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At least 217 records · Page 12Linked to original sources

USGS2005

A regional classification scheme for estimating reference water quality in streams using land-use-adjusted spatial regression-tree analysis

Various approaches are used to subdivide large areas into regions containing streams that have similar reference or background water quality and that respond similarly to different factors. For many applications, such as establishing reference conditions, it is preferable to use physical characteristics that are not affected by human activities to delineate these regions. However, most approaches, such as ecoregion classifications, rely on land use to delineate regions or have difficulties compensating for the effects of land use. Land use not only directly affects water quality, but it is often correlated with the factors used to define the regions. In this article, we describe modifications to SPARTA (spatial regression-tree analysis), a relatively new approach applied to water-quality and environmental characteristic data to delineate zones with similar factors affecting water quality. In this modified approach, land-use-adjusted (residualized) water quality and environmental characteristics are computed for each site. Regression-tree analysis is applied to the residualized data to determine the most statistically important environmental characteristics describing the distribution of a specific water-quality constituent. Geographic information for small basins throughout the study area is then used to subdivide the area into relatively homogeneous environmental water-quality zones. For each zone, commonly used approaches are subsequently used to define its reference water quality and how its water quality responds to changes in land use. SPARTA is used to delineate zones of similar reference concentrations of total phosphorus and suspended sediment throughout the upper Midwestern part of the United States. ?? 2006 Springer Science+Business Media, Inc.

Environmental Management↗

USGS2013

A review of selected inorganic surface water quality-monitoring practices: are we really measuring what we think, and if so, are we doing it right?

Successful environmental/water quality-monitoring programs usually require a balance between analytical capabilities, the collection and preservation of representative samples, and available financial/personnel resources. Due to current economic conditions, monitoring programs are under increasing pressure to do more with less. Hence, a review of current sampling and analytical methodologies, and some of the underlying assumptions that form the bases for these programs seems appropriate, to see if they are achieving their intended objectives within acceptable error limits and/or measurement uncertainty, in a cost-effective manner. That evaluation appears to indicate that several common sampling/processing/analytical procedures (e.g., dip (point) samples/measurements, nitrogen determinations, total recoverable analytical procedures) are generating biased or nonrepresentative data, and that some of the underlying assumptions relative to current programs, such as calendar-based sampling and stationarity are no longer defensible. The extensive use of statistical models as well as surrogates (e.g., turbidity) also needs to be re-examined because the hydrologic interrelationships that support their use tend to be dynamic rather than static. As a result, a number of monitoring programs may need redesigning, some sampling and analytical procedures may need to be updated, and model/surrogate interrelationships may require recalibration.

Environmental Science & Technology↗

USGS2007

Measurement of atmospheric mercury species with manual sampling and analysis methods in a case study in Indiana

Ground-level concentrations of three atmospheric mercury species were measured using manual sampling and analysis to provide data for estimates of mercury dry deposition. Three monitoring stations were operated simultaneously during winter, spring, and summer 2004, adjacent to three mercury wet-deposition monitoring stations in northern, central, and southern Indiana. The monitoring locations differed in land-use setting and annual mercury-emissions level from nearby sources. A timer-controlled air-sampling system that contained a three-part sampling train was used to isolate reactive gaseous mercury, particulate-bound mercury, and elemental mercury. The sampling trains were exchanged every 6 days, and the mercury species were quantified in a laboratory. A quality-assurance study indicated the sampling trains could be held at least 120 h without a significant change in reactive gaseous or particulate-bound mercury concentrations. The manual sampling method was able to provide valid mercury concentrations in 90 to 95% of samples. Statistical differences in mercury concentrations were observed during the project. Concentrations of reactive gaseous and elemental mercury were higher in the daytime samples than in the nighttime samples. Concentrations of reactive gaseous mercury were higher in winter than in summer and were highest at the urban monitoring location. The results of this case study indicated manual sampling and analysis could be a reliable method for measurement of atmospheric mercury species and has the capability for supplying representative concentrations in an effective manner from a long-term deposition-monitoring network. ?? 2007 Springer Science+Business Media B.V.

