About

Joshua Apte is an assistant professor jointly appointed in the School of Public Health and the Department of Civil and Environmental Engineering at UC Berkeley. His research investigates human exposure to air pollution, focusing on issues from the neighborhood scale to the global scale. His work encompasses air pollution, atmospheric aerosols, exposure assessment, risk assessment, environmental justice, environmental engineering, environmental sensors, climate change mitigation, and environmental issues in developing countries. Apte holds an ScB in Environmental Science from Brown University and both MS and PhD degrees in Energy and Resources from UC Berkeley. Prior to his current position, he was an Assistant Professor at the University of Texas at Austin in the Department of Civil, Architectural and Environmental Engineering from 2015 to 2020.

Research topics

• Geography
• Environmental science
• Political Science
• Medicine
• Meteorology
• Environmental health
• Atmospheric sciences
• Chemistry
• Physics
• Environmental chemistry
• Geology
• Waste management
• Natural resource economics
• Engineering
• Mineralogy
• Environmental protection
• Ecology
• Demographic economics
• Climatology
• Demography
• Thermodynamics
• Inorganic chemistry
• Psychology
• Biology

Selected publications

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

  2026-03-14

  articleOpen accessSenior author

  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 accessSenior author

  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

• Urban Natural Gas Seasonality is Associated with Commercial Areas in Berkeley, California

  2026-03-13

  articleOpen accessSenior author

  Recent measurement studies have found that urban natural gas (NG) emissions are 3.9× larger than bottom-up inventory estimates, on average, across various North American cities.1 Several studies have proposed that post-meter emissions may be a substantial missing source in urban methane (CH4) estimates, but the role of diffuse residential and commercial NG consumption in overall emissions remains uncertain. Long-term, continuous eddy covariance flux measurements can help clarify possible post-meter contributions by providing localized, high-resolution observations of cumulative emissions. Here we present nearly 3 years of CH4 flux measurements collected between July, 2022 and April, 2025 from a 42 m tall stationary tower located in downtown Berkeley, California, USA. Methane source types were characterized using contemporaneous ethane and δ13CH4 measurements, and spatially resolved population, building, and land use datasets were used to determine possible post-meter emissions drivers. Average annual CH4 fluxes in Berkeley were 152 nmol m-2 s-1 [95%: 150,155] and were primarily attributed to natural gas. Fluxes were dominated by a persistent spatial gradient wherein higher fluxes were associated with increased commercial building space and lower population density in the downtown core, with estimated average annual fluxes ranging from 85 nmol m-2 s-1 [95%: 82.8, 88.3] in residential areas to 218 nmol m-2 s-1 [95%: 214, 223] downtown. Flux diurnal trends were distinct between different seasons and dominant land uses, but no significant weekday-weekend differences were observed. Residential areas had lower diurnal variation and higher springtime fluxes—exhibiting no positive correlation with NG consumption. In denser commercial areas, CH4 fluxes were significantly lower during warmer months, and monthly emissions were positively correlated with NG consumption at rates of 0.21% and 0.23%. Overall fluxes were 5× larger than the highest inventory estimates and were elevated relative to urban eddy covariance studies in similarly sized European and Asian cities. Our results emphasize how eddy covariance studies can help identify and track the drivers of larger urban CH4 emissions trends and the importance of evaluating these trends across different spatial scales.[1] Vollrath, et al. (2025) Environ. Res. Lett.

  PublisherDOI

• A Decade of Progress: Quantifying Air Pollution Reductions in West Oakland, CA with Hyperlocal Monitoring (2015-2025)

  2026-03-13

  articleOpen accessSenior author

  West Oakland, California has experienced disproportionate exposure to diesel-related air pollution due to its proximity to the Port of Oakland, major freeways, and freight corridors. In the last decade, California has increased statewide diesel truck emission regulations while Assembly Bill 617 (AB617) has directed targeted local mitigation investments through community-engaged planning. This study quantifies changes in air pollution across West Oakland spanning the decade of 2015-2025 to evaluate these multilevel interventions. We augmented Google Street View vehicle measurements from 2015-2017 by deploying the UC Berkeley Mobile Air Pollution Laboratory to systematically map air pollution at a 30 m resolution on all accessible roads in West Oakland throughout 2025. We focus on black carbon (BC) and nitrogen oxides (NO, NO2) and also draw on additional extensive gas and particle phase air toxic measurements. Spatial patterns were analyzed across seven community-identified impact zones and supplemented with long-term regulatory monitoring trends contextualized against California and national networks. We find substantial reductions of all pollutants between 2015 and 2025. On average, BC, NO and NO2 decreased by 55%, 39% and 38% respectively, with most impact zones meeting community-designated air quality targets. The largest improvements were seen on diesel-heavy port and freeway corridors from which concentration gradients diminished, indicating reduced near-source exposures. West Oakland's improvements exceeded regional trends at other monitoring sites, suggesting local interventions provided benefits beyond statewide policies. Overall, we demonstrate the effectiveness of multilevel approaches combining regulatory standards with targeted, community-guided local investments in overburdened communities.

