大气14CO2的时空分异特征及其在化石源CO2示踪中的应用

大气CO₂中放射性碳同位素(¹⁴C)的水平可以反映化石源CO₂的影响程度,这对于评估我国目前化石源CO₂的排放状况和制定节能减排政策具有重要的指导意义。本文在概述大气¹⁴CO₂采样和分析方法的基础上,简要介绍了大气¹⁴CO₂观测的起源和主要的源汇过程,重点论述了大气¹⁴CO₂的时空分异特征及其驱动因素;阐述了化石源CO₂浓度的估算方法及¹⁴CO₂在国内外化石源CO₂示踪中的应用现状,并对大气¹⁴CO₂观测在我国化石源CO₂示踪中的应用前景进行了展望;旨在为我国正确地开展大气¹⁴CO₂的观测研究,深刻地理解特定区域大气¹⁴CO₂的时空分异特征和化石源CO₂的分布状况提供参考。     

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。     

连续流反应器短程硝化的快速启动与维持机制

如何快速稳定地启动短程硝化工艺对低C/N比废水的处理具有十分重要的实际应用价值.针对城市污水厂以连续流工艺为主的现状,故对连续流反应器短程硝化的快速启动与维持进行研究.结果表明,利用间歇曝气,依次控制3个阶段的停/曝时间(15 min/45 min、45 min/45 min和30 min/30 min),连续流反应器经过60 d左右的运行,可以成功实现短程硝化的快速启动.控制停/曝时间为30 min/30 min,进水氨氮浓度为50或100 mg·L~(-1)时,亚硝化率分别可达90%或95%.另外,间歇曝气有利于抑制硝化菌(NOB)的活性,而缩短水力停留时间(HRT)可淘洗出NOB,两者结合可以更好地维持短程硝化.     

三峡库区水体中可溶性C、N变化及影响因素

为了解三峡库区水体中可溶性C、N的变化,本研究于2011年3月至2012年8月在三峡水库涪陵段进行了每周一次的水样采集,分析其中可溶性C、N成分的变化及其来源特征.结果表明,库区水中DOC浓度范围为0.64~9.07 mg·L~(-1),且有明显的季节变化,表现为:夏季春季、秋季冬季,DOC年入库总量为1.78×109kg,入库量具有与浓度相似的季节变化趋势;DTN的浓度范围为2.59~4.35 mg·L~(-1),春季冬季夏季秋季,年入库总量为1.32×109kg,入库量的季节变化特征:夏季秋季春季冬季,其中DON、NO-3-N分别占DTN的30.35%~63.45%、35.87%~67.72%.DOC受降水和温度的影响明显,水中DOC主要来自外源输入,春季、夏季降雨径流其外源输入量增加,而秋季、冬季则内源贡献有所增加;DTN受人为排放和水体稀释的影响相对较大.相关分析表明,DOC与DON呈显著负相关(P0.05),通常以DOC/DON比值反映水中DOM的来源,库区DOC/DON范围为0.35~7.28,DOM来源具有明显的季节特征.夏季DOC/DON较高,DOM主要来自流域侵蚀;冬季DOC/DON较低,DOM主要来自生活污水排放和内源现场产生;春季、秋季DOC/DON介于两者之间,DOM来源包括流域侵蚀、生活污水排放及现场产生等.     

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.     

闽江口互花米草入侵过程对短叶茳芏沼泽沉积物硝化-反硝化作用的影响

2014年4、7、10月和2015年1月在闽江口鳝鱼滩湿地选择未被入侵的短叶茳芏(Cyperus malaccensis)群落(A)、互花米草入侵斑块边缘(B)以及互花米草(Spartina alterniflora)入侵斑块中央(C)为研究对象,基于时空互代研究方法,探讨互花米草入侵序列下湿地沉积物硝化-反硝化变化规律.结果表明:在互花米草入侵序列中,湿地沉积物硝化速率为0.19~1.66μmol·m~(-2)·h~(-1),反硝化速率为12.41~27.19μmol·m~(-2)·h~(-1).沉积物硝化-反硝化作用存在明显的季节变化,硝化速率表现为夏季春季秋季冬季,反硝化速率表现为夏季秋季冬季春季;入侵不同状态下,沉积物硝化速率表现为BCA,反硝化速率表现为CBA.互花米草入侵提高了沉积物-水界面N_2O交换通量.互花米草入侵引起的沉积物pH、NH_4~+-N、含水量、容重和电导率等理化性质的改变是导致不同入侵阶段沉积物硝化-反硝化速率以及N_2O释放差异的重要原因.     

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.     

蚯蚓细胞色素P450酶系对土壤中芘或苯并[a]芘的响应

以赤子爱胜蚓(Eisenia fetida)为受试生物,通过人工污染草甸棕壤微宇宙试验方法,研究了暴露于不同环境浓度芘或苯并[a]芘(添加量分别为0.12、0.24、0.48、0.96 mg·kg~(-1))污染的土壤中14 d后,蚯蚓体内细胞色素P450(CYPs)酶系,如CYPs总量、CYP1A1及CYP2C9活性的变化.结果显示,芘或苯并[a]芘暴露导致蚯蚓CYPs总量、CYP1A1及CYP2C9活性的变化不同.其中,CYPs总量与CYP1A1活性对芘的响应趋势相似:试验3 d后,0.96 mg·kg~(-1)芘导致二者都显著高于对照水平;14 d后,都显著低于对照水平;CYP1A1活性对芘的响应更为敏感.CYPs总量与CYP2C9活性对苯并[a]芘的响应趋势相似:暴露初期,苯并[a]芘最高暴露剂量(0.96 mg·kg~(-1))导致二者都显著低于对照水平;试验结束后,苯并[a]芘最低暴露剂量(0.12 mg·kg~(-1))导致二者都显著高于对照水平,最高剂量暴露导致二者都显著低于对照水平,CYP2C9活性对苯并[a]芘的响应更为敏感.因此,推断CYPs亚酶对污染物的响应具有选择性且亚酶的响应敏感度高于CYPs总量.将CYPs总量与CYP1A1活性结合,或与CYP2C9活性结合分别诊断土壤芘污染或并[a]芘污染,较为准确有效.     

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.