Skip to main content

Advertisement

SpringerLink
Log in

Spatial-temporal prediction of ambient nitrogen dioxide and ozone levels over Italy using a Random Forest model for population exposure assessment

  • Published:
Air Quality, Atmosphere & Health Aims and scope Submit manuscript

Abstract

We developed an integrated approach coupling a chemical transport model (CTM) with machine learning (ML) techniques to produce high spatial resolution NO2 and O3 daily concentration fields over Italy. Three years (2013–2015) simulations, at a spatial resolution of 5 km, performed by the Flexible Air quality Regional Model (FARM) were used as predictors, together with other spatial-temporal data, such as population, land-use, surface greenness and road networks, by a ML Random Forest (ML-RF) algorithm to produce daily concentrations at higher resolution (1 km) over the national territory. The evaluation of the adopted integrated approach was based on NO2 and O3 observations available from 530 and 293 monitoring stations across Italy, respectively. A good performance for NO2 and excellent results for O3 were obtained from the application of the CTM; as for NO2, the levels at urban traffic stations were not captured by the simulations due to the adopted horizontal resolution and related emissions uncertainties. Performance improvements were achieved with ML-RF predictions, reducing NO2 underestimation (near zero fractional bias results) and better capturing spatial contrasts. The results obtained in this work were used to support the national exposure assessment and environmental epidemiology studies planned in the BEEP (Big data in Environmental and occupational Epidemiology) project and confirm the potential of machine learning methods to adequately predict air pollutant levels at high spatial and temporal resolutions.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Araki S, Shima M, Yamamoto K (2018) Spatiotemporal land use random forest model for estimating metropolitan NO2 exposure in Japan. Sci Total Environ 634:1269–1277. https://doi.org/10.1016/j.scitotenv.2018.03.324

    Article  CAS  Google Scholar 

  • Bessagnet B, Pirovano G, Mircea M, Cuvelier C, Aulinger A, Calori G, Ciarelli G, Manders A, Stern R, Tsyro S, García Vivanco M, Thunis P, Pay M-T, Colette A, Couvidat F, Meleux F, Rouïl L, Ung A, Aksoyoglu S, Baldasano JM, Bieser J, Briganti G, Cappelletti A, D’Isidoro M, Finardi S, Kranenburg R, Silibello C, Carnevale C, Aas W, Dupont J-C, Fagerli H, Gonzalez L, Menut L, Prévôt ASH, Roberts P, White L (2016) Presentation of the EURODELTA III intercomparison exercise - evaluation of the chemistry transport models’ performance on criteria pollutants and joint analysis with meteorology. Atmos. Chem. Phys. 16:12667–12701. https://doi.org/10.5194/acp-16-12667-2016

    Article  CAS  Google Scholar 

  • Binkowski FS, Roselle SJ (2003) Models-3 Community Multiscale Air Quality (CMAQ) model aerosol component, 1, model description. J. Geophys. Res. 108(D6):4183. https://doi.org/10.1029/2001JD001409

    Article  Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  Google Scholar 

  • Brook RD, Newby DE, Rajagopalan S (2017) Air pollution and cardiometabolic disease: an update and call for clinical trials. Am J Hypertens 1–10:1–10. https://doi.org/10.1093/ajh/hpx109

    Article  CAS  Google Scholar 

  • Carter, W.P.L., 2000. Implementation of the SAPRC-99 chemical mechanism into the models-3 framework. Report to the United States Environmental Protection Agency. Available at: http://www.cert.ucr.edu/carter/absts.htm#s99mod3S

  • Cattani G, Gaeta A, Di Menno di Bucchianico A, De Santis A, Gaddi R, Cusano M, Ancona C, Badaloni C, Forastiere F, Gariazzo C, Sozzi R, Inglessis M, Silibello C, Salvatori E, Manes F, Cesaroni G (2017) Development of land-use regression models for exposure assessment to ultrafine particles in Rome, Italy. Atmos Environ 156:52–60. https://doi.org/10.1016/j.atmosenv.2017.02.028

