About

Robert Harley is a Professor of Civil and Environmental Engineering at UC Berkeley and the inaugural holder of the Carl W. Johnson Endowed Chair in Civil and Environmental Engineering. His research interests include air quality modeling, atmospheric chemistry, sustainable transportation, climate change mitigation and adaptation, and emission source characterization and control. Harley's group employs mathematical models and data from field experiments to understand air pollution problems, atmospheric chemistry, and related issues. His work encompasses the development of air quality models to analyze the formation of pollutants such as tropospheric ozone and PM2.5 from precursor emissions, which involve complex photochemical reactions in the atmosphere. Additionally, Harley has led the development of a fuel-based approach to estimating motor vehicle emissions, measuring vehicle activity through fuel consumption and expressing emission rates per unit of fuel burned. This approach has contributed to policy-relevant revisions in national and state-level emission inventories. Harley's research aims to address the heavy burden of air pollution from fossil fuel combustion and promote more sustainable, environmentally benign energy practices.

Research topics

• Environmental science
• Engineering
• Political Science
• Environmental engineering
• Meteorology
• Environmental protection
• Waste management
• Geography
• Chemistry
• Economics
• Environmental planning
• Ecology
• Environmental health
• Automotive engineering
• Natural resource economics
• Transport engineering
• Business

Selected publications

• Health Implications and Deployment Potential of Post-Combustion CCS in the U.S. Power Sector

  2026-03-13

  articleOpen access

  Post-combustion carbon capture and storage (CCS) can substantially reduce CO2 emissions from coal and natural gas combined cycle (NGCC) power plants before entering the atmosphere. Little is known about the maximum potential of CCS across U.S. thermoelectric power plants or the potential air pollution and resulting human health effects associated with its retrofit. Integrating CCS affects other air pollutant emissions as well. The flue gas must be adequately pretreated to remove air pollutants that react with solvents to cause losses, while solvents can break down and lead to ammonia (NH3) emissions. In this study, we explore the air pollution and CO2 emissions impacts of national-scale post-combustion CCS adoption at coal and NGCC plants in the U.S. using monoethanolamine (MEA) and CESAR1 as representative first- and second-generation solvents, respectively. We quantify the effects of CCS on human health using the InMAP Source-Receptor Matrix (ISRM), which transforms emissions of primary PM2.5 and precursors of secondary PM2.5 into total changes in concentrations. This analysis brings together four main components in an integrated assessment model: (1) power plant CCS retrofit scenarios for coal and NGCC plants, (2) grid mix and generation scenario modeling, (3) plant-level emissions changes, and (4) the quantification of human health and greenhouse gas (GHG) emissions impacts.If CCS retrofits are only viable on newer facilities, 97% of NGCC plant emissions are addressable compared to only 27% of coal plant emissions. Potential human health benefits of CCS retrofits are concentrated at coal plants, where the net benefits of added flue gas pretreatment are substantial, regardless of solvent. NGCC plants, however, require NH3 emissions controls and/or modern solvents, as using MEA without NH3 emissions controls could increase net human health burdens fourfold. This study shows that post-combustion CCS using amine-based solvents can have human health co-benefits or co-burdens depending on the solvent choice, fuel type, existing flue gas concentration, and presence of NH3 emission controls. Further, we provide policy-relevant recommendations for achieving greenhouse gas reduction benefits while limiting air pollution-related human health effects.

  PublisherDOI

• A Bayesian Inverse Modeling Approach to Achieving Triple Wins in Air Quality, Climate, and Equity

