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At least 631 records · Page 35Linked to original sources

USGS2013

Increases in dissolved organic carbon accelerate loss of toxic Al in Adirondack lakes recovering from acidification

Increasing pH and decreasing Al in surface waters recovering from acidification have been accompanied by increasing concentrations of dissolved organic carbon (DOC) and associated organic acids that partially offset pH increases and complicate assessments of recovery from acidification. To better understand the processes of recovery, monthly chemistry from 42 lakes in the Adirondack region, NY, collected from 1994 to 2011, were used to (1) evaluate long-term changes in DOC and associated strongly acidic organic acids and (2) use the base-cation surplus (BCS) as a chemical index to assess the effects of increasing DOC concentrations on the Al chemistry of these lakes. Over the study period, the BCS increased (p < 0.01) and concentrations of toxic inorganic monomeric Al (IMAl) decreased (p < 0.01). The decreases in IMAl were greater than expected from the increases in the BCS. Higher DOC concentrations that increased organic complexation of Al resulted in a decrease in the IMAl fraction of total monomeric Al from 57% in 1994 to 23% in 2011. Increasing DOC concentrations have accelerated recovery in terms of decreasing toxic Al beyond that directly accomplished by reducing atmospheric deposition of strong mineral acids.

Environmental Science & Technology↗

USGS2016

Predicting arsenic in drinking water wells of the Central Valley, California

Probabilities of arsenic in groundwater at depths used for domestic and public supply in the Central Valley of California are predicted using weak-learner ensemble models (boosted regression trees, BRT) and more traditional linear models (logistic regression, LR). Both methods captured major processes that affect arsenic concentrations, such as the chemical evolution of groundwater, redox differences, and the influence of aquifer geochemistry. Inferred flow-path length was the most important variable but near-surface-aquifer geochemical data also were significant. A unique feature of this study was that previously predicted nitrate concentrations in three dimensions were themselves predictive of arsenic and indicated an important redox effect at >10 μg/L, indicating low arsenic where nitrate was high. Additionally, a variable representing three-dimensional aquifer texture from the Central Valley Hydrologic Model was an important predictor, indicating high arsenic associated with fine-grained aquifer sediment. BRT outperformed LR at the 5 μg/L threshold in all five predictive performance measures and at 10 μg/L in four out of five measures. BRT yielded higher prediction sensitivity (39%) than LR (18%) at the 10 μg/L threshold–a useful outcome because a major objective of the modeling was to improve our ability to predict high arsenic areas.

California↗

USGS2021

Exposure and transport of alkaloids and phytoestrogens from soybeans to agricultural soils and streams in the Midwestern United States

Phytotoxins are naturally produced toxins with potencies similar/higher than many anthropogenic micropollutants. Nevertheless, little is known regarding their environmental fate and off-field transport to streams. To fill this research gap, a network of six basins in the Midwestern United States with substantial soybean production was selected for the study. Stream water ( n = 110), soybean plant tissues ( n = 8), and soil samples ( n = 16) were analyzed for 12 phytotoxins (5 alkaloids and 7 phytoestrogens) and 2 widely used herbicides (atrazine and metolachlor). Overall, at least 1 phytotoxin was detected in 82% of the samples, with as many as 11 phytotoxins detected in a single sample (median = 5), with a concentration range from below detection to 37 and 68 ng/L for alkaloids and phytoestrogens, respectively. In contrast, the herbicides were ubiquitously detected at substantially higher concentrations (atrazine: 99% and metolachlor: 83%; the concentrations range from below detection to 150 and 410 ng/L, respectively). There was an apparent seasonal pattern for phytotoxins, where occurrence prior to and during harvest season (September to November) and during the snow melt season (March) was higher than that in December–January. Runoff events increased phytotoxin and herbicide concentrations compared to those in base-flow conditions. Phytotoxin plant concentrations were orders of magnitude higher compared to those measured in soil and streams. These results demonstrate the potential exposure of aquatic and terrestrial organisms to soybean-derived phytotoxins.

