Skip to main content
SpringerLink
Log in

Modeling and forecasting of monthly PM2.5 emission of Paris by periodogram-based time series methodology

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

In this study, monthly particulate matter (PM2.5) of Paris for the period between January 2000 and December 2019 is investigated by utilizing a periodogram-based time series methodology. The main contribution of the study is modeling the PM2.5 of Paris by extracting the information purely from the examined time series data, where proposed model implicitly captures the effects of other factors, as all their periodic and seasonal effects reside in the air pollution data. Periodicity can be defined as the patterns embedded in the data other than seasonality, and it is crucial to understand the underlying periodic dynamics of air pollutants to better fight pollution. The method we use successfully captures and accounts for the periodicities, which could otherwise be mixed with seasonality under an alternative methodology. Upon the unit root test based on periodograms, it is revealed that the investigated data has periodicities of 1 year and 20 years, so harmonic regression is utilized as an alternative to Box-Jenkins methodology. As the harmonic regression displayed a better performance both in and out-of-sample forecasts, it can be considered as a powerful alternative to model and forecast time series with a periodic structure.

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.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  • Abdulrazzaq, L. R., Abdulkareem, M. N., Yazid, M. R. M., Borhan, M. N., & Mahdi, M. S. (2020). Traffic congestion: Shift from private car to public transportation. Civil Engineering Journal, 6(8), 1547–1554.

    Article  Google Scholar 

  • Akdi, Y., & Dickey, D. A. (1998). Periodograms of unit root time series: Distributions and tests. Communications in Statistics-Theory and Methods, 27(1), 69–87.

    Article  Google Scholar 

  • Akdi, Y., Okkaoğlu, Y., Gölveren, E., & Yücel, M. E. (2020a) Estimation and forecasting of PM 10 air pollution in Ankara via time series and harmonic regressions. International Journal of Environmental Science and Technology, 1–14.

  • Akdi, Y., Varlik, S., & Berument, M. H. (2020b) Duration of global financial cycles. Physica A: Statistical Mechanics and its Applications, 124331.

  • Akdi, Y., Gölveren, E., & Okkaoğlu, Y. (2020c) Daily electrical energy consumption: Periodicity, harmonic regression method and forecasting. Energy, 191, 116524.

  • Akdi, Y., & Ünlü, K. D. (2020). Periodicity in precipitation and temperature for monthly data of Turkey. Theoretical and Applied Climatology. https://doi.org/10.1007/s00704-020-03459-y

    Article  Google Scholar 

  • Anderson, J. O., Thundiyil, J. G., & Stolbach, A. (2012). Clearing the air: A review of the effects of particulate matter air pollution on human health. Journal of Medical Toxicology, 8, 166–175.

    Article  CAS  Google Scholar 

  • Angelevska, B., Atanasova, V., & Andreevski, I. (2021). Urban air quality guidance based on measures categorization in road transport. Civil Engineering Journal, 7(2), 253–267.

    Article  Google Scholar 

  • Ashrafzadeh, A., Kişi, O., Aghelpour, P., Biazar, S. M., & Masouleh, M. A. (2020). Comparative study of time series models, support vector machines, and GMDH in forecasting long-term evapotranspiration rates in northern Iran. Journal of Irrigation and Drainage Engineering, 146(6), 04020010.

    Article  Google Scholar 

  • Ashrafzadeh, A., Kişi, O., Aghelpour, P., Mostafa Biazar, S., & Askarizad Masouleh, M. (2021). Closure to “comparative study of time series models, support vector machines, and gmdh in forecasting long-term evapotranspiration rates in northern Iran” by Afshin Ashrafzadeh, Ozgur Kişi, Pouya Aghelpour, Seyed Mostafa Biazar, and Mohammadreza Askarizad Masouleh. Journal of Irrigation and Drainage Engineering, 147(6), 07021006.

