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.     

Is love of nature in the US becoming love of electronic media? 16-year downtrend in national park visits explained by watching movies,playing video games,internet use,and oil prices

After 50 years of steady increase, per capita visits to US national parks have declined since 1988. This decline, coincident with the rise in electronic entertainment media, may represent a shift in recreation choices with broader implications for the value placed on biodiversity conservation and environmentally responsible behavior. We compared the decline in per capita visits with a set of indicators representing alternate recreation choices and constraints. The Spearman correlation analyses found this decline in NPV to be significantly negatively correlated with several electronic entertainment indicators: hours of television, (rs=-0.743, P<0.001), video games (rs=-0.773, P<0.001), home movies (rs=-0.788, P<0.001), theatre attendance (rs=-0.587, P<0.025) and internet use (rs=-0.783, P<0.001). There were also significant negative correlations with oil prices (rs=-0.547, P<0.025), foreign travel (rs=-0.452, P<0.05), and Appalachian Trail hikers (rs=-0.785, P<0.001). Income was significantly positively correlated with foreign travel (rs=0.621, P<0.005) but negatively correlated with national park visits (rs=-0.697, P<0.005). There was no significant correlation of mean number of vacation days, indicating available vacation time is probably not a factor. Federal funding actually increased during this period, and so was rejected as a probable factor. Park capacity was rejected as limiting since both total overnight stays and visits at the seven most popular parks rose well into the mid-1990s. Aging of baby boomers was also rejected as they are only now reaching retirement age, and thus during the period of visitation decline were still of prime family vacation age. Multiple linear regression of four of the entertainment media variables as well as oil prices explains 97.5% of this recent decline (r=0.975, multiple r2=0.950, adjusted multiple r2=0.925, SE=0.015, F=37.800, P<0.0001). We may be seeing evidence of a fundamental shift away from people's appreciation of nature (biophilia, Wilson 1984) to 'videophilia,' which we here define as "the new human tendency to focus on sedentary activities involving electronic media." Such a shift would not bode well for the future of biodiversity conservation.     

水中粪大肠菌群检测方法的比较

采用标准菌株、实际水样和国际标准样品,比较纸片快速法与多管发酵法的一致性。标准菌株试验表明,两种方法在粪大肠菌群的定性检测上没有显著性差异;实际水样试验表明,纸片快速法的检测结果略低于多管发酵法,但两种方法检测结果的回归关系显著;国际标准样品试验表明,两种方法的精密度与准确度均无统计学意义上的显著性差异。     

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.     

Disinfection byproducts in indoor swimming pool water: Detection and human lifetime health risk assessment

Quantification of regulated and emerging disinfection byproducts (DBPs) in swimming pool water, as well as the assessment of their lifetime health risk are limited in China. In this study, the occurrence of regulated DBPs (e.g., trihalomethanes, haloacetic acids) and emerging DBPs (e.g., haloacetonitriles, haloacetaldehydes) in indoor swimming pool water and the corresponding source water at a city in Eastern China were determined. The concentrations of DBPs in swimming pool water were 1-2 orders of magnitude higher than that in source water. Lifetime cancer and non-cancer risks of DBPs stemming from swimming pool water were also estimated. Inhalation and dermal exposure were the most significant exposure routes related to swimming pool DBP cancer and non-cancer risks. For the first time, buccal and aural exposure were considered, and were proven to be important routes of DBP exposure (accounting for 17.9%-38.9% of total risk). The cancer risks of DBPs for all swimmers were higher than 10⁻⁶ of lifetime exposure risk recommended by United States Environmental Protection Agency, and the competitive adult swimmers experienced the highest cancer risk (7.82 × 10⁻⁵). These findings provide important information and perspectives for future efforts to lower the health risks associated with exposure to DBPs in swimming pool water.     

Multiple sulphatase deficiency: Prenatal diagnosis using chorionic villi

Multiple sulphatase deficiency was diagnosed in the first trimester of pregnancy by demonstrating markedly reduced activities of arylsulphatases and heparin sulphamidase by direct assays on chorionic villi (CV). The diagnosis was confirmed by assays on cell cultures of villi and fetal skin fibroblasts. Two further pregnancies of this mother were monitored similarly and predicted to be unaffected; one produced a normal healthy infant, the other miscarried shortly after CV sampling.     

Environmentally conscious long-range planning and design of supply chain networks

In this paper, a mathematical programming-based methodology is presented for the explicit inclusion of life cycle assessment (LCA) criteria as part of the strategic investment decisions related to the design and planning of supply chain networks. By considering the multiple environmental concerns together with the traditional economic criteria, the planning task is formulated as a multi-objective optimization problem. Over a long-range planning horizon, the methodology utilizes mixed integer modelling techniques to address strategic decisions involving the selection, allocation and capacity expansion of processing technologies and assignment of transportation links required to satisfy the demands at the markets. At the operational level, optimal production profiles and flows of material between various components within the supply chain are determined. As such, the formulation presented here combines the elements of the classical plant location and capacity expansion problems with the principles of LCA to develop a quantitative decision-support tool for environmentally conscious strategic investment planning.