Short-term PM2.5 forecasting using a unique ensemble technique for proactive environmental management initiatives
Particulate matter with a diameter of 2.5 microns or less ( PM2.5 ) is a significant type of air pollution that affects human health due to its ability to persist in the atmosphere and penetrate the respiratory system. Accurate forecasting of particulate matter is crucial for the healthcare sector o...
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2024
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| author | Iftikhar, Hasnain Qureshi, Moiz Zywiołek, Justyna López Gonzales, Javier Linkolk Albalawi, Olayan |
| author_browse | Albalawi, Olayan Iftikhar, Hasnain López Gonzales, Javier Linkolk Qureshi, Moiz Zywiołek, Justyna |
| author_facet | Iftikhar, Hasnain Qureshi, Moiz Zywiołek, Justyna López Gonzales, Javier Linkolk Albalawi, Olayan |
| author_sort | Iftikhar, Hasnain |
| collection | Producción científica |
| description | Particulate matter with a diameter of 2.5 microns or less ( PM2.5 ) is a significant type of air pollution that affects human health due to its ability to persist in the atmosphere and penetrate the respiratory system. Accurate forecasting of particulate matter is crucial for the healthcare sector of any country. To achieve this, in the current work, a new time series ensemble approach is proposed based on various linear (autoregressive, simple exponential smoothing, autoregressive moving average, and theta) and nonlinear (nonparametric autoregressive and neural network autoregressive) models. Three ensemble models are also developed, each employing distinct weighting strategies: equal distribution of weight among all single models (ESME), weight assignment based on training average accuracy errors (ESMT), and weight assignment based on validation mean accuracy measures (ESMV). This technique was applied to daily PM2.5 concentration data from 1 January 2019, to 31 May 2023, in Pakistan’s main cities, including Lahore, Karachi, Peshawar, and Islamabad, to forecast short-term PM2.5 concentrations. When compared to other models, the best ensemble model (ESMV) demonstrated mean errors ranging from 3.60% to 25.79% in Islamabad, 0.81%–13.52% in Lahore, 1.08%–7.06% in Karachi, and 1.09%–12.11% in Peshawar. These results indicate that the proposed ensemble approach is more efficient and accurate for short-term PM2.5 forecasting than existing models. Furthermore, using the best ensemble model, a forecast was made for the next 15 days (June 1 to 15 June 2023). The forecast showed that in Lahore, the highest PM2.5 value (236.00 μg/m3 ) was observed on 8 June 2023. Other days also displayed higher and poor air quality throughout the 15 days. Conversely, Karachi experienced moderate PM2.5 concentration levels between 50 μg/m3 and 80 μg/m3 . In Peshawar, the PM2.5 concentration levels were consistently unhealthy, with the highest peak (153.00 μg/m3 ) observed on 9 June 2023. This forecasting experience can assist environmental monitoring organizations in implementing cost-effective planning to minimize air pollution. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/410 |
| institution | UPeU |
| language | English |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/4102026-10-30T16:14:36ZShort-term PM2.5 forecasting using a unique ensemble technique for proactive environmental management initiativesIftikhar, HasnainQureshi, MoizZywiołek, JustynaLópez Gonzales, Javier LinkolkAlbalawi, Olayanair pollutionconcentrationshort-term PM 2.5 forecastingsingle time series modelsensemble time series modelssustainable developmentParticulate matter with a diameter of 2.5 microns or less ( PM2.5 ) is a significant type of air pollution that affects human health due to its ability to persist in the atmosphere and penetrate the respiratory system. Accurate forecasting of particulate matter is crucial for the healthcare sector of any country. To achieve this, in the current work, a new time series ensemble approach is proposed based on various linear (autoregressive, simple exponential smoothing, autoregressive moving average, and theta) and nonlinear (nonparametric autoregressive and neural network autoregressive) models. Three ensemble models are also developed, each employing distinct weighting strategies: equal distribution of weight among all single models (ESME), weight assignment based on training average accuracy errors (ESMT), and weight assignment based on validation mean accuracy measures (ESMV). This technique was applied to daily PM2.5 concentration data from 1 January 2019, to 31 May 2023, in Pakistan’s main cities, including Lahore, Karachi, Peshawar, and Islamabad, to forecast short-term PM2.5 concentrations. When compared to other models, the best ensemble model (ESMV) demonstrated mean errors ranging from 3.60% to 25.79% in Islamabad, 0.81%–13.52% in Lahore, 1.08%–7.06% in Karachi, and 1.09%–12.11% in Peshawar. These results indicate that the proposed ensemble approach is more efficient and accurate for short-term PM2.5 forecasting than existing models. Furthermore, using the best ensemble model, a forecast was made for the next 15 days (June 1 to 15 June 2023). The forecast showed that in Lahore, the highest PM2.5 value (236.00 μg/m3 ) was observed on 8 June 2023. Other days also displayed higher and poor air quality throughout the 15 days. Conversely, Karachi experienced moderate PM2.5 concentration levels between 50 μg/m3 and 80 μg/m3 . In Peshawar, the PM2.5 concentration levels were consistently unhealthy, with the highest peak (153.00 μg/m3 ) observed on 9 June 2023. This forecasting experience can assist environmental monitoring organizations in implementing cost-effective planning to minimize air pollution.Articlehttps://public-pages-files-2025.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2024.1442644/pdfhttps://doi.org/10.3389/fenvs.2024.144264410.3389/fenvs.2024.1442644https://hdl.handle.net/20.500.15037/410W4402423025enFrontiers in Environmental Science2296-665X2024-09-102026-06-13T08:12:58Z2026-06-13T08:12:58Z |
| spellingShingle | air pollution concentration short-term PM 2.5 forecasting single time series models ensemble time series models sustainable development Iftikhar, Hasnain Qureshi, Moiz Zywiołek, Justyna López Gonzales, Javier Linkolk Albalawi, Olayan Short-term PM2.5 forecasting using a unique ensemble technique for proactive environmental management initiatives |
| title | Short-term PM2.5 forecasting using a unique ensemble technique for proactive environmental management initiatives |
| title_full | Short-term PM2.5 forecasting using a unique ensemble technique for proactive environmental management initiatives |
| title_fullStr | Short-term PM2.5 forecasting using a unique ensemble technique for proactive environmental management initiatives |
| title_full_unstemmed | Short-term PM2.5 forecasting using a unique ensemble technique for proactive environmental management initiatives |
| title_short | Short-term PM2.5 forecasting using a unique ensemble technique for proactive environmental management initiatives |
| title_sort | short term pm2 5 forecasting using a unique ensemble technique for proactive environmental management initiatives |
| topic | air pollution concentration short-term PM 2.5 forecasting single time series models ensemble time series models sustainable development |
| topic_facet | air pollution concentration short-term PM 2.5 forecasting single time series models ensemble time series models sustainable development |
| url | https://public-pages-files-2025.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2024.1442644/pdf https://doi.org/10.3389/fenvs.2024.1442644 https://hdl.handle.net/20.500.15037/410 |
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