Estimating Watershed Level Nonagricultural Pesticide Use From Golf Courses Using Geospatial Methods1

Abstract: Limited information exists on pesticide use for nonagricultural purposes, making it difficult to estimate pesticide loadings from nonagricultural sources to surface water and to conduct environmental risk assessments. A method was developed to estimate the amount of pesticide use on recreational turf grasses, specifically golf course turf grasses, for watersheds located throughout the conterminous United States (U.S.). The approach estimates pesticide use: (1) based on the area of recreational turf grasses (used as a surrogate for turf associated with golf courses) within the watershed, which was derived from maps of land cover, and (2) from data on the location and average treatable area of golf courses. The area of golf course turf grasses determined from these two methods was used to calculate the percentage of each watershed planted in golf course turf grass (percent crop area, or PCA). Turf‐grass PCAs derived from the two methods were used with recommended application rates provided on pesticide labels to estimate total pesticide use on recreational turf within 1,606 watersheds associated with surface‐water sources of drinking water. These pesticide use estimates made from label rates and PCAs were compared to use estimates from industry sales data on the amount of each pesticide sold for use within the watershed. The PCAs derived from the land‐cover data had an average value of 0.4% of a watershed with minimum of 0.01% and a maximum of 9.8%, whereas the PCA values that are based on the number of golf courses in a watershed had an average of 0.3% of a watershed with a minimum of <0.01% and a maximum of 14.2%. Both the land‐cover method and the number of golf courses method produced similar PCA distributions, suggesting that either technique may be used to provide a PCA estimate for recreational turf. The average and maximum PCAs generally correlated to watershed size, with the highest PCAs estimated for small watersheds. Using watershed specific PCAs, combined with label rates, resulted in greater than two orders of magnitude over‐estimation of the pesticide use compared to estimates from sales data.     

Temporal and Spatial Relationships Between Watershed Land Use and Salt Marsh Disturbance in a Pacific Estuary

Historical and recent remote sensing data can be used to address temporal and spatial relationships between upland land cover and downstream vegetation response at the watershed scale. This is demonstrated for sub-watersheds draining into Elkhorn Slough, California, where salt marsh habitat has diminished because of the formation of sediment fans that support woody riparian vegetation. Multiple regression models were used to examine which land cover variables and physical properties of the watershed most influenced sediment fan size within 23 sub-watersheds (1.4 ha to 200 ha). Model explanatory power increased (adjusted R(2) = 0.94 vs. 0.75) among large sub-watersheds (>10 ha) and historical watershed variables, such as average farmland slope, flowpath slope, and flowpath distance between farmland and marsh, were significant. It was also possible to explain the increase in riparian vegetation by historical watershed variables for the larger sub-watersheds. Sub-watershed area is the overriding physical characteristic influencing the extent of sedimentation in a salt marsh, while percent cover of agricultural land use is the most influential land cover variable. The results also reveal that salt marsh recovery depends on relative cover of different land use classes in the watershed, with greater chances of recovery associated with less intensive agriculture. This research reveals a potential delay between watershed impacts and wetland response that can be best revealed when conducting multi-temporal analyses on larger watersheds.     

Feasibility of Applying a Stable Isotopic Tracer for Direct Determination of Dry Particulate Deposition to Soybean Plants

ABSTRACT

A stable rare-earth isotopic tracer was used to measure the deposition of KNO₃ particles on soybean leaves by direct measurement of the tracer on the plant surfaces by thermal-ionization mass spectrometry. Submicrometer particles, made from a solution containing 3 |mg mL^-1 neodymium isotope (¹⁴⁸Nd, 87.9%) and 1,000 mg mL^-1 KNO₃, were dispersed with a two-fluid nozzle and released upwind of a soybean field. Total suspended- and size-fractionated-aerosol particles were collected on an open-face filter and in a micro-orifice impactor, respectively, at a distance of 40 m from the release point. Soybean leaves exposed to the plume were collected at distances ranging from 25 to 100 m. As little as 5.5 pg of the tracer (i.e., excess ¹⁴⁸Nd) was detected in soybean leaves at signal-to-noise ratios ranging from 7,500 to 240,000, in the presence of 200 to 2,700 pg of naturally occurring Nd. The dry-particle deposition velocity, determined from the ratio of the aerial concentration and directly deposited aerosol (geometric mass mean diameter, 0.20 mm) flux, and its corresponding analytical uncertainty were 0.30 cm sec^-1 and 2.5%, respectively.     

