A multi-year dataset of measurements of CO2 concentrations, eddy covariance fluxes, and meteorological parameters over the city centre of Florence (Italy) has been analysed to assess the role of anthropogenic emissions and meteorology in controlling urban CO2 concentrations. The latter exhibited a negative correlation with air temperature, wind speed, solar radiation, and sensible heat flux and a positive one with relative humidity and emissions. A linear and an artificial neural network (ANN) model have been developed and validated for short-term modelling of 3-h CO2 concentrations. The ANN model performed better, with mean bias of 0.58 ppm, root mean square error within 30 ppm, and r2=0.49. Data clustering through the self-organized maps allowed to disentangle the role played by emissions and meteorological parameters in influencing CO2 concentrations. Sensitivity analysis of CO2 concentrations revealed a primary role played by the meteorological parameters, particularly wind speed. These results highlighted that (i) emission reduction actions at local urban scale should be better tied to actual and expected meteorological conditions and (ii) those actions alone have limited effects (e.g. a 20% emission reduction would result in a 3% CO2 concentrations reduction). For all these reasons, large-scale policies would be needed.
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Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19(6):716–723
Aubinet M, Grelle A, Ibrom A, Rannik Ü, Moncrieff J, Foken T et al (2000) Estimates of the annual net carbon and water exchange of forests: the EUROFLUX methodology. Adv Ecol Res 30:113–175. https://doi.org/10.1016/S0065-2504(08)60018-5
Baklanov A, Schlünzen K, Suppan P, Baldasano J, Brunner D, Aksoyoglu S, Carmichael G, Douros J, Flemming J, Forkel R, Galmarini S, Gauss M, Grell G, Hirtl M, Joffre S, Jorba O, Kaas E, Kaasik M, Kallos G, Kong X, Korsholm U, Kurganskiy A, Kushta J, Lohmann U, Mahura A, Manders-Groot A, Maurizi A, Moussiopoulos N, Rao ST, Savage N, Seigneur C, Sokhi RS, Solazzo E, Solomos S, Sørensen B, Tsegas G, Vignati E, Vogel B, Zhang Y (2014) Online coupled regional meteorology chemistry models in Europe: current status and prospects. Atmos Chem Phys 14:317–398. https://doi.org/10.5194/acp-14-317-2014
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. https://doi.org/10.5194/acp-11-1813-2011
Belsley DA, Kuh E, Welsch RE (1980) Regression diagnostics: identifying influential data and sources of collinearity. Wiley, New York
Bowman AW (2018). ‘sm’ package: smoothing methods for nonparametric regression and density estimation. Version 2.2-5.6, 2018/09/27. Available at: https://cran.r-project.org/web/packages/sm/index.html. Accessed 27/02/2020
Bowman AW, Azzalini A (1997) Applied smoothing techniques for data analysis: the kernel approach with S-Plus illustrations. Oxford University Press, Oxford
Brilli F, Gioli B, Fares S, Terenzio Z, Zona D, Gielen B, Loreto F, Janssens IA, Ceulemans R (2016) Rapid leaf development drives the seasonal pattern of volatile organic compound (VOC) fluxes in a ‘coppiced’ bioenergy poplar plantation. Plant Cell Environ 39(3):539–555
Calcagno V, de Mazancourt C (2010) ‘glmulti’: an R package for easy automated model selection with (generalized) linear models. J Stat Softw 34(12):1–29
Carotenuto F, Gualtieri G, Miglietta F, Riccio A, Toscano P, Wohlfahrt G, Gioli B (2018) Industrial point source CO2 emission strength estimation with aircraft measurements and dispersion modelling. Environ Monit Assess 190(3):165
Christen A (2014) Atmospheric measurement techniques to quantify greenhouse gas emissions from cities. Urban Clim 10(2):241–260. https://doi.org/10.1016/j.uclim.2014.04.006
Fiore AM, Naik V, Leibensperger EM (2015) Air quality and climate connections. J Air Waste Manage Assoc 65(6):645–685