    Article  CAS  Google Scholar 

  • Cesaroni G, Porta D, Badaloni C, Stafoggia M, Eeftens M, Meliefste K, Forastiere F (2012) Nitrogen dioxide levels estimated from land use regression models several years apart and association with mortality in a large cohort study. Environ Health 11:48. https://doi.org/10.1186/1476-069X-11-48

    Article  CAS  Google Scholar 

  • Cesaroni G, Forastiere F, Stafoggia M, Andersen ZJ, Badaloni C, Beelen R, Caracciolo B, de Faire U, Erbel R, Eriksen KT, Fratiglioni L, Galassi C, Hampel R, Heier M, Hennig F, Hilding A, Hoffmann B, Houthuijs D, Jöckel K-H, Korek M, Lanki T, Leander K, Magnusson PKE, Migliore E, Ostenson C-G, Overvad K, Pedersen NL, J JP, Penell J, Pershagen G, Pyko A, Raaschou-Nielsen O, Ranzi A, Ricceri F, Sacerdote C, Salomaa V, Swart W, Turunen AW, Vineis P, Weinmayr G, Wolf K, de Hoogh K, Hoek G, Brunekreef B, Peters A (2014) Long term exposure to ambient air pollution and incidence of acute coronary events: prospective cohort study and meta-analysis in 11 European cohorts from the ESCAPE Project. BMJ 348:f7412. https://doi.org/10.1136/bmj.f7412

    Article  CAS  Google Scholar 

  • Chang JC, Hanna SR (2004) Air quality model performance evaluation. Meteorog Atmos Phys 87:167–196. https://doi.org/10.1007/s00703-003-0070-7

    Article  Google Scholar 

  • Chen H, Kwong JC, Copes R, Hystad P, van Donkelaar A, Tu K, Brook JR, Goldberg MS, Martin RV, Murray BJ, Wilton AS, Kopp A, Burnett RT (2017) Exposure to ambient air pollution and the incidence of dementia: a population-based cohort study. Environ Int 108:271–277. https://doi.org/10.1016/j.envint.2017.08.020

    Article  CAS  Google Scholar 

  • Chen G, Li S, Knibbs LD et al (2018a) A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information. Sci. Total Environ 636:52–60. https://doi.org/10.1016/j.scitotenv.2018.04.251

    Article  CAS  Google Scholar 

  • Chen G, Wang Y, Li S, Cao W, Ren H, Knibbs LD, Abramson MJ, Guo Y (2018b) Spatiotemporal patterns of PM10 concentrations over China during 2005–2016: a satellite-based estimation using the random forests approach. Environ Pollut 242:605–613. https://doi.org/10.1016/j.envpol.2018.07.012

    Article  CAS  Google Scholar 

  • Chen J, de Hoog K, Gulliver J, Hoffmann B, Hertel O, Ketzel M, Bauwelinck M, van Donkelaar A, Hvidtfeldt UA, Katsouyanni K, Janssen NAH, Martin RV, Samoli E, Schwartz PE, Stafoggia M, Bellander T, Strak M, Wolf K, Vienneau D, Vermeulen R, Brunekreef B, Hoek G (2019) A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide. Environ Int 130:104934. https://doi.org/10.1016/j.envint.2019.104934

    Article  CAS  Google Scholar 

  • Chiusolo M, Cadum E, Stafoggia M, Galassi C, Berti G, Faustini A, Bisanti L, Vigotti MA, Dessì MP, Cernigliaro A, Mallone S, Pacelli B, Minerba S, Simonato L, Forastiere F, and on behalf of the EpiAir Collaborative Group (2011) Short-term effects of nitrogen dioxide on mortality and susceptibility factors in 10 Italian cities: the EpiAir Study. Environ Health Perspect 119:9–1238. https://doi.org/10.1289/ehp.1002904

