三岔湖浮游植物四季群落结构变化

2008年5月—2009年4月对三岔湖浮游植物群落结构进行调查的结果显示,浮游植物有8门78属299种,浮游生物群落结构、优势种和密度有明显的季节变化。春末和夏季蓝藻占绝对优势,冬季和初春硅藻和绿藻占优势。浮游植物优势种有梅尼小环藻(Cyclotella meneghiniana)、尖针杆藻(Synedra acus Kutzing)、小舟形藻(Naviculaminima)、细小平裂藻(M erism opdeia minima)、湖泊鞘丝藻(Lyngbya lim netia)等。浮游植物优势种群和密度显示,湖区的水体已进入富营养状态。     

松花江流域水生态环境质量评价研究

采用生物完整性指数(IBI)法评价松花江流域的水生态环境质量。对25个候选生物参数进行敏感度分析、Pearson相关性分析,最终筛选出由总分类单元数、EPT分类单元数、EPT密度、敏感种分类单元比例、敏感物种数量、Hilsenhoff生物指数(HBI)6个核心参数构成的IBI评价指标。采用95%分位数法建立了IBI评价标准,将IBI评价结果划分为5个等级:大于35.84为优,26.88~35.84为良好,17.92~26.88为一般,8.96~17.92为较差,小于8.96为很差。结果表明,建立的IBI评价方法适用于松花江流域水生态环境质量评价,松花江流域各位点30.0%生物状况为优和良好,23.3%为一般;46.7%为较差和很差,说明流域内近一半区域的水生态质量存在不同程度的受损。流域生境质量主要处于一般-良好的状态;水质处于轻度污染。     

双怠速法机动车尾气排放检测结果的影响因素分析

针对双怠速法机动车尾气排放检测过程中经常大量出现过量空气系数(λ)不合格现象,结合相关标准和工作实践对影响检测结果的因素进行分析。结果显示,取样管路的气密性差、系统响应时间未达标准、测试软件程序设置错误、高怠速转速设置不合理及由于车辆特性导致的选用标准及操作错误均会导致λ测试结果超标,影响双怠速检测法的准确性。     

实时荧光定量聚合酶链反应快速检测产毒铜绿微囊藻

以铜绿微囊藻(Microcystis aeruginosa)产毒株微囊藻毒素合成酶基因(mcyA)为靶序列设计了一对特异性引物,运用实时荧光定量聚合酶链反应(qPCR)方法,对铜绿微囊藻进行了定性定量检测。结果表明,仅含铜绿微囊藻脱氧核糖核酸(DNA)模板的样品有扩增,对照组小球藻(Chlorella)没有检测到扩增信号;扩增产物熔解曲线平稳,峰尖且窄,熔解温度为(83±1)℃,证明该聚合酶链反应扩增产物极为特异。以重组质粒pMD-18T-mcyA为标准品,所得标准曲线具有高度线性相关性,重复性好,符合制备实时定量聚合酶链反应标准曲线的要求。利用该方法建立的标准曲线对实验室培养获得的铜绿微囊藻DNA样品进行定量检测,与显微镜计数结果基本一致。     

