@inproceedings{money_2023, title={A Modern Curriculum for Modern GIS}, booktitle={Esri Higher Education Webinar}, author={Money, E.S.}, year={2023} }
 @inproceedings{money_petras_2023, title={Engaging with the Center for Geospatial Analytics}, booktitle={North Carolina GIS Conference}, author={Money, E.S. and Petras, V.}, year={2023} }
 @inproceedings{money_2023, place={Denver, CO}, title={Teaching Modern GIS}, booktitle={Association of American Geographers Annual Conference}, author={Money, E.S.}, year={2023} }
 @inproceedings{money_2021, title={Challenges and Opportunities from Implementing an Informal K-12 After School GIS Program}, booktitle={North Carolina GIS Conference}, author={Money, E.S.}, year={2021} }
 @inproceedings{developing a geospatial technologies focused professional development to promote interdisciplinary approaches in stem education_2021, booktitle={Association for Science Teacher Education}, year={2021} }
 @inproceedings{gst-integrated pd to promote interdisciplinary approaches to stem education._2020, booktitle={National Association for Research in Science Teaching}, year={2020} }
 @article{house_hall_park_planchart_money_maguire_huang_mattingly_skaar_tzeng_et al._2019, title={Cadmium exposure and MEG3 methylation differences between Whites and African Americans in the NEST Cohort}, volume={5}, ISSN={2058-5888}, url={http://dx.doi.org/10.1093/eep/dvz014}, DOI={10.1093/eep/dvz014}, abstractNote={Abstract}, number={3}, journal={Environmental Epigenetics}, publisher={Oxford University Press (OUP)}, author={House, John S and Hall, Jonathan and Park, Sarah S and Planchart, Antonio and Money, Eric and Maguire, Rachel L and Huang, Zhiqing and Mattingly, Carolyn J and Skaar, David and Tzeng, Jung Ying and et al.}, editor={Skinner, MikeEditor}, year={2019}, month={Jul} }
 @inproceedings{money_2019, title={Geospatial Applications for Problem Solving (GAPS) for Hi-Tech Teens}, booktitle={Burroughs Wellcome Fund Awardee Conference}, author={Money, E.S.}, year={2019} }
 @inproceedings{money_2019, title={Using GIS for Sustainability}, booktitle={NC State – University of Sao Paulo Collaboration Meeting}, author={Money, E.S.}, year={2019} }
 @inproceedings{money_2018, title={From tangible interfaces to virtual reality: advanced geospatial analytics and technologies for the digital age}, booktitle={University Global Partnership Network (UGPN) Annual Conference}, author={Money, E.S.}, year={2018} }
 @inproceedings{increasing underrepresented high school students' stem career awareness and interest: an informal geospatial science program_2018, booktitle={American Geophysical Union Fall Meeting}, year={2018}, month={Dec} }
 @inproceedings{money_2018, title={Innovations in decision making using geospatial analytics}, booktitle={Duke GIS Day Celebration}, author={Money, E.S.}, year={2018} }
 @inproceedings{money_2017, title={Geospatial Applications for Problem Solving (GAPS) for Hi-Tech Teens}, booktitle={Burroughs Wellcome Fund Awardee Conference}, author={Money, E.S.}, year={2017} }
 @inproceedings{money_2017, title={Research and education at the frontiers of geospatial sciences and technology}, booktitle={Duke GIS Day Celebration}, author={Money, E.S.}, year={2017} }
 @article{tonini_dillon_money_meentemeyer_2016, title={Spatio-temporal reconstruction of missing forest microclimate measurements}, volume={218}, ISSN={["1873-2240"]}, DOI={10.1016/j.agrformet.2015.11.004}, abstractNote={Scientists and land managers are increasingly monitoring forest microclimate environments to better understand ecosystem processes, such as carbon sequestration and the population dynamics of species. Obtaining reliable time-series measurements of microclimate conditions is often hindered by missing and erroneous values. In this study, we compare spatio-temporal techniques, space–time kriging (probabilistic) and empirical orthogonal functions (deterministic), for reconstructing hourly time series of near-surface air temperature recorded by a dense network of 200 forest understory sensors across a heterogeneous 349 km2 region in northern California. The reconstructed data were also aggregated to daily mean, minimum, and maximum in order to understand the sensitivity of model predictions to temporal scale of measurement. Empirical orthogonal functions performed best at both the hourly and daily time