    Article  Google Scholar 

  • Cilluffo G, Ferrante G, Fasola S, Montalbano L, Malizia V, Piscini A, Romaniello V, Silvestri M, Stramondo S, Stafoggia M, Ranzi A, Viegi G, la Grutta S (2018) Associations of greenness, greyness and air pollution exposure with children’s health: a cross-sectional study in Southern Italy. Environ Health 17:1–12. https://doi.org/10.1186/s12940-018-0430-x

    Article  Google Scholar 

  • de Hoogh K, Saucy A, Shtein A, Schwartz J, l West EA, Strassmann A, Puhan M, Röösli M, Stafoggia M, Kloog I (2019) Predicting fine-scale daily NO2 for 2005–2016 incorporating OMI satellite data across Switzerland. Environmental Science & Technology 53(17):10279–10287. https://doi.org/10.1021/acs.est.9b03107

    Article  CAS  Google Scholar 

  • Di Q, Amini H, Shi L, Kloog I, Silvern R, Kelly J, Sabath MB, Choirat C, Koutrakis P, Lyapustin A, Wang Y, Mickley LJ, Schwartz J (2019) An ensemble-based model of PM2.5 concentration across the contiguous United States with high spatiotemporal resolution. Environment International 130:104909. https://doi.org/10.1016/j.envint.2019.104909

    Article  CAS  Google Scholar 

  • EEA (European Environmental Agency), 2000. Corine Land Cover Technical Guide – Addendum 2000. Technical Report No. 40, Copenhagen, Denmark

  • Elvidge CD, Baugh K, Zhizhin M, Hsu FC, Ghosh T (2017) VIIRS night-time lights. Int J Remote Sens 38(21):5860–5879. https://doi.org/10.1080/01431161.2017.1342050

    Article  Google Scholar 

  • Fasola S, Maio S, Baldacci S, La Grutta S, Ferrante G, Forastiere F, Stafoggia M, Gariazzo C, Viegi G (2020) Effects of particulate matter on the incidence of respiratory diseases in the Pisan longitudinal study. Int. J. Environ. Res. Public Health 17(7):2540. https://doi.org/10.3390/ijerph17072540

    Article  CAS  Google Scholar 

  • Gariazzo C, Silibello C, Finardi S, Radice P, Piersanti A, Calori G, Cecinato A, Perrino C, Nussio F, Cagnoli M, Pelliccioni A, Gobbi GP, Di Filippo P (2007) A gas/aerosol air pollutants study over the urban area of Rome using a comprehensive chemical transport model. Atmos Environ 41:7286–7303. https://doi.org/10.1016/j.atmosenv.2007.05.018

    Article  CAS  Google Scholar 

  • Gariazzo C, Carlino G, Silibello C, Renzi M, Finardi S, Pepe N, Radice P, Forastiere F, Michelozzi P, Viegi G, Stafoggia M, on behalf of BEEP collaborative group (2020) A multi-city air pollution population exposure study: combined use of chemical-transport and random-Forest models with dynamic population data. Sci Total Environ 724:138102. https://doi.org/10.1016/j.scitotenv.2020.138102

    Article  CAS  Google Scholar 

  • Hoek G (2017) Methods for assessing long-term exposures to outdoor air pollutants. Curr Envir Health Rpt In Topical Collection on Air Pollution and Health Springer ed DOI 4:450–462. https://doi.org/10.1007/s40572-017-0169-5

    Article  CAS  Google Scholar 

  • Hoek G, Beelen R, de Hoogh K, Vienneau D, Gulliver J, Fischer P, Briggs D (2008) A review of land-use regression models to assess spatial variation of outdoor air pollution. Atmos Environ 42(33):7561–7578. https://doi.org/10.1016/j.atmosenv.2008.05.057

    Article  CAS  Google Scholar 

  • ISPRA, 2016 Qualità dell’aria nell’ambiente urbano. XII rapporto Edizione 2016. ISBN: 978–88–448-0793-1 (in Italian, available at: https://www.isprambiente.gov.it/it/pubblicazioni/stato-dellambiente/xii-rapporto-qualita-dell2019ambiente-urbano-edizione-2016)

  • ISPRA, 2018 Analisi dei trend dei principali inquinanti atmosferici in Italia (2008 – 2017). Report 302/2018 ISBN: 978-88-448-0938-6 (in Italian)

