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Matthew J. Ferrari

Publications and source records attributed to Matthew J. Ferrari.

At least 19 recordsLinked to original sources
USGS2023

Multiple models for outbreak decision support in the face of uncertainty

Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.

PNAS↗
USGS2022

Vote-processing rules for combining control recommendations from multiple models

Mathematical modelling is used during disease outbreaks to compare control interventions. Using multiple models, the best method to combine model recommendations is unclear. Existing methods weight model projections, then rank control interventions using the combined projections, presuming model outputs are directly comparable. However, the way each model represents the epidemiological system will vary. We apply electoral vote-processing rules to combine model-generated rankings of interventions. Combining rankings of interventions, instead of combining model projections, avoids assuming that projections are comparable as all comparisons of projections are made within each model. We investigate four rules: First-past-the-post, Alternative Vote (AV), Coombs Method and Borda Count. We investigate rule sensitivity by including models that favour only one action or including those that rank interventions randomly. We investigate two case studies: the 2014 Ebola outbreak in West Africa (37 compartmental models) and a hypothetical foot-and-mouth disease outbreak in UK (four individual-based models). The Coombs Method was least susceptible to adding models that favoured a single action, Borda Count and AV were most susceptible to adding models that ranked interventions randomly. Each rule chose the same intervention as when ranking interventions by mean projections, suggesting that combining rankings provides similar recommendations with fewer assumptions about model comparability.

Philosophical Transactions of the Royal Society A:↗
USGS2021

Synergistic interventions to control COVID-19: Mass testing and isolation mitigates reliance on distancing

Stay-at-home orders and shutdowns of non-essential businesses are powerful, but socially costly, tools to control the pandemic spread of SARS-CoV-2. Mass testing strategies, which rely on widely administered frequent and rapid diagnostics to identify and isolate infected individuals, could be a potentially less disruptive management strategy, particularly where vaccine access is limited. In this paper, we assess the extent to which mass testing and isolation strategies can reduce reliance on socially costly non-pharmaceutical interventions, such as distancing and shutdowns. We develop a multi-compartmental model of SARS-CoV-2 transmission incorporating both preventative non-pharmaceutical interventions (NPIs) and testing and isolation to evaluate their combined effect on public health outcomes. Our model is designed to be a policy-guiding tool that captures important realities of the testing system, including constraints on test administration and non-random testing allocation. We show how strategic changes in the characteristics of the testing system, including test administration, test delays, and test sensitivity, can reduce reliance on preventative NPIs without compromising public health outcomes in the future. The lowest NPI levels are possible only when many tests are administered and test delays are short, given limited immunity in the population. Reducing reliance on NPIs is highly dependent on the ability of a testing program to identify and isolate unreported, asymptomatic infections. Changes in NPIs, including the intensity of lockdowns and stay at home orders, should be coordinated with increases in testing to ensure epidemic control; otherwise small additional lifting of these NPIs can lead to dramatic increases in infections, hospitalizations and deaths. Importantly, our results can be used to guide ramp-up of testing capacity in outbreak settings, allow for the flexible design of combined interventions based on social context, and inform future cost-benefit analyses to identify efficient pandemic management strategies.

PLOS Computational Biology↗
USGS2021

Strategic testing approaches for targeted disease monitoring can be used to inform pandemic decision-making

More than 1.6 million Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) tests were administered daily in the United States at the peak of the epidemic, with a significant focus on individual treatment. Here, we show that objective-driven, strategic sampling designs and analyses can maximize information gain at the population level, which is necessary to increase situational awareness and predict, prepare for, and respond to a pandemic, while also continuing to inform individual treatment. By focusing on specific objectives such as individual treatment or disease prediction and control (e.g., via the collection of population-level statistics to inform lockdown measures or vaccine rollout) and drawing from the literature on capture–recapture methods to deal with nonrandom sampling and testing errors, we illustrate how public health objectives can be achieved even with limited test availability when testing programs are designed a priori to meet those objectives.