    Article  Google Scholar 

  • AIRPARIF. “Air quality”. https://www.airparif.asso.fr/en/telechargement/telechargement-polluant. Accessed on 14 August 2020.

  • Apte, J. S., Marshall, J. D., Cohen, A. J., & Brauer, M. (2015). Addressing global mortality from ambient PM2.5. Environmental Science & Technology, 49, 8057–8066.

    Article  CAS  Google Scholar 

  • Bai, Y., Zeng, B., Li, C., & Zhang, J. (2019a). An ensemble long short-term memory neural network for hourly PM2. 5 concentration forecasting. Chemosphere, 222, 286–294.

    Article  CAS  Google Scholar 

  • Bai, Y., Li, Y., Zeng, B., Li, C., & Zhang, J. (2019b). Hourly PM2. 5 concentration forecast using stacked autoencoder model with emphasis on seasonality. Journal of Cleaner Production, 224, 739–750.

    Article  CAS  Google Scholar 

  • Biancofiore, F., Busilacchio, M., Verdecchia, M., Tomassetti, B., Aruffo, E., Bianco, S., ... & Di Carlo, P. (2017) Recursive neural network model for analysis and forecast of PM10 and PM2. 5. Atmospheric Pollution Research, 8(4), 652–659.

  • Breysse, P. N., Delfino, R. J., Dominici, F., Elder, A. C. P., Frampton, M. W., Froines, J. R., et al. (2013). US EPA particulate matter research centers: Summary of research results for 2005–2011. Air Quality, Atmosphere & Health, 6, 333–355.

    Article  Google Scholar 

  • Brook, R. D., Newby, D. E., & Rajagopalan, S. (2017). The global threat of outdoor ambient air pollution to cardiovascular health: Time for intervention. JAMA Cardiology, 2(4), 353–354.

    Article  Google Scholar 

  • Brockwell, P. J., & Davis, R. A. (1987). Time series: Theory and methods. Springer-Verlag.

    Book  Google Scholar 

  • Brunekreef, B., & Holgate, S. T. (2002). Air pollution and health. The Lancet, 360(9341), 1233–1242.

    Article  CAS  Google Scholar 

  • Chang, F. J., Chang, L. C., Kang, C. C., Wang, Y. S., & Huang, A. (2020a) Explore spatio-temporal PM2. 5 features in northern Taiwan using machine learning techniques. Science of The Total Environment, 139656.

  • Chang, Y. S., Abimannan, S., Chiao, H. T., Lin, C. Y., & Huang, Y. P. (2020b) An ensemble learning based hybrid model and framework for air pollution forecasting. Environmental Science and Pollution Research, 1–14.

  • Cheng, Y., Zhang, H., Liu, Z., Chen, L., & Wang, P. (2019). Hybrid algorithm for short-term forecasting of PM2. 5 in China. Atmospheric Environment, 200, 264–279.

    Article  CAS  Google Scholar 

  • Du, J., Qiao, F., & Yu, L. (2019). Temporal characteristics and forecasting of PM2. 5 concentration based on historical data in Houston, USA. Resources, Conservation and Recycling, 147, 145–156.

    Article  Google Scholar 

  • Dockery, D. W. (2009). Health effects of particulate air pollution. Annals of Epidemiology, 19(4), 257–263.

    Article  Google Scholar 

  • Dockery, D. W., & Pope, C. A. (1994). Acute respiratory effects of particulate air pollution. Annual Review of Public Health, 15, 107–132.

    Article  CAS  Google Scholar 

  • Dockery, D. W., Pope, C. A., Xu, X., Spengler, J. D., Ware, J. H., Fay, M. E., et al. (1993) An association between air pollution and mortality in six U.S. cities. The New England Journal of Medicine, 329(24), 1753–1759.

  • Evans, K. A., Halterman, J. S., Hopke, P. K., Fagnano, M., & Rich, D. Q. (2014). Increased ultrafine particles and carbon monoxide concentrations are asociated with asthma exacerbation among urban children. Environmental Research, 129, 11–19.

