Prof. Joshua Apte

We've moved!

Effective July 2020, we've moved to UC Berkeley. Please visit our new website at http://apte.berkeley.edu/

From 2015-2020, I was an assistant professor in the Department of Civil, Architectural, and Environmental Engineering at the University of Texas. Effective 7/1/2020, I have moved to UC Berkeley, and maintain an affiliate appointment at UT-Austin. Prior to joining UT-Austin, I was the ITRI-Rosenfeld Postdoctoral Fellow at Lawrence Berkeley National Laboratory.

My core training is in air quality engineering and in techniques for air pollution exposure assessment: understanding the sources, physicochemical transformations, and spatial patterns of the pollution that people breathe, and methods for reducing these exposures. I also have experience in data science, energy analysis, and health risk assessment. Much of my research is motivated by a desire to identify technologies, policies and strategies for improving the environmental sustainability of cities and the built environment, and to reduce inequities in exposures to environmental contaminants. I have a strong regional interest in Asia and elsewhere in the developing world.


Postdoctoral, 2014, Environmental Energy Technologies, Lawrence Berkeley National Laboratory
PhD, 2013, Energy and Resources, UC Berkeley
MS, 2008, Energy and Resources, UC Berkeley
ScB, 2004 Environmental Science, Brown University
[Download CV] [Google Scholar Profile] [ReseacherID Profile]


email: JSApte {at} utexas {dot} edu
mail: 301 E. Dean Keeton St. Stop C1700, Austin, Texas 78712-0273
office: Room 5.422, Ernest Cockrell, Jr. Hall (ECJ)

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Research groups

Department of Civil, Architectural and Environmental Engineering, University of Texas at Austin
Energy Technologies Area, Lawrence Berkeley National Laboratory

Data sets

OpenAQ Open, real-time air quality data from around the world
SpatialModel Database of spatially resolved air quality surfaces from around the world, including satellite remote sensing and empirical models
Global Burden of Disease Data Comparative global regional analyses of causes and risks for ill health
NASA MERRA reanalysis
Gridded climate and meteorology dataset
Atlas of Urban Expansion City population and urban land cover dataset for ~3600 cities
MODIS 500m global urban extents Remotely sensed urban land cover
Delhi Pollution Control Committee Official air quality data for Delhi, including archives since 2013
Beijing/China AQ Data Unofficial archival measurements of PM2.5 for US Embassy and Consulates in China
AAAR 2019 Tutorial - Sensor Data Science Bootcamp, with John Volckens

Collaborators and colleagues

Lea Hildebrandt Ruiz, University of Texas at Austin
Center for Atmospheric Particle Studies
, Carnegie Mellon University
Thomas W. Kirchstetter, Energy Technologies Area, Lawrence Berkeley National Laboratory
Julian D. Marshall, Civil Engineering, University of Washington
Gazala Habib, Indian Institute of Technology, Delhi
Sarath Guttikunda, UrbanEmissions.info, Goa/New Delhi
William W Nazaroff,
Civil and Environmental Engineering, UC Berkeley

Professional societies

American Association for Aerosol Research
International Society of Exposure Science
International Society for Environmental Epidemiology

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Mapping air pollution with Google Street View cars

We used two specially equipped Google Street View cars to repeatedly map gaseous and particulate air pollution, block-by-block, in Oakland, California. By using a year of repeated measurements, our algorithms were able to map pollution at 30 meter scales - an unprecedented resolution for a measurement dataset. We find that air quality can persistently vary even within individual city blocks. We determined that the data requirements for making stable, high-resolution pollution maps are surprisingly modest. This straightforward measurement approach is now being scaled up to other cities around the world.

Quick links:
[Journal article, open access] [Interactive Maps] [UT Press Release] [Google Blog Post] [Request data access]


The challenge: how does air quality vary within cities?
Most routine air pollution measurements in cities are collected at a small number of "ambient" monitoring sites that provide information on urban-background concentrations. In the US cities, there are generally 2-3 ambient monitoring sites for every million people. Therefore, despite the fact that air pollution can vary sharply in urban areas, our understanding of how pollution varies at the block-to-block scale is quite limited. This study demonstrates a new approach to filling this data gap by using specially equipped Google Street View cars to map urban air pollution every 30 meters, at 100,000× higher spatial resolution than possible with official monitors.