scale. We analyzed several scenarios to understand the effects that spatial coverage and patterns of missing data may have on model accuracy: (a) random reduction of the sample size/density by 25%, 50%, and 75% (spatial coverage); and (b) random removal of either 50% of the data, or three consecutive months of observations at randomly chosen stations (random and seasonal temporal missingness, respectively). Here, space–time kriging was less sensitive to scenarios of spatial coverage, but more sensitive to temporal missingness, with less marked differences between the two approaches when data were aggregated on a daily time scale. This research contextualizes trade-offs between techniques and provides practical guidelines, with free source code, for filling data gaps depending on the spatial density and coverage of measurements.}, journal={AGRICULTURAL AND FOREST METEOROLOGY}, publisher={Elsevier BV}, author={Tonini, Francesco and Dillon, Whalen W. and Money, Eric S. and Meentemeyer, Ross K.}, year={2016}, month={Mar}, pages={1–10} }
 @article{king_darrah_money_meentemeyer_maguire_nye_michener_murtha_jirtle_murphy_et al._2015, title={Geographic clustering of elevated blood heavy metal levels in pregnant women}, volume={15}, ISSN={["1471-2458"]}, DOI={10.1186/s12889-015-2379-9}, abstractNote={Cadmium (Cd), lead (Pb), mercury (Hg), and arsenic (As) exposure is ubiquitous and has been associated with higher risk of growth restriction and cardiometabolic and neurodevelopmental disorders. However, cost-efficient strategies to identify at-risk populations and potential sources of exposure to inform mitigation efforts are limited. The objective of this study was to describe the spatial distribution and identify factors associated with Cd, Pb, Hg, and As concentrations in peripheral blood of pregnant women.Heavy metals were measured in whole peripheral blood of 310 pregnant women obtained at gestational age ~12 weeks. Prenatal residential addresses were geocoded and geospatial analysis (Getis-Ord Gi* statistics) was used to determine if elevated blood concentrations were geographically clustered. Logistic regression models were used to identify factors associated with elevated blood metal levels and cluster membership.Geospatial clusters for Cd and Pb were identified with high confidence (p-value for Gi* statistic <0.01). The Cd and Pb clusters comprised 10.5 and 9.2 % of Durham County residents, respectively. Medians and interquartile ranges of blood concentrations (μg/dL) for all participants were Cd 0.02 (0.01-0.04), Hg 0.03 (0.01-0.07), Pb 0.34 (0.16-0.83), and As 0.04 (0.04-0.05). In the Cd cluster, medians and interquartile ranges of blood concentrations (μg/dL) were Cd 0.06 (0.02-0.16), Hg 0.02 (0.00-0.05), Pb 0.54 (0.23-1.23), and As 0.05 (0.04-0.05). In the Pb cluster, medians and interquartile ranges of blood concentrations (μg/dL) were Cd 0.03 (0.02-0.15), Hg 0.01 (0.01-0.05), Pb 0.39 (0.24-0.74), and As 0.04 (0.04-0.05). Co-exposure with Pb and Cd was also clustered, the p-values for the Gi* statistic for Pb and Cd was <0.01. Cluster membership was associated with lower education levels and higher pre-pregnancy BMI.Our data support that elevated blood concentrations of Cd and Pb are spatially clustered in this urban environment compared to the surrounding areas. Spatial analysis of metals concentrations in peripheral blood or urine obtained routinely during prenatal care can be useful in surveillance of heavy metal exposure.}, number={1}, journal={BMC PUBLIC HEALTH}, publisher={Springer Science and Business Media LLC}, author={King, Katherine E. and Darrah, Thomas H. and Money, Eric and Meentemeyer, Ross and Maguire, Rachel L. and Nye, Monica D. and Michener, Lloyd and Murtha, Amy P. and Jirtle, Randy and Murphy, Susan K. and et al.}, year={2015}, month={Oct} }
 @inproceedings{money_2015, place={Fayetteville, NC}, title={Geospatial Programs and Partnerships}, booktitle={Geospatial Intelligence at Historically Black Colleges and Universities Annual Conference (GEOINT-HBCU)}, author={Money, E.S.}, year={2015} }
