Abstract
The importance of road traffic, residential heating and meteorological conditions as major drivers of urban PM10 concentrations during air pollution critical episodes has been assessed in the city of Florence (Italy) during the winter season. The most significant meteorological variables (wind speed and atmospheric stability) explained 80.5–85.5 % of PM10 concentrations variance, while a marginal role was played by major emission sources such as residential heating (12.1 %) and road traffic (5.7 %). The persistence of low wind speeds and unstable atmospheric conditions was the leading factor controlling PM10 during critical episodes. A specific PM10 critical episode was analysed, following a snowstorm that caused a “natural” scenario of 2-day dramatic road traffic abatement (−43 %), and a massive (up to +48 %) and persistent (8 consecutive days) increase in residential heating use. Even with such a strong variability in local PM10 emissions, the role of meteorological conditions was prominent, revealing that short-term traffic restrictions are insufficient countermeasures to reduce the health impacts and risks of PM10 critical episodes, while efforts should be made to anticipate those measures by linking them with air quality and weather forecasts.
Similar content being viewed by others
References
Acero JA, Simon A, Padro A, Coloma OS (2012) Impact of local urban design and traffic restrictions on air quality in a medium-sized town. Environ Technol 33(21):2467–2477
Aldrin M, Haff IH (2005) Generalised additive modelling of air pollution, traffic volume and meteorology. Atmos Environ 39:2145–2155
Amodio M, Andriani E, de Gennaro G, Loiotile AD, Di Gilio A, Placentino MC (2012) An integrated approach to identify the origin of PM10 exceedances. Environ Sci Pollut Res 19(8):3132–3141
Barmpadimos I, Hueglin C, Keller J, Henne S, Prévôt ASH (2011) Influence of meteorology on PM10 trends and variability in Switzerland from 1991 to 2008. Atmos Chem Phys 11:1813–1835
Cai H, Xie S (2011) Traffic-related air pollution modeling during the 2008 Beijing Olympic Games: the effects of an odd-even day traffic restriction scheme. Sci Total Environ 409(10):1935–1948
Casale F, Nieddu G, Burdino E, Vignati DAL, Ferretti C, Ugazio G (2009) Monitoring of submicron particulate matter concentrations in the air of Turin city, Italy. Influence of traffic-limitations. Water Air Soil Pollut 196:141–149
Chaloulakou A, Kassomenos P, Spyrellis N, Demokritou P, Koutrakis P (2003) Measurements of PM10 and PM2.5 particle concentrations in Athens, Greece. Atmos Environ 37:649–660
Cusack M, Alastuey A, Pérez N, Pey J, Querol X (2012) Trends of particulate matter (PM2.5) and chemical composition at a regional background site in the Western Mediterranean over the last nine years (2002–2010). Atmos Chem Phys 12(18):8341–8357
Dupont E, Menut L, Carissimo B, Pelon J, Flamant P (1999) Comparison between the atmospheric boundary layer in Paris and its rural suburbs during the ECLAP experiment. Atmos Environ 33:979–994
EC (2008) Directive 2008/50/EC of the European Parliament and of the Council on ambient air quality and cleaner air for Europe, 21/05/2008
EEA (2014) Air quality in Europe – 2014 report. Tech. report No. 5/2014. doi:10.2800/22847
Galindo N, Varea M, Gil-Moltó J, Yubero E, Nicolás J (2011) The influence of meteorology on particulate matter concentrations at an urban Mediterranean location. Water Air Soil Pollut 215:365–372
Genc DD, Yesilyurt C, Tuncel G (2010) Air pollution forecasting in Ankara, Turkey using air pollution index and its relation to assimilative capacity of the atmosphere. Environ Monit Assess 166:11–27
Gioli B, Toscano P, Lugato E, Matese A, Miglietta F, Zaldei A et al (2012) Methane and carbon dioxide fluxes and source partitioning in urban areas: the case study of Florence, Italy. Environ Pollut 164(14):125–131
Grömping U (2006) Relative importance for linear regression in R: the package relaimpo. J Stat Softw 17(1):1–27
Gualtieri G, Crisci A, Tartaglia M, Toscano P, Vagnoli C, Andreini BP, Gioli B (2014) Analysis of 20-year air quality trends in the city of Florence (Italy). Urban Clim 10:530–549
Holst J, Mayer H, Holst T (2008) Effect of meteorological exchange conditions on PM10 concentration. Meteorol Z 17(3):273–282
Hooyberghs J, Mensink C, Dumont G, Fierens F, Brasseur O (2005) A neural network forecast for daily average PM10 concentrations in Belgium. Atmos Environ 39:3279–3289
INERIS (2015). PREV’AIR: forecast desert dust. http://www.prevair.org/en/prevision_pous_desert.php. Accessed 26 Feb 2015
Invernizzi G, Ruprecht A, Mazza R, De Marco C, Močnik G, Sioutas C et al (2011) Measurement of black carbon concentration as an indicator of air quality benefits of traffic restriction policies within the ecopass zone in Milan, Italy. Atmos Environ 45(21):3522–3527
