Geology topics

Tammy M. Zimmerman

Publications and source records attributed to Tammy M. Zimmerman.

17 recordsLinked to original sources

USGS2025

Tracking status and trends in seven key indicators of river and stream condition in the Chesapeake Bay watershed

Freshwater streams and rivers are recognized as vital habitats within the Chesapeake Bay watershed, which has been undergoing extensive restoration efforts for more than 30 years. Resource managers need to understand stream and river condition and how these conditions are changing over time to determine whether regional long-term restoration and conservation goals are being met. The objective of this report was to document the spatial and temporal variability of conditions for seven indicators of river and stream health across the nontidal Chesapeake Bay watershed. The framework for the U.S. Geological Survey’s Nontidal Network (NTN), a network of more than 100 nutrient and suspended sediment monitoring locations, was extended to assess conditions for six additional indicators of stream health: temperature, salinity, toxic contaminants, streamflow, hydromorphology, and biological aquatic communities. For each indicator, the latest available data from multiple sources were compiled and harmonized, and key metrics were identified to describe indicator conditions across space and time. A status condition was defined for each indicator to describe overall spatial variability in recent condition, and trend analyses were used to describe changes in each indicator metric over time. The analysis revealed clear differences in spatial and temporal data coverage across the seven indicators, so individual indicator trend analyses were not constrained to a common time interval. However, a status snapshot was conducted across all indicators for the 2015–17 period to simultaneously explore spatial variability across all indicators. The status snapshot highlighted general degraded conditions across multiple indicators in large metropolitan regions, such as the Baltimore–Washington, D.C., metropolitan area. Regression analysis between indicator status metrics and major land cover for the sites suggest urbanization as a potential driver of degraded conditions for many of the indicator metrics, including total phosphorus, salinity, temperature, high-flow frequency, and metrics of habitat and biological assemblage quality. A final analysis exploring the spatial representation of each indicator network showed that some indicator monitoring networks did not cover certain settings, such as small watersheds. These results provided an initial assessment of stream health status and trends and will continue to be leveraged to describe conditions across the Chesapeake Bay watershed to help inform local and regional management decisions. These results also highlighted the need for improved coordination among monitoring organizations to support long-term multi-indicator monitoring and assessment.

Chesapeake Bay watershed↗

USGS2023

Tracking status and trends in seven key indicators of stream health in the Chesapeake Bay watershed

“The Bay Connects us, the Bay reflects us” writes Tom Horton in the book “Turning the Tide—Saving the Chesapeake Bay”. The Chesapeake Bay watershed contains the largest estuary in the United States. The watershed stretches north to Cooperstown, New York, south to Lynchburg and Virginia Beach, Virginia, west to Pendleton County, West Virginia, and east to Seaford, Delaware, and Scranton, Pennsylvania. The watershed is more than 64,000 square miles that contain 150 major rivers and streams, hereafter referred to collectively as streams, that total more than 100,000 miles in length. The watershed contains thousands of smaller creeks and tributaries, large numbers of plants and animals, and, in 2020, more than 18.4 million people. As changes occur in population, land use, and climate within the watershed, so too do the diversity and health of the Bay's ecosystems.

Chesapeake Bay watershed↗

USGS2020

Effects of legacy sediment removal and effects on nutrients and sediment in Big Spring Run, Lancaster County, Pennsylvania, 2009–15

Big Spring Run is a 1.68-square mile watershed underlain by mostly carbonate rock in a mixed land-use setting (part agricultural and part developed) in Lancaster County, Pennsylvania. Big Spring Run is a subwatershed of Mill Creek, a tributary to the Conestoga River. These watersheds are known contributors of nutrient and sediment loads to the Chesapeake Bay and several stream reaches are on the Pennsylvania impaired waters list. Big Spring Run is listed as impaired and was selected by the Pennsylvania Department of Environmental Protection to evaluate a novel best management practice to restore natural aquatic ecosystems by removing legacy sediment. The study was designed to quantify sediment and nutrient contributions in pre- and postrestoration periods (water years 2009–11 and 2012–15, respectively) using an intensive monitoring approach at three surface-water sites within the watershed. Instrumentation at each site continuously measured (15-minute intervals) streamflow, water temperature, and turbidity. Water-quality samples were collected routinely (generally monthly and during selected storms); sampling frequency varied by site and constituent at the three monitoring sites. Effects of legacy sediment removal and restoration on nutrient concentrations varied in surface water samples depending on the form (particulate, dissolved, organic, inorganic). For example, total phosphorus concentrations at the downstream site decreased from a median of 0.19 milligram per liter (mg/L) to 0.04 mg/L, pre- and postrestoration periods, respectively. Concentrations of orthophosphate, the dissolved form of phosphorus, were not significantly different pre- to postrestoration at the downstream site. Similarly, nitrate concentrations, the dominant form of nitrogen in Big Spring Run surface-water samples (92.3 percent of total nitrogen) were not significantly different in the pre- compared to the postrestoration periods. Legacy sediment removal and restoration had significant effects on suspended-sediment concentrations and loads. Median suspended-sediment concentrations at the downstream site decreased from 556 mg/L prerestoration to 74 mg/L postrestoration even though streamflow hydrographs during the two periods were similar. In the postrestoration period, the mean annual suspended-sediment load conveyed to the restoration area from the upstream sites was 839 tons, whereas mean annual suspended-sediment load at the downstream site was reduced to 242 tons. Streamflow during storms transports a large proportion of the suspended-sediment load; there were a total of 320 storms over the study period. In Big Spring Run, a single storm event can transport more than 25 percent of the annual suspended-sediment load. The greatest single-storm contribution to suspended-sediment load was 38 percent in water year 2015 at the downstream site. Although streamflow magnitudes during storms varied greatly over the study period, median streamflow was 17.5 cubic feet per second and median duration was about 3 hours and 24 minutes. Results observed for this study using the newly proposed best management practice were compared with other best management practices intended to reduce sediment. For example, during a previous study, statistically significant reductions in suspended-sediment concentration were observed when streambank fencing was implemented in an adjacent watershed; however, suspended-sediment reductions were an order of magnitude less than the reductions observed in the current study. Median suspended-sediment concentration at the downstream site was reduced by 482 mg/L in the current study compared to only 30 to 46 mg/L as a result of streambank fencing.

