Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Using a network of lower-cost monitors to identify the influence of modifiable factors driving spatial patterns in fine particulate matter concentrations in an urban environment



There is substantial interest in using networks of lower-cost air quality sensors to characterize urban population exposure to fine particulate matter mass (PM2.5). However, sensor uncertainty is a concern with these monitors.


(1) Quantify the uncertainty of lower-cost PM2.5 sensors; (2) Use the high spatiotemporal resolution of a lower-cost sensor network to quantify the contribution of different modifiable and non-modifiable factors to urban PM2.5.


A network of 64 lower-cost monitors was deployed across Pittsburgh, PA, USA. Measurement and sampling uncertainties were quantified by comparison to local reference monitors. Data were sorted by land-use characteristics, time of day, and wind direction.


Careful calibration, temporal averaging, and reference site corrections reduced sensor uncertainty to 1 μg/m3, ~10% of typical long-term average PM2.5 concentrations in Pittsburgh. Episodic and long-term enhancements to urban PM2.5 due to a nearby large metallurgical coke manufacturing facility were 1.6 ± 0.36 μg/m3 and 0.3 ± 0.2 μg/m3, respectively. Daytime land-use regression models identified restaurants as an important local contributor to urban PM2.5. PM2.5 above EPA and WHO daily health standards was observed at several sites across the city.


With proper management, a large network of lower-cost sensors can identify statistically significant trends and factors in urban exposure.

This is a preview of subscription content, access via your institution

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Lower-cost monitoring network in the Pittsburgh, PA region.
Fig. 2: Two types of uncertainy plotted against averaging time.
Fig. 3: Long-term average PM2.5 concentrations in the Pittsburgh, PA area.
Fig. 4: Average concentrations by site type.
Fig. 5: The impact of Clairton Coke Works on PM2.5 concentrations in the Pittsburgh region.
Fig. 6: Days per year with PM2.5 concentrations exceeding 24-hour standards.


  1. Brook RD, Rajagopalan S, Pope CA, Brook JR, Bhatnagar A, Diez-Roux AV, et al. Particulate matter air pollution and cardiovascular disease. Circulation. 2010;121:2331–78.

    Article  CAS  Google Scholar 

  2. WHO Europe. Health Aspects of Air Pollution Results from the WHO Project ‘Systematic Review of Health Aspects of Air Pollution in Europe’. 2004

  3. Pope A, Burnett R, Thun M, Calle E, Krewski D, Ito K, et al. Long-term exposure to fine particulate air pollution. JAMA. 2002;287:1192.

    Google Scholar 

  4. Pope CA, Lefler JS, Ezzati M, Higbee JD, Marshall JD, Kim S-Y, et al. Mortality risk and fine particulate air pollution in a large, representative cohort of U.S. adults. Environ Health Perspect. 2019;127:077007.

    Article  CAS  Google Scholar 

  5. Lefler JS, Higbee JD, Burnett RT, Ezzati M, Coleman NC, Mann DD et al. Air pollution and mortality in a large, representative U.S. cohort: multiple-pollutant analyses, and spatial and temporal decompositions. Environ Heal. 2019. 10.1186/s12940-019-0544-9.

  6. Eeftens M, Beelen R, De Hoogh K, Bellander T, Cesaroni G, Cirach M, et al. Development of land use regression models for PM2.5, PM 2.5 absorbance, PM10 and PMcoarse in 20 European study areas; Results of the ESCAPE project. Environ Sci Technol. 2012;46:11195–205.

    Article  CAS  Google Scholar 

  7. Malings C, Tanzer R, Hauryliuk A, Saha PK, Robinson AL, Presto AA, et al. Fine particle mass monitoring with low-cost sensors: corrections and long-term performance evaluation. Aerosol Sci Technol. 2019;0:1–15.

    Google Scholar 

  8. De Nazelle A, Seto E, Donaire-Gonzalez D, Mendez M, Matamala J, Nieuwenhuijsen MJ, et al. Improving estimates of air pollution exposure through ubiquitous sensing technologies. Environ Pollut. 2013;176:92–99.

    Article  Google Scholar 

  9. Schneider P, Castell N, Dauge FR, Vogt M, Lahoz WA, Bartonova A. A network of low-cost air quality sensors and its use for mapping urban air quality. In: Earth Systems Data and Models Mobile Information Systems Leveraging Volunteered Geographic Information for Earth Observation. Cham: Springer International Publishing; 2018, p. 93–110.

  10. Popoola OAM, Carruthers D, Lad C, Bright VB, Mead MI, Stettler MEJ, et al. Use of networks of low cost air quality sensors to quantify air quality in urban settings. Atmos Environ. 2018;194:58–70.

    Article  CAS  Google Scholar 

  11. Masiol M, Zíková N, Chalupa DC, Rich DQ, Ferro AR, Hopke PK. Hourly land-use regression models based on low-cost PM monitor data. Environ Res. 2018;167:7–14.