Article: Apte JS, Messier KP, Gani S, Brauer M, Kirchstetter TW, Lunden MM, Marshall JD, Portier CJ, Vermeulen RCH,  Hamburg SP. High resolution air pollution mapping with Google Street View cars: Exploiting big data. Environ. Sci. Tech, 2017[open access, link]. Selected as ES&T's "Top Environmental Technology Article of 2017."

Measurement approach
We made repeated measurements of air pollution in Oakland, California using two specially equipped Google Street View cars. Aclima equipped these cars with their Environmental Intelligence sensing platform, which for this study incorporated research-grade fast-response gas and particle analyzers measuring at 1 second time resolution. Here, we focused on three urban air pollutants - black carbon particles (BC), nitric oxide (NO) and NO2 (nitrogen dioxide). For over a year, the cars repeatedly mapped the streets of three Oakland neighborhoods during daytime hours. The cars visited different neighborhoods on different days, collecting more than 3 million data points and logging more than 15,000 miles. Each city block was sampled on an average of 31 different days throughout the year. Our group then developed data analysis algorithms to convert this mobile monitoring dataset, perhaps the largest of its kind, into maps that represent consistent long-term trends of daytime, weekday air quality.

Fine-scale pollution data
By using data from repeated measurements on each city block over an entire year, our data analysis approach produces stable estimates of the long-term daytime median concentration for each 30 meter segment of roadway that we sampled. Our analyses suggest that concentration estimates for individual road segments are precise to within ± 10-20%.

We find that air pollution is surprisingly variable inside neighborhoods: concentrations can persistently vary by as much 5-8× within an individual city block. We were especially surprised to find small, consistent pollution hotspots on many city blocks. Each of these hotspots may arise for its own idiosyncratic reasons. We believe that common causes of hotspots include traffic congestion, industrial emissions, cooking emissions, and local truck or bus traffic. For our study area, pollution at the single official air quality monitoring site were similar to the levels we measured on quiet residential streets. However, many streets that we measured were consistently much more polluted than the official air quality data would suggest.

Explore interactive maps

The partnership
We conducted this study in collaboration with several organizations. The scientific team included researchers from UT-Austin, Utrecht University, University of British Columbia, University of Washington, Lawrence Berkeley National Laboratory, Environmental Defense Fund, and Aclima, Inc. Funding for the research was provided by the Environmental Defense Fund. Google supported the operation of their Google Street View cars, which Aclima equipped with their Ei sensing platform. We worked with local community groups, including the West Oakland Environmental Indicators Project, to interpret our results and understand their implications for the community. Finally, the Bay Area Air Quality Management district provided us with access to their ambient monitoring datasets. 

Towards scalability: Measure or model?
To scale this measurement approach around the world, it may be helpful to identify sampling approaches that minimize the amount of "driving effort" required to collect a city's dataset. One approach to doing so would be to simply reduce the number of repeated measurement visits to every street -- sacrificing precision, but gaining efficiency. An alternative approach is to predict air quality everywhere using models trained on measurements made on only a subset of a city's streets. Our unusually rich dataset from Oakland allowed us to investigate the relative advantages of each of these two approaches. While each approach has advantages, we demonstrate that useful air quality maps can be estimated from mobile monitoring campaigns even with minimal data collection. 

Article: Messier KP, Chambliss SE, Gani S, Alvarez RA, Brauer M, Choi JJ, Hamburg SP, Kerckhoffs J, LaFranchi B, Lunden MM, Marshall JD, Portier CJ, Roy A, Szpiro AA, Vermeulen RCH, Apte JS. 2018. Mapping air pollution with Google Street View cars: Efficient approaches with mobile monitoring and land use regression. Accepted, Environ. Sci. Tech. [open access]

Explore the interactive data maps | Download the data

Our partners at Environmental Defense Fund have prepared an interactive version of our dataset. These interactive maps allow the user to explore how air pollution varies within neighborhoods of Oakland, California. The full dataset from this study is available via the EDF website.

