Articles | Volume 15, issue 2
https://doi.org/10.5194/amt-15-321-2022
© Author(s) 2022. 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-15-321-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Evaluating uncertainty in sensor networks for urban air pollution insights
Environmental Defense Fund, 301 Congress Ave #1300, Austin, TX
78701, USA
Olalekan A. M. Popoola
Yusuf Hamied Department of Chemistry, University of Cambridge,
Cambridge, CB2 1EW, UK
Roderic L. Jones
Yusuf Hamied Department of Chemistry, University of Cambridge,
Cambridge, CB2 1EW, UK
Nicholas A. Martin
Air Quality and Aerosol Metrology Group, Atmospheric Environmental
Science Department, National Physical Laboratory, Hampton Road, Teddington,
Middlesex, TW11 0LW, UK
Jim Mills
ACOEM Air Monitors Ltd., Ground Floor Offices, C1 The Courtyard,
Tewkesbury Business Park, Tewkesbury, Gloucestershire, GL20 8GD, UK
Elizabeth R. Fonseca
Environmental Defense Fund Europe, 3rd Floor, 41 Eastcheap,
London, EC3M 1DT, UK
Amy Stidworthy
Cambridge Environmental Research Consultants Ltd., 3 King's Parade,
Cambridge, CB2 1SJ, UK
Ella Forsyth
Cambridge Environmental Research Consultants Ltd., 3 King's Parade,
Cambridge, CB2 1SJ, UK
David Carruthers
Cambridge Environmental Research Consultants Ltd., 3 King's Parade,
Cambridge, CB2 1SJ, UK
Megan Dupuy-Todd
Environmental Defense Fund, 301 Congress Ave #1300, Austin, TX
78701, USA
now at: Clean Air Task Force, 114 State Street, 6th Floor, Boston, MA
02109, USA
Felicia Douglas
Environmental Defense Fund Europe, 3rd Floor, 41 Eastcheap,
London, EC3M 1DT, UK
Katie Moore
Environmental Defense Fund, 301 Congress Ave #1300, Austin, TX
78701, USA
now at: Clarity Movement Co., 808 Gilman Street, Berkeley, CA 94710,
USA
Rishabh U. Shah
Environmental Defense Fund, 301 Congress Ave #1300, Austin, TX
78701, USA
Lauren E. Padilla
Environmental Defense Fund, 301 Congress Ave #1300, Austin, TX
78701, USA
Ramón A. Alvarez
Environmental Defense Fund, 301 Congress Ave #1300, Austin, TX
78701, USA
Related authors
No articles found.
Sebastian Diez, Stuart Lacy, Hugh Coe, Josefina Urquiza, Max Priestman, Michael Flynn, Nicholas Marsden, Nicholas A. Martin, Stefan Gillott, Thomas Bannan, and Pete M. Edwards
Atmos. Meas. Tech., 17, 3809–3827, https://doi.org/10.5194/amt-17-3809-2024, https://doi.org/10.5194/amt-17-3809-2024, 2024
Short summary
Short summary
In this paper we present an overview of the QUANT project, which to our knowledge is one of the largest evaluations of commercial sensors to date. The objective was to evaluate the performance of a range of commercial products and also to nourish the different applications in which these technologies can offer relevant information.
Joanna E. Dyson, Lisa K. Whalley, Eloise J. Slater, Robert Woodward-Massey, Chunxiang Ye, James D. Lee, Freya Squires, James R. Hopkins, Rachel E. Dunmore, Marvin Shaw, Jacqueline F. Hamilton, Alastair C. Lewis, Stephen D. Worrall, Asan Bacak, Archit Mehra, Thomas J. Bannan, Hugh Coe, Carl J. Percival, Bin Ouyang, C. Nicholas Hewitt, Roderic L. Jones, Leigh R. Crilley, Louisa J. Kramer, W. Joe F. Acton, William J. Bloss, Supattarachai Saksakulkrai, Jingsha Xu, Zongbo Shi, Roy M. Harrison, Simone Kotthaus, Sue Grimmond, Yele Sun, Weiqi Xu, Siyao Yue, Lianfang Wei, Pingqing Fu, Xinming Wang, Stephen R. Arnold, and Dwayne E. Heard
Atmos. Chem. Phys., 23, 5679–5697, https://doi.org/10.5194/acp-23-5679-2023, https://doi.org/10.5194/acp-23-5679-2023, 2023
Short summary
Short summary
The hydroxyl (OH) and closely coupled hydroperoxyl (HO2) radicals are vital for their role in the removal of atmospheric pollutants. In less polluted regions, atmospheric models over-predict HO2 concentrations. In this modelling study, the impact of heterogeneous uptake of HO2 onto aerosol surfaces on radical concentrations and the ozone production regime in Beijing in the summertime is investigated, and the implications for emissions policies across China are considered.
