Environmental Health Sciences · University of Arizona
Mahdi Faraji
What you cannot see is still a dose.
Air QualityGround–Satellite IntegrationClimate Health EffectsDigital Public HealthAI
Connected through Digital Public Health
I — Exposure
The invisible burden
The particles that matter most sit below the threshold of sight. The smallest travel deepest, past every filter the body has.
Size decides depth. Exposure becomes dose without announcing itself.
II — Measurement
A reading is not yet evidence
Instruments became cheap before they became trustworthy. A sensor earns its number beside a reference, and only against a future it has not seen.
Machine learning belongs here as an instrument, held to the standard of one.
III — Resolution
Certainty is a matter of density
Regulators answer at the scale of a county. People live at the scale of a block.
Orbit gives coverage, the ground gives resolution. The work is in the fusion. Heat arrives the same way, and with the same inequity.
IV — Intelligence
An instrument you can ask
A sensor network produces a number every minute. A person needs an answer once.
An interface is where measurement becomes prevention. A model can carry a calibrated reading to the person standing in it — if it stays grounded in what was observed, and honest about what was not.
V — Profile
Mahdi Faraji
I am a PhD researcher in Environmental Health Sciences at the University of Arizona, with a doctoral minor in Digital Public Health.
My research develops machine learning methods that calibrate low-cost air quality sensor networks to the standard exposure assessment requires, and combines ground measurement with satellite retrieval to resolve air pollution and heat exposure at the scale of a neighborhood.
My training in occupational health engineering, combined with research on the health effects of noise and air pollution in communities and professional experience in environmental health and safety, has shaped my approach to research. These experiences continue to inform my emphasis on environmental epidemiology, public health, methodological rigor, and the depth of insight that robust environmental data can support.