Predictive modeling of carbon monoxide emissions using deep learning and environmental features from a Mexican border city
DOI:
https://doi.org/10.37636/recit.v8n4e412Keywords:
Artificial neural network, Backpropagation, Carbon monoxideAbstract
Carbon monoxide (CO) poisoning constitutes a critical issue with global ramifications, resulting in impacts on the changing atmospheric composition that affect air quality and lead to fatalities worldwide. The prediction of CO concentration levels is of utmost importance due to the negative impacts of CO on human health. The present work aims to advance the field of emission science and reduction strategies by introducing an enhanced neural network model. This model integrates a methodology based on a feed-forward artificial neural network with meteorological factors—specifically, wind speed (WS), wind direction (WD), and outdoor temperature (OT). Hourly measurements taken throughout a year, alongside two time-series variables (day and month), are utilized to feed the neural network during its training-testing process. The input data are sourced from an air pollutant-monitoring station situated in a Mexican border city. The proposed neural network model demonstrates its efficacy and reliability in predicting CO concentrations, affirming its potential to inform regulatory measures, protect atmospheric resources, and advance future research efforts in atmospheric science.
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[1] E. Mencías Rodriguez and L. M. Mayero Franco, Manual de toxicología básica, 1st ed. Madrid, Spain: Editorial Díaz de Santos 2000. Accessed: Aug. 25, 2025. [Online]. Available: https://www.editdiazdesantos.com/libros/mencias-rodriguez-emilio-manual-de-toxicologia-basica-L03004360601.html?articulo=03004360601
[2] World Health Organization, WHO global air quality guidelines: Particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. Geneva, Switzerland: WHO, 2021. [Online]. Available: https://www.who.int/publications/i/item/9789240034228 (accessed Aug. 25, 2025).
[3] A. Sibón Olano, P. Martínez-García, M. A. Vizcaya Rojas, and J. L. Romero Palanco, "Intoxicación por monóxido de carbono," Cuad. Med. Forense, no. 47, pp. 65–69, 2007.
[4] R. N. Yadava and V. Bhatt, "Carbon monoxide: Risk assessment, environmental, and health hazard," in Hazardous Gases, J. Singh, R. D. Kaushik, and M. Chawla, Eds. Cambridge, MA, USA: Academic Press, 2021, ch. 8, pp. 83–96, doi: 10.1016/B978-0-323-89857-7.00030-X.
[5] Z. Yuan, L. K. De La Cruz, X. Yang, and B. Wang, "Carbon monoxide signaling: Examining its engagement with various molecular targets in the context of binding affinity, concentration, and biologic responses," Pharmacol. Rev., vol. 74, no. 3, pp. 825–875, Jul. 2022, doi: 10.1124/pharmrev.121.000564.
[6] C. G. Douglas, J. S. Haldane, and J. B. S. Haldane, "The laws of combination of hemoglobin with carbon monoxide and oxygen," J. Physiol., vol. 44, no. 4, pp. 275–304, Jun. 1912, doi: 10.1113/jphysiol.1912.sp001517.
[7] L. W. Kao and K. A. Nañagas, "Carbon monoxide poisoning," Emerg. Med. Clin. North Am., vol. 22, no. 4, pp. 985–1018, Nov. 2004, doi: 10.1016/j.emc.2004.05.003.
[8] U.S. Environmental Protection Agency, What is carbon monoxide? [Online]. Available: https://www.epa.gov/indoor-air-quality-iaq/what-carbon-monoxide (accessed Aug. 25, 2025).
[9] U.S. Environmental Protection Agency, Automobiles and carbon monoxide. Office of Mobile Sources, 1993. [Online]. Available: https://nepis.epa.gov/Exe/ZyNET.exe/P1002J4W.TXT (accessed Aug. 25, 2025).
[10] S. Dey and N. S. Mehta, “Automobile pollution control using catalysis,” Resour. Environ. Sustain., vol. 2, p. 100006, Dec. 2020, doi: 10.1016/j.resenv.2020.100006.
[11] Secretaría de Desarrollo Urbano y Ecología (SEDUE), Gaceta Ecológica, no. 17, Ciudad de México, 1991. [Online]. Available: https://paot.org.mx/centro/ine-semarnat/gaceta.html (accessed Aug. 25, 2025).
[12] Secretaría de Medio Ambiente y Recursos Naturales (SEMARNAT), Norma Oficial Mexicana NOM-172-SEMARNAT-2019, Lineamientos para la obtención y comunicación del Índice de Calidad del Aire y Riesgos a la Salud. Ciudad de México, México: SEMARNAT, 2019.
[13] A. Mendoza and M. R. García, "Application of a second-generation air quality model to the Guadalajara metropolitan area, Mexico," Rev. Int. Contam. Ambient., vol. 25, no. 2, pp. 73–85, 2009.
[14] X. Li, S. A. Hussain, S. Sobri, and M. S. Md Said, "Overviewing the air quality models on air pollution in Sichuan Basin, China," Chemosphere, vol. 271, p. 129502, May 2021, doi: 10.1016/j.chemosphere.2020.129502.
