Research Article
BibTex RIS Cite

Ampirik Mod Ayrıştırmasına Dayalı ARIMA Modeli Kullanılarak Van İli Hava Kirliliğinin Tahmini

Year 2023, Volume: 28 Issue: 2, 495 - 509, 31.08.2023
https://doi.org/10.53433/yyufbed.1220578

Abstract

Hava kirliliği, yaşam kalitesini doğrudan tehdit eden ana unsurlardan birisidir. Hava kirleticilerindeki değişimlerin öngörülmesi, hava kirliliği kontrolünde ve yönetiminde önemli bir role sahiptir. Günümüzde kullanılan Çift Üstel Düzeltme (DES) ve Bütünleşik Otoregresif Hareketli Ortalama (ARIMA) gibi geleneksel yöntemler, hava kirliliğinin tahmin edilmesinde çoğu zaman yetersiz kalmaktadır. Bu nedenle, hava kalitesinin belirlenebilmesi için daha etkili tekniklerin üretilmesine ihtiyaç vardır. Bu araştırmanın temel amacı, yukarıdaki sorunları ele alarak doğruluğu yüksek bir hava kirliliği tahmin teorisi geliştirmektir. Önerilen yaklaşım, Ampirik Mod Ayrıştırması (EMD) algoritması ve ARIMA modelinin bir arada uygulandığı melez bir yöntemdir. EMD-ARIMA yönteminin tahmin becerisini belirlemek için Türkiye’nin Van şehir merkezindeki PM10 ve SO2 hava kirleticilerine ait 2019-2020 kış dönemindeki veriler kullanılmıştır. MAE, MAPE, RMSE ve R2 performans ölçütlerine göre EMD-ARIMA modeli ile PM10 ve SO2 için sırasıyla 7.25 µg/m3, %20.58, 8.84 µg/m3, 0.87 ve 7.58 µg/m3, %20.73, 8.96 µg/m3, 0.71 değerleri elde edilmiştir. Bulgular EMD-ARIMA yönteminin, geleneksel DES ve ARIMA tahmin modellerine göre daha hassas bir tahmin becerisine sahip olduğunu ortaya koymaktadır. Önerilen melez yaklaşım, hava kirliliğinin öngörülmesi ve azaltılmasına yönelik basit ve etkili bir yöntem olarak kullanılabilir.