Marsailidh M. Twigg, Augustinus J. C. Berkhout, Nicholas Cowan, Sabine Crunaire, Enrico Dammers, Volker Ebert, Vincent Gaudion, Marty Haaima, Christoph Häni, Lewis John, Matthew R. Jones, Bjorn Kamps, John Kentisbeer, Thomas Kupper, Sarah R. Leeson, Daiana Leuenberger, Nils O. B. Lüttschwager, Ulla Makkonen, Nicholas A. Martin, David Missler, Duncan Mounsor, Albrecht Neftel, Chad Nelson, Eiko Nemitz, Rutger Oudwater, Celine Pascale, Jean-Eudes Petit, Andrea Pogany, Nathalie Redon, Jörg Sintermann, Amy Stephens, Mark A. Sutton, Yuk S. Tang, Rens Zijlmans, Christine F. Braban, and Bernhard Niederhauser
Atmos. Meas. Tech., 15, 6755–6787, https://doi.org/10.5194/amt-15-6755-2022, https://doi.org/10.5194/amt-15-6755-2022, 2022
Short summary
Short summary
Ammonia (NH3) gas in the atmosphere impacts the environment, human health, and, indirectly, climate. Historic NH3 monitoring was labour intensive, and the instruments were complicated. Over the last decade, there has been a rapid technology development, including “plug-and-play” instruments. This study is an extensive field comparison of the currently available technologies and provides evidence that for routine monitoring, standard operating protocols are required for datasets to be comparable.
Sebastian Diez, Stuart E. Lacy, Thomas J. Bannan, Michael Flynn, Tom Gardiner, David Harrison, Nicholas Marsden, Nicholas A. Martin, Katie Read, and Pete M. Edwards
Atmos. Meas. Tech., 15, 4091–4105, https://doi.org/10.5194/amt-15-4091-2022, https://doi.org/10.5194/amt-15-4091-2022, 2022
Short summary
Short summary
Regardless of the cost of the measuring instrument, there are no perfect measurements. For this reason, we compare the quality of the information provided by cheap devices when they are used to measure air pollutants and we try to emphasise that before judging the potential usefulness of the devices, the user must specify his own needs. Since commonly used performance indices/metrics can be misleading in qualifying this, we propose complementary visual analysis to the more commonly used metrics.
Le Yuan, Olalekan A. M. Popoola, Christina Hood, David Carruthers, Roderic L. Jones, Haitong Zhe Sun, Huan Liu, Qiang Zhang, and Alexander T. Archibald
Atmos. Chem. Phys., 22, 8617–8637, https://doi.org/10.5194/acp-22-8617-2022, https://doi.org/10.5194/acp-22-8617-2022, 2022
Short summary
Short summary
Emission estimates represent a major source of uncertainty in air quality modelling. We developed a novel approach to improve emission estimates from existing inventories using air quality models and routine in situ observations. Using this approach, we derived improved estimates of NOx emissions from the transport sector in Beijing in 2016. This approach has great potential in deriving timely updates of emissions for other pollutants, particularly in regions undergoing rapid emission changes.
