Monitoring toxics by group testing

Monitoring the environment for a large list of organic compounds present at low levels is costly. The primary purpose of such monitoring is to assure that hazardous levels of such compounds are not released into, or present in, the environment. Viewing this as a quality control problem, we suggest that samples from different sources can be composited using group testing procedures prior to analysis. Our purpose is to describe the basic concepts and suggest problems requiring study.     

Spatial and temporal variation of below-ground N transfer from a leguminous tree to an associated grass in an agroforestry system

Nitrogen (N) transfer from leguminous trees can be a major N source for the associated crop in low-input agroforestry systems. The aim of this study was to identify the main climatic and soil factors controlling N transfer from the leguminous tree Gliricidia sepium (Jacq.) Walp to the associated grass Dichanthium aristatum (Poir.) C.E. Hubb, in a 16-year-old tropical agroforestry system. Nitrogen transfer was estimated using the natural ¹⁵N abundance method. Before tree pruning, total N transfer represented 57% of the N uptake of the grass, including 31% coming from N₂ fixation. The spatial variation induced by the tree was well described by soil organic N content (ON). In this system, ON is an index of soil available N as well as of tree root density. Rainfall (R) and evapotranspiration (ETP) were the main climatic factors controlling N transfer. Multiple regression analysis indicated that R, ETP and ON explained 79% of the temporal and spatial variation of N transfer. Transferred N cannot be estimated after pruning because of the change in the isotopic signature of the soil N source. This was related to N release from root turnover. The results suggest that grass showed a preferential uptake of N coming from the tree, which could be due to a lower energy cost compared to obtaining absorbed N from the clayey soil used in this work.     

Melanin- and carotenoid-dependent signals of great tits (<Emphasis Type="Italic">Parus major</Emphasis>) relate differently to metal pollution

Due to their high phenotypic plasticity, the expression of secondary sexual characteristics is particularly sensitive to stress. Here, we investigated the expression of two conspicuous visual signals in great tits (Parus major) in a metal pollution gradient. In three study sites with marked differences in metal contamination (mainly lead, cadmium, copper and zinc), we compared melanin and carotenoid colouration of great tits. While carotenoid colouration (yellow breast) was negatively related to metal pollution, the size of a melanin trait (breast stripe) was larger in the most polluted sites. Environmental pollutants not only affect the expression of conspicuous signals but may even enhance, directly or indirectly, a signal of male quality such as breast stripe. Our results also support the multiple messages hypothesis predicting that different signals highlight different aspects of geno- and phenotypic condition of the bearer.     

Instream and offstream environmental conditions and stream biotic integrity: Importance of scale and site similarities for learning and prediction

Two different methods to predict biotic integrity were tested and compared in the present paper. The first one tries to predict the fish indices of biotic integrity (IBI) at the state or regional scale based on the most similar observations to a specific target site of interest using the simple to implement k-nearest neighbors (or kNN) method. Two different distance functions were considered to find the k-nearest neighbors: the Euclidean and the Mahalanobis. The second method was applied on the same datasets and consisted of a step-wise multiple regression. The two modeling approaches yielded similar results but kNN proved to be more time-efficient and very fast computationally for the given dataset sizes, which allowed applying a leave-one-out cross validation.In an attempt to reveal the importance of scale in the prediction of IBI, regression models were constructed at the state (or regional) scale and at the more refined cluster of sampling sites scale. Clusters of sites were extracted using Kohonen's self-organizing maps (SOM) followed by k-means clustering of the SOM neurons. Cluster-level regression models, constructed after site patterning, performed better in IBI prediction than global regression models constructed without any previous site patterning. The importance of identifying groups of sites with similar environmental characteristics affecting the IBI was revealed. The combined use of site patterning and regression modeling for IBI prediction also helped identifying important variables acting at the local scale which remain latent at the global scale.     

A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia,China

Two models, artificial neural network (ANN) and multiple linear regression (MLR), were developed to estimate typical grassland aboveground dry biomass in Xilingol River Basin, Inner Mongolia, China. The normalized difference vegetation index (NDVI) and topographic variables (elevation, aspect, and slope) were combined with atmospherically corrected reflectance from the Landsat ETM+ reflective bands as the candidate input variables for building both models. Seven variables (NDVI, aspect, and bands 1, 3, 4, 5 and 7) were selected by the ANN model (implemented in Statistica 6.0 neural network module), while six (elevation, NDVI, and bands 1, 3, 5 and 7) were picked to fit the MLR function after a stepwise analysis was executed between the candidate input variables and the above ground dry biomass. Both models achieved reasonable results with RMSEs ranging from 39.88% to 50.08%. The ANN model provided a more accurate estimation (RMSE_r = 39.88% for the training set, and RMSE_r = 42.36% for the testing set) than MLR (RMSE_r = 49.51% for the training, and RMSE_r = 53.20% for the testing). The final above ground dry biomass maps of the research area were produced based on the ANN and MLR models, generating the estimated mean values of 121 and 147 g/m², respectively.     

