Using multivariate regression modeling for sampling and predicting chemical characteristics of mixed waste in old landfills

Municipal solid waste landfills pose a threat on environment and human health, especially old landfills which lack facilities for collection and treatment of landfill gas and leachate. Consequently, missing information about emission flows prevent site-specific environmental risk assessments. To overcome this gap, the combination of waste sampling and analysis with statistical modeling is one option for estimating present and future emission potentials. Optimizing the tradeoff between investigation costs and reliable results requires knowledge about both: the number of samples to be taken and variables to be analyzed.This article aims to identify the optimized number of waste samples and variables in order to predict a larger set of variables. Therefore, we introduce a multivariate linear regression model and tested the applicability by usage of two case studies. Landfill A was used to set up and calibrate the model based on 50 waste samples and twelve variables. The calibrated model was applied to Landfill B including 36 waste samples and twelve variables with four predictor variables.The case study results are twofold: first, the reliable and accurate prediction of the twelve variables can be achieved with the knowledge of four predictor variables (Loi, EC, pH and Cl). For the second Landfill B, only ten full measurements would be needed for a reliable prediction of most response variables. The four predictor variables would exhibit comparably low analytical costs in comparison to the full set of measurements. This cost reduction could be used to increase the number of samples yielding an improved understanding of the spatial waste heterogeneity in landfills.Concluding, the future application of the developed model potentially improves the reliability of predicted emission potentials. The model could become a standard screening tool for old landfills if its applicability and reliability would be tested in additional case studies.     

Manure placement depth impacts on crop yields and N retained in soil

The objective of this study was to determine the impact of manure placement depth on crop yield and N retention in soil. Experimental treatments were deep manure injection (45 cm), shallow manure injection (15 cm), and conventional fertilizer-based management with at least three replications per site. Water infiltration, and changes in soil N and P amounts were measured for up to 30 months and crop yield monitored for three seasons following initial treatment. Deep and shallow manure injections differed in soil inorganic N distributions. For example, in the manure slot the spring following application, NO₃-N in the surface 60 cm was higher (p < .01) when injected 15 cm (21.4 μ g/g) into the soil than 45 cm (11.7 μ g/g), whereas NH₄-N had opposite results with shallow injection having less (p = 0.045) NH₄-N (102 μ g/g) than deep (133 μ g/g) injection. In the fall one year after the manure was applied, NO₃-N and NH₄-N were lower (p = 0.001) in the shallow injection than the deep injection. The net impact of manure placement on total N was that deep injection had 31, 59, and 44 more kg N ha^{? 1} than the shallow injection treatment 12, 18, and 30 months after application, respectively. Deep manure injection did not impact soybean (Glycine max L.) yield, however corn (Zea mays L.) yield increased if N was limiting. The higher corn yield in the deep injected treatment was attributed to increased N use efficiency. Higher inorganic N amounts in the deep injection treatment were attributed to reduced N losses through ammonia volatilization, leaching, or denitrification. Results suggest that deep manure placement in glacial till soil may be considered a technique to increase energy, N use efficiency, and maintain surface and ground water quality. However, this technique may not work in glacial outwash soils due to the inability to inject into a rocky subsurface.     

