的改进和数据收集工作.同时,对我国排放源清单开发中不确定性分析提出建议.      

长春市餐饮源PM2.5和VOCS排放清单

采用自下而上的四种不同清单编制方法（以人口、就餐次数、用油量和灶头为核算基准）,评估了长春市辖区2014年不同餐饮活动（家庭餐饮、社会餐饮和食堂餐饮）的大气污染物PM_2.5和VOC_S的排放量,编制了餐饮源大气污染物PM_2.5和挥发性有机物（VOC_S）排放清单,并分析了餐饮源排放的时空分布特征.结果表明：2014年长春市辖区餐饮源PM_2.5总排放量183~770t,VOC_S总排放量9~586t;长春市辖区餐饮源PM_2.5和VOC_S的最大排放源是家庭餐饮,其贡献率分别为74%~81%和28%~78%,食堂餐饮的贡献率分别为8%~22%和3%~26%,社会餐饮的贡献率分别为2%~17%和2%~69%;排放强度空间分布表明,长春市辖区餐饮源污染物在排放区域上：朝阳区 > 南关区 > 绿园区 > 二道区 > 宽城区 > 双阳区;时间变化特征显示,日变化峰值为07：00~08：00,11：30~12：30,18：00~20：00;周变化中周三、周六和周日排放量较大;季节变化中,冬季排放强度高于其他季节,其中十二月份贡献率最大（9.98%）;不同方法计算的大气污染物排放清单中,以人口为基准的不确定性最大,VOC_S的不确定性为302%,以用油量为基准的不确定性最小,PM_2.5和VOC_S的不确定性分别为31%和61%,可以作为区域餐饮源大气污染物清单推荐方法.未来的工作将侧重于典型餐饮源本地排放因子的测定,从而有效减少排放清单的不确定性.     

江苏省人为源挥发性有机物排放清单

掌握VOCs排放特征是研究区域大气复合污染特征和控制策略的前提. 对江苏省VOCs人为源进行系统分类,收集活动水平数据,应用国内外排放因子研究成果及江苏省行业调研结果,采用排放因子法建立了江苏省2010年分行业、分城市的人为源VOCs排放清单. 结果表明:江苏省人为源VOCs排放总量约为179.20×104t,其中化石燃料燃烧源、生物质燃烧源、工业过程源、溶剂使用源、移动源、油品储运源的排放量分别占排放总量的24.1%、3.3%、22.3%、25.3%、18.4%和6.6%,工业过程源中石油炼制、有机化工、医药制造是重点行业,溶剂使用源中机械装备制造、电子设备制造是重点行业. 南京、苏州、无锡、常州、南通5个苏南城市VOCs排放量明显高于苏北和苏中地区,占全省总排放量的60.0%,苏州、南京、无锡排放量居前3位. 各城市化石燃料燃烧源和移动源排放所占比例均超过10.0%,其他重点行业差异显著,其中南京市为石油炼制、有机化工,苏州市为有机化工、机械涂装,无锡市为有机化工、电子设备制造.      

江门市人为源挥发性有机物排放清单

将江门市人为源挥发性有机物(VOCs)排放分为工业源、移动源、生活源和农业源四大类,以2014年为基准年,根据江门市的统计数据和实地调研结果,采用"自上而下"和"自下而上"结合的排放因子法建立了江门市人为源VOCs排放清单.结果显示江门市2014年人为源VOCs排放总量约为75.09 kt,工业源、移动源、农业源和生活源VOCs排放量为41.37、19.16、11.07和3.50 kt,占比分别为55.09%、25.51%、14.74%和4.65%.工业源中摩托车制造、集装箱制造、涂料、油墨、颜料及类似产品制造、印刷及包装印刷、塑料及橡胶制品、人造革制造、皮革鞣制加工、化石燃料燃烧、基础化学原料制造、电子制造、胶黏剂制造、家具制造等行业的VOCs排放量均超过1 000 t,为江门市重点VOCs排放行业.江门市蓬江区、江海区、鹤山市这3地以工业源排放为主,占比均超过50%,而恩平市、台山市等地则以农业源排放为主.各区和县级市在进行VOCs减排政策制定时要针对本土化的VOCs清单特征,进行精细化管控,才能取得较好减排效果.     