    Article  CAS  Google Scholar 

  • Fuller, W. A. (1996). Introduction to statistical time series. Wiley.

    Google Scholar 

  • Franceschi, F., Cobo, M., & Figueredo, M. (2018) Discovering relationships and forecasting PM10 and PM2. 5 concentrations in Bogotá, Colombia, using artificial neural networks, principal component analysis, and k-means clustering. Atmospheric Pollution Research, 9(5), 912–922.

  • Gasana, J., Dillikar, D., Mendy, A., Forno, E., & Vieira, E. R. (2012). Motor vehicle air pollution and asthma in children: A meta-analysis. Environmental Research, 117, 36–45.

    Article  CAS  Google Scholar 

  • Golly, B., Waked, A., Weber, S., Samake, A., Jacob, V., Conil, S., & Besombes, J. L. (2019). Organic markers and OC source apportionment for seasonal variations of PM2. 5 at 5 rural sites in France. Atmospheric Environment, 198, 142–157.

    Article  CAS  Google Scholar 

  • Gibergans Bàguena, J., Hervada Sala, C., & Jarauta Bragulat, E. (2020). The quality of urban air in Barcelona: A new approach applying compositional data analysis methods. Emerging Science Journal, 4(2), 113–121.

    Article  Google Scholar 

  • Guaita, R., Pichiule, M., Mate, T., Linares, C., & Diaz, J. (2011) Short-term impact of particulate matter (PM2.5) on respiratory mortality in Madrid. International Journal of Environmental Health Research, 21(4), 260–274.

  • Hamra, G. B., Guha, N., Cohen, A., Laden, F., Raaschou-Nielsen, O., Samet, J. M., et al. (2014). Outdoor particulate matter exposure and lung cancer: A systematic review and meta-analysis. Environmental Health Perspectives, 122(9), 906–911.

    Article  Google Scholar 

  • Han, X., Liu, Y., Gao, H., Ma, J., Mao, X., Wang, Y., et al. (2017). Forecasting PM2.5 induced male lung cancer morbidity in China using satellite retrieved PM2.5 and spatial analysis. Science of the Total Environment, 607–608, 1009–1017.

    Article  CAS  Google Scholar 

  • Hadley, O. L. (2017). Background PM2. 5 source apportionment in the remote Northwestern United States. Atmospheric Environment, 167, 298–308.

    Article  CAS  Google Scholar 

  • Hart, J. E., Liao, X., Hong, B., Puett, R. C., Yanosky, J. D. Suh, H., et al. (2015) The association of long-term exposure to PM2.5 on all-cause mortality in the nurses’ health study and the impact of measurement-error correction. Environmental Health, 14(38), 1–9.

  • Hoek, G., Krishnan, R. M., Beelen, R., Peters, A., Ostro, B., Brunekreef, B., et al. (2013). Long-term air pollution exposure and cardio-respiratory mortality: A review. Environmental Health, 12(43), 1–15.

    Google Scholar 

  • Kim, K. H., Kabir, E., & Kabir, S. (2015). A review on the human health impact of airborne particulate matter. Environment International, 74, 136–143.

    Article  CAS  Google Scholar 

  • Kow, P. Y., Wang, Y. S., Zhou, Y., Kao, I. F., Issermann, M., Chang, L. C., & Chang, F. J. (2020) Seamless integration of convolutional and back-propagation neural networks for regional multi-step-ahead PM2. 5 forecasting. Journal of Cleaner Production, 121285.

  • Lelieveld, J., Evans, J. S., Fnais, M., Giannadaki, D., & Pozzer, A. (2015). The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature, 525(7569), 367–371.

    Article  CAS  Google Scholar 

  • Lin, S., Munsie, J. P., Hwang, S. A., Fitzgerald, E., & Cayo, M. R. (2002). Childhood asthma hospitalization and residential exposure to state route traffic. Environmental Research, 88(2), 73–81.