Pennsylvania↗

USGS2019

Real-time assessments of water quality—A nowcast for Escherichia coli and cyanobacterial toxins

Threats to our recreational and drinking waters include disease-causing (pathogenic) organisms from fecal contamination and toxins produced by some species of cyanobacteria (cyanotoxins) that can cause acute and (or) chronic illnesses. Because traditional laboratory methods for detecting these threats take too long for prompt public health protection, tools for real-time assessments are needed to protect public health. To address this need, the U.S. Geological Survey is collaborating with State and local partners to develop models that provide real-time estimates of Escherichia coli ( E . coli ) (for pathogens) and (or) microcystin (for freshwater cyanotoxins) levels at inland and Great Lakes beaches and drinking-water intakes. Model results are then used to inform the public of water-quality conditions in near-real time through the Great Lakes NowCast ( https://ny.water.usgs.gov/maps/nowcast/ ). Behind the scenes, the NowCast provides speed and efficiency for managers by automating data management and standardizing methods among agencies.

Fact Sheet↗

USGS2015

The quality of our nation's waters: Water quality in the Principal Aquifers of the Piedmont, Blue Ridge, and Valley and Ridge regions, eastern United States, 1993-2009

The aquifers of the Piedmont, Blue Ridge, and Valley and Ridge regions underlie an area with a population of more than 40 million people in 10 states. The suburban and rural population is large, growing rapidly, and increasingly dependent on groundwater as a source of supply, with more than 550 million gallons per day withdrawn from domestic wells for household use. Water from some of these aquifers does not meet human-health benchmarks for drinking water for contaminants with geologic or human sources. Water from samples in crystalline- and siliciclastic-rock aquifers frequently exceeded standards for contaminants with geologic sources, and samples in carbonate-rock aquifers frequently exceeded standards for contaminants with human sources, most often nitrate and bacteria. Major Findings Many contaminants in groundwater have geologic sources, but geochemical conditions control whether or not those contaminants dissolve and move through groundwater Concentrations of nitrate and bacteria—the main drinking-water contaminants with human sources—were high in carbonate-rock aquifers and frequently exceeded human-health benchmarks Large contributions of nitrate and phosphorus from groundwater to streams have a negative effect on ecological health of estuaries, such as the Chesapeake Bay and Albemarle-Pamlico Sound

Piedmont, Blue Ridge, Valley and Ridge regions↗

USGS2013

Developing and implementing the use of predictive models for estimating water quality at Great Lakes beaches

