Network design for water quality monitoring of surface freshwaters: a review

To date, many water quality monitoring networks for surface freshwaters have been rather haphazardly designed without a consistent or logical design strategy. Moreover, design practices in recent years indicate a need for cost-effective and logistically adaptable network design approaches. There are many variables that need to be included in a comprehensive yet practical monitoring network: a holistic appraisal of the monitoring objectives, representative sampling locations, suitable sampling frequencies, water quality variable selection, and budgetary and logistical constraints are examples. In order to investigate the factors which affect the development of an effective water quality monitoring network design methodology, a review of past and current approaches is presented.     

辽河流域与英国中部河湖水体中溶解有机质的荧光特性

运用三维荧光光谱技术对辽河流域和英国中部水体中溶解有机质(DOM)的荧光光谱特性进行了分析.结果表明,辽河流域各研究水体中DOM包含4种荧光组分,即类色氨酸、类酪氨酸、可见光区和紫外光区类腐殖质,通过对荧光特性定量指标的分析可知,辽河流域各研究断面水体中类腐殖质物质内源作用大于陆源,DOM具有较强的新近自生源,各河流水体中DOM腐殖化程度顺序为:浑河中游海城河中游蒲河下游太子河下游.相比之下,英国河湖中,除了River Tame含有以上4种荧光组分外,其它水体中均没有发现类酪氨酸荧光物质,甚至在River Tern中仅含有类腐殖质荧光物质,河湖中DOM含有较少的新近自生源组分,以陆源影响为主.另外,辽河流域水体中类蛋白与类腐殖质荧光峰强度比值大于英国各河流水体的平均值.由此可以推断,辽河流域各研究断面大量污染物的输入,是造成其严重内源污染的主要原因,从而使水体中DOM含量增加,英国各河流断面其DOM主要来自于水体本身存在的大量微生物和浮游植物的代谢活动以及土壤有机质经雨水的冲刷流入水体的贡献.     

Trace metals used as natural markers for discriminating some karstic freshwaters near Trieste (Italy)

Some heavy metals have been determined using voltammetry in freshwaters forming part of a karstic hydrological system (the Timavo River, north of Trieste), during attempts to characterize and discriminate the different sources of the springs. The trace elements can be considered natural markers, with lead appearing to be the most discriminating variable. Non parametric statistics and cluster analysis were used to estimate the significance of the determined parameters with respect to the considered ecosystem.     

Predicting Freshwater Critical Loads from Catchment Characteristics using National Datasets

Maps of freshwater critical loads are used toguide emission strategies for sulphur and nitrogen bothnationally and internationally. Water chemistry data arerequired to calculate critical loads and the production ofnational maps therefore relies on the existence of extensivechemistry datasets. However, the data required to calculatecritical loads are not readily available for all sites. Thisarticle explores how empirical statistical models mightpotentially be used to predict critical loads using nationallyavailable datasets representing a range of catchmentcharacteristics. Initially a global regression model forexplaining freshwater critical load variation across a broadspectrum of catchment types (from lowland agricultural tomountain lakes) throughout mainland Britain is described. Whenattention is focused on more specific catchment types (i.e.upland and non-arable) it is shown that the global model hasless explanatory power. A regionalisation of Great Britain(based on 100 km grid squares) shows that the global modelcannot necessarily be applied successfully within a narrowerregional context. Separate analyses were undertaken on each ofthe regional subsets using backward selection regression. Thevariables emerging as significant predictors variedsubstantially across the regions, as did the explanatory powerof the models. This was also the case when the analysis wasconfined to upland and non-arable catchments. This approachcould be developed so that critical loads assessments can bemade for populations of standing waters rather than simplythose for which water chemistry is available.