美国氮氧化物排放交易经验及启示

综述了美国氮氧化物排放交易项目的演进过程,总结其实践经验,并探讨美国经验对中国氮氧化物排放控制的借鉴意义。     

Two-step accelerated mineral carbonation and decomposition analysis for the reduction of CO2 emission in the eco-industrial parks

Carbon dioxide（CO2） emissions are a leading contributor to the negative effects of global warming. Globally, research has focused on effective means of reducing and mitigating CO2 emissions. In this study, we examined the efficacy of eco-industrial parks（EIPs） and accelerated mineral carbonation techniques in reducing CO2 emissions in South Korea.First, we used Logarithmic Mean Divisia Index（LMDI） analysis to determine the trends in carbon production and mitigation at the existing EIPs. We found that, although CO2 was generated as byproducts and wastes of production at these EIPs, improved energy intensity effects occurred at all EIPs, and we strongly believe that EIPs are a strong alternative to traditional industrial complexes for reducing net carbon emissions. We also examined the optimal conditions for using accelerated mineral carbonation to dispose of hazardous fly ash produced through the incineration of municipal solid wastes at these EIPs. We determined that this technique most efficiently sequestered CO2 when micro-bubbling, low flow rate inlet gas, and ammonia additives were employed.     

农村沼气在北方农业源减排中的应用浅析

农村沼气作为一种特殊的清洁能源,在沈阳市农村节能减排、应对气候变化和发展低碳经济、促进新农村建设方面发挥了巨大的作用。本文通过对沼气工程在北方地区应用的阐述,力图推动北方农村沼气在农业源减排工作中的应用。     

京津冀区域生产和消费CO2排放的时空特点分析

区分消费和生产二氧化碳排放是对开放的经济区域进行排放责任划分的基础,日渐受到政策制定者的关注.利用经济投入产出-生命周期分析模型,对京津冀区域1997年、2002年和2007年的消费和生产二氧化碳排放时空特征及二氧化碳排放平衡进行分析.结果表明,京津冀区域消费和生产二氧化碳排放呈约4%的年均增长;贸易隐含二氧化碳排放比例为30%~83%,并以国内贸易隐含二氧化碳排放为主;河北的消费和生产二氧化碳排放占区域主导,增速和二氧化碳排放强度高于北京和天津;京津冀区域为二氧化碳排放净流入区域,存在部分排放责任转移;京津为二氧化碳排放净转入地区,冀为二氧化碳排放净转出地区;京津冀三地二氧化碳排放关键部门分布集中且相似度较高,可以考虑区域联合控制.其中,电力、蒸汽、热水生产和供应业和金属冶炼及压延加工业对二氧化碳排放的依赖性最大,承担较大的其他部门的二氧化碳排放责任.投入产出分析解析了地区生产和消费二氧化碳排放情况,有利于区域减排的精细化管理和制定相应对策,并促进区域减排合作.     

碳排放现状及驱动因素分解实证分析研究——以东莞市为例

根据IPCC碳排放计算指南核算东莞市2005年-2011年期间能源消费的碳排放量,并进行现状分析和采用对数平均权重分解法（LMDI）因素分解分析.研究结果表明：碳排放总量以5.8％速度增长,而碳排放强度以5.8％速度递减;煤是碳排放主源,但所占比重逐年下降;工业碳排放量比重最大,但是增速减慢;交通业、服务业和居民生活碳排放量及所占比重逐年增加;经济规模的扩大是拉动东莞市人均碳排放的决定因素,累计效应远高于能源效率和能源结构对碳减排影响.     

浅谈黑龙江及乌苏里江流域废水中污染物排放特征

以跨界流域黑龙江和乌苏里江为研究对象,通过合理的流域断面设置,对流域废水中的污染物及农药残留进行分析测定,通过监测结果为以后的污染防治提供依据。     

京津冀地区主要排放源减排对PM2.5污染改善贡献评估

研究选取2012年1月和7月作为冬夏两季代表时段,利用CMAQ/2D-VBS模型分析了冬夏两季京津冀地区主要排放源减排30%对改善区域PM_(2.5)污染的效果.结果表明,工业源对PM_(2.5)污染的贡献最大,其次是民用源,但工业源单位减排量贡献低于民用源,交通源和电厂源的整体贡献和单位减排量贡献均较小.工业部门内贡献最大的为钢铁冶金行业,其次是水泥、工业锅炉、炼焦、石灰砖瓦和化工行业.与各部门各物种排放量的比较反映出各排放源贡献大小与其一次PM_(2.5)排放水平高度相关.因京津冀地区冬季NO_x减排对PM_(2.5)形成的促进作用,以及冬季较弱的大气垂直扩散作用,各排放源夏季减排比冬季普遍更有效,交通源、电厂源以及工业源中的水泥、工业锅炉和石灰砖瓦行业夏季减排效果相比冬季优势明显.民用源由于采暖季排放较高而冬季贡献更明显,农业源因秸秆开放燃烧量大,冬季单位减排量贡献十分显著.从同等幅度减排考虑,应将工业源作为控制重点,优先控制其一次PM_(2.5)排放,在部门内进一步重点控制钢铁冶金行业的NO_x和SO_2排放、水泥行业的夏季NO_x排放以及炼焦行业的SO_2和NMVOC排放.民用源排放应着重在冬季采暖期控制.     