Water, Air, & Soil Pollution↗

USGS2013

Predictive occurrence models for coastal wetland plant communities: delineating hydrologic response surfaces with multinomial logistic regression

Understanding plant community zonation along estuarine stress gradients is critical for effective conservation and restoration of coastal wetland ecosystems. We related the presence of plant community types to estuarine hydrology at 173 sites across coastal Louisiana. Percent relative cover by species was assessed at each site near the end of the growing season in 2008, and hourly water level and salinity were recorded at each site Oct 2007–Sep 2008. Nine plant community types were delineated with k-means clustering, and indicator species were identified for each of the community types with indicator species analysis. An inverse relation between salinity and species diversity was observed. Canonical correspondence analysis (CCA) effectively segregated the sites across ordination space by community type, and indicated that salinity and tidal amplitude were both important drivers of vegetation composition. Multinomial logistic regression (MLR) and Akaike's Information Criterion (AIC) were used to predict the probability of occurrence of the nine vegetation communities as a function of salinity and tidal amplitude, and probability surfaces obtained from the MLR model corroborated the CCA results. The weighted kappa statistic, calculated from the confusion matrix of predicted versus actual community types, was 0.7 and indicated good agreement between observed community types and model predictions. Our results suggest that models based on a few key hydrologic variables can be valuable tools for predicting vegetation community development when restoring and managing coastal wetlands.

Estuarine, Coastal and Shelf Science↗

USGS2021

In vitro effects-based method and water quality screening model for use in pre- and post-distribution treated waters

Recent urban public water supply contamination events emphasize the importance of screening treated drinking water quality after distribution. In vitro bioassays, when run concurrently with analytical chemistry methods, are effective tools to evaluating the efficacy of water treatment processes and water quality. We tested 49 water samples representing the Chicago Department of Water Management service areas for estrogen, (anti)androgen, glucocorticoid receptor-activating contaminants and cytotoxicity. We present a tiered screening approach suitable to samples with anticipated low-level activity and initially tested all extracts for statistically identifiable endocrine activity; performing a secondary dilution-response analysis to determine sample EC 50 and biological equivalency values (BioEq). Estrogenic activity was detected in untreated Lake Michigan intake water samples using mammalian (5/49; median: 0.21 ng E2Eq/L) and yeast cell (5/49; 1.78 ng E2Eq/L) bioassays. A highly sensitive (anti)androgenic activity bioassay was applied for the first time to water quality screening and androgenic activity was detected in untreated intake and treated pre-distribution samples (4/49; 0.93 ng DHTEq/L). No activity was identified above method detection limits in the yeast androgenic, mammalian anti-androgenic, and both glucocorticoid bioassays. Known estrogen receptor agonists were detected using HPLC/MS-MS (estrone: 0.72-1.4 ng/L; 17α-estradiol: 1.3-1.5 ng/L; 17β-estradiol: 1.4 ng/L; equol: 8.8 ng/L), however occurrence did not correlate with estrogenic bioassay results. Many studies have applied bioassays to water quality monitoring using only relatively small samples sets often collected from surface and/or wastewater effluent. However, to realistically adapt these tools to treated water quality monitoring, water quality managers must have the capacity to screen potentially hundreds of samples in short timeframes. Therefore, we provided a tiered screening model that increased sample screening speed, without sacrificing statistical stringency, and detected estrogenic and androgenic activity only in pre-distribution Chicago area samples.

Illinois, Indiana↗

USGS2018

Long-term population dynamics and conservation risk of migratory bull trout in the upper Columbia River basin

We used redd count data from 88 bull trout (Salvelinus confluentus) populations in the upper Columbia River basin to quantify local and regional patterns in population dynamics, including adult abundance, long-term trend, and population synchrony. We further used this information to assess conservation risk of metapopulations using eight population dynamic metrics associated with persistence. Local population abundances were generally low (<20 redds annually) and the majority of trends were either stable (85%) or declining (13%). Evidence of synchrony among populations was apparent but not related to fluvial distance between streams. Variability in annual abundances was 1.4–2.5 times lower in metapopulations than local populations, indicating moderate portfolio effects across the regional stock complex. Importantly, most metrics of conservation risk were uncorrelated with one another, emphasizing that multiple statistics describing population dynamics at various scales are needed for monitoring and assessing recovery. We provide a composite description of conservation risk based on local and regional population dynamics that can help inform conservation management decisions for bull trout and other freshwater fishes.