PLoS Biology↗
USGS2021

Causes of delayed outbreak responses and their impacts on epidemic spread

Livestock diseases have devastating consequences economically, socially and politically across the globe. In certain systems, pathogens remain viable after host death, which enables residual transmissions from infected carcasses. Rapid culling and carcass disposal are well-established strategies for stamping out an outbreak and limiting its impact; however, wait-times for these procedures, i.e. response delays, are typically farm-specific and time-varying due to logistical constraints. Failing to incorporate variable response delays in epidemiological models may understate outbreak projections and mislead management decisions. We revisited the 2001 foot-and-mouth epidemic in the United Kingdom and sought to understand how misrepresented response delays can influence model predictions. Survival analysis identified farm size and control demand as key factors that impeded timely culling and disposal activities on individual farms. Using these factors in the context of an existing policy to predict local variation in response times significantly affected predictions at the national scale. Models that assumed fixed, timely responses grossly underestimated epidemic severity and its long-term consequences. As a result, this study demonstrates how general inclusion of response dynamics and recognition of partial controllability of interventions can help inform management priorities during epidemics of livestock diseases.

Journal of the Royal Society Interface↗
USGS2020

Anticipating future learning affects current control decisions: A comparison between passive and active adaptive management in an epidemiological setting

Infectious disease epidemics present a difficult task for policymakers, requiring the implementation of control strategies under significant time constraints and uncertainty. Mathematical models can be used to predict the outcome of control interventions, providing useful information to policymakers in the event of such an epidemic. However, these models suffer in the early stages of an outbreak from a lack of accurate, relevant information regarding the dynamics and spread of the disease and the efficacy of control. As such, recommendations provided by these models are often incorporated in an ad hoc fashion, as and when more reliable information becomes available. In this work, we show that such trial-and-error-type approaches to management, which do not formally take into account the resolution of uncertainty and how control actions affect this, can lead to sub-optimal management outcomes. We compare three approaches to managing a theoretical epidemic: a non-adaptive management (AM) approach that does not use real-time outbreak information to adapt control, a passive AM approach that incorporates real-time information if and when it becomes available, and an active AM approach that explicitly incorporates the future resolution of uncertainty through gathering real-time information into its initial recommendations. The structured framework of active AM encourages the specification of quantifiable objectives, models of system behaviour and possible control and monitoring actions, followed by an iterative learning and control phase that is able to employ complex control optimisations and resolve system uncertainty. The result is a management framework that is able to provide dynamic, long-term projections to help policymakers meet the objectives of management. We investigate in detail the effect of different methods of incorporating up-to-date outbreak information. We find that, even in a highly simplified system, the method of incorporating new data can lead to different results that may influence initial policy decisions, with an active AM approach to management providing better information that can lead to more desirable outcomes from an epidemic.

Journal of Theoretical Biology↗
USGS2020

Harnessing multiple models for outbreak management

The coronavirus disease 2019 (COVID-19) pandemic has triggered efforts by multiple modeling groups to forecast disease trajectory, assess interventions, and improve understanding of the pathogen. Such models can often differ substantially in their projections and recommendations, reflecting different policy assumptions and objectives, as well as scientific, logistical, and other uncertainty about biological and management processes (1). Disparate predictions during any outbreak can hinder intervention planning and response by policy-makers (2, 3), who may instead choose to rely on single trusted sources of advice, or on consensus where it appears. Thus, valuable insights and information from other models may be overlooked, limiting the opportunity for decision-makers to account for risk and uncertainty and resulting in more lives lost or resources used than necessary. We advocate a more systematic approach, by merging two well-established research fields. The first element involves formal expert elicitation methods applied to multiple models to deliberately generate, retain, and synthesize valuable individual model ideas and share important insights during group discussions, while minimizing various cognitive biases. The second element uses a decision-theoretic framework to capture and account for within- and between-model uncertainty as we evaluate actions in a timely manner to achieve management objectives.