  • Kuhn M (2008) Building predictive models in R using the caret package. J. Stat. Softw. 28(5):1–26. https://doi.org/10.18637/jss.v028.i05

    Article  Google Scholar 

  • Kukkonen J, Olsson T, Schultz DM, Baklanov A, Klein T, Miranda AI, Monteiro A, Hirtl M, Tarvainen V, Boy M, Peuch VH, Poupkou A, Kioutsioukis I, Finardi S, Sofiev M, Sokhi R, Lehtinen KEJ, Karatzas K, San José R, Astitha M, Kallos G, Schaap M, Reimer E, Jakobs H, Eben K (2012) A review of operational, regional-scale, chemical weather forecasting models in Europe. Atmos Chem Phys 12(1):1–87. https://doi.org/10.5194/acp-12-1-2012

    Article  CAS  Google Scholar 

  • Kukkonen J, Karl M, Keuken MP, Denier Van Der Gon HAC, Denby BR, Singh V et al (2016) Modelling the dispersion of particle numbers in five European cities. Geoscientific Model Dev 9(2):451–478. https://doi.org/10.5194/gmd-9-451-2016

    Article  Google Scholar 

  • Liaw A, Wiener M (2002) Classification and regression by random forest. R News 2:18–22

    Google Scholar 

  • Liu R, Ma Z, Liu Y, Shao Y, Zhao W, Bi J (2020) Spatiotemporal distributions of surface ozone levels in China from 2005 to 2017: a machine learning approach. Environ Int 142:105823. https://doi.org/10.1016/j.envint.2020.105823

    Article  CAS  Google Scholar 

  • Marinaccio A, Scortichini M, Gariazzo C, Leva A, Bonafede M, De’Donato FK, Stafoggia M, Viegi G, Michelozzi P, Carla A et al (2019) Nationwide epidemiological study for estimating the effect of extreme outdoor temperature on occupational injuries in Italy. Environ Int 2019(133):105176. https://doi.org/10.1016/j.envint.2019.105176

    Article  Google Scholar 

  • Ren X, Mi Z, Georgopoulos PG (2020) Comparison of machine learning and land use regression for fine scale spatiotemporal estimation of ambient air pollution: modeling ozone concentrations across the contiguous United States. Environ Int 142:105827. https://doi.org/10.1016/j.envint.2020.105827

    Article  CAS  Google Scholar 

  • Renzi M, Cerza F, Gariazzo C, Agabiti N, Cascini S, Di Domenicantonio R, Davoli M, Forastiere F, Cesaroni G (2018) Air pollution and occurrence of type 2 diabetes in a large cohort study. Environ Int 112:68–76. https://doi.org/10.1016/j.envint.2017.12.007

    Article  CAS  Google Scholar 

  • Scheers H, Jacobs L, Casas L, Nemery B, Nawrot TS (2015) Long-term exposure to particulate matter air pollution is a risk factor for stroke: meta-analytical evidence. Stroke 46:3058–3066. https://doi.org/10.1161/STROKEAHA.115.009913

    Article  CAS  Google Scholar 

  • Schikowski T, Adam M, Marcon A, Cai Y, Vierkotter A, Carsin AE, Jacquemin B, Al Kanani Z, Beelen R, Birk M, Bridevaux PO, Brunekeef B, Burney P, Cirach M, Cyrys J, De Hoogh K, De Marco R, De Nazelle A, Declercq C, Forsberg B, Hardy R, Heinrich J, Hoek G, Jarvis D, Keidel D, Kuh D, Kuhlbusch T, Migliore E, Mosler G, Nieuwenhuijsen MJ, Phuleria H, Rochat T, Schindler C, Villani S, Tsai MY, Zemp E, Hansell A, Kauffmann F, Sunyer J, Probst-Hensch N, Kramer U, Kunzli N (2014) Association of ambient air pollution with the prevalence and incidence of COPD. Eur Respir J 44:614–626. https://doi.org/10.1183/09031936.00132213