Is Urban Stream Restoration Worth It?1

Kenney, Melissa A., Peter R. Wilcock, Benjamin F. Hobbs, Nicholas E. Flores, and Daniela C. Martínez, 2012. Is Urban Stream Restoration Worth It? Journal of the American Water Resources Association (JAWRA) 48(3): 603-615. DOI: 10.1111/j.1752-1688.2011.00635.x Abstract: Public investment in urban stream restoration is growing, yet little has been done to quantify whether its benefits outweigh its cost. The most common drivers of urban stream projects are water quality improvement and infrastructure protection, although recreational and aesthetic benefits are often important community goals. We use standard economic methods to show that these contributions of restoration can be quantified and compared to costs. The approach is demonstrated with a case study in Baltimore, Maryland, a city with a legal mandate to reduce its pollutant load. Typical urban stream restoration costs of US$500-1,200 per foot are larger than the cost of the least expensive alternatives for management of nitrogen loads from stormwater (here, detention ponds, equivalent to $30-120 per foot of restored stream) and for protecting infrastructure (rip-rap armoring of streambanks, at $0-120 per foot). However, the higher costs of stream restoration can in some cases be justified by its aesthetic and recreational benefits, valued using a contingent valuation survey at $560-1,100 per foot. We do not intend to provide a definitive answer regarding the worth of stream restoration, but demonstrate that questions of worth can be asked and answered. Broader application of economic analysis would provide a defensible basis for understanding restoration benefits and for making restoration decisions.     

An Ex Post Evaluation of Ohio’s Great Miami Water Quality Trading Program1

Newburn, David A. and Richard T. Woodward, 2011. An Ex Post Evaluation of Ohio’s Great Miami Water Quality Trading Program. Journal of the American Water Resources Association (JAWRA) 48(1): 156‐169. DOI: 10.1111/j.1752‐1688.2011.00601.x Abstract: Market‐based approaches to address water quality problems have resulted in only limited success, especially in trading programs involving both point and nonpoint sources. We analyze one of the largest point‐nonpoint trading programs – the Great Miami Trading Program in Ohio, administered by the Miami Conservancy District (MCD). Our evaluation focuses on the economic and institutional aspects of the program, including cost effectiveness, efficiency of bidding, transaction costs, trading ratios, and innovation. We use a unique dataset consisting of all bids from agricultural nonpoint sources and interviews of soil and water conservation district (SWCD) agents in the watershed. We find that the MCD’s reliance on county‐level SWCD offices to recruit and advise farmers has been essential to achieve relatively high rates of farmer participation. Additionally, the MCD is able to partly free ride on the administrative costs that SWCD offices receive to assist federal conservation programs, which is helpful to lower costs for a fledgling trading program. However, the involvement of SWCD offices reduced the potential cost savings from the reverse auction structure because some agents were able to learn about the threshold price over the six rounds of bidding and help farmers bid strategically. Overall, the program structure serves as an effective model for future trading programs in other regions that seek to involve agricultural nonpoint sources.     

Review of Pesticide Retention Processes Occurring in Buffer Strips Receiving Agricultural Runoff1

Arora, Kapil, Steven K. Mickelson, Matthew J. Helmers, and James L. Baker, 2010. Review of Pesticide Retention Processes Occurring in Buffer Strips Receiving Agricultural Runoff. Journal of the American Water Resources Association (JAWRA) 46(3):618-647. DOI: 10.1111/j.1752-1688.2010.00438.x Abstract: Review of the published results shows that the retention of the two pesticide carrier phases (runoff volume and sediment mass) influences pesticide mass transport through buffer strips. Data averaged across different studies showed that the buffer strips retained 45% of runoff volume (ranging between 0 and 100%) and 76% of sediment mass (ranging between 2 and 100%). Sorption (soil sorption coefficient, K_oc) is one key pesticide property affecting its transport with the two carrier phases through buffer strips. Data from different studies for pesticide mass retention for weakly (K_oc < 100), moderately (100 < K_oc < 1,000), and strongly sorbed pesticides (K_oc > 1,000) averaged (with ranges) 61 (0-100), 63 (0-100), and 76 (53-100) %, respectively. Because there are more data for runoff volume and sediment mass retention, the average retentions of both carrier phases were used to calculate that the buffer strips would retain 45% of weakly to moderately sorbed and 70% of strongly sorbed pesticides on an average basis. As pesticide mass retention presented is only an average across several studies with different experimental setups, the application of these results to actual field conditions should be carefully examined.     