References

  • Akbostancı, E., Türüt-Aşık, S., & Tunç, G. İ. (2009). The relationship between income and environment in Turkey: is there an environmental Kuznets curve?. Energy policy, 37(3), 861 -867. doi:10.1016/j.enpol.2008.09.088
  • Aladag, E. (2023). The influence of meteorological factors on air quality in the province of Van, Turkey. Water, Air, & Soil Pollution, 234(4), 259. doi:10.1007/s11270-023-06265-0
  • Aladağ, E. (2021). Forecasting of particulate matter with a hybrid ARIMA model based on wavelet transformation and seasonal adjustment. Urban Climate, 39, 100930. doi:10.1016/j.uclim.2021.100930
  • Alkan, A. (2018). Hava kirliliğinin ciddi boyutlara ulaştığı kentlere bir örnek: Siirt. Bitlis Eren Üniversitesi Sosyal Bilimler Dergisi, 7(2), 641-666.
  • Bayram, H. (2005). Türkiye’de hava kirliliği sorunu: Nedenleri, alınan önlemler ve mevcut durum. Toraks Dergisi, 6(2), 159-165.
  • Bilik, M. B. (2021). Deprem tehlikelerine karşı Van kent merkezinin sosyo-mekansal zarar görebilirliği. Resilience, 5(1), 67-92. doi:10.32569/resilience.886414
  • Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control. Hoboken, USA: John Wiley & Sons.
  • 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. doi:10.1016/j.atmosenv.2018.12.025
  • Cujia, A., Agudelo-Castañeda, D., Pacheco-Bustos, C., & Teixeira, E. C. (2019). Forecast of PM10 time-series data: A study case in Caribbean cities. Atmospheric Pollution Research, 10(6), 2053-2062. doi:10.1016/j.apr.2019.09.013
  • ÇŞİDB. (2022). Ulusal Hava Kalitesi İzleme Ağı. Çevre, Şehircilik ve İklim Değişikliği Bakanlığı. http://www.havaizleme.gov.tr Erişim tarihi: 11.10.2022.
  • Elbir, T., Müezzinoğlu, A., & Bayram, A. (2000). Evaluation of some air pollution indicators in Turkey. Environment International, 26(1-2), 5-10. doi: 10.1016/S0160-4120(00)00071-4
  • EPA. (2022). Air quality index (AQI) basics. Çevre Koruma Ajansı. https://airnow.gov/index.cfm?action=aqibasics.aqi Erişim tarihi: 03.12.2022.
  • Gautam, D., & Bolia, N. B. (2020). Air pollution: Impact and interventions. Air Quality, Atmosphere & Health, 13(2), 209-223. doi:10.1007/s11869-019-00784-8
  • Gopu, P., Panda, R. R., & Nagwani, N. K. (2021). Time series analysis using ARIMA model for air pollution prediction in Hyderabad city of India. 3rd International Conference on Soft Computing and Signal Processing, Haydarabad. doi:10.1007/978-981-33-6912-2_5
  • Güzel, Ş., & Özer, P. (2022). Türkiye’de hava kirliliği ve sağlık harcamaları. Sağlık ve Sosyal Refah Araştırmaları Dergisi, 4(2), 186-202. doi:10.55050/sarad.1138629
  • Hao, Y., Peng, H., Temulun, T., Liu, L.-Q., Mao, J., Lu, Z.-N., & Chen, H. (2018). How harmful is air pollution to economic development? New evidence from PM2.5 concentrations of Chinese cities. Journal of Cleaner Production, 172, 743-757. doi:10.1016/j.jclepro.2017.10.195
  • HKDYY. (2008, 6 Haziran). T. C. Başbakanlık, Hava Kalitesi Değerlendirme ve Yönetimi Yönetmeliği. Resmi Gazete (Sayı: 26898). Erişim adresi: https://www.mevzuat.gov.tr/mevzuat?MevzuatNo=12188&MevzuatTur=7&MevzuatTertip=5
  • Holt, C. C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting, 20(1), 5-10. doi:10.1016/j.ijforecast.2003.09.015
  • Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., … & Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 454(1971), 903-995. doi:10.1098/rspa.1998.0193
  • Huang, J., Li, C., & Yu, J. (2012). Resource prediction based on double exponential smoothing in cloud computing. 2nd International Conference on Consumer Electronics, Communications and Networks, Yichang. doi:10.1109/CECNet.2012.6201461
  • Kurt, A., & Oktay, A. B. (2010). Forecasting air pollutant indicator levels with geographic models 3 days in advance using neural networks. Expert Systems with Applications, 37(12), 7986-7992. doi:10.1016/j.eswa.2010.05.093
  • Leong, W. C., Kelani, R. O., & Ahmad, Z. (2020). Prediction of air pollution index (API) using support vector machine (SVM). Journal of Environmental Chemical Engineering, 8(3), 103208. doi:10.1016/j.jece.2019.103208
  • Levy, H., Horowitz, L. W., Schwarzkopf, M. D., Ming, Y., Golaz, J.-C., Naik, V., & Ramaswamy, V. (2013). The roles of aerosol direct and indirect effects in past and future climate change. Journal of Geophysical Research: Atmospheres, 118(10), 4521-4532. doi:10.1002/jgrd.50192
  • Liu, M.-D., Ding, L., & Bai, Y.-L. (2021). Application of hybrid model based on empirical mode decomposition, novel recurrent neural networks and the ARIMA to wind speed prediction. Energy Conversion and Management, 233, 113917. doi:10.1016/j.enconman.2021.113917
  • Ma, Z., Chen, H., Wang, J., Yang, X., Yan, R., Jia, J., & Xu, W. (2020). Application of hybrid model based on double decomposition, error correction and deep learning in short-term wind speed prediction. Energy Conversion and Management, 205, 112345. doi:10.1016/j.enconman.2019.112345
