New York State regulates construction and demolition waste (CDW)—its generation, recycling and reuse—and collects all data on CDW from private waste haulers and transfer stations/recycling facilities. There is no city source of data for CDW. For the city to innovate policy with respect to CDW, which is a source of embedded carbon, by leveraging its capital program to close material loops, generating environmental sustainability and financial sustainability benefits, it is important to understand where CDW goes after the demolition process through the transfer and recycling processes.

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Chronic exposure to arsenic (As) in groundwater is a staggering global public health crisis and yet, we lack a complete understanding of the environmental conditions that govern As mobility and toxicity in groundwater and are unable predict groundwater As concentrations with enough confidence to make effective management decisions. The objective of this project is to identify key hydrologic and biogeochemical variables that control groundwater As concentrations and heterogeneity across spatial scales in Southeast Asia and the USA. We then aim to develop clear mechanistic linkages and high-resolution geospatial information that can be used with machine learning to evaluate and predict groundwater As contamination. This project involves the integration of various types of large datasets from remotely-sensed and field-collected measurements (e.g., surface hydrology and topography, groundwater geochemistry, climate, and population density). We are looking for a student to advance the connections between key environmental variables and groundwater As contamination across scales. The student will receive experience and mentorship in cutting-edge research that crosses interdisciplinary fields, and will have the opportunity to lead their own project and acquire analytical skills using creative measures, which can involve remote sensing, geospatial methods, statistics and graphing, machine learning, and predictive modeling.

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Getting a better approximation of the age of a NYC’s building can improve assigning the building to a structural type that includes type of construction and relevant building code in effect. Mapping the age and type of building would help NYC DOB and the City on a number of fronts, which include enabling NYC DOB to be more effective in enforcing building and construction safety and evaluating risk when adjacent or nearby subsurface construction is proposed. Furthermore, the more precise characterization of NYC buildings will improve efforts by the City to craft policies aimed at energy efficiency (TWG) as it drives to 80% GHG reductions by 2050 (80X50) and determining natural disaster vulnerability of its building stock (HAZUS).

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The State regulates Construction and Demolition Waste (CDW) — its generation, recycling and reuse — and collects all data on CDW. There is no city source of data for CDW. For the city to innovate policy with respect to CDW by leveraging its capital program as one way to close material loops, which would generate environmental sustainability and financial sustainability benefits, understanding where CDW goes from the demolition process through the recycling process is the most important single step.

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DEP uses near real-time water quality data to guide its operations (i.e., the selection and routing of water) to achieve optimum quality for consumers. Historical data is used to evaluate the effectiveness of watershed protection programs, and model predictions of future water quality are used to understand potential impacts to the water supply under different infrastructure and climate scenarios.

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The main goal of this work is to assess if storms have increased in frequency over Antarctica. It is theorized that climate change will increase the intensity of the winds and frequency of the storms. With ICESat 2 satellite laser altimetry, we can count the number of storms and blowing snow events. ICESat 2 is a photon counting laser and generates terrabytes of data each day. Innovative data science techniques are needed to handle the data and analyze it. This project is, therefore, a suitable topic for a masters student that combines an important problem in Geophysics and climate science with a great Data Science application.

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