Craig Connolly merges environmental chemistry, biogeochemistry, satellite remote-sensing, geospatial analysis, and data science—all for the benefit of public health. His purpose: To identify the key environmental conditions associated with arsenic’s mobilization and toxicity in groundwater, especially when that water is being used for drinking, and apply his findings for the benefit of vulnerable and affected communities.

“Arsenic contamination in groundwater used for drinking, unfortunately, is a concern everywhere around the world, including the U.S.,” Connolly said. “It’s important to me to improve fundamental knowledge in science while also producing work with real-world applications for people’s health and livelihood.”

As a postdoctoral research fellow in the Department of Environmental Health Sciences at Columbia University Mailman School of Public Health, Connolly combines techniques, including analysis of satellite-derived data and remote-sensing data products, to predict groundwater arsenic levels and variability around the world. His work may be used to help identify areas and populations at risk for excessive arsenic exposure, and points these communities towards the reasons for contamination and where they may find safer drinking water sources. He also uses data to forecast groundwater arsenic levels in regions facing climate-driven changes. 

Connolly received his Ph.D. in marine science from the University of Texas at Austin. He came to Columbia’s Lamont-Doherty Earth Observatory in 2019 as a U.S. Geological Survey John Wesley Powell Center for Analysis and Synthesis fellow. Today, he works with Columbia’s Benjamin Bostick and Ana Navas-Acien and Union College’s Mason Stahl to integrate international and U.S.-based water quality data, improve understanding of arsenic distribution, and develop better solutions to manage arsenic contamination in groundwater.

“We focus on variables that can be acquired remotely—geospatial, satellite, etc. This data is very important, especially for building predictive models, where we need many samples across an entire state,” Connolly explained.

In a recent project, the colleagues combined a unified dataset for global arsenic contamination with geospatial satellite data on the presence or absence of surface water to examine the environmental drivers of groundwater arsenic contamination in south and southeast Asia. Without needing to collect any data in the field, the team built a machine learning model that accurately predicts groundwater arsenic concentrations in Cambodia, Vietnam, and Bangladesh, and helped determine that monsoonal surface flooding is a key driver for groundwater arsenic contamination. An article describing their findings is currently under review for publication.

Connolly received a fellowship from the National Institute of Environmental Health Sciences to conduct research through Mailman and the Data Science Institute in 2020. He applies what he learned in Asia to the U.S. for a collaboration with the Columbia Superfund Research Program. This research is also linked to the Strong Heart Study, which looks at the connection between arsenic exposure and heart disease in Native American communities. 

Connolly’s investigations into arsenic contamination also include rice, which is an important staple for human consumption globally and a vital source of livelihood for small-scale farmers in south and southeast Asia. Another recent project with Bostick and Stahl explores how climate change and flooding affect arsenic levels in groundwater and rice in both Cambodia and the U.S. This project leverages an AI for Earth grant from Microsoft and data from Planetary Computer.

“What interests me most is doing science that has a real world impact on people’s lives,” Connolly said. “Given how important these issues are globally, I want to use my skills and experience to understand the key drivers.”

— Karina Alexanyan, Ph.D.