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。     

南水北调中线陕西水源区污染源排放时空变化特征

基于2008—2012年污染源环境统计数据,采用Spearman秩相关系数法等分析南水北调中线陕西水源区污废水及污染物年排放总量变化趋势,借助ArcGIS空间分析功能表征水源区污染源排放的空间分布特征。结果表明:2008—2012年水源区污废水和NH_3-N年排放总量呈显著上升趋势,COD、As、Pb、Cd、Cr和Hg年排放总量呈抛物线型变化,总体上COD、Pb、Cd年排放总量增加,As、Cr和Hg年排放总量降低。水源区污废水及污染物排放量空间差异明显,污废水、COD和NH_3-N排放涉及流域所有区县,其排放量从干流到流域边缘呈现较明显的梯度变化,即位于流域中心或地级市行政中心的区县排放量明显高于位于流域边缘的区县;As、Pb、Cd、Cr和Hg排放量呈现明显的区域分布,主要分布在勉县等8个区县。     

铅冶炼区土壤重金属总量和有效态含量的函数分析

采集铅冶炼企业周边3 000 m范围内220个表层土壤样品,测定了有毒有害元素铅、镉、砷和汞的总量和有效态含量,探讨了它们之间的关系。结果表明:研究区土壤受到汞、砷、铅、镉的污染依次明显严重,土壤重金属的总量和有效态含量的变异系数均大于100%,土壤镉、铅、汞、砷的生物有效性系数平均值分别为25.9%、17.2%、0.58%、0.11%。土壤铅、镉和砷的总量与其有效态含量呈显著正相关(P0.001),而汞的总量与其有效态含量的相关性不显著(P0.05)。土壤铅和镉的总量和有效态含量可以用直线函数和幂函数表达,函数反推的有效态值和对应统计值的变异系数不大于10%。     

基于环境卫星CCD数据的黄海浒苔遥感监测

2007年以来,黄海海域每年夏季都发生大面积浒苔绿藻潮,影响沿岸人民的生活、生产。考虑到其分布范围动则几千、上万平方公里,水面监测很难实施,尝试利用遥感技术对其分布和发展变化情况进行监测。基于中国自主产权的环境卫星(HJ1)数据,采用经典植被指数算法和人工辅助判读方法,对2013年黄海浒苔暴发全过程进行了连续遥感监测,分析了此次浒苔过程的分布和漂移路线。监测结果表明,浒苔绿藻潮首发于江苏盐城东部海域,由南向北逐渐漂移,面积逐渐扩大,结束于青岛东北部海域;该期间,浒苔最大覆盖面积达663.54平方公里。     

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.     

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.