Articles | Volume 11, issue 1
https://doi.org/10.5194/amt-11-291-2018
© Author(s) 2018. 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-11-291-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
15 Jan 2018
Research article | Highlight paper |
| 15 Jan 2018
| 15 Jan 2018
A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring
Naomi Zimmerman
Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Albert A. Presto
Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Sriniwasa P. N. Kumar
Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Jason Gu
Sensevere LLC, Pittsburgh, PA 15222, USA
Aliaksei Hauryliuk
Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Ellis S. Robinson
Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Allen L. Robinson
Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA 15213, USA
R. Subramanian
Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA 15213, USA
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Cited articles
Air Quality England: Air Pollution Report, 1st January to 31st December 2016, Cambridge Parker Street (Site ID: CAM 1), 1–4, available at: http://www.airqualityengland.co.uk/site/statistics?site_id=CAM1 (last access: 22 June 2017), 2015.
Bart, M., Williams, D. E., Ainslie, B., McKendry, I., Salmond, J., Grange, S. K., Alavi-Shoshtari, M., Steyn, D., and Henshaw, G. S.: High density ozone monitoring using gas sensitive semi-conductor sensors in the lower Fraser valley, British Columbia, Environ. Sci. Technol., 48, 3970–3977, https://doi.org/10.1021/es404610t, 2014.
Borrego, C., Costa, A. M., Ginja, J., Amorim, M., Coutinho, M., Karatzas, K., Sioumis, T., Katsifarakis, N., Konstantinidis, K., De Vito, S., Esposito, E., Smith, P., Andre, N., Gerard, P., Francis, L. A., Castell, N., Schneider, P., Viana, M., Minguillon, M. C., Reimringer, W., Otjes, R. P., von Sicard, O., Pohle, R., Elen, B., Suriano, D., Pfister, V., Prato, M., Dipinto, S., and Penza, M.: Assessment of air quality microsensors versus reference methods: The EuNetAir joint exercise, Atmos. Environ., 147, 246–263, https://doi.org/10.1016/j.atmosenv.2016.09.050, 2016.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, 2001.
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.
Cross, E. S., Williams, L. R., Lewis, D. K., Magoon, G. R., Onasch, T. B., Kaminsky, M. L., Worsnop, D. R., and Jayne, J. T.: Use of electrochemical sensors for measurement of air pollution: correcting interference response and validating measurements, Atmos. Meas. Tech., 10, 3575–3588, https://doi.org/10.5194/amt-10-3575-2017, 2017.
Delgado-Saborit, J. M.: Use of real-time sensors to characterise human exposures to combustion related pollutants, J. Environ. Monit., 14, 1824–1837, https://doi.org/10.1039/C2EM10996D, 2012.
De Vito, S., Massera, E., Piga, M., Martinotto, L., and Di Francia, G.: On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario, Sensor. Actuat. B-Chem., 129, 750–757, https://doi.org/10.1016/j.snb.2007.09.060, 2008.
De Vito, S., Piga, M., Martinotto, L., and Di Francia, G.: CO, NO2 and NOx urban pollution monitoring with on-field calibrated electronic nose by automatic bayesian regularization, Sensor. Actuat. B-Chem., 143, 182–191, https://doi.org/10.1016/j.snb.2009.08.041, 2009.
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 (Switzerland), 16, 1698, https://doi.org/10.3390/s16101698, 2016.
Esposito, E., De Vito, S., Salvato, M., Bright, V., Jones, R. L., and Popoola, O.: Dynamic neural network architectures for on field stochastic calibration of indicative low cost air quality sensing systems, Sensor. Actuat. B-Chem., 231, 701–713, https://doi.org/10.1016/j.snb.2016.03.038, 2016.
Hagan, D. H., Issacman-Vanwertz, G., Franklin, J. P., Wallace, L. M. M., Kocar, B. D., Heald, C. L., and Kroll, J. H.: Calibration and assessment of electrochemical air quality sensors by co-location with reference-grade instruments, Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2017-296, in review, 2017.