Illinois, Indiana, Iowa, Minnesota, Missouri, Sout↗

USGS1996

Hydrologic and microbiological factors affecting persistence and migration of petroleum hydrocarbons spilled in a continuous-permafrost region

Fuel spills, totaling about 1300 m3, occurred between 1976 and 1978 adjacent to Imikpuk Lake, a drinking water source near Barrow, AK. Substantial contamination of soils and groundwater near the lake persists. We examined the magnitude and direction of groundwater flux and the microbial activity at this site to understand the persistence of contamination and its effect on the lake. We found that groundwater flux is small due to shallow permafrost, which restricts the cross-sectional area available for flow, and to the short annual thaw season (ca. 90 days). The small flux and limited depth also constrain contaminant transport and dispersion, resulting in persistent, shallow contamination. The numbers of hydrocarbon-oxidizing microorganisms and their laboratory mineralization potentials for benzene (at 10 ??C) were higher in samples from contaminated areas than in reference samples. Benzene mineralization potentials in groundwater samples were comparable to more temperate systems (0.1-0.5 mg of benzene mineralized L-1 day-1) and were stimulated by nutrient additions. Field measurements of dissolved oxygen, nitrate, ferrous iron, and sulfide in groundwater provided evidence that biodegradation of petroleum hydrocarbons is occurring in situ. Despite evidence of an active microbial population, microbial processes, like contaminant transport, are likely limited at this site by the short annual thaw season.

Environmental Science & Technology↗

USGS2021

Assessing the impact of drought on arsenic exposure from private domestic wells in the conterminous United States

This study assesses the potential impact of drought on arsenic exposure from private domestic wells by using a previously developed statistical model that predicts the probability of elevated arsenic concentrations (>10 μg per liter) in water from domestic wells located in the conterminous United States (CONUS). The application of the model to simulate drought conditions used systematically reduced precipitation and recharge values. The drought conditions resulted in higher probabilities of elevated arsenic throughout most of the CONUS. While the increase in the probability of elevated arsenic was generally less than 10% at any one location, when considered over the entire CONUS, the increase has considerable public health implications. The population exposed to elevated arsenic from domestic wells was estimated to increase from approximately 2.7 million to 4.1 million people during drought. The model was also run using total annual precipitation and groundwater recharge values from the year 2012 when drought existed over a large extent of the CONUS. This simulation provided a method for comparing the duration of drought to changes in the predicted probability of high arsenic in domestic wells. These results suggest that the probability of exposure to arsenic concentrations greater than 10 μg per liter increases with increasing duration of drought. These findings indicate that drought has a potentially adverse impact on the arsenic hazard from domestic wells throughout the CONUS.

Conterminous United States↗

USGS2010

Influence of coal-tar sealcoat and other carbonaceous materials on polycyclic aromatic hydrocarbon loading in an urban watershed

Carbonaceous material (CM) particles are the principal vectors transporting polycyclic aromatic hydrocarbons (PAHs) into urban waters via runoff; however, characteristics of CM particles in urban watersheds and their relative contributions to PAH contamination remain unclear. Our objectives were to identify the sources and distribution of CM particles in an urban watershed and to determine the types of CMs that were the dominant sources of PAHs in the lake and stream sediments. Samples of soils, parking lot and street dust, and streambed and lake sediment were collected from the Lake Como watershed in Fort Worth, Texas. Characteristics of CM particles determined by organic petrography and a significant correlation between PAH concentrations and organic carbon in coal tar, asphalt, and soot indicate that these three CM particle types are the major sources and carriers of PAHs in the watershed. Estimates of the distribution of PAHs in CM particles indicate that coal-tar pitch, usedinsomepavementsealcoats, isadominant source of PAHs in the watershed, and contributes as much as 99% of the PAHs in sealed parking lot dust, 92% in unsealed parking lot dust, 88% in commercial area soil, 71% in streambed sediment, and 84% in surficial lake sediment. ?? 2010 American Chemical Society.