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Air pollution research


41. Crilley L, Singh A, Kramer LJ, Shaw MD, Alam MS, Apte JS, Bloss WJ, Ruiz LH, Fu P, Fu W, Gani S, Gatari M, Ilyinskaya E, Lewis AC, Ng’ang’a D, Sun Y, Whitty RCW, Yue S, Young S, Pope FD. 2020. Effect of aerosol composition on the performance of low-cost optical particle counter correction factors. Atmospheric Measurement Technology  13, 1181–1193. [open access]

40. Ye Q, Li HZ, Gu P, Robinson ES, Sullivan RC, Apte JS, Robinson AL, Donahue NM, Presto AA. 2020. Moving beyond fine particle mass: High-spatial resolution exposure to source-resolved atmospheric particle number and chemical mixing state. Environmental Health Perspectives 128, 17009. [open access]

39. Bhandari S, Gani S, Patel K, Wang DS, Soni P, Arub Z, Habib G, Apte JS, Hildebrandt Ruiz L. 2020. Sources and atmospheric dynamics of organic aerosol in New Delhi, India: Insights from receptor modeling. Atmospheric Chemistry and Physics, 20, 735–752. [open access]

38. Zimmerman N, Li HZ, Ellis A, Hauryliuk A, Robinson ES, Gu P, Shah RU, Ye Q, Snell L, Subramanian R, Robinson AL, Apte JS, Presto AA. Improving correlations between land use and air pollutant concentrations using wavelet analysis: insights from a low-cost sensor network. Accepted, Aerosol and Air Quality Research. [open access]


37. Anenberg SC, Achakulwisut P, Brauer M, Moran D, Apte JS, Henze D. 2019. Particulate matter mortality in cities worldwide: a challenge and an opportunity for co-benefits from low carbon development. Scientific Reports 9, 11552. [open access]

36. Hagan D, Gani S, Bhandari S, Patel K, Habib G, Apte JS, Hildebrandt Ruiz L, Kroll JH. 2019. Inferring aerosol sources from low-cost air quality sensor measurements: a case study in Delhi, India. Environmental Science & Technology Letters 6, 467-472. [open access]

35. Robinson ES, Shah RU, Messier KP, Gu P, Li HZ, Apte JS, Robinson AL, Presto AA. 2019. Land use regressi on modeling of source-resolved fine particulate matter components from mobile sampling. Environmental Science & Technology 53, 8925-8937 [link]

34. Spears D, Dey S, Chowdhury S, Scorovnick N, Vyas S, Apte JS. 2019. The association of early-life exposure to ambient PM2.5 and later-childhood height-for-age in India: An observational study. Environmental Health 18:62[open access]

33. Saha P, Li Z, Apte JS, Robinson AL, Presto AA. 2019. Urban ultrafine particle exposure assessment with land-use regression: Influence of sampling strategy. Environmental Science & Technology 53, 7326-7336. [link]

32. Fantke P, McKone TE, Tainio M, Jolliet O, Apte JS, Stylianou K, Illner N, Marshall JD, Choma EF, Evans JS. 2019. Global effect factors for exposure to fine particulate matter. Environmental Science & Technology 53, 6855-6868. [open access]

31. Apte JS and Pant P. Towards cleaner air for a billion Indians. 2019. Proceedings of the National Academy of Sciences 116, 10614-10616. [link] [link for UT students]

30. Gani S, Bhandari S, Seraj S, Wang DS, Patel K, Soni P, Arub Z, Habib G, Hildebrandt Ruiz L, Apte JS. 2019. Submicron aerosol composition in the world’s most polluted megacity: The Delhi Aerosol Supersite study. Atmospheric Chemistry and Physics 19, 6843-6859. [open access]

29. Tessum CW, Apte JS, Goodkind AL, Muller NZ, Mullins KA, Paolella DA, Polasky S, Springer NP, Thakrar SK, Marshall JD, Hill JD. 2019. Inequity in consumption of goods and services widens racial-ethnic disparities in air pollution exposure. In press, Proceedings of the National Academy of Sciences. doi: 10.1073/pnas.1818859116. [open access]

28. Li HZ, Gu P, Ye Q, Zimmerman N, Robinson ES, Subramanian R, Apte JS, Robinson AL, Presto AA. 2019. Spatially dense air pollutant sampling: Implications of spatial variability on the representativeness of stationary air pollutant monitors. Atmospheric Environment X 2, 100012. [open access]

27. Saha P, Zimmerman N, Mailings C, Hauryliuk A, Li Z, Snell L, Subramanian R, Lipsky EM, Apte JS, Robinson AL, Presto AA. 2019. Quantifying high-resolution spatial variations and local source impacts of urban ultrafine particle concentration. Science of the Total Environment 655, 473-481. [link]


