镇江地区土壤中重金属Cu和Pb污染状况与空间分布

为了锯镇江地区土壤中重金属Cu和Ph的污染状况与空间分布,对镇江地区表层土壤中的Cu和Pb进行了采样监测。结果表明,镇江地区表层土壤中cu的含量为19．2～273mg／kg,Pb的含量为20．6～3846mg／kg。与全省土壤背景值相比,均有一定程度的富集。对cu和Pb的相互关系进行分析可得出,镇江地区土壤在一定程度上受到农业面源的污染。     

2013年北京市PM2.5重污染日时空分布特征研究

根据2013年北京市环境保护监测中心监测的PM2.5数据,系统分析了北京市重污染日PM2.5污染的时空分布特征,并利用克里格插值初步统计了全年和重污染日PM2.5不同浓度区间的国土面积。2013年全市PM2.5年均浓度为89.5μg/m3,重污染日平均浓度为218μg/m3,重污染日主要集中在冬季;PM2.5年均浓度呈现明显的南高北低梯度分布特征,而重污染日空间分布较均匀,南部及城六区存在明显的高污染区,平均浓度在180μg/m3以上;2013年北京市重污染日PM2.5平均浓度为150~250μg/m3,其对应的国土面积约为12 656 km2,PM2.5平均浓度在250μg/m3以上的国土面积约为883 km2,而全年无PM2.5平均浓度在150μg/m3以上所对应的国土面积。     

Land Cover Changes as a Result of Environmental Restrictions on Nitrate Leaching in Dairy Farming

Nitrate leaching forms an important environmental problem because it causes pollution of groundwater and surface water, and adds to already problematic eutrophication. This study analyses the impact of reductions in nitrate leaching on land cover decisions of dairy farms, of which the activities make an important contribution to nitrate leaching. As the level of nitrate leaching depends on groundwater depth as well as on the supply of nitrogen, spatial variation in groundwater levels will cause a spatial variation in land cover under restrictions on nitrate leaching. A non-linear partial optimisation model for the economic and ecological aspects of the problem were used to show how land cover and dairy farms' financial balances change when nitrate losses are reduced. The model is spatially explicit, and describes nitrate leakage and yields of maize and grass as a function of groundwater depth, including the effects of various grazing systems. The model analyses the decisions of a risk neutral agent who minimises costs under the following constraints: (i) production, feed requirements and mass balances for fodder; (ii) constraints for nitrate leaching. Economic costs are attributed to increased costs of fodder and processing of manure when nitrate restrictions are tightened. An important result of the study is the variation in compliance costs and land cover for maize and grass production brought about by spatial variation in groundwater depth. While the effects are negligible for some shallow groundwater classes, it is extremely difficult in other classes – if not impossible – to obtain the EU standard of maximum admissible losses of 34 kg N ha^–1 at low costs. The study shows an important reduction in land cover by maize.     

京杭运河苏州市区段大型底栖动物种群结构特征与评价

对京杭运河苏州市区段4个点位进行了底栖动物群落结构和多样性进行多年研究,并进行了水质评价。结果表明,底栖动物共12种,寡毛类的克拉泊水丝蚓为各点位的优势种;从时间尺度来看,S1点位的物种种类数量从2008年开始明显增加,软体类物种出现,种群密度也在增加,生物污染指数下降,水质明显改善;从空间尺度来看,京杭运河苏州市区段上游和中游物种种类数量要多于下游和老运河城区段,下游水质有所下降。     

基于污染指数与ArcGIS的聊城市耕地OCPs污染研究

按8 km×8 km网格将聊城市耕地划分为136个采样点,调查六六六(BHCs)、滴滴涕(DDTs)、艾氏剂、狄氏剂、异狄氏剂、六氯苯和七氯等7种有机氯农药(OCPs)的残留状况。采用单因子污染指数和综合污染指数法进行污染评价,以此判断OCPs的污染程度。对7种OCPs在各样点中的含量进行差异分析,并应用ArcGIS8.3,以污染评价结果为数据支持,进行OCPs污染空间分布研究。结果表明,聊城市OCPs污染程度依次为DDTs>狄氏剂>七氯>艾氏剂>BHCs>异狄氏剂>六氯苯,总体变异程度依次为DDTs>狄氏剂>异狄氏剂>BHCs>七氯>六氯苯>艾氏剂。各OCPs空间分布没有固定的变化趋势;各采样点综合污染水平存在地域差异,以临清市污染最为严重,其他各县市污染较轻。     

