Estimating hourly concentrations of PM2.5 across a metropolitan area using low-cost particle monitors

Sensors (Switzerland)

Volumn 17, Issue 8, 2017, Pages

  Zikova, Nadezda ^a,b   Masiol, Mauro ^b,c   Chalupa, David C ^d   Rich, David Q ^c   Ferro, Andrea R ^e   Hopke, Philip K ^b,c  

^a Environmental Centre   (Czech Republic)

^b CLARKSON UNIVERSITY   (United States)

^c UNIVERSITY OF ROCHESTER SCHOOL OF MEDICINE AND DENTISTRY   (United States)

^d UNIVERSITY OF ROCHESTER MEDICAL CENTER   (United States)

^e Clarkson University   (United States)

Author keywords

Hourly concentrations; Low cost monitors; Particulate matter; Spatial variability

Indexed keywords

CORRELATION METHODS; WIND;

AIRBORNE PARTICULATE MATTERS; LOW COSTS; PARTICULATE MATTER; PEARSON CORRELATION COEFFICIENTS; SIMULTANEOUS MEASUREMENT; SPATIAL AND TEMPORAL VARIABILITY; SPATIAL AND TEMPORAL VARIATION; SPATIAL VARIABILITY;

COSTS;

EID: 85028335497     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s17081922     Document Type: Article

References (32)