Michael Biggart, Jenny Stocker, Ruth M. Doherty, Oliver Wild, David Carruthers, Sue Grimmond, Yiqun Han, Pingqing Fu, and Simone Kotthaus
Atmos. Chem. Phys., 21, 13687–13711, https://doi.org/10.5194/acp-21-13687-2021, https://doi.org/10.5194/acp-21-13687-2021, 2021
Short summary
Short summary
Heat-related illnesses are of increasing concern in China given its rapid urbanisation and our ever-warming climate. We examine the relative impacts that land surface properties and anthropogenic heat have on the urban heat island (UHI) in Beijing using ADMS-Urban. Air temperature measurements and satellite-derived land surface temperatures provide valuable means of evaluating modelled spatiotemporal variations. This work provides critical information for urban planners and UHI mitigation.
Claire E. Reeves, Graham P. Mills, Lisa K. Whalley, W. Joe F. Acton, William J. Bloss, Leigh R. Crilley, Sue Grimmond, Dwayne E. Heard, C. Nicholas Hewitt, James R. Hopkins, Simone Kotthaus, Louisa J. Kramer, Roderic L. Jones, James D. Lee, Yanhui Liu, Bin Ouyang, Eloise Slater, Freya Squires, Xinming Wang, Robert Woodward-Massey, and Chunxiang Ye
Atmos. Chem. Phys., 21, 6315–6330, https://doi.org/10.5194/acp-21-6315-2021, https://doi.org/10.5194/acp-21-6315-2021, 2021
Short summary
Short summary
The impact of isoprene on atmospheric chemistry is dependent on how its oxidation products interact with other pollutants, specifically nitrogen oxides. Such interactions can lead to isoprene nitrates. We made measurements of the concentrations of individual isoprene nitrate isomers in Beijing and used a model to test current understanding of their chemistry. We highlight areas of uncertainty in understanding, in particular the chemistry following oxidation of isoprene by the nitrate radical.
Lisa K. Whalley, Eloise J. Slater, Robert Woodward-Massey, Chunxiang Ye, James D. Lee, Freya Squires, James R. Hopkins, Rachel E. Dunmore, Marvin Shaw, Jacqueline F. Hamilton, Alastair C. Lewis, Archit Mehra, Stephen D. Worrall, Asan Bacak, Thomas J. Bannan, Hugh Coe, Carl J. Percival, Bin Ouyang, Roderic L. Jones, Leigh R. Crilley, Louisa J. Kramer, William J. Bloss, Tuan Vu, Simone Kotthaus, Sue Grimmond, Yele Sun, Weiqi Xu, Siyao Yue, Lujie Ren, W. Joe F. Acton, C. Nicholas Hewitt, Xinming Wang, Pingqing Fu, and Dwayne E. Heard
Atmos. Chem. Phys., 21, 2125–2147, https://doi.org/10.5194/acp-21-2125-2021, https://doi.org/10.5194/acp-21-2125-2021, 2021
Short summary
Short summary
To understand how emission controls will impact ozone, an understanding of the sources and sinks of OH and the chemical cycling between peroxy radicals is needed. This paper presents measurements of OH, HO2 and total RO2 taken in central Beijing. The radical observations are compared to a detailed chemistry model, which shows that under low NO conditions, there is a missing OH source. Under high NOx conditions, the model under-predicts RO2 and impacts our ability to model ozone.
Yiqun Han, Wu Chen, Lia Chatzidiakou, Anika Krause, Li Yan, Hanbin Zhang, Queenie Chan, Ben Barratt, Rod Jones, Jing Liu, Yangfeng Wu, Meiping Zhao, Junfeng Zhang, Frank J. Kelly, Tong Zhu, and the AIRLESS team
Atmos. Chem. Phys., 20, 15775–15792, https://doi.org/10.5194/acp-20-15775-2020, https://doi.org/10.5194/acp-20-15775-2020, 2020
Short summary
Short summary
Panel studies might be the most suitable way to link intensive air monitoring campaigns for a wide range of pollutant species and personal exposure in different micro-environments, together with epidemiological studies of detailed biological changes in humans. Panel studies are intensive, but related papers are very limited. With the successful collection of a rich dataset, we believe AIRLESS sets a good example for the design of a multidisciplinary study.