Environmental Science & Technology↗

USGS2022

Weakly supervised spatial deep learning for Earth image segmentation based on imperfect polyline labels

In recent years, deep learning has achieved tremendous success in image segmentation for computer vision applications. The performance of these models heavily relies on the availability of large-scale high-quality training labels (e.g., PASCAL VOC 2012). Unfortunately, such large-scale high-quality training data are often unavailable in many real-world spatial or spatiotemporal problems in earth science and remote sensing (e.g., mapping the nationwide river streams for water resource management). Although extensive efforts have been made to reduce the reliance on labeled data (e.g., semi-supervised or unsupervised learning, few-shot learning), the complex nature of geographic data such as spatial heterogeneity still requires sufficient training labels when transferring a pre-trained model from one region to another. On the other hand, it is often much easier to collect lower-quality training labels with imperfect alignment with earth imagery pixels (e.g., through interpreting coarse imagery by non-expert volunteers). However, directly training a deep neural network on imperfect labels with geometric annotation errors could significantly impact model performance. Existing research that overcomes imperfect training labels either focuses on errors in label class semantics or characterizes label location errors at the pixel level. These methods do not fully incorporate the geometric properties of label location errors in the vector representation. To fill the gap, this article proposes a weakly supervised learning framework to simultaneously update deep learning model parameters and infer hidden true vector label locations. Specifically, we model label location errors in the vector representation to partially reserve geometric properties (e.g., spatial contiguity within line segments). Evaluations on real-world datasets in the National Hydrography Dataset (NHD) refinement application illustrate that the proposed framework outperforms baseline methods in classification accuracy.

ACM Transactions on Intelligent Systems and Techno↗

USGS2020

Reduction in drinking water arsenic exposure and health risk through arsenic treatment among private well households in Maine and New Jersey, USA

Over 2 million people in the United States (U.S.) drink water from private wells that contain arsenic (As) exceeding the U.S. Environmental Protection Agency (USEPA) Maximum Contaminant Level (MCL) of 10 micrograms per liter (μg/L). While there are a number of commercially available treatment technologies for removing As from drinking water, it is up to the private well households to decide whether to treat for As or not. However, how well existing treatment technologies perform in real world situations, and to what extent they reduce health risks, are not well understood. This study evaluates the effectiveness of household As treatment systems in southern-central Maine (ME, n=156) and northern New Jersey (NJ, n=94) and ascertains how untreated well water chemistry and other factors influence As removal. Untreated and treated water samples, as well as a treatment questionnaire, were collected. Most ME households in this study had point-of-use reverse-osmosis systems (POU RO), while in NJ, dual-tank point-of-entry (POE) whole house systems were popular. Arsenic treatment systems reduced well water arsenic concentrations ([As]) by up to two orders of magnitude, i.e. from a median of 71.7 to 0.8 μg/L and from a mean of 105 to 14.3 μg/L in ME, and from a median of 8.6 to 0.2 μg/L and a mean of 15.8 to 2.1 μg/L in NJ. More than half (53%) of the systems in ME reduced water [As] to below 1 µg/L, compared to 69% in NJ. The treatment system failure rates were 19% in ME (> USEPA MCL 10 µg/L) and 16% in NJ (> NJ standard 5 μg/L). In both states, the higher the untreated well water [As] and the As(III)/As ratio, the higher the rate of treatment failure. POE systems failed less than POU systems, as did the treatment systems installed and maintained by vendors than those by homeowners. The 7-fold reduction of [As] in the treated water reduced skin cancer risk alone from 3,765 to 514 in 1 million in ME, and from 568 to 75 in 1 million in NJ.

