Suchergebnisse

Investigates whether and to what extent the promotion of more land-intensive housing patterns is compatible with the fiscal incentives of local governments.

Recycling irrigation water can provide water during periods of drought for horticulture operations and can reduce nonpoint-source pollution, but water recycling increases production costs and can increase risk of disease infestation from waterborne pathogens such as Pythium and Phytophthora. This study of water recycling adoption by horticultural growers in Virginia, Maryland, and Pennsylvania finds that the potential for increased disease infestation would reduce growers probability of adopting water recycling. Widespread adoption of recycling irrigation water would require government incentives or coercion or growers ability to pass cost increases on to customers. ; This work was supported by the U.S. Department of Agriculture, National Institute of Food and Agriculture, Hatch project 227572, and by Specialty Crop Research Initiative Project #2010-51181- 21140, Integrated management of zoosporic pathogens and irrigation water quality for a sustainable green industry. The authors appreciate the research assistance of Nicole DAlessio and Gwen Rees.

Extensive efforts to adaptively manage nutrient pollution rely on Chesapeake Bay Program's (Phase 6) Watershed Model, called Chesapeake Assessment Scenario Tool (CAST), which helps decision-makers plan and track implementation of Best Management Practices (BMPs). We describe mathematical characteristics of CAST and develop a constrained nonlinear BMP-subset model, software, and visualization framework. This represents the first publicly available optimization framework for exploring least-cost strategies of pollutant load control for the United States' largest estuary. The optimization identifies implementation options for a BMP subset modeled with load reduction effectiveness factors, and the web interface facilitates interactive exploration of >30,000 solutions organized by objective, nutrient control level, and for similar to 200 counties. We assess framework performance and demonstrate modeled cost improvements when comparing optimization-suggested proposals with proposals inspired by jurisdiction plans. Stakeholder feedback highlights the framework's current utility for investigating cost-effective tradeoffs and its usefulness as a foundation for future analysis of restoration strategies. ; United States Environmental Protection Agency (USEPA) Chesapeake Bay Program Office [CB96350501, CB96325901, CB96365601, CB96351401] ; Published version ; This material is based upon work funded wholly or in part by the United States Environmental Protection Agency (USEPA) Chesapeake Bay Program Office, including direct salary support for multiple partners within the USEPA-administered Chesapeake Bay Program (including co-authors Shenk and Linker as well as numerous assisting support staff) , assistance agreements CB96350501 to Chesapeake Research Consortium (CRC) , Inc. (co-authors Kaufman, Ball, Bosch, Ellis, Hobbs, Van Houtven, and McGarity) , CB96325901 and CB96365601 to the Uni-versity of Maryland Center for Environmental Science (co-author Asplen) , and CB96351401 to Pennsylvania State University (co-author Bhatt) . The contents of this document do not necessarily reflect the views and policies of the Environmental Protection Agency. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.; The authors thank Rich Batiuk and the modeling team at the Ches-apeake Bay Program Office (Cuiyin Wu, Andrew Sommerlot, Richard Tian, Isabella Bertani) for constructive input throughout the project. The authors thank and acknowledge Stuart Schwartz of the University of Maryland, Baltimore County, who was an original member of CRC's Advisory and Support Committee and provided important early contributions of both conceptual and practical nature. Many thanks to John Massey for technical support with cloud resources and batch processing and Megan McNeilly for User Experience improvements to the online decision-support tool. Further thanks to Brian Burch, Megan Thynge, Tim Paris, Martin Koslof, and the entire CBPO software development and information technology team.