 @article{grieger_redmon_money_widder_van der schalie_beaulieu_womack_2014, title={A relative ranking approach for nano-enabled applications to improve risk-based decision making: a case study of Army materiel}, volume={35}, ISSN={2194-5403 2194-5411}, url={http://dx.doi.org/10.1007/s10669-014-9531-4}, DOI={10.1007/s10669-014-9531-4}, number={1}, journal={Environment Systems and Decisions}, publisher={Springer Science and Business Media LLC}, author={Grieger, Khara D. and Redmon, Jennifer Hoponick and Money, Eric S. and Widder, Mark W. and van der Schalie, William H. and Beaulieu, Stephen M. and Womack, Donna}, year={2014}, month={Dec}, pages={42–53} }
 @article{powers_grieger_hendren_meacham_gurevich_lassiter_money_lloyd_beaulieu_2014, title={A web-based tool to engage stakeholders in informing research planning for future decisions on emerging materials}, volume={470-471}, ISSN={0048-9697}, url={http://dx.doi.org/10.1016/j.scitotenv.2013.10.016}, DOI={10.1016/j.scitotenv.2013.10.016}, abstractNote={Prioritizing and assessing risks associated with chemicals, industrial materials, or emerging technologies is a complex problem that benefits from the involvement of multiple stakeholder groups. For example, in the case of engineered nanomaterials (ENMs), scientific uncertainties exist that hamper environmental, health, and safety (EHS) assessments. Therefore, alternative approaches to standard EHS assessment methods have gained increased attention. The objective of this paper is to describe the application of a web-based, interactive decision support tool developed by the U.S. Environmental Protection Agency (U.S. EPA) in a pilot study on ENMs. The piloted tool implements U.S. EPA's comprehensive environmental assessment (CEA) approach to prioritize research gaps. When pursued, such research priorities can result in data that subsequently improve the scientific robustness of risk assessments and inform future risk management decisions. Pilot results suggest that the tool was useful in facilitating multi-stakeholder prioritization of research gaps. Results also provide potential improvements for subsequent applications. The outcomes of future CEAWeb applications with larger stakeholder groups may inform the development of funding opportunities for emerging materials across the scientific community (e.g., National Science Foundation Science to Achieve Results [STAR] grants, National Institutes of Health Requests for Proposals).}, journal={Science of The Total Environment}, publisher={Elsevier BV}, author={Powers, Christina M. and Grieger, Khara D. and Hendren, Christine Ogilvie and Meacham, Connie A. and Gurevich, Gerald and Lassiter, Meredith Gooding and Money, Eric S. and Lloyd, Jennifer M. and Beaulieu, Stephen M.}, year={2014}, month={Feb}, pages={660–668} }
 @article{money_barton_dawson_reckhow_wiesner_2014, title={Validation and sensitivity of the FINE Bayesian network for forecasting aquatic exposure to nano-silver}, volume={473-474}, DOI={10.1016/j.scitotenv.2013.12.100}, abstractNote={The adaptive nature of the Forecasting the Impacts of Nanomaterials in the Environment (FINE) Bayesian network is explored. We create an updated FINE model (FINEAgNP-2) for predicting aquatic exposure concentrations of silver nanoparticles (AgNP) by combining the expert-based parameters from the baseline model established in previous work with literature data related to particle behavior, exposure, and nano-ecotoxicology via parameter learning. We validate the AgNP forecast from the updated model using mesocosm-scale field data and determine the sensitivity of several key variables to changes in environmental conditions, particle characteristics, and particle fate. Results show that the prediction accuracy of the FINEAgNP-2 model increased approximately 70% over the baseline model, with an error rate of only 20%, suggesting that FINE is a reliable tool to predict aquatic concentrations of nano-silver. Sensitivity analysis suggests that fractal dimension, particle diameter, conductivity, time, and particle fate have the most influence on aquatic exposure given the current knowledge; however, numerous knowledge gaps can be identified to suggest further research efforts that will reduce the uncertainty in subsequent exposure and risk forecasts.}, journal={Science of The Total Environment}, publisher={Elsevier BV}, author={Money, Eric S. and Barton, Lauren E. and Dawson, Joseph and Reckhow, Kenneth H. and Wiesner, Mark R.}, year={2014}, month={Mar}, pages={685–691} }