26. Shah RU, Robinson ES, Gu P, Robinson AL, Apte JS, Presto AA. High spatial resolution mapping of aerosol composition and sources in Oakland, California using mobile aerosol mass spectrometry. 2018. Atmospheric Chemistry and Physics 18, 16325-16344.  [open access]

25. Messier KP, Chambliss SE, Gani S, Alvarez RA, Brauer M, Choi JJ, Hamburg SP, Kerckhoffs J, LaFranchi B, Lunden MM, Marshall JD, Portier CJ, Roy A, Szpiro AA, Vermeulen RCH, Apte JS. 2018. Mapping air pollution with Google Street View cars: Efficient approaches with mobile monitoring and land use regression. Environmental Science & Technology 52, 12563-12572[open access]

24. Gu P, Li HZ, Ye Q, Robinson ES, Apte JS, Robinson AL, Presto AA. 2018. Intra-city variability of PM exposure is driven by carbonaceous sources and correlated with land use variables. Environmental Science & Technology, 52, 11545 - 11554. [link]

23. Burnett R, Chen H, Szyszkowicz M, Fann N, Hubbell B, Pope CA III, Apte JS, Brauer M, Cohen A, Weichenthal S, Coggins J, Di Q, Brunekreef B, Frostad J, Lim SS, Kan H, Walker KD, Thurston G, Hayes RB, Lim CC, Turner MC, Jerrett M, Krewski D, Gapstur SM, Diver WR, Ostro B, Goldberg D, Crouse DL, Martin RV, Peters P, Pinault L, Tjepkema M, van Donkelaar A, Villeneueve PJ, Miller, AB, Yin P, Zhou M, Wang L, Janssen NAH, Marra M, Atkinson RW, Tsang H, Thach TQ, Cannon JB, Allen RT, Hart J, Laden F, Cesaroni G, Forastiere F, Weinmayr G, Jaensch A, Nagel G, Concin H, Spadaro JV. 2018. Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter.  Proceedings of the National Academy of Sciences, 115, 9592-9597. [link, open access]

22. Apte JS, Brauer M, Cohen AJ, Ezzati M, Pope CA III. 2018. Ambient PM2.5 reduces global and regional life expectancy. Environmental Science & Technology Letters 5, 546-551. [link, open access]

21. Robinson ES, Gu P, Ye Q, Li ZH, Shah RU, Apte JS, Robinson AL, Presto AA. 2018. Restaurant impacts on outdoor air quality: elevated organic aerosol mass from restaurant cooking with neighborhood-scale plume extents. Environmental Science & Technology 52, 9285–9294. [link]

20. Paolella D, Tessum CW, Adams P, Apte JS, Chambliss SE, Hill J, Muller NZ, Marshall JD. 2018. Effect of model spatial resolution on estimates of fine particulate matter exposure and exposure disparities in the United States. Environmental Science & Technology Letters 5, 436-441. [link]

19. Ye Q, Gu P, Li HZ, Robinson ES, Lipsky EM, Kaltsonoudis C, Lee AKY, Apte JS, Robinson AL, Sullivan RC, Presto AA, Donahue NM. 2018. Characterization of spatial variability of sources and mixing state of atmospheric particles in a metropolitan area. Environmental Science & Technology 52, 6807-6815.[link]

18. Saha P, Robinson ES, Shah RU, Zimmerman N, Apte JS, Robinson AL, Presto AA. 2018. Reduced ultrafine particle concentration in urban air: Changes in nucleation and anthropogenic emissions. Environmental Science & Technology 52, 6798–6806. [link]

17.  Alexeef SE, Roy A, Shan J, Liu X, Messier K, Apte JS, Portier C, Sidney S, van den Eeden SK. 2018. High-resolution mapping of traffic related air pollution with Google Street View cars and incidence of cardiovascular events within neighborhoods in Oakland, CA. Environmental Health 17:38 [link, open access]


16. Fantke P, Jolliet O, Apte JS, Hodas N, Evans J, Weschler CJ, Stylianou K.S, Jantunen M, McKone TE. 2017. Characterizing aggregated exposure to primary particulate matter: Recommended intake fractions for indoor and outdoor sources. Environmental Science & Technology 51, 9089-9100. [link, open access]