Predictive models have been used at beaches to improve the timeliness and accuracy of recreational water-quality assessments over the most common current approach to water-quality monitoring, which relies on culturing fecal-indicator bacteria such as Escherichia coli (E. coli.) . Beach-specific predictive models use environmental and water-quality variables that are easily and quickly measured as surrogates to estimate concentrations of fecal-indicator bacteria or to provide the probability that a State recreational water-quality standard will be exceeded. When predictive models are used for beach closure or advisory decisions, they are referred to as “nowcasts.” During the recreational seasons of 2010-12, the U.S. Geological Survey (USGS), in cooperation with 23 local and State agencies, worked to improve existing nowcasts at 4 beaches, validate predictive models at another 38 beaches, and collect data for predictive-model development at 7 beaches throughout the Great Lakes. This report summarizes efforts to collect data and develop predictive models by multiple agencies and to compile existing information on the beaches and beach-monitoring programs into one comprehensive report. Local agencies measured E. coli concentrations and variables expected to affect E. coli concentrations such as wave height, turbidity, water temperature, and numbers of birds at the time of sampling. In addition to these field measurements, equipment was installed by the USGS or local agencies at or near several beaches to collect water-quality and metrological measurements in near real time, including nearshore buoys, weather stations, and tributary staff gages and monitors. The USGS worked with local agencies to retrieve data from existing sources either manually or by use of tools designed specifically to compile and process data for predictive-model development. Predictive models were developed by use of linear regression and (or) partial least squares techniques for 42 beaches that had at least 2 years of data (2010-11 and sometimes earlier) and for 1 beach that had 1 year of data. For most models, software designed for model development by the U.S. Environmental Protection Agency (Virtual Beach) was used. The selected model for each beach was based on a combination of explanatory variables including, most commonly, turbidity, day of the year, change in lake level over 24 hours, wave height, wind direction and speed, and antecedent rainfall for various time periods. Forty-two predictive models were validated against data collected during an independent year (2012) and compared to the current method for assessing recreational water quality-using the previous day’s E. coli concentration (persistence model). Goals for good predictive-model performance were responses that were at least 5 percent greater than the persistence model and overall correct responses greater than or equal to 80 percent, sensitivities (percentage of exceedances of the bathing-water standard that were correctly predicted by the model) greater than or equal to 50 percent, and specificities (percentage of nonexceedances correctly predicted by the model) greater than or equal to 85 percent. Out of 42 predictive models, 24 models yielded over-all correct responses that were at least 5 percent greater than the use of the persistence model. Predictive-model responses met the performance goals more often than the persistence-model responses in terms of overall correctness (28 versus 17 models, respectively), sensitivity (17 versus 4 models), and specificity (34 versus 25 models). Gaining knowledge of each beach and the factors that affect E. coli concentrations is important for developing good predictive models. Collection of additional years of data with a wide range of environmental conditions may also help to improve future model performance. The USGS will continue to work with local agencies in 2013 and beyond to develop and validate predictive models at beaches and improve existing nowcasts, restructuring monitoring activities to accommodate future uncertainties in funding and resources.

Great Lakes↗

USGS2012

Evaluating changes in matrix based, recovery-adjusted concentrations in paired data for pesticides in groundwater

Pesticide concentration data for waters from selected carbonate-rock aquifers in agricultural areas of Pennsylvania were collected in 1993–2009 for occurrence and distribution assessments. A set of 30 wells was visited once in 1993–1995 and again in 2008–2009 to assess concentration changes. The data include censored matched pairs (nondetections of a compound in one or both samples of a pair). A potentially improved approach for assessing concentration changes is presented where (i) concentrations are adjusted with models of matrix-spike recovery and (ii) area-wide temporal change is tested by use of the paired Prentice-Wilcoxon (PPW) statistical test. The PPW results for atrazine, simazine, metolachlor, prometon, and an atrazine degradate, deethylatrazine (DEA), are compared using recovery-adjusted and unadjusted concentrations. Results for adjusted compared with unadjusted concentrations in 2008–2009 compared with 1993–1995 were similar for atrazine and simazine (significant decrease; 95% confidence level) and metolachlor (no change) but differed for DEA (adjusted, decrease; unadjusted, increase) and prometon (adjusted, decrease; unadjusted, no change). The PPW results were different on recovery-adjusted compared with unadjusted concentrations. Not accounting for variability in recovery can mask a true change, misidentify a change when no true change exists, or assign a direction opposite of the true change in concentration that resulted from matrix influences on extraction and laboratory method performance. However, matrix-based models of recovery derived from a laboratory performance dataset from multiple studies for national assessment, as used herein, rather than time- and study-specific recoveries may introduce uncertainty in recovery adjustments for individual samples that should be considered in assessing change.

Pennsylvania↗

USGS2011

Factors affecting groundwater quality in the Valley and Ridge aquifers, eastern United States, 1993-2002

Chemical and microbiological analyses of water from 230 wells and 35 springs in the Valley and Ridge Physiographic Province, sampled between 1993 and 2002, indicated that bedrock type (carbonate or siliciclastic rock) and land use were dominant factors influencing groundwater quality across a region extending from northwestern Georgia to New Jersey. The analyses included naturally occurring compounds (major mineral ions and radon) and anthropogenic contaminants [pesticides and volatile organic compounds (VOCs)], and contaminants, such as nitrate and bacteria, which commonly increase as a result of human activities. Natural factors, such as topographic position and the mineral composition of underlying geology, act to produce basic physical and geochemical conditions in groundwater that are reflected in physical properties, such as pH, temperature, specific conductance, and alkalinity, and in chemical concentrations of dissolved oxygen, radon, and major mineral ions. Anthropogenic contaminants were most commonly found in water from wells and springs in carbonate-rock aquifers. Nitrate concentrations exceeded U.S. Environmental Protection Agency maximum contaminant levels in 12 percent of samples, most of which were from carbonate-rock aquifers. Escherichia coli (E. coli), pesticide, and VOC detection frequencies were significantly higher in samples from sites in carbonate-rock aquifers. Naturally occurring elements, such as radon, iron, and manganese, were found in higher concentrations in siliciclastic-rock aquifers. Radon levels exceeded the proposed maximum contaminant level of 300 picocuries per liter in 74 percent of the samples, which were evenly distributed between carbonate- and siliciclastic-rock aquifers. The land use in areas surrounding wells and springs was another significant explanatory variable for the occurrence of anthropogenic compounds. Nitrate and pesticide concentrations were highest in samples collected from sites in agricultural areas and lowest in samples collected from sites in undeveloped areas. Volatile organic compounds were detected most frequently and in highest concentrations in samples from sites in urban areas, and least frequently in agricultural and undeveloped areas. No volatile organic compound concentrations and concentrations from only one pesticide, dieldrin, exceeded human-health benchmarks.