Maine, New Jersey↗

USGS2016

Effects of flow regime on metal concentrations and the attainment of water quality standards in a remediated stream reach, Butte, Montana

Low-flow synoptic sampling campaigns are often used as the primary tool to characterize watersheds affected by mining. Although such campaigns are an invaluable part of site characterization, investigations which focus solely on low-flow conditions may yield misleading results. The objective of this paper is to demonstrate this point and elucidate the mechanisms responsible for the release of metals during rainfall runoff. This objective is addressed using data from diel and synoptic sampling campaigns conducted over a two-day period. Low-flow synoptic sampling results indicate that concentrations of most constituents meet aquatic standards. This finding is in contrast to findings from a diel sampling campaign that captured dramatic increases in concentrations during rainfall runoff. Concentrations during the rising limb of the hydrograph were 2–23 times concentrations observed during synoptic sampling (most increases were >10-fold), remaining elevated during the receding limb of the hydrograph to produce a clockwise hysteresis loop. Hydrologic mechanisms responsible for the release of metals include increased transport due to resuspension of streambed solids, erosion of alluvial tailings, and overland flow. Rainfall also elevated the alluvial groundwater table and increased infiltration through the vadose zone, likely resulting in dissolution from alluvial tailings that were dry prior to the event.

Montana↗

USGS2006

Modeling the transport and inactivation of E. coli and enterococci in the near-shore region of Lake Michigan

To investigate the transport and fate of fecal pollution at Great Lakes beaches and the health risks associated with swimming, the near-shore waters of Lake Michigan and two tributaries discharging into it were examined for bacterial indicators of human fecal pollution. The enterococcus human fecal pollution marker, which targets a putative virulence factor the enterococcal surface protein (esp) in Enterococcus faecium , was detected in 2/28 samples (7%) in the tributaries draining into Lake Michigan and in 6/30 samples (20%) in Lake Michigan beaches. This was indicative of human fecal pollution being transported in the tributaries and occurrence at Lake Michigan beaches. To understand the relative importance of different processes influencing pollution transport and inactivation, a finite-element model of surf-zone hydrodynamics (coupled with models for temperature, E. coli and enterococci) was used. Enterococci appear to survive longer than E. coli , which was described using an overall first-order inactivation coefficient in the range 0.5−2.0 per day. Our analysis suggests that the majority of fecal indicator bacteria variation can be explained based on loadings from the tributaries. Sunlight is a major contributor to inactivation in the surf-zone and the formulation based on sunlight, temperature and sedimentation is preferred over the first-order inactivation formulation.

Environmental Science & Technology↗

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↗

USGS1999

Seasonal and annual load of herbicides from the Mississippi River Basin to the Gulf of Mexico

Water samples collected from rivers in the Mississippi River Basin were analyzed for selected herbicides to evaluate their discharge to the Gulf of Mexico and to identify their predominant source areas within the basin. Samples were collected from the Mississippi River at Baton Rouge, LA from 1991 to 1997 and from sites on the upper Mississippi, the Missouri, and the Ohio Rivers from 1996 to 1997. Atrazine, metolachlor, and alachlor ESA (an alachlor metabolite) were the most frequently detected herbicides in the Mississippi River at Baton Rouge, and, in general, were present in the largest concentrations. The peak annual herbicide load was in 1993 when about 640 metric tons of atrazine, 320 metric tons of cyanazine, 215 metric tons of metolachlor, 53 metric tons of simazine, and 50 metric tons of alachlor were discharged to the Gulf of Mexico. The annual load of atrazine and cyanazine was generally 1−3% of the amount annually applied in the Mississippi River drainage basin; the annual load of acetochlor, alachlor, and metolachlor was generally less than 1%. During 1996−1997 the Ohio River contributed about 50% of the discharge and 50% or more of the herbicide load to the Gulf of Mexico.