Science↗
USGS2019

Concurrent assessment of epidemiological and operational uncertainties for optimal outbreak control: Ebola as a case study

Determining how to best manage an epidemiological outbreak may be hindered by both epidemiological uncertainty (i.e. about epidemiological processes) and operational uncertainty (i.e. about the effectiveness of candidate interventions). These two uncertainties are rarely addressed concurrently in epidemic studies, impeding decision-making. We present an approach to simultaneously address both sources of uncertainty. Epidemiological uncertainty is represented by a large ensemble of models of the 2014 West African Ebola outbreak. Operational uncertainty about the effectiveness of three classes of intervention is assessed for a wide range of potential effectiveness for each intervention. We ranked each intervention in terms of caseload reduction in each model, initially assuming an unlimited budget. To explore the role of budget limitation, we assessed the influence of three candidate cost functions relating intervention effectiveness and cost for different budget levels. The Value of Information (VoI) to resolve uncertainty is generally high in this study; appropriate information gain could reduce expected caseload by more than 50%. The ranking of interventions is jointly determined by the underlying epidemiological process, the effectiveness of the interventions and the size of the budget available for the program. An epidemiologically effective intervention might not be optimal if its costs outweigh its epidemiological benefit. Under higher budget conditions, resolution of epidemiological uncertainty is most valuable. When budgets are tight, however, operational and epidemiological uncertainty are equally important. Overall, our study demonstrates that significant public health improvements could result from a careful examination of both epidemiological and operational uncertainties within the same modelling structure. This approach can be applied to decision-making for management of other diseases for which multiple models and multiple interventions are available.

Proceedings of the Royal Society B↗
USGS2019

Context matters: Using reinforcement learning to develop human-readable, state-dependent outbreak response policies

The number of all possible epidemics of a given infectious disease that could occur on a given landscape is large for systems of real-world complexity. Furthermore, there is no guarantee that the control actions that are optimal, on average, over all possible epidemics are also best for each possible epidemic. Reinforcement learning (RL) has been used to develop machine-readable context-dependent solutions for complex problems with many possible realisations ranging from video-games to the game of Go. RL could be a valuable tool to generate context-dependent policies for outbreak response, though translating the resulting policies into simple rules that can be read and interpreted by human decision-makers remains a challenge. Here we illustrate the application of RL to the development of context-dependent outbreak response policies to minimise outbreaks of foot-and-mouth disease. We show that control based on the resulting context-dependent policies, which adapt interventions to the specific outbreak, result in smaller outbreaks than static policies. We further illustrate two approaches for translating the complex machine-readable policies into simple heuristics that can be evaluated by human decision-makers.

Philosophical Transactions of the Royal Society B:↗
USGS2018

Real-time decision-making during emergency disease outbreaks

In the event of a new infectious disease outbreak, mathematical and simulation models are commonly used to inform policy by evaluating which control strategies will minimize the impact of the epidemic. In the early stages of such outbreaks, substantial parameter uncertainty may limit the ability of models to provide accurate predictions, and policymakers do not have the luxury of waiting for data to alleviate this state of uncertainty. For policymakers, however, it is the selection of the optimal control intervention in the face of uncertainty, rather than accuracy of model predictions, that is the measure of success that counts. We simulate the process of real-time decision-making by fitting an epidemic model to observed, spatially-explicit, infection data at weekly intervals throughout two historical outbreaks of foot-and-mouth disease, UK in 2001 and Miyazaki, Japan in 2010, and compare forward simulations of the impact of switching to an alternative control intervention at the time point in question. These are compared to policy recommendations generated in hindsight using data from the entire outbreak, thereby comparing the best we could have done at the time with the best we could have done in retrospect. Our results show that the control policy that would have been chosen using all the data is also identified from an early stage in an outbreak using only the available data, despite high variability in projections of epidemic size. Critically, we find that it is an improved understanding of the locations of infected farms, rather than improved estimates of transmission parameters, that drives improved prediction of the relative performance of control interventions. However, the ability to estimate undetected infectious premises is a function of uncertainty in the transmission parameters. Here, we demonstrate the need for both real-time model fitting and generating projections to evaluate alternative control interventions throughout an outbreak. Our results highlight the use of using models at outbreak onset to inform policy and the importance of state-dependent interventions that adapt in response to additional information throughout an outbreak.