Alabama;Georgia;Tennessee;North Carolina;Virginia;↗

USGS2009

Pesticides in ground water in selected agricultural land-use areas and hydrogeologic settings in Pennsylvania, 2003-07

This report was prepared by the U.S. Geological Survey (USGS) in cooperation with the Pennsylvania Department of Agriculture (PDA) as part of the Pennsylvania Pesticides and Ground Water Strategy (PPGWS). Monitoring data and extensive quality-assurance data on the occurrence of pesticides in ground water during 2003–07 are presented and evaluated; decreases in the land area used for agriculture and corresponding changes in the use of pesticides also are documented. In the Pennsylvania ground waters assessed since 2003, concentrations of pesticides did not exceed any maximum contaminant or health advisory levels established by the U.S. Environmental Protection Agency; PPGWS actions are invoked by the PDA at fractions of these levels and were needed only in areas designated by the PDA for special ground-water protection. Previous investigations through 1998 of pesticides in Pennsylvania ground water identified land use, as a surrogate for pesticide use, and rock type of the aquifer combined with physiography as key hydrogeologic setting variables for understanding aquifer vulnerability to contamination and the common occurrence of atrazine and metolachlor in ground water. Of 20 major hydrogeologic settings in a framework established in 1999 for pesticide monitoring in Pennsylvania, 9 were identified as priorities for data collection in order to change the monitoring status from "inadequate" to "adequate" for the PPGWS. Agricultural and forested land-use areas are decreasing because of urban and suburban growth. In the nine hydrogeologic settings evaluated using 1992 and 2001 data, decreases of up to 12 percent for agricultural land and 10 percent for forested land corresponded to increases of up to 11 percent for urban land. Changes in agricultural pesticide use were computed from crop data. For example, from 1996 to 2004–05, atrazine use declined by about 15 percent to 1,314,000 lb/yr (pounds per year) and metolachlor use increased by about 20 percent to 895,000 lb/yr; these compounds are the two most-used agricultural pesticides statewide. In 2003–07, a baseline assessment of pesticides was conducted in five of nine hydrogeologic settings with inadequate monitoring data—the Blue Ridge crystalline and Triassic Lowland siliciclastic, Eastern Lake surficial, Devonian-Silurian carbonate, Great Valley siliciclastic, and Northeastern Glaciated surficial settings. Between 20 and 30 wells in each setting were monitored. Of the 126 wells sampled, 96 well-water samples were analyzed for at least 52 pesticide compounds at the USGS National Water Quality Laboratory (NWQL) using a method with a minimum reporting level (MRL) at or above 0.002 µg/L (micrograms per liter). Of the 96 well waters analyzed by NWQL, 43 had measureable concentrations of one or more pesticides. Atrazine and (or) deethylatrazine (CIAT), a degradation product of atrazine, were reported at or above the MRL in 39 of the 43 well waters. Neither atrazine nor CIAT were reported at concentrations exceeding 0.10 µg/L; all measured concentrations in these five settings were below PPGWS action levels. Metolachlor was present in 7 of the 43 well waters with measureable concentrations of 1 or more pesticides; however, concentrations were below the MRL. The other 30 samples (10 of 20 wells in the Blue Ridge crystalline and Triassic Lowland siliciclastic setting and all 20 wells in the Eastern Lake surficial setting) were analyzed for at least 19 pesticide compounds at the Pennsylvania Department of Environmental Protection Laboratory (PADEPL); the PADEPL reported no concentrations of pesticides at or above an MRL of 0.10 µg/L. Statistical tests using the NWQL analytical results showed correlations between pesticide occurrence and two indicators of water-quality degradation—the occurrence of total coliform bacteria and nitrate concentration. A 2 × 2 contingency-table test indicated a relation between presence or absence of atrazine or metolachlor and presence or absence of bacteria only for the 10 wells representing the Blue Ridge crystalline and Triassic Lowland siliciclastic setting. Results of Spearman’s rank test showed strong positive correlations in the Devonian-Silurian carbonate setting between 1) the number of pesticides above the MRLs and nitrate concentration, and 2) concentrations of atrazine and nitrate. Atrazine concentration and nitrate concentration also showed a statistically significant positive correlation in the Great Valley siliciclastic setting. An additional component of baseline monitoring was to evaluate changes in pesticide concentration in water from wells representing hydrogeologic settings most vulnerable to contamination from pesticides. In 2003, 16 wells originally sampled in the 1990s were resampled—4 each in the Appalachian Mountain carbonate, Triassic Lowland siliciclastic, Great Valley carbonate, and Piedmont carbonate settings. Nine of these wells, where pesticide concentrations from 1993 and 2003 were analyzed at the NWQL, were chosen for a paired-sample analysis using concentrations of atrazine and metolachlor. A statistically significant decrease in atrazine concentration was identified using the Wilcoxon signed-rank test (p = 0.004); significant temporal changes in metolachlor concentrations were not observed (p = 0.625). Monitoring in three areas of special ground-water protection, where selected pesticide concentrations in well water were at or above the PPGWS action levels, was done at wells BE 1370 (Berks County, Oley Township), BA 437 (Blair County, North Woodbury Township), and LN 1842 (Lancaster County, Earl Township). Co-occurrence of pesticide-degradation products with parent compounds was documented for the first time in ground-water samples collected from these three wells. Degradation products of atrazine, cyanazine, acetochlor, alachlor, and metolachlor were commonly at larger concentrations than the parent compound in the same water sample. Pesticide occurrence in water from wells neighboring the hot-spot wells was highly variable; however, the same sets of pesticide compounds that were present in wells BA 437, BE 1370, and LN 1842 were present to some degree in water from neighboring wells. To evaluate temporal changes in concentration, nonparametric statistical tests were used to determine overall and seasonal monotonic trends. Concentrations of alachlor, atrazine, metolachlor, and nitrate were examined using the 5-year (2003–07) and the long-term data from wells BA 437 and LN 1842 (1996–2007 and 1995–2007, respectively), and the long-term data for well BE 1370 (1998–2007); results showed either downward trends or no trends. Trends in acetochlor concentrations were tested only at well LN 1842 using the 5-year data; no trends were observed. Homogeneity of trend tests indicated statistically significant downward concentration trends in the long-term data were due to seasonal trends as follows: BA 437—alachlor and atrazine (summer); BE 1370—atrazine and metolachlor (winter) and alachlor (winter and spring); LN 1842—alachlor (summer and fall) and atrazine (spring and fall).

