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Durham, NH, United States

Gopalakrishnan G.,Argonne National Laboratory | Cristina Negri M.,Argonne National Laboratory | Salas W.,Applied Geosolutions Llc
GCB Bioenergy

Current research on the environmental sustainability of bioenergy has largely focused on the potential of bioenergy crops to sequester carbon and mitigate greenhouse gas emissions and possible impacts on water quality and quantity. A key assumption in these studies is that bioenergy crops will be grown in a manner similar to current agricultural crops such as corn and hence would affect the environment similarly. In this study, we investigate an alternative cropping system where bioenergy crops are grown in buffer strips adjacent to current agricultural crops such that nutrients present in runoff and leachate from the traditional row-crops are reused by the bioenergy crops (switchgrass, miscanthus and native prairie grasses) in the buffer strips, thus providing environmental services and meeting economic needs of farmers. The process-based biogeochemical model Denitrification-Decomposition (DNDC) was used to simulate crop yield, nitrous oxide production and nitrate concentrations in leachate for a typical agricultural field in Illinois. Model parameters have been developed for the first time for miscanthus and switchgrass in DNDC. Results from model simulations indicated that growing bioenergy crops in buffer strips mitigated nutrient runoff, reduced nitrate concentrations in leachate by 60-70% and resulted in a reduction of 50-90% in nitrous oxide emissions compared with traditional cropping systems. While all the bioenergy crop buffers had significant positive environmental benefits, switchgrass performed the best with respect to minimizing nutrient runoff and nitrous oxide emissions, while miscanthus had the highest yield. Overall, our model results indicated that the bioenergy crops grown in these buffer strips achieved yields that are comparable to those obtained for traditional agricultural systems while simultaneously providing environmental services and could be used to design sustainable agricultural landscapes. © 2012 Blackwell Publishing Ltd. Source

Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 148.01K | Year: 2012

DESCRIPTION (provided by applicant): Research at the intersection of limnology and public health is showing that toxins produced from cyanobacteria act as an environmental trigger for Amyotrophic Lateral Sclerosis (ALS) commonly known as Lou Gehrig's Disease. Additional linkages are being made between cyanobacteria and Parkinson's and several acute illnesses and neurological disorders. A primary obstacle in advancing our understanding of linkages between cyanobacteria blooms, toxicity, and human health is water quality information on the presence, extent, magnitude, and intensity of these harmful algal blooms in freshwater bodies. Little to no exposure data is available to the public health community which has created an opportunity and innovation gap. Remote sensing science has now advanced to the point where operational assessment of cyanobacteria and water quality is feasible. The innovation of this NIH SBIR Phase 1 is the development of an operational cyanobacteria indicator tool (Cyano-Map) that utilizesNASA satellite remote sensing platforms and state-of-the-art geosciences methods. Cyano-Map will be capable mapping and monitoring phycocyanin (PC) abundance and cyanobacteria (i.e., microcystis, anabaena, and planktothrix) over space and time for inlandwaters. Cyano-Map will utilize multiple NASA platforms to extract the strengths of multiple sensors for the optimal spatial, temporal, spectral and radiometric resolutions. PUBLIC HEALTH RELEVANCE: Indicators and risk maps for cyanobacteria blooms in water systems and mapping environmental triggers for ALS and Parkinson's Disease.

Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 728.71K | Year: 2014

Agriculture faces major challenges in the decades to come due to increasing resource pressures, severe weather and climate change, population growth and shifting diets, and economic development. Rice is one of the most important crops globally considering its role in the Earth system, food security, and providing livelihoods with more than 1 billion people depending on rice. Tools and systems that can help monitor production and support risk management are needed for decision making by many end users and governments. Futures are a tool used to manage or hedge risk, reduce volatility, improve food security, and maximize efficiency and profit on the open market. Currently, the rice futures market has little high quality and timely information available to make strategic or application specific decisions to reduce risk and maximize profit. The global rice futures market is thinly traded causing extreme price fluctuation orders of magnitude. The innovation of Rice Decision Support System (RiceDSS) is the seamless fusion of operational satellite remote sensing monitoring metrics of rice agriculture, rice yield modeling, and weather forecasts to generate near real time information on rice extent, growth stages, production forecasts and statistical uncertainty. RiceDSS uses a state-of-the-art open source framework with advanced automation routines, web-GIS, and mobile technologies to support visualization and delivery of information to support global food security programs and commodity markets.

Agency: NSF | Branch: Standard Grant | Program: | Phase: | Award Amount: 149.98K | Year: 2014

There is broad scientific consensus that some diseases are influenced by the environment. Recent work has suggested that harmful algae in lakes are linked to some illnesses and diseases. This project will study relationships among lake water quality, harmful algae, and Amyotrophic lateral sclerosis (ALS), also known as Lou Gehrigs disease. An interdisciplinary team of geographers, neurologists, epidemiologists, and statisticians will collaborate to assess how and under what conditions algal levels and water quality impact ALS incidence, a very poorly understood disease. This work will contribute to research on disease ecology as well as inform a wide audience concerned with health outcomes including public health and environmental agencies, medical centers, those potentially exposed to the risk factors, and property owners.

Amyotrophic lateral sclerosis (ALS) is a progressive, fatal disease with an average life expectancy of two to five years from time of diagnosis. Approximately 90% of ALS cases have no known genetic cause and are commonly known as sporadic ALS (sALS). Despite many recent discoveries about the genetics of ALS, the etiology or causal origins of sALS remain largely unknown. It is most likely that sALS results from a combination of underlying genetic susceptibility coupled with environmental exposure to one or more toxins. Recent work has shown linkages between lake water quality and high ALS incidence, with the [algal] cyanotoxin beta methyl-amino-alanine (BMAA) as a potential trigger. The overarching goal of this study is to characterize the relationship between sALS incidence and lake water quality parameters that favor cyanobacteria growth. Multi-scale satellite remote sensing imagery will be used to map lake water quality attributes including cyanobacteria, phycocyanin, chlorophyll-a, and total nitrogen in freshwater lakes in northern New England. An ALS database with completed questionnaire surveys, including information on residential history and related risk factors, will be integrated into a spatial analysis framework that will be generalizable to other similar freshwater / residential systems. Eco-epidemiological modeling will be conducted to test the relationships among lake conditions, risk factors, and sALS. This work will develop tools for assessing stressor response relationships improve understanding of sALS. The results will assist in identifying potential causal factors as well as and means by which they may be remotely monitored, thereby contributing to the quantification of the role of freshwater aquatic ecosystems in human health risk.

Agency: Department of Agriculture | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 460.00K | Year: 2011

Agricultural row crops occupy hundreds of million acres of land in the United States. Decisions regarding the implementation of tillage practices in these agricultural areas have a significant effect on other environmental outcomes including soil erosion, water quality, and carbon sequestration. In addition, the effects of tillage practices can vary due to soil type and topographic conditions. There is currently no systematic and cost-effective method for documenting tillage practices, or the resulting effects, over a large region. Currently, agricultural tillage practices are typically mapped at the regional level using a drive-by survey method, which is time consuming, expensive, and limited in spatial extent and temporal sampling. The high cost, in time and resources, of identifying tillage practices across a large region using this traditional drive-by survey is prohibitive. The use of remote sensing data for mapping tillage practices across large regions represents a cost efficient solution. With funding from the USDA, we will build a prototype operational tillage practice monitoring platform (OpTIS) that will systematically provide information about the spatial and temporal dynamics of tillage practices through a web-GIS environment.

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