PLOS Computational Biology↗
USGS2017

Essential information: Uncertainty and optimal control of Ebola outbreaks

Early resolution of uncertainty during an epidemic outbreak can lead to rapid and efficient decision making, provided that the uncertainty affects prioritization of actions. The wide range in caseload projections for the 2014 Ebola outbreak caused great concern and debate about the utility of models. By coding and running 37 published Ebola models with five candidate interventions, we found that, despite this large variation in caseload projection, the ranking of management options was relatively consistent. Reducing funeral transmission and reducing community transmission were generally ranked as the two best options. Value of information (VoI) analyses show that caseloads could be reduced by 11% by resolving all model-specific uncertainties, with information about model structure accounting for 82% of this reduction and uncertainty about caseload only accounting for 12%. Our study shows that the uncertainty that is of most interest epidemiologically may not be the same as the uncertainty that is most relevant for management. If the goal is to improve management outcomes, then the focus of study should be to identify and resolve those uncertainties that most hinder the choice of an optimal intervention. Our study further shows that simplifying multiple alternative models into a smaller number of relevant groups (here, with shared structure) could streamline the decision-making process and may allow for a better integration of epidemiological modeling and decision making for policy.

Proceedings of the National Academy of Sciences of↗
USGS2016

Decision-making for foot-and-mouth disease control: Objectives matter

Formal decision-analytic methods can be used to frame disease control problems, the first step of which is to define a clear and specific objective. We demonstrate the imperative of framing clearly-defined management objectives in finding optimal control actions for control of disease outbreaks. We illustrate an analysis that can be applied rapidly at the start of an outbreak when there are multiple stakeholders involved with potentially multiple objectives, and when there are also multiple disease models upon which to compare control actions. The output of our analysis frames subsequent discourse between policy-makers, modellers and other stakeholders, by highlighting areas of discord among different management objectives and also among different models used in the analysis. We illustrate this approach in the context of a hypothetical foot-and-mouth disease (FMD) outbreak in Cumbria, UK using outputs from five rigorously-studied simulation models of FMD spread. We present both relative rankings and relative performance of controls within each model and across a range of objectives. Results illustrate how control actions change across both the base metric used to measure management success and across the statistic used to rank control actions according to said metric. This work represents a first step towards reconciling the extensive modelling work on disease control problems with frameworks for structured decision making.

Epidemics↗
USGS2014

Adaptive management and the value of information: learning via intervention in epidemiology

Optimal intervention for disease outbreaks is often impeded by severe scientific uncertainty. Adaptive management (AM), long-used in natural resource management, is a structured decision-making approach to solving dynamic problems that accounts for the value of resolving uncertainty via real-time evaluation of alternative models. We propose an AM approach to design and evaluate intervention strategies in epidemiology, using real-time surveillance to resolve model uncertainty as management proceeds, with foot-and-mouth disease (FMD) culling and measles vaccination as case studies. We use simulations of alternative intervention strategies under competing models to quantify the effect of model uncertainty on decision making, in terms of the value of information, and quantify the benefit of adaptive versus static intervention strategies. Culling decisions during the 2001 UK FMD outbreak were contentious due to uncertainty about the spatial scale of transmission. The expected benefit of resolving this uncertainty prior to a new outbreak on a UK-like landscape would be £45–£60 million relative to the strategy that minimizes livestock losses averaged over alternate transmission models. AM during the outbreak would be expected to recover up to £20.1 million of this expected benefit. AM would also recommend a more conservative initial approach (culling of infected premises and dangerous contact farms) than would a fixed strategy (which would additionally require culling of contiguous premises). For optimal targeting of measles vaccination, based on an outbreak in Malawi in 2010, AM allows better distribution of resources across the affected region; its utility depends on uncertainty about both the at-risk population and logistical capacity. When daily vaccination rates are highly constrained, the optimal initial strategy is to conduct a small, quick campaign; a reduction in expected burden of approximately 10,000 cases could result if campaign targets can be updated on the basis of the true susceptible population. Formal incorporation of a policy to update future management actions in response to information gained in the course of an outbreak can change the optimal initial response and result in significant cost savings. AM provides a framework for using multiple models to facilitate public-health decision making and an objective basis for updating management actions in response to improved scientific understanding.

