Removal of nitrogen and phosphate from wastewater by addition of bittern

Removal of nitrogen and phosphate through crystallization of struvite (MgNH(4)PO(4).6H(2)O) has gained increasing interest. Since wastewaters tend to be low in magnesium relative to ammonia and phosphates, addition of this mineral is usually required to effect the struvite crystallization process. The present study evaluated the feasibility of using bittern, a byproduct of salt manufacture, as a low-cost source of magnesium ions. High reaction rates were observed; the extent of nitrogen and phosphorus removals did not change beyond 10 min. Phosphorus removals from pure solutions with bittern added were equivalent to those obtained with MgCl(2) or seawater. Nitrogen removals with bittern were somewhat lower than with the alternate Mg(2+) sources, however. Application of bittern to biologically treated wastewater from a swine farm achieved high phosphate removal, but ammonia removals were limited by imbalance in the nitrogen:phosphorus ratio.     

Estimating the extent and structure of trade in horticultural orchids via social media

The wildlife trade is a lucrative industry involving thousands of animal and plant species. The increasing use of the internet for both legal and illegal wildlife trade is well documented, but there is evidence that trade may be emerging on new online technologies such as social media. Using the orchid trade as a case study, we conducted the first systematic survey of wildlife trade on an international social‐media website. We focused on themed forums (groups), where people with similar interests can interact by uploading images or text (posts) that are visible to other group members. We used social‐network analysis to examine the ties between 150 of these orchid‐themed groups to determine the structure of the network. We found 4 communities of closely linked groups based around shared language. Most trade occurred in a community that consisted of English‐speaking and Southeast Asian groups. In addition to the network analysis, we randomly sampled 30 groups from the whole network to assess the prevalence of trade in cultivated and wild plants. Of 55,805 posts recorded over 12 weeks, 8.9% contained plants for sale, and 22–46% of these posts pertained to wild‐collected orchids. Although total numbers of posts about trade were relatively small, the large proportion of posts advertising wild orchids for sale supports calls for better monitoring of social media for trade in wild‐collected plants.     

Using the land transformation model to forecast vacant land

Growing or shrinking cities can experience increases in vacant land. As urban populations and boundaries fluctuate, holes can open in once tight urban areas. Many cities chase growth-oriented approaches to dealing with vacancies. It is critical to understand land-use alteration to accurately predict transformations of physical change in order to make better informed decisions about this phenomenon. This research utilizes the land transformation model (LTM), an artificial neural networking mechanism in Geographic Information Systems, to forecast vacant land. Variable influence on vacant land prediction and accuracy of the LTM is assessed by comparing input factors and patterns, using time-series data from 1990 to 2010 in Fort Worth, Texas, USA. Results indicate that the LTM can be useful in simulating vacant land-use changes but more precise mechanisms are necessary to increase accuracy. This will allow for more proactive decisions to better regulate the process of urban decline and regeneration.     

SIMULATION AND DECISIONMAKING: THE PLATTE RIVER BASIN IN NEBRASKA1

Substantial conflict exists over water management and allocation in the Platte River Basin of Nebraska. An interdisciplinary computer simulation model, representing the water quantity, water quality, environmental, and economic dimensions of the conflict, was developed in order to analyze the tradeoffs among allocation scenarios. Most importantly, decisionmakers and interest groups were involved in model development. Simulation results for a base case and two scenarios are presented. One scenario favors protection of instream flow for wildlife; the other favors water diversions for agriculture. Impacts of the instream flow scenario, as measured by the amount of land irrigated, groundwater levels, the amount of wildlife habitat for cranes and catfish, and net agricultural benefits did not differ greatly from those of the base case. However, impacts of the diversion scenario were substantial. On the negative side, instream flows and wildlife habitat declined an average of 39 percent; while, on the positive side, groundwater levels and net agricultural benefits each increased 6 percent. The modeling process was successful insofar as it promoted an understanding among the highly diverse interest groups of the systems nature of the Basin. One agreement on a water diversion schedule among three of the parties has been reached, partly as a result of this process. More comprehensive compromises have not yet been forged. Our experience, however, indicates that modeling success at the policymaking level depends more on the extent to which the policymakers understand the model than it does on model sophistication.     