  2026-03-14

  articleOpen access

  With increasing urgency to mitigate air pollution, climate change, and racialized exposure disparities, decision-makers in the United States (US) are faced with three distinct challenges that arise from the same sources but are often managed separately. This is in part because traditional environmental policies are generally designed based on a forward simulation approach: formulating an idea, estimating emission-changes, and modeling the resulting changes to air pollution, climate mitigation, and environmental justice. This process is computationally inefficient for testing multiple strategies and poorly suited for optimizing outcomes that address multiple objectives. Here, we reverse this pipeline to derive emission-reduction pathways that represent the optimal “triple win” strategy for mitigating air pollution exposure, climate change, and exposure inequity across the contiguous US.To do this, we build upon our novel receptor-oriented, Bayesian optimization method by incorporating an additional cost function that reweights reductions for other priorities. Our approach begins from an atmospheric inverse modeling framework, whereby we set an idealized concentration surface — meeting the US National Ambient Air Quality Standard for particulate matter (PM2.5) everywhere — as the target variable. Using an alternating gradient descent algorithm, we perturb this optimal solution to find the co- or triple-benefits associated with advancing climate or equity goals. We consider four optimal emission reduction scenarios representing distinct combinations of policy goals: PM2.5 Exposure Alone, Climate Priority, Equity Priority, and Triple Win. Our solutions are discretized in space, by precursor pollutant, and by the economic sector of emission.Although all scenarios meet the PM2.5 standard, preliminary results suggest that meeting different combinations of goals requires attention to diverse locations, chemical species, and sectors. While the difference in total aggregate emissions reduction is small when comparing the PM2.5 Exposure Alone case with the other priorities, incorporating additional priorities up front enables the direct identification of distinct mitigation pathways in space and by sector (e.g., marine vessels are important for climate mitigation). We demonstrate how a non-optimal emission reduction pathway results in lesser or neutral air quality and climate benefits; however, the non-optimal reduction pathway can also result in significant harms in terms of environmental injustices. This framework could have strong implications for how we think about the challenge of how environmental policy can advance action against compounding risks. Our approach provides a data-driven and scalable strategy for simultaneously achieving a triple win across exposure, climate, and equity goals.

  PublisherDOI

• Planning-Oriented Receptor Modeling: Apportioning Emissions Reductions Required for PM2.5 Attainment

  2026-03-14

  articleOpen access

  Air-quality concentration standards inherently do not specify which emissions controls are necessary to achieve them. Such standards set up a planning challenge that is fundamentally underdetermined, since many distinct emissions pathways can achieve the standard. Forward scenario testing rarely reveals which control levers are truly required versus merely sufficient, and does not necessarily identify optimal approaches. Here, we present a planning-oriented receptor modeling framework that inverts the traditional source apportionment approach. Instead of attributing observed concentrations to sources, we apportion the emissions reductions required for attainment to specific locations, precursors, and sectors, conditional on receptor-based concentration constraints.We couple a source–receptor sensitivity matrix (mapping emissions changes to downwind concentration responses at receptors) with a constrained Bayesian inverse problem that infers the minimal, spatially explicit emissions changes needed to meet a fine particulate matter (PM2.5) concentration target everywhere (or within a specified attainment definition). An emissions prior regularizes solutions toward a baseline inventory, while constraints enforce physical and policy realism (e.g., non-negativity, sectoral controllability, optional caps/targets by precursor or region). This yields a transparent “control apportionment” output dictating how much each source category must change and where, in order to satisfy receptor targets. In addition, the model estimates uncertainty-aware diagnostics of which receptors bind and which sources dominate the required controls.In application across the contiguous United States, we show that strategies with comparable economy-wide reductions (~10%) can produce dramatically different attainment outcomes depending on spatial allocation, ranging from near-universal compliance to minimal improvements in population exposure. By systematically exploring the feasible solution space, we quantify a compliance penalty for misallocation: the additional emissions reductions required when controls are applied non-optimally. Together, the framework bridges receptor modeling and attainment planning by producing source-resolved, defensible control requirements and actionable diagnostics that help agencies benchmark, compare, and stress-test attainment strategies.

  PublisherDOI

• Connecting Urban Black Carbon Emissions and Measured Concentrations: A Fusion of Hyperlocal Monitoring and Bayesian Techniques

  2025-03-14

  preprintOpen access

  Understanding urban air pollution at fine scales is essential for pinpointing emission sources that disproportionately impact vulnerable communities. Traditional emission inventories often suffer from insufficient spatial granularity and lack observational grounding, thus hampering effective source-specific interventions.Here, we introduce a novel application of receptor-oriented models (RMs) for the hyperlocal source apportionment of black carbon (BC). By integrating a rich dataset from both dense mobile monitoring and temporally detailed fixed-site measurements into a Bayesian inversion framework using the WRF-STILT model, we quantify BC contributions in the community of West Oakland, CA, USA from diverse urban sources including on-road vehicles (notably diesel trucks), locomotives, port cargo-handling equipment, and maritime vessels, with a high spatial resolution of 150 meters (~0.02 km2). We reveal a wide variety of uninventoried neighborhood-scale emissions sources that substantially impact this overburdened community.Our method employs a data-driven spatiotemporal model that combines both mobile and fixed-site data within a factor analysis framework, providing robust observational constraints for Bayesian inference. The robustness of our method is particularly notable given the uncertainties in prior emissions inventories. Moreover, we demonstrate that with only 10 strategically placed stationary sensors within a 15 km2 area, supplemented by time-averaged mobile measurements, reliable source apportionment can be achieved.This study advances the methodology of RMs by providing a scalable and adaptable approach for incorporating hyperlocal measurements, providing critical insights into the effectiveness of these models in real-world urban scenarios. Future applications of the method would support observationally constrained strategies for fine-scale urban emissions tracking and community-centered air quality improvements.

