Rainfall-induced fecal indicator organisms transport from manured fields: model sensitivity analysis.

Microbial quality of surface waters attracts attention due to food- and waterborne disease outbreaks. Fecal indicator organisms (FIOs) are commonly used for the microbial pollution level evaluation. Models predicting the fate and transport of FIOs are required to design and evaluate best management practices that reduce the microbial pollution in ecosystems and water sources and thus help to predict the risk of food and waterborne diseases. In this study we performed a sensitivity analysis for the KINEROS/STWIR model developed to predict the FIOs transport out of manured fields to other fields and water bodies in order to identify input variables that control the transport uncertainty. The distributions of model input parameters were set to encompass values found from three-year experiments at the USDA-ARS OPE3 experimental site in Beltsville and publicly available information. Sobol' indices and complementary regression trees were used to perform the global sensitivity analysis of the model and to explore the interactions between model input parameters on the proportion of FIO removed from fields. Regression trees provided a useful visualization of the differences in sensitivity of the model output in different parts of the input variable domain. Environmental controls such as soil saturation, rainfall duration and rainfall intensity had the largest influence in the model behavior, whereas soil and manure properties ranked lower. The field length had only moderate effect on the model output sensitivity to the model inputs. Among the manure-related properties the parameter determining the shape of the FIO release kinetic curve had the largest influence on the removal of FIOs from the fields. That underscored the need to better characterize the FIO release kinetics. Since the most sensitive model inputs are available in soil and weather databases or can be obtained using soil water models, results indicate the opportunity of obtaining large-scale estimates of FIO transport from fields based on publicly available rather than site-specific information.

Characterizing the isotopic composition of atmospheric ammonia emission sources using passive samplers and a combined oxidation-bacterial denitrifier approach.

RATIONALE: Ammonia (NH3) emissions are a substantial source of nitrogen pollution to sensitive terrestrial, aquatic, and marine ecosystems and dependable quantification of NH3 sources is of growing importance due to recently observed increases in ammonium (NH4(+)) deposition rates. While determination of the nitrogen isotopic composition of NH3 (δ(15)N-NH3) can aid in the quantification of NH3 emission sources, existing methods have precluded a comprehensive assessment of δ(15)N-NH3 values from major emission sources. METHODS: We report an approach for the δ(15)N-NH4(+) analysis of low concentration NH4(+) samples that couples the bromate oxidation of NH4(+) to NO2(-) and the microbial denitrifier method for δ(15)N-NO2(-) analysis. This approach reduces the required sample mass by 50-fold relative to standard elemental analysis (EA) procedures, is capable of high throughput, and eliminates toxic chemicals used in a prior method for the analysis of low concentration samples. Using this approach, we report a comprehensive inventory of δ(15)N-NH3 values from major emission sources (including livestock operations, marine sources, vehicles, fertilized cornfields) collected using passive sampling devices. RESULTS: The δ(15)N-NH4(+) analysis approach developed has a standard deviation of ±0.7‰ and was used to analyze passively collected NH3 emissions with a wide range of ambient NH3 concentrations (0.2 to 165.6 µg/m(3)). The δ(15)N-NH3 values reveal that the NH3 emitted from volatilized livestock waste and fertilizer has relatively low δ(15)N values (-56 to -23‰), allowing it to be differentiated from NH3 emitted from fossil fuel sources that are characterized by relatively high δ(15)N values (-15 to +2‰). CONCLUSIONS: The isotopic source signatures presented in this emission inventory can be used as an additional tool in identifying NH3 emission sources and tracing their transport across localized landscapes and regions. The insight into the transport of NH3 emissions provided by isotopic investigation is an important step in devising strategies to reduce future NH3 emissions, a mounting concern for air quality scientists, epidemiologists, and policy-makers.

Comparison of field-scale herbicide runoff and volatilization losses: an eight-year field investigation.