Foken T, Wichura B (1996) Tools for quality assessment of surface-based flux measurements. Agric For Meteorol 78(1-2):83–105. https://doi.org/10.1016/0168-1923(95)02248-1
Font A, Grimmond CSB, Kotthaus S, Morguí J-A, Stockdale C, O'Connor E, Priestman M, Barratt B (2015) Daytime CO2 urban surface fluxes from airborne measurements, eddy-covariance observations and emissions inventory in Greater London. Environ Pollut 196:98–106. https://doi.org/10.1016/j.envpol.2014.10.001
Fox J, Kleiber C, Zeileis A (2020) ‘ivreg’: two-stage least-squares regression with diagnostics. Version 0.5-0, 2020/09/03. Available at: https://cran.r-project.org/web/packages/ivreg/index.html. Accessed 08/01/2021
Fragkias M, Lobo J, Strumsky D, Seto KC (2013) Does size matter? Scaling of CO2 emissions and U.S. urban areas. PLoS One 8(6):e64727. https://doi.org/10.1371/journal.pone.0064727
Friendly M (2002) Corrgrams: exploratory displays for correlation matrices. Am Stat 56:316–324
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. https://doi.org/10.1007/s10661-009-0981-y
Gioli B, Toscano P, Lugato E, Matese A, Miglietta F, Zaldei A, Vaccari FP (2012) Methane and carbon dioxide fluxes and source partitioning in urban areas: the case study of Florence, Italy. Environ Pollut 164:125–131. https://doi.org/10.1016/j.envpol.2012.01.019
Gioli B, Gualtieri G, Busillo C, Calastrini F, Zaldei A, Toscano P (2015) Improving high resolution emission inventories with local proxies and urban eddy covariance flux measurements. Atmos Environ 115:246–256. https://doi.org/10.1016/j.atmosenv.2015.05.068
Gualtieri G, Toscano P, Crisci A, Di Lonardo S, Tartaglia M, Vagnoli C et al (2015) Influence of road traffic, residential heating and meteorological conditions on PM10 concentrations during air pollution critical episodes. Environ Sci Pollut Res 22(23):19027–19038. https://doi.org/10.1007/s11356-015-5099-x
Gualtieri G, Carotenuto F, Finardi S, Tartaglia M, Toscano P, Gioli B (2018) Forecasting PM10 hourly concentrations in northern Italy: insights on models performance and PM10 drivers through self-organizing maps. Atmos Pollut Res 9(6):1204–1213
Guenther F (2019). Package ‘neuralnet’, 07/02/2019. Available at:https://cran.r-project.org/web/packages/neuralnet/neuralnet.pdf. Accessed 27/02/2020
Haiduc I, Beldean-Galea MS (2011) Variation of greenhouse gases in urban areas–case study: CO2, CO and CH4 in three Romanian cities. In: Popovic D (ed) Air quality – models and applications. INTech, UK, pp 289–318
Hassan AGA (2015) Diurnal and monthly variations in atmospheric CO2 level in Qena, Upper Egypt. Resour Environ 5:59–65
Hausman JA (1978) Specification tests in econometrics. Econometrica 46(6):1251–1271. https://doi.org/10.2307/1913827
Hewitson BC, Crane RG (2002) Self-organizing maps: applications to synoptic climatology. Clim Res 22(1):13–26. https://doi.org/10.3354/cr022013
Holst J, Mayer H, Holst T (2008) Effect of meteorological exchange conditions on PM10 concentration. Meteorol Z 17(3):273–282. https://doi.org/10.1127/0941-2948/2008/0283
Hong JW, Lee SD, Lee K, Hong J (2020). Seasonal variations in the surface energy and CO2 flux over a high-rise, high-population, residential urban area in the East Asian monsoon region. Int J Climatol 1–24
Huang X, Wang T, Talbot R, Xie M, Mao H, Li S, Zhuang B, Yang X, Fu C, Zhu J, Huang X, Xu R (2015) Temporal characteristics of atmospheric CO2 in urban Nanjing, China. Atmos Res 153:437–450
Hutyra LR, Duren R, Gurney KR, Grimm N, Kort EA, Larson E, Shrestha G (2014) Urbanization and the carbon cycle: current capabilities and research outlook from the natural sciences perspective. Earth’s Future 2:473–495. https://doi.org/10.1002/2014EF000255
Iwata H, Okada K, Samreth S (2010) Empirical study on environmental Kuznets curve for CO2 in France: the role of nuclear energy. Energy Policy 38:4057–4063. https://doi.org/10.1016/j.enpol.2010.03.031