Environmental Science & Technology↗

USGS2001

Occurrence of cyanazine compounds in groundwater: Degradates more prevalent than the parent compound

A recently developed analytical method using liquid chromatography/mass spectrometry was used to investigate the occurrence of cyanazine and its degradates cyanazine acid (CAC), cyanazine amide (CAM), deethylcyanazine (DEC), and deethylcyanazine acid (DCAC) in groundwater. This research represents some of the earliest data on the occurrence of cyanazine degradates in groundwater. Although cyanazine was infrequently detected in the 64 wells across Iowa sampled in 1999, cyanazine degradates were commonly found during this study. The most frequently detected cyanazine compound was DCAC (32.8%) followed by CAC (29.7%), CAM (17.2%), DEC (3.1%), and cyanazine (3.1%). The frequency of detection for cyanazine or one or more of its degradates (CYTOT) was more than 12-fold over that of cyanazine alone (39.1% for CYTOT versus 3.1% for cyanazine). Of the total measured concentration of cyanazine, only 0.2% was derived from its parent compound - with DCAC (74.1%) and CAC (18.4%) comprising 92.5% of this total. Thus, although DCAC and CAC had similar frequencies of detection, DCAC was generally present in higher concentrations. No concentrations of cyanazine compounds for this study exceeded water-quality criteria for the protection of human health. Only cyanazine, however, has such a criteria established. Nevertheless, because these cyanazine degradates are still chlorinated, they may have similar toxicity as their parent compound - similar to what has been found with the chlorinated degradates of atrazine. Thus, the results of this study documented that data on the degradates for cyanazine are critical for understanding its fate and transport in the hydrologic system. Furthermore, the prevalence of the chlorinated degradates of cyanazine found in groundwater suggests that to accurately determine the overall effect on human health and the environment from cyanazine its degradates should also be considered. In addition, because CYTOT was found in 57.6% of the samples collected from alluvial aquifers, about 2-5 times more frequently than the other major aquifer types (glacial drift, bedrock/karst, bedrock/nonkarst) under investigation, this finding has long-term implications for the occurrence of CYTOT in streams. It is anticipated that low-level concentrations of CYTOT will continue to be detected in streams for years after the use of cyanazine has terminated (scheduled for the year 2000 in the United States), primarily through its movement from groundwater into streams during base-flow conditions.

Iowa↗

USGS2021

Machine learning models of arsenic in private wells throughout the conterminous United States as a tool for exposure assessment in human health studies

Arsenic from geologic sources is widespread in groundwater within the United States (U.S.). In several areas, groundwater arsenic concentrations exceed the U.S. Environmental Protection Agency maximum contaminant level of 10 μg per liter (μg/L). However, this standard applies only to public-supply drinking water and not to private-supply, which is not federally regulated and is rarely monitored. As a result, arsenic exposure from private wells is a potentially substantial, but largely hidden, public health concern. Machine learning models using boosted regression trees (BRT) and random forest classification (RFC) techniques were developed to estimate probabilities and concentration ranges of arsenic in private wells throughout the conterminous U.S. Three BRT models were fit separately to estimate the probability of private well arsenic concentrations exceeding 1, 5, or 10 μg/L whereas the RFC model estimates the most probable category (≤5, >5 to ≤10, or >10 μg/L). Overall, the models perform best at identifying areas with low concentrations of arsenic in private wells. The BRT 10 μg/L model estimates for testing data have an overall accuracy of 91.2%, sensitivity of 33.9%, and specificity of 98.2%. Influential variables identified across all models included average annual precipitation and soil geochemistry. Models were developed in collaboration with public health experts to support U.S.-based studies focused on health effects from arsenic exposure.