Ag+-Cl--荧光素体系的离子缔合纳米微粒的共振非线性散射光谱及其在环境分析中的应用

在pH 3.5~4.4的醋酸盐缓冲溶液中, Ag⁺与Cl^-离子反应形成AgCl。当Ag⁺离子适当过量时,AgCl能与Ag⁺结合形成[AgCl·Ag]⁺阳离子,它能借静电引力和疏水作用力与荧光素一价阴离子(HL^-)反应形成离子缔合物[(AgCl·Ag)HL],该疏水性的离子缔合物能在水相挤压作用和范德华力的作用下彼此靠近而进一步聚集,形成平均粒径约为20 nm的纳米微粒[(AgCl·Ag)HL]_n。此时仅能引起吸收光谱和荧光光谱的微小变化,但能导致倍频散射(FDS)和二级散射(SOS)等共振非线性散射(RNLS)的显著增强,其最大FDS和SOS波长分别位于350和560 nm处。两种散射增强(ΔI^FDS和ΔI^SOS)在一定范围内均与氯离子浓度成正比,均可用于氯离子的测定。其中以FDS最灵敏,对于氯离子的检测,其线性范围是0.04~1.22 μg/mL, 检出限为10.9 ng/mL;在环境空气或无组织排放废气HCl的检测中,当采气体积为60L时,其线性范围是0.007~0.21 mg/m³,检出限为1.9×10^-3 mg/m³;在有组织排放废气样品中,当采气体积为10 L时,其线性范围是0.04~1.25 mg/m³,检出限为1.1×10^-2 mg/m³。该文研究了纳米微粒对吸收、RNLS光谱的影响、反应的适宜条件及影响因素,考察了共存物质的影响,表明方法有良好的选择性,据此利用上述反应发展了一种用SOS和FDS技术高灵敏度、高选择性和简便、快速测定环境空气和废气中HCl及环境水样中氯化物的新方法。文中还对反应机理进行了讨论。     

江南Z市农村饮水有机污染物健康风险评估

应用化学法及吹扫捕集、气相色谱-质谱联用仪(GC-MS),检测Z市农村不同类型饮用水与癌症相关的有机污染物,对污染物的剂量与癌症标化死亡率进行Spearman等级相关分析。该市河水、池塘水、浅井水中部分样品的COD_Mn、BOD₅和NH₃-N指标超过地表水环境质量Ⅲ类标准,微生物指标超过生活饮用水卫生标准,检出NH₃-N及NO₃-N、NO₂-N合成致癌物NAD的2类前体物及NDMA等直接致癌物。Z市农村河水、池塘水、浅井水主要受生活排污和农业生产污染,不宜作生活饮用水。不同类型饮用水有机污染物丰水期高于枯水期(P值均小于0.05)。癌症标化死亡率与不同类型饮用水有机污染浓度呈等级正相关(P值均小于0.05),与NO₂-N、NDMA、NDEA含量呈剂量-效应关系。直接饮用污染的地表水是造成该市农村癌症高发聚集分布的主要原因之一。     

机动车尾气排放检测方法对比分析

通过怠速、双怠速法对机动车尾气检测试验和数据对比分析表明,不同车龄段的电喷型轿车采用怠速法检测尾气达标率在73.3%～93.5%之间;双怠速法检测尾气达标率在40.0%～82.6%之间.不同车龄段的化油器型轿车采用怠速法检测尾气达标率在25.0%～57.1%之间;双怠速法检测尾气达标率在18.8%～57.1%之间.指出双怠速法对化油器车辆尾气控制同样有效果,双怠速法对筛选高污染车辆比怠速法更严格和科学.     

黄河流域甘肃段面源污染估算与空间分析

采用遥感分布式面源污染评估模型（DPeRS）,对2018年黄河流域(甘肃段)面源污染空间分布特征进行分析,具体包括多类型污染量产排特征解析和流域优先管控单元识别。结果表明,污染量上,2018年黄河流域（甘肃段）总氮(TN)、总磷(TP)、氨氮(NH₃^-N)、化学需氧量(COD_Cr)的面源污染排放负荷分别为65.6,11.8,19.1和77.2 kg/km²,入河量分别为836.7,33.3,220.2和1 353.3 t;空间分布上,氮型(TN和NH₃^-N)排放负荷高值区主要分布在流域中部和东部局部地区,流域大部分地区TP排放负荷均较高,COD_Cr面源污染排放负荷高值区分布较为零散。与排放负荷相比,黄河流域（甘肃段）面源污染入河负荷并不突出,这与该地区水资源量少有密切关系。筛选出黄河流域（甘肃段）面源污染优先控制单元15个,面积占比为85.2%,I类优控单元主要分布在庆阳市、天水市、兰州市和白银市等地区,II类优控单元主要分布在甘南藏族自治州,且TN、TP、NH₃^-N和COD_Cr面源污染优控单元识别结果的平均精度达到80%。     

