Application of the use of Time Series Models: Tropospheric Nitrogen Dioxide (NO2) in Different Meteorological Systems in Two Districts of the City of Lima
Application of the use of Time Series Models: Tropospheric Nitrogen Dioxide (NO2) in Different Meteorological Systems in Two Districts of the City of Lima |
||
 |
 |
|
| © 2023 by IJETT Journal | ||
| Volume-71 Issue-10 |
||
| Year of Publication : 2023 | ||
| Author : Airton Fabrizio Molina-Cueva, Renzo Aaron Cueva-Roldan, Yvan Jesus Garcia-Lopez, Juan Carlos Quiroz-Flores |
||
| DOI : 10.14445/22315381/IJETT-V71I10P201 | ||
How to Cite?
Airton Fabrizio Molina-Cueva, Renzo Aaron Cueva-Roldan, Yvan Jesus Garcia-Lopez, Juan Carlos Quiroz-Flores, "Application of the use of Time Series Models: Tropospheric Nitrogen Dioxide (NO2) in Different Meteorological Systems in Two Districts of the City of Lima," International Journal of Engineering Trends and Technology, vol. 71, no. 10, pp. 1-10, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I10P201
Abstract
This research will address air pollution, a severe problem in all world cities, because it negatively affects people's health and deteriorates the ecosystem. NO2 is a gas linked to acid rain formation and various reactions with greenhouse gases. Meteorological variables influence the behavior of tropospheric NO2 concentration. During the period of confinement due to the COVID-19 pandemic, the concentration levels of pollutants dropped abruptly, which meant relief for the ecosystem. The application of Time Series models allows us to graphically identify the concentration of contaminants in various areas and make accurate forecasts to mitigate environmental problems in the future. The research analysis shows that the SARIMA model effectively forecasts the pollutant concentration in the San Borja and San Martin de Porres districts in Lima. Error tests such as R2, MAE, MAPE, MSE, and RSME, as well as Dickey-Fuller Test, AIC, BIC, Skew, and Kurtosis, provide information on the performance of the SARIMA model and show that it is the most suitable.
Keywords
Air pollution, Time series, ARIMA, SARIMA, Tropospheric NO2.
References
[1] Faqih Hamami, and Inayatul Fithriyah, “Classification of Air Pollution Levels using Artificial Neural Network,” International Conference on Information Technology Systems and Innovation, pp. 217-220, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Nishant Raj Kapoor et al., “Machine Learning-Based CO2 Prediction for Office Room: A Pilot Study,” Wireless Communications and Mobile Computing, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Gonzalo Carreño et al., “Machine Learning Models to Predict Critical Episodes of Environmental Pollution for PM2.5 and PM10 in Talca, Chile,” Mathematics, vol. 10, no. 3, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Ahmed Alnaim, Ziheng Sun, and Daniel Tong, “Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants,” Remote Sensing, vol. 14, no. 3, pp. 729, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Pratima Kumari, and Durga Toshniwal, “Impact of Lockdown on Air Quality Over Major Cities Across the Globe During the COVID-19 Pandemic," Urban Climate, vol. 34, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Yanding Wang et al., “Prediction and Analysis of COVID-19 Daily New Cases and Cumulative Cases: Times Series Forecasting and Machine Learning Models,” BMC Infectious Diseases, vol. 22, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Erick Chávez Flores, “Incidence of Covid-19 Quarantine on Air Quality (NO2) in the City of Lima,” Journal of Research Institute of the Faculty of Mines, Metallurgy and Geographical Sciences, vol. 23, no. 46, pp. 65-71, 2020.
[CrossRef] [Publisher Link]
[8] Vilma Tapia et al., “Vehicle Rearrangement and Environmental Pollution by Particulate Matter (2.5 And 10), Sulfur Dioxide and Nitrogen Dioxide in Lima Metropolitana, Peru,” Peruvian Journal of Experimental Medicine and Public Health, vol. 35, no. 2, pp. 190-197, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Jaime Yelsin Rosales Malpartida, “Prediction of Air Pollution Generated by CO2 Emissions in Peru using ARIMA Methods and Neural Networks,” Journal BioFab, vol. 2, no. 1, pp. 130-142, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Raghavendra Kumar, Pardeep Kumar, and Yugal Kumar, “Time Series Data Prediction using IoT and Machine Learning Technique,” Procedia Computer Science, vol. 167, pp. 373-381, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Pooja Gopu et al., “Time Series Analysis Using ARIMA Model for Air Pollution Prediction in Hyderabad City of India,” Soft Computing and Signal Processing, vol. 1325, pp. 47-56, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Stylianos I. Vagropoulos et al., “Comparison of SARIMAX, SARIMA, Modified SARIMA, and ANN-Based Models for Short-Term PV Generation Forecasting,” IEEE International Energy Conference, pp. 1-6, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Norizan Mohamed et al., “Improving Short Term Load Forecasting Using Double Seasonal Arima Model,” World Applied Sciences Journal, vol. 15, no. 2, pp. 223-231, 2011.
[Google Scholar] [Publisher Link]
[14] K.Krishna Samal et al., “Time Series Based Air Pollution Forecasting using SARIMA and Prophet Model,” ITCC 2019: Proceedings of the 2019 International Conference on Information Technology and Computer Communications, pp. 80-85, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Francesca Lazzeri, Machine Learning for Time Series Forecasting with Python, 1st Edition., Wiley, pp. 22, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Esperanza Manrique Rojas, “Machine Learning: Analysis of Programming Languages and Development Tools,” Iberian Journal of Information Systems and Technologies, vol. 4, no. 28, pp. 586-599, 2020.
[Publisher Link]
[17] Małgorzata Murat et al., “Forecasting Daily Meteorological Time Series using ARIMA and Regression Models,” International Agrophysics, vol. 32, no. 2, pp. 253-264, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Gonzalo Ríos, and Carlos Hurtado, “Time Series,” Topics in Data Mining, 2008.
[Publisher Link]
[19] Liming Ye et al., “Time-Series Modeling and Prediction of Global Monthly Absolute Temperature for Environmental Decision Making,” Advances in Atmospheric Sciences, vol. 30, no. 2, pp. 382-396, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[20] George E.P. Box et al., Time Series Analysis: Forecasting and Control, 5th Edition, Ed. Hoboken, New Jersey: Wiley, vol. 37, no. 5, pp. 712, 2015.
[Google Scholar] [Publisher Link]
[21] Shreya Kusrey, Avinash Rai, and Vineeta (Nigam) Saxena, “Zigbee Based Air Pollution Monitoring and Control System using WSN,” SSRG International Journal of Electronics and Communication Engineering, vol. 4, no. 6, pp. 7-11, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Patrima Kumari, and Durga Toshniwal, “Impact of Lockdown on Air Quality Over Major Cities across the Globe During the COVID-19 Pandemic,” Urban Climate, vol. 34, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Fahad Radhi Alharbi, and Dénes Csala, “A Seasonal Autoregressive Integrated Moving Average with Exogenous Factors (SARIMAX) Forecasting Model-Based Time Series Approach,” Inventions, vol. 7, no. 4, pp. 94, 2022.
[CrossRef] [Google Scholar] [Publisher Link]