Estimated losses of plant biodiversity across the U.S. from historical N deposition from 1985—2010.

Although nitrogen (N) deposition is a significant threat to herbaceous plant biodiversity worldwide, it is not a new stressor for many developed regions. Only recently has it become possible to estimate historical impacts nationally for the United States. We used 26 years (1985–2010) of deposition data, with ecosystem-specific functional responses from local field experiments and a national critical loads (CL) database, to generate scenario-based estimates of herbaceous species loss. Here we show that, in scenarios using the low end of the CL range, N deposition exceeded critical loads over 0.38, 6.5, 13.1, 88.6, and 222.1 million ha for the Mediterranean California, North American Desert, Northwestern Forested Mountains, Great Plains, and Eastern Forest ecoregions, respectively, with corresponding species losses ranging from \u3c1% to 30%. When we ran scenarios assuming ecosystems were less sensitive (using a common CL of 10 kg.ha-1.yr-1, and the high end of the CL range) minimal losses were estimated. The large range in projected impacts among scenarios implies uncertainty as to whether current critical loads provide protection to terrestrial plant biodiversity nationally and urge greater research in refining critical loads for U.S. ecosystems

Growing a Sustainable Biofuels Industry: Economics, Environmental Considerations, and the Role of the Conservation Reserve Program

Biofuels are expected to be a major contributor to renewable energy in the coming decades under the Renewable Fuel Standard (RFS). These fuels have many attractive properties including the promotion of energy independence, rural development, and the reduction of national carbon emissions. However, several unresolved environmental and economic concerns remain. Environmentally, much of the biomass is expected to come from agricultural expansion and/or intensification, which may greatly affect the net environmental impact, and economically, the lack of a developed infrastructure and bottlenecks along the supply chain may affect the industry\u27s economic vitality. The approximately 30 million acres (12 million hectares) under the Conservation Reserve Program (CRP) represent one land base for possible expansion. Here, we examine the potential role of the CRP in biofuels industry development, by (1) assessing the range of environmental effects on six end points of concern, and (2) simulating differences in potential industry growth nationally using a systems dynamics model. The model examines seven land-use scenarios (various percentages of CRP cultivation for biofuel) and five economic scenarios (subsidy schemes) to explore the benefits of using the CRP. The environmental assessment revealed wide variation in potential impacts. Lignocellulosic feedstocks had the greatest potential to improve the environmental condition relative to row crops, but the most plausible impacts were considered to be neutral or slightly negative. Model simulations revealed that industry growth was much more sensitive to economic scenarios than land-use scenarios—similar volumes of biofuels could be produced with no CRP as with 100% utilization. The range of responses to economic policy was substantial, including long-term market stagnation at current levels of first-generation biofuels under minimal policy intervention, or RFS-scale quantities of biofuels if policy or market conditions were more favorable. In total, the combination of the environmental assessment and the supply chain model suggests that large-scale conversion of the CRP to row crops would likely incur a significant environmental cost, without a concomitant benefit in terms of biofuel production

The Vulnverability Cube: A Multi-Dimensional Framework for Assessing Relative Vulnerability

The diversity and abundance of information available for vulnerability assessments can present a challenge to decision-makers. Here we propose a framework to aggregate and present socioeconomic and environmental data in a visual vulnerability assessment that will help prioritize management options for communities vulnerable to environmental change. Socioeconomic and environmental data are aggregated into distinct categorical indices across three dimensions and arranged in a cube, so that individual communities can be plotted in a three-dimensional space to assess the type and relative magnitude of the communities’ vulnerabilities based on their position in the cube. We present an example assessment using a subset of the USEPA National Estuary Program (NEP) estuaries: coastal communities vulnerable to the effects of environmental change on ecosystem health and water quality. Using three categorical indices created from a pool of publicly available data (socioeconomic index, land use index, estuary condition index), the estuaries were ranked based on their normalized averaged scores and then plotted along the three axes to form a vulnerability cube. The position of each community within the three-dimensional space communicates both the types of vulnerability endemic to each estuary and allows for the clustering of estuaries with like-vulnerabilities to be classified into typologies. The typologies highlight specific vulnerability descriptions that may be helpful in creating specific management strategies. The data used to create the categorical indices are flexible depending on the goals of the decision makers, as different data should be chosen based on availability or importance to the system. Therefore, the analysis can be tailored to specific types of communities, allowing a data rich process to inform decision-making