  PublisherDOI

• Impact of truck electrification on air pollution disparities in the United States

  Nature Sustainability · 2025-02-11 · 25 citations

  articleOpen access

  Abstract Electrifying heavy-duty trucks reduces on-road diesel emissions but shifts the burden of supplying energy to power-generation facilities. The combined effect of Inflation Reduction Act investments in grid decarbonization and truck electrification will alter the magnitude and distribution of air pollution burdens across the United States. These investments are intended to facilitate a just energy transition, with 40% of the benefits flowing to disadvantaged communities per the Justice40 Initiative. Here we evaluate the combined effects of Inflation Reduction Act grid decarbonization and truck electrification investments on a national scale to determine whether the air pollution benefits would meet this 40% goal for both disadvantaged communities and the most exposed racial–ethnic groups. We find that truck electrification and decarbonization reduce air-pollution-related premature mortality in disadvantaged communities. However, the relative disparity between disadvantaged and non-disadvantaged communities increases, suggesting that a disproportionate share of benefits accrue to non-disadvantaged communities. Whereas absolute disparity in grid emissions decreases over time for all racial–ethnic groups, relative disparity remains largely unchanged, with Black populations being the most exposed. Electrifying drayage corridors would result in comparatively large health benefits for disadvantaged communities, suggesting that increasing targeted electrification investments in short-haul routes near urban areas (for example, ports) could be promising.

  PublisherOA PDFDOI

• Hyperlocal Sensing and Inverse Modeling Reveal Community Impacts of Urban Air Pollutant Emissions

  ChemRxiv · 2025-10-13

  article

  Addressing urban air pollution requires identifying, quantifying, and mitigating emission sources, yet bottom-up emissions inventories often lack fine spatiotemporal precision and rarely capture unpermitted or informally operated sources. For the first time, we combine dense mobile and fixed-site air pollution measurements in a Bayesian inverse modeling framework to produce hourly, hyperlocal (150 m × 150 m ≈ 0.02 km2) black carbon emission maps that directly reveal how and where key sources may be underrepresented. In a test case in West Oakland, CA — a community with long-documented elevated diesel particulate matter exposures and proximity to major freight infrastructure — our approach identifies previously unaccounted and underestimated emission sources, increasing total estimated BC emissions by ~33%. Crucially, these corrections quadruple the contribution of neighborhood and port-related sources to population-weighted exposures (from 5 to 20%), disproportionately affecting low-income neighborhoods adjacent to freight corridors. Our results demonstrate that combining multi-platform, dense observational data with inverse modeling enables reliable detection and quantification of overlooked emissions, supporting more precisely targeted policy intervention.

  PublisherDOI

• Temperature and Stagnation Effects on Ozone Sensitivity to NO _x and VOC: An Adjoint Modeling Study in Central California