  PublisherDOI

• Super-Emitters On California Roads -On-road VOC fingerprinting from Mobile Monitoring

  2026-03-13

  articleOpen accessSenior author

  Various programs, regulations, and technologies targeting emissions from the vehicle fleet on roadways around the world have made significant air quality gains over the past few decades. However, recent monitoring in the San Francisco Bay and surrounding areas by the UC Berkeley Mobile Air Pollution Laboratory (CalMAPLab) has shown that high- emission vehicles (“super-emitters”) are likely now having an outsized impact on total fleet emissions. Fingerprinting and bounding the emission factors for these super-emitters is therefore critical in assessing the overall impact on air toxics from these vehicles. Here we present extensive chemical speciation (VOCs, combustion tracers, GHGs) from on- road and on-highway emissions measurements around the Bay Area performed by the CalMAPLab in 2025. We present and compare the speciated fingerprints for vehicles powered by gasoline and diesel, and super-emitters in these classes.

  PublisherDOI

• Supplementary material to "Design, operation and characterization of a mobile laboratory for community-scale atmospheric research"

  2026-03-11

  articleOpen accessSenior author
  PublisherDOI

• Methodological Design Choices Can Affect Air Pollution Exposure Disparity Estimates: A Case Study on California’s Agricultural Sector

  Environmental Science & Technology · 2026-02-05

  articleOpen accessSenior authorCorresponding

  People of color in the United States are disproportionately and unfairly exposed to air pollution. Equity-oriented scientific evaluations quantifying these disparities often use population-average exposure metrics to capture the overall inequality within a system. Utilizing these metrics involves choices about the exposure input for assessing disparity, the study geography, and the reference population, which are critical to understanding disparities and effectively designing interventions. Here, we use a case study of exposure to fine particulate matter (PM2.5) from California’s agricultural sector to dissect the implications of these decisions. Using a reduced-complexity model and emissions of PM2.5 and precursors, we compare estimates of racial and ethnic disparities in exposure resulting from different combinations of these methodological choices. The full population distributions highlight differences between disparities at the extremes (e.g., 90th percentile) and at the mean. Additionally, the selection of study geography and reference population can influence the magnitude and relative ordering of exposure disparities. Thus, methodological choices can lead to different conclusions for the same concentration and population surfaces; this can impact not only the findings of an individual study but also have implications for mitigation strategies. We conclude with recommendations for best practices for making, justifying, and communicating these methodological decisions.

  PublisherDOI

• Design, operation and characterization of a mobile laboratory for community-scale atmospheric research

  2026-03-11

  articleOpen accessSenior author

  Abstract. Mobile laboratories equipped with research grade instrumentation make it possible to accurately observe fine scale (< 10 m) concentration gradients driven by local emissions, chemistry and meteorology. The flexibility afforded in measurement location makes mobile monitoring well suited to community pollution source characterization and rapid response to natural and anthropogenic situations. However, constructing a platform capable of these measurements requires simultaneous consideration of many engineering challenges and previous examples are rarely documented sufficiently for replication. Here, we present the design process and engineering decisions behind the UC Berkeley Mobile Air Pollution Laboratory (CalMAPLab). Built into a Ford Transit 250 van, the laboratory delivers extensive chemical speciation of air pollution in the gaseous and particulate phases. We characterize the performance of the electrical system, climate control and instrumentation suite for mobile measurements with over 500 hours of test driving. In addition, we introduce a fully open-source data acquisition system with live geospatial visualization that facilitates emissions plume mapping throughout a community. Our presentation of the fully described open design of the facility is intended to provide a transferable blueprint for high performance mobile monitoring in community-scale atmospheric research.

  PublisherOA PDFDOI

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

  ChemRxiv · 2025-10-13

  articleSenior author

  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

• Changes in PM _2.5 -Attributable Mortality in the US by Sector, 2002–2019

  Environmental Science & Technology Letters · 2025-11-21

  article

  Levels of fine particulate matter (PM2.5) air pollution in the United States have declined substantially in recent decades, yielding substantial benefits to public health. This study evaluates emission reductions across five key economic sectors─electricity, industrial, transportation, agriculture, and residential─and their impact on air quality and health. We employ a recently developed sector-specific inventory that provides emissions and their spatial disaggregation across time in a self-consistent framework. Using a national source-receptor matrix, we estimate annual PM2.5-attributable mortality and its variability spatiotemporally and by sector. We find that annual PM2.5-attributable mortality decreased 51% between 2002 (197,000 deaths) and 2019 (96,000 deaths). The largest reductions were from electricity and transportation, especially secondary PM2.5 from NOx, SOx, and VOC emissions. Emissions reductions from industrial and residential sectors were more modest. In contrast, agricultural emissions, especially NH3, increased over time; the importance of agriculture among the five sectors increased from second-smallest (2002) to the largest (2019). While the reductions in PM2.5-attributable mortality have been large (approximately a factor of 2), future progress may need to focus greater attention on agricultural emissions, in addition to traditionally dominant sources such as transportation and industry.

  PublisherDOI

Frequent coauthors

• Julian Marshall

  Seattle University

  92 shared

• Allen L. Robinson

  Carnegie Mellon University

  44 shared

• Shahzad Gani

  The University of Texas at Austin

  43 shared

• Lea Hildebrandt Ruiz

  The University of Texas at Austin

  41 shared

• Sahil Bhandari

  Environmental Defense Fund

  38 shared

• Gazala Habib

  Indian Institute of Technology Delhi

  38 shared

• Michael Bräuer

  University of Washington

  32 shared

• Christopher W. Tessum

  University of Illinois Urbana-Champaign

  28 shared