  • Maleki, H., Sorooshian, A., Goudarzi, G., Baboli, Z., Tahmasebi Birgani, Y., & Rahmati, M. (2019). Air pollution prediction by using an artificial neural network model. Clean Technologies and Environmental Policy, 21(6), 1341-1352. doi:10.1007/s10098-019-01709-w
  • Mujtaba, G., & Shahzad, S. J. H. (2021). Air pollutants, economic growth and public health: Implications for sustainable development in OECD countries. Environmental Science and Pollution Research, 28(10), 12686-12698. doi:10.1007/s11356-020-11212-1
  • Murray, C. J., Aravkin, A. Y., Zheng, P., Abbafati, C., Abbas, K. M., Abbasi-Kangevari, M., Abd-Allah, F., Abdelalim, A., Abdollahi, M., & Abdollahpour, I. (2020). Global burden of 87 risk factors in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. The Lancet, 396(10258), 1223-1249. doi:10.1016/S0140-6736(20)30752-2
  • Ostro, B., Malig, B., Broadwin, R., Basu, R., Gold, E. B., Bromberger, J. T., … & Green, R. (2014). Chronic PM2.5 exposure and inflammation: Determining sensitive subgroups in mid-life women. Environmental Research, 132, 168-175. doi:10.1016/j.envres.2014.03.042
  • Öztürk, D., & Bayram, T. (2019). Van ili kent merkezinde hava kirliliği. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 8(3), 1142-1153. doi:10.17798/bitlisfen.529099
  • Quah, E., & Boon, T. L. (2003). The economic cost of particulate air pollution on health in Singapore. Journal of Asian Economics, 14(1), 73-90. doi:10.1016/S1049-0078(02)00240-3
  • Ruchiraset, A., & Tantrakarnapa, K. (2022). Association of climate factors and air pollutants with pneumonia incidence in Lampang province, Thailand: Findings from a 12-year longitudinal study. International Journal of Environmental Health Research, 32(3), 691-700. doi:10.1080/09603123.2020.1793919
  • Shams, S. R., Jahani, A., Kalantary, S., Moeinaddini, M., & Khorasani, N. (2021). The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models for predicting SO2 concentration. Urban Climate, 37, 100837. doi:10.1016/j.uclim.2021.100837
  • Sheng, N., & Tang, U. W. (2016). The first official city ranking by air quality in China—A review and analysis. Cities, 51, 139-149. doi:10.1016/j.cities.2015.08.012
  • Sümer, G. Ç. (2014). Hava kirliği kontrolü: Türkiye’de hava kirliliğini önlemeye yönelik yasal düzenlemelerin ve örgütlenmelerin incelenmesi. Uluslararası İktisadi ve İdari İncelemeler Dergisi, 13, 37-56. doi:10.18092/ulikidince.232135
  • THHP. (2021). Kara Rapor: Hava kirliliği ve sağlığa etkileri. Temiz Hava Hakkı Platformu. https://www.temizhavahakki.org/wp-content/uploads/2021/09/KaraRapor2021.pdf Erişim tarihi: 21.11.2022.
  • Tırınk, S., & Öztürk, B. (2022). Evaluation of PM10 concentration by using Mars and XGBOOST algorithms in Iğdır Province of Türkiye. International Journal of Environmental Science and Technology, 20, 5349–5358. doi:10.1007/s13762-022-04511-2
  • Varaprasad, V., Kanawade, V., & Narayana, A. (2021). Spatio-temporal variability of near-surface air pollutants at four distinct geographical locations in Andhra Pradesh State of India. Environmental Pollution, 268, 115899. doi:10.1016/j.envpol.2020.115899
  • Volna, V., Blažek, Z., & Krejčí, B. (2021). Assessment of air pollution by PM10 suspended particles in the urban agglomeration of Central Europe in the period from 2001 to 2018. Urban Climate, 39, 100959. doi:10.1016/j.uclim.2021.100959
  • Wang, H., Liu, L., Dong, S., Qian, Z., & Wei, H. (2016). A novel work zone short-term vehicle-type specific traffic speed prediction model through the hybrid EMD–ARIMA framework. Transportmetrica B: Transport Dynamics, 4(3), 159-186. doi:10.1080/21680566.2015.1060582
  • WHO. (2022). Ambient (outdoor) air pollution. Dünya Sağlık Örgütü. https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health Erişim tarihi: 03.12.2022.
  • Wu, Q., & Lin, H. (2019). Daily urban air quality index forecasting based on variational mode decomposition, sample entropy and LSTM neural network. Sustainable Cities and Society, 50, 101657. doi:10.1016/j.scs.2019.101657
  • Yağımlı, M., & Ergin, H. (2017). Türkiye’de iş kazalarının üssel düzeltme metodu ile tahmin edilmesi. Marmara Fen Bilimleri Dergisi, 29(4), 118-123. doi:10.7240/marufbd.305236
  • Zeydan, Ö., & Pekkaya, M. (2021). Evaluating air quality monitoring stations in Turkey by using multi criteria decision making. Atmospheric Pollution Research, 12(5), 101046. doi:10.1016/j.apr.2021.03.009
  • 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. doi:10.1016/j.ecolind.2018.08.032
  • Zhang, Z., & Xia, D. (2022). An improved PM2.5 forecasting method based on correlation denoising and ensemble learning strategy. International Journal of Environmental Science and Technology, 20, 8641–8654. doi:10.1007/s13762-022-04525-w
  • Zhu, S., Lian, X., Liu, H., Hu, J., Wang, Y., & Che, J. (2017). Daily air quality index forecasting with hybrid models: A case in China. Environmental Pollution, 231, 1232-1244. doi:10.1016/j.envpol.2017.08.069