    Article  CAS  Google Scholar 

  • Shtein A, Kloog I, Schwartz J, Silibello C, Michelozzi P, Gariazzo C, Viegi G, Forastiere F, Karnieli A, Just AC, Stafoggia M (2020) Estimating Daily PM2.5 and PM10 over Italy using an ensemble model. Environmental Science & Technology 54(1):120–128. https://doi.org/10.1021/acs.est.9b04279

    Article  CAS  Google Scholar 

  • Silibello C, Calori G, Brusasca G, Giudici A, Angelino E, Fossati G, Peroni E, Buganza E (2008) Modelling of PM10 concentrations over Milano urban area using two aerosol modules. Environ Model Softw 23:333–343. https://doi.org/10.1016/j.envsoft.2007.04.002

    Article  Google Scholar 

  • Skamarock, W.C., J.B. Klemp, J.Dudhia, D.O.Gill, D.M.Barker, M.G. Duda, X.Y. Huang, W. Wang, and J.G. Powers, 2008: A description of the advanced research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp., DOI: https://doi.org/10.5065/D68S4MVH

  • Stafoggia M, Cesaroni G, Peters A, Andersen ZJ, Badaloni C, Beelen R, Caracciolo B, Cyrys J, de Faire U, Gigante B, Havulinna AS, Hennig F, Hilding A, Hoek G, Hoffmann B, Houthuijs D, Korek M, Lanki T, Leander K, Magnusson PK, Meisinger C, Migliore E, Overvad K, Östenson C, Pedersen NL, Pekkanen J, Penell J, Ranzi A, Ricceri F, Sacerdote C, Swart WJR, Turunen AW (2014) Long-term exposure to ambient air pollution and incidence of cerebrovascular events: results from 11 European cohorts within the ESCAPE project. Environ Health Perspect 122:919–925. https://doi.org/10.1289/ehp.1307301

    Article  CAS  Google Scholar 

  • Stafoggia M, Schwartz J, Badaloni C, Bellander T, Alessandrini E, Cattani G, de’ Donato F, Gaeta A, Leone G, Lyapustin A, Sorek-Hamer M, de Hoogh K, Di Q, Forastiere F, Kloog I (2017) Estimation of daily PM10 concentrations in Italy (2006–2012) using finely resolved satellite data, land use variables and meteorology. Environ Int 99:234–244. https://doi.org/10.1016/j.envint.2016.11.024

    Article  CAS  Google Scholar 

  • Stafoggia M, Bellander T, Bucci S, Davoli M, de Hoogh K, de’ Donato F, Gariazzo C et al (2019) Estimation of daily PM10 and PM2.5 concentrations in Italy, 2013–2015, using a spatiotemporal land-use random-forest model. Environ Int 124:170–179. https://doi.org/10.1016/j.envint.2019.01.016

    Article  CAS  Google Scholar 

  • Willmott CJ (1981) On the validation of models. Phys Geogr 2:184–194. https://doi.org/10.1080/02723646.1981.10642213

    Article  Google Scholar 

  • Wright MN, Ziegler A (2017) Ranger: A fast implementation of random forests for high dimensional data in C++ and R. J. Stat. Softw. 77:1–17. https://doi.org/10.18637/jss.v077.i01

    Article  Google Scholar 

  • Zhang Y, Bocquet M, Mallet V, Seigneur C, Baklanov A (2012) Real-time air quality forecasting, part I: history, techniques, and current status. Atmos Environ 60:632–655. https://doi.org/10.1016/j.atmosenv.2012.06.031