    Article  CAS  Google Scholar 

  12. Castell N, Dauge FR, Schneider P, Vogt M, Lerner U, Fishbain B et al. Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates? Environ Int. 2017. 10.1016/j.envint.2016.12.007.

  13. Schneider P, Castell N, Vogt M, Dauge FR, Lahoz WA, Bartonova A Mapping urban air quality in near real-time using observations from low-cost sensors and model information. Environ Int. 2017. 10.1016/j.envint.2017.05.005.

  14. Mead MI, Popoola OAM, Stewart GB, Landshoff P, Calleja M, Hayes M, et al. The use of electrochemical sensors for monitoring urban air quality in low-cost, high-density networks. Atmos Environ. 2013;70:186–203.

    Article  CAS  Google Scholar 

  15. Jiao W, Hagler G, Williams R, Sharpe R, Brown R, Garver D, et al. Community Air Sensor Network (CAIRSENSE) project: evaluation of low-cost sensor performance in a suburban environment in the southeastern United States. Atmos Meas Tech. 2016;9:5281–92.

    Article  CAS  Google Scholar 

  16. Zheng T, Bergin MH, Johnson KK, Tripathi SN, Shirodkar S, Landis MS, et al. Field evaluation of low-cost particulate matter sensors in high-and low-concentration environments. Atmos Meas Tech. 2018;11:4823–46.

    Article  CAS  Google Scholar 

  17. Crilley LR, Shaw M, Pound R, Kramer LJ, Price R, Young S, et al. Evaluation of a low-cost optical particle counter (Alphasense OPC-N2) for ambient air monitoring. Atmos Meas Tech. 2018;11:709–20.

    Article  Google Scholar 

  18. Piedrahita R, Xiang Y, Masson N, Ortega J, Collier A, Jiang Y, et al. The next generation of low-cost personal air quality sensors for quantitative exposure monitoring. Atmos Meas Tech. 2014;7:3325–36.

    Article  Google Scholar 

  19. Tanzer R, Malings C, Hauryliuk A, Subramanian R, Presto AA. Demonstration of a low-cost multi-pollutant network to quantify intra-urban spatial variations in air pollutant source impacts and to evaluate environmental justice. Int J Environ Res Public Health. 2019;16:2523.

    Article  CAS  Google Scholar 

  20. Subramanian R, Ellis A, Torres-Delgado E, Tanzer R, Malings C, Rivera F, et al. Air quality in puerto rico in the aftermath of hurricane maria: a case study on the use of lower cost air quality monitors. ACS Earth Sp Chem. 2018;2:1179–86.

    Article  CAS  Google Scholar 

  21. Zimmerman N, Li HZ, Ellis A, Hauryliuk A, Robinson ES, Gu P, et al. Improving correlations between land use and air pollutant concentrations using wavelet analysis: insights from a low-cost sensor network. Aerosol Air Qual Res. 2019;5:1–15.

    Google Scholar 

  22. Hoek G, Beelen R, de Hoogh K, Vienneau D, Gulliver J, Fischer P, et al. A review of land-use regression models to assess spatial variation of outdoor air pollution. Atmos Environ. 2008;42:7561–78.

    Article  CAS  Google Scholar 

  23. Henderson SB, Beckerman B, Jerrett M, Brauer M Application of land use regression to estimate long-term concentrations of traffic-related nitrogen oxides and fine particulate matter. Environ Sci Technol. 2007. 10.1021/es0606780.

  24. Beelen R, Hoek G, Pebesma E, Vienneau D, de Hoogh K, Briggs DJ. Mapping of background air pollution at a fine spatial scale across the European Union. Sci Total Environ. 2009;407:1852–67.

    Article  CAS  Google Scholar 

  25. Clougherty JE, Kheirbek I, Eisl HM, Ross Z, Pezeshki G, Gorczynski JE, et al. Intra-urban spatial variability in wintertime street-level concentrations of multiple combustion-related air pollutants: The New York City Community Air Survey (NYCCAS). J Expo Sci Environ Epidemiol. 2013;23:232–40.

    Article  CAS  Google Scholar 

  26. Lenschow P, Abraham HJ, Kutzner K, Lutz M, Preuß JD, Reichenbficher W. Some ideas about the sources of PM10. Atmos Environ. 2001;35:23–33.

    Article  Google Scholar 

  27. Thunis P. On the validity of the incremental approach to estimate the impact of cities on air quality. Atmos Environ. 2018;173:210–22.

    Article  CAS  Google Scholar 

  28. Association AL State of the Air 2018.

  29. Zimmerman N, Presto AA, Kumar SPN, Gu J, Hauryliuk A, Robinson ES, et al. A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring. Atmos Meas Tech. 2018;11:291–313.