Prediction of Air Pollution in Van Province Using ARIMA Model Based on Empirical Mode Decomposition

Year 2023, Volume: 28 Issue: 2, 495 - 509, 31.08.2023
https://doi.org/10.53433/yyufbed.1220578

Abstract

Air pollution is one of the main factors that directly threatens the quality of life. Predicting changes in air pollutants has an important role in air pollution control and management. Traditional methods such as Double Exponential Smoothing (DES) and Autoregressive Integrated Moving Average (ARIMA) used today are often insufficient for estimating air pollution. Therefore, more effective techniques are needed to determine air quality. The main purpose of this research is to develop a highly accurate air pollution prediction theory by addressing the above problems. The proposed approach is a hybrid method in which Empirical Mode Decomposition (EMD) algorithm and ARIMA model are applied together. To determine the estimation ability of the EMD-ARIMA method, data on PM10 and SO2 air pollutants in the city center of Van, Turkey, in the winter period of 2019-2020 were used. According to MAE, MAPE, RMSE and R2 performance criteria, values of 7.25 µg/m3, 20.58%, 8.84 µg/m3, 0.87 and 7.58 µg/m3, 20.73%, 8.96 µg/m3, 0.71 were obtained for PM10 and SO2 with EMD-ARIMA model, respectively. The findings reveal that the EMD-ARIMA method has a more sensitive estimation ability than traditional DES and ARIMA estimation models. The proposed hybrid approach can be used as a simple and effective method for predicting and reducing air pollution.

