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Dan Dawson, Ph.D.

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Dan Dawson, Ph.D.
Senior Scientist

Dan Dawson, Ph.D.

Senior Scientist (667) 225-5426 Raleigh, NC ddawson@integral-corp.com

Dr. Dan Dawson is an ecologist and environmental toxicologist with a general interest in the use of quantitative approaches to understanding and addressing human and ecological problems. With more than 12 years of graduate and post-graduate experience, he has worked with private organizations, academic institutions, and government agencies to tackle a diverse set of questions. Along the way, he has acquired expertise in a wide variety of topics and quantitative toolsets, including chemical and electromagnetic field exposure modeling, population modeling, disease transmission modeling, machine learning/quantitative structure–activity relationship (QSAR) modeling, habitat equivalency analysis (HEA), and statistical modeling. Dr. Dawson has published much of his work in the peer-reviewed literature, and he has frequently presented research at scientific conferences. Finally, he is interested in continually improving science communication and believes that scientific information should be conveyed clearly, concisely, and with as little jargon as possible.

Using Ecological Risk Assessment to Assess EMF Impacts to Marine Life From offshore Wind Infrastructure

Publication

July 01 2024

Using an Agent-Based Model to Estimate Effects of EMF Exposure from Offshore Wind Infrastructure on Benthic Marine Organisms

Poster

November 18 2023

Applying AQUATOX to Aquatic Mesocosms for Higher-Tier Pesticide Risk Assessment

Poster

April 28 2023

Data Science

Environmental Fate of Water-Soluble Polymers, United States Used AI-based screening tools to conduct literature reviews on the environmental fate of water-soluble polymers in groundwater, soil, sediment, and wastewater. Work also included assembling information about available monitoring and modeling approaches and making recommendations on best practices for modeling the fate of water-soluble polymers.

Ecological Modeling

Modeling Offshore Wind Impacts to the Environment, United States Contributed to and coordinated modeling for preconstruction environmental impact assessments of several offshore wind development projects. Work areas included modeling electromagnetic field exposure from energized undersea cables to benthic organisms, and modeling movement and acoustic impacts of construction activities to marine mammals and sea turtles.
Modeling Impacts of Nectar-Based Pesticide to Honeybees Using an Agent-Based Model, Office of Research and Development, EPA Integrated a nectar-based pesticide exposure and effects module into BeeHave, an existing agent-based model of honeybee colony dynamics. The model was calibrated using Approximate Bayesian Computation, and model behavior and structure will be compared with other established models of honeybee colony dynamics. This research will be communicated in upcoming peer-reviewed journal articles.

Natural Resource Damage Assessment

Natural Resource Damage Assessment, United States Contributed to background research, injury estimation, and HEA as part of natural resource damage analyses for several different clients. Contributed to development of Microsoft® Excel-based HEA tools to help make the natural resource damage assessment estimations more nimble and more transparent.

Human Health Risk Assessment

Exposure Assessment of 1,4-Dioxane from Drinking Water and Product Use, EPA, United States Created an R-based exposure modeling workflow that considered relative exposure sources of 1,4‑dioxane in humans. This research was communicated in a peer-reviewed journal article. Also created a novel Microsoft® Excel-based model to estimate upstream sources of 1,4-dioxane to downstream surface water drinking plants. This work is being used by the EPA Office of Pollution Prevention and Toxics as part of a reevaluation of risks posed by 1,4-dioxane under TSCA authority.

Statistical Modeling

Understanding How Data Structure and Processing Decisions Influence the Output of Transmission Models, North Carolina State University, Raleigh, North Carolina Used a data set of cattle locations in a feedlot and a network-based simulation model of contact-based disease transmission, and investigated how data processing influences the outcome of simulated epidemics. By converting time series of contact data to a measure of information (entropy), demonstrated that different processing decisions have significant implications for resultant simulations. This research was communicated in a peer-reviewed journal article.
Modeling Mosquito Populations in Response to Environmental Stressors and Mosquito Control, Tarrant County, Texas Created a regression-based model of mosquito population based on environmental drivers, spatial information, and mosquito control records. This research was communicated in a peer-reviewed journal article.
Modeling Drivers of Bartonella henselae Exposure in Dogs in North Carolina Assisted in the creation of a statistical model providing inference into social, environmental, and spatial drivers of Bartonella henselae infection in dogs. This research was communicated in a peer-reviewed journal article.

Product Stewardship

Secondary Safety Assessments, United States Reviewed safety assessments of ingredients used in or planned for use in personal care products. Initial reviews were conducted by third party toxicologists. Reviews included considerations of lines of evidence across endpoints, exposure estimates, and risk estimations.

Toxicology

Development of Several Machine Learning Models of Toxicokinetic Model Parameters, EPA, Office of Research and Development, United States Helped to develop publicly available models of important toxicokinetic parameters (intrinsic clearance, fraction unbound to protein) for a large domain of chemicals, with predictions for several thousand chemicals of the Tox21 data set incorporated into the open-source high-throughput toxicokinetic R package, httk. Also contributed to a machine learning model of serum half-lives (t1/2) of 11 per- and polyfluoroalkyl substances (PFAS) across four species. These projects were communicated in peer-reviewed journal articles.