Cited articles
Apte, J. S., Messier, K. P., Gani, S., Brauer, M., Kirchstetter, T. W.,
Lunden, M. M., Marshall, J. D., Portier, C. J., Vermeulen, R. C. H., and
Hamburg, S. P.: High-resolution Air Pollution Mapping with Google Street
View Cars: Exploiting Big Data, Environ. Sci. Technol., 51, 6999–7008,
https://doi.org/10.1021/acs.est.7b00891, 2017.
AQMesh: https://www.aqmesh.com/products/aqmesh/, last access:
15 June 2021.
AQ-SPEC: AQMesh (v.4.0) – field evaluation, South Coast AQMD, available at:
http://www.aqmd.gov/aq-spec/sensordetail/aqmesh-(v.4.0) (last access: 7 January 2022), Diamond Bar, CA,
2015.
Bi, J., Stowell, J., Seto, E. Y. W., English, P. B., Al-Hamdan, M. Z.,
Kinney, P. L., Freedman, F. R., and Liu, Y.: Contribution of low-cost sensor
measurements to the prediction of PM2.5 levels: A case study in Imperial
County, California, USA, Environ. Res., 180, 108810,
https://doi.org/10.1016/j.envres.2019.108810, 2020.
Breathe London: AQMesh fixed sensor network data quality assurance and
control procedures, available at:
https://www.globalcleanair.org/files/2021/01/Breathe-London-Fixed-Sensor-Network-QAQC-Procedures.pdf (last access: 7 January 2022),
2020.
Breathe London: Breathe London archival website, available at:
http://breathelondon.edf.org/, last access: 15 June 2021, 2021a.
Breathe London: The Breathe London Blueprint, available at:
https://www.globalcleanair.org/files/2021/02/EDF-Europe-BreatheLondon_Blueprint-guide.pdf (last access: 7 January 2022), 2021b.
Breathe London: Breathe London Stationary, OpenAQ [data set], available at: https://openaq.org/#/project/28967, last access: 7 January 2022.
Carruthers, D., Stidworthy, A., Clarke, D., Dicks, J., Jones, R., Leslie,
I., Popoola, O. A. M., and Seaton, M.: Urban emission inventory optimisation
using sensor data, an urban air quality model and inversion techniques,
Int. J. Environ. Pollut., 66, 252, https://doi.org/10.1504/IJEP.2019.104878, 2019.
Carslaw, D.: worldmet: Import Surface Meteorological Data from NOAA
Integrated Surface Database (ISD), R package version 0.9.2, available at: https://CRAN.R-project.org/package=worldmet (last access: 7 January 2022), 2020.
Carslaw, D. and Ropkins, K.: openair – An R package for air quality data
analysis, Environ. Model. Softw., 27–28, 52–61,
https://doi.org/10.1016/j.envsoft.2011.09.008, 2012.
Castell, N., Dauge, F. R., Schneider, P., Vogt, M., Lerner, U., Fishbain,
B., Broday, D., and Bartonova, A.: Can commercial low-cost sensor platforms
contribute to air quality monitoring and exposure estimates?, Environ.
Int., 99, 293–302, https://doi.org/10.1016/j.envint.2016.12.007,
2017.
Castell, N., Schneider, P., Grossberndt, S., Fredriksen, Mirjam. F.,
Sousa-Santos, G., Vogt, M., and Bartonova, A.: Localized real-time
information on outdoor air quality at kindergartens in Oslo, Norway using
low-cost sensor nodes, Environ. Res., 165, 410–419,
https://doi.org/10.1016/j.envres.2017.10.019, 2018.
Caubel, J. J., Cados, T. E., Preble, C. V., and Kirchstetter, T. W.: A
Distributed Network of 100 Black Carbon Sensors for 100 Days of Air Quality
Monitoring in West Oakland, California, Environ. Sci. Technol., 53,
7564–7573, https://doi.org/10.1021/acs.est.9b00282, 2019.
CERC: Final report, Breathe London project, available at:
https://www.globalcleanair.org/files/2021/02/BL-CERC-Final-Report.pdf (last access: 7 January 2022), 2021.
Clark, L. P., Millet, D. B., and Marshall, J. D.: National Patterns in
Environmental Injustice and Inequality: Outdoor NO2 Air Pollution in the
United States, PLOS ONE, 9, e94431,
https://doi.org/10.1371/journal.pone.0094431, 2014.