References

  • Akbostancı, E., Türüt-Aşık, S., & Tunç, G. İ. (2009). The relationship between income and environment in Turkey: is there an environmental Kuznets curve?. Energy policy, 37(3), 861 -867. doi:10.1016/j.enpol.2008.09.088
  • Aladag, E. (2023). The influence of meteorological factors on air quality in the province of Van, Turkey. Water, Air, & Soil Pollution, 234(4), 259. doi:10.1007/s11270-023-06265-0
  • Aladağ, E. (2021). Forecasting of particulate matter with a hybrid ARIMA model based on wavelet transformation and seasonal adjustment. Urban Climate, 39, 100930. doi:10.1016/j.uclim.2021.100930
  • Alkan, A. (2018). Hava kirliliğinin ciddi boyutlara ulaştığı kentlere bir örnek: Siirt. Bitlis Eren Üniversitesi Sosyal Bilimler Dergisi, 7(2), 641-666.
  • Bayram, H. (2005). Türkiye’de hava kirliliği sorunu: Nedenleri, alınan önlemler ve mevcut durum. Toraks Dergisi, 6(2), 159-165.
  • Bilik, M. B. (2021). Deprem tehlikelerine karşı Van kent merkezinin sosyo-mekansal zarar görebilirliği. Resilience, 5(1), 67-92. doi:10.32569/resilience.886414
  • Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control. Hoboken, USA: John Wiley & Sons.
  • 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. doi:10.1016/j.atmosenv.2018.12.025
  • Cujia, A., Agudelo-Castañeda, D., Pacheco-Bustos, C., & Teixeira, E. C. (2019). Forecast of PM10 time-series data: A study case in Caribbean cities. Atmospheric Pollution Research, 10(6), 2053-2062. doi:10.1016/j.apr.2019.09.013
  • ÇŞİDB. (2022). Ulusal Hava Kalitesi İzleme Ağı. Çevre, Şehircilik ve İklim Değişikliği Bakanlığı. http://www.havaizleme.gov.tr Erişim tarihi: 11.10.2022.
  • Elbir, T., Müezzinoğlu, A., & Bayram, A. (2000). Evaluation of some air pollution indicators in Turkey. Environment International, 26(1-2), 5-10. doi: 10.1016/S0160-4120(00)00071-4
  • EPA. (2022). Air quality index (AQI) basics. Çevre Koruma Ajansı. https://airnow.gov/index.cfm?action=aqibasics.aqi Erişim tarihi: 03.12.2022.
  • Gautam, D., & Bolia, N. B. (2020). Air pollution: Impact and interventions. Air Quality, Atmosphere & Health, 13(2), 209-223. doi:10.1007/s11869-019-00784-8
  • Gopu, P., Panda, R. R., & Nagwani, N. K. (2021). Time series analysis using ARIMA model for air pollution prediction in Hyderabad city of India. 3rd International Conference on Soft Computing and Signal Processing, Haydarabad. doi:10.1007/978-981-33-6912-2_5
  • Güzel, Ş., & Özer, P. (2022). Türkiye’de hava kirliliği ve sağlık harcamaları. Sağlık ve Sosyal Refah Araştırmaları Dergisi, 4(2), 186-202. doi:10.55050/sarad.1138629
  • Hao, Y., Peng, H., Temulun, T., Liu, L.-Q., Mao, J., Lu, Z.-N., & Chen, H. (2018). How harmful is air pollution to economic development? New evidence from PM2.5 concentrations of Chinese cities. Journal of Cleaner Production, 172, 743-757. doi:10.1016/j.jclepro.2017.10.195
  • HKDYY. (2008, 6 Haziran). T. C. Başbakanlık, Hava Kalitesi Değerlendirme ve Yönetimi Yönetmeliği. Resmi Gazete (Sayı: 26898). Erişim adresi: https://www.mevzuat.gov.tr/mevzuat?MevzuatNo=12188&MevzuatTur=7&MevzuatTertip=5
  • Holt, C. C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting, 20(1), 5-10. doi:10.1016/j.ijforecast.2003.09.015
  • Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., … & Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 454(1971), 903-995. doi:10.1098/rspa.1998.0193