PLoS Biology↗
USGS2008

Ground-water quality data in the Central Sierra study unit, 2006— Results from the California GAMA Program

Ground-water quality in the approximately 950 square kilometer (370 square mile) Central Sierra study unit (CENSIE) was investigated in May 2006 as part of the Priority Basin Assessment project of the Groundwater Ambient Monitoring and Assessment (GAMA) Program. The GAMA Priority Basin Assessment project was developed in response to the Ground-Water Quality Monitoring Act of 2001, and is being conducted by the U.S. Geological Survey (USGS) in cooperation with the California State Water Resources Control Board (SWRCB). This study was designed to provide a spatially unbiased assessment of the quality of raw ground water used for drinking-water supplies within CENSIE, and to facilitate statistically consistent comparisons of ground-water quality throughout California. Samples were collected from thirty wells in Madera County. Twenty-seven of the wells were selected using a spatially distributed, randomized grid-based method to provide statistical representation of the study area (grid wells), and three were selected to aid in evaluation of specific water-quality issues (understanding wells). Ground-water samples were analyzed for a large number of synthetic organic constituents (volatile organic compounds [VOCs], gasoline oxygenates and degradates, pesticides and pesticide degradates), constituents of special interest (N-nitrosodimethylamine, perchlorate, and 1,2,3-trichloropropane), naturally occurring inorganic constituents [nutrients, major and minor ions, and trace elements], radioactive constituents, and microbial indicators. Naturally occurring isotopes [tritium, and carbon-14, and stable isotopes of hydrogen, oxygen, nitrogen, and carbon], and dissolved noble gases also were measured to help identify the sources and ages of the sampled ground water. In total, over 250 constituents and water-quality indicators were investigated. Quality-control samples (blanks, replicates, and samples for matrix spikes) were collected at approximately one-sixth of the wells, and the results for these samples were used to evaluate the quality of the data for the ground-water samples. Results from field blanks indicated contamination was not a noticeable source of bias in the data for ground-water samples. Differences between replicate samples were within acceptable ranges, indicating acceptably low variability. Matrix spike recoveries were within acceptable ranges for most constituents. This study did not attempt to evaluate the quality of water delivered to consumers; after withdrawal from the ground, water typically is treated, disinfected, or blended with other waters to maintain water quality. Regulatory thresholds apply to water that is served to the consumer, not to raw ground water. However, to provide some context for the results, concentrations of constituents measured in the raw ground water were compared with health-based thresholds established by the U.S. Environmental Protection Agency (USEPA) and California Department of Public Health (CDPH), and thresholds established for aesthetic concerns (Secondary Maximum Contaminant Levels, SMCL-CA) by CDPH. Therefore, any comparisons of the results of this study to drinking-water standards only is for illustrative purposes and is not indicative of compliance or non-compliance to those standards. Most constituents that were detected in ground-water samples were found at concentrations below drinking-water standards or thresholds. Six constituents? fluoride, arsenic, molybdenum, uranium, gross-alpha radioactivity, and radon-222?were detected at concentrations higher than thresholds set for health-based regulatory purposes. Three additional constituents?pH, iron and manganese?were detected at concentrations above thresholds set for aesthetic concerns. Volatile organic compounds (VOCs) and pesticides, were detected in less than one-third of the samples and generally at less than one one-hundredth of a health-based threshold.

California↗
USGS2006

Factors affecting occurrence and distribution of selected contaminants in ground water from selected areas in the Piedmont Aquifer System, Eastern United States, 1993-2003