Bioaccessibility of metals in soils: comparison between chemical extractions and in vitro tests

It is well-known that the total metal content in soils is not a good indicator of their harmful effects, leading to an overestimation of risks. Toxicological and environmental hazards depend on the chemical species and on its bioavailability to target organisms. Because a good estimation of bioavailability is difficult, a good compromise is to assess bioaccessibility, defined as the maximum amount of a pollutant which is potentially absorbable by a target organism. This study presents a comparison of different strategies to measure metal bioaccessibility in soils. Three procedures were applied to real soil samples with different levels of metal contamination: pseudo-total metal attack, selective sequential extractions and in vitro tests (deliberately developed to simulate human or mammals digestion). Considering the first step of the selective extraction procedure, which can provide the bioaccessible fraction for deposit-feeder organisms, data obtained for each metal were lower than those obtained from in vitro tests. Therefore, it is possible to highlight that this extraction tends to underestimate metal bioaccessibility in soils for humans, while in vitro tests certainly will overestimate bioaccessibility for organisms as invertebrates. If the sum of first and second step of sequential procedure is considered, results are quite similar to those obtained from in vitro tests, but this kind of procedure would require two days of work rather than a few hours required to perform an in vitro test. Results highlight the diversity among the differently defined bioaccessible fractions and the need to apply the most suitable procedure depending on the target organism.     

Investigation of PCDD/Fs and dl-PCBs in fish from the southern Baltic Sea during the 2002–2006 period

Concentrations of polychlorinated dibenzo-p-dioxins (PCDDs), polychlorinated dibenzofurans (PCDFs), and dioxin-like polychlorinated biphenyls (dl-PCBs) in the southern Baltic herring (Clupea harengus), sprat (Sprattus sprattus), and salmon (Salmo salar) are reported.The significant interspecies and season-specific differences in PCDD/Fs and dl-PCBs concentrations were observed. The contribution of dl-PCBs to the total toxicity equivalents (TEQs) ranged between 50% and 70%, dependently on fish species. In all samples, concentrations of PCDF were higher in comparison with that of PCDD.The permissible limits of the content of PCDD/Fs and dl-PCBs (8 pg g⁻¹ fresh weight for the sum of PCDD/Fs and dl-PCBs, and 4 pg g⁻¹ fresh weight for the PCDD/Fs) were exceeded mainly in salmon samples (in 80% of salmon samples studied). The elevated levels of PCDD/F and dl-PCB were observed only in two of seventy two herring samples, and in ten of 62 sprat samples.The multiple regression analysis revealed that fish lipid content, and concentration of PCB 153, used simultaneously, might be useful in the prediction of TEQ values of fish samples. These two variables explained more than 80% of total variance. For all fish species studied, the correlation coefficients obtained with the multiple regression analysis were higher than that obtained with the regression analysis involving total TEQ values, and only one independent variable: fish lipid content.     

丹江口库区可持续发展能力分析

从区域可持续发展的角度出发,以湖北省丹江口库区为例,首先定性分析库区可持续发展的优势与问题,认为库区移民安置工作困难,库区可持续发展受到限制;经济增长缓慢,基础设施建设滞后;城镇化水平较低,库区社会矛盾突出;自然资源丰富与开发不足并存,库区淹没加剧土地资源稀缺;环境质量较好,生态基础脆弱。然后采用多因素综合评价模型定量分析了1999～2005年库区可持续发展的变化趋势。结果表明：库区可持续发展能力从1999年到2003年呈现明显的上升趋势,却从2004年开始缓慢下降;而人口、经济、社会、资源和环境各子系统也呈现出不同的特征。最后针对定性和定量评价结果提出相应的对策建议：做好库区移民安置,实现移民稳定就业;减少和消除移民贫困,增加基础设施建设;加快小城镇建设步伐,解决库区社会矛盾;发挥水资源优势,改善库区生态环境。     

固体废弃物资源化利用评价体系及研究方法

从经济、社会政策和环境影响三个角度,运用层次分析法、模糊决策理论和多目标分析3种评价方法以及矩阵、德尔斐法和混合优序图等数学工具,分别对上海市固体废弃物资源化利用所采用的不同方案进行全面综合评价,不同评价方法的研究结果具有一致性,均表明环境影响是相对最重要的影响因素,固体废弃物双向分离系统的建立是最优化的固体废弃物资源化利用方案,也是今后上海固体废弃物资源化利用研究的重点以及努力发展的方向.