  • Huang, J., Li, C., & Yu, J. (2012). Resource prediction based on double exponential smoothing in cloud computing. 2nd International Conference on Consumer Electronics, Communications and Networks, Yichang. doi:10.1109/CECNet.2012.6201461
  • Kurt, A., & Oktay, A. B. (2010). Forecasting air pollutant indicator levels with geographic models 3 days in advance using neural networks. Expert Systems with Applications, 37(12), 7986-7992. doi:10.1016/j.eswa.2010.05.093
  • Leong, W. C., Kelani, R. O., & Ahmad, Z. (2020). Prediction of air pollution index (API) using support vector machine (SVM). Journal of Environmental Chemical Engineering, 8(3), 103208. doi:10.1016/j.jece.2019.103208
  • Levy, H., Horowitz, L. W., Schwarzkopf, M. D., Ming, Y., Golaz, J.-C., Naik, V., & Ramaswamy, V. (2013). The roles of aerosol direct and indirect effects in past and future climate change. Journal of Geophysical Research: Atmospheres, 118(10), 4521-4532. doi:10.1002/jgrd.50192
  • Liu, M.-D., Ding, L., & Bai, Y.-L. (2021). Application of hybrid model based on empirical mode decomposition, novel recurrent neural networks and the ARIMA to wind speed prediction. Energy Conversion and Management, 233, 113917. doi:10.1016/j.enconman.2021.113917
  • Ma, Z., Chen, H., Wang, J., Yang, X., Yan, R., Jia, J., & Xu, W. (2020). Application of hybrid model based on double decomposition, error correction and deep learning in short-term wind speed prediction. Energy Conversion and Management, 205, 112345. doi:10.1016/j.enconman.2019.112345
  • Maleki, H., Sorooshian, A., Goudarzi, G., Baboli, Z., Tahmasebi Birgani, Y., & Rahmati, M. (2019). Air pollution prediction by using an artificial neural network model. Clean Technologies and Environmental Policy, 21(6), 1341-1352. doi:10.1007/s10098-019-01709-w
  • Mujtaba, G., & Shahzad, S. J. H. (2021). Air pollutants, economic growth and public health: Implications for sustainable development in OECD countries. Environmental Science and Pollution Research, 28(10), 12686-12698. doi:10.1007/s11356-020-11212-1
  • Murray, C. J., Aravkin, A. Y., Zheng, P., Abbafati, C., Abbas, K. M., Abbasi-Kangevari, M., Abd-Allah, F., Abdelalim, A., Abdollahi, M., & Abdollahpour, I. (2020). Global burden of 87 risk factors in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. The Lancet, 396(10258), 1223-1249. doi:10.1016/S0140-6736(20)30752-2
  • Ostro, B., Malig, B., Broadwin, R., Basu, R., Gold, E. B., Bromberger, J. T., … & Green, R. (2014). Chronic PM2.5 exposure and inflammation: Determining sensitive subgroups in mid-life women. Environmental Research, 132, 168-175. doi:10.1016/j.envres.2014.03.042
  • Öztürk, D., & Bayram, T. (2019). Van ili kent merkezinde hava kirliliği. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 8(3), 1142-1153. doi:10.17798/bitlisfen.529099
  • Quah, E., & Boon, T. L. (2003). The economic cost of particulate air pollution on health in Singapore. Journal of Asian Economics, 14(1), 73-90. doi:10.1016/S1049-0078(02)00240-3
  • Ruchiraset, A., & Tantrakarnapa, K. (2022). Association of climate factors and air pollutants with pneumonia incidence in Lampang province, Thailand: Findings from a 12-year longitudinal study. International Journal of Environmental Health Research, 32(3), 691-700. doi:10.1080/09603123.2020.1793919
  • Shams, S. R., Jahani, A., Kalantary, S., Moeinaddini, M., & Khorasani, N. (2021). The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models for predicting SO2 concentration. Urban Climate, 37, 100837. doi:10.1016/j.uclim.2021.100837