[15] M. W. Gardner and S. R. Dorling, "Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences," Atmos. Environ., vol. 32, no. 14, pp. 2627–2636, Aug. 1998, doi: 10.1016/S1352-2310(97)00447-0.
[16] D. Seng, Q. Zhang, X. Zhang, G. Chen, and X. Chen, "Spatiotemporal prediction of air quality based on LSTM neural network," Alex. Eng. J., vol. 60, no. 2, pp. 2021–2032, Apr. 2021, doi: 10.1016/j.aej.2020.12.009.
[17] A. Coman, A. Ionescu, and Y. Candau, "Hourly ozone prediction for a 24-h horizon using neural networks," Environ. Model. Softw., vol. 23, no. 12, pp. 1407–1421, Dec. 2008, doi: 10.1016/j.envsoft.2008.04.004.
[18] G. Chattopadhyay and S. Chattopadhyay, "Autoregressive forecast of monthly total ozone concentration: A neurocomputing approach," Comput. Geosci., vol. 35, no. 9, pp. 1925–1932, Sep. 2009, doi: 10.1016/j.cageo.2008.11.007.
[19] I. Chairez, R. Fuentes, T. Poznyak, M. Franco, and A. Poznyak, "Numerical modeling of the benzene reaction with ozone in gas phase using differential neural networks," Catal. Today, vol. 151, no. 1, pp. 159–165, Apr. 2010, doi: 10.1016/j.cattod.2010.02.057.
[20] S. M. Al-Alawi, S. A. Abdul-Wahab, and C. S. Bakheit, "Combining principal component regression and artificial neural networks for more accurate predictions of ground-level ozone," Environ. Model. Softw., vol. 23, no. 4, pp. 396–403, Apr. 2008, doi: 10.1016/j.envsoft.2006.08.007.
[21] H. R. Maier and G. C. Dandy, "Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications," Environ. Model. Softw., vol. 15, no. 1, pp. 101–124, Jan. 2000, doi: 10.1016/S1364-8152(99)00007-9.
[22] S. Salcedo-Sanz, J. L. Camacho, Á. M. Pérez-Bellido, E. G. Ortiz-Garcia, A. Portilla-Figueras, and E. Hernández-Martín, "Improving the prediction of average total ozone in column over the Iberian Peninsula using neural networks banks," Neurocomputing, vol. 74, no. 9, pp. 1492–1496, Apr. 2011, doi: 10.1016/j.neucom.2011.01.003.
[23] M. Kolehmainen, H. Martikainen, and J. Ruuskanen, “Neural networks and periodic components used in air quality forecasting,” Atmos. Environ., vol. 35, no. 5, pp. 815–825, Jan. 2001, doi: 10.1016/S1352-2310(00)00385-X.
[24] S. R. Shams, A. Jahani, S. Kalantary, M. Moeinaddini, and N. Khorasani, "The evaluation of artificial neural networks (ANN) and multiple linear regression (MLR) models for predicting SO2 concentration," Urban Clim., vol. 37, p. 100837, May 2021, doi: 10.1016/j.uclim.2021.100837.
[25] L. Zhang, P. Liu, L. Zhao, G. Wang, W. Zhang, and J. Liu, "Air quality predictions with a semi-supervised bidirectional LSTM neural network," Atmos. Pollut. Res., vol. 12, no. 1, pp. 328–339, Jan. 2021, doi: 10.1016/j.apr.2020.09.003.
[26] R. Navares and J. L. Aznarte, "Predicting air quality with deep learning LSTM: Towards comprehensive models," Ecol. Inform., vol. 55, p. 101019, Jan. 2020, doi: 10.1016/j.ecoinf.2019.101019.
[27] A. Masood and K. Ahmad, "A review on emerging artificial intelligence (AI) techniques for air pollution forecasting: Fundamentals, application and performance," J. Clean. Prod., vol. 322, p. 129072, Nov. 2021, doi: 10.1016/j.jclepro.2021.129072.
[28] N. Zaini, L. W. Ean, and A. N. Ahmed, "Forecasting of carbon monoxide concentration based on sequence-to-sequence deep learning approach," in Advances in Visual Informatics, Nov. 2021, pp. 518–529, doi: 10.1007/978-3-030-90235-3_45.
[29] M. A. Almubaidin et al., "Machine learning predictions for carbon monoxide levels in urban environments," Results Eng., vol. 22, p. 102114, Jun. 2024, doi: 10.1016/j.rineng.2024.102114.
[30] S. Bedi, K. Tiwari, P. A. P., S. H. Kota, and N. M. A. Krishnan, "A neural operator for forecasting carbon monoxide evolution in cities," npj Clean Air, vol. 1, no. 1, p. 2, Mar. 2025, doi: 10.1038/s44407-024-00002-5.
[31] F. Inal, "Artificial neural network prediction of tropospheric ozone concentrations in Istanbul, Turkey," CLEAN – Soil Air Water, vol. 38, no. 10, pp. 897–908, 2010, doi: 10.1002/clen.201000138.
