改善太仓城区水环境原型调水实验研究及模型建立

为达到改善太仓城区水环境的目的,于2004年4月21日进行了调水实验.调水历时14 h,期间向城区调水21.8万m3.通过对监测点采样,得到水量、水质实测数据.根据实验数据,建立了符合太仓河网的水量水质数学模型,分析了从长江引水对太仓城区水环境的改善程度.同时对不同引水方案情况下城区水环境改善效果,以及截污和清淤对水环境的影响做了分析.研究表明,引水只能短期改善水环境;要根治水环境,必须从源头抓起,进行截污.     

Apparent relationships between anthropogenic factors and climate change indicators and POPs deposition in a lacustrine system

Climate change and anthropogenic activities are expected to impact the environmental behaviors and fates of persistent organic pollutants(POPs), however, quantitative studies on these combined factors are scarce. In this study, dichlorodiphenyltrichloroethane(DDTs), polycyclic aromatic hydrocarbons(PAHs), and polychlorinated biphenyls(PCBs)were used as examples to identify how and when those factors may be related to the deposition of POPs in the sediment of Lake Chaohu, China, using generalized additive models(GAMs). Three historical trends of DDT, PAH, and PCB deposition were delineated in a dated sediment core encompassing ～100 years of historical record: a steady state or gradually increasing stage, a rapidly increasing stage, and a declining stage. The GAM results showed that aquatic total phosphorus(TP) concentrations and regional GDP(anthropogenic factors) were dominant contributors to POP accumulation rates in the lake sediment. The fitted relationships between air temperature and sedimentary DDT and PAH concentrations were linear and negative, while a positive linear relationship was found for PCBs, suggesting that Lake Chaohu may have become a net source for DDTs and PAHs, and a sink for PCBs, under a progressively warming climate.     

室内空气污染预测方法的实证研究

在大型(30m3)、小型(1m3)环境箱实验获得数据的基础上构建释放模型,并将模拟结果和现场监测得到的浓度水平进行对比,对开发的室内空气污染预测方法进行了实证研究.以一新装修房间为例,分别利用大型和小型环境箱实验,研究了复杂整装材料和简单层状材料的释放规律,建立了相应材料的污染物释放模型;依据单元内物质守恒理论和污染物充分混合的假设将释放模型组合,建立了室内空气污染预测方法,并预测了该房间甲醛和TVOC(total volatile organic compounds)浓度的变化过程.在考虑了0.03ACH(air change per hour)的换气率之后,模型预测与现场监测的污染物浓度变化趋势基本吻合,甲醛和TVOC污染预测误差(正则化标准差)分别为2.8%和1.6%.模型分析表明,各污染源对于甲醛污染的贡献,家具＞涂料＞地板;对于TVOC污染的贡献,涂料＞地板＞家具.结论表明,该预测方法可以真实反映现场污染物浓度变化趋势,可以用来分析各污染源对于整体污染的贡献、指导装修材料的选择和作为室内污染评估和控制的有效工具.     

Integrating intraseasonal grassland dynamics in cross-scale distribution modeling to support waterbird recovery plans

Despite much discussion about the utility of remote sensing for effective conservation, the inclusion of these technologies in species recovery plans remains largely anecdotal. We developed a modeling approach for the integration of local, spatially measured ecosystem functional dynamics into a species distribution modeling (SDM) framework in which other ecologically relevant factors are modeled separately at broad scales. To illustrate the approach, we incorporated intraseasonal water-vegetation dynamics into a cross-scale SDM for the Common Snipe (Gallinago gallinago), which is highly dependent on water and vegetation dynamics. The Common Snipe is an Iberian grassland waterbird characteristic of European agricultural meadows and a member of one of the most threatened bird guilds. The intraseasonal dynamics of water content of vegetation were measured using the standard deviation of the normalized difference water index time series computed from bimonthly images of the Sentinel-2 satellite. The recovery plan for the Common Snipe in Galicia (northwestern Iberian Peninsula) provided an opportunity to apply our modeling framework. Model accuracy in predicting the species’ distribution at a regional scale (resulting from integration of downscaled climate projections with regional habitat–topographic suitability models) was very high (area under the curve [AUC] of 0.981 and Boyce's index of 0.971). Local water-vegetation dynamic models, based exclusively on Sentinel-2 imagery, were good predictors (AUC of 0.849 and Boyce's index of 0.976). The predictive power improved (AUC of 0.92 and Boyce's index of 0.98) when local model predictions were restricted to areas identified by the continental and regional models as priorities for conservation. Our models also performed well (AUC of 0.90 and Boyce's index of 0.93) when projected to updated water-vegetation conditions. Our modeling framework enabled incorporation of key ecosystem processes closely related to water and carbon cycles while accounting for other factors ecologically relevant to endangered grassland waterbirds across different scales, allowed identification of priority areas for conservation, and provided an opportunity for cost-effective recovery planning by monitoring management effectiveness from space.     

