Riverine Threat Indices to Assess Watershed Condition and Identify Primary Management Capacity of Agriculture Natural Resource Management Agencies

Managers can improve conservation of lotic systems over large geographies if they have tools to assess total watershed conditions for individual stream segments and can identify segments where conservation practices are most likely to be successful (i.e., primary management capacity). The goal of this research was to develop a suite of threat indices to help agriculture resource management agencies select and prioritize watersheds across Missouri River basin in which to implement agriculture conservation practices. We quantified watershed percentages or densities of 17 threat metrics that represent major sources of ecological stress to stream communities into five threat indices: agriculture, urban, point-source pollution, infrastructure, and all non-agriculture threats. We identified stream segments where agriculture management agencies had primary management capacity. Agriculture watershed condition differed by ecoregion and considerable local variation was observed among stream segments in ecoregions of high agriculture threats. Stream segments with high non-agriculture threats were most concentrated near urban areas, but showed high local variability. 60 % of stream segments in the basin were classified as under U.S. Department of Agriculture’s Natural Resources Conservation Service (NRCS) primary management capacity and most segments were in regions of high agricultural threats. NRCS primary management capacity was locally variable which highlights the importance of assessing total watershed condition for multiple threats. Our threat indices can be used by agriculture resource management agencies to prioritize conservation actions and investments based on: (a) relative severity of all threats, (b) relative severity of agricultural threats, and (c) and degree of primary management capacity.     

Risk and resilience lessons from Venice

In the history of disasters in Venice, there are implications for modern times in terms of complex systems management and emerging threats, in particular from examples of risk management and resilience achieved by the Venetian state during outbreaks of the plague. In fourteenth century Venice, risk assessment the way we practice it today would fail to provide meaningful recommendations to reduce the casualty rate of the plague epidemic because the cause and transmission of the disease was not understood. Instead, a set of systemic actions across the social, economic, and transportation networks of the city taken by officials and doctors eventually slowed and arguably stopped the spread of the disease. These latter actions are an early example of what is now considered resilience management. Resilience management improves a complex system’s ability to prepare, absorb, recover, and adapt to unexpected threats and does so by address the capabilities at a system, rather than component, level. Resilience management can be a guide to addressing current issues of population growth and rising sea level in modern day Venice and across the globe. This paper calls for integration of resilience assessment in comprehensive risk and resilience management framework.     

Balancing environmental and industry sustainability: A case study of the US gold mining industry

Mandatory insurance requirements and/or mitigation fees (royalties) for mining companies may help reduce environmental risk exposure for the federal government. Mining is examined since the Environmental Protection Agency (EPA) Toxic Release Inventory reveals that this sector produces more hazardous waste than any other industrial sector. Although uncommon, environmental expense can exceed hundreds of millions of dollars per development. Of particular concern is the potential for mines to become unfunded Superfund sites. Monte Carlo simulation of risk exposure is used to establish a plausible range of unfunded federal liabilities associated with cyanide-leach gold mining. A model is developed to assess these costs and their impact on both the federal budget and corporate profitability (i.e., industry sustainability), particularly if such costs are borne by offending firms.     

Optimal temporal scale for the correlation of AOD and ground measurements of PM2.5 in a real-time air quality estimation system

Aerosol optical depth (AOD), an indirect estimate of particulate matter using satellite observations, has shown great promise in improving estimates of PM_2.5 (particulate matter with aerodynamic diameter less than or equal to 2.5 μm) surface. Currently, few studies have been conducted to explore the optimal way to apply AOD data to improve the model accuracy of PM_2.5 in a real-time air quality system. We believe that two major aspects may be worthy of consideration in that area: 1) an approach that integrates satellite measurements with ground measurements in the estimates of pollutants and 2) identification of an optimal temporal scale to calculate the correlation of AOD and ground measurements. This paper is focused on the second aspect, identifying the optimal temporal scale to correlate AOD with PM_2.5. Five following different temporal scales were chosen to evaluate their impact on the model performance: 1) within the last 3 days, 2) within the last 10 days, 3) within the last 30 days, 4) within the last 90 days, and 5) the time period with the highest correlation in a year. The model performance is evaluated for its accuracy, bias, and errors based on the following selected statistics: the Mean Bias, the Normalized Mean Bias, the Root Mean Square Error, Normalized Mean Error, and the Index of Agreement. This research shows that the model with the temporal scale: within the last 30 days, displays the best model performance in a southern multi-state area centered in Mississippi using 2004 and 2005 data sets.     