Modelling plant species richness using functional groups

Conservation biologists increasingly rely on spatial predictive models of biodiversity to support decision-making. Therefore, highly accurate and ecologically meaningful models are required at relatively broad spatial scales. While statistical techniques have been optimized to improve model accuracy, less focus has been given to the question: How does the autecology of a single species affect model quality? We compare a direct modelling approach versus a cumulative modelling approach for predicting plant species richness, where the latter gives more weight to the ecology of functional species groups. In the direct modelling approach, species richness is predicted by a single model calibrated for all species. In the cumulative modelling approach, the species were partitioned into functional groups, with each group calibrated separately and species richness of each group was cumulated to predict total species richness. We hypothesized that model accuracy depends on the ecology of individual species and that the cumulative modelling approach would predict species richness more accurately. The predictors explained plant species richness by ca. 25%. However, depending on the functional group the deviance explained varied from 3 to 67%. While both modelling approaches performed equally well, the models of the different functional groups highly varied in their quality and their spatial richness pattern. This variability helps to improve our understanding on how plant functional groups respond to ecological gradients.     

Theoretical and Empirical Analysis on Influence Factors of Modern Agricultural Equipment Level in the Processof Agricultural Sustainable Development

To promote modern agricultural equipment level is one characteristic of constructing and developing modern agriculture in China.This paper makes up stepwise linear regression analysis model of influence factors of modern agricultural equipment level,and chooses rural labor,per capita income of rural residents,rural investment,proportion of people at secondary education level and at higher level in per hundred rural labor force and arable land area as independent variables,and total power of machine as induced variable.The major results show that the relativity of modern agricultural equipment level,rural investment and education level of peasants is remarkable,and they are the major influence factors of modern agricultural equipment level.Raising investment level of rural infrastructure construction as well as and research and development and promotion of advanced and applicable modern agricultural equipment,improving quality and education level of peasants can accelerate the development of China’s modern agricultural equipment effectively in the process of agricultural sustainable development.     

COMPARISON OF RESERVOIR LINEAR OPERATION RULES USING LINEAR AND DYNAMIC PROGRAMMING1

ABSTRACT: Mathematical optimization techniques are used to study the operation and design of a single, multi-purpose reservoir system. Optimal monthly release policies are derived for Hoover Reservoir, located in Central Ohio, using chance-constrained linear programming and dynamic programming-regression methodologies. Important characteristics of the former approach are derived, discussed, and graphically illustrated using Hoover Reservoir as a case example. Simulation procedures are used to examine and compare the overall performance of the optimal monthly reservoir release policies derived under the two approaches. Results indicate that, for the mean detention time and the corresponding safe yield target water supply release under existing design of Hoover Reservoir, the dynamic programming policies produce lower average annual losses (as defined by a two-sided quadratic loss function) while achieving at least as high reliability levels when compared to policies derived under the chance-constrained linear programming method. In making this comparison, the reservoir release policies, although not identical, are assumed to be linear. This restricted form of the release policy is necessary to make the chance-constrained programming method mathematically tractable.     

DERIVING THE NONLINEAR RISK-BENEFIT ALGORITHM FOR RESERVOIRS1

ABSTRACT: The Nonlinear Risk-Benefit (NRB) Algorithm includes risk as one of the objectives in a multiple-objective optimization problem. The NRB Algorithm is derived by extending the Surrogate Worth Trade-Off method to quadratic programming. This category of problem is common in water resources planning and design, especially multipurpose reservoir systems. Consequently, an example is given using the algorithm for optimally operating a multipurpose reservoir.     

AN EVALUATION OF ALTERNATIVE POLICIES FOR CONTROLLING AGRICULTURAL NONPOINT SOURCE POLLUTION1

An economic analysis of nonpoint source pollution management was conducted for the Nansemond River and Chuckatuck Creek watersheds in Southeast Virginia. The potential effects of alternative public policies on farm income, land use, and pollution loadings were investigated. Regulatory programs could have quite different impacts depending on which pollutant is targeted. Cost-share rates greater than 50 percent would have little additional effect on pollution from crop enterprises, but would reduce pollution from livestock