Research

Overview

My research focuses on understanding the fundamental chemistry of natural and engineered systems using a combination of laboratory, field, and computer-based modeling studies for a sustainable future. Specifically, I am interested in understanding the effects of natural and anthropogenic activities on the fate of pollutants and carbon cycling, and gaining mechanistic insights into the chemical, physical, and biological fate of synthetic contaminants and carbon in the environment. Currently, I am interested in two main research topics:

(i) Application of instrumentation and chemistry principles to identify and understand the fate of synthetic contaminants and carbon cycling in natural environmental compartments and engineered systems.

(ii) Using cheminformatics, metabolomics, artificial intelligence, and/or machine learning for small molecules and natural organic matter.

Research Area 1

Experimental and Analytical Environmental Science to Unravel Processes in Natural and Engineered Systems

Transformation processes occurring in natural and engineered environments control the persistence of contaminants, the cycling of carbon, and ultimately the quality of our water resources and ecosystems. Yet many of these processes remain poorly understood because they occur through complex molecular interactions that cannot be resolved using conventional environmental measurements alone. My research seeks to understand these processes by combining field investigations, laboratory experiments, and molecular characterization to reveal the chemical mechanisms that govern environmental systems. At the center of my experimental research is the application of molecular characterization approaches1, together with complementary analytical techniques to characterize environmental systems. Rather than viewing analytical chemistry solely as a tool for identifying compounds, I use molecular characterization to understand how chemical composition changes in response to natural processes, engineered treatment, and environmental stressors. These molecular-level and analytical chemistry observations provide mechanistic insight into environmental processes and establish the experimental foundation for predictive environmental science.

Research Area 2

ML, AI, and Data Science in Environmental Science and Engineering.

The rapid growth of experimental data, public databases, and scientific literature has fundamentally changed how environmental science and engineering can be studied. High-resolution analytical techniques now generate molecular datasets of unprecedented size and complexity, yet extracting meaningful scientific knowledge from these data remains a major challenge. While ML has transformed fields such as drug discovery, protein engineering, and materials science, its application to environmental science and engineering remains relatively underdeveloped. Environmental datasets are often heterogeneous, sparsely labeled, and distributed across experimental studies, making them difficult to integrate into robust predictive models. Addressing these challenges requires more than simply applying existing ML algorithms. It requires the development of computational methods designed specifically for molecular environmental data. My research seeks to establish molecular artificial intelligence as a foundation for environmental discovery.

Research Area 3

Intelligent Environmental Discovery

Experimental and computational methods are most powerful when they inform one another. Modern experiments generate increasingly complex information, from high-resolution mass spectra and molecular measurements to multivariable process data, while decades of experimental knowledge are distributed across databases and scientific literature. Machine learning and artificial intelligence provide an opportunity not only to interpret these data, but also to uncover relationships that are difficult to recognize through conventional analysis, predict system behavior, and guide future experiments. My third research area brings these capabilities together by integrating experimental and analytical science with ML and AI. The goal is to develop discovery frameworks in which experimental evidence improves computational models, while the resulting predictions and insights help determine what should be measured, tested, or optimized next.

Previous Research

Environmental DOM Photochemistry and Photophysics

Dissolved organic matter (DOM) is a critical component of aquatic systems that serves many purposes. Among them is the production of reactive intermediates when irradiated. Examples of RIs produced when DOM is irradiated include the excited triplet states of dissolved organic matter (3DOM*), singlet oxygen (1O2), hydroxyl radicals (·OH), carbonate radicals, and halide radicals all of which contribute to the degradation of environmental contaminants and disinfection of pathogens. It is therefore critical to understand how the cycling, transformation, and composition of DOM may affect the production of reactive intermediates in aquatic systems. The DOM’s ability to produce RIs was studied using probe compounds that have been well-studied and established as methods for the quantification of RIs. These probe compounds included terephthalic acid (TPA) for •OH, furfuryl alcohol (FFA) for 1O2, 2,4,6-trimethylphenol (TMP) as an electron transfer probe for 3DOM* (), and trans,trans-2,4-hexadien-1-ol (t,t-HDO or sorbic alcohol) as an energy transfer probe for 3DOM*. Overall, this dissertation contributes to the growing knowledge about the photochemical reactivity of DOM in aquatic systems. It advances our understanding of the roles of several natural and anthropogenically influenced activities toward the photochemical reactivity of DOM.

See the publications page for results from this study.

Organic Contaminants Screening and Quantification

In 2017 while planning a trip to Uganda to visit my family, I had an idea that I could use the opportunity to contribute to the scientific knowledge base in Africa. There is a big research gap between Africa and the developed world, yet Africa faces tremendous environmental challenges. Uganda has antiquated wastewater treatment and disposal facilities which have resulted in the contamination of the environment. I decided to carry out a study aimed at understanding the current occurrence and distribution of organic micropollutants (OMPs) in drinking water sources, wastewater treatment plants as well as waterways of Kampala. I wrote a proposal and obtained funding to carry out sampling in Uganda. I initiated and led this collaborative study between Syracuse University and Makerere University in Uganda. Our results prioritized and confirmed 157 OMPs in Kampala samples for target quantification. Many OMPs detected in Kampala samples occurred within concentration ranges similar to those documented in previous studies reporting OMP occurrence in sub-Saharan Africa (SSA), but some have never or rarely been quantified in environmental water samples from SSA. This work has been published as a peer-reviewed journal article and it is the first larger study to evaluate the spatial and temporal distribution of organic micropollutants in a major city in sub-Saharan Africa.

See the publications page for results from this study.

Water Reuse and insitu contaminant sensors

This research aimed at assessing the use of fluorescence spectroscopy as a real time surveillance tool in advanced water reuse operations. I evaluated how temperature changes affects the in-situ fluorescence sensors deployed in water, wastewater, and water reuse operations, and suggested correction protocols to compensate sensor performance for temperature fluctuation. I also I evaluated and compared the potential of both in situ and 3D benchtop fluorescence equipment to monitor contaminants in water, wastewater, and water reuse operation. Another section of this study focused on evaluating the suitability of fluorescence spectroscopy as a tool to monitor membrane fouling during ultrafiltration as well as understanding changes in dissolved organic matter (DOM) bulk properties during the process. Overall, this work demonstrated the potential application of fluorescence spectroscopy to monitor contaminants in advanced water treatment and reuse facilities and developing in situ, quick, and robust contaminant sensors. Fluorescence sensors could serve as an early warning system for source water protection in water operations.

See the publicatiosn page for results from this study.

Summary

The application of cheminformatics in environmental chemistry is not as popular as in other areas like drug discovery, material science, and energy research. My research aims to bridge the gap between environmental analytical chemistry and the fast-growing data science and artificial intelligence industry.

Page updated

Google Sites

Report abuse