珠江三角洲机动车污染物排放特征及分担率

建立了2006年珠江三角洲(以下简称为珠三角)地区机动车排放清单,获得了该地区分车型、区域以及燃料类型的机动车排放分担率. 结果表明:①珠三角地区不同车型的机动车污染排放分担率有显著差别,其中柴油大货车、汽油小客车和柴油小货车是机动车排放NO_x的主要来源,摩托车、汽油小货车和汽油小客车是机动车排放VOCs的主要来源,柴油大货车、柴油大客车和柴油小货车是机动车排放PM₁₀的主要来源;②广州、佛山、东莞和深圳等经济发达和快速发展地区的机动车污染物排放量均较大,这4个城市机动车NO_x,VOCs和PM₁₀的排放量之和分别占珠三角地区机动车排放总量的79.7%,69.8%和77.9%;③机动车燃油比例对污染排放影响显著. 汽油车(含摩托车)对VOCs的排放分担率较大(约为93.8%),而柴油车对NO_x和PM₁₀的排放分担率较大(分别为61.5%和84.0%).      

A Method for Locating Influential Pollution Sources and Estimating Their Contributions

This work develops a source-oriented approach to locate the influential pollution sources and estimate their contributions to pollutant concentrations observed at a receptor site. The domain containing possible influential pollution sources is divided into systematic grid cells, and the influential grid sources are determined based on the locations of the segment endpoints of air trajectories arriving at the receptors. The contribution of each grid source is initially calculated using a formula derived from a Lagrangian box model and including the effects of source emissions, atmospheric dilution, and chemical transformation and deposition. The formula is described in detail in this study. Finally, the average contribution of each grid source is determined based on numerous analyzed events. The proposed approach was used to locate influential pollution sources and determine their contributions to a rural monitoring station during periods of high SO₂ pollution in southern Taiwan. The contributions of sources in various 2 km by 2 km grid cells, five districts, three source categories, 8 industrial areas, and a power plant were evaluated. The results show that the major influential sources were in the northwestern region of south Taiwan, and belonged to three district and point sources. Furthermore, two industrial areas close to the evaluated stations were also very significant, and contributed about 30% of the total pollution.     

Major air pollutants in Bursa,Turkey: their levels,temporal changes,interactions, and sources

Bursa is one of the largest cities of Turkey and it hosts 17 organized industrial zones. Parallel to the increase in population, rapidly growing energy consumption, and increased numbers of transport vehicles have impacts on the air quality of the city. In this study, regularly calibrated automatic samplers were employed to get the levels of air pollution in Bursa. The concentrations of CH₄ and N-CH₄ as well as the major air pollutants including PM₁₀, PM_2.5, NO, NO₂, NO_x, SO₂, CO, and O₃, were determined for 2016 and 2017 calendar years. Their levels were 1641.62?±?718.25, 33.11?±?5.45, 42.10?±?10.09, 26.41?±?9.01, 19.47?±?16.51, 46.73?±?16.56, 66.23?±?32.265, 7.60?±?3.43, 659.397?±?192.73, and 51.92?±?25.63 µg/m³ for 2016, respectively. Except for O₃, seasonal concentrations were higher in winter and autumn for both years. O₃, CO, and SO₂ had never exceeded the limit values specified in the regulations yet PM₁₀, PM_2.5, and NO₂ had violated the limits in some days. The ratios of CO/NO_x, SO₂/NO_x, and PM_2.5/PM₁₀ were examined to characterize the emission sources. Generally, domestic and industrial emissions were dominated in the fall and winter seasons, yet traffic emissions were effective in spring and summer seasons. As a result of the correlation process between O_x and NO_x, it was concluded that the most important source of O_x concentrations in winter was NO_x and O₃ was in summer.     

铅同位素解析技术在工业园污染溯源中的应用

针对土壤中重金属污染溯源及解析不同污染源对土壤中铅的相对贡献率问题,以陕西西部某工业园区为例,采集土壤样品、铅锌冶炼厂矿石混合样、热电厂煤样及焦化厂混合煤样共40个,用ICP-MS测定样品的铅浓度及铅同位素比值(208Pb/204Pb、207Pb/204Pb、206Pb/204Pb).借助于铅同位素比值散点图来对比分析采样点土壤、背景点土壤和可能的污染源样品的铅同位素特征分布,判定其主要污染来源.用混合多元模型计算不同污染区域各污染源的贡献率,结果表明,土壤中的铅同位素比值落在铅锌矿石、炼焦用煤及热电燃煤及背景点的铅同位素比值之间,说明各端元介质均有可能对工业园区土壤中的铅污染有贡献.在铅同位素比值散点图中土壤铅浓度大于60 mg/kg的采样点的铅同位素比值集中落在热电燃煤与炼焦用煤附近.统计定量解析结果可知,对工业园区土壤铅污染的贡献大小依次为热电厂、焦化厂和铅锌冶炼厂.研究表明,铅同位素指纹解析技术用于定性与定量解析工业园区土壤铅污染效果较为理想.     