15. Apte JS, Messier KP, Gani S, Brauer M, Kirchstetter TW, Lunden MM, Marshall JD, Portier CJ, Vermeulen RCH,  Hamburg SP. 2017. High resolution air pollution mapping with Google Street View cars: Exploiting big data. Environmental Science & Technology 51, 6999-7008. [link, open access]

14. Kirchstetter TW, Preble CV, Hadley OC, Bond TC, Apte JS. 2017. Large reductions in black carbon concentrations in the United States between 1965 and 2000. Atmospheric Environment 151, 17-23. [link]


13. Brauer M, Freedman G, Frostad J, van Donkelaar A, Martin R, Dentener F, van Dingenen, Rita, Estep K, Amini H, Apte JS, Balakrishnan K, Barregard L, Broday D, Feigin V, Ghosh S, Hopke P, Knibbs L, Kokubo Y, Liu Y, Ma S, Morawska L, Texcalac Sangrador J-L, Shaddick G, Anderson HR, Vos T, Forouzanfar M, Burnett R, Cohen A. 2016. Ambient air pollution exposure estimation for the Global Burden of Disease 2013. Environmental Science & Technology 50, 79-88. [link]


12. Marshall JD, Apte JS, Coggins JS, Goodkind AL. 2015. Blue skies bluer? Environmental Science & Technology 49, 13929-13936. [link]

11. Apte JS, Marshall JD, Cohen AJ, Brauer M. 2015. Addressing global mortality from ambient PM2.5. Environmental Science & Technology 49, 8057-8066. [link, open access]

10. Tang NW, Apte JS, Martien PM, Kirchstetter TW. 2015. Black carbon emissions from in-use diesel-electric passenger locomotives. Atmospheric Environment 115, 295-303. [link]

9. Su J, Apte JS, Lipsitt J, Garcia-Gonzales, DA, Beckerman BS, de Nazelle A, Texcalac-Sagrandor J-L, Jerrett M. 2015. Identification of population potentially exposed to traffic in seven world cities. Environment International 78, 82-89. [link]

8. Fantke P, Jolliet O, Evans JS, Apte JS, Cohen AJ, Hänninen OO, Hurley F, Jantunen MJ, Jerrett M, Levy JI, Loh MM, Marshall JD, Miller BG, Preiss P, Spadaro JV, Tainio M, Tuomisto JT, Weschler CJ, McKone TE. 2015. Health effects of fine particulate matter in life cycle impact assessment: findings from the Basel Guidance Workshop. International Journal of Life Cycle Assessment 20, 276-288. [link]


7. Krzyzanowski M, Apte JS, Bonjour SP, Brauer M, Cohen AJ, Prüss-Üstün A. 2014. Air pollution in the megacities. Current Environmental Health Reports 1, 185-191. [link]


6.  Saraswat A , Apte JS, Kandlikar M, Brauer M, Henderson SB, Marshall JD, 2013. Spatiotemporal land use regression models of fine, ultrafine, and black carbon particulate matter in New Delhi, India. Environmental Science & Technology 47, 12903–12911. [link]


5.  Apte JS, Bombrun E, Marshall JD, Nazaroff WW, 2012. Global intraurban intake fractions for air pollutants from vehicles and other distributed sources. Environmental Science & Technology 46, 3415-3423.
[link, open access]

4. Grieshop AP, Boland D, Reynolds CCO, Gouge B, Apte JS , Rogak S, Kandlikar M. 2011. Modeling air pollutant emissions from Indian auto-rickshaws: model development and implications for fleet emission rate estimates.  Atmospheric Environment 50, 148-15. [link]


3. Apte JS, Kirchstetter TW, Reich AH, Deshpande SJ, Kaushik G, Chel A, Marshall JD, Nazaroff WW. 2011. Concentrations of fine, ultrafine, and black carbon particles in auto-rickshaws in New Delhi, India. Atmospheric Environment 45, 4470-4480. [link]

2. Sager J, Apte JS, Lemoine DM, Kammen DM. 2011. Reduce the growth rate of light duty vehicle travel to meet 2050 global climate goals. Environmental Research Letters 6, 024018. [link]


1. Coffey B, Borgeson S, Selkowitz S, Apte JS, Mathew P, Haves P. 2009. Towards a very low-energy building stock: modeling the US commercial building sector to support policy and innovation planning. Building Research & Information 37, 610-624. [link]

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