Pennsylvania↗

USGS2008

Modeling to Predict Escherichia coli at Presque Isle Beach 2, City of Erie, Erie County, Pennsylvania

The Lake Erie beaches in Pennsylvania are a valuable recreational resource for Erie County. Concentrations of Escherichia coli (E. coli) at monitored beaches in Presque Isle State Park in Erie, Pa., occasionally exceed the single-sample bathing-water standard of 235 colonies per 100 milliliters resulting in potentially unsafe swimming conditions and prompting beach managers to post public advisories or to close beaches to recreation. To supplement the current method for assessing recreational water quality (E. coli concentrations from the previous day), a predictive regression model for E. coli concentrations at Presque Isle Beach 2 was developed from data collected during the 2004 and 2005 recreational seasons. Model output included predicted E. coli concentrations and exceedance probabilities--the probability that E. coli concentrations would exceed the standard. For this study, E. coli concentrations and other water-quality and environmental data were collected during the 2006 recreational season at Presque Isle Beach 2. The data from 2006, an independent year, were used to test (validate) the 2004-2005 predictive regression model and compare the model performance to the current method. Using 2006 data, the 2004-2005 model yielded more correct responses and better predicted exceedances of the standard than the use of E. coli concentrations from the previous day. The differences were not pronounced, however, and more data are needed. For example, the model correctly predicted exceedances of the standard 11 percent of the time (1 out of 9 exceedances that occurred in 2006) whereas using the E. coli concentrations from the previous day did not result in any correctly predicted exceedances. After validation, new models were developed by adding the 2006 data to the 2004-2005 dataset and by analyzing the data in 2- and 3-year combinations. Results showed that excluding the 2004 data (using 2005 and 2006 data only) yielded the best model. Explanatory variables in the 2005-2006 model were log10 turbidity, bird count, and wave height. The 2005-2006 model correctly predicted when the standard would not be exceeded (specificity) with a response of 95.2 percent (178 out of 187 nonexceedances) and correctly predicted when the standard would be exceeded (sensitivity) with a response of 64.3 percent (9 out of 14 exceedances). In all cases, the results from predictive modeling produced higher percentages of correct predictions than using E. coli concentrations from the previous day. Additional data collected each year can be used to test and possibly improve the model. The results of this study will aid beach managers in more rapidly determining when waters are not safe for recreational use and, subsequently, when to close a beach or post an advisory.