Uncertainty-based quantitative assessment of sustainability for higher education institutions

Evaluation of sustainability in various facets of life is gaining increasing importance. Traditionally, different multi-criteria decision-making methods have been used for sustainability assessment. “Sustainability” can be a qualitative concept, and as such several researchers have attempted fuzzy logic for the quantitative assessment of sustainability. This paper outlines a new evaluation model based on fuzzy multi-criteria decision-making. The model is tested for sustainability assessment of higher education institutions (HEIs). It is based on a driving force–pressure–state–exposure–effect–action (DPSEEA) framework and is called uncertainty-based DPSEEA-Sustainability index Model (uD-SiM). The uD-SiM is a causality-based model in which the sustainability index is an outcome of nonlinear impacts of sustainability indicators in different stages of DPSEEA. The percent contribution of driving forces on the sustainability index of HEI is investigated using sensitivity analysis. The study reveals that education in sustainability and global and local research trends are the major driving forces for achieving sustainability in HEI, followed by financial and economic growth rate, social equity, energy requirements rate, and institutional enhancement, in descending order. The results of uD-SiM were found to be more realistic and rational than our earlier proposed approach, D-SiM.     

Species Distribution Models of Freshwater Stream Fishes in Maryland and Their Implications for Management

Species distribution models (SDMs) are often used in conservation planning, but their utility can be improved by assessing the relationships between environmental and species response variables. We constructed SDMs for 30 stream fishes of Maryland, USA, using watershed attributes as environmental variables and presence/absence as species responses. SDMs showed substantial agreement between observed and predicted values for 17 species. Most important variables were natural attributes (e.g., ecoregion, watershed area, latitude/longitude); land cover (e.g., %impervious, %row crop) was important for three species. Focused analyses on four representative species (central stoneroller, creek chub, largemouth bass, and white sucker) showed the probability of presence of each species increased non-linearly with watershed area. For these species, SDMs built to predict absent, low, and high densities were similar to presence/absence predictions but provided probable locations of high densities (e.g., probability of high-density creek chub decreased rapidly with watershed area). We applied SDMs to predict suitability of watersheds within the study area for each species. Maps of suitability and the environmental and species response relationships can help develop better management plans.     

Use of fugacity model to analyze temperature-dependent removal of micro-contaminants in sewage treatment plants

Effluents from sewage treatment plants (STPs) are known to contain residual micro-contaminants including endocrine disrupting chemicals (EDCs) despite the utilization of various removal processes. Temperature alters the efficacy of removal processes; however, experimental measurements of EDC removal at various temperatures are limited. Extrapolation of EDC behavior over a wide temperature range is possible using available physicochemical property data followed by the correction of temperature dependency. A level II fugacity-based STP model was employed by inputting parameters obtained from the literature and estimated by the US EPA’s Estimations Programs Interface (EPI) including EPI’s BIOWIN for temperature-dependent biodegradation half-lives. EDC removals in a three-stage activated sludge system were modeled under various temperatures and hydraulic retention times (HRTs) for representative compounds of various properties. Sensitivity analysis indicates that temperature plays a significant role in the model outcomes. Increasing temperature considerably enhances the removal of β-estradiol, ethinyestradiol, bisphenol, phenol, and tetrachloroethylene, but not testosterone with the highest biodegradation rate. The shortcomings of BIOWIN were mitigated by the correction of highly temperature-dependent biodegradation rates using the Arrhenius equation. The model predicts well the effects of operating temperature and HRTs on the removal via volatilization, adsorption, and biodegradation. The model also reveals that an impractically long HRT is needed to achieve a high EDC removal. The STP model along with temperature corrections is able to provide some useful insight into the different patterns of STP performance, and useful operational considerations relevant to EDC removal at winter low temperatures.