    Article  Google Scholar 

  30. Malings C, Tanzer R, Hauryliuk A, Kumar SPN, Zimmerman N, Kara LB, et al. Development of a general calibration model and long-term performance evaluation of low-cost sensors for air pollutant gas monitoring. Atmos Meas Tech. 2019;12:903–20.

    Article  Google Scholar 

  31. Local Climatological Data (LCD) | Data Tools | Climate Data Online (CDO) | National Climatic Data Center (NCDC). Accessed 15 Jan 2020.

  32. US EPA. 3-Year Quality Assurance Report for Calendar Years 2011, 2012, and 2013 PM2.5 Ambient Air Monitoring Program. 2015

  33. Allegheny County GIS OPen Data.

  34. Brunekreef B. Study manual for the European Study of Cohorts for Air Pollution Effects. The Netherlands: Institute for Risk Assessment Sciences, Utrecht University; 2008. p. 1–66.

  35. Lachenbruch PA, Mickey MR. Estimation of error rates in discriminant analysis. Technometrics. 1968;10:1.

    Article  Google Scholar 

  36. Tang W, Raymond T, Wittig B, Davidson C, Pandis S, Robinson A, et al. Spatial variations of PM2.5 during the Pittsburgh air quality study. Aerosol Sci Technol. 2004;38:80–90.

    Article  CAS  Google Scholar 

  37. Gu P, Li HZ, Ye Q, Robinson ES, Apte JS, Robinson AL, et al. Intracity variability of particulate matter exposure is driven by carbonaceous sources and correlated with land-use variables. Environ Sci Technol. 2018;52:11545–54.

    Article  CAS  Google Scholar 

  38. US EPA. 2017 National Emissions Inventory (NEI) Data. 2017.

  39. Chu N, Kadane JB, Davidson CI. Identifying likely PM 2.5 sources on days of elevated concentration: A simple statistical approach. Environ Sci Technol. 2009;43:2407–11.

    Article  CAS  Google Scholar 

  40. Anderson RR, Martello DV, White CM, Crist KC, John K, Modey WK, et al. The regional nature of PM 2.5 episodes in the upper Ohio River Valley. J Air Waste Manag Assoc. 2012;54:971–84.

    Article  Google Scholar 

  41. Allegheny county Health Department. Proposed Revision to the Allegheny County Portion of the Pennsylvania State Implementation Plan. 2019

  42. Liu C, Henderson BH, Wang D, Yang X, Peng ZR. A land use regression application into assessing spatial variation of intra-urban fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations in City of Shanghai, China. Sci Total Environ. 2016;565:607–15.

    Article  CAS  Google Scholar 

  43. Li HZ, Dallmann TR, Gu P, Presto AA. Application of mobile sampling to investigate spatial variation in fine particle composition. Atmos Environ. 2016;142:71–82.

    Article  CAS  Google Scholar 

  44. Robinson ES, Gu P, Ye Q, Li HZ, Shah RU, Apte JS, et al. Restaurant impacts on outdoor air quality: elevated organic aerosol mass from restaurant cooking with neighborhood-scale plume extents. Environ Sci Technol. 2018;52:9285–94.

    Article  CAS  Google Scholar 

  45. Saha PK, Zimmerman N, Malings C, Hauryliuk A, Li Z, Snell L, et al. Quantifying high-resolution spatial variations and local source impacts of urban ultrafine particle concentrations. Sci Total Environ. 2019;655:473–81.

    Article  CAS  Google Scholar 

  46. Karner AA, Eisinger DS, Niemeier DEBA. Near-roadway air quality: synthesizing the findings from real-world. Data 2010;44:5334–44.

    CAS  Google Scholar 

  47. Di Q, Dai L, Wang Y, Zanobetti A, Choirat C, Schwartz JD, et al. Association of short-term exposure to air pollution with mortality in older adults. J Am Med Assoc. 2017;318:2446–56.

    Article  CAS  Google Scholar 

Download references


The authors thank Eric Lipsky, Naomi Zimmerman, Aja Ellis, and Rebecca Tanzer for assistance with instrument setup and operation.


This was developed as  part of the Center for Air, Climate and Energy Solution (CACES). Funding was provided by the United States Environmental Protection Agency (assistance agreement nos. RD83587301 and 83628601) and the Heinz Endowments Fund (grants E2375 and E3145). It has not been formally reviewed by the EPA.The views expressed in this document are solely those of authors and do not necessarily reflect those of the funders. The funders do not endorse any products or commercial services mentioned in this publication.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Allen L. Robinson.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Rose Eilenberg, S., Subramanian, R., Malings, C. et al. Using a network of lower-cost monitors to identify the influence of modifiable factors driving spatial patterns in fine particulate matter concentrations in an urban environment. J Expo Sci Environ Epidemiol 30, 949–961 (2020).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • Air pollution
  • Sensors
  • Particulate matter
  • Spatial variation
  • Urban air pollution
  • low-cost monitors

This article is cited by


Quick links