A hierarchical approach to fisheries planning and modeling in the Columbia River Basin

The Columbia River Basin is the scene of a massive effort to restore populations of Pacific salmon (Oncorhynchus spp.) and steelhead (O. mykiss). Efficient restoration is confounded by a high level of complexity, competing sociopolitical goals and values, and uncertainty about key system properties. Simulation models and other tools of systems analysis are important to development of a comprehensive, regionally acceptable strategy. Hierarchy theory provides a useful paradigm for organized complexity within the Columbia Basin and the basis for a trilevel hierarchical structure for organizing and integrating models. Life-stage models compose the most basic simulation units at the lowest level in the proposed hierarchical modeling structure. Each life-stage model simulates a distinct period in the life cycle of anadromous salmonids. Population models at the intermediate level simulate the complete life cycles of salmon and steelhead populations. At the highest level in the hierarchy, interpopulation models simulate extensive, long-term processes that affect multiple species and stocks. A hierarchical system of models is preferable to a single model or to a group of models lacking formal structure. A principal advantage is that models have the correct spatial and temporal resolution for analyzing questions at different scales. A hierarchical structure also facilitates the flow of information among models, and aids in understanding the impacts of uncertainty. Constructing a hierarchy of models should involve both bottom-up and top-down perspectives that maintain logical consistency among models, while allowing unique model structures appropriate for each level in the hierarchy.     

Estimation of the shadow prices of pollutants with production/environment inefficiency taken into account: a nonparametric directional distance function approach

This paper deals with the estimation of the shadow prices of pollutants with a nonparametric directional distance function approach, where the inefficiency involved in the production process is taken into account unlike the previous studies. The directional vector, which is critical to the estimation and subject to the criterion for an appropriate efficiency rule proposed here, is calculated by using the annual plans of power plants in terms of production and environment. In the empirical study for Korea's electric power industry during the period of 1990-1995, we find that the average shadow prices of sulfur oxides (SOx), nitrogen oxides (NOx), and total suspended particulates (TSP) are approximately 10% lower than those calculated under the assumption of full efficiency. The methodology we propose and the findings obtained in the empirical study allow us to undertake better decision-making over a broad range of environmental policy issues.     

APPLICATION OF GREY MODEL AND ARTIFICIAL NEURAL NETWORKS TO FLOOD FORECASTING1

The main focus of this study was to compare the Grey model and several artificial neural network (ANN) models for real time flood forecasting, including a comparison of the models for various lead times (ranging from one to six hours). For hydrological applications, the Grey model has the advantage that it can easily be used in forecasting without assuming that forecast storm events exhibit the same stochastic characteristics as the storm events themselves. The major advantage of an ANN in rainfall‐runoff modeling is that there is no requirement for any prior assumptions regarding the processes involved. The Grey model and three ANN models were applied to a 2,509 km² watershed in the Republic of Korea to compare the results for real time flood forecasting with from one to six hours of lead time. The fifth‐order Grey model and the ANN models with the optimal network architectures, represented by ANN1004 (34 input nodes, 21 hidden nodes, and 1 output node), ANN1010 (40 input nodes, 25 hidden nodes, and 1 output node), and ANN1004T (14 input nodes, 21 hidden nodes, and 1 output node), were adopted to evaluate the effects of time lags and differences between area mean and point rainfall. The Grey model and the ANN models, which provided reliable forecasts with one to six hours of lead time, were calibrated and their datasets validated. The results showed that the Grey model and the ANN1010 model achieved the highest level of performance in forecasting runoff for one to six lead hours. The ANN model architectures (ANN1004 and ANN1010) that used point rainfall data performed better than the model that used mean rainfall data (ANN1004T) in the real time forecasting. The selected models thus appear to be a useful tool for flood forecasting in Korea.