Considine, E. M., Reid, C. E., Ogletree, M. R., and Dye, T.: Improving accuracy of air pollution exposure measurements: Statistical correction of a municipal low-cost airborne particulate matter sensor network, Environ. Pollut., 268, 115833, https://doi.org/10.1016/j.envpol.2020.115833, 2021.
Dajnak, D., Evangelopoulos, D., Kitwiroon, N., Beevers, S. D., and Walton,
H.: London health burden of current air pollution and future health benefits
of mayoral air quality policies, available at:
http://erg.ic.ac.uk/research/home/resources/ERG_ImperialCollegeLondon_HIA_AQ_LDN_11012021.pdf (last access: 7 January 2022), 2021.
Duvall, R. M., Long, R. W., Beaver, M. R., Kronmiller, K. G., Wheeler, M.
L., and Szykman, J. J.: Performance Evaluation and Community Application of
Low-Cost Sensors for Ozone and Nitrogen Dioxide, Sensors, 16, 1698,
https://doi.org/10.3390/s16101698, 2016.
EU: Directive 2008/50/EC of the European Parliament and of the Council of 21
May 2008 on ambient air quality and cleaner air for Europe, Off. J. Eur.
Union, 152, 1–44, 2008.
Greater London Authority (GLA): Guide for monitoring air quality in London,
available at:
https://www.london.gov.uk/sites/default/files/air_quality_monitoring_guidance_january_2018.pdf (last access: 7 January 2022), 2018.
Greater London Authority (GLA): Air pollution monitoring data in London:
2016 to 2020, available at:
https://www.london.gov.uk/sites/default/files/air_pollution_monitoring_data_in_london_2016_to_2020_feb2020.pdf (last access: 7 January 2022), 2020a.
Greater London Authority (GLA): Central London ultra low emission zone –
ten month report, available at:
https://www.london.gov.uk/sites/default/files/ulez_ten_month_evaluation_report_23_april_2020.pdf (last access: 7 January 2022),
2020b.
Greater London Authority (GLA): Monitoring and predicting air pollution,
available at:
https://www.london.gov.uk/what-we-do/environment/pollution-and-air-quality/monitoring-and-predicting-air-pollution (last access: 7 January 2022),
2021.
Gupta, P., Doraiswamy, P., Levy, R., Pikelnaya, O., Maibach, J., Feenstra,
B., Polidori, A., Kiros, F., and Mills, K. C.: Impact of California Fires on
Local and Regional Air Quality: The Role of a Low-Cost Sensor Network and
Satellite Observations, GeoHealth, 2, 172–181, https://doi.org/10.1029/2018GH000136,
2018.
Health Effects Institute (HEI): State of Global Air 2020, Health Effects
Institute, Boston, MA, 2020.
Heimann, I., Bright, V. B., McLeod, M. W., Mead, M. I., Popoola, O. A. M.,
Stewart, G. B., and Jones, R. L.: Source attribution of air pollution by
spatial scale separation using high spatial density networks of low cost air
quality sensors, Atmos. Environ., 113, 10–19,
https://doi.org/10.1016/j.atmosenv.2015.04.057, 2015.
Jiao, W., Hagler, G., Williams, R., Sharpe, R., Brown, R., Garver, D., Judge, R., Caudill, M., Rickard, J., Davis, M., Weinstock, L., Zimmer-Dauphinee, S., and Buckley, K.: Community Air Sensor Network (CAIRSENSE) project: evaluation of low-cost sensor performance in a suburban environment in the southeastern United States, Atmos. Meas. Tech., 9, 5281–5292, https://doi.org/10.5194/amt-9-5281-2016, 2016.
Karagulian, F., Barbiere, M., Kotsev, A., Spinelle, L., Gerboles, M.,
Lagler, F., Redon, N., Crunaire, S., and Borowiak, A.: Review of the
Performance of Low-Cost Sensors for Air Quality Monitoring, Atmosphere, 10, 506,
https://doi.org/10.3390/atmos10090506, 2019.