The Steubenville Comprehensive Air Monitoring Program (SCAMP): Overview and Statistical Considerations

Abstract

Average concentrations of particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM_2.5) in Steubenville, OH, have decreased by more than 10 μg/m³ since the landmark Harvard Six Cities Study¹ associated the city’s elevated PM_2.5 concentrations with adverse health effects in the 1980s. Given the promulgation of a new National Ambient Air Quality Standard (NAAQS) for PM_2.5 in 1997, a current assessment of PM_2.5 in the Steubenville region is warranted. The Steubenville Comprehensive Air Monitoring Program (SCAMP) was conducted from 2000 through 2002 to provide such an assessment. The program included both an outdoor ambient air monitoring component and an indoor and personal air sampling component. This paper, which is the first in a series of four that will present results from the outdoor portion of SCAMP, provides an overview of the outdoor ambient air monitoring program and addresses statistical issues, most notably autocorrelation, that have been overlooked by many PM_2.5 data analyses. The average PM_2.5 concentration measured in Steubenville during SCAMP (18.4 μg/m³) was 3.4g/m³ above the annual PM_2.5 NAAQS. On average, sulfate and organic material accounted for ～31% and 25%, respectively, of the total PM_2.5 mass. Local sources contributed an estimated 4.6 μg/m³ to Steubenville’s mean PM_2.5 concentration. PM_2.5 and each of its major ionic components were significantly correlated in space across all pairs of monitoring sites in the region, suggesting the influence of meteorology and long-range transport on regional PM_2.5 concentrations. Statistically significant autocorrelation was observed among time series of PM_2.5 and component data collected at daily and 1-in-4-day frequencies during SCAMP. Results of spatial analyses that accounted for autocorrelation were generally consistent with findings from previous studies that did not consider autocorrelation; however, these analyses also indicated that failure to account for autocorrelation can lead to incorrect conclusions about statistical significance.     

Aerosol species concentrations and source apportionment of ammonia at Rocky Mountain National Park

Changes in ecosystem function at Rocky Mountain National Park (RMNP) are occurring because of emissions of nitrogen and sulfate species along the Front Range of the Colorado Rocky Mountains, as well as sources farther east and west. The nitrogen compounds include both oxidized and reduced nitrogen. A year-long monitoring program of various oxidized and reduced nitrogen species was initiated to better understand their origins as well as the complex chemistry occurring during transport from source to receptor. Specifically, the goals of the study were to characterize the atmospheric concentrations of nitrogen species in gaseous, particulate, and aqueous phases (precipitation and clouds) along the east and west sides of the Continental Divide; identify the relative contributions to atmospheric nitrogen species in RMNP from within and outside of the state of Colorado; identify the relative contributions to atmospheric nitrogen species in RMNP from emission sources along the Colorado Front Range versus other areas within Colorado; and identify the relative contributions to atmospheric nitrogen species from mobile sources, agricultural activities, and large and small point sources within the state of Colorado. Measured ammonia concentrations are combined with modeled releases of conservative tracers from ammonia source regions around the United States to apportion ammonia to its respective sources, using receptor modeling tools.

Implications: Increased deposition of nitrogen in RMNP has been demonstrated to contribute to a number of important ecosystem changes. The rate of deposition of nitrogen compounds in RMNP has crossed a crucial threshold called the “critical load.” This means that changes are occurring to park ecosystems and that these changes may soon reach a point where they are difficult or impossible to reverse. Several key issues need attention to develop an effective strategy for protecting park resources from adverse impacts of elevated nitrogen deposition. These include determining the importance of previously unquantified nitrogen inputs within the park and identification of important nitrogen sources and transport pathways.     

The influence of use,environmental and managerial factors on the width of recreational trails

This paper evaluates the relative influences of use, managerial and environmental factors on trail width, from a survey of all formal trails in Acadia National Park, Maine, USA. A Trimble GPS was used to navigate to and collect data at sample points spaced at a 152.4 m (500 ft) interval across all National Park Service trails on Mount Desert Island. Regression analyses focus on increasing understanding of factors that influence the width of formal hiking trails. ANOVA analyses demonstrate differences in trail width based on trail surface type (class), and the presence or absence of trail borders. A novel approach of comparing intended widths to actual widths enabled us to look specifically at the avoidable and undesirable impacts associated with having a trail that is wider than intended.     

