Abstract
Air pollution prediction based on variables in environmental monitoring data gains further importance with increasing concerns about climate change and the sustainability of cities. Modeling of the complex relationships between these variables by sophisticated methods in machine learning is a promising field. The objectives of this work are to develop a supervised model for the prediction of air pollution by using real sensor data and to transfer the model between cities. The combination of a convolutional neural network and a long short-term memory deep neural network model was proposed to predict the concentration of air pollutants in multiple locations of a city by using spatial-temporal relationships. Two approaches have been adopted: the univariate model contains the information of one pollutant whereas the multivariate model contains the information of all pollutants and meteorology data for prediction. The study was carried out for different pollutants which are in the publicly available data of the cities of Barcelona, Kocaeli, and İstanbul. The hyperparameters of the model (filter, frame, and batch sizes; number of convolutional/LSTM layers and hidden units; learning rate; and parameters for sample selection, pooling, and validation) were tuned to determine the architecture that achieved the lowest test error. The proposed model improved the prediction performance (measured by the root mean square error) by 11–53% for particulate matter, 20–31% for ozone, 9–47% for nitrogenoxides, and 18–46% for sulfurdioxide with respect to the 1-hidden layer long short-term memory networks utilized in the literature. The multivariate model without using meteorological data revealed the best results. Regarding transfer learning, the network weights were transferred from the source city to the target city. The model has more accurate prediction performance with the transfer of the network from Kocaeli to İstanbul as those neighbor cities have similar air pollution and meteorological characteristics.
Similar content being viewed by others
References
Aceves-Fernandez M, Domínguez-Guevara R, Pedraza Ortega J C, Vargas-Soto J (2020) Evaluation of key parameters using deep convolutional neural networks for airborne pollution (pm10) prediction. Discret Dyn Nat Soc 2020:1–14. https://doi.org/10.1155/2020/2792481
Al-Janabi S, Mohammad M, Al-Sultan A (2019) A new method for prediction of air pollution based on intelligent computation. Soft Comput 24:661–680. https://doi.org/10.1007/s00500-019-04495-1
Al-Janabi S, Alkaim A, Al-Janabi E, A Aljeboree MM (2021) Intelligent forecaster of concentrations (pm2.5, pm10, no2, co, o3, so2) caused air pollution (ifcsap). Neural Computing and Applications. https://doi.org/10.1007/s00521-021-06067-7
Bakici T, Almirall E, Wareham J (2012) A smart city initiative: The case of barcelona. J Knowl Econ:4. https://doi.org/10.1007/s13132-012-0084-9
Barcelona City Council (2020) Open data bcn. https://opendata-ajuntament.barcelona.cat/en/https://opendata-ajuntament.barcelona.cat/en/, (last accessed: 15.04.2021)
Bashir Shaban K, Kadri A, Rezk E (2016) Urban air pollution monitoring system with forecasting models. IEEE Sens J 16(8):2598–2606. https://doi.org/10.1109/JSEN.2016.2514378
Chen Q, Wang W, Wu F, De S, Wang R, Zhang B, Huang X (2019) A survey on an emerging area: Deep learning for smart city data. IEEE Trans Emerging Top Comput Intell 3(5):392–410. https://doi.org/10.1109/TETCI.2019.2907718
Chu H J, Lin C Y, Cj Liau, Kuo Y M (2012) Identifying controlling factors of ground-level ozone levels over southwestern Taiwan using a decision tree. Atmos Environ 60:142–152. https://doi.org/10.1016/j.atmosenv.2012.06.032