    Article  CAS  Google Scholar 

  • Lippmann, M. (2014) Toxicological and epidemiological studies of cardiovascular effects of ambient air fine particulate matter (PM2.5) and its chemical components: Coherence and public health implications. Critical Reviews in Toxicology, 44(4), 299–347.

  • Liu, H. Y., Dunea, D. N. Iordache, S., & Pohoata, A. (2018). A review of airborne particulate matter effects on young children’s respiratory symptoms and diseases. Atmosphere, 9(4).

  • Loftus, C., Yost, M., Sampson, P., Arias, G., Torres, E., Vasquez, V. B., et al. (2015). Regional PM2.5 and asthma morbidity in an agricultural community: A panel study. Environmental Research, 136, 505–512.

    Article  CAS  Google Scholar 

  • Lopez-Restrepo, S., Yarce, A., Pinel, N., Quintero, O. L., Segers, A., & Heemink, A. W. (2020) Forecasting PM10 and PM2. 5 in the Aburrá Valley (Medellín, Colombia) via EnKF based data assimilation. Atmospheric Environment, 117507.

  • Lv, B., Cobourn, W. G., & Bai, Y. (2016). Development of nonlinear empirical models to forecast daily PM2. 5 and ozone levels in three large Chinese cities. Atmospheric Environment, 147, 209–223.

    Article  CAS  Google Scholar 

  • Maji, K. J., Dikshit, A. K., Arora, M., & Deshpande, A. (2018). Estimating premature mortality attributable to PM2.5 exposure and benefit of air pollution control policies in China for 2020. Science of the Total Environment, 612, 683–693.

    Article  CAS  Google Scholar 

  • Maji, K. J., Dikshit, A. K., & Deshpande, A. (2017). Disability-adjusted life years and economic cost assessment of the health effects related to PM2.5 and PM10 pollution in Mumbai and Delhi, in India from 1991 to 2015. Environmental Science and Pollution Research, 24, 4709–4730.

    Article  CAS  Google Scholar 

  • Mannucci, P. M., & Franchini, M. (2017). Health effects of ambient air pollution in developing countries. International Journal of Environmental Research and Public Health, 14(9), 1048.

    Article  CAS  Google Scholar 

  • Moisan, S., Herrera, R., & Clements, A. (2018) A dynamic multiple equation approach for forecasting PM2. 5 pollution in Santiago, Chile. International Journal of Forecasting, 34(4), 566–581.

  • Okkaoğlu, Y., Akdi, Y., & Ünlü, K. D. (2020). Daily PM10, periodicity and harmonic regression model: The case of London. Atmospheric Environment, 117755.

  • Orru, H., Maasikmets, M., Lai, T., Tamm, T., Kaasik, M., Kimmel, V., et al. (2011). Health impacts of particulate matter in five major Estonian towns: Main Sources of Exposure and Local Differences. Air Quality, Atmosphere & Health, 4, 247–258.

    Article  CAS  Google Scholar 

  • Ostro, B. D., Lipsett, M. J., & Das, R. (1998). Particulate Matter and Asthma: A quantitative assessment of the current evidence. Applied Occupational and Environmental Hygiene, 13(6), 453–460.

    Article  Google Scholar 

  • Peled, R. (2011). Air pollution exposure: Who is at high risk? Atmospheric Environment, 45(10), 1781–1785.

    Article  CAS  Google Scholar 

  • Perez, P., Menares, C., & Ramírez, C. (2020) PM2. 5 forecasting in Coyhaique, the most polluted city in the Americas. Urban Climate, 32, 100608.

  • Perez, P., & Gramsch, E. (2016). Forecasting hourly PM2. 5 in Santiago de Chile with emphasis on night episodes. Atmospheric Environment, 124, 22–27.