Sources of ambient volatile organic compounds and their contributions to photochemical ozone formation at a site in the Pearl River Delta, southern China

The Positive Matrix Factorization (PMF) receptor model and the Observation Based Model (OBM) were combined to analyze volatile organic compound (VOC) data collected at a suburban site (WQS) in the PRD region. The purposes are to estimate the VOC source apportionment and investigate the contributions of these sources and species of these sources to the O₃ formation in PRD. Ten VOC sources were identified. We further applied the PMF-extracted concentrations of these 10 sources into the OBM and found "solvent usage 1", "diesel vehicular emissions" and "biomass/biofuel burning" contributed most to the O₃ formation at WQS. Among these three sources, higher Relative Incremental Reactivity (RIR)-weighted values of ethene, toluene and m/p-xylene indicated that they were mainly responsible for local O₃ formation in the region. Sensitivity analysis revealed that the sources of "diesel vehicular emissions", "biomass/biofuel burning" and "solvent usage 1" had low uncertainties whereas "gasoline evaporation" showed the highest uncertainty.     

杭州城区大气颗粒物污染特征及PM2.5潜在源区研究

基于2015年9月1日至2016年8月25日杭州城区观测点PM₁、PM_2.5、PM₁₀小时浓度数据进行分析,利用HYSPLIT模型、潜在源贡献因子（PSCF）方法和浓度权重轨迹（CWT）方法,探讨了杭州城区PM₁、PM_2.5、PM₁₀时间分布特征和PM_2.5潜在来源。结果表明：研究期间PM₁季节平均浓度表现为冬季 > 秋季、春季 > 夏季,PM_1~2.5、PM_2.5~10浓度则表现为冬季 > 春季 > 秋季 > 夏季;PM₁浓度日变化呈现明显的双峰现象,而PM_1~2.5和PM_2.5~10在同一时段均无明显浓度峰值;杭州城区PM_2.5受外源输送污染具有明显的季节性变化特征,夏季、秋季杭州城区PM_2.5的潜在源区主要是浙江北部、安徽东南部等,春季PM_2.5的潜在源区主要是浙江中部、江苏南部等,冬季PM_2.5的潜在源区主要是山东南部、江苏西南部、浙江北部、安徽南部、江西中部等地区。     

利用后向轨迹模式研究上海市PM2.5来源分布及传输特征

利用后向轨迹模型,结合上海 PM2．5的浓度数据计算了2012年6月27日-2013年6月26日以上海为起始点的后向轨迹,并通过轨迹相关的分析方法,研究不同来源区域对上海 PM2．5浓度的贡献影响。结果表明：长三角地区的排放对上海的贡献最为显著;苏北、山东等地区的排放对上海也有较明显的贡献;来自海面的贡献总体低于大陆。所采用的轨迹多元回归分析法为 PM2．5的来源分布及传输特征研究提供了新思路。     

应用ARPS-CMAQ模拟研究石景山污染对北京的影响

建立了中尺度气象模型ARPS与区域多尺度空气质量模型CMAQ的耦合模型系统,应用该模型系统模拟研究了2002年1月份和8月份石景山区污染物排放对北京市空气质量的影响.模拟结果显示,1、8两个月份石景山区污染对北京市PM10的月均贡献浓度分别为29.68μg·m-3和32.09μg·m-3,其中污染较重日的日均贡献浓度在50μg·m-3左右,说明石景山污染对北京市空气质量影响较大.同时,进一步对两月典型污染过程进行深入分析,研究发现,在所选两个污染过程中,石景山对北京PM10的贡献浓度与监测浓度同步升高或降低.首先,在污染过程的前期,石景山贡献浓度较低且变化不大,之后贡献浓度与监测浓度同步快速上升,该阶段贡献浓度增量对监测浓度增量的贡献较大,分别占到了同期各自监测浓度增量的52.89%和44.78%,最后,贡献浓度与监测浓度同步降低,说明,在所选污染过程中贡献浓度短时间内的迅速增加是导致北京市监测浓度骤然升高的一个重要原因.     

关于结构振型参与系数和振型贡献的分析

采用振型分解反应谱法求解多自由度弹性体系的地震反应时,为了在满足所需计算精度的前提下减少工作量,需要对振型数量进行合理的选择,而振型数的确定主要取决于结构各阶振型对总体反应的贡献。通过数学推导,对振型贡献及振型数量的选择问题进行了研究。首先,讨论了振型参与系数的性质,在此基础上给出了能够反映结构振型贡献参数的数学表达式,对这些参数的力学含义进行了解释,并给出了相关证明;其次,对有效质量法、振型位移控制法等基于不同振型贡献标准的确定振型数的方法进行了分析比较,指出了其合理性和不足。本文研究对进一步理解结构振型贡献和振型数的选择问题具有一定的理论意义。     

中国各省市科技进步贡献率测算的实证研究

依据新经济增长理论,提出了一种基于C-D生产函数和索罗余值法测算科技进步对经济增长贡献率的新方法.该方法包括三个步骤:首先,对中国31个省市按照科技进步水平进行软分类.得到各地区隶属于各类别的隶属度;第二,分别计算不同科技水平类别的科技进步对经济增长的贡献率;第三,把得到的各个类别的贡献率与各个地区隶属于各类别的隶属度相乘,得到各个地区科技进步对经济增长的贡献率.最后,运用该方法计算了中国31个省(市)1998-2007年间的科技进步贡献率.结果表明:中国区域科技水平发展不平衡,上海的科技进步贡献率最高为54.19%:北京次之,为48.90%;四川最低为28.59%.另外,中国大部分地区经济增长都主要依靠资金投入,资金投入贡献率在60%以上.因此,必须注重科技水平的发展,充分发挥科技进步在经济增长中的作用,从而带动其他生产要素贡献率的提高,促进经济水平的整体增长.