Environmental Science and Technology↗

USGS2011

Differential effects of dissolved organic carbon upon re-entrainment and surface properties of groundwater bacteria and bacteria-sized microspheres during transport through a contaminated, sandy aquifer

Injection-and-recovery studies involving a contaminated, sandy aquifer (Cape Cod, Massachusetts) were conducted to assess the relative susceptibility for in situ re-entrainment of attached groundwater bacteria ( Pseudomonas stuzeri ML2, and uncultured, native bacteria) and carboxylate-modified microspheres (0.2 and 1.0 μm diameters). Different patterns of re-entrainment were evident for the two colloids in response to subsequent injections of groundwater (hydrodynamic perturbation), deionized water (ionic strength alteration), 77 μM linear alkylbenzene sulfonates (LAS, anionic surfactant), and 76 μM Tween 80 (polyoxyethylene sorbitan monooleate, a very hydrophobic nonionic surfactant). An injection of deionized water was more effective in causing detachment of micrsopheres than were either of the surfactants, consistent with the more electrostatic nature of microsphere’s attachment, their extreme hydrophilicity (hydrophilicity index, HI, of 0.99), and negative charge (zeta potentials, ζ, of −44 to −49 mv). In contrast, Tween 80 was considerably more effective in re-entraining the more-hydrophobic native bacteria. Both the hydrophilicities and zeta potentials of the native bacteria were highly sensitive to and linearly correlated with levels of groundwater dissolved organic carbon (DOC), which varied modestly from 0.6 to 1.3 mg L −1 . The most hydrophilic (0.52 HI) and negatively charged (ζ −38.1 mv) indigenous bacteria were associated with the lowest DOC. FTIR spectra indicated the latter community had the highest average density of surface carboxyl groups. In contrast, differences in groundwater (DOC) had no measurable effect on hydrophilicity of the bacteria-sized microspheres and only a minor effect on their ζ. These findings suggest that microspheres may not be very good surrogates for bacteria in field-scale transport studies and that adaptive (biological) changes in bacterial surface characteristics may need to be considered where there is longer-term exposure to contaminant DOC.

Environmental Science & Technology↗

USGS2012

Humic acid facilitates the transport of ARS-labeled hydroxyapatite nanoparticles in iron oxyhydroxide-coated sand

Hydroxyapatite nanoparticles (nHAP) have been widely used to remediate soil and wastewater contaminated with metals and radionuclides. However, our understanding of nHAP transport and fate is limited in natural environments that exhibit significant variability in solid and solution chemistry. The transport and retention kinetics of Alizarin red S (ARS)-labeled nHAP were investigated in water-saturated packed columns that encompassed a range of humic acid concentrations (HA, 0–10 mg L –1 ), fractional surface coverage of iron oxyhydroxide coatings on sand grains (λ, 0–0.75), and pH (6.0–10.5). HA was found to have a marked effect on the electrokinetic properties of ARS-nHAP, and on the transport and retention of ARS-nHAP in granular media. The transport of ARS-nHAP was found to increase with increasing HA concentration because of enhanced colloidal stability and the reduced aggregate size. When HA = 10 mg L –1 , greater ARS-nHAP attachment occurred with increasing λ because of increased electrostatic attraction between negatively charged nanoparticles and positively charged iron oxyhydroxides, although alkaline conditions (pH 8.0 and 10.5) reversed the surface charge of the iron oxyhydroxides and therefore decreased deposition. The retention profiles of ARS-nHAP exhibited a hyperexponential shape for all test conditions, suggesting some unfavorable attachment conditions. Retarded breakthrough curves occurred in sands with iron oxyhydroxide coatings because of time-dependent occupation of favorable deposition sites. Consideration of the above effects is necessary to improve remediation efficiency of nHAP for metals and actinides in soils and subsurface environments.