Calibration and validation of a regionally and seasonally stratified macroinvertebrate index for West Virginia wadeable streams

Multimetric indices (MMIs) are routinely used by federal, state, and tribal entities to assess the quality of aquatic resources. Because of their diversity, abundance, ubiquity, and sensitivity to environmental stress, benthic macroinvertebrates are well suited for MMIs. West Virginia has used a statewide family-level stream condition index (WVSCI) since 2002. We describe the development, validation, and application of a geographically- and seasonally partitioned genus-level index of most probable stream status (GLIMPSS) for West Virginia wadeable streams. Natural classification strata were evaluated with reference site communities using mean similarity analysis and non-metric multidimensional scaling ordination. Forty-one metrics spanning six ecological categories (richness, composition, tolerance, dominance, trophic groups, and habits) were evaluated for sensitivity, responsiveness, redundancy, range and variability across seasonal (spring and summer) and regional (mountains and plateau) strata. Through a step-wise metric selection process, 8–10 metrics were aggregated to comprise four stratum-specific GLIMPSS models (mountain/plateau and spring/summer). A comparison of GLIMPSS with WVSCI exhibited marked improvements where GLIMPSS detecting greater stream impacts. A variation of the GLIMPSS, which differs only in the family-level taxonomic identification of Chironomidae (GLIMPSS (CF)), was comparable to the full GLIMPSS. These MMIs are robust yet practical tools for evaluating impacts to water quality, instream and riparian habitat, and aquatic wildlife in wadeable riffle-run streams based on sensitivity, responsiveness, precision, and independent validation. These models may be used effectively to detect degradation of the naturally occurring benthic community, assess causes of biological degradation, and plan and evaluate remediation of damaged stream ecosystems.     

Ozone and Other Air Quality-Related Variables Affecting Visibility in the Southeast United States

Abstract

An analysis of ozone (O₃) concentrations and several other air quality-related variables was performed to elucidate their relationship with visibility at five urban and semi-urban locations in the southeast United States during the summer seasons of 1980-1996. The role and impact of O₃ on aerosols was investigated to ascertain a relationship with visibility. Regional trend analysis over the 1980s reveals an increase in maximum O₃ concentration coupled with a decrease in visibility. However, a similar analysis for the 1990s shows a leveling-off of both O₃ and visibility; in both cases, the results were not statistically significant at the 5% level. A case study of site-specific trends at Nashville, TN, followed similar trends. To better understand the relationships between O₃ concentration and visibility, the analysis was varied from yearly through daily to hourly averaged values. This increased temporal resolution showed a statistically significant inverse relationship between visibility and O₃. Site-specific hourly r² values ranged from 0.02 to 0.43. Additionally, by performing back-trajectory analysis, it was found that the visibility degraded by air mass migration over polluted areas.     

Anticipating Forest and Range Land Development in Central Oregon (USA) for Landscape Analysis, with an Example Application Involving Mule Deer

Forest policymakers, public lands managers, and scientists in the Pacific Northwest (USA) seek ways to evaluate the landscape-level effects of policies and management through the multidisciplinary development and application of spatially explicit methods and models. The Interagency Mapping and Analysis Project (IMAP) is an ongoing effort to generate landscape-wide vegetation data and models to evaluate the integrated effects of disturbances and management activities on natural resource conditions in Oregon and Washington (USA). In this initial analysis, we characterized the spatial distribution of forest and range land development in a four-county pilot study region in central Oregon. The empirical model describes the spatial distribution of buildings and new building construction as a function of population growth, existing development, topography, land-use zoning, and other factors. We used the model to create geographic information system maps of likely future development based on human population projections to inform complementary landscape analyses underway involving vegetation, habitat, and wildfire interactions. In an example application, we use the model and resulting maps to show the potential impacts of future forest and range land development on mule deer (Odocoileus hemionus) winter range. Results indicate significant development encroachment and habitat loss already in 2000 with development located along key migration routes and increasing through the projection period to 2040. The example application illustrates a simple way for policymakers and public lands managers to combine existing data and preliminary model outputs to begin to consider the potential effects of development on future landscape conditions.