Scientific Investigations Report↗

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, ↗

USGS2006

Monitoring and modeling to predict Escherichia coli at Presque Isle Beach 2, City of Erie, Erie County, Pennsylvania

The Lake Erie shoreline in Pennsylvania spans nearly 40 miles and is a valuable recreational resource for Erie County. Nearly 7 miles of the Lake Erie shoreline lies within Presque Isle State Park in Erie, Pa. Concentrations of Escherichia coli (E. coli) bacteria at permitted Presque Isle beaches occasionally exceed the single-sample bathing-water standard, resulting in unsafe swimming conditions and closure of the beaches. E. coli concentrations and other water-quality and environmental data collected at Presque Isle Beach 2 during the 2004 and 2005 recreational seasons were used to develop models using tobit regression analyses to predict E. coli concentrations. All variables statistically related to E. coli concentrations were included in the initial regression analyses, and after several iterations, only those explanatory variables that made the models significantly better at predicting E. coli concentrations were included in the final models. Regression models were developed using data from 2004, 2005, and the combined 2-year dataset. Variables in the 2004 model and the combined 2004-2005 model were log10 turbidity, rain weight, wave height (calculated), and wind direction. Variables in the 2005 model were log10 turbidity and wind direction. Explanatory variables not included in the final models were water temperature, streamflow, wind speed, and current speed; model results indicated these variables did not meet significance criteria at the 95-percent confidence level (probabilities were greater than 0.05). The predicted E. coli concentrations produced by the models were used to develop probabilities that concentrations would exceed the single-sample bathing-water standard for E. coli of 235 colonies per 100 milliliters. Analysis of the exceedence probabilities helped determine a threshold probability for each model, chosen such that the correct number of exceedences and nonexceedences was maximized and the number of false positives and false negatives was minimized. Future samples with computed exceedence probabilities higher than the selected threshold probability, as determined by the model, will likely exceed the E. coli standard and a beach advisory or closing may need to be issued; computed exceedence probabilities lower than the threshold probability will likely indicate the standard will not be exceeded. Additional data collected each year can be used to test and possibly improve the model. This study will aid beach managers in more rapidly determining when waters are not safe for recreational use and, subsequently, when to issue beach advisories or closings.

Pennsylvania↗

USGS2006

Fecal-indicator bacteria in the Allegheny, Monongahela, and Ohio Rivers and selected tributaries, Allegheny County, Pennsylvania, 2001-2005

Concentrations of fecal-indicator bacteria were determined in 1,027 water-quality samples collected from July 2001 through August 2005 during dry- (72-hour dry antecedent period) and wet-weather (48-hour dry antecedent period and at least 0.3 inch of rain in a 24-hour period) conditions in the Allegheny, Monongahela, and Ohio Rivers (locally referred to as the Three Rivers) and selected tributaries in Allegheny County. Samples were collected at five sampling sites on the Three Rivers and at eight sites on four tributaries to the Three Rivers having combined sewer overflows. Water samples were analyzed for three fecal-indicator organisms fecal coliform, Escherichia coli (E. coli), and enterococci bacteria. Left-bank and right-bank surface-water samples were collected in addition to a cross-section composite sample at each site. Concentrations of fecal coliform, E. coli, and enterococci were detected in 98.6, 98.5, and 87.7 percent of all samples, respectively. The maximum fecal-indicator bacteria concentrations were collected from Sawmill Run, a tributary to the Ohio River; Sawmill Run at Duquesne Heights had concentrations of fecal coliform, E. coli, and enterococci of 410,000, 510,000, and 180,000 col/100 mL, respectively, following a large storm. The samples collected in the Three Rivers and selected tributaries frequently exceeded established recreational standards and criteria for bacteria. Concentrations of fecal coliform exceeded the Pennsylvania water-quality standard (200 col/100 mL) in approximately 63 percent of the samples. Sample concentrations of E. coli and enterococci exceeded the U.S. Environmental Protection Agency (USEPA) water-quality criteria (235 and 61 col/100 mL, respectively) in about 53 and 47 percent, respectively, of the samples. Fecal-indicator bacteria were most strongly correlated with streamflow, specific conductance, and turbidity. These correlations most frequently were observed in samples collected from tributary sites. Fecal-indicator bacteria concentrations and turbidity were correlated to the location of sample collection in the cross section. Most differences were between bank and composite samples; differences between right-bank and left-bank samples were rarely observed. The Allegheny River sites had more significant correlations than the Monongahela or Ohio River sites. Comparisons were made between fecal-indicator bacteria in composite samples collected during dry-weather, wet-weather day-one, wet-weather day-two (tributary sites only), and wet-weather day-three (Three Rivers sites only) events in the Three Rivers and selected tributary sites. The lowest median bacteria concentrations generally were observed in the dry-weather composite samples. All median bacteria concentrations in dry-weather composite samples in the five Three Rivers sites were below water-quality standards and criteria; bacteria concentrations in the upstream tributary sites rarely met all standards or criteria. Only Turtle Creek, Thompson Run, and Chartiers Creek had at least one median bacteria concentration below water-quality standards or criteria. Median bacteria concentrations in the composite samples generally were higher the day after a wet-weather event compared to dry-weather composite samples and other wet-weather composite samples collected. In the five Three Rivers sites, median bacteria concentrations 3 days after a wet-weather event in composite samples tended to fall below the water-quality standards and criteria; in the eight tributary sites, median bacteria concentrations in the dry-weather and wet-weather composite samples generally were above the water-quality standards or criteria. Composite samples collected at the upstream sites on the Three Rivers and selected tributaries generally had lower median bacteria concentrations than composite samples collected at the downstream sites during dry- and wet-weather events. Higher concentrations downstream may be because o