Results of ground-water sampling from 255 wells and 19 springs in 11 studies done by the U.S. Geological Survey National Water-Quality Assessment (NAWQA) Program within the Piedmont Aquifer System (PAS) were analyzed to determine the factors affecting occurrence and distribution of selected contaminants. The contaminants, which were selected on the basis of potential human-health effects, included nitrate, pesticides, volatile organic compounds (VOCs), and radon. The PAS was subdivided on the basis of the general rock type of the aquifers into three areas for the study—crystalline, carbonate, and siliciclastic. The 11 studies were designed to areally represent an individual aquifer rock type and overall are representative of the PAS in their distribution; 7 studies are in the crystalline-rock aquifers, 3 studies are in the siliciclasticrock aquifers, and 1 study is in the carbonate-rock aquifers. Four of the studies were focused on land use, 1 in an agricultural area and 3 in urban areas. The remaining studies had wells representing a range of land-use types. Analysis of results of nitrate sampling indicated that in 8 of the 10 areas where nitrate concentrations were measured, median concentrations of nitrate were below 3 mg/L (milligrams per liter); 2 of the 10 areas had statistically significant higher median concentrations when compared to the other 8 areas. The agricultural land-use study in the carbonate-rock aquifer in the Lower Susquehanna River Basin had the highest median nitrate concentration (11 mg/L), and 60 percent of the wells sampled exceeded the U.S. Environmental Protection Agency (USEPA) Maximum Contaminant Level (MCL) of 10 mg/L. The major aquifer study in the crystalline-rock aquifer of the Lower Susquehanna River Basin Study Unit had the second-highest median nitrate concentration. Nitrate concentrations were positively correlated to the percentage of agricultural land use around the well, the total input of nitrogen from all sources, dissolved oxygen concentration, lithology, depth to water, and soil-matrix characteristics. A linear regression model was used to determine that increases in the percentage of agricultural land use, the input of nitrogen from all sources, and dissolved oxygen were the most significant variables affecting increased concentration of nitrate. A logistic regression model was used to determine that those same factors were the most significant variables affecting whether or not the nitrate concentration would exceed 4 mg/L. Of the analysis of samples from 253 wells and 19 springs for 47 pesticides, no sample had a pesticide concentration that exceeded any USEPA MCL. The most frequently detected pesticide was desethyl atrazine, a degradation product of atrazine; the detection frequency was 47 percent. Other frequently detected pesticides included atrazine, metolachlor, simazine, alachlor, prometon, and dieldrin. Detection frequency was affected by the analytical reporting limits; the frequency of detection was somewhat lower when all pesticides were censored to the highest common detection limit. Source factors such as agricultural land use (for agricultural herbicides), urban land use (for insecticides), and the application rate were found to have positive statistical correlations with pesticide concentration. Transport factors such as depth to water and percentage of well-drained soils, sand, or silt typically were positively correlated with higher pesticide concentrations. Sampling for VOCs was conducted in 187 wells and 19 springs that were sampled for 59 VOCs. There were 137 detections of VOCs above the common censoring limit of 0.2 µg/L. The most frequently detected VOCs were chloroform, a trihalomethane, and methyl-tert butyl ether (MTBE), a fuel oxygenate. Seventy-nine wells had at least one VOC detected. The detections were related to land use and well depth. Kendall’s tau correlations indicated a significant positive correlation between chloroform concentration and urban land use, leaking underground storage tanks, population density, and well depth. MTBE concentrations also were positively correlated to urban land use, leaking underground storage tanks, population density, and well depth. Radon was sampled at 205 sites. The subdivisions used for analysis of other contaminants were not adequate for analysis of radon because radon varies on the basis of variations in mineralogy that are not reflected by the general lithologic categories used for the rest of the studies. Concentrations of radon were highest in areas where the crystalline-rock aquifers had felsic mineralogy, and the lowest concentrations of radon were in areas where the crystalline-rocks aquifer had mafic mineralogy. Water from wells in siliciclastic-rock aquifers had concentrations of radon lower than that in the felsic crystalline-rock aquifers. More than 90 percent of the wells sampled for radon exceeded the proposed MCL of 300 pCi/L (picoCuries per liter); however, only 13 percent of those wells had concentrations in water that exceeded the alternative maximum contaminant level (AMCL), a higher level that can be used by municipalities addressing other sources of radon exposure. Overall, concentrations of constituents were related to land-use factors for nitrate, pesticides, VOCs, and to aquifer lithology for radon. None of the 47 pesticides or 59 VOCs analyzed exceeded the MCLs where those constituents were sampled. Concentrations exceeded the MCL for nitrate in 11 percent of the wells sampled. Nearly 91 percent of the wells sampled exceeded the proposed MCL for radon. Additional sampling in selected areas would improve overall understanding of the PAS and increase the possibility of creating predictive models of ground-water quality in this area.