Idaho, Montana↗

USGS2002

Preserving the distribution of inorganic arsenic species in groundwater and acid mine drainage samples

The distribution of inorganic arsenic species must be preserved in the field to eliminate changes caused by metal oxyhydroxide precipitation, photochemical oxidation, and redox reactions. Arsenic species sorb to iron and manganese oxyhydroxide precipitates, and arsenite can be oxidized to arsenate by photolytically produced free radicals in many sample matrices. Several preservatives were evaluated to minimize metal oxyhydroxide precipitation, such as inorganic acids and ethylenediaminetetraacetic acid (EDTA). EDTA was found to work best for all sample matrices tested. Storing samples in opaque polyethylene bottles eliminated the effects of photochemical reactions. The preservation technique was tested on 71 groundwater and six acid mine drainage samples. Concentrations in groundwater samples reached 720 ??g-As/L for arsenite and 1080 ??g-As/L for arsenate, and acid mine drainage samples reached 13 000 ??g-As/L for arsenite and 3700 ??g-As/L for arsenate. The arsenic species distribution in the samples ranged from 0 to 90% arsenite. The stability of the preservation technique was established by comparing laboratory arsenic speciation results for samples preserved in the field to results for subsamples speciated onsite. Statistical analyses indicated that the difference between arsenite and arsenate concentrations for samples preserved with EDTA in opaque bottles and field speciation results were analytically insignificant. The percentage change in arsenite:arsenate ratios for a preserved acid mine drainage sample and groundwater sample during a 3-month period was -5 and +3%, respectively.

Environmental Science & Technology↗

USGS2024

Novel quantitative methods to enable multispectral identification of high-purity water ice exposures on Mars using High Resolution Imaging Science Experiment (HiRISE) images

Reliable detection and characterization of water ice on the Martian surface is pivotal to not only understand its present and past climate, but to also provide valuable information on in-situ resource availability and distribution for future human exploration missions. Ice-rich features are currently identified with visible/near-IR (VNIR), thermal IR and radar data. However, their coarse spatial scale sometimes limits confident characterization of small (i.e., meter-scale) icy exposures resulting from recent activity like new impacts. Water ice bearing materials possess weaker spectral characteristics at wavelengths shorter than ∼1030 nm that may be resolved by VNIR imaging instruments like the High Resolution Imaging Science Experiment (HiRISE) and the Colour and Stereo Surface Imaging System (CaSSIS). Our study assesses the spectral capability of HiRISE colour observations to help distinguish high purity water ice exposures from ice-poor materials. We report detailed methodologies for reliable colour characterization of icy surface using unfiltered HiRISE images. We present the first quantitative approach to uniquely characterize high-purity ice-rich materials through spectral shape and spectral parameterization methods at high spatial resolution (∼50 cm/pixel). We also present three spectral parameters to aid detection of pure water ice features, while also providing statistical constraints to enable a quantitative interpretation scheme. Our methods are observed to work well in characterizing and separating ice-rich features uniquely from ice-poor and ferrous materials. However, we do observe that these methods have a lower grain size detection limit of ∼250–300 μm, and may not be able to uniquely separate frosts from ground ice exposures. We also apply these methods to better constrain the composition of bright materials exposed by recent impacts identified in previous surveys, where substantial evidence for ice-bearing materials was previously unavailable. Overall, our work proposes HiRISE colour-based methods as a novel approach for high-resolution multispectral characterization of ice-rich features on the Martian surface, which is of particular value since the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) has ceased operations.

Icarus↗

USGS2018

Real-time nowcasting of microbiological water quality at recreational beaches: A wavelet and artificial neural network-based hybrid modeling approach

The number of beach closings caused by bacterial contamination has continued to rise in recent years, putting beachgoers at risk of exposure to contaminated water. Current approaches predict levels of indicator bacteria using regression models containing a number of explanatory variables. Data-based modeling approaches can supplement routine monitoring data and provide highly accurate short-term forecasts of beach water quality. In this paper, we apply the nonlinear autoregressive network with exogenous inputs (NARX) method with explanatory variables to predict Escherichia coli concentrations at four Lake Michigan beach sites. We also apply the nonlinear input–output network (NIO) and nonlinear autoregressive neural network (NAR) methods in addition to a hybrid wavelet-NAR (WA-NAR) model and demonstrate their application. All models were tested using 3 months of observed data. Results revealed that the NARX models provided the best performance and that the WA-NAR model, which requires no explanatory variables, outperformed the NIO and NAR models; therefore, the WA-NAR model is suitable for application to data scarce regions. The models proposed in this paper were evaluated using multiple performance metrics, including sensitivity and specificity measures, and produced results comparable or superior to those of previous mechanistic and statistical models developed for the same beach sites. The relatively high R 2 values between data and the NARX models ( R 2 values of ∼0.8 for the beach sites and ∼0.9 for the river site) indicate that the new class of models shows promise for beach management.