Kelly, K. E., Whitaker, J., Petty, A., Widmer, C., Dybwad, A., Sleeth, D.,
Martin, R., and Butterfield, A.: Ambient and laboratory evaluation of a
low-cost particulate matter sensor, Environ. Pollut., 221, 491–500,
https://doi.org/10.1016/j.envpol.2016.12.039, 2017.
Kim, J., Shusterman, A. A., Lieschke, K. J., Newman, C., and Cohen, R. C.: The BErkeley Atmospheric CO2 Observation Network: field calibration and evaluation of low-cost air quality sensors, Atmos. Meas. Tech., 11, 1937–1946, https://doi.org/10.5194/amt-11-1937-2018, 2018.
LondonAir: Data Downloads, Imperial College London [data set], available at: https://www.londonair.org.uk/london/asp/datadownload.asp, last access: 7 January 2022.
London Air Quality Network (LAQN): LAQN Pollution Episodes, available at:
https://londonair.org.uk/london/asp/publicepisodes.asp?region=0&site=&postcode=&la_id=&level=All&bulletindate=03%2F12%2F2019&MapType=Google&zoom=&lat=51.4750&lon=-0.119824&VenueCode=&bulletin=explanation&episodeID=pol3to4Dec2019&pageID=page1&cm-djitdk-djitdk=
(last access 20 January 2021), 2019.
Lewis, A. C., Lee, J. D., Edwards, P. M., Shaw, M. D., Evans, M. J., Moller,
S. J., Smith, K. R., Buckley, J. W., Ellis, M., Gillot, S. R., and White,
A.: Evaluating the performance of low cost chemical sensors for air
pollution research, Faraday Discuss., 189, 85–103,
https://doi.org/10.1039/C5FD00201J, 2016.
Lopez-Restrepo, S., Yarce, A., Pinel, N., Quintero, O. L., Segers, A., and
Heemink, A. W.: Urban Air Quality Modeling Using Low-Cost Sensor Network and
Data Assimilation in the Aburrá Valley, Colombia, Atmosphere, 12, 91,
https://doi.org/10.3390/atmos12010091, 2021.
McHugh, C. A., Carruthers, D. J., and Edmunds, H. A.: ADMS–Urban: an air
quality management system for traffic, domestic and industrial pollution, Int. J. Environ. Pollut., 8,
666–674, 1997.
Mead, M. I., Popoola, O. A. M., Stewart, G. B., Landshoff, P., Calleja, M.,
Hayes, M., Baldovi, J. J., McLeod, M. W., Hodgson, T. F., Dicks, J., Lewis,
A., Cohen, J., Baron, R., Saffell, J. R., and Jones, R. L.: The use of
electrochemical sensors for monitoring urban air quality in low-cost,
high-density networks, Atmos. Environ., 70, 186–203,
https://doi.org/10.1016/j.atmosenv.2012.11.060, 2013.
Miller, D. J., Actkinson, B., Padilla, L., Griffin, R. J., Moore, K., Lewis,
P. G. T., Gardner-Frolick, R., Craft, E., Portier, C. J., Hamburg, S. P.,
and Alvarez, R. A.: Characterizing Elevated Urban Air Pollutant Spatial
Patterns with Mobile Monitoring in Houston, Texas, Environ. Sci. Technol.,
54, 2133–2142, https://doi.org/10.1021/acs.est.9b05523, 2020.
Munir, S., Mayfield, M., Coca, D., Jubb, S. A., and Osammor, O.: Analysing
the performance of low-cost air quality sensors, their drivers, relative
benefits and calibration in cities-a case study in Sheffield, Environ. Monit.
Assess., 191, 94, https://doi.org/10.1007/s10661-019-7231-8, 2019.
NOAA: Integrated surface database (ISD), available at:
https://www.ncdc.noaa.gov/isd (last access: 7 January 2022), 2021.
Pinder, R. W., Klopp, J. M., Kleiman, G., Hagler, G. S. W., Awe, Y., and
Terry, S.: Opportunities and challenges for filling the air quality data gap
in low- and middle-income countries, Atmos. Environ., 215, 116794,
https://doi.org/10.1016/j.atmosenv.2019.06.032, 2019.