 @article{hendren_lowry_grieger_money_johnston_wiesner_beaulieu_2013, title={Modeling Approaches for Characterizing and Evaluating Environmental Exposure to Engineered Nanomaterials in Support of Risk-Based Decision Making}, volume={47}, ISSN={0013-936X 1520-5851}, url={http://dx.doi.org/10.1021/es302749u}, DOI={10.1021/es302749u}, abstractNote={As the use of engineered nanomaterials becomes more prevalent, the likelihood of unintended exposure to these materials also increases. Given the current scarcity of experimental data regarding fate, transport, and bioavailability, determining potential environmental exposure to these materials requires an in depth analysis of modeling techniques that can be used in both the near- and long-term. Here, we provide a critical review of traditional and emerging exposure modeling approaches to highlight the challenges that scientists and decision-makers face when developing environmental exposure and risk assessments for nanomaterials. We find that accounting for nanospecific properties, overcoming data gaps, realizing model limitations, and handling uncertainty are key to developing informative and reliable environmental exposure and risk assessments for engineered nanomaterials. We find methods suited to recognizing and addressing significant uncertainty to be most appropriate for near-term environmental exposure modeling, given the current state of information and the current insufficiency of established deterministic models to address environmental exposure to engineered nanomaterials.}, number={3}, journal={Environmental Science & Technology}, publisher={American Chemical Society (ACS)}, author={Hendren, Christine Ogilvie and Lowry, Michael and Grieger, Khara D. and Money, Eric S. and Johnston, John M. and Wiesner, Mark R. and Beaulieu, Stephen M.}, year={2013}, month={Feb}, pages={1190–1205} }
 @article{money_reckhow_wiesner_2012, title={The use of Bayesian networks for nanoparticle risk forecasting: Model formulation and baseline evaluation}, volume={426}, DOI={10.1016/j.scitotenv.2012.03.064}, abstractNote={We describe the use of Bayesian networks as a tool for nanomaterial risk forecasting and develop a baseline probabilistic model that incorporates nanoparticle specific characteristics and environmental parameters, along with elements of exposure potential, hazard, and risk related to nanomaterials. The baseline model, FINE (Forecasting the Impacts of Nanomaterials in the Environment), was developed using expert elicitation techniques. The Bayesian nature of FINE allows for updating as new data become available, a critical feature for forecasting risk in the context of nanomaterials. The specific case of silver nanoparticles (AgNPs) in aquatic environments is presented here (FINEAgNP). The results of this study show that Bayesian networks provide a robust method for formally incorporating expert judgments into a probabilistic measure of exposure and risk to nanoparticles, particularly when other knowledge bases may be lacking. The model is easily adapted and updated as additional experimental data and other information on nanoparticle behavior in the environment become available. The baseline model suggests that, within the bounds of uncertainty as currently quantified, nanosilver may pose the greatest potential risk as these particles accumulate in aquatic sediments.}, journal={Science of The Total Environment}, publisher={Elsevier BV}, author={Money, Eric S. and Reckhow, Kenneth H. and Wiesner, Mark R.}, year={2012}, month={Jun}, pages={436–445} }
 @inproceedings{money_wiesner_2011, title={A Bayesian Network Model for Nanomaterial Risk Forecasting and Characterization}, booktitle={Gordon Research Conference on Environmental Nanotechnology}, author={Money, E.S. and Wiesner, M.R.}, year={2011} }
 @inproceedings{money_mccall_reckhow_wiesner_2011, title={Ecological Risk Forecasting for Nanomaterials Using Bayesian Networks: A Case Study of Nano-Ag Exposure Potential in a North Carolina River Basin}, booktitle={International Conference on the Environmental Implications of NanoTechnology (ICEIN)}, author={Money, E.S. and McCall, C.M. and Reckhow, K.H. and Wiesner, M.R.}, year={2011} }