石家庄市冬季大气中VOCs污染特征分析

为弄清石家庄市冬季大气中VOCs的污染特征,采用美国环保局TO-15方法对石家庄市冬季大气中VOCs组成进行了定性和定量分析。在此基础上,进行了VOCs的月度变化分析、春节期间的变化分析,并进行了VOCs与空气质量指数AQI、PM2.5等之间的相关性分析;根据VOCs组成及变化情况和相关性,分析了其可能的来源。结果表明,石家庄市冬季大气中VOCs的质量浓度为145.7~1 410.7μg/m3,VOCs组分主要有丙酮、二氯甲烷、苯、乙酸乙酯、甲苯、1,2-二氯丙烷、三氯甲烷。春节期间,大气中VOCs的浓度有大幅的下降,比日常均值下降了40.9%。AQI较高时,大气中VOCs浓度有所升高。石家庄市冬季大气中丙酮、二氯甲烷、乙酸乙酯等主要来源于医药化工生产活动,苯、甲苯主要来源于煤燃烧。     

长湖流域水质时空分布特征及影响因子

利用2009—2014年长湖5个水质监测点数据,采用时间序列法分析了长湖水质的时间变化规律,采用相关性分析法,分析了流域水污染的影响因子。结果表明:在时间上,长湖水污染物质量浓度季节变化明显,COD、TN、NH3-N均为7、9月较低,1、3月较高,丰水期水质好于枯水期。入湖地区TP质量浓度7月达最高值,且7月份入湖地区的桥河口、关沮口的NH3-N、TN含量稍高于5月。空间上,西北部入湖地区水质劣于湖心及东南部出湖地区。工业、生活等点源污水,以耕地为主的农业非点源以及天然降水量和径流量是影响水质的主要因素,入湖排污量、降水量和径流量与长湖水质呈显著相关关系(P0.05)。     

乌鲁木齐市近年来大气降尘变化规律及空间分布特征

基于乌鲁木齐市多年监测的降尘数据,对乌鲁木齐市城区的降尘量进行了统计分析,得出了乌鲁木齐市近10年来降尘的总体变化规律;利用方差分析方法检验了不同季节降尘的显著性差异,发现采暖期的降尘量明显大于非采暖期;按行政区分析降尘量的分布变化,结果为不同行政区存在差别;利用秩相关系数法对其进行了趋势分析。结果表明,乌鲁木齐市的降尘呈下降趋势。利用Arc GIS9.3中的普通克里格插值方法绘制了乌鲁木齐市城市自然降尘的空间分布图。     

洪泽湖水体富营养化时空分布特征与影响因素分析

通过2014年—2017年对洪泽湖12个水质断面定期调查,采用营养状态指数(TLI)综合评价其水体富营养状态,同时应用主成分分析方法(PCA)分析其富营养化状态的时空变化特征。结果表明,洪泽湖70%以上的调查断面水质全年处于轻度富营养化状态,夏季是其富营养化最严重的季节;洪泽湖年内水体水质差异较大,而其水华特征并未呈现明显差异;洪泽湖富营养化很大程度上受制于营养盐的积累程度,并与湖泊透明度呈现极显著的负相关关系(p0.001),与湖水pH值呈现极显著的正相关关系。     

Using Multi-Scale Sampling and Spatial Cross-Correlation to Investigate Patterns of Plant Species Richness

Land managers need better techniques to assess exoticplant invasions. We used the cross-correlationstatistic, I _YZ, to test for the presence ofspatial cross-correlation between pair-wisecombinations of soil characteristics, topographicvariables, plant species richness, and cover ofvascular plants in a 754 ha study site in RockyMountain National Park, Colorado, U.S.A. Using 25 largeplots (1000 m²) in five vegetation types, 8 of 12variables showed significant spatial cross-correlationwith at least one other variable, while 6 of 12variables showed significant spatial auto-correlation. Elevation and slope showed significant spatialcross-correlation with all variables except percentcover of native and exotic species. Percent cover ofnative species had significant spatialcross-correlations with soil variables, but not withexotic species. This was probably because of thepatchy distributions of vegetation types in the studyarea. At a finer resolution, using data from ten1 m² subplots within each of the 1000 m² plots, allvariables showed significant spatial auto- andcross-correlation. Large-plot sampling was moreaffected by topographic factors than speciesdistribution patterns, while with finer resolutionsampling, the opposite was true. However, thestatistically and biologically significant spatialcorrelation of native and exotic species could only bedetected with finer resolution sampling. We foundexotic plant species invading areas with high nativeplant richness and cover, and in fertile soils high innitrogen, silt, and clay. Spatial auto- andcross-correlation statistics, along with theintegration of remotely sensed data and geographicinformation systems, are powerful new tools forevaluating the patterns and distribution of native andexotic plant species in relation to landscape structure.