南宁城市内河沉积物中SVOCs的分布与风险评价

采用加速溶剂萃取-气相色谱-质谱联用技术对南宁市7条城市内河表层沉积物中的64种半挥发性有机物(SVOCs)进行了分析测定,定量检出12种SVOCs,包括7种多环芳烃类(PAHs)、1种酚类和4种酯类,其中属于我国优先控制污染物的有8种,属于美国优先控制污染物的有11种。南宁市城市内河沉积物中的SVOCs总量范围为6.75~34.45 μg/g(以干质量计),均值为15.85 μg/g(以干质量计),以竹排冲的含量最高,污染程度与工业污染源的关系十分密切。特征污染物为酞酸酯,在各测点的比重均占检出总含量的83%以上。除相思湖外的6条城市内河的沉积物均检出PAHs,区域内的工业生产活动对城市内河沉积物的PAHs有重要贡献。应用效应区间低值(ERL)和效应区间中值(ERM)指标对沉积物进行的生态风险评价表明,南宁市城市内河沉积物的生态风险较小。     

2022年春季南京臭氧污染特征及成因分析

2022年春季,受新一轮新冠疫情影响,长三角各城市采取了一系列管控措施,使得大气污染物排放水平降低。对2022年春季(3—5月)南京及长三角地区的六项污染物尤其是臭氧(O₃)的变化特征进行了分析,从气象因素和O₃前体物方面,同时利用基于观测的模型(OBM)对南京O₃污染变化原因进行了研究,并分析了南京挥发性有机物(VOCs)的关键活性组分和来源。结果表明:2022年春季,南京PM_2.5、PM₁₀、NO₂和CO均值浓度均同比下降,但O₃日最大8 h滑动平均质量浓度(O₃-8 h)同比上升19.8%,O₃-8 h超标时间同比增加9 d;长三角区域O₃-8 h同比上升17.9%,O₃-8 h超标天数为2021年同期的2.5倍。南京O₃浓度上升的原因:一方面是由于不利的气象条件,另一方面是由于南京O₃生成处于VOCs控制区,但氮氧化物(NO_x)降幅大于VOCs降幅,同时结合O₃前体物削减方案的分析结果发现,VOCs和NO_x不当的削减比例会导致O₃浓度不降反升。南京O₃生成的关键VOC活性物种依次为乙醛、丙烯、间/对二甲苯、丙烯醛和乙烯;正定矩阵因子分解(PMF)解析结果显示,机动车尾气是南京城区VOCs的主要来源,其次为液化石油气/天然气使用和石油化工。     

Diurnal models of traffic-generated CO for Penang,Malaysia

Georgetown of Penang, an old city, is noted for its narrow streets. The existing traffic dispersal system is utterly inadequate to cope with the ever increasing number of cars and motorcycles on the road. The principal objective of this study is to build prediction models of CO to be employed as one of the planning tools in the future design of Penang urban traffic dispersal system. This study involves the monitoring of kerbside CO levels at selected sites and the fitting of hourly-averaged CO data to linear regression models incorporating the residual effect of CO emission due to traffic in the earlier periods and also different categories of vehicles. The best overall regression model appears to be the one based upon the total traffic count of motorcycles. This can be accounted for by the fact that the traffic counts of motorcycles and cars are highly correlated in most cases and that the emissions of CO from motorcycles are more readily detected as they travel closer to the kerb. The inclusion of residual CO in the models significantly improves the correlation coefficient from about 0.4 to about 0.7.     