  2025-08-22

  articleOpen accessSenior author

  Abstract. Extreme weather events like heatwaves and stagnation are increasing with climate change. While their effects on ozone levels have been extensively studied, how extreme weather alters O3-NOx-VOC sensitivity and optimal mitigation strategies is less explored. Here, we apply the CMAQ adjoint model over central California to quantify ozone sensitivity to spatiotemporally resolved precursor emissions under three meteorological scenarios (baseline, high-T, and stagnation) and three emission years (2000, 2012, and 2022). Results show that meteorology-induced changes in sensitivity are comparable in magnitude to those from decadal emission reductions. Higher temperature (+5 °C) amplifies ozone sensitivity to both NOx and VOC, with the largest relative increase in biogenic VOC sources. High-T conditions shift ozone chemistry toward NOx limitation under a VOC-limited emission scenario, but increase the relative importance of VOC control for a NOx-limited scenario. Stagnation consistently pushes ozone chemistry toward VOC limitation across emission scenarios, increasing VOC sensitivity by a factor of ~3–4. Stagnation also spatially shifts influential source areas, especially for NOx, and temporally amplifies prior-day emission impacts due to enhanced pollutant carryover. As the study domain transitions to a NOₓ-limited regime over time, we identify a growing subset of "climate-resilient" source targets that remain impactful across meteorological scenarios, along with spatial convergence in optimal locations for NOx and VOC emission control. These findings underscore both the need and feasibility to consider meteorological extremes in the design of ozone mitigation strategies for a warming climate.

  PublisherOA PDFDOI

• Supplementary material to "Temperature and Stagnation Effects on Ozone Sensitivity to NO _x and VOC: An Adjoint Modeling Study in Central California"

  2025-08-22

  articleOpen accessSenior author
  PublisherDOI

• Modeling Optimal Pathways to a Triple Win in Air Quality, Climate, and Equity

  ChemRxiv · 2025-09-15

  preprintOpen access

  “Triple win” strategies in air quality are those that benefit health, climate, and equity. Traditional air pollution modeling estimates these outcomes based on simulated changes in emissions. Here, we develop an inverse modeling approach to identify optimal “triple wins” for the contiguous United States. We use receptor-oriented Bayesian optimization to derive spatially-explicit emission-reductions that meet national standards for fine particulate matter (PM2.5) concentrations while (1) reducing CO2 emissions and (3) reducing disparities in exposure to PM2.5. We compare our optimal solutions against a solution space of over a million runs derived from conventional “forward engineering” approaches. Our approach demonstrates important advantages over conventional methods. It is modular across space, sector, and priority set, and provides a data-driven and scalable framework for explicitly identifying multivariable solutions for air, climate, health, and equity.

  PublisherOA PDFDOI

• Temperature and stagnation effects on ozone sensitivity to NO _<i>x</i> and VOC: an adjoint modeling study in central California

  Atmospheric chemistry and physics · 2025-12-04 · 1 citations

  articleOpen accessSenior author

  Abstract. Extreme weather events like heatwaves and stagnation are increasing with climate change. While their effects on ozone levels have been extensively studied, how extreme weather alters O3-NOx-VOC sensitivity and optimal mitigation strategies is less explored. Here, we apply the CMAQ adjoint model over central California to quantify ozone sensitivity to spatiotemporally resolved precursor emissions under three meteorological scenarios (baseline, high-T, and stagnation) and three emission years (2000, 2012, and 2022). Results show that meteorology-induced changes in sensitivity are comparable in magnitude to those from decadal emission reductions. Higher temperature (+5 °C) amplifies ozone sensitivity to both NOx and VOC, with the largest relative increase in biogenic VOC sources. High-T conditions shift ozone chemistry toward NOx limitation under a VOC-limited emission scenario, but increase the relative importance of VOC control for a NOx-limited scenario. Stagnation consistently pushes ozone chemistry toward VOC limitation across emission scenarios, increasing VOC sensitivity by a factor of ∼ 3–4. Stagnation also spatially shifts influential source areas, especially for NOx, and temporally amplifies prior-day emission impacts due to enhanced pollutant carryover. As the study domain transitions to a NOx-limited regime over time, we identify a growing subset of “climate-resilient” source targets that remain impactful across meteorological scenarios, along with spatial convergence in optimal locations for NOx and VOC emission control. These findings underscore both the need and feasibility to consider meteorological extremes in the design of ozone mitigation strategies for a warming climate.

  PublisherOA PDFDOI

Frequent coauthors

• Thomas W. Kirchstetter

  University of California, Berkeley

  101 shared

• Gary R. Kendall

  Bay Area Air Quality Management District

  43 shared

• Brett C. Singer

  41 shared

• W. C. Kuster

  National Oceanic and Atmospheric Administration

  38 shared

• A. H. Goldstein

  37 shared

• Nancy J. Brown

  Yale University

  34 shared

• Brian McDonald

  NOAA Earth System Research Laboratory

  34 shared

• D. D. Parrish

  32 shared

Awards & honors

• Carl W. Johnson Endowed Chair in Civil and Environmental Eng…