[32] J. Yi and V. R. Prybutok, "A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area," Environ. Pollut., vol. 92, no. 3, pp. 349–357, Jan. 1996, doi: 10.1016/0269-7491(95)00078-X.
[33] W. Wang, W. Lu, X. Wang, and A. Y. T. Leung, "Prediction of maximum daily ozone level using combined neural network and statistical characteristics," Environ. Int., vol. 29, no. 5, pp. 555–562, Aug. 2003, doi: 10.1016/S0160-4120(03)00013-8.
[34] W. Mao, W. Wang, L. Jiao, S. Zhao, and A. Liu, "Modeling air quality prediction using a deep learning approach: Method optimization and evaluation," Sustain. Cities Soc., vol. 65, p. 102567, Feb. 2021, doi: 10.1016/j.scs.2020.102567.
[35] M. Zeinalnezhad, A. G. Chofreh, F. A. Goni, and J. J. Klemeš, "Air pollution prediction using semi-experimental regression model and adaptive neuro-fuzzy inference system," J. Clean. Prod., vol. 261, p. 121218, Jul. 2020, doi: 10.1016/j.jclepro.2020.121218.
[36] Doreswamy, H. Ks, Y. Km, and I. Gad, "Forecasting air pollution particulate matter (PM2.5) using machine learning regression models," Procedia Comput. Sci., vol. 171, pp. 2057–2066, Jan. 2020, doi: 10.1016/j.procs.2020.04.221.
[37] Y. Libao, Y. Tingting, Z. Jielian, L. Guicai, L. Yanfen, and M. Xiaoqian, "Prediction of CO2 emissions based on multiple linear regression analysis," Energy Procedia, vol. 105, pp. 4222–4228, May 2017, doi: 10.1016/j.egypro.2017.03.906.
[38] Gobierno del Estado de Baja California, Programa para mejorar la calidad del aire de Mexicali: 2000–2005. Mexicali, B.C., Mexico: ProAire, 2010. [Online]. Available: https://www.gob.mx/cms/uploads/attachment/file/69316/12_PROAIRE_MEXICALI_2000-2005.pdf (accessed Aug. 25, 2025).
[39] Gobierno del Estado de Baja California, Programa de gestión para mejorar la calidad del aire del Estado de Baja California (ProAire BC 2011–2020). Mexicali, B.C., Mexico: Gobierno de B.C., 2011. [Online]. Available: https://www.gob.mx/cms/uploads/attachment/file/310361/24_ProAire_Baja_California.pdf (accessed Aug. 25, 2025).
[40] E. Salazar-Ruiz, J. B. Ordieres, E. P. Vergara, and S. F. Capuz-Rizo, "Development and comparative analysis of tropospheric ozone prediction models using linear and artificial intelligence-based models in Mexicali, Baja California (Mexico) and Calexico, California (US)," Environ. Model. Softw., vol. 23, no. 8, pp. 1056–1069, Aug. 2008, doi: 10.1016/j.envsoft.2007.11.009.
[41] H. Allende, C. Moraga, and R. Salas, "Artificial neural networks in time series forecasting: A comparative analysis," Kybernetika, vol. 38, no. 6, pp. 685–707, 2002.
[42] K. J. Cios, W. Pedrycz, and R. W. Swiniarski, Data Mining Methods for Knowledge Discovery. New York, NY, USA: Springer, 2012.
[43] B. Drozdowicz, S. J. Benz, A. S. M. Santa Cruz, and N. J. Scenna, "A neural network based model for the analysis of carbon monoxide contamination in the urban area of Rosario," WIT Trans. Ecol. Environ., vol. 21, p. 8, 1997, doi: 10.2495/AIR970641.
[44] S. M. S. Nagendra and M. Khare, "Modelling urban air quality using artificial neural network," Clean Technol. Environ. Policy, vol. 7, no. 2, pp. 116–126, Feb. 2005, doi: 10.1007/s10098-004-0267-6.
[45] S. M. LaValle, Planning Algorithms. Cambridge, U.K.: Cambridge Univ. Press, 2006.
[46] B. Rosner, Fundamentals of Biostatistics, 6th ed. Belmont, CA, USA: Thomson-Brooks/Cole, 2006. [Online]. Available: http://catdir.loc.gov/catdir/enhancements/fy1514/2004117046-t.html (accessed Aug. 26, 2025).
[47] R. R. Lutz and R. M. Woodhouse, "Requirements analysis using forward and backward search," Ann. Softw. Eng., vol. 3, no. 1, pp. 459–475, Jan. 1997, doi: 10.1023/A:1018929719710.
[48] M. R. Mohebbi, A. Karimi Jashni, M. Dehghani, and K. Hadad, "Short-term prediction of carbon monoxide concentration using artificial neural network (NARX) without traffic data: Case study: Shiraz City," Iran. J. Sci. Technol. Trans. Civ. Eng., vol. 43, no. 3, pp. 533–540, Nov. 2018, doi: 10.1007/s40996-018-0210-4.
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