Emission Factors Determination of Euro III 1,200- to 1,400-cc Petrol Passenger Cars with a PLS Multivariate Regression Model

This paper presents emission factors of a class of passenger cars obtained by applying a statistical model developed to evaluate average emission factors based on driving cycle emission measurements. A multivariate regression method based on principal components, namely, the partial least squares (PLS) method, is applied to calculate the model. The method was applied to emission data from a sample of petrol Euro III 1,200- to 1,400-cc passenger cars taken from the ARTEMIS database. A vehicle effect analysis showed that vehicle effect is considerable, in some cases comparable to or greater than the driving cycle effect. Determination of emission factors is obviously affected by these aspects. Thus, the CO2 PLS model fit results are good, CO, HC and NOX more or less sufficient. PLS-predicted quantities were compared with corresponding quantities estimated by a multiple regression model (GLM) based on a quadratic polynomial equation of sub-cycle overall mean speed. GLM goodness of fit was poorer than PLS ones. A validation effort of models is in progress, which is considering the ARTEMIS database extended with tests performed within other national or international projects. In this way, an extended population of combinations of vehicles and driving cycles will provide a better calculation of models and emission factors.     

Assessment of chlorophyll-a variations in high- and low-flow seasons in Apalachicola Bay by MODIS 250-m remote sensing

Chlorophyll-a (chl-a) is considered as a primary indicator for water quality and foods for oyster growth in Apalachicola estuarine ecosystem. Assessment of chl-a concentration variation in response to river inflow is important for estuarine environmental research and management. In this study, remote sensing analysis has been conducted to evaluate the effects of river inflow on chlorophyll concentrations in Apalachicola Bay of Florida in the northeast Gulf of Mexico. A remote sensing model for chl-a was improved and applied to map spatial distributions of chl-a by using Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m resolution imageries in high-flow and low-flow seasons in 2001 and 2008. Chl-a values approximately ranged from the minimum 6 μg/l to the maximum 29 μg/l in the study period. Maximum chl-a concentration in high-flow season was almost twice above that in low-flow season. The averaged mean and minimum chl-a level in the high-flow season were approximately 42 and 28 % higher than those in low-flow season, respectively. The remote sensing mapping of chl-a was able to show spatial variations of chl-a in the entire bay under different flow conditions, which indicated its advantage over the traditional field data sampling for monitoring water quality over a large area of estuary. The MODIS 250-m remote sensing regression model presented from this study can be used to support monitoring and assessment of the spatial chl-a distribution in the bay for environmental research and management in Apalachicola Bay.     

Management of air quality in the vicinity of congested area in Kuwait

An assessment of air quality in the vicinity of a selected school has been carried out by monitoring the concentrations of primary pollutants. The results has shown that during the school hours, the measured pollutants emitted from the cars next to the selected school, such as CO and NO(2), are always under the allowable limits for Kuwaiti air quality standards. On the other hand, the concentrations of non methane hydrocarbon pollutant (nm-HC), some of which are considered to be cancergenic, are found to be above the Kuwaiti standard most of the times. A traffic counter is used to record the number of cars in the main road next to the school in 15 min intervals for 10 days during the monitoring period for air quality. Statistical analysis is performed to develop a relation for prediction of the necessary reduction in traffic, based on CO concentrations, during morning and afternoon periods on working days. A computer dispersion model (CALINE4) is also used to assess the CO concentrations based on recorded flow of traffic and emission inventory with the prevailing meteorological conditions existed at the specified time. After the validation of model, different scenarios have been evaluated to provide an acceptable solution to resolve the traffic congestion problem near the schools in the early morning hours with substantial reduction in pollution levels. The optimal solution for CO concentration reduction by managing smooth traffic flow is to reduce the traffic intensity by half in early morning and afternoon rush hours. The results of the predicted CO concentration in the vicinity of the school for the model and the statistical analysis has shown reduction of 30% and 42% respectively, for approximately 50% decrease in the car use. On the other hand the predicted CO concentration for the model and the statistics reached 24% and 33% respectively when 50% of students opted for buses instead of using private cars.     

Prediction of lichen diversity in an UNESCO biosphere reserve – correlation of high resolution remote sensing data with field samples

The present study focuses on developing models to predict lichen species richness in a UNESCO Biosphere Reserve of the Swiss Pre-Alps following a gradient of land-use intensity combining remote sensing data and regression models. The predictive power of the models and the obtained r ranging from 0.5 for lichens on soil to 0.8 for lichens on trees can be regarded as satisfactory to good, respectively. The study revealed that a combination of airborne and spaceborne remote sensing data produced a variety of ecological meaningful variables.     