Alabama, Delaware, Georgia, Maryland, New Jersey, ↗
USGS2004

Water Quality in the Delmarva Peninsula, Delaware, Maryland, and Virginia, 1999-2001

This report contains the major findings of a 1999-2001 assessment of water quality in the Delmarva Peninsula. It is one of a series of reports by the National Water-Quality Assessment (NAWQA) Program that present major findings in 51 major river basins and aquifer systems across the Nation. In these reports, water quality is assessed at many scales?from local ground-water flow paths to regional ground-water networks and in surface water?and is discussed in terms of local, State, and regional issues. Conditions in the Delmarva Peninsula are compared to conditions found elsewhere and to selected national benchmarks, such as those for drinking-water quality and the protection of aquatic organisms. This report is intended for individuals working with water-resource issues in Federal, State, or local agencies; universities; public interest groups; or in the private sector. The information will be useful in addressing a number of current issues, such as the effects of agricultural and urban land use on water quality, human health, drinking water, source-water protection, hypoxia and excessive growth of algae and plants, pesticide registration, and monitoring and sampling strategies. This report is also for individuals who wish to know more about the quality of streams and ground water in areas near where they live, and how that water quality compares to the quality of water in other areas across the Nation. Other products describing water-quality conditions in the Delmarva Peninsula are available. Detailed technical information, data and analyses, methodology, models, graphs, and maps that support the findings presented in this report can be accessed from http://md.water.usgs.gov/delmarva. Other reports in this series and data collected from other basins can be accessed from the national NAWQA Web site (http://water.usgs.gov/nawqa).

Circular↗
USGS2001

Occurrence and distribution of selected contaminants in public drinking-water supplies in the surficial aquifer in Delaware

Water samples were collected from August through November 2000 from 30 randomly selected public drinking-water supply wells screened in the unconfined aquifer in Delaware, and analyzed to assess the occurrence and distribution of selected pesticide compounds, volatile organic compounds, major inorganic ions, and nutrients. Water from a subset of 10 wells was sampled and analyzed for radium and radon. The average age of ground water entering the well screens in all the wells was determined to be generally less than 20 years. Low concentrations of pesticide compounds and volatile organic compounds were detected throughout the State of Delaware, with several compounds often detected in each water sample. Pesticide and metabolite (pesticide degradation products) concentrations were generally less than 1 microgram per liter, and were detected in sam-ples from 27 of 30 wells. Of the 45 pesticides and 13 metabolites analyzed, 19 compounds (13 pesticides and 6 metabolites) were detected in at least 1 of the 30 samples. Desethylatrazine, alachlor ethane sulfonic acid, metolachlor ethane sulfonic acid, metolachlor, and atrazine were the most frequently detected pesticide compounds, and were present in at least half the samples. None of the pesticide detections was above the U.S. Environmental Protection Agency's Primary Maximum Contaminant Levels or Health Advisories. Volatile organic compounds also were present at low concentrations (generally less than 1 microgram per liter) in samples from all 30 wells. Of the 85 volatile organic com-pounds analyzed, 34 compounds were detected in at least 1 of the 30 samples. Chloroform, tetrachloroethene, and methyl tert-butyl ether were the most frequently detected volatile organic compounds, and were found in at least half the samples. None of the volatile organic compound detections was above U.S. Environmental Protection Agency's Primary Maximum Contaminant Levels or Health Advisories. A few samples contained compounds with concentrations above the U.S. Environmental Protection Agency's Primary Maximum Contaminant Levels or Secondary Maximum Contaminant Levels for inorganic compounds and radionuclides. One sample out of 30 contained a concentration of nitrite plus nitrate above the U.S. Environmental Protection Agency's Primary Maximum Contaminant Level of 10 milligrams per liter as nitrogen. Iron and manganese concentrations above the U.S. Environmental Protection Agency's Secondary Maximum Contaminant Levels were found in 7 of 30 ground-water samples, most of them from Sussex County. In the 10 wells sampled for radionuclides, only one sample had detectable levels of radium-224 and -226, and another sample contained detectable levels of radium-228; both of these samples also had detectable gross-alpha and gross-beta activities. None of these activities were above the U.S. Environ-mental Protection Agency's Primary Maximum Contaminant Levels or Secondary Maximum Contaminant Levels. Radon was detected in all 10 samples, but was above the current U.S. Environmental Protection Agency's proposed Primary Maximum Contaminant Level of 300 picocuries per liter in only one sample.

Open-File Report↗