基于化石能源消耗的重庆市二氧化碳排放峰值预测

首先利用重庆市能源平衡表,采用IPCC方法 1对重庆市1997—2012年的碳排放进行核算;其次依据重庆市经济社会发展状况,通过LMDI因素分解法将影响碳排放的因素分解为:人口、人均GDP、产业结构、能源结构、能源强度和碳排放系数;然后利用扩展的重庆市STIRPAT碳排放模型,在9个情景模式下对2013—2050年重庆市碳排放进行预测;最后对比分析了各情景下的峰值大小及出现时间.研究发现:基准模式下的重庆市碳排放在2035年出现32135.38万t的峰值;提高能源利用技术、增加清洁能源使用比例和大力发展第三产业,能在不降低经济发展的情况下有效降低碳排放;消极因素中的第二产业占比下降比碳排放强度下降对碳排放的抑制作用更加明显;积极因素对碳排放峰值的影响比消极因素更有效.     

Source characterization of ambient fine particles at multiple sites in the Seattle area

To identify major PM_2.5 (particulate matter ≤2.5 μm in aerodynamic diameter) sources with a particular emphasis on the ship engine emissions from a major port, integrated 24 h PM_2.5 speciation data collected between 2000 and 2005 at five United State Environmental Protection Agency's Speciation Trends Network monitoring sites in Seattle, WA were analyzed. Seven to ten PM_2.5 sources were identified through the application of positive matrix factorization (PMF). Secondary particles (12–26% for secondary nitrate; 17–20% for secondary sulfate) and gasoline vehicle emissions (13–31%) made the largest contributions to the PM_2.5 mass concentrations at all of the monitoring sites except for the residential Lake Forest site, where wood smoke contributed the most PM_2.5 mass (31%). Other identified sources include diesel vehicle emissions, airborne soil, residual oil combustion, sea salt, aged sea salt, metal processing, and cement kiln. Residual oil combustion sources identified at multiple monitoring sites point clearly to the Port of Seattle suggesting ship emissions as the source of oil combustion particles. In addition, the relationship between sulfate concentrations and the oil combustion emissions indicated contributions of ship emissions to the local sulfate concentrations. The analysis of spatial variability of PM_2.5 sources shows that the spatial distributions of several PM_2.5 sources were heterogeneous within a given air shed.     

Spatial & temporal variations of PM10 and particle number concentrations in urban air

The size of particles in urban air varies over four orders of magnitude (from 0.001 μm to 10 μm in diameter). In many cities only particle mass concentrations (PM10, i.e. particles <10 μm diameter) is measured. In this paper we analyze how differences in emissions, background concentrations and meteorology affect the temporal and spatial distribution of PM10 and total particle number concentrations (PNC) based on measurements and dispersion modeling in Stockholm, Sweden. PNC at densely trafficked kerbside locations are dominated by ultrafine particles (<0.1 μm diameter) due to vehicle exhaust emissions as verified by high correlation with NOx. But PNC contribute only marginally to PM10, due to the small size of exhaust particles. Instead wear of the road surface is an important factor for the highest PM10 concentrations observed. In Stockholm, road wear increases drastically due to the use of studded tires and traction sand on streets during winter; up to 90% of the locally emitted PM10 may be due to road abrasion. PM10 emissions and concentrations, but not PNC, at kerbside are controlled by road moisture. Annual mean urban background PM10 levels are relatively uniformly distributed over the city, due to the importance of long range transport. For PNC local sources often dominate the concentrations resulting in large temporal and spatial gradients in the concentrations. Despite these differences in the origin of PM10 and PNC, the spatial gradients of annual mean concentrations due to local sources are of equal magnitude due to the common source, namely traffic. Thus, people in different areas experiencing a factor of 2 different annual PM10 exposure due to local sources will also experience a factor of 2 different exposure in terms of PNC. This implies that health impact studies based solely on spatial differences in annual exposure to PM10 may not separate differences in health effects due to ultrafine and coarse particles. On the other hand, health effect assessments based on time series exposure analysis of PM10 and PNC, should be able to observe differences in health effects of ultrafine particles versus coarse particles.