Articles | Volume 13, issue 6
https://doi.org/10.5194/amt-13-3277-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-13-3277-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Jun 2020
Research article |
| 30 Jun 2020
| 30 Jun 2020
Mobile-platform measurement of air pollutant concentrations in California: performance assessment, statistical methods for evaluating spatial variations, and spatial representativeness
Paul A. Solomon
Independent consultant, Henderson, NV 89052, USA
formerly at: Office of Research and Development, US Environmental Protection Agency,
Las Vegas, NV 89119, USA
Dena Vallano
Air and Radiation
Division, Region 9, US Environmental Protection Agency, 75 Hawthorne St, San Francisco, CA 94105, USA
Melissa Lunden
Aclima, Inc., 10 Lombard St, Suite 200, San Francisco, CA 94111, USA
Brian LaFranchi
Aclima, Inc., 10 Lombard St, Suite 200, San Francisco, CA 94111, USA
Charles L. Blanchard
CORRESPONDING AUTHOR
Envair, 526 Cornell Avenue, Albany, CA 94706, USA
Stephanie L. Shaw
Electric Power Research Institute, 3420 Hillview Ave, Palo Alto, CA
94304, USA
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Cited
17 citations as recorded by crossref.
- Determinants of Spatial Variability of Urban Air Pollutant Concentrations Measured Using a Mobile Laboratory and a Drone L. Järvi et al. 10.2139/ssrn.4094701
- Drone-Assisted Monitoring of Atmospheric Pollution—A Comprehensive Review J. Jońca et al. 10.3390/su141811516
- Impacts of lake breeze meteorology on ozone gradient observations along Lake Michigan shorelines in Wisconsin P. Cleary et al. 10.1016/j.atmosenv.2021.118834
- Urban crowdsensing by personal mobility vehicles to manage air pollution P. Jiménez et al. 10.1016/j.trpro.2023.11.071
- Data Insights for Sustainable Cities: Associations between Google Street View-Derived Urban Greenspace and Google Air View-Derived Pollution Levels M. Sabedotti et al. 10.1021/acs.est.3c05000
- Developing the Maximum Incremental Reactivity for Volatile Organic Compounds in Major Cities of Central‐Eastern China Y. Zhang et al. 10.1029/2022JD037296
- Evaluating the Performance and Practicality of a Multi-Parameter Assessment System with Design, Comparative Analysis, and Future Directions Z. Zlatev et al. 10.3390/su16104124
- Aerosol particle number concentration, ultrafine particle number fraction, and new particle formation measurements near the international airports in Berlin, Germany – First results from the BEAR study S. Kecorius et al. 10.1016/j.envint.2024.109086
- Assessment of the impact of sensor error on the representativeness of population exposure to urban air pollutants T. Leo Hohenberger et al. 10.1016/j.envint.2022.107329
- Pinpointing sources of pollution using citizen science and hyperlocal low-cost mobile source apportionment D. Bousiotis et al. 10.1016/j.envint.2024.109069
- Integrating Mobile and Fixed-Site Black Carbon Measurements to Bridge Spatiotemporal Gaps in Urban Air Quality C. Manchanda et al. 10.1021/acs.est.3c10829
- In-cabin and outdoor environmental monitoring in vehicular scenarios with distributed computing E. Ramos-Sorroche et al. 10.1016/j.iot.2023.101009
- Determinants of spatial variability of air pollutant concentrations in a street canyon network measured using a mobile laboratory and a drone L. Järvi et al. 10.1016/j.scitotenv.2022.158974
- Mobile air quality monitoring and comparison to fixed monitoring sites for instrument performance assessment A. Whitehill et al. 10.5194/amt-17-2991-2024
- Characterizing the distribution pattern of submicron and coarse particles on high-density container truck roads through mobile monitoring H. Zhao et al. 10.1016/j.apr.2022.101561
- Opportunistic mobile air quality mapping using sensors on postal service vehicles: from point clouds to actionable insights J. Hofman et al. 10.3389/fenvh.2023.1232867
- Emission Rate Estimation of Industrial Air Pollutant Emissions Based on Mobile Observation X. Cui et al. 10.3390/atmos15080969
17 citations as recorded by crossref.
- Determinants of Spatial Variability of Urban Air Pollutant Concentrations Measured Using a Mobile Laboratory and a Drone L. Järvi et al. 10.2139/ssrn.4094701
- Drone-Assisted Monitoring of Atmospheric Pollution—A Comprehensive Review J. Jońca et al. 10.3390/su141811516
- Impacts of lake breeze meteorology on ozone gradient observations along Lake Michigan shorelines in Wisconsin P. Cleary et al. 10.1016/j.atmosenv.2021.118834
- Urban crowdsensing by personal mobility vehicles to manage air pollution P. Jiménez et al. 10.1016/j.trpro.2023.11.071
- Data Insights for Sustainable Cities: Associations between Google Street View-Derived Urban Greenspace and Google Air View-Derived Pollution Levels M. Sabedotti et al. 10.1021/acs.est.3c05000
- Developing the Maximum Incremental Reactivity for Volatile Organic Compounds in Major Cities of Central‐Eastern China Y. Zhang et al. 10.1029/2022JD037296
- Evaluating the Performance and Practicality of a Multi-Parameter Assessment System with Design, Comparative Analysis, and Future Directions Z. Zlatev et al. 10.3390/su16104124
- Aerosol particle number concentration, ultrafine particle number fraction, and new particle formation measurements near the international airports in Berlin, Germany – First results from the BEAR study S. Kecorius et al. 10.1016/j.envint.2024.109086
- Assessment of the impact of sensor error on the representativeness of population exposure to urban air pollutants T. Leo Hohenberger et al. 10.1016/j.envint.2022.107329
- Pinpointing sources of pollution using citizen science and hyperlocal low-cost mobile source apportionment D. Bousiotis et al. 10.1016/j.envint.2024.109069
- Integrating Mobile and Fixed-Site Black Carbon Measurements to Bridge Spatiotemporal Gaps in Urban Air Quality C. Manchanda et al. 10.1021/acs.est.3c10829
- In-cabin and outdoor environmental monitoring in vehicular scenarios with distributed computing E. Ramos-Sorroche et al. 10.1016/j.iot.2023.101009
- Determinants of spatial variability of air pollutant concentrations in a street canyon network measured using a mobile laboratory and a drone L. Järvi et al. 10.1016/j.scitotenv.2022.158974
- Mobile air quality monitoring and comparison to fixed monitoring sites for instrument performance assessment A. Whitehill et al. 10.5194/amt-17-2991-2024
- Characterizing the distribution pattern of submicron and coarse particles on high-density container truck roads through mobile monitoring H. Zhao et al. 10.1016/j.apr.2022.101561
- Opportunistic mobile air quality mapping using sensors on postal service vehicles: from point clouds to actionable insights J. Hofman et al. 10.3389/fenvh.2023.1232867
- Emission Rate Estimation of Industrial Air Pollutant Emissions Based on Mobile Observation X. Cui et al. 10.3390/atmos15080969
Latest update: 17 Jan 2025
Short summary
Analyzing street-level air pollutants (2016–2017), this assessment indicates that mobile measurement is precise and accurate (5 % to 25 % bias) relative to regulatory sites, with higher spatial resolution. Collocated sensor measurements in California showed differences less than 20 %, suggesting that greater differences represent spatial variability. Mobile data confirm regulatory-site spatial representation and that pollutant levels can also be 6 to 8 times higher just blocks apart.
Analyzing street-level air pollutants (2016–2017), this assessment indicates that mobile...