Structurally-consistent estimation of use and nonuse values for landscape-wide environmental change

We address the problem of estimating the use and nonuse value derived from a landscape-wide programme of environmental change. Working in the random utility framework, we develop a structural model that describes both demand for recreational trips to the landscape's quality-differentiated natural areas and preferences over different landscape-wide patterns of environmental quality elicited in a choice experiment. The structural coherence of the model ensures that the parameters of the preference function can be simultaneously estimated from the combination of revealed and stated preference data. We explore the properties of the model in a Monte Carlo experiment and then apply it to a study of preferences for changes in the ecological quality of rivers in northern England. This implementation reveals plausible estimates of the use and nonuse parameters of the model and provides insights into the distance decay in those two different forms of value.     

Effects of spatial autocorrelation and sampling design on estimates of protected area effectiveness

Estimating the effectiveness of protected areas (PAs) in reducing deforestation is useful to support decisions on whether to invest in better management of areas already protected or to create new ones. Statistical matching is commonly used to assess this effectiveness, but spatial autocorrelation and regional differences in protection effectiveness are frequently overlooked. Using Colombia as a case study, we employed statistical matching to account for confounding factors in park location and accounted for for spatial autocorrelation to determine statistical significance. We compared the performance of different matching procedures—ways of generating matching pairs at different scales—in estimating PA effectiveness. Differences in matching procedures affected covariate similarity between matched pairs (balance) and estimates of PA effectiveness in reducing deforestation. Independent matching yielded the greatest balance. On average 95% of variables in each region were balanced with independent matching, whereas 33% of variables were balanced when using the method that performed worst. The best estimates suggested that average deforestation inside protected areas in Colombia was 40% lower than in matched sites. Protection significantly reduced deforestation, but PA effectiveness differed among regions. Protected areas in Caribe were the most effective, whereas those in Orinoco and Pacific were least effective. Our results demonstrate that accounting for spatial autocorrelation and using independent matching for each subset of data is needed to infer the effectiveness of protection in reducing deforestation. Not accounting for spatial autocorrelation can distort the assessment of protection effectiveness, increasing type I and II errors and inflating effect size. Our method allowed improved estimates of protection effectiveness across scales and under different conditions and can be applied to other regions to effectively assess PA performance.     

Using chemical models for evaluating the geochemical confinement capacity of a geological barrier application to the Centre de Stockage de l'Aube (France)

The feasibility of using a chemical reaction-based approach for evaluating and modelling the role of adsorption reactions in determining the geochernical confinement capacity of natural geological barriers is being studied as part of an on-going R & D programme. The confined superficial aquifer underlying the Centre de Stockage de l'Aube facility, a geological barrier for this site, has been used as a case study with the following aims. First, development of a site characterisation protocol and demonstration of its use to determine the principal geochemical characteristics of aquifer materials using batch experiments and to represent the information obtained in terms of a chemical model. The experimental results obtained for Ni²⁺ partitioning as a function of total Ni, pH, total Ca and total solid can be satisfactorily represented in terms of reactions with an ion exchange site and a single amphoteric surface hydroxyl site with ferrihydrite reaction constants. A second objective is the incorporation of the reactions in a coupled geochemistry/transport code, and to verify the applicability of the coupled code predictions for Ni²⁺ mass transfer by comparison with the results obtained during column tracer experiments. The breakthrough curve and equilibrium solid phase Ni loading, predicted by a one-dimensional coupled model for a column tracer experiment, agree closely with observed data.Additional studies are underway to reduce model conditionality, to extend the adsorption model to other analogue cations and anions, to incorporate the effect of natural organic matter and to take into consideration precipitation/dissolution of amorphous Fe surface phases.     