 @article{money_sackett_aday_serre_2011, title={Using River Distance and Existing Hydrography Data Can Improve the Geostatistical Estimation of Fish Tissue Mercury at Unsampled Locations}, volume={45}, ISSN={["1520-5851"]}, DOI={10.1021/es2003827}, abstractNote={Mercury in fish tissue is a major human health concern. Consumption of mercury-contaminated fish poses risks to the general population, including potentially serious developmental defects and neurological damage in young children. Therefore, it is important to accurately identify areas that have the potential for high levels of bioaccumulated mercury. However, due to time and resource constraints, it is difficult to adequately assess fish tissue mercury on a basin wide scale. We hypothesized that, given the nature of fish movement along streams, an analytical approach that takes into account distance traveled along these streams would improve the estimation accuracy for fish tissue mercury in unsampled streams. Therefore, we used a river-based Bayesian Maximum Entropy framework (river-BME) for modern space/time geostatistics to estimate fish tissue mercury at unsampled locations in the Cape Fear and Lumber Basins in eastern North Carolina. We also compared the space/time geostatistical estimation using river-BME to the more traditional Euclidean-based BME approach, with and without the inclusion of a secondary variable. Results showed that this river-based approach reduced the estimation error of fish tissue mercury by more than 13% and that the median estimate of fish tissue mercury exceeded the EPA action level of 0.3 ppm in more than 90% of river miles for the study domain.}, number={18}, journal={ENVIRONMENTAL SCIENCE & TECHNOLOGY}, publisher={American Chemical Society (ACS)}, author={Money, Eric S. and Sackett, Dana K. and Aday, D. Derek and Serre, Marc L.}, year={2011}, month={Sep}, pages={7746–7753} }
 @inproceedings{money_2010, title={BayesNets for Nanomaterial Risk Assessment}, booktitle={Society for Risk Analysis Annual Meeting}, author={Money, E.S.}, year={2010} }
 @article{love_lovelace_money_sobsey_2010, title={Microbial Fecal Indicator Concentrations in Water and Their Correlation to Environmental Parameters in Nine Geographically Diverse Estuaries}, volume={2}, DOI={10.1007/s12403-010-0026-3}, number={2}, journal={Water Quality, Exposure and Health}, publisher={Springer Science and Business Media LLC}, author={Love, David C. and Lovelace, Greg L. and Money, Eric S. and Sobsey, Mark D.}, year={2010}, month={May}, pages={85–95} }
 @inproceedings{money_reckhow_2010, title={Modeling an uncertain risk: using belief networks to assess the environmental implications of nanotechnology}, booktitle={Environmental Decisions: Risks and Uncertainties}, author={Money, E.S. and Reckhow, K.H.}, year={2010} }
 @inproceedings{money_reckhow_2009, title={A probabilistic network modeling approach for nanoparticle risk assessments}, booktitle={Society for Risk Analysis Annual Meeting}, author={Money, E.S. and Reckhow, K.H.}, year={2009} }
 @article{money_carter_serre_2009, title={Modern Space/Time Geostatistics Using River Distances: Data Integration of Turbidity andE. coliMeasurements to Assess Fecal Contamination Along the Raritan River in New Jersey}, volume={43}, DOI={10.1021/es803236j}, abstractNote={Escherichia coli (E. coli) is a widely used indicator of fecal contamination in water bodies. External contact and subsequent ingestion of bacteria coming from fecal contamination can lead to harmful health effects. Since E. coli data are sometimes limited, the objective of this study is to use secondary information in the form of turbidity to improve the assessment of E. coli at unmonitored locations. We obtained all E. coli and turbidity monitoring data available from existing monitoring networks for the 2000-2006 time period for the Raritan River Basin, New Jersey. Using collocated measurements, we developed a predictive model of E. coli from turbidity data. Using this model, soft data are constructed for E. coli given turbidity measurements at 739 space/time locations where only turbidity was measured. Finally, the Bayesian Maximum Entropy (BME) method of modern space/time geostatistics was used for the data integration of monitored and predicted E. coli data to produce maps showing E. coli concentration estimated daily across the river basin. The addition of soft data in conjunction with the use of river distances reduced estimation error by about 30%. Furthermore, based on these maps, up to 35% of river miles in the Raritan Basin had a probability of E coli impairment greater than 90% on the most polluted day of the study period.}, number={10}, journal={Environmental Science & Technology}, publisher={American Chemical Society (ACS)}, author={Money, Eric S. and Carter, Gail P. and Serre, Marc L.}, year={2009}, month={May}, pages={3736–3742} }