Heat-Related Health Impacts under Scenarios of Climate and Population Change

Recent assessments have found that a warming climate, with associated increases in extreme heat events, could profoundly affect human health. This paper describes a new modeling and analysis framework, built around the Benefits Mapping and Analysis Program—Community Edition (BenMAP), for estimating heat-related mortality as a function of changes in key factors that determine the health impacts of extreme heat. This new framework has the flexibility to integrate these factors within health risk assessments, and to sample across the uncertainties in them, to provide a more comprehensive picture of total health risk from climate-driven increases in extreme heat. We illustrate the framework’s potential with an updated set of projected heat-related mortality estimates for the United States. These projections combine downscaled Coupled Modeling Intercomparison Project 5 (CMIP5) climate model simulations for Representative Concentration Pathway (RCP)4.5 and RCP8.5, using the new Locating and Selecting Scenarios Online (LASSO) tool to select the most relevant downscaled climate realizations for the study, with new population projections from EPA’s Integrated Climate and Land Use Scenarios (ICLUS) project. Results suggest that future changes in climate could cause approximately from 3000 to more than 16,000 heat-related deaths nationally on an annual basis. This work demonstrates that uncertainties associated with both future population and future climate strongly influence projected heat-related mortality. This framework can be used to systematically evaluate the sensitivity of projected future heat-related mortality to the key driving factors and major sources of methodological uncertainty inherent in such calculations, improving the scientific foundations of risk-based assessments of climate change and human health

Grasslands, wetlands, and agriculture: the fate of land expiring from the Conservation Reserve Program in the Midwestern United States

The Conservation Reserve Program (CRP) is the largest agricultural land-retirement program in the United States, providing many environmental benefits, including wildlife habitat and improved air, water, and soil quality. Since 2007, however, CRP area has declined by over 25% nationally with much of this land returning to agriculture. Despite this trend, it is unclear what types of CRP land are being converted, to what crops, and where. All of these specific factors greatly affect environmental impacts. To answer these questions, we quantified shifts in expiring CRP parcels to five major crop-types (corn, soy, winter and spring wheat, and sorghum) in a 12-state, Midwestern region of the United States using a US Department of Agriculture (USDA), field-level CRP database and USDA’s Cropland Data Layer. For the years 2010 through 2013, we estimate almost 30%, or more than 530 000 ha, of expiring CRP land returned to the production of these five crops in our study area, with soy and corn accounting for the vast majority of these shifts. Grasslands were the largest type of CRP land converted (360 000 ha), followed by specifically designated wildlife habitat (76 000 ha), and wetland areas (53 000 ha). These wetland areas were not just wetlands themselves, but also a mix of land covers enhancing or protecting wetland ecosystem services (e.g., wetland buffers). Areas in the Dakotas, Nebraska, and southern Iowa were hotspots of change, with the highest areas of CRP land moving back to agriculture. By contrast, we estimate only a small amount (∼3%) of the expiring land shifted into similar, non-CRP land-retirement or easement programs. Reconciling needs for food, feed, fuel, and healthy ecosystems is an immense challenge for farmers, conservationists, and state and federal agencies. Reduced enrollment and the turnover of CRP land from conservation to agriculture raises questions about sustaining ecosystem services in this region