Pennsylvania↗

USGS2005

Drought-sensitive aquifer settings in southeastern Pennsylvania

This report describes the results of a study conducted by the U.S. Geological Survey, in cooperation with the Pennsylvania Department of Conservation and Natural Resources, Bureau of Topographic and Geologic Survey, to determine drought-sensitive aquifer settings in southeastern Pennsylvania. Because all or parts of southeastern Pennsylvania have been in drought-warning or drought-emergency status during 6 of the past 10 years from 1994 through 2004, this information should aid well owners, drillers, and water-resource managers in guiding appropriate well construction and sustainable use of Pennsylvania's water resources. 'Drought-sensitive' aquifer settings are defined for this study as areas unable to supply adequate quantities of water to wells during drought. Using information from previous investigations and a knowledge of the hydrogeology and topography of the study area, drought-sensitive aquifer settings in southeastern Pennsylvania were hypothesized as being associated with two factors - a water-table decline (WTD) index and topographic setting. The WTD index is an estimate of the theoretical water-table decline at the ground-water divide for a hypothetical aquifer with idealized geometry. The index shows the magnitude of ground-water decline after cessation of recharge is a function of (1) distance from stream to divide, (2) ground-water recharge rate, (3) transmissivity, (4) specific yield, and (5) duration of the drought. WTD indices were developed for 39 aquifers that were subsequently grouped into categories of high, moderate, and low WTD index. Drought-sensitive settings determined from the hypothesized factors were compared to locations of wells known to have been affected (gone dry, replaced, or deepened) during recent droughts. Information collected from well owners, drillers, and public agencies identified 2,016 wells affected by drought during 1998-2002. Most of the available data on the location of drought-affected wells in the study area were from Chester and Montgomery Counties because those counties have well-construction regulations that identify wells that failed during drought. The locations of drought-affected wells in Chester and Montgomery Counties indicated the most highly sensitive settings are uplands and slopes in aquifers with high WTD index and uplands in aquifers with moderate WTD index. The least sensitive settings are in aquifers with low WTD index, in valleys, or on slopes. A map was developed showing the relative drought sensitivity (low, moderate, and high) of aquifers in southeastern Pennsylvania. Study results were limited by the inability to obtain much information about the location of drought-affected wells, with the exception of Montgomery and Chester Counties. Also, the construction characteristics (particularly depth) of drought-affected wells generally were not available. Well depth could be used to distinguish between problems caused by shallow well depth (generally less than 100 ft) and those caused by deficiency of the aquifer to supply water. With the exception of owner-derived information from a public survey on drought-affected wells (35 wells), depth data were not obtained. Data from the 35 drought-affected wells indicated most were drilled (not dug) and were completed to depths greater than 100 feet. This finding indicates that the affects of recent droughts in southeastern Pennsylvania were not restricted to shallow dug wells, but also affected deeper drilled wells.

Pennsylvania↗

USGS2002

Microbiological quality of water from noncommunity supply wells in carbonate and crystalline aquifers of Pennsylvania

Samples were collected from 59 noncommunity water supplies in the Commonwealth of Pennsylvania from September 2000 to January 2001 and analyzed for pathogens and microbiological indicator organisms. The pathogens sampled were culturable viruses and Helicobacter pylori (H. pylori). The indicator organisms sampled were total coliform, Escherichia coli ( E. coli ), Clostridium perfringens ( C. perfringens ), somatic coliphage, male- specific coliphage, and enterococcus. The two primary areas sampled for the project completed by the U.S. Geological Survey, in cooperation with the Pennsylvania Department of Environmental Protection (PaDEP), were carbonate aquifers and crystalline aquifers. The results of all sampling showed culturable viruses were detected in 8 percent of the wells, H. pyloriin 7 percent of the wells, E. coli in 12 percent of the wells, total coliform in 46 percent of the wells, C. perfringens in 15 percent of the wells, somatic coliphage in 8 percent of the wells, male-specific coliphage in 5 percent of the wells, and enterococcus in 14 percent of the wells. Carbonate aquifers tended to have higher detection rates for the pathogens and indicators sampled than the crystalline aquifers. Detections of the pathogens and indicator organisms were not related statistically to the amounts of urban, agricultural, or forested area in a 1,500-foot radius around the sampled well. Somatic and male-specific coliphage showed the best relation to occurrence of culturable viruses. Culturable viruses and H. pylori were detected in wells in which no indicator organisms were present; therefore, none of the indicator organisms sampled provide complete assurance of pathogenfree water. The best predictive tool for virus screening was a combination of indicator organisms.