    Article  CAS  Google Scholar 

  • Pope, C. A., Burnett, R. T., Thun, M. J., Calle, E. E., Krewski, D., Ito, K., et al. (2002). Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. JAMA, 287(9), 1132–1141.

    Article  CAS  Google Scholar 

  • Pope, C. A., & Dockery, D. W. (2006). Health effects of fine particulate air pollution: Lines that connect. Journal of the Air & Waste Management Association, 56(6), 709–742.

    Article  CAS  Google Scholar 

  • Pui, D. Y. H., Chen, S. C., & Zuo, Z. (2014). PM2.5 in China: Measurements, sources, visibility and health effects, and mitigation. Particuology, 13, 1–26.

    Article  CAS  Google Scholar 

  • Qi, Y., Li, Q., Karimian, H., & Liu, D. (2019). A hybrid model for spatiotemporal forecasting of PM2. 5 based on graph convolutional neural network and long short-term memory. Science of the Total Environment, 664, 1–10.

    Article  CAS  Google Scholar 

  • Querol, X., Alastuey, A., Rodriguez, S., Plana, F., Ruiz, C. R., Cots, N., ... & Puig, O. (2001) PM10 and PM2. 5 source apportionment in the Barcelona Metropolitan area, Catalonia, Spain. Atmospheric Environment, 35(36), 6407–6419.

  • Rao, M., D'Elia, I., & Piersanti, A. (2018) An uncertainty quantification of PM2. 5 emissions from residential wood combustion in Italy. Atmospheric Pollution Research, 9(3), 526–533.

  • Rojas-Bracho, L., Suh, H. H., & Koutrakis, P. (2000). Relationships among personal, indoor, and outdoor fine and coarse particle concentrations for individuals with COPD. Journal of Exposure Analysis and Environmental Epidemiology, 10, 294–306.

    Article  CAS  Google Scholar 

  • Saxon, A., & Diaz-Sanchez, D. (2000). Diesel exhaust as a model xenobiotic in allergic inflammation. Immunopharmacology, 48(3), 325–327.

    Article  CAS  Google Scholar 

  • Schikowski, T., Mills, I. C., Anderson, H. R., Cohen, A., Hansell, A., Kauffmann, F., et al. (2014). Ambient air pollution: A cause of COPD? European Respiratory Journal, 43, 250–263.

    Article  Google Scholar 

  • Schwartz, J., Dockery, D. W., & Neas, L. M. (1996). Is daily mortality associated specifically with fine particles? Journal of the Air & Waste Management Association, 46(10), 927–939.

    Article  CAS  Google Scholar 

  • Shou, Y., Huang, Y., Zhu, X., Liu, C., Hu, Y., & Wang, H. (2019). A review of the possible associations between ambient PM2.5 exposures and the development of Alzheimer’s disease. Ecotoxicology and Environmental Safety, 174, 344–352.

    Article  CAS  Google Scholar 

  • Stanek, L. W., Sacks, J. D., Dutton, S. J., & Dubois, J. J. B. (2011). Attributing health effects to apportioned components and sources of particulate matter: An evaluation of collective results. Atmospheric Environment, 45(32), 5655–5663.

    Article  CAS  Google Scholar 

  • Shang, Z., Deng, T., He, J., & Duan, X. (2019). A novel model for hourly PM2. 5 concentration prediction based on CART and EELM. Science of the Total Environment, 651, 3043–3052.

    Article  CAS  Google Scholar 

  • Shahid, I., Kistler, M., Mukhtar, A., Ghauri, B. M., Ramirez-Santa Cruz, C., Bauer, H., & Puxbaum, H. (2016). Chemical characterization and mass closure of PM10 and PM2. 5 at an urban site in Karachi-Pakistan. Atmospheric Environment, 128, 114–123.