1. Kim, K.H.; Kabir, E.; Kabir, S. A review on the human health impact of airborne particulate matter. Environ. Int. 2015, 74, 136–143. [CrossRef] [PubMed]
2. 2.5 Chemical Speciation Monitoring Networks—CSN and IMPROVE: Description of networks. J. Air Waste Manag. Assoc. 2014, 64, 1410–1438. [CrossRef] [PubMed]
3. 10. Available online: https://www.osti.gov/scitech/biblio/678946 (accessed on 17 August 2017).
4. 2 Monitoring Technical Assistance Document. Available online: https://trid.trb.org/view.aspx?id=1212411 (accessed on 17 August 2017).
5. 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.; Hamburg, S.P. High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data. Environ. Sci. Technol. 2017, 51, 6999–7008. [CrossRef] [PubMed]
6. 2.5 concentration associated with increased cardiovascular mortality in Guangzhou, China. J. Expo. Sci. Environ. Epidemiol. 2017, 27, 333–338. [CrossRef] [PubMed]
7. Kumar, P.; Morawska, L.; Martani, C.; Biskos, G.; Neophytou, M.; Di Sabatino, S.; Bell, M.; Norford, L.; Britter, R. The rise of low-cost sensing for managing air pollution in cities. Environ. Int. 2015, 75, 199–205. [CrossRef] [PubMed]
8. Northcross, A.L.; Edwards, R.J.; Johnson, M.A.; Wang, Z.-M.; Zhu, K.; Allen, T.; Smith, K.R. A low-cost particle counter as a realtime fine-particle mass monitor. Environ. Sci. Process. Impacts 2013, 15, 433–439. [CrossRef] [PubMed]
9. 2.5 in Xi’an, China. Environ. Pollut. 2015, 199, 56–65. [CrossRef] [PubMed]
10. Steinle, S.; Reis, S.; Sabel, C.E. Quantifying human exposure to air pollution-Moving from static monitoring to spatio-temporally resolved personal exposure assessment. Sci. Total Environ. 2013, 443, 184–193. [CrossRef] [PubMed]
11. Holstius, D.M.; Pillarisetti, A.; Smith, K.R.; Seto, E. Field calibrations of a low-cost aerosol sensor at a regulatory monitoring site in California. Atmos. Meas. Tech. 2014, 7, 1121–1131. [CrossRef]
12. Budde, M.; Zhang, L.; Beigl, M. Distributed, Low-Cost Particulate Matter Sensing: Scenarios, Challenges, Approaches. In Proceedings of the 1st International Conference on Atmospheric Dust, Castellaneta Marina, Italy, 1–6 June 2014; pp. 230–236.
13. Lewis, A.; Edwards, P. Validate personal air-pollution sensors. Nature 2016, 535, 29–31. [CrossRef] [PubMed]
14. Sousan, S.; Koehler, K.; Thomas, G.; Park, J.H.; Hillman, M.; Halterman, A.; Peters, T.M. Inter-comparison of low-cost sensors for measuring the mass concentration of occupational aerosols. Aerosol Sci. Technol. 2016, 50, 462–473. [CrossRef]
15. 2.5 in indoor and outdoor microenvironments. Sci. Total Environ. 2015, 508, 383–394. [CrossRef] [PubMed]
16. Williams, R.; Kaufman, A.; Hanley, T.; Rice, J.; Garvey, S. Evaluation of Field-Deployed Low Cost PM Sensors. Available online: https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=297517 (accessed on 17 August 2017).
17. Manikonda, A.; Zikova, N.; Hopke, P.K.; Ferro, A.R. Laboratory assessment of low-cost PM monitors. J. Aerosol Sci. 2016, 102, 29–40. [CrossRef]
18. 2.5 concentrations measurements. J. Aerosol Sci. 2017, 105, 24–34. [CrossRef]
19. Wang, Y.; Hopke, P.K.; Utell, M.J. Urban-scale spatial-temporal variability of black carbon and winter residential wood combustion particles. Aerosol Air Qual. Res. 2011, 11, 473–481. [CrossRef]
20. Wang, Y.; Hopke, P.K.; Rattigan, O.V.; Chalupa, D.C.; Utell, M.J. Multiple-year black carbon measurements and source apportionment using Delta-C in Rochester, New York. J. Air Waste Manag. Assoc. 2012, 62, 880–887. [CrossRef] [PubMed]
21. Wang, Y.; Hopke, P.K.; Xia, X.; Rattigan, O.V.; Chalupa, D.C.; Utell, M.J. Source apportionment of airborne particulate matter using inorganic and organic species as tracers. Atmos. Environ. 2012, 55, 525–532. [CrossRef]
22. 2 Source Impacts. Sci. Total Environ. 2017. submitted.
23. Wilcoxon, F. Individual comparisons by ranking methods. Biom. Bull. 1945, 1, 80–83. [CrossRef]
24. 2.5 ambient aerosol chemical databases. Environ. Sci. Technol. 1998, 32, 3926–3934. [CrossRef]
25. 2.5 in Urban Areas in the United States. J. Air Waste Manag. Assoc. 2004, 54, 440–449. [CrossRef] [PubMed]
26. Wilson, J.G.; Kingham, S.; Pearce, J.; Sturman, A.P. A review of intraurban variations in particulate air pollution: Implications for epidemiological research. Atmos. Environ. 2005, 39, 6444–6462. [CrossRef]
27. Marshall, J.D.; Nethery, E.; Brauer, M. Within-urban variability in ambient air pollution: comparison of estimation methods. Atmos. Environ. 2008, 42, 1359–1369. [CrossRef]
28. Beelen, R.; Hoek, G.; Pebesma, E.; Vienneau, D.; de Hoogh, K.; Briggs, D.J. Mapping of background air pollution at a fine spatial scale across the European Union. Sci. Total Environ. 2009, 407, 1852–1867. [CrossRef] [PubMed]
29. Dodson, R.; Marks, D. Daily air temperature interpolation at high spatial resolution over a large mountainous region. Clim. Res. 1997, 8, 1–20. [CrossRef]
30. Uria-Tellaetxe, I.; Carslaw, D.C. Conditional bivariate probability function for source identification. Environ. Model. Softw. 2014, 59, 1–9. [CrossRef]
31. Ashbaugh, L.L.; Malm, W.C.; Sadeh, W.Z. A residence time probability analysis of sulfur concentrations at Grand Canyon national park. Atmos. Environ. 1985, 19, 1263–1270. [CrossRef]
32. Wang, Y.; Hopke, P.K.; Utell, M.J. Urban-scale Spatial-Temporal Variability of Ultrafine Particle Number. Water Air Soil Pollut. 2012, 223, 2223–2235. [CrossRef]