USE OF A GEOGRAPHIC INFORMATION SYSTEM TO ASSEMBLE INPUT-DATA SETS FOR A FINITE-DIFFERENCE MODEL OF GROUND-WATER FLOW1

ABSTRACT: A Geographic Information System (GIS) was used to expedite assembly of input-data sets for a model of ground-water flow in the Middle Patuxent River basin in Howard County, Maryland. The model grid was developed with GIS and used to select attributes from GIS data coverages. These attributes were then output from GIS into the input-data sets, which included model boundaries, regolith thickness, bedrock-surface altitude, stream locations, stream length, stream-bottom altitude, and trans-missivity. The ability to change large sets of spatial data quickly and accurately with GIS enhances the model-calibration process. (KEY TERMS: GIS; ground water; Piedmont; Maryland; MODFLOW.)     

Development of a unique topology for a hard-turning micro-scale machine tool

A micro-scale machine tool (mMT) topology is developed for turning hardened steel bearing components. The topology utilizes the principle of leverage to increase accuracy and stiffness and incorporates decoupling to reduce unwanted motion of the tool. Performance specifications required that the static stiffness is at least 10 N/μm and dynamic stiffness is at least 30 N/μm in all directions. The kinematics for the topology are developed to enable control over the position and orientation of the tool tip. The effect of the topology on rake angle is determined and the topology is adjusted so as to minimize the rake angle variation during the cut. Cutting tests are performed to determine cutting parameters for achieving a low surface roughness and to estimate the accuracy of the machine. Tests show that the hard-turning mMT can achieve surface roughness below 25 nm Ra, diametrical accuracy of 1 μm and peak-to-valley roundness deviation (RONt) below 0.35 μm.     

Preferences and willingness to pay for close to home nature for outdoor recreation in Sweden

In Sweden there is a strong tradition of using nature areas for outdoor recreation. This paper reports on a study which explored preferences and willingness to pay for outdoor recreation close to home (≤100 km away from home and ≤24 hours stay at a recreation site) using the contingent valuation method. The data originated from a mail survey that involved Swedish residents who were randomly selected from a national register. An ordinary least squares regression model was used to account for factors influencing willingness to pay. The results showed that approximately 50% of the respondents used nature areas close to their home for recreation and their average frequency of visits to these areas was 74 times annually. Areas dominated by forests were the most preferred, followed by water. The respondents were willing to pay approximately 7200 SEK (US$1080) annually. Their willingness to pay was influenced by factors such as type of nature area, distance to and time spent at the recreation site and income. The results provide one input to the land use planning process by considering the demand for nature-based outdoor recreation close to home.     

LAND COVER AS A FRAMEWORK FOR ASSESSING RISK OF WATER POLLUTION1

ABSTRACT: A survey of numerous field studies shows that nitrogen and phosphorous export coefficients are significantly different across forest, agriculture, and urban land‐cover types. We used simulations to estimate the land‐cover composition at which there was a significant risk of nutrient loads representative of watersheds without forest cover. The results suggest that at between 20 percent and 30 percent nonforest cover, there is a 10 percent or greater chance of N or P nutrient loads being equivalent to the median values of predominantly agricultural or urban watersheds. The methods apply to environmental management for assessing the risk to increased nonpoint nutrient pollution. Interpretation of the risk measures are discussed relative to their application for a single watershed and across a region comprised of several watersheds.     

Impact of desiccation and heat exposure stress on Salmonella tolerance to acidic conditions

In a recent study, the pH of commonly used Salmonella pre-enrichment media became acidic (pH 4.0 to 5.0) when feed or feed ingredients were incubated for 24 h. Acidic conditions have been reported to injure or kill Salmonella. In this study, cultures of four known feed isolates (S. montevideo, S. senftenberg, S. tennessee, and S. schwarzengrund) and four important processing plant isolates (S. typhimurium, S. enteritidis, S. infantis, and S. heidelberg) were grown on meat and bone meal and later subjected to desiccation and heat exposure to stress the microorganism. The impact of stress on the isolates ability to survive in acidic conditions ranging from pH 4.0 to 7.0 was compared to the non-stressed isolate. Cell injury was determined on xylose lysine tergitol 4 (XLT4) and cell death determined on nutrient agar (NA). When measured by cell death in non-stressed Salmonella, S. typhimurium was the most acid tolerant and S. heidelberg was the most acid sensitive whereas in stressed Salmonella, S. senftenberg was the most acid tolerant and S. tennessee was the most acid sensitive. The pH required to cause cell injury varied among isolates. With some isolates, the pH required for 50% cell death and 50% cell injury was similar. In other isolates, cell injury occurred at a more neutral pH. These findings suggest that the pH of pre-enrichment media may influence the recovery and bias the serotype of Salmonella recovered from feed during pre-enrichment.     

Where Did the Agricultural Nonpoint Source Trades Go? Lessons from Virginia Water Quality Trading Programs

Governmental agencies, nongovernmental organizations, and agricultural organizations promote water quality trading programs as an innovative policy to engage agricultural producers in conservation activities. Cost analyses suggest regulated sources can reduce compliance costs by purchasing agricultural nonpoint source credits. Yet, such “point‐nonpoint” trades are rare. This article assesses the demand for agricultural nonpoint sources in well‐developed nutrient trading programs in Virginia for industrial and municipal wastewater treatment plants, municipal stormwater programs, and land developers. Evidence suggests nutrient trading programs in Virginia will not stimulate investments in pollutant reduction practices on working agricultural lands. The lack of demand for agricultural nonpoint source credits can be attributed to a substantial degree to the design features and incentives present in multiple overlapping regulatory programs. The legal setting that dampens regulated source demand for nonpoint source credits in Virginia is broadly representative of conditions found elsewhere in the United States.     

Estimating Root Zone Soil Moisture at Continental Scale Using Neural Networks

This study investigates the feasibility of artificial neural networks (ANNs) to retrieve root zone soil moisture (RZSM) at the depths of 20 cm (SM20) and 50 cm (SM50) at a continental scale, using surface information. To train the ANNs to capture interactions between land surface and various climatic patterns, data of 557 stations over the continental United States were collected. A sensitivity analysis revealed that the ANNs were able to identify input variables that directly affect the water and energy balance in root zone. The data important for RZSM retrieval in a large area included soil texture, surface soil moisture, and the cumulative values of air temperature, surface soil temperature, rainfall, and snowfall. The results showed that the ANNs had high skill in retrieving SM20 with a correlation coefficient above 0.7 in most cases, but were less effective at estimating SM50. The comparison of the ANNs showed that using soil texture data improved the model performance, especially for the estimation of SM50. It was demonstrated that the ANNs had high flexibility for applications in different climatic regions. The method was used to generate RZSM in North America using Soil Moisture and Ocean Salinity (SMOS) soil moisture data, and achieved a spatial soil moisture pattern comparable to that of Global Land Data Assimilation System Noah model with comparable performance to the SMOS surface soil moisture retrievals. The models can be efficient alternatives to assimilate remote sensing soil moisture data for shallow RZSM retrieval.