江苏省2019年S/IVOCs排放清单及分布特征

通过收集各类S/IVOCs排放源的活动水平数据,选取合适的排放因子和估算方法,建立了2019年江苏省半/中等挥发性有机物(S/IVOCs)排放清单,分析了江苏省各地市以及各排放源的排放特征.结果表明,2019年江苏省排放S/IVOCs约637.31 Gg,其中工业源排放最多,占比达到63.42%,其次为道路移动源(22.23%),非道路移动源占比最少(0.06%).江苏省13地市中,苏州市S/IVOCs排放量最高(161.86 Gg),占江苏省S/IVOCs排放总量的25.40%;单位面积排放强度苏州市最高(18.70 t·km^-2),而单位GDP排放强度连云港市最高(22.45 t·亿元^-1).江苏南部S/IVOCs的排放量较中部和北部地区高,各地市S/IVOCs总排放量、单位面积排放强度和单位GDP排放强度相差均较大.全省S/IVOCs排放量的不确定范围在-88.46%～224.38%,其中生物质燃烧源的不确定范围最大,为-96.40%～277.17%.     

Uncertainty in Agricultural CH<Subscript>4</Subscript> AND N<Subscript>2</Subscript>O Emissions from Finland – Possibilities to Increase Accuracy in Emission Estimates

According to the United Nations Framework Convention on Climate Change (UNFCCC) and Kyoto Protocol under it, industrial countries have to estimate their greenhouse gas emissions annually, and assess the uncertainties in these estimates. In Finland, agricultural methane (CH₄) and nitrous oxide (N_2O) emissions represent 7% of anthropogenic greenhouse gas emissions, and globally the share is much higher. Agriculture is one of the most uncertain emission categories (representing over 20% of greenhouse gas inventory uncertainty in Finland), due to both high natural variability of the emission sources and poor knowledge of the emission-generating processes. In this paper, we present an uncertainty estimate of agricultural CH₄ and N₂O emissions from Finland in 2002. Uncertainties were estimated based on measurement data, literature and expert judgement, and total uncertainty in agriculture was calculated using Monte Carlo simulation. According to the calculations, agricultural CH₄ and N₂O emissions from Finland were 3.7 to 7.8 Tg carbon dioxide (CO₂) equivalents, 5.4 Tg being the mean value.Estimates of CH₄ emissions are more reliable than those of N₂O. N₂O from agricultural soils was the most uncertain emission category, and the uncertainty was not reduced by using available national measurement data of N₂O fluxes. Sensitivity study revealed that the uncertainty in total agricultural inventory could be 7% points lower, if more accurate emission estimation methods were used, including 1) improved data collection in area estimates of organic soils, 2) climate-specific methods for N₂O from agricultural soils as already presented in literature, and 3) more detailed CH₄ estimation methods for enteric fermentation which can be achieved by investigating national circumstances and digestible systems of animals in more detail.     

浙江省2008~2018年人为源氨排放清单及分布特征

以浙江省为研究区域,通过收集11个地级市各类氨排放的活动水平数据,采用排放因子法建立了2008~2018年浙江省人为源氨排放清单,并利用ArcGIS进行1 km×1 km空间网格分配.结果表明,2008~2018年浙江省人为源氨排放量总体呈现下降趋势,年均下降率约3.97%;2018年浙江省氨排放量为108.52 kt,其中农业源为90.02 kt,非农业源为18.50 kt,排放强度为1.03 t ·km^-2;杭州市、嘉兴市和温州市的氨排放量高于其他城市,分别占全省总量的14.72%、11.86%和11.80%;空间分布特征显示,氨排放主要分布在浙江省北部区域,呈现出"北高南低"的排放趋势;不确定性分析表明,氨排放量模拟平均值为108.37 kt,在95%置信区间的不确定度范围为-5.40%~5.60%.