Environmental Science & Technology↗

USGS2005

Murre eggs ( Uria aalge and Uria lomvia ) as indicators of mercury contamination in the Alaskan marine environment

Sixty common murre ( Uria aalge ) and 27 thick-billed murre ( Uria lomvia ) eggs collected by the Seabird Tissue Archival and Monitoring Project (STAMP) in 1999−2001 from two Gulf of Alaska and three Bering Sea nesting colonies were analyzed for total mercury (Hg) using isotope dilution cold vapor inductively coupled mass spectrometry. Hg concentrations (wet mass) ranged from 0.011 μg/g to 0.357 μg/g (relative standard deviation = 76%), while conspecifics from the same colonies and years had an average relative standard deviation of 33%. Hg levels in eggs from the Gulf of Alaska (0.166 μg/g ± 0.011 μg/g) were significantly higher ( p < 0.0001) than in the Bering Sea (0.047 μg/g ± 0.004 μg/g). Within the Bering Sea, Hg was significantly higher ( p = 0.0007) in eggs from Little Diomede Island near the arctic than at the two more southern colonies. Although thick-billed and common murres are ecologically similar, there were significant species differences in egg Hg concentrations within each region ( p < 0.0001). In the Bering Sea, eggs from thick-billed murres had higher Hg concentrations than eggs from common murres, while in the Gulf of Alaska, common murre eggs had higher concentrations than those of thick-billed murres. A separate one-way analysis of variance on the only time−trend data currently available for a colony (St. Lazaria Island in the Gulf of Alaska) found significantly lower Hg concentrations in common murre eggs collected in 2001 compared to 1999 ( p = 0.017). Results from this study indicate that murre eggs may be effective monitoring units for detecting geographic, species, and temporal patterns of Hg contamination in marine food webs. The relatively small intracolony variation in egg Hg levels and the ability to consistently obtain adequate sample sizes both within and among colonies over a large geographic range means that monitoring efforts using murre eggs will have suitable statistical power for detecting environmental patterns of Hg contamination. The potential influences of trophic effects, physical transport patterns, and biogeochemical processes on these monitoring efforts are discussed, and future plans to investigate the sources of the observed variability are presented.

Environmental Science & Technology↗

USGS2008

Documentation of the U.S. Geological Survey Oceanographic time-series measurement database

The U.S. Geological Survey (USGS) Oceanographic Time-Series Measurement Database contains oceanographic observations made as part of studies designed to increase understanding of sediment transport processes and associated dynamics. Analysis of these data has contributed to more accurate prediction of the movement and fate of sediments and other suspended materials in the coastal ocean. The measurements were collected primarily by investigators at the USGS Woods Hole Coastal and Marine Science Center (WHCMSC) and colleagues, beginning in 1975. Most of the field experiments were carried out on the U.S. continental shelf and slope. This report describes the instrumentation and platforms used to make the measurements; the methods used to process, apply quality-control criteria, and archive the data; the data storage format, and how the data are released and distributed. The report also includes instructions on how to access the data from the online database at http://stellwagen.er.usgs.gov/ . As of 2016, the database contains about 5,000 files, which may include observations of current velocity, wave statistics, ocean temperature, conductivity, pressure, and light transmission at one or more depths over some duration of time.

