Projects:
- Heavy metal contamination in US drinking water & sociodemographic factors
- Sociodemographic disparities in mercury exposure from US coal-fired electricity generating units
- Evaluating household tap water variability in the Nurses' Health Study
Publications:
Dai, M. Q., Geyman, B. M., Hu, X. C., Thackray, C. P. & Sunderland, E. M. Sociodemographic Disparities in Mercury Exposure from United States Coal-Fired Power Plants. Environ. Sci. Technol. Lett. (2023) doi:10.1021/acs.estlett.3c00216
Hu, X.C., Dai, M., Sun, J.M.Sunderland, E.M.The Utility of Machine Learning Models for Predicting Chemical Contaminants in Drinking Water: Promise, Challenges, and Opportunities. Curr Envir Health Rpt 10, 45–60 (2023). https://doi.org/10.1007/s40572-022-00389-x
de Vera, G.A. Brown, B.Y., Cortesa, S., Dai, M., Bruno, J., LaPier, J., Sule, N., Hancock, M., Yoon, B., Chalah, A., Sunderland, E.M., Wofsy, S.C. HazeL: A Low-Cost Learning Platform for Aerosol Measurements. Journal of Chemical Education, 99(9), pp. 3203–3210. (2022). https://doi.org/10.1021/acs.jchemed.2c00535
Dai, M., Euling, S.Y., Phillips, L., Rice, G.E. ExpoKids: An R-based tool for characterizing aggregate chemical
exposure during childhood. J Expo Sci Environ Epidemiol (2020). https://doi.org/10.1038/s41370-020-00265-6