Khedairia S, Khadir MT (2012) Impact of clustered meteorological parameters on air pollutants concentrations in the region of Annaba, Algeria. Atmos Res 113:89–101. https://doi.org/10.1016/j.atmosres.2012.05.002
Kim S (2015) ‘ppcor’: an R Package for a fast calculation to semi-partial correlation coefficients. Commun Stat Appl Methods 22(6):665–674
Kohonen T (1997) Self-organizing Maps, 2nd edn. Springer, Heidelberg
Kolehmainen M, Martikainen H, Ruuskanen J (2001) Neural networks and periodic components used in air quality forecasting. Atmos Environ 35(5):815–825. https://doi.org/10.1016/S1352-2310(00)00385-X
Kotthaus S, Grimmond CSB (2012) Identification of micro-scale anthropogenic CO2, heat and moisture sources – processing eddy covariance fluxes for a dense urban environment. Atmos Environ 57:301–316. https://doi.org/10.1016/j.atmosenv.2012.04.024
Kukkonen J, Partanen L, Karppinen A, Ruuskanen J, Junninen H, Kolehmainen M et al (2003) Extensive evaluation of neural network models for the prediction of NO2 and PM10 concentrations, compared with a deterministic modelling system and measurements in central Helsinki. Atmos Environ 37:4539–4550. https://doi.org/10.1016/S1352-2310(03)00583-1
Langner M, Draheim T, Endlicher W (2011) Particulate matter in the urban atmosphere: concentration, distribution, reduction-results of studies in the Berlin metropolitan area. In: Endlicher W (ed) Perspectives in urban ecology. Springer, Berlin Heidelberg, pp 15–41
Lapira E, Brisset D, Ardakani HD, Siegel D, Lee J (2012) Wind turbine performance assessment using multi-regime modeling approach. Renew Energy 45:86–95. https://doi.org/10.1016/j.renene.2012.02.018
Lian J, Bréon FM, Broquet G, Zaccheo TS, Dobler J, Ramonet M, Staufer J, Santaren D, Xueref-Remy I, Ciais P (2019) Analysis of temporal and spatial variability of atmospheric CO2 concentration within Paris from the GreenLITE™ laser imaging experiment. Atmos Chem Phys 19(22):13809–13825
Louhichi K, Jacquet F, Butault JP (2012) Estimating input allocation from heterogeneous data sources: a comparison of alternative estimation approaches. Agric Econ Rev 13(2):83–102
Luyssaert S, Janssens IA, Sulkava M, Papale D, Dolman AJ, Reichstein M et al (2007) Photosynthesis drives anomalies in net carbon-exchange of pine forests at different latitudes. Glob Chang Biol 13:2110–2127. https://doi.org/10.1111/j.1365-2486.2007.01432.x
Magliulo V, Toscano P, Grimmond CSB, Kotthaus S, Järvi L, Setälä H et al (2014) Environmental measurements in BRIDGE case studies. In: Chrysoulakis N, de Castro EA, Moors EJ (eds) Understanding urban metabolism. Routledge, pp 45–57 ISBN 9780415835114. Available at: http://centaur.reading.ac.uk/52793. Accessed 23/03/2020
Makar PA, Gong W, Hogrefe C, Zhang Y, Curci G, Žabkar R, Milbrandt J, Im U, Balzarini A, Baró R, Bianconi R, Cheung P, Forkel R, Gravel S, Hirtl M, Honzak L, Hou A, Jiménez-Guerrero P, Langer M, Moran MD, Pabla B, Pérez JL, Pirovano G, San José R, Tuccella P, Werhahn J, Zhang J, Galmarini S (2015) Feedbacks between air pollution and weather, part 2: effects on chemistry. Atmos Environ 115:499–526. https://doi.org/10.1016/j.atmosenv.2014.10.021
Matese A, Gioli B, Vaccari FP, Zaldei A, Miglietta F (2009) Carbon dioxide emissions of the city center of Firenze, Italy: measurement, evaluation, and source partitioning. J Appl Meteorol Climatol 48(9):1940–1947. https://doi.org/10.1175/2009JAMC1945.1
Mitchell LE, Crosman ET, Jacques AA, Fasoli B, Leclair-Marzolf L, Horel J, Bowling DR, Ehleringer JR, Lin JC (2018) Monitoring of greenhouse gases and pollutants across an urban area using a light-rail public transit platform. Atmos Environ 187:9–23
Montgomery DC, Peck EA (2006) Introduction to linear regression analysis, 4th edn. Wiley Blackwell, New York, p 2006