Environmental Science & Technology↗

USGS2013

Transport and fate of microbial pathogens in agricultural settings

An understanding of the transport and survival of microbial pathogens (pathogens hereafter) in agricultural settings is needed to assess the risk of pathogen contamination to water and food resources, and to develop control strategies and treatment options. However, many knowledge gaps still remain in predicting the fate and transport of pathogens in runoff water, and then through the shallow vadose zone and groundwater. A number of transport pathways, processes, factors, and mathematical models often are needed to describe pathogen fate in agricultural settings. The level of complexity is dramatically enhanced by soil heterogeneity, as well as by temporal variability in temperature, water inputs, and pathogen sources. There is substantial variability in pathogen migration pathways, leading to changes in the dominant processes that control pathogen transport over different spatial and temporal scales. For example, intense rainfall events can generate runoff and preferential flow that can rapidly transport pathogens. Pathogens that survive for extended periods of time have a greatly enhanced probability of remaining viable when subjected to such rapid-transport events. Conversely, in dry seasons, pathogen transport depends more strongly on retention at diverse environmental surfaces controlled by a multitude of coupled physical, chemical, and microbiological factors. These interactions are incompletely characterized, leading to a lack of consensus on the proper mathematical framework to model pathogen transport even at the column scale. In addition, little is known about how to quantify transport and survival parameters at the scale of agricultural fields or watersheds. This review summarizes current conceptual and quantitative models for pathogen transport and fate in agricultural settings over a wide range of spatial and temporal scales. The authors also discuss the benefits that can be realized by improved modeling, and potential treatments to mitigate the risk of waterborne disease transmission.

Critical Reviews in Environmental Science and Tech↗

USGS2012

Archive of digital boomer subbottom data collected during USGS cruise 05FGS01 offshore east-central Florida, July 17-29, 2005

In July of 2005, the U.S. Geological Survey (USGS), in cooperation with the Florida Geological Survey (FGS), conducted a geophysical survey of the Atlantic Ocean offshore of Florida's east coast from Flagler Beach to Daytona Beach. This report serves as an archive of unprocessed digital boomer subbottom data, trackline maps, navigation files, Geographic Information System (GIS) files, Field Activity Collection System (FACS) logs and formal Federal Geographic Data Committee (FGDC) metadata. Filtered and gained (showing a relative increase in signal amplitude) digital images of the seismic profiles are also provided. Refer to the Acronyms page for expansions of acronyms and abbreviations used in this report. The USGS Saint Petersburg Coastal and Marine Science Center (SPCMSC) assigns a unique identifier to each cruise or field activity. For example, 05FGS01 tells us the data were collected in 2005 for cooperative work with the FGS and the data were collected during the first field activity for that project in that calendar year. Refer to http://walrus.wr.usgs.gov/infobank/programs/html/definition/activity.html for a detailed description of the method used to assign the field activity ID. The boomer subbottom processing system consists of an acoustic energy source that is made up of capacitors charged to a high voltage and discharged through a transducer in the water. The transducer is towed on a sled floating on the water surface and when discharged emits a short acoustic pulse, or shot, which propagates through the water column and shallow stratrigraphy below. The acoustic energy is reflected at density boundaries (such as the seafloor or sediment layers beneath the seafloor), detected by the receiver (a hydrophone streamer), and recorded by a PC-based seismic acquisition system. This process is repeated at timed intervals (for example, 0.5 s) and recorded for specific intervals of time (for example, 100 ms). In this way, a two-dimensional (2-D) vertical image of the shallow geologic structure beneath the ship track is produced. Figure 1 displays the acquisition geometry. Refer to table 1 for a summary of acquisition parameters and table 2 for trackline statistics. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG Y format (Barry and others, 1975), except an ASCII format is used for the first 3,200 bytes of the card image header instead of the standard EBCDIC format. For a detailed description about the recorded trace headers, refer to the SEG Y Format page. The SEG Y files may be downloaded and processed with commercial or public domain software such as Seismic Unix (Cohen and Stockwell, 2005). See the How To Download SEG Y Data page for download instructions. The printable profiles provided here are GIF images that were processed and gained using SU software; refer to the Software page for links to example SU processing scripts. The processed SEG Y data were also exported to Chesapeake Technology, Inc. (CTI) SonarWeb software to produce a geospatially interactive version of the profile that allows the user to obtain a geographic location and depth from the profile for a given cursor position; this information is displayed in the status bar of the browser. Please note that clicking on the profile image switches it to "Expanded View" (a compressed image of the entire line) and cursor tracking is not available in this mode.

Florida↗