Pope, F. D., Gatari, M., Ng'ang'a, D., Poynter, A., and Blake, R.: Airborne particulate matter monitoring in Kenya using calibrated low-cost sensors, Atmos. Chem. Phys., 18, 15403–15418, https://doi.org/10.5194/acp-18-15403-2018, 2018.
Popoola, O. A. M., Carruthers, D., Lad, C., Bright, V. B., Mead, M. I.,
Stettler, M. E. J., Saffell, J. R., and Jones, R. L.: Use of networks of low
cost air quality sensors to quantify air quality in urban settings,
Atmos. Environ., 194, 58–70,
https://doi.org/10.1016/j.atmosenv.2018.09.030, 2018.
Popoola, O. A. M. and Jones, R. L.: A novel calibration method for
hyperlocal measurements of air quality using a low-cost sensor network, Air
Sensors International Conference (ASIC): Virtual Fall Series, October 2020,
available at:
https://www.youtube.com/watch?v=sPzwmLNiP1w&ab_channel=UCDavisAirQualityResearchCenter (last access: 7 January 2022), 2020.
Popoola, O. A. M., Fleming, J., Peters, D. R., Alvarez, R. A., Ma, G., Stidworthy, A., Forsyth, E., Martin, N. A., Mills, J., Carruthers, L. E., Fonseca, E. R., and Jones, R. L.: A cloud based calibration method for atmospheric measurement networks, in preparation, 2022.
Sahu, R., Nagal, A., Dixit, K. K., Unnibhavi, H., Mantravadi, S., Nair, S., Simmhan, Y., Mishra, B., Zele, R., Sutaria, R., Motghare, V. M., Kar, P., and Tripathi, S. N.: Robust statistical calibration and characterization of portable low-cost air quality monitoring sensors to quantify real-time O3 and NO2 concentrations in diverse environments, Atmos. Meas. Tech., 14, 37–52, https://doi.org/10.5194/amt-14-37-2021, 2021.
Shah, R. U., Robinson, E. S., Gu, P., Apte, J. S., Marshall, J. D.,
Robinson, A. L., and Presto, A. A.: Socio-economic disparities in exposure
to urban restaurant emissions are larger than for traffic, Environ. Res.
Lett., 15, 114039, https://doi.org/10.1088/1748-9326/abbc92, 2020.
Spinelle, L., Gerboles, M., Villani, M. G., Aleixandre, M., and
Bonavitacola, F.: Field calibration of a cluster of low-cost available
sensors for air quality monitoring. Part A: Ozone and nitrogen dioxide,
Sensors and Actuators B: Chemical, 215, 249–257,
https://doi.org/10.1016/j.snb.2015.03.031, 2015.
The Guardian: UK has broken air pollution limits for a decade, EU court
finds, available at:
https://www.theguardian.com/environment/2021/mar/04/uk-has-broken-air-pollution-limits-for-a-decade-eu-court-finds (last access: 7 January 2022),
2021.
US GAO: Air pollution: Opportunities to better sustain and modernize the
national air quality monitoring system, Washington, D.C., GAO-21-38, 2020.
WHO: Ambient (outdoor) air pollution, available at:
https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health
(last access: 28 December 2020), 2018.
WMO: An Update on Low-cost Sensors for the Measurement of Atmospheric
Composition, Geneva, Switzerland, WMO-No. 1215, 2021.
Zimmerman, N., Presto, A. A., Kumar, S. P. N., Gu, J., Hauryliuk, A., Robinson, E. S., Robinson, A. L., and R. Subramanian: A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring, Atmos. Meas. Tech., 11, 291–313, https://doi.org/10.5194/amt-11-291-2018, 2018.
Short summary
We present more than 2 years of NO2 pollution measurements from a sensor network in Greater London and compare results to an extensive network of expensive reference-grade monitors. We show the ability of our lower-cost network to generate robust insights about local air pollution. We also show how irregularities in sensor performance lead to some uncertainty in results and demonstrate ways that future users can characterize and mitigate uncertainties to get the most value from sensor data.
We present more than 2 years of NO2 pollution measurements from a sensor network in Greater...