Significant pollution heterogeneity persists across New York State's (NYS) underserved
communities, which are disproportionately burdened by air pollution from sources that traditional,
sparse regulatory monitoring networks fail to capture. This fine-scale variability limits effective
public health interventions and perpetuates environmental injustice. This research project directly
addresses this challenge by deploying a dense, community-driven air quality monitoring network
across targeted neighborhoods. My doctoral research is built upon five years of international
fieldwork experience managing air quality monitoring projects, from network design and leading
field teams in Ghana and abroad, to advanced data analysis and community engagement.
A critical challenge with low-cost sensors is ensuring data reliability, as performance is
highly dependent on local pollution sources and meteorological conditions. Therefore, a core
component of my dissertation will be to leverage my experience from the Air Sensor Evaluation
and Training Facility for West Africa project to develop and validate a new, robust calibration
protocol specifically tailored for the NYS environment. I will utilize statistical models including
hygroscopic growth correction, multiple linear regression and advanced machine learning to
produce a scientifically reliable and credible, two-year dataset of key pollutants, including climate forcing
black carbon (BC), PM2.5, and ozone, from 15 fixed ambient sites and 200 homes.
The validated data will be used in the source apportionment analysis, applying EPA Positive
Matrix Factorization (PMF) to identify and quantify community-specific pollution sources, such
as traffic and residential wood burning. Rooted in a community-based participatory research
framework, this project will provide actionable, hyperlocal understanding to complement
traditional monitoring. The outcomes will be transformative, producing a novel, high-quality
dataset for the scientific community while simultaneously empowering residents with the evidence
needed to advocate for targeted mitigation initiatives. This work directly fulfills the APERG
mission to fund research that solves critical air pollution problems and contributes meaningfully
to the well-being of society.
communities, which are disproportionately burdened by air pollution from sources that traditional,
sparse regulatory monitoring networks fail to capture. This fine-scale variability limits effective
public health interventions and perpetuates environmental injustice. This research project directly
addresses this challenge by deploying a dense, community-driven air quality monitoring network
across targeted neighborhoods. My doctoral research is built upon five years of international
fieldwork experience managing air quality monitoring projects, from network design and leading
field teams in Ghana and abroad, to advanced data analysis and community engagement.
A critical challenge with low-cost sensors is ensuring data reliability, as performance is
highly dependent on local pollution sources and meteorological conditions. Therefore, a core
component of my dissertation will be to leverage my experience from the Air Sensor Evaluation
and Training Facility for West Africa project to develop and validate a new, robust calibration
protocol specifically tailored for the NYS environment. I will utilize statistical models including
hygroscopic growth correction, multiple linear regression and advanced machine learning to
produce a scientifically reliable and credible, two-year dataset of key pollutants, including climate forcing
black carbon (BC), PM2.5, and ozone, from 15 fixed ambient sites and 200 homes.
The validated data will be used in the source apportionment analysis, applying EPA Positive
Matrix Factorization (PMF) to identify and quantify community-specific pollution sources, such
as traffic and residential wood burning. Rooted in a community-based participatory research
framework, this project will provide actionable, hyperlocal understanding to complement
traditional monitoring. The outcomes will be transformative, producing a novel, high-quality
dataset for the scientific community while simultaneously empowering residents with the evidence
needed to advocate for targeted mitigation initiatives. This work directly fulfills the APERG
mission to fund research that solves critical air pollution problems and contributes meaningfully
to the well-being of society.