Open-File Report↗

USGS2014

Volcanology: Lessons learned from Synthetic Aperture Radar imagery

Twenty years of continuous Earth observation by satellite SAR have resulted in numerous new insights into active volcanism, including a better understanding of subsurface magma storage and transport, deposition of volcanic materials on the surface, and the structure and development of volcanic edifices. This massive archive of data has resulted in fundamental leaps in our understanding of how volcanoes work – for example, identifying magma accumulation at supposedly quiescent volcanoes, even in remote areas or in the absence of ground-based data. In addition, global compilations of volcanic activity facilitate comparison of deformation behavior between different volcanic arcs and statistical evaluation of the strong link between deformation and eruption. SAR data are also increasingly used in timely hazard evaluation thanks to decreases in data latency and growth in processing and analysis techniques. The existing archive of SAR imagery is on the cusp of being enhanced by a new generation of satellite SAR missions, in addition to ground-based and airborne SAR systems, which will provide enhanced temporal and spatial resolution, broader geographic coverage, and improved availability of data to the scientific community. Now is therefore an opportune time to review the contributions of SAR imagery to volcano science, monitoring, and hazard mitigation, and to explore the future potential for SAR in volcanology. Provided that the ever-growing volume of SAR data can be managed effectively, we expect the future application of SAR data to expand from being a research tool for analyzing volcanic activity after the fact, to being a monitoring and research tool capable of imaging a wide variety of processes on different temporal and spatial scales as those processes are occurring. These data can then be used to develop new models of how volcanoes work and to improve quantitative forecasts of volcanic activity as a means of mitigating risk from future eruptions.

Journal of Volcanology and Geothermal Research↗

USGS2016

Comparative evaluation of statistical and mechanistic models of Escherichia coli at beaches in southern Lake Michigan

Statistical and mechanistic models are popular tools for predicting the levels of indicator bacteria at recreational beaches. Researchers tend to use one class of model or the other, and it is difficult to generalize statements about their relative performance due to differences in how the models are developed, tested, and used. We describe a cooperative modeling approach for freshwater beaches impacted by point sources in which insights derived from mechanistic modeling were used to further improve the statistical models and vice versa. The statistical models provided a basis for assessing the mechanistic models which were further improved using probability distributions to generate high-resolution time series data at the source, long-term “tracer” transport modeling based on observed electrical conductivity, better assimilation of meteorological data, and the use of unstructured-grids to better resolve nearshore features. This approach resulted in improved models of comparable performance for both classes including a parsimonious statistical model suitable for real-time predictions based on an easily measurable environmental variable (turbidity). The modeling approach outlined here can be used at other sites impacted by point sources and has the potential to improve water quality predictions resulting in more accurate estimates of beach closures.

Lake Michigan, Ogden Dunes↗

USGS2000

No consistent effect of plant diversity on productivity

Hector et al . (1) reported on BIODEPTH, a major international experiment on the response of plant productivity to variation in the number of plant species. They found “an overall log-linear reduction of average aboveground biomass with loss of species,” leading to what the accompanying Perspective (2) described as “a rule of thumb—that each halving of diversity leads to a 10 to 20% reduction in productivity.” These conclusions, if true, imply that the continuing high rate of plant extinction threatens the future productivity of Earth's natural and managed ecosystems and could impair their ability to produce resources essential for human survival and to regulate the concentration of atmospheric CO 2 . The three sites with proper experimental design (Portugal, Sweden, and Sheffield) all showed significant positive regressions of productivity across two or three doublings of species richness [Fig. 1; (12)]. This is the pattern expected from random selection from a set of objects with different properties (13–15), because the probability of including any specific member of the set—such as a plant species that grows rapidly or fixes nitrogen—increases with the number of objects selected. Such a pattern, found consistently in randomly assembled experimental plant communities but only rarely in natural plant communities (4, 5,13–15), has been identified as a statistical artifact of experimental design (5, 13, 14). Although one study (15) suggested that the pattern constitutes a natural mechanism by which diversity affects productivity, this requires the biologically unrealistic assumption that plant communities are randomly assembled with respect to productivity (5).

Science↗

USGS2019

Modeling groundwater nitrate exposure in private wells of North Carolina for the Agricultural Health Study

Unregulated private wells in the United States are susceptible to many groundwater contaminants. Ingestion of nitrate, the most common anthropogenic private well contaminant in the United States, can lead to the endogenous formation of N-nitroso-compounds, which are known human carcinogens. In this study, we expand upon previous efforts to model private well groundwater nitrate concentration in North Carolina by developing multiple machine learning models and testing against out-of-sample prediction. Our purpose was to develop exposure estimates in unmonitored areas for use in the Agricultural Health Study (AHS) cohort. Using approximately 22,000 private well nitrate measurements in North Carolina, we trained and tested continuous models including a censored maximum likelihood-based linear model, random forest, gradient boosted machine, support vector machine, neural networks, and kriging. Continuous nitrate models had low predictive performance (R 2 < 0.33), so multiple random forest classification models were also trained and tested. The final classification approach predicted <1 mg/L, 1–5 mg/L, and ≥5 mg/L using a random forest model with 58 variables and maximizing the Cohen's kappa statistic. The final model had an overall accuracy of 0.75 and high specificity for the higher two categories and high sensitivity for the lowest category. The results will be used for the categorical prediction of private well nitrate for AHS cohort participants that reside in North Carolina.

