© 2021 IIETA. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).
OPEN ACCESS
Air pollution is a major environmental cause of morbidity and mortality worldwide, representing a top public health objective, especially in areas interested by the presence of anthropic emissions sources. Correctly assessing how pollutant emissions influence the air quality is, therefore, crucial for the design and/or implementation of effective measures from the public health perspectives. The impact of local emission sources on air quality is strongly modulated by meteorological conditions, which can mask the real trends in the observed pollutant concentrations. However, the confounding effect of meteorology in air quality time series can be accounted for by techniques of meteorological normalisation. In this study, the performances of a meteorological normalisation technique based on machine learning (ML) algorithms were investigated. To these purposes, two Ml models (gradient boosted regression (GBM) and random forest (RF)) were developed and subsequently used to calculate meteorologically normalised trends of nitrogen oxide (NOx ) concentrations time series. Both models were trained on daily averaged data of NOx concentrations and meteorological parameters, as well as on temporal variables; data were acquired, over the 2013–2019 period, in a rural area affected by anthropic sources of air pollutants. Results obtained show that both models are able to explain more than 70% of the variance in the NOx observed concentrations and that the meteorological normalization technique based on both algorithms represent a robust method to account for the confounding effect of meteorology in air quality time series. Moreover, the GBM/RF ML models allowed to analyse the dependence of the observed concentrations on each explanatory variables used in the models, shedding light on the role of local meteorological processes in the observed pollutant concentrations. This knowledge can help in defining air pollution control strategies that are increasingly effective in preventing and/or mitigating health damage associated with exposure to atmospheric pollution.
air pollution, boosted regression trees, machine learning, meteorology, random forest, trend analysis
[1] World Health Organization, Air Pollution, WHO, available at https://www.who.int/health-topics/air-pollution#tab=tab_1 (accessed 19 May 2021).
[2] Fiore, A.M., Naik, V. & Leibensperger, E.M., Air quality and climate connections. Journal of the Air & Waste Management Association, 65(6), pp. 645–685, 2015.
[3] Donateo, A., Villani, M., Lo Feudo, T., Chianese, E., Recent Advances of Air Pollution Studies in Italy. Atmosphere, vol. 11, https://doi.org/10.3390/atmos11101054, 2020.
[4] F. Giorgi, Climate change hot spot, Geophys Res Lett., vol. 33, 10.1029/2006GL025734, 2006.
[5] Kinney, P.L., Climate change, air quality, and human health. American Journal of Preventive Medicine, vol. 35(5), pp. 459–467, 2008.
[6] Thompson, M.R.J., Cox, L., Guttorp, P. & Sampson, P., A review of statistical methods for the meteorological adjustment of tropospheric ozone. Atmospheric Environment, 35, pp. 617–630, 2001.
[7] Grange, S., Carslaw, D., Lewis, A., Boleti, E. & Hueglin, C., Random forest meteorological normalisation models for Swiss PM10 trend analysis. Atmospheric Chemistry and Physics, 18, pp. 6223–6239, 2018.
[8] Grange, S. & Carslaw, D.,Using meteorological normalisation to detect interventions in air quality time series. Science of the Total Environment, 653, pp. 578–588, 2019.
[9] Petetin, H., Bowdalo, D., Soret, A., Guervara, M., Jorba, O., Serradell, K., Perez Gar-cia-Pardo, C., Meteorology-normalized impact of COVID-19 lokdown upon NO2 pol-lution in Spain. Atmos. Chem. Phys., vol. 20, pp. 11119–11141, 2020.
[10] Friedman, J., Stochastic gradient boosting. Computational Statistics & Data Analysis, 38(4), pp. 367–378, 2002.
[11] Breiman, L., Random forests. Machine Learning, 45, pp. 5–32, 2001.
[12] Gagliardi, R.V., Andenna, C. A Machine Learning Approach to Investigate the Surface Ozone Behavior. Atmosphere, vol. 11, https://doi.org/10.3390/atmos11111173, 2020.
[13] Brokamp, C., Jandarov, R., Hossain, M. & Ryan, P., Predicting daily urban fine particulate matter concentrations using a random forest model. Environmental Science & Technology, 52, pp. 4173–4179, 2018.
[14] Sayegh, A., Munir, S. & Habeedullah, T., Comparing the performance of statistical models for predicting PM10. Aerosol and Air Quality Research, 14, pp. 653–665, 2014.
[15] Nunifu, T. & Fu, L., Methods and Procedures for Trend Analysis of Air Quality Data. Government of Alberta, Ministry of Environment and Parks: Edmonton, 2019.
[16] ENI, Il centro Olio Val d’agri, https://www.eni.com/eni-basilicata/chi-siamo/centro-olio-val-d-agri.page. Accessed on: 10 Mar. 2021.
[17] ARPAB, Inquinanti monitorati, http://www.arpab.it/aria/inquinanti.asp. Accessed on: 30 Mar. 2020.
[18] Seinfeld, J. & Pandis, S., Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, John Wiley & Sons: New York, 2006.
[19] ARPAB, Gli Open Data - qualità dell’aria www.arpab.it/opendata/q_aria_serie.asp. Accessed on: 10 Jan. 2020.
[20] Greenwell, B., Boehmke, B., Cunningham, J., Developers, G., gbm: geeneralized boosted regression models. r package version 2.1.5, 2019. https://cran.r-project.org/web/packages/gbm/index.html. Accessed on: 10 Jan. 2021.
[21] Probst, P., Wright, M., Boulestei, A., Hyperparameters and Tuning Strategies for Random Forest. https://arxiv.org/pdf/1804.03515.pdf, Accessed on: 20 December 2018.386 Roberta Valentina Gagliardi & Claudio Andenna, Int. J. Environ. Impacts, Vol. 4, No. 4 (2021)
[22] Shi, Z., Song, C., Liu, B., Lu, G., Xu, J., Vu, T., Elliot, R., Li, W., Bloss, W. & Harrison, R., Abrupt but smaller than expected changes in surface air quality attributable to COVID-19 lockdowns. Science Advances, 7, pp. 1–10, 2021.
[23] Carslaw, D., Deweather, http://github.com/davidcarslaw/deweather. Accessed on: 10 March 2019.
[24] Legislative Decree 155/10. Attuazione della direttiva 2008/50/CE relativa alla qual-ità dell’aria ambiente e per un’aria più pulita in Europa. Gazzetta Ufficiale n. 216 del 15.09.2010 - Suppl. Ordinario n. 217, 2010.
[25] Directive 2008/50/EC on ambient air quality and cleaner air for Europe, Offcial Journal of the European Union, L 152/1, pp. 1–44, 11.6.2008.
[26] Arpab, Rapporto Ambientale Anuale, http://www.arpab.it/public/Rapporto-Ambien-tale-anno-2019.pdf. Accessed on: 10 Jun 2020.
[27] Prefettura - Ufficio Territoriale del Governo di Potenza, PEE Centro Olio Val d?agri di Viggiano, http://www.prefettura.it/potenza/contenuti/Pee_centro_olio_val_d_agri_di_viggiano_edizione_2013-64403.htm. Accessed on: 30 Mar. 2021.
[28] Carslaw, D.C., Beevers, S.D., Ropkins, K. & Bell, M.C., Detecting and quantifying aircraft and other on-airport contributions to ambient nitrogen oxides in the vicinity of a large international airport. Atmospheric Environment, 40, pp. 5424–5434, 2006.