Water-Resources Investigations Report↗

USGS2001

Summary of and factors affecting pesticide concentrations in streams and shallow wells of the lower Susquehanna River basin, Pennsylvania and Maryland, 1993-95

This report presents the detection frequency of 83 analyzed pesticides, describes the concentrations of those pesticides measured in water from streams and shallow wells, and presents conceptual models of the major factors affecting seasonal and areal patterns of pesticide concentrations in water from streams and shallow wells in the Lower Susquehanna River Basin. Seasonal and areal patterns of pesticide concentrations were observed in 577 samples and nearly 40,000 pesticide analyses collected from 155 stream sites and 169 shallow wells from 1993 to 1995. For this study, shallow wells were defined as those generally less than 200 feet deep. The most commonly detected pesticides were agricultural herbicides?atrazine, metolachlor, simazine, prometon, alachlor, and cyanazine. Atrazine and metolachlor are the two most-used agricultural pesticides in the Lower Susquehanna River Basin. Atrazine was detected in 92 percent of all the samples and in 98 percent of the stream samples. Metolachlor was detected in 83 percent of all the samples and in 95 percent of the stream samples. Nearly half of all the analyzed pesticides were not detected in any sample. Of the 45 pesticides that were detected at least once, the median concentrations of 39 of the pesticides were less than the detection limit for the individual compounds, indicating that for at least 50 percent of the samples collected, those pesticides were not detected. Only 10 (less than 0.025 percent) of the measured concentrations exceeded any established drinking-water standards; 25 concentrations exceeded 2 mg/L (micrograms per liter) and 55 concentrations exceeded 1 mg/L. None of the elevated concentrations were measured in samples collected from streams that are used for public drinking-water supplies, and 8 of the 10 were measured in storm-affected samples. The timing and rate of agricultural pesticide applications affect the seasonal and areal concentration patterns of atrazine, simazine, chlorpyrifos, and diazinon observed in water from wells and streams in the Lower Susquehanna River Basin. Average annual pesticide use for agricultural purposes and nonagricultural pesticide use indicators were used to explain seasonal and areal patterns. Elevated concentrations of some pesticides in streams during base-flow and storm-affected conditions were related to the seasonality of agricultural-use applications and local climate conditions. Agricultural-use patterns affected areal concentration patterns for the high-use pesticides, but indicators of nonagricultural use were needed to explain concentration patterns of pesticides with smaller amounts used for agricultural purposes. Bedrock type influences the movement and discharge of ground water, which in turn affects concentration patterns of pesticides. The ratio of atrazine concentrations in stream base flow to concentrations in shallow wells varied among the different general rock types found in the Lower Susquehanna River Basin. Median concentrations of atrazine in well water and stream base flow tended to be similar in individual areas underlain by carbonate bedrock, indicating the connectivity of water in streams and shallow wells in these areas. In areas underlain by noncarbonate bedrock, median concentrations of atrazine tended to be significantly higher in stream base flow than in well water. This suggests a deep ground-water system that delivers water to shallow wells and a near-surficial system that supplies base-flow water to streams. In addition to the presence or absence of carbonate bedrock, pesticide leaching potential and persistence, soil infiltration capacity, and agricultural land use affected areal patterns in detection frequency and concentration differences between samples collected from streams during base-flow conditions and shallow wells.

Water-Resources Investigations Report↗

USGS2001

Relation between selected well-construction characteristics and occurrence of bacteria in private household-supply wells, south-central and southeastern Pennsylvania

Total coliform and Escherichia coli ( E. coli ) bacteria were analyzed in ground water sampled from 78 private household-supply wells as part of a study by the U.S. Geological Survey in cooperation with the Pennsylvania Department of Environmental Protection to evaluate the relation between well construction characteristics and the occurrence of bacteria in ground water. Sampling was done in eight counties in south-central and southeastern Pennsylvania from September 2000 to March 2001. All samples were collected from wells in close proximity to agricultural land-use areas. Total coliform bacteria were found in water from 62 percent (48 of 78) of the wells, and bacteria were just as likely to be found in sanitary wells (grouted/loose-fitting well cap or grouted/sanitary sealed well cap) as in nonsanitary wells (nongrouted/ loose-fitting well cap). The areas underlain by carbonate bedrock had the highest percentages of total coliform detected (about 75 percent). Nearly half of the samples collected in the areas underlain by noncarbonate bedrock also were found to have total coliform present. E. coli bacteria were found in water from 10 percent of the wells. Seventeen percent of the samples that were positive for total coliform also were positive for E. coli . The presence of E. coli bacteria was more likely in water from nonsanitary wells. Additionally, the presence of E. coli bacteria was more likely in ground water from wells underlain by carbonate bedrock. A further breakdown of the data into four groups on the basis of sanitary construction and bedrock type indicated the presence of E. coli was more likely in water from nonsanitary wells in areas underlain by carbonate bedrock. Statistical analysis of other well-construction characteristics that might relate to occurrence of bacteria showed that the presence of total coliform bacteria was related to the depth to water-bearing zone in both sanitary and nonsanitary wells in areas underlain by carbonate bedrock. Relations also are present between the presence of total coliform bacteria and casing length in nonsanitary wells in areas underlain by noncarbonate bedrock. Bacteria were found in wells both with and without insects observed on the underside of the well cap. Because of the small number of wells sampled that had sanitary sealed caps, it is uncertain whether installation of sanitary sealed well caps would reduce the incidence of bacteria in ground water from wells or if the presence of bacteria is because of a combination of well-construction characteristics or aquifer-wide contamination of limited or broad areal extent.

Pennsylvania↗