Heat-Related Health Impacts under Scenarios of Climate and Population Change

Recent assessments have found that a warming climate, with associated increases in extreme heat events, could profoundly affect human health. This paper describes a new modeling and analysis framework, built around the Benefits Mapping and Analysis Program—Community Edition (BenMAP), for estimating heat-related mortality as a function of changes in key factors that determine the health impacts of extreme heat. This new framework has the flexibility to integrate these factors within health risk assessments, and to sample across the uncertainties in them, to provide a more comprehensive picture of total health risk from climate-driven increases in extreme heat. We illustrate the framework’s potential with an updated set of projected heat-related mortality estimates for the United States. These projections combine downscaled Coupled Modeling Intercomparison Project 5 (CMIP5) climate model simulations for Representative Concentration Pathway (RCP)4.5 and RCP8.5, using the new Locating and Selecting Scenarios Online (LASSO) tool to select the most relevant downscaled climate realizations for the study, with new population projections from EPA’s Integrated Climate and Land Use Scenarios (ICLUS) project. Results suggest that future changes in climate could cause approximately from 3000 to more than 16,000 heat-related deaths nationally on an annual basis. This work demonstrates that uncertainties associated with both future population and future climate strongly influence projected heat-related mortality. This framework can be used to systematically evaluate the sensitivity of projected future heat-related mortality to the key driving factors and major sources of methodological uncertainty inherent in such calculations, improving the scientific foundations of risk-based assessments of climate change and human health

Estimated losses of plant biodiversity in the United States from historical N deposition (1985–2010)

Although nitrogen (N) deposition is a significant threat to herbaceous plant biodiversity worldwide, it is not a new stressor for many developed regions. Only recently has it become possible to estimate historical impacts nationally for the United States. We used 26 years (1985–2010) of deposition data, with ecosystem-specific functional responses from local field experiments and a national critical loads (CL) database, to generate scenario-based estimates of herbaceous species loss. Here we show that, in scenarios using the low end of the CL range, N deposition exceeded critical loads over 0.38, 6.5, 13.1, 88.6, and 222.1 million ha for the Mediterranean California, North American Desert, Northwestern Forested Mountains, Great Plains, and Eastern Forest ecoregions, respectively, with corresponding species losses ranging from ,1% to 30%. When we ran scenarios assuming ecosystems were less sensitive (using a common CL of 10 kg ha⁻¹ year ⁻¹, and the high end of the CL range) minimal losses were estimated. The large range in projected impacts among scenarios implies uncertainty as to whether current critical loads provide protection to terrestrial plant biodiversity nationally and urge greater research in refining critical loads for U.S. ecosystems.Journal ArticlePublishe

Appendix A. Detailed description of methodology and supplemental results on time lags.

Detailed description of methodology and supplemental results on time lags

Spatiotemporal Prediction of Fine Particulate Matter During the 2008 Northern California Wildfires Using Machine Learning

Estimating population exposure to particulate matter during wildfires can be difficult because of insufficient monitoring data to capture the spatiotemporal variability of smoke plumes. Chemical transport models (CTMs) and satellite retrievals provide spatiotemporal data that may be useful in predicting PM_2.5 during wildfires. We estimated PM_2.5 concentrations during the 2008 northern California wildfires using 10-fold cross-validation (CV) to select an optimal prediction model from a set of 11 statistical algorithms and 29 predictor variables. The variables included CTM output, three measures of satellite aerosol optical depth, distance to the nearest fires, meteorological data, and land use, traffic, spatial location, and temporal characteristics. The generalized boosting model (GBM) with 29 predictor variables had the lowest CV root mean squared error and a CV-R² of 0.803. The most important predictor variable was the Geostationary Operational Environmental Satellite Aerosol/Smoke Product (GASP) Aerosol Optical Depth (AOD), followed by the CTM output and distance to the nearest fire cluster. Parsimonious models with various combinations of fewer variables also predicted PM_2.5 well. Using machine learning algorithms to combine spatiotemporal data from satellites and CTMs can reliably predict PM_2.5 concentrations during a major wildfire event