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors wish to thank the *BEEP Collaborative Group: Carla Ancona, Simone Bucci, Francesca de’ Donato, Paola Michelozzi, Matteo Renzi, Matteo Scortichini, DEPLAZIO, Roma, Italy; Michela Bonafede, Alessandro Marinaccio, INAIL DIMEILA, Roma, Italy; Stefania Argentini, Roberto Sozzi, CNR-ISAC, Roma, Italy; Sergio Bonomo, Salvatore Fasola, Stefania La Grutta, CNR-IRIB, Palermo, Italy; Achille Cernigliaro, Salvatore Scondotto, DASOE, Palermo, Italy; Sandra Baldacci, Sara Maio, CNR-IFC, Italy, Pisa; Gaetano Licitra, Antonino Moro, CNR-IPCF, Pisa, Italy; Paola Angelini, Regione ER, Bologna; Laura Bonvicini, Serena Broccoli, Marta Ottone, Paolo Giorgi Rossi, AUSL-RE, Reggio Emilia, Italy; Annamaria Colacci, Federica Parmagnani, Andrea Ranzi, ARPAE, Modena, Italy; Claudia Galassi, Enrica Migliore, CPO, Torino, Italy; Lucia Bisceglia, Antonio Chieti, ARES Puglia, Bari, Italy; Giuseppe Brusasca, Giuseppe Calori, Alessandro Nanni, Gianni Tinarelli, ARIANET srl, Milano, Italy.

Funding

This work has been funded by the National Institute for Insurance against Accidents at Work, within the project “BEEP” (project code B72F17000180005). The funding institute had no role in the study design; in the collection, analysis and interpretation of data; and in the decision to submit the article for publication.

Author information

Authors and Affiliations

  1. Arianet, Milan, Italy

    Camillo Silibello, Sandro Finardi, Nicola Pepe & Paola Radice

  2. Simularia, Torino, Italy

    Giuseppe Carlino

  3. Department of Epidemiology, Lazio Regional Health Service, ASL Roma 1, Rome, Italy

    Massimo Stafoggia

  4. Occupational and Environmental Medicine, Epidemiology and Hygiene Department, Italian Workers’ Compensation Authority (INAIL), Monte Porzio Catone, RM, Italy

    Claudio Gariazzo

  5. Institute for Biomedical Research and Innovation, National Research Council, Palermo, Italy

    Francesco Forastiere & Giovanni Viegi

  6. Environmental Research Group, Imperial College, London, UK

    Francesco Forastiere

  7. Institute of Clinical Physiology, National Research Council, Pisa, Italy

    Giovanni Viegi

Authors
  1. Camillo Silibello
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Giuseppe Carlino
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Massimo Stafoggia
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Claudio Gariazzo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sandro Finardi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nicola Pepe
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Paola Radice
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Francesco Forastiere
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Giovanni Viegi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Camillo Silibello: Software, Formal analysis, Validation, Writing–Original Draft; Giuseppe Carlino: Software, Formal analysis, Validation, Writing–Original Draft; Massimo Stafoggia: Conceptualization, Methodology, Supervision, Writing–Review and Editing; Claudio Gariazzo: Conceptualization, Resources, Methodology, Writing–Original Draft; Sandro Finardi: Resources, Software, Formal analysis; Nicola Pepe: Resources, Software, Formal analysis; Paola Radice: Resources, Software, Formal analysis; Francesco Forastiere: Writing–Review and Editing; Giovanni Viegi: Project administration, Writing–Review and Editing.

Corresponding author

Correspondence to Camillo Silibello.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

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

Highlights

• Novel approach, based on a combination of a Chemical Transport and Machine Learning models to obtain air pollutants exposure at high spatial resolution over Italy;

• Evaluation of the adopted approach based on a comparison between observed and predicted NO2 and O3 available from 530 and 293 monitoring stations across Italy, respectively;

• Differences in predicted concentration from CTM and ML models were identified with a strong geographical gradient;

• The results obtained in this work have been used to support nation-wide exposure assessment and environmental epidemiology studies performed in the BEEP Project.

Supplementary information

ESM 1

(DOC 13.2 mb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Silibello, C., Carlino, G., Stafoggia, M. et al. Spatial-temporal prediction of ambient nitrogen dioxide and ozone levels over Italy using a Random Forest model for population exposure assessment. Air Qual Atmos Health 14, 817–829 (2021). https://doi.org/10.1007/s11869-021-00981-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11869-021-00981-4

Keywords

Search

Navigation