Hitchman, M. L., Cade, N. J., Kim Gibbs, T., and Hedley, N. J. M.: Study of the Factors Affecting Mass Transport in Electrochemical Gas Sensors, Analyst, 122, 1411–1417, https://doi.org/10.1039/a703644b, 1997.
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.
Jolliff, J. K., Kindle, J. C., Shulman, I., Penta, B., Friedrichs, M. A. M., Helber, R., and Arnone, R. A.: Summary diagrams for coupled hydrodynamic-ecosystem model skill assessment, J. Marine Syst., 76, 64–82, https://doi.org/10.1016/j.jmarsys.2008.05.014, 2009.
Kuhn, M., Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., Cooper, T., Mayer, Z., Kenkel, B., The R Core Team, Benesty, M., Lescarbeau, R., Ziem, A., Scrucca, L., Tang, Y., Candan, C., and Hunt., T.: caret: Classification and Regression Training, available at: https://cran.r-project.org/package=caret, last access: 23 October 2017.
Lewis, A. and Edwards, P.: Validate personal air-pollution sensors, Nature, 535, 29–31, 2016.
Marshall, J. D., Nethery, E., and Brauer, M.: Within-urban variability in ambient air pollution?: Comparison of estimation methods, Atmos. Environ., 42, 1359–1369, https://doi.org/10.1016/j.atmosenv.2007.08.012, 2008.
Massachusetts Department of Environmental Protection: Massachusetts 2015 Air Quality Report, available at: http://www.mass.gov/eea/docs/dep/air/priorities/15aqrpt.pdf (last access: 23 June 2017), 2016.
Masson, N., Piedrahita, R., and Hannigan, M.: Approach for quantification of metal oxide type semiconductor gas sensors used for ambient air quality monitoring, Sensor. Actuat. B-Chem., 208, 339–345, https://doi.org/10.1016/j.snb.2014.11.032, 2015a.
Masson, N., Piedrahita, R., and Hannigan, M.: Quantification method for electrolytic sensors in long-term monitoring of ambient air quality, Sensors (Switzerland), 15, 27283–27302, https://doi.org/10.3390/s151027283, 2015b.
McKercher, G. R., Salmond, J. A., and Vanos, J. K.: Characteristics and applications of small, portable gaseous air pollution monitors, Environ. Pollut., 223, 102–110, https://doi.org/10.1016/j.envpol.2016.12.045, 2017.
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.
Moltchanov, S., Levy, I., Etzion, Y., Lerner, U., Broday, D. M., and Fishbain, B.: On the feasibility of measuring urban air pollution by wireless distributed sensor networks, Sci. Total Environ., 502, 537–547, https://doi.org/10.1016/j.scitotenv.2014.09.059, 2015.
Nazelle, A. De, Rodríguez, D. A., and Crawford-Brown, D.: Science of the Total Environment The built environment and health?: Impacts of pedestrian-friendly designs on air pollution exposure, Sci. Total Environ., 407, 2525–2535, https://doi.org/10.1016/j.scitotenv.2009.01.006, 2009.
Oshiro, T. M., Perez, P. S., and Baranauskas, J. A.: How Many Trees in a Random Forest?, in: Machine Learning and Data Mining in Pattern Recognition: 8th International Conference, MLDM 2012, Berlin, Germany, July 13–20, 2012. Proceedings, edited by: Perner, P., Springer Berlin Heidelberg, 154–168, 2012.
Pang, X., Shaw, M. D., Lewis, A. C., Carpenter, L. J., and Batchellier, T.: Electrochemical ozone sensors: A miniaturised alternative for ozone measurements in laboratory experiments and air-quality monitoring, Sensor. Actuat. B-Chem., 240, 829–837, https://doi.org/10.1016/j.snb.2016.09.020, 2017.
Pearson, R.: Assessing Variable Importance for Predictive Models of Arbitrary Type, available at: https://cran.r-project.org/web/packages/datarobot/vignettes/VariableImportance.html, last access: 23 October 2017.
Perpinan Lamigueiro, O.: tdr: Target Diagram, available at: https://cran.r-project.org/package=tdr (last access: 25 June 2017), 2015.