Napiorkowska M, Tomaszewska M(2013). The relationship between carbon dioxide (CO2) /derived/ from SCIAMACHY.ENVISAT-1, meteorological parameters, and vegetation indices – case study of Poland. Geoinformation (5)
Sarrat C, Noilhan J, Lacarrère P, Ceschia E, Ciais P, Dolman AJ, Elbers JA, Gerbig C, Gioli B, Lauvaux T, Miglietta F, Neininger B, Ramonet M, Vellinga O, Bonnefond JM (2009) Mesoscale modelling of the CO2 interactions between the surface and the atmosphere applied to the April 2007 CERES field experiment. Biogeosciences 6(4):633–646
Schmidt A, Hanson C, Kathilankal J, Law BE (2011) Classification and assessment of turbulent fluxes above ecosystems in North America with self-organizing feature map networks. Agric For Meteorol 151(4):508–520. https://doi.org/10.1016/j.agrformet.2010.12.009
Schmutz M, Vogt R, Feigenwinter C, Parlow E (2016) Ten years of eddy covariance measurements in Basel, Switzerland: seasonal and interannual variabilities of urban CO2 mole fraction and flux. J Geophys Res-Atmos 121:8649–8667
Spiridonov V, Jakimovski B, Spiridonova I, Pereira G (2019) Development of air quality forecasting system in Macedonia, based on WRF-Chem model. Air Qual Atmos Health 12(7):825–836
Sreenivas G, Mahesh P, Subin J, Kanchana AL, Rao PVN, Dadhwal VK (2016) Influence of meteorology and interrelationship with greenhouse gases (CO2 and CH4) at a suburban site of India. Atmos Chem Phys 16:3953–3967
UN (United Nations) (2018) World urbanization prospects: the 2018 Revision. ST/ESA/SER.A/420. Available at: https://population.un.org/wup/Publications/Files/WUP2018-Report.pdf. Accessed 27/02/2020
Vaccari FP, Gioli B, Toscano P, Perrone C (2013) Carbon dioxide balance assessment of the city of Florence (Italy) and implications for urban planning. Landsc Urban Plan 120:138–146. https://doi.org/10.1016/j.landurbplan.2013.08.004
Vickers D, Mahrt L (1997) Quality control and flux sampling problems for tower and aircraft data. J Atmos Ocean Technol 14(3):512–526
Ward HC, Evans JG, Grimmond CSB (2013) Multi-season eddy covariance observations of energy, water and carbon fluxes over a suburban area in Swindon, UK. Atmos Chem Phys 13:4645–4666. https://doi.org/10.5194/acp-13-4645-2013
Webb EK, Pearman GI, Leuning R (1980) Correction of flux measurements for density effects due to heat and water vapour transfer. Q J R Meteorol Soc 106(447):85–100. https://doi.org/10.1002/qj.49710644707
Wei T, Simko V, Levy M, Xie Y, Jin Y, Zemla J (2017). Package ‘corrplot’, 17/10/2017. Available at: https://cran.r-project.org/web/packages/corrplot/corrplot.pdf. Accessed 27/02/2020
Ying CS (2015). Measurement and analysis of carbon dioxide concentration in the outdoor environment. Physics Department, from Chinese University of Hong Kong. Available at: http://www.phy.cuhk.edu.hk/new/internshipandjobs/hko/2010/chan%20so%20yin_20110118.pdf. Accessed 26/02/2020
Zaiontz C (2018). Real statistical analysis using Excel. Available at: www.real-statistics.com. Accessed 08/01/2021
Zannetti P (1990) Air pollution modelling: theories, computational methods and available software. Computational Mechanism Publications, Southampton
Zhang Y, Bocquet M, Mallet V, Seigneur C, Baklanov A (2012) Real-time air quality forecasting, part II: state of the science, current research needs, and future prospects. Atmos Environ 60:656–676. https://doi.org/10.1016/j.atmosenv.2012.02.041
The authors wish to thank Emilio Borchi and Renzo Macii (Ximeniano Observatory) for their support to the measurement infrastructure such as the meteorological and the EC stations.
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Gualtieri, G., Di Lonardo, S., Carotenuto, F. et al. The role of emissions and meteorology in driving CO2 concentrations in urban areas. Environ Sci Pollut Res (2021). https://doi.org/10.1007/s11356-021-12754-8
- Eddy covariance
- CO2 concentrations
- Urban CO2 fluxes
- Meteorological conditions
- Artificial neural network
- Self-organized maps