Iovino P, Canzano S, Leone V, Berto C, Salvestrini S, Capasso S (2014) Contribution of vehicular traffic and industrial facilities to PM10 concentrations in a suburban area of Caserta (Italy). Environ Sci Pollut Res 21(23):13169–13174
ISPRA (2013) Qualità dell’ambiente urbano. Rep. no. 45/2013. ISBN 978-88-448-0621-7 [In Italian]
Lonati G, Giugliano M, Cernuschi S (2006) The role of traffic emissions from weekends’ and weekdays’ fine PM data in Milan. Atmos Environ 40:5998–6011
Marcazzan GM, Vaccaro S, Valli G, Vecchi R (2001) Characterisation of PM10 and PM2.5 particulate matter in the ambient air of Milan (Italy). Atmos Environ 35(27):4639–4650
Marcazzan GM, Valli G, Vecchi R (2002) Factors influencing mass concentration and chemical composition of fine aerosols during a PM high pollution episode. Sci Total Environ 298(1):65–79
Matese A, Gioli B, Vaccari FP, Zaldei A, Miglietta F (2009) Carbon dioxide emission of the city center of Firenze, Italy: measurement, evaluation, and source partitioning. J Appl Meteorol Climatol 48(9):1940–1947
Nava S, Lucarelli F, Amato F, Becagli S, Calzolai G, Chiari M et al (2015) Biomass burning contributions estimated by synergistic coupling of daily and hourly aerosol composition records. Sci Total Environ 511:11–20
Parker JD, Mendola P, Woodruff TJB (2008) Preterm birth after the Utah Valley steel mill closure: a natural experiment. Epidemiology 19(6):820–823
Perrino C, Catrambone M, Pietrodangelo A (2008) Influence of atmospheric stability on the mass concentration and chemical composition of atmospheric particles: a case study in Rome, Italy. Environ Int 34(5):621–628
Pope CA III (2007) Mortality effects of longer term exposures to fine particulate air pollution: review of recent epidemiological evidence. Inhal Toxicol 19(suppl 1):33–38
Pope CA III, Dockery DW (2006) Health effects of fine particulate air pollution: lines that connect. J Air Waste Manag 56(6):709–742
Pope CA III, Ezzati M, Dockery DW (2009) Fine-particulate air pollution and life expectancy in the United States. N Engl J Med 360(4):376–386
R Core Team (2013) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. http://www.r-project.org. Accessed 26 Feb 2015
Rajšić SF, Tasić MD, Novaković VT, Tomašević MN (2004) First assessment of the PM10 and PM2. 5 particulate level in the ambient air of Belgrade city. Environ Sci Pollut Res 11(3):158–164
Smith S, Stribley FT, Milligan P, Barratt B (2001) Factors influencing measurements of PM10 during 1995–1997 in London. Atmos Environ 35:4651–4662
Tuscany Region (2011) Progetto regionale PATOS: Il materiale particolato fine PM10. Final report [In Italian]. Available at: http://www.regione.toscana.it/-/progetto-regionale-patos. Accessed 26 Feb 2015
Tuscany Region (2015) Inventario Regionale sulle Sorgenti di Emissione in aria ambiente IRSE – Emissioni inquinanti e gas serra. Aggiornamento anno 2010. [In Italian]. Available at: http://servizi2.regione.toscana.it/aria. Accessed 26 Feb 2015
Unal YS, Toros H, Deniz A, Incecik S (2011) Influence of meteorological factors and emission sources on spatial and temporal variations of PM10 concentrations in Istanbul metropolitan area. Atmos Environ 45:5504–5513
Van der Wal JT, Janssen LHJM (2000) Analysis of spatial and temporal variations of PM10 concentrations in the Netherlands using Kalman filtering. Atmos Environ 34:3675–3687
Vecchi R, Marcazzan G, Valli G (2007) A study on nighttime-daytime PM10 concentration and elemental composition in relation to atmospheric dispersion in the urban area of Milan (Italy). Atmos Environ 41(10):2136–2144
Acknowledgments
The Authors wish to thank ARPAT for providing PM10 and PM2.5 concentrations data; the Florence municipal council for providing road traffic data; Società Toscana Energia S.p.A. for providing gas consumption data; the LaMMA Consortium for providing emission data from the IRSE inventory; Emilio Borchi and Renzo Macii (Ximeniano Observatory) for their support to the eddy covariance infrastructure management.
Author information
Authors and Affiliations
Corresponding author
Additional information
Responsible editor: Constantini Samara
Highlights
• Urban-scale PM10 critical episodes are mainly caused by the strong variability of weather conditions
• Wind speed and atmospheric stability explain 80.5–85.5 % of PM10 concentrations variance
• The persistence of lower wind regimes and unstable conditions is pivotal
• The influence of local emission drivers (residential heating and road traffic) is minor
• Short-term traffic restrictions are insufficient countermeasures to prevent PM10 critical episodes
Rights and permissions
About this article
Cite this article
Gualtieri, G., Toscano, P., Crisci, A. et al. Influence of road traffic, residential heating and meteorological conditions on PM10 concentrations during air pollution critical episodes. Environ Sci Pollut Res 22, 19027–19038 (2015). https://doi.org/10.1007/s11356-015-5099-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-015-5099-x