Piedrahita, R., Xiang, Y., Masson, N., Ortega, J., Collier, A., Jiang, Y., Li, K., Dick, R. P., Lv, Q., Hannigan, M., and Shang, L.: The next generation of low-cost personal air quality sensors for quantitative exposure monitoring, Atmos. Meas. Tech., 7, 3325–3336, https://doi.org/10.5194/amt-7-3325-2014, 2014.
Pugh, T. A. M., Mackenzie, A. R., Whyatt, J. D., and Hewitt, C. N.: Effectiveness of Green Infrastructure for Improvement of Air Quality in Urban Street Canyons, Environ. Sci. Technol., 46, 7692–7699, 2012.
Strobl, C., Boulesteix, A.-L., Kneib, T., Augustin, T., and Zeileis, A.: Conditional variable importance for random forests, BMC Bioinformatics, 9, 307, doi:10.1186/1471-2105-9-307, 2008.
Snyder, E. G., Watkins, T. H., Solomon, P. A., Thoma, E. D., Williams, R. W., Hagler, G. S. W., Shelow, D., Hindin, D. A., Kilaru, V. J., and Preuss, P. W.: The changing paradigm of air pollution monitoring, Environ. Sci. Technol., 47, 11369–77, https://doi.org/10.1021/es4022602, 2013.
Spinelle, L., Aleixandre, M., and Gerboles, M.: Protocol of evaluation and calibration of low-cost gas sensors for the monitoring of air pollution, Eur. Comm. JRC Techical Reports, EUR 26112, https://doi.org/10.2788/9916, 2013.
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, Sensor. Actuat. B-Chem., 215, 249–257, https://doi.org/10.1016/j.snb.2015.03.031, 2015.
Spinelle, L., Gerboles, M., Villani, M. G., Aleixandre, M., and Bonavitacola, F.: Field calibration of a cluster of low-cost commercially available sensors for air quality monitoring. Part B: NO, CO and CO2, Sensor. Actuat. B-Chem., 238, 706–715, https://doi.org/10.1016/j.snb.2016.07.036, 2017.
Tan, Y., Lipsky, E. M., Saleh, R., Robinson, A. L., and Presto, A. A.: Characterizing the Spatial Variation of Air Pollutants and the Contributions of High Emitting Vehicles in Pittsburgh, PA, Environ. Sci. Technol., 48, 14186–14194, https://doi.org/10.1021/es5034074, 2014.
United States Environmental Protection Agency: Appendix D, Measurement Quality Objectives and Validation Templates, in: QA Handbook Volume II, 5–12, 2014.
Williams, D. E., Henshaw, G. S., Bart, M., Laing, G., Wagner, J., Naisbitt, S., and Salmond, J. A.: Validation of low-cost ozone measurement instruments suitable for use in an air-quality monitoring network, Meas. Sci. Technol., 24, 065803, https://doi.org/10.1088/0957-0233/24/6/065803, 2013.
Williams, R., Kilaru, V. J., Snyder, E. G., Kaufman, A., Dye, T., Ruttler, A., Russell, A., and Hafner, H.: Air Sensor Guidebook, EPA/600/R-14/159, United States Environmental Protection Agency, 2014.
Zhou, Y. and Levy, J. I.: Factors influencing the spatial extent of mobile source air pollution impacts: a meta-analysis, BMC Public Health, 7, 89, https://doi.org/10.1186/1471-2458-7-89, 2007.
Zimmerman, N., Presto, A., Kumar, S., Gu, J., Hauryliuk, A., Robinson, E., Robinson, A., and Subramanian, R.: A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring (Version v1), https://doi.org/10.5281/zenodo.1146109, 2018.
Short summary
Low-cost sensors promise neighborhood-scale air quality monitoring but have been plagued by inconsistent performance for precision, accuracy, and drift. CMU and SenSevere collaborated to develop the RAMP, which uses electrochemical sensors. We present a machine learning algorithm that overcomes previous performance issues and meets US EPA's data quality recommendations for personal exposure for NO2 and tougher "supplemental monitoring" standards for CO & ozone across 19 RAMPs for several months.
Low-cost sensors promise neighborhood-scale air quality monitoring but have been plagued by...
