大连市臭氧污染特征及典型污染日成因

通过对大连市区10个空气监测子站的监测数据进行分析,探讨了大连市臭氧污染的时空分布、气象条件对臭氧污染的影响,对臭氧污染日进行了归类分析。结果表明,大连市臭氧污染主要出现在4—10月。在强紫外辐射、高温、低湿、低压和低风速的气象条件下,监测点位的臭氧浓度较高。臭氧污染日的日变化分为单峰型、双峰型和夜间持续升高型3种类型。通过对2015年的一次高浓度臭氧污染过程的气象条件、污染物浓度和污染气团轨迹进行分析,发现臭氧浓度在夜间持续升高现象与区域输送密切相关。     

我国流域水生态完整性评价方法构建

流域水生态完整性评价是指通过对水生态系统中不同水生态指标(生物和非生物)的监测以及由数学方法综合形成的综合评价指数,来反映水生态系统完整性状况。近年来,世界各国水环境管理政策发生了变化,开始强调生态保护,重视水体的生态质量。中国现行的常规理化监测指标(如COD、氨氮、BOD5)很难满足水环境管理的需求,难以全面准确地反映水环境质量变化的趋势。因此,在借鉴欧美发达国家流域水生态完整性评价方法的基础上,结合中国目前监测现状以及流域水环境管理需求,构建了包括物理生境指标、理化指标、水生生物指标在内的流域水生态完整性监测与评价方法,以期为中国流域水质目标管理技术体系的业务化运行提供可资借鉴的技术支撑,实现从单一的化学指标监测转向综合的水生态系统监测,实现流域水生态完整性的监测与评价。     

高效率颗粒物微物理模型简介与重污染过程应用分析

中国细颗粒物(PM2.5)导致的空气污染和雾霾天气问题越来越突出,在低层对流层中很多地方都有显著的二次颗粒物生成过程,因此对颗粒物物理化学转化过程的研究具有重要意义,其转化过程对空气质量模式模拟和预报的准确性有重要影响。高效率颗粒物微物理(APM)模型经过近20多年的研究和发展,在全球化学传输模型(GEOS-Chem)、区域性气象和化学预报模型(WRF-Chem)通用地球系统模型(CESM-CAM5)等气象及环境空气质量预报模型中取得了很好的应用验证,能够在全球/区域等不同尺度对粒子生成、长大、凝聚、消除等微物理过程以及气溶胶光学厚度(AOD)、直接/间接辐射强迫等进行模拟。应用GEOS-Chem-APM模式模拟了2014年2月京津冀区域的重污染过程。结果显示,湿度模拟的准确性对其他物理和污染物浓度等模拟结果有重要作用;2月20—26日的重污染过程模拟结果与实际观测较为吻合,主要污染特点是较高的湿度(90%以上),同时,二次颗粒物和包裹了二次物种的一次颗粒物(黑碳/有机碳)是低能见度的最大贡献者。     

浅谈滨海湿地生态环境退化监测与评价

指出了滨海湿地面临的主要环境问题。简述了美国、欧洲、澳大利亚等滨海湿地现状及监测评价方法。对我国滨海湿地管理与研究进展进行了分析探索。     

美国地表水环境监测管理体系及对我国的启示

简述了美国地表水监测管理体系,指出其健全的环境监测体系、完善的标准体系以及充分的信息公开和数据共享是保障水环境质量的基石、关键和枢纽。结合我国水环境监测管理的现状,提出,应加强水环境质量监测的立法工作,进一步完善水污染物排污许可证制度,建立以水环境质量为目标的水环境管理制度体系,进一步加大监测信息公开和数据共享力度,修订更适合我国的水环境质量监测指标。     

Diversity of crude oil-degrading bacteria and alkane hydroxylase (<Emphasis Type="Italic">alkB</Emphasis>) genes from the Qinghai-Tibet Plateau