PFAS

Development of Several Machine Learning Models of Toxicokinetic Model Parameters, EPA, Office of Research and Development, United States Helped to develop publicly available models of important toxicokinetic parameters (intrinsic clearance, fraction unbound to protein) for a large domain of chemicals, with predictions for several thousand chemicals of the Tox21 data set incorporated into the open-source high-throughput toxicokinetic R package, httk. Also contributed to a machine learning model of serum half-lives (t1/2) of 11 per- and polyfluoroalkyl substances (PFAS) across four species. These projects were communicated in peer-reviewed journal articles.
Public Comments to TSCA PFAS Regulations, United States Coordinated and contributed to client comments published to the public record evaluating the scientific integrity of PFAS regulations proposed by EPA and other government agencies.

Statistical Analysis

Understanding How Data Structure and Processing Decisions Influence the Output of Transmission Models, North Carolina State University, Raleigh, North Carolina Used a data set of cattle locations in a feedlot and a network-based simulation model of contact-based disease transmission, and investigated how data processing influences the outcome of simulated epidemics. By converting time series of contact data to a measure of information (entropy), demonstrated that different processing decisions have significant implications for resultant simulations. This research was communicated in a peer-reviewed journal article.
Modeling Mosquito Populations in Response to Environmental Stressors and Mosquito Control, Tarrant County, Texas Created a regression-based model of mosquito population based on environmental drivers, spatial information, and mosquito control records. This research was communicated in a peer-reviewed journal article.
Modeling Drivers of Bartonella henselae Exposure in Dogs in North Carolina Assisted in the creation of a statistical model providing inference into social, environmental, and spatial drivers of Bartonella henselae infection in dogs. This research was communicated in a peer-reviewed journal article.

Dawson, D., C. Lau, P. Pradeep, R. Sayre, R. Judson, R. Tornero-Velez, and J. Wambaugh. 2023. A machine learning model to estimate toxicokinetic half-lives of per- and polyfluoro-alkyl substances (PFAS) in multiple species. Toxics 11(2):98. https://doi.org/10.3390/toxics11020098

Dawson, D., F. Hunter, A. Noble, Q. Meng, A.C. Doherty, Y. Sakano, D. Vallero, R. Tornero-Velez, and E.A. Cohen-Hubal. 2022. An assessment of non-occupational 1,4-dioxane exposure pathways from drinking water and product use. Environ. Sci. Technol. 56(8): 5266–5275.

Dawson, D., D. Rasmussen, X. Peng, and C. Lanzas. 2021. Inferring environmental transmission using phylodynamics: A case-study using simulated evolution of an enteric pathogen. J. R. Soc. Interface 18(179):20210041. doi: 10.1098/rsif.2021.0041.

Dawson, D., B. Ingle, K. Phillips, J. Nichols, J. Wambaugh, and R. Tornero-Velez. 2021. Designing QSARS for parameters of high-throughput toxicokinetic models using open-source descriptors. Environ. Sci. and Technol. 55(9):6505–6517.

Dawson, D., N. Dacko, A. Kurian, and C. Salice. 2019. A model of Culex quinquefasciatus SAY abundance constructed using routine surveillance and treatment data in Tarrant County, Texas. J. Am. Mosquito Contr. 35(1):1–10.

Dawson, D., T. Farthing, M. Sanderson., and C. Lanzas. 2019. Transmission on empirical dynamic contact networks is influenced by processing decisions. Epidemics 26:32–42.

Dawson, D., J. Keung, M. Napoles, M. Vella, S. Chen, M. Sanderson, and C. Lanzas. 2018. Investigating behavioral drivers of seasonal Shiga-toxigenic Escherichia coli (STEC) patterns in grazing cattle using an agent-based model. Plos One 13(10):e0205418.

Dawson, D., D. Preziosi, R. Pastorok, et al. 2023. Using an agent-based model to estimate EMF exposure from offshore wind infrastructure to benthic marine organisms. Poster presented at the Annual Meeting of the Society of Environmental Toxicological and Chemistry. Louisville, KY. November 12–16.

Dawson, D., J. Minucci, and T. Purucker. 2021. Getting BeeHave to behave: Extending and calibrating an agent-based model of honey bee dynamics for pesticide exposure data using approximate Bayesian computation (ABC). 42nd Annual North American Meeting of the Society of Environmental Toxicology and Chemistry, November 14–18 (Virtual).

Dawson, D., R. Tornero-Velez., P. Pradeep, R. Judson, and J.A. Wambaugh. 2021. A quantitative structure-activity relationship (QSAR) model to estimate half-lives of perfluoro-alkyl substances (PFAS) in multiple species. 60th Annual Meeting of the Society of Toxicology, March.

Dawson, D., F. Hunter, A.E. Noble, Q. Meng, A.C. Doherty, D. Vallero, R. Tornero-Velez, and E. Cohen-Hubal. 2021. Developing scientific workflows for exposure assessments: a case study with 1,4-dioxane. Annual Meeting of the International Society of Exposure Scientists.

Dawson, D., B. Ingle, K. Phillips, J. Nicols, J. Wambaugh, and R. Tornero-Velez. 2020. Designing QSARS for parameters of high-throughput toxicokinetic models using open-source descriptors. 41st Annual Meeting of the Society of Environmental Toxicology and Chemistry, November 15–19 (Virtual).

Dawson, D.E., D. Rasmussen, X. Peng, and C. Lanzas. 2019. Inferring environmental transmission using phylodynamics: A case-study using simulated evolution of an enteric pathogen. Epidemics: 7th International Conference on Infectious Disease Dynamics. Charleston, SC

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