水体中泥沙对藻类光谱特征位置的影响分析

自然水体叶绿素a浓度的遥感反演中,泥沙的存在影响着反演精度.如何消除这种影响是提高叶绿素a遥感反演精度的关键,而了解泥沙对藻类光谱特征的影响是消除影响的前提.文章在人工控制条件下获取了不同泥沙浓度下藻类光谱曲线,通过分析光谱曲线特征位置的漂移和数值变化,总结泥沙对藻类光谱的影响并提出了消除影响的可能性.     

遥感技术在大气环境监测中的应用综述

综合论述了近20多年来国内外对大气环境遥感监测的研究现状,介绍了应用于大气环境遥感监测的多种方法并着重阐述了被动式空基遥感和主动式地基遥感在大气环境遥感中的应用以及探测气溶胶的卫星传感器的发展历程和特点。最后,对我国大气环境遥感研究中存在问题和发展前景进行了讨论。     

遥感技术在水生态环境管理的应用与前景

随着遥感数据源的不断丰富,遥感技术不断提高,可以解决越来越多的水环境问题。指出了当前水生态环境管理方面的主要需求,结合目前遥感技术的发展,对国内外的水环境遥感研究进展进行综述。以湖泊富营养化监测与评估、核电站温排水遥感监测及城市黑臭水体遥感监测为案例,具体阐述遥感在水环境管理中的应用方法及成效。未来水生态环境管理发展趋势将以水污染防治为主向水污染防治和水生态修复与保护并重发展。基于此趋势,提出遥感在水生态修复的应用潜力,利于更多地方部门积极有效应用遥感技术,解决水生态环境问题。     

A Classification Procedure for Mapping Topo-climatic Conditions for Strategic Vegetation Planning

Environmental classification addresses issues involving the representation and analysis of continuous and variable ecological data. This study creates a methodology to define topo-climatic landscapes (TCL) in the north-west of Catalonia, which is situated in the north-east of the Iberian Peninsula. TCL provide data regarding the ecological behavior of a landscape in terms of its topography, physiognomy, and climate, which are the main drivers of an ecosystem. The variables selected are derived from a variety of different sources, such as remote sensing and climatic atlases. The methodology employed combines unsupervised iterative cluster classification with supervised fuzzy classification. Twenty eight TCL, which can be differentiated in terms of their vegetation physiognomy and vegetation altitudinal range type, were selected for the study area. Furthermore, a hierarchy among the TCL is established which permits the merging of clusters and allows for changes in thematic resolution. By using the topo-climatic landscape map, managers can identify patches with similar environmental conditions and at the same time assess the uncertainty involved in classification.     

Trends in exhaust emissions from in-use Mexico City vehicles, 2000–2006. A remote sensing study

A remote sensing study was conducted in year 2006 in four locations of the Metropolitan Area of Mexico City (MAMC). Two of the sites were the same studied back by us in year 2000 and by others in year 1994. A database was compiled containing 11,289 valid measurements for the carbon monoxide (CO), total hydrocarbons (THC), and nitric oxide (NO) exhaust vehicles emissions. Valid measurements were binned for each pollutant by the vehicle specific power (between −5 and 20 kW tonne⁻¹) for the 2000 and 2006 databases. The mean average CO, THC, and NO emissions for year 2006 were determined to be 1.10 ± 0.18 vol.%, 299 ± 88.4 ppm, and 610 ± 115.0 ppm, respectively. Matching the vehicle driving patterns of the fleet measured in year 2000 with the emissions factors obtained in this work, allows estimating the trends in the exhaust emissions of vehicles in the MAMC. The adjusted results of the remote sensing study performed in year 2006 shows that the fleet has decrease 22% in CO and 17% in NO emissions, with small change in total hydrocarbons emissions. The improvements could be related with the introduction in year 2001 of vehicles that met tighter emissions standards, particularly for nitrogen oxides.