The aim of this study was to survey the response of the microbial community to crude oil and the diversity of alkane hydroxylase (alkB) genes in soil samples from the Qinghai-Tibet Plateau (QTP). The enrichment cultures and clone libraries were used. Finally, 53 isolates and 94 alkB sequences were obtained from 10 pristine soil samples after enrichment at 10 °C with crude oil as sole carbon source. The isolates fell into the phyla Proteobacteria, Actinobacteria, and Bacteroidetes, with the dominance of Pseudomonas and Acinetobacter. The composition of degraders was different from polar habitats where Acinetobacter sp. is not a predominant responder of alkane degradative microbial communities. Phylogenetic analysis showed that the alkB genes from isolates and enrichment communities formed eight clusters and mainly related with alkB genes of Pseudomonas, Rhodococcus, and Acinetobacter. The alkB gene diversity in the QTP was lower than marine environments and polar soil samples. In particular, a total of 10 isolates exhibiting vigorous growth with crude oil could detect no crude oil degradation-related gene sequences, such as alkB, P450, almA, ndoB, and xylE genes. The Shannon-Wiener index of the alkB clone libraries from the QTP ranged from 1.00 to 2.24 which is similar with polar pristine soil samples but lower than that of contaminated soils. These results indicated that the Pseudomonas, Acinetobacter, and Rhodococcus genera are the candidate for in situ bioremediation, and the environment of QTP may be still relatively uncontaminated by crude oil.     

低温浓缩-气质联用分析污染源废气中环氧氯丙烷

建立了气袋采样-液氮低温浓缩-气质联用分析污染源废气中环氧氯丙烷的方法,优化了相关监测参数。优化后的方法在环氧氯丙烷质量浓度10~500μg/m~3范围内线性良好,相关系数0.999,加标回收率为72.1%~85.7%,相对标准偏差5%,气体进样量为5.0 mL时,检出限为4.37μg/m~3,可以满足废气中环氧氯丙烷监测的要求。     

松花江流域生物完整性时空变化分析

采用大型底栖动物生物完整性指数(B-IBI)法对松花江流域的水生态环境质量进行研究分析。结果表明,2015年松花江流域各点位水生态环境质量17.24%为优、10.34%为良好、24.14%为一般、41.38%为较差、6.90%为很差。与2012年松花江流域水生态环境质量的比较和趋势分析表明,松花江干流下游、第二松花江、松花江支流的水生态质量均出现不同程度的改善,松花江干流下游改善尤为显著,松花江干流上游生态质量基本保持不变,嫩江水生态质量有下降迹象。流域总体水生态环境质量得到进一步改善,总物种数和清洁指示类群EPT丰度均有增加。     

Spatial succession modeling of biological communities: a multi-model approach

Strong spatial correlation may exist in the spatial succession of biological communities, and the spatial succession can be mathematically described. It was confirmed by our study on spatial succession of both plant and arthropod communities along a linear transect of natural grassland. Both auto-correlation and cross-correlation analyses revealed that the succession of plant and arthropod communities exhibited a significant spatial correlation, and the spatial correlation for plant community succession was stronger than arthropod community succession. Theoretically it should be reasonable to infer a site's community composition from the last site in the linear transect. An artificial neural network for state space modeling (ANNSSM) was developed in present study. An algorithm (i.e., Importance Detection Method (IDM)) for determining the relative importance of input variables was proposed. The relative importance for plant families Gramineae, Compositae and Leguminosae, and arthropod orders Homoptera, Diptera and Orthoptera, were detected and analyzed using IDM. ANNSSM performed better than multivariate linear regression and ordinary differential equation, while ordinary differential equation exhibited the worst performance in the simulation and prediction of spatial succession of biological communities. A state transition probability model (STPM) was proposed to simulate the state transition process of biological communities. STPM performed better than multinomial logistic regression in the state transition modeling. We suggested a novel multi-model framework, i.e., the joint use of ANNSSM and STPM, to predict the spatial succession of biological communities. In this framework, ANNSSM and STPM can be separately used to simulate the continuous and discrete dynamics.