    Article  CAS  Google Scholar 

  • Turner, M. C., Krewski, D., Pope, C. A., Chen, Y., Gapstur, S. M., & Thun, M. J. (2011). Long-term ambient fine particulate matter air pollution and lung cancer in a large cohort of never smokers. American Journal of Respiratory and Critical Care Medicine, 184(12), 1374–1381.

    Article  Google Scholar 

  • Wang, P., Zhang, G., Chen, F., & He, Y. (2019) A hybrid-wavelet model applied for forecasting PM2. 5 concentrations in Taiyuan city, China. Atmospheric Pollution Research, 10(6), 1884–1894.

  • Wei, W. W. S. (2006). Time series analysis: Univariate and multivariate methods. Pearson Education.

    Google Scholar 

  • Weiland, S. K., Mundt, K. A., Diplstat, A. R., & Keil, U. (1994). Self-reported wheezing and allergic rhinitis in children and traffic density on street of residence. Annals of Epidemiology, 4(3), 243–247.

    Article  CAS  Google Scholar 

  • WHO. World Health Organization, (2020a) “Air pollution”. https://www.who.int/health-topics/air-pollution#tab=tab_1. Accessed on 21 September 2020.

  • WHO. Regional Office for Europe, (2020b) “Air quality”. https://www.euro.who.int/en/health-topics/environment-and-health/air-quality. Accessed on 23 September 2020.

  • Xu, X., Tong, T., Zhang, W., & Meng, L. (2020) Fine-grained prediction of PM2. 5 concentration based on multisource data and deep learning. Atmospheric Pollution Research. https://doi.org/10.1016/j.apr.2020.06.032

  • Yuan, W., Wang, K., Bo, X., Tang, L., & Wu, J. (2019) A novel multi-factor & multi-scale method for PM2. 5 concentration forecasting. Environmental Pollution, 255, 113187.

  • Zhang, L., Lin, J., Qiu, R., Hu, X., Zhang, H., Chen, Q., & Wang, J. (2018). Trend analysis and forecast of PM2. 5 in Fuzhou, China using the ARIMA model. Ecological Indicators, 95, 702–710.

    Article  CAS  Google Scholar 

  • Zhou, Y., Chang, F. J., Chang, L. C., Kao, I. F., Wang, Y. S., & Kang, C. C. (2019). Multi-output support vector machine for regional multi-step-ahead PM2. 5 forecasting. Science of the Total Environment, 651, 230–240.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank to the associate editor and the anonymous teviewers for their valuable suggestions and corrections.

Author information

Authors and Affiliations

  1. Department of Statistics, Faculty of Science, Ankara University, Ankara, Turkey

    Yılmaz Akdi

  2. Department of Econometrics, Faculty of Economics and Administrative Sciences, Ataturk University, Erzurum, Turkey

    Elif Gölveren

  3. Department of Mathematics, Atilim University, Ankara, Turkey

    Kamil Demirberk Ünlü

  4. Department of Economics, Faculty of Economics, Administrative and Social Sciences, İhsan Dogramaci Bilkent University, Ankara, Turkey

    Mustafa Eray Yücel

Authors
  1. Yılmaz Akdi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Elif Gölveren
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kamil Demirberk Ünlü
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mustafa Eray Yücel
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

YA: Design of methodology. Application of statistical, mathematical and computational techniques to analyze study data. EG: Formulation of overarching research goals and aims. Management activities to produce metadata. KDU: Formulation of overarching research goals and aims. Conducting a research and investigation process. Preparation and presentation of the published work. MEY: Verification of the overall reproducibility, experiments and other research outputs. Preparation and presentation of the published work.

Corresponding author

Correspondence to Kamil Demirberk Ünlü.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interest

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.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Akdi, Y., Gölveren, E., Ünlü, K.D. et al. Modeling and forecasting of monthly PM2.5 emission of Paris by periodogram-based time series methodology. Environ Monit Assess 193, 622 (2021). https://doi.org/10.1007/s10661-021-09399-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-021-09399-y

Keywords

Search

Navigation