An 8-yr study was conducted to better understand factors influencing year-to-year variability in field-scale herbicide volatilization and surface runoff losses. The 21-ha research site is located at the USDA-ARS Beltsville Agricultural Research Center in Beltsville, MD. Site location, herbicide formulations, and agricultural management practices remained unchanged throughout the duration of the study. Metolachlor [2-chloro--(2-ethyl-6-methylphenyl)--(2-methoxy-1-methylethyl) acetamide] and atrazine [6-chloro--ethyl--(1-methylethyl)-1,3,5-triazine-2,4-diamine] were coapplied as a surface broadcast spray. Herbicide runoff was monitored from a month before application through harvest. A flux gradient technique was used to compute volatilization fluxes for the first 5 d after application using herbicide concentration profiles and turbulent fluxes of heat and water vapor as determined from eddy covariance measurements. Results demonstrated that volatilization losses for these two herbicides were significantly greater than runoff losses ( < 0.007), even though both have relatively low vapor pressures. The largest annual runoff loss for metolachlor never exceeded 2.5%, whereas atrazine runoff never exceeded 3% of that applied. On the other hand, herbicide cumulative volatilization losses after 5 d ranged from about 5 to 63% of that applied for metolachlor and about 2 to 12% of that applied for atrazine. Additionally, daytime herbicide volatilization losses were significantly greater than nighttime vapor losses ( < 0.05). This research confirmed that vapor losses for some commonly used herbicides frequently exceeds runoff losses and herbicide vapor losses on the same site and with the same management practices can vary significantly year to year depending on local environmental conditions.

Soil moisture and metolachlor volatilization observations over three years.

A 3-yr study was conducted to focus on the impact of surface soil water content on metolachlor (2-chloro-N-(2-ethyl-6-methylphenyl)-N-(2-methoxy-1-methylethyl) acetamide) volatilization from a field with different surface soil water regimes created by subsurface water flow paths. Metolachlor vapor fluxes were measured at two locations within the field where local meteorological and soil conditions were relatively constant, except for surface soil water content, which differed significantly. Surface soil water content at the two sites differed in response to the presence of subsurface flow pathways. Detailed soil moisture observations over the duration of the study showed that for the first 2 yr (2004 and 2005), surface soil water contents at the dry location (V1) were nearly half those at the wetter location (V2). Cumulative metolachlor vapor fluxes during 2004 and 2005 at V1 were also about half that at V2. In the third year (2006), early-season drought conditions rendered the soil water content at the two locations to be nearly identical, resulting in similar metolachlor volatilization losses. Analysis of infrared soil surface temperatures suggests a correlation between surface soil temperatures and metolachlor volatilization when soils are wet (2004 and 2005) but not when the soils are dry (2006). Field-averaged metolachlor volatilization losses were highly correlated with increasing surface soil water contents (r(2) = 0.995).

Solar radiation, relative humidity, and soil water effects on metolachlor volatilization.

Pesticide volatilization is a significant loss pathway that may have unintended consequences in nontarget environments. Field-scale pesticide volatilization involves the interaction of a number of complex variables. There is a need to acquire pesticide volatilization fluxes from a location where several of these variables can be held constant. Accordingly, soil properties, tillage practices, surface residue management, and pesticide formulations were held constant while fundamental information regarding metolachlor volatilization (a pre-emergent pesticide) was monitored over a five-year period as influenced by meteorological variables and soil water content. Metolachlor vapor concentrations were measured continuously for 120 h after each application using polyurethane foam plugs in a logarithmic profile above the soil surface. A flux gradient technique was used to compute volatilization fluxes from metolachlor concentration profiles and turbulent fluxes of heat and water vapor (as determined from eddy covariance measurements). Differences in meteorological conditions and surface soil water contents resulted in variability of the volatilization losses over the years studied. The peak volatilization losses for each year occurred during the first 24 h after application with a maximum flux rate in 2001 (1500 ng m(-2) s(-1)) associated with wet surface soil conditions combined with warm temperatures. The cumulative volatilization losses for the 120-hour period following metolachlor application varied over the years from 5 to 25% of the applied active ingredient, with approximately 87% of the losses occurring during the first 72 h. In all of the years studied, volatilization occurred diurnally and accounted for between 43 and 86% during the day and 14 and 57% during the night of the total measured loss. The results suggest that metolachlor volatilization is influenced by multiple factors involving meteorological, surface soil, and chemical factors.