Estimating Hourly Concentrations of PM2.5 across a Metropolitan Area Using Low-Cost Particle Monitors

. 2017 Aug 21 ; 17 (8) : . [epub] 20170821

Status PubMed-not-MEDLINE Jazyk angličtina Země Švýcarsko Médium electronic

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/pmid28825680

There is concern regarding the heterogeneity of exposure to airborne particulate matter (PM) across urban areas leading to negatively biased health effects models. New, low-cost sensors now permit continuous and simultaneous measurements to be made in multiple locations. Measurements of ambient PM were made from October to April 2015-2016 and 2016-2017 to assess the spatial and temporal variability in PM and the relative importance of traffic and wood smoke to outdoor PM concentrations in Rochester, NY, USA. In general, there was moderate spatial inhomogeneity, as indicated by multiple pairwise measures including coefficient of divergence and signed rank tests of the value distributions. Pearson correlation coefficients were often moderate (~50% of units showed correlations >0.5 during the first season), indicating that there was some coherent variation across the area, likely driven by a combination of meteorological conditions (wind speed, direction, and mixed layer heights) and the concentration of PM2.5 being transported into the region. Although the accuracy of these PM sensors is limited, they are sufficiently precise relative to one another and to research grade instruments that they can be useful is assessing the spatial and temporal variations across an area and provide concentration estimates based on higher-quality central site monitoring data.

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Kim K.H., Kabir E., Kabir S. A review on the human health impact of airborne particulate matter. Environ. Int. 2015;74:136–143. doi: 10.1016/j.envint.2014.10.005. PubMed DOI

Solomon P.A., Crumpler D., Flanagan J.B., Jayanty R.K.M., Rickman E.E., McDade C.E. U.S. National PM2.5 Chemical Speciation Monitoring Networks—CSN and IMPROVE: Description of networks. J. Air Waste Manag. Assoc. 2014;64:1410–1438. doi: 10.1080/10962247.2014.956904. PubMed DOI

Watson J.G., Chow J.C., Dubois D., Green M., Frank N. Guidance for the Network Design and Optimum Site Exposure for PM2.5 and PM10. [(accessed on 17 August 2017)]; Available online: https://www.osti.gov/scitech/biblio/678946.

Watkins N., Baldauf R. Near-Road NO2 Monitoring Technical Assistance Document. [(accessed on 17 August 2017)]; Available online: https://trid.trb.org/view.aspx?id=1212411.

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. doi: 10.1021/acs.est.7b00891. PubMed DOI

Lin H., Liu T., Xiao J., Zeng W., Guo L., Li X., Xu Y., Zhang Y., Chang J.J., Vaughn M.G., et al. Hourly peak PM2.5 concentration associated with increased cardiovascular mortality in Guangzhou, China. J. Expo. Sci. Environ. Epidemiol. 2017;27:333–338. doi: 10.1038/jes.2016.63. PubMed DOI

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. doi: 10.1016/j.envint.2014.11.019. PubMed DOI

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. doi: 10.1039/C2EM30568B. PubMed DOI

Gao M., Cao J., Seto E. A distributed network of low-cost continuous reading sensors to measure spatiotemporal variations of PM2.5 in Xi’an, China. Environ. Pollut. 2015;199:56–65. doi: 10.1016/j.envpol.2015.01.013. PubMed DOI

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. doi: 10.1016/j.scitotenv.2012.10.098. PubMed DOI

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. doi: 10.5194/amt-7-1121-2014. DOI

Budde M., Zhang L., Beigl M. Distributed, Low-Cost Particulate Matter Sensing: Scenarios, Challenges, Approaches; Proceedings of the 1st International Conference on Atmospheric Dust; Castellaneta Marina, Italy. 1–6 June 2014; pp. 230–236.

Lewis A., Edwards P. Validate personal air-pollution sensors. Nature. 2016;535:29–31. doi: 10.1038/535029a. PubMed DOI

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. doi: 10.1080/02786826.2016.1162901. PubMed DOI PMC

Steinle S., Reis S., Sabel C.E., Semple S., Twigg M.M., Braban C.F., Leeson S.R., Heal M.R., Harrison D., Lin C., et al. Personal exposure monitoring of PM2.5 in indoor and outdoor microenvironments. Sci. Total Environ. 2015;508:383–394. doi: 10.1016/j.scitotenv.2014.12.003. PubMed DOI

Williams R., Kaufman A., Hanley T., Rice J., Garvey S. Evaluation of Field-Deployed Low Cost PM Sensors. [(accessed on 17 August 2017)]; Available online: https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=297517.

Manikonda A., Zikova N., Hopke P.K., Ferro A.R. Laboratory assessment of low-cost PM monitors. J. Aerosol Sci. 2016;102:29–40. doi: 10.1016/j.jaerosci.2016.08.010. DOI

Zikova N., Hopke P.K., Ferro A.R. Evaluation of new low-cost particle monitors for PM2.5 concentrations measurements. J. Aerosol Sci. 2017;105:24–34. doi: 10.1016/j.jaerosci.2016.11.010. DOI

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. doi: 10.4209/aaqr.2011.01.0005. DOI

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. doi: 10.1080/10962247.2012.671792. PubMed DOI

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. doi: 10.1016/j.atmosenv.2012.03.073. DOI

Emami F., Masiol M., Hopke P.K. The Air Pollution at Rochester, NY: Long-Term Trends and Multivariate Analysis of Upwind SO2 Source Impacts. Sci. Total Environ. 2017 submitted. PubMed

Wilcoxon F. Individual comparisons by ranking methods. Biom. Bull. 1945;1:80–83. doi: 10.2307/3001968. DOI

Wongphatarakul V., Friedlander S.K., Pinto J.P. A comparative study of PM2.5 ambient aerosol chemical databases. Environ. Sci. Technol. 1998;32:3926–3934. doi: 10.1021/es9800582. DOI

Pinto J.P., Lefohn A.S., Shadwick D.S. Spatial Variability of PM2.5 in Urban Areas in the United States. J. Air Waste Manag. Assoc. 2004;54:440–449. doi: 10.1080/10473289.2004.10470919. PubMed DOI

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. doi: 10.1016/j.atmosenv.2005.07.030. DOI

Marshall J.D., Nethery E., Brauer M. Within-urban variability in ambient air pollution: Comparison of estimation methods. Atmos. Environ. 2008;42:1359–1369. doi: 10.1016/j.atmosenv.2007.08.012. DOI

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. doi: 10.1016/j.scitotenv.2008.11.048. PubMed DOI

Dodson R., Marks D. Daily air temperature interpolation at high spatial resolution over a large mountainous region. Clim. Res. 1997;8:1–20. doi: 10.3354/cr008001. DOI

Uria-Tellaetxe I., Carslaw D.C. Conditional bivariate probability function for source identification. Environ. Model. Softw. 2014;59:1–9. doi: 10.1016/j.envsoft.2014.05.002. DOI

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. doi: 10.1016/0004-6981(85)90256-2. DOI

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. doi: 10.1007/s11270-011-1018-z. DOI

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