MULTIPLICATIVE,SEASONAL ARIMA MODELS FOR LAKE ERIE AND LAKE ONTARIO WATER LEVELS1

ABSTRACT: Federal agencies in the U.S. and Canada continuously examine methods to improve understanding and forecasting of Great Lakes water level dynamics in an effort to reduce the negative impacts of fluctuating levels incurred by interests using the lakes. The short term, seasonal and long term water level dynamics of lakes Erie and Ontario are discussed. Multiplicative, seasonal ARIMA models are developed for lakes Erie and Ontario using standardized, monthly mean level data for the period 1900 to 1986. The most appropriate model identified for each lake had the general form: (1 0 1)(0 1 1)₁₂. The data for each lake were subdivided by time periods (1900 to 1942;1 943 to 1986) and the model coefficients estimated for the subdivided data were similar, indicating general model stability for the entire period of record. The models estimated for the full data sets were used to forecast levels 1,2,3, and 6 months ahead for a period of high levels (1984 to 1986). The average absolute forecast error for Lake Erie was 0.049m, 0.076m, 0.091 m and 0.128m for the 1, 2,3, and 6 month forecasts, respectively. The average absolute forecast error for Lake Ontario was 0.058m, 0.095m, 0.120m and 0.136m for the 1,2,3, and 6 month forecasts, respectively. The ARIMA models provide additional information on water level time series structure and dynamics. The models also could be coordinated with current forecasting methods, possibly improving forecasting accuracy.     

Island biogeographical theory: Can it be used to predict lotic recovery rates?

Classic island biogeographic theory predicts that equilibrium will be reached when immigration and extinction rates are equal. These rates are modified by number of species in source area, number of intermediate islands, distance to recipient island, and size of intermediate islands. This general model has been variously modified and proposed to be a stochastic process with minimal competitive interaction or heavily deterministic. Predictive models of recovery (regardless of the end point chosen) have been based on the appropriateness of the MacArthur-Wilson models. Because disturbance frequency, severity, and intensity vary in their effect on community dynamics, we propose that disturbance levels should first be defined before evaluating the applicability of island biogeographical theory. Thus, we suggest a classification system of four disturbance levels based on recovery patterns by primary and secondary succession and faunal organization by primary (invasion of vacant areas) and secondary (remnant of previous community remains) processes. Level 1A disturbances completely destroy communities with no upstream or downstream sources of colonizers, while some component of near surface interstitial or hyporheic flora and fauna survive level 1B disturbances. Recovery has been reported to take from five years to longer than 25 years, when most invading colonists do not have an aerial form. Level 2 disturbances destroy the communities but leave upstream and downstream colonization sources (level 2A) and, sometimes, a hyporheic pool of colonizers (level 2B). Recovery studies have indicated primary succession and faunal structuring patterns (2A) with recovery times of 90–400 days or secondary succession and faunal structuring patterns (2B) with recovery times of 40–250 days. Level 3 disturbances result in reduction in species abundance and diversity along a stream reach; level 4 disturbances result in reduction of abundance and diversity in discrete patches. Both disturbance types lead to secondary succession and secondary faunal organization. Recovery rates can be quite rapid, varying from less than 10 days to 100 or more days. We suggest that island biogeographical models seem appropriate to recovery by secondary processes after level 3 and 4 disturbances, where competition may be an important organizing factor, while models of numerical abundance and resource tracking are probably of better use where community development is by primary succession (levels 1 and 2). Development of predictive recovery models requires research that addresses a number of fundamental questions. These include the role of hydrologic patterns on colonization dynamics, the role of nonaerial colonizers in recovery from level 1 disturbances, and assessment of the impact of changes in the order of invasion by colonizers of varying energetic efficiencies. Finally, we must be able to assemble these data and determine whether information that guides community organization at one level of disturbance can provide insights into colonization dynamics at other levels.