Di Antonio L, Rosato A, Colaiuda V, Lombardi A, Tomassetti B, Panella M (2019) Multivariate prediction of pm 10 concentration by lstm neural networks. pp 423–431. https://doi.org/10.1109/PIERS-Fall48861.2019.9021929
Djalalova I, Delle Monache L, Wilczak J (2015) Pm2.5 analog forecast and kalman filter post-processing for the community multiscale air quality (cmaq) model. Atmos Environ:108. https://doi.org/10.1016/j.atmosenv.2015.02.021
Eessaar E (2016) The database normalization theory and the theory of normalized systems: finding a common ground. Baltic J Modern Comput 4:5–33
EU (2021) Explore. https://eu-smartcities.eu/, (last accessed: 15.04.2021
Eurepean Environment Agency (2019) Air quality in europe — 2019 report. Tech. Rep. EEA Report 10/2019
European Commission (2017) 2030 climate and energy framework. https://ec.europa.eu/clima/policies/strategies/2030_en, (last accessed: 15.04.2021)
Khan S, Paul D, Momtahan P, Aloqaily M (2018) Artificial intelligence framework for smart city microgrids: state of the art, challenges, and opportunities. In: 2018 Third International Conference on Fog and Mobile Edge Computing (FMEC), pp 283–288
Kloeckl K, Senn O, Ratti C (2012) Enabling the real-time city: live singapore! J Urban Technol:19. https://doi.org/10.1080/10630732.2012.698068
Li X, Peng L, Shao J, Chi T (2016) Deep learning architecture for air quality predictions. Environ Sci Pollut Res:23. https://doi.org/10.1007/s11356-016-7812-9
Lv M, Li Y, Chen L, Chen T (2019) Air quality estimation by exploiting terrain features and multi-view transfer semi-supervised regression. Inf Sci:483. https://doi.org/10.1016/j.ins.2019.01.038
Ma J, Cheng J, Lin C, Tan Y, Zhang J (2019) Improving air quality prediction accuracy at larger temporal resolutions using deep learning and transfer learning techniques. Atmos Environ 214:116885. https://doi.org/10.1016/j.atmosenv.2019.116885
Ma J, Ding Y, Gan VJL, Lin C, Wan Z (2019) Spatiotemporal prediction of pm2.5 concentrations at different time granularities using idw-blstm. IEEE Access 7:107897–107907
Nwankpa C, Ijomah W, Gachagan A, Marshall S (2018) Activation functions: Comparison of trends in practice and research for deep learning. arXiv:1811.03378
Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22 (10):1345–1359. https://doi.org/10.1109/TKDE.2009.191
Park S, Kim M, Kim M, Namgung H G, Kim K T, Cho K, Kwon S B (2017) Predicting pm 10 concentration in seoul metropolitan subway stations using artificial neural network (ann). J Hazard Mater:341. https://doi.org/10.1016/j.jhazmat.2017.07.050
Qi Z, Wang T, Song G, Hu W, Li X, Zhang Z (2018) Deep air learning: interpolation, prediction, and feature analysis of fine-grained air quality. IEEE Trans Knowl Data Eng 30(12):2285–2297. https://doi.org/10.1109/TKDE.2018.2823740
Qin D, Yu J, Zou G, Yong R, Zhao Q, Zhang B (2019) A novel combined prediction scheme based on cnn and lstm for urban pm2.5 concentration. IEEE Access 7:20050–20059. https://doi.org/10.1109/ACCESS.2019.2897028
Republic of Turkey Ministry of Environment and Urbanization (2019) National air quality monitoring network (in turkish). https://sim.csb.gov.tr/, (last accessed 15.04.2021)
Schürholz D, Kubler S, Zaslavsky A (2020) Artificial intelligence-enabled context-aware air quality prediction for smart cities. J Cleaner Prod:121941. https://doi.org/10.1016/j.jclepro.2020.121941
Scovronick N (2015) Reducing global health risks through mitigation of short-lived climate pollutants
Sánchez L, Muñoz L, Galache J, Sotres P, Santana J, Gutierrez V, Ramdhany R, Gluhak A, Krco S, Theodoridis E, Pfisterer D (2013) Smartsantander: Iot experimentation over a smart city testbed. Computer Networks. https://doi.org/10.1016/j.bjp.2013.12.020
