Postdoctoral Scholar · Stanford University
I build methods that turn air-pollution measurements into decisions about where and how to cut emissions.
My work combines measurements, atmospheric models, Bayesian inference, and optimization to study pollution at the scales where people experience it and policy acts on it.
My dissertation moved from reconstructing urban pollution, to estimating the emissions behind it, to using those same inverse methods to design efficient emission-control strategies.
Google Street View measurements and fixed sensors combined into high-resolution urban black-carbon fields using compressive sensing.
ES&T 2024 ↗︎Bayesian inversion used observed concentration patterns to update emissions and evaluate localized interventions such as freight rerouting.
Preprint ↗︎Repurposing inverse modeling to meet air quality, climate, and equity objectives with the least cumulative emissions reductions or cost.
Preprint ↗︎I am an environmental engineer and a postdoctoral scholar in the ECHO Lab at Stanford’s Doerr School of Sustainability, working with Marshall Burke.
I completed my Ph.D. in Environmental Engineering at UC Berkeley in 2026 with Joshua Apte and Robert Harley. My dissertation combined mobile and fixed-site measurements to reconstruct pollution at city-block scales, then used those concentration fields to estimate the emissions that produced them.
Earlier, at IIT Delhi, I worked with chemically speciated PM2.5 measurements during Diwali and the COVID-19 lockdown. Across these projects, the common thread is using measurements to learn something actionable about sources, exposure, or control.
I’m especially interested in methods that connect observations to decisions without throwing away the spatial and temporal structure in between.
Mobile monitors, fixed sensors, aircraft, and satellites each see a different slice of the atmosphere. I work on methods that combine those observations while keeping their different resolutions and uncertainties explicit.
Now: extending high-resolution data fusion to satellite and aircraft observations, including wildfire-smoke applications.
I use Bayesian inversion to update bottom-up emissions inventories from measured concentration patterns. At fine resolution, this makes it possible to distinguish individual corridors, facilities, and neighborhoods.
Now: faster transport models and differentiable emulators for repeated inversion and scenario testing.
I use optimization to compare reductions by location, sector, and chemical precursor, including air-quality, greenhouse-gas, and exposure-disparity objectives.
Now: finding multiple feasible policy options and representing how households and firms respond after a policy is introduced.
C. Manchanda*, L. Koolik*, A. Ünal, I. Fung, J. Marshall, R. Morello-Frosch, A. Turner, R. Harley, and J. Apte. Inverse modeling identifies efficient emission control strategy for mitigating PM2.5 pollution. Environmental Science & Technology.
C. Manchanda, L. Koolik, A. Ünal, R. Harley, J. Marshall, and J. Apte. Deconstructing the distributional impacts of air pollution controls. Environmental Science & Technology.
L. Koolik*, C. Manchanda*, A. Ünal, I. Fung, J. Marshall, R. Morello-Frosch, A. Turner, R. Harley, and J. Apte. Modeling optimal pathways to a triple win in air quality, climate, and equity.
C. Manchanda, R. Cohen, R. Alvarez, T. Thompson, M. Harris, A. Turner, J. Marshall, R. Harley, and J. Apte. Hyperlocal sensing and inversion reveal community impacts of urban air pollutant emissions. Science Advances.
J. Apte and C. Manchanda. High-resolution urban air pollution mapping. Science, 385, 380–385, 2024.
C. Manchanda, R. Harley, J. Marshall, A. Turner, and J. Apte. Integrating mobile and fixed-site black carbon measurements to bridge spatiotemporal gaps in urban air quality. Environmental Science & Technology, 58, 12563–12574, 2024.
C. Manchanda, M. Kumar, V. Singh, N. Hazarika, M. Faisal, V. Lalchandani, A. Shukla, J. Dave, N. Rastogi, and S. N. Tripathi. Chemical speciation and source apportionment of ambient PM2.5 in New Delhi before, during, and after the Diwali fireworks. Atmospheric Pollution Research, 13, 101428, 2022.
C. Manchanda, M. Kumar, and V. Singh. Meteorology governs the variation of Delhi's high particulate-bound chloride levels. Chemosphere, 291, 132879, 2021.
C. Manchanda, M. Kumar, V. Singh, M. Faisal, N. Hazarika, A. Shukla, V. Lalchandani, V. Goel, N. Thamban, D. Ganguly, and S. N. Tripathi. Variation in chemical composition and sources of PM2.5 during the COVID-19 lockdown in Delhi. Environment International, 153, 106541, 2021.
A. Saxena, E. Ng, C. Manchanda, and T. Canchi. Cardiac thermal pulse at the neck-skin surface as a measure of stenosis in the carotid artery. Thermal Science and Engineering Progress, 19, 100603, 2020.
A. Saxena, E. Ng, M. Mathur, C. Manchanda, and N. Jajal. Effect of carotid artery stenosis on neck skin tissue heat transfer. International Journal of Thermal Sciences, 145, 106010, 2019.
* Co-first authorship. Current list on Google Scholar ↗︎ and ORCID ↗︎.
J. Apte, R. Harley, C. Manchanda, L. Koolik, and J. Marshall. Systems, methods, and program products for reducing air pollution for one or more pollutants in a locality.
J. Apte, R. Harley, C. Manchanda, J. Marshall, and A. Turner. Systems, methods, and program products for detecting emissions of an airborne pollutant on a hyperlocal scale.
Postdoctoral Scholar · ECHO Lab · Doerr School of Sustainability
Graduate Student Researcher · Environmental Engineering
Research Associate · Air Quality Research Group
NTU–India Connect Research Scholar