 @inproceedings{money_robichaud_reckhow_2009, title={Nano-risk and Macro-Uncertainty: Using Probability Networks to Model the Environmental Implications of Nanotechnology}, booktitle={International Consortium on the Environmental Implications of NanoTechnology (ICEIN) Annual Meeting}, author={Money, E.S. and Robichaud, C.O. and Reckhow, K.H.}, year={2009} }
 @article{coulliette_money_serre_noble_2009, title={Space/Time Analysis of Fecal Pollution and Rainfall in an Eastern North Carolina Estuary}, volume={43}, DOI={10.1021/es803183f}, abstractNote={The Newport River Estuary (NPRE) is a high-priority shellfish harvesting area in eastern North Carolina that is impaired due to fecal contamination, specifically exceeding recommended levels for fecal coliforms. A hydrologic-driven mean trend model was developed, as a function of antecedent rainfall, in the NPRE to predict levels of Escherichia coli (EC, measured as a proxyforfecal coliforms). This mean trend model was integrated in a Bayesian Maximum Entropy (BME) framework to produce informative space/time (S/T) maps depicting fecal contamination across the NPRE during winter and summer months. These maps showed that during dry winter months, corretponding to the oyster harvesting season in North Carolina (October 1-March 30), predicted EC concentrations were below the shellfish harvesting standard (14 MPN/100 mL). However, after substantial rainfall of 3.81 cm (1.5 in.), the NPRE did not appear to mee this requirement. Warmer months resulted in the predicted EC concentrations exceeding the threshold for the NPRE. Predicted ENT concentrations were generally below the recreational water quality threshold (104 MPN/100 mL), except for warmer months after substantial rainfall. Once established, this combined approach produces near real-time visual information on which to base water quality management decisions.}, number={10}, journal={Environmental Science & Technology}, publisher={American Chemical Society (ACS)}, author={Coulliette, Angela D. and Money, Eric S. and Serre, Marc L. and Noble, Rachel T.}, year={2009}, month={May}, pages={3728–3735} }
 @article{money_carter_serre_2009, title={Using river distances in the space/time estimation of dissolved oxygen along two impaired river networks in New Jersey}, volume={43}, DOI={10.1016/j.watres.2009.01.034}, abstractNote={Understanding surface water quality is a critical step towards protecting human health and ecological stability. Because of resource deficiencies and the large number of river miles needing assessment, there is a need for a methodology that can accurately depict river water quality where data do not exist. The objective of this research is to implement a methodology that incorporates a river metric into the space/time analysis of dissolved oxygen data for two impaired river basins. An efficient algorithm is developed to calculate river distances within the BMElib statistical package for space/time geostatistics. We find that using a river distance in a space/time context leads to an appreciable 10% reduction in the overall estimation error, and results in maps of DO that are more realistic than those obtained using a Euclidean distance. As a result river distance is used in the subsequent non-attainment assessment of DO for two impaired river basins in New Jersey.}, number={7}, journal={Water Research}, publisher={Elsevier BV}, author={Money, Eric and Carter, Gail P. and Serre, Marc L.}, year={2009}, month={Apr}, pages={1948–1958} }
 @article{coulliette_money_serre_noble_2008, title={A Space/Time Analysis Framework for Fecal Indicator Bacteria in a North Carolina Estuary}, volume={19}, DOI={10.1097/01.ede.0000340103.90644.88}, number={6}, journal={Epidemiology}, author={Coulliette, A. and Money, E. and Serre, M. and Noble, R.}, year={2008}, pages={S200} }
 @inproceedings{money_sackett_aday_serre_2008, title={An Integrated Spatiotemporal Approach to Improve Mercury Estimation and Exposure Assessment}, booktitle={International Society for Environmental Epidemiology & International Society of Exposure Analysis Joint Annual Conference}, author={Money, E. and Sackett, D.K. and Aday, D. and Serre, M.L.}, year={2008} }