Soh P, Chang J, Huang J (2018) Adaptive deep learning-based air quality prediction model using the most relevant spatial-temporal relations. IEEE Access 6:38186–38199. https://doi.org/10.1109/ACCESS.2018.2849820
Tai A, Mickley L, Jacob D (2010) Correlations between fine particulate matter (pm2.5) and meteorological variables in the united states: implications for the sensitivity of pm2.5 to climate change. Atmos Environ 44:3976–3984. https://doi.org/10.1016/j.atmosenv.2010.06.060
Tao Q, Liu F, Li Y, Sidorov D (2019) Air pollution forecasting using a deep learning model based on 1d convnets and bidirectional gru. IEEE Access 7:76690–76698. https://doi.org/10.1109/ACCESS.2019.2921578
Wei Y, Zheng Y, Yang Q (2016) Transfer knowledge between cities. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16. Association for Computing Machinery, New York, pp 1905–1914, https://doi.org/10.1145/2939672.2939830
Weiss K, Khoshgoftaar T, Wang D (2016) A survey of transfer learning. J Big Data:3. https://doi.org/10.1186/s40537-016-0043-6
WHO (2019) Healthy environments for healthier populations: why do they matter, and what can we do?
Zanella A, Bui N, Castellani A, Vangelista L, Zorzi M (2014) Internet of things for smart cities. IEEE Internet Things J 1(1):22–32. https://doi.org/10.1109/JIOT.2014.2306328
Zeinalnezhad M, Gholamzadeh Chofreh A, Goni F, Klemes J (2020) Air pollution prediction using semi-experimental regression model and adaptive neuro-fuzzy inference system. J Cleaner Prod:121218. https://doi.org/10.1016/j.jclepro.2020.121218
Zhang Y, Wang Y, Gao M, Ma Q, Zhao J, Zhang R, Wang Q, Huang L (2019) A predictive data feature exploration-based air quality prediction approach. IEEE Access 7:30732–30743. https://doi.org/10.1109/ACCESS.2019.2897754
Zhang Z, Zeng Y, Yan K (2021) A hybrid deep learning technology for pm2.5 air quality forecasting. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-021-12657-8
Zhao G, Huang G, He H, Wang Q (2019) Innovative spatial-temporal network modeling and analysis method of air quality. IEEE Access 7:26241–26254. https://doi.org/10.1109/ACCESS.2019.2900997
Zhao Z, Qin J, He Z, Li H, Yang Y, Zhang R (2020) Combining forward with recurrent neural networks for hourly air quality prediction in northwest of china. Environ Sci Pollution Res Int 27(23):28931–28948. https://doi.org/10.1007/s11356-020-08948-1
Zhou Y, Chang F J, Chang L C, Kao I F, Wang Y S (2018) Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts. J Clean Prod:209. https://doi.org/10.1016/j.jclepro.2018.10.243
Zivot E, Wang J (2003) Rolling Analysis of Time Series, pp 299–346. https://doi.org/10.1007/978-0-387-21763-5_9
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Additional information
Responsible Editor: Marcus Schulz
Author contribution
Aysenur Gilik has contributed to conceptual design, data processing, algorithm development, implementation, writing and review. Arif Selcuk Ogrenci has contributed to conceptual design, algorithm development, writing and review. Atilla Ozmen has contributed to conceptual design, algorithm development, writing and review.
Data availability
The data used in this paper are publicly available at Ministry of Environment and Urbanization Continuous Monitoring Center (URL: https://sim.csb.gov.tr/) and Ajuntament de Barcelona’s open data service (URL: https://opendata-ajuntament.barcelona.cat/en/). The codes are not packaged into a library however the codes can be shared with interested readers.
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Gilik, A., Ogrenci, A.S. & Ozmen, A. Air quality prediction using CNN+LSTM-based hybrid deep learning architecture. Environ Sci Pollut Res 29, 11920–11938 (2022). https://doi.org/10.1007/s11356-021-16227-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-021-16227-w