North Carolina↗

USGS2007

The FORE-SCE model: a practical approach for projecting land cover change using scenario-based modeling

A wide variety of ecological applications require spatially explicit, historic, current, and projected land use and land cover data. The U.S. Land Cover Trends project is analyzing contemporary (1973–2000) land-cover change in the conterminous United States. The newly developed FORE-SCE model used Land Cover Trends data and theoretical, statistical, and deterministic modeling techniques to project future land cover change through 2020 for multiple plausible scenarios. Projected proportions of future land use were initially developed, and then sited on the lands with the highest potential for supporting that land use and land cover using a statistically based stochastic allocation procedure. Three scenarios of 2020 land cover were mapped for the western Great Plains in the US. The model provided realistic, high-resolution, scenario-based land-cover products suitable for multiple applications, including studies of climate and weather variability, carbon dynamics, and regional hydrology.

Journal of Land Use Science↗

USGS2006

On the influence of substrate morphology and surface area on phytofauna

The independent effects and interactions between substrate morphology and substrate surface area on invertebrate density or biomass colonizing artificial plant beds were assessed in a clear-water and a turbid playa lake in Castro County, Texas, USA. Total invertebrate density and biomass were consistently greater on filiform substrates than on laminar substrates with equivalent substrate surface areas. The relationship among treatments (substrates with different morphologies and surface areas) and response (invertebrate density or biomass) was assessed with equally spaced surface areas. Few statistically significant interactions between substrate morphology and surface area were detected, indicating that these factors were mostly independent from each other in their effect on colonizing invertebrates. Although infrequently, when substrate morphology and surface area were not independent, the effects of equally spaced changes in substrate surface area on the rate of change of phytofauna density or biomass per unit of substrate surface area were dependent upon substrate morphology. The absence of three-way interactions indicated that effects of substrate morphology and substrate area on phytofauna density or biomass were independent of environmental conditions outside and inside exclosures. ?? 2006 Springer Science+Business Media B.V.

Hydrobiologia↗

USGS2016

Assessing the relationship between groundwater nitrate and animal feeding operations in Iowa (USA)

Nitrate-nitrogen is a common contaminant of drinking water in many agricultural areas of the United States of America (USA). Ingested nitrate from contaminated drinking water has been linked to an increased risk of several cancers, specific birth defects, and other diseases. In this research, we assessed the relationship between animal feeding operations (AFOs) and groundwater nitrate in private wells in Iowa. We characterized AFOs by swine and total animal units and type (open, confined, or mixed), and we evaluated the number and spatial intensities of AFOs in proximity to private wells. The types of AFO indicate the extent to which a facility is enclosed by a roof. Using linear regression models, we found significant positive associations between the total number of AFOs within 2 km of a well (p trend < 0.001), number of open AFOs within 5 km of a well (p trend < 0.001), and number of mixed AFOs within 30 km of a well (p trend < 0.001) and the log nitrate concentration. Additionally, we found significant increases in log nitrate in the top quartiles for AFO spatial intensity, open AFO spatial intensity, and mixed AFO spatial intensity compared to the bottom quartile (0.171 log(mg/L), 0.319 log(mg/L), and 0.541 log(mg/L), respectively; all p < 0.001). We also explored the spatial distribution of nitrate-nitrogen in drinking wells and found significant spatial clustering of high-nitrate wells (> 5 mg/L) compared with low-nitrate (≤ 5 mg/L) wells ( p = 0.001). A generalized additive model for high-nitrate status identified statistically significant areas of risk for high levels of nitrate. Adjustment for some AFO predictor variables explained a portion of the elevated nitrate risk. These results support a relationship between animal feeding operations and groundwater nitrate concentrations and differences in nitrate loss from confined AFOs vs. open or mixed types.

Iowa↗