 @book{money_carter_serre_2008, title={Covariance models for directed tree river networks}, number={2008-08}, author={Money, E. and Carter, G. and Serre, M.L.}, year={2008} }
 @inproceedings{money_2008, title={Geostatistical Estimation of Water Quality Along River Networks}, booktitle={North Carolina Water Resources Research Institute Annual Conference}, author={Money, E.}, year={2008} }
 @inproceedings{money_carter_serre_2008, title={Improving the Assessment of E.coli Exposure Levels Along Un-monitored Stream Reaches}, booktitle={International Society for Environmental Epidemiology & International Society of Exposure Analysis, 2008 Joint Annual Conference}, author={Money, E. and Carter, G.P. and Serre, M.L.}, year={2008} }
 @inproceedings{money_carter_serre_2007, title={Data integration of E.coli and turbidity data to assess fecal contamination in the Raritan River Basin, New Jersey}, booktitle={2007 International Society for Exposure Analysis Conference}, author={Money, E. and Carter, G. and Serre, M.L.}, year={2007} }
 @article{coulliette_gronewold_money_serre_noble_2007, title={Examining the Relationship Between Wet Weather and Fecal Contamination in a North Carolina Estuary}, volume={2007}, ISSN={1938-6478}, url={http://dx.doi.org/10.2175/193864707786619855}, DOI={10.2175/193864707786619855}, number={5}, journal={Proceedings of the Water Environment Federation}, publisher={Water Environment Federation}, author={Coulliette, Angela D. and Gronewold, Andrew D. and Money, Eric S. and Serre, Marc L. and Noble, Rachel T.}, year={2007}, month={Oct}, pages={1019–1031} }
 @inproceedings{money_carter_serre_2007, title={Geostatistical Methods for Assessing Fecal Contamination Along Unmonitored River Reaches}, booktitle={Environmental Protection Agency and Centers for Disease Control, 2nd Annual Southeast Regional Environmental Science & Health Symposium}, author={Money, E. and Carter, G. and Serre, M.L.}, year={2007} }
 @inproceedings{money_serre_2007, title={The Effects of River Network Complexity on the Spatiotemporal Estimation of Water Quality}, booktitle={38th Annual Binghamton Geomorphology Symposium}, author={Money, E. and Serre, M.L.}, year={2007} }
 @article{serre_carter_money_2004, title={Geostatistical space/time estimation of water quality along the Raritan River Basin in New Jersey}, DOI={10.1016/s0167-5648(04)80189-8}, abstractNote={The assessment of the river water quality across space and time is a considerable public health concern and it is an important issue for the efficient management of our natural water resources. The state of New Jersey is mandated by the federal Clean Water Act to assess water quality along all streams and rivers in the state, which is critical to designate use attainment and to direct total maximum daily load (TMDL) development. However due to budget and scientific limitations less than 30% of the state's non-tidal stream miles have been assessed. Therefore there is a need to develop a method that can use the partial monitoring information available to estimate water quality along the unmonitored network of streams and rivers. However the high natural variability of water quality over space and time, the limited number of water samples, and the varying levels of measurement errors between samples introduce major sources of uncertainty in the estimation of water quality along rivers and over time. In this work we present the Bayesian Maximum Entropy (BME) framework to rigorously process information about the space/time variability of water quality in its aquatic environment, the uncertainty and scarcity of the monitoring data, and the relevant flow and transport governing laws, in order to obtain statistical estimate of water quality at unmonitored reaches. We implement the BME method for a case study involving the estimation of phosphate along the Raritan river basin from 1990 to 2002, and we find through cross validation that the BME space/time analysis is a substantial improvement over a purely spatial analysis.}, journal={Computational Methods in Water Resources: Volume 2, Proceedings of the XVth International Conference on Computational Methods in Water Resources}, publisher={Elsevier}, author={Serre, Marc L. and Carter, Gail and Money, Eric}, year={2004}, pages={1839–1852} }