  • Sheng, N., & Tang, U. W. (2016). The first official city ranking by air quality in China—A review and analysis. Cities, 51, 139-149. doi:10.1016/j.cities.2015.08.012
  • Sümer, G. Ç. (2014). Hava kirliği kontrolü: Türkiye’de hava kirliliğini önlemeye yönelik yasal düzenlemelerin ve örgütlenmelerin incelenmesi. Uluslararası İktisadi ve İdari İncelemeler Dergisi, 13, 37-56. doi:10.18092/ulikidince.232135
  • THHP. (2021). Kara Rapor: Hava kirliliği ve sağlığa etkileri. Temiz Hava Hakkı Platformu. https://www.temizhavahakki.org/wp-content/uploads/2021/09/KaraRapor2021.pdf Erişim tarihi: 21.11.2022.
  • Tırınk, S., & Öztürk, B. (2022). Evaluation of PM10 concentration by using Mars and XGBOOST algorithms in Iğdır Province of Türkiye. International Journal of Environmental Science and Technology, 20, 5349–5358. doi:10.1007/s13762-022-04511-2
  • Varaprasad, V., Kanawade, V., & Narayana, A. (2021). Spatio-temporal variability of near-surface air pollutants at four distinct geographical locations in Andhra Pradesh State of India. Environmental Pollution, 268, 115899. doi:10.1016/j.envpol.2020.115899
  • Volna, V., Blažek, Z., & Krejčí, B. (2021). Assessment of air pollution by PM10 suspended particles in the urban agglomeration of Central Europe in the period from 2001 to 2018. Urban Climate, 39, 100959. doi:10.1016/j.uclim.2021.100959
  • Wang, H., Liu, L., Dong, S., Qian, Z., & Wei, H. (2016). A novel work zone short-term vehicle-type specific traffic speed prediction model through the hybrid EMD–ARIMA framework. Transportmetrica B: Transport Dynamics, 4(3), 159-186. doi:10.1080/21680566.2015.1060582
  • WHO. (2022). Ambient (outdoor) air pollution. Dünya Sağlık Örgütü. https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health Erişim tarihi: 03.12.2022.
  • Wu, Q., & Lin, H. (2019). Daily urban air quality index forecasting based on variational mode decomposition, sample entropy and LSTM neural network. Sustainable Cities and Society, 50, 101657. doi:10.1016/j.scs.2019.101657
  • Yağımlı, M., & Ergin, H. (2017). Türkiye’de iş kazalarının üssel düzeltme metodu ile tahmin edilmesi. Marmara Fen Bilimleri Dergisi, 29(4), 118-123. doi:10.7240/marufbd.305236
  • Zeydan, Ö., & Pekkaya, M. (2021). Evaluating air quality monitoring stations in Turkey by using multi criteria decision making. Atmospheric Pollution Research, 12(5), 101046. doi:10.1016/j.apr.2021.03.009
  • 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. doi:10.1016/j.ecolind.2018.08.032
  • Zhang, Z., & Xia, D. (2022). An improved PM2.5 forecasting method based on correlation denoising and ensemble learning strategy. International Journal of Environmental Science and Technology, 20, 8641–8654. doi:10.1007/s13762-022-04525-w
  • Zhu, S., Lian, X., Liu, H., Hu, J., Wang, Y., & Che, J. (2017). Daily air quality index forecasting with hybrid models: A case in China. Environmental Pollution, 231, 1232-1244. doi:10.1016/j.envpol.2017.08.069
There are 47 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Engineering and Architecture / Mühendislik ve Mimarlık
Authors

Erdinç Aladağ 0000-0003-1354-0930

Publication Date August 31, 2023
Submission Date December 17, 2022
Published in Issue Year 2023 Volume: 28 Issue: 2

Cite

APA Aladağ, E. (2023). Ampirik Mod Ayrıştırmasına Dayalı ARIMA Modeli Kullanılarak Van İli Hava Kirliliğinin Tahmini. Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 28(2), 495-509. https://doi.org/10.53433/yyufbed.1220578