Air quality and urban sustainable development: the application of machine learning tools

[EN] Air quality has an efect on a population¿s quality of life. As a dimension of sustainable urban development, governments have been concerned about this indicator. This is refected in the references consulted that have demonstrated progress in forecasting pollution events to issue early warnings using conventional tools which, as a result of the new era of big data, are becoming obsolete. There are a limited number of studies with applications of machine learning tools to characterize and forecast behavior of the environmental, social and economic dimensions of sustainable development as they pertain to air quality. This article presents an analysis of studies that developed machine learning models to forecast sustainable development and air quality. Additionally, this paper sets out to present research that studied the relationship between air quality and urban sustainable development to identify the reliability and possible applications in diferent urban contexts of these machine learning tools. To that end, a systematic review was carried out, revealing that machine learning tools have been primarily used for clustering and classifying variables and indicators according to the problem analyzed, while tools such as artifcial neural networks and support vector machines are the most widely used to predict diferent types of events. The nonlinear nature and synergy of the dimensions of sustainable development are of great interest for the application of machine learning tools.Molina-Gómez, NI.; Díaz-Arévalo, JL.; López Jiménez, PA. (2021). Air quality and urban sustainable development: the application of machine learning tools. International Journal of Environmental Science and Technology. 18(4):1-18. https://doi.org/10.1007/s13762-020-02896-6S118184Al-Dabbous A, Kumar P, Khan A (2017) Prediction of airborne nanoparticles at roadside location using a feed–forward artificial neural network. Atmos Pollut Res 8:446–454. https://doi.org/10.1016/j.apr.2016.11.004Antanasijević D, Pocajt V, Povrenović D, Ristić M, Perić-Grujić A (2013) PM10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization. Sci Total Environ 443:511–519. https://doi.org/10.1016/j.scitotenv.2012.10.110Brink H, Richards JW, Fetherolf M (2016) Real-world machine learning. Richards JW, Fetherolf M (eds) Manning Publications Co. Berkeley, CA. https://www.manning.com/books/real-world-machine-learning. Accessed 26 Apr 2020Cervone G, Franzese P, Ezber Y, Boybeyi Z (2008) Risk assessment of atmospheric emissions using machine learning. Nat Hazard Earth Syst 8:991–1000. https://doi.org/10.5194/nhess-8-991-2008Chen S, Kan G, Li J, Liang K, Hong Y (2018) Investigating China’s urban air quality using big data, information theory, and machine learning. Pol J Environ Stud 27:565–578. https://doi.org/10.15244/pjoes/75159Corani (2005) Air quality prediction in Milan: feed-forward neural networks, pruned neural networks and lazy learning. Ecol Model 185:513–529. https://doi.org/10.1016/j.ecolmodel.2005.01.008Cruz C, Gómez A, Ramírez L, Villalva A, Monge O, Varela J, Quiroz J, Duarte H (2017) Calidad del aire respecto de metales (Pb, Cd, Ni, Cu, Cr) y relación con salud respiratoria: caso Sonora, México. Rev Int Contam Ambient 33:23–34. https://doi.org/10.20937/RICA.2017.33.esp02.02de Hoogh K, Héritier H, Stafoggia M, Künzli N, Kloog I (2018) Modelling daily PM2.5 concentrations at high spatio-temporal resolution across Switzerland. Environ Pollut 233:1147–1154. https://doi.org/10.1016/j.envpol.2017.10.025Franceschi F, Cobo M, Figueredo M (2018) Discovering relationships and forecasting PM10 and PM2.5 concentrations in Bogotá, Colombia, using Artificial Neural Networks, Principal Component Analysis, and k-means clustering. Atmos Pollut Res 9:912–922. https://doi.org/10.1016/j.apr.2018.02.006García N, Combarro E, del Coz J, Montañes E (2013) A SVM-based regression model to study the air quality at local scale in Oviedo urban area (Northern Spain): a case study. Appl Math Comput 219:8923–8937. https://doi.org/10.1016/j.amc.2013.03.018Gibert K, Sànchez-Màrre M, Sevilla B (2012) Tools for environmental data mining and intelligent decision support. In iEMSs. Leipzig, Germany. http://www.iemss.org/society/index.php/iemss-2012-proceedings. Accessed 26 Nov 2018Gibert K, Sànchez-Marrè M, Izquierdo J (2016) A survey on pre-processing techniques: relevant issues in the context of environmental data mining. Ai Commun 29:627–663. https://doi.org/10.3233/AIC-160710Gounaridis D, Chorianopoulos I, Koukoulas S (2018) Exploring prospective urban growth trends under different economic outlooks and land-use planning scenarios: the case of Athens. Appl Geogr 90:134–144. https://doi.org/10.1016/j.apgeog.2017.12.001Holloway J, Mengersen K (2018) Statistical machine learning methods and remote sensing for sustainable development goals: a review. Remote Sens 10:1–21. https://doi.org/10.3390/rs10091365Ifaei P, Karbassi A, Lee S, Yoo Ch (2017) A renewable energies-assisted sustainable development plan for Iran using techno-econo-socio-environmental multivariate analysis and big data. Energy Convers Manag 153:257–277. https://doi.org/10.1016/j.enconman.2017.10.014Kadiyala A, Kumar A (2017a) Applications of R to evaluate environmental data science problems. Environ Prog Sustain 36:1358–1364. https://doi.org/10.1002/ep.12676Kadiyala A, Kumar A (2017b) Vector time series-based radial basis function neural network modeling of air quality inside a public transportation bus using available software. Environ Prog Sustain 36:4–10. https://doi.org/10.1002/ep.12523Karimian H, Li Q, Wu Ch, Qi Y, Mo Y, Chen G, Zhang X, Sachdeva S (2019) Evaluation of different machine learning approaches to forecasting PM2.5 mass concentrations. Aerosol Air Qual Res 19:1400–1410. https://doi.org/10.4209/aaqr.2018.12.0450Krzyzanowski M, Apte J, Bonjour S, Brauer M, Cohen A, Prüss-Ustun A (2014) Air pollution in the mega-cities. Curr Environ Health Rep 1:185–191. https://doi.org/10.1007/s40572-014-0019-7Lässig K, Morik (2016) Computat sustainability. Springer, Berlin. https://doi.org/10.1007/978-3-319-31858-5Li Y, Wu Y-X, Zeng Z-X, Guo L (2006) Research on forecast model for sustainable development of economy-environment system based on PCA and SVM. In: Proceedings of the 2006 international conference on machine learning and cybernetics, vol 2006. IEEE, Dalian, China, pp 3590–3593. https://doi.org/10.1109/ICMLC.2006.258576Liu B-Ch, Binaykia A, Chang P-Ch, Tiwari M, Tsao Ch-Ch (2017) Urban air quality forecasting based on multi- dimensional collaborative support vector regression (SVR): a case study of Beijing-Tianjin-Shijiazhuang. PLoS ONE 12:1–17. https://doi.org/10.1371/journal.pone.0179763Lubell M, Feiock R, Handy S (2009) City adoption of environmentally sustainable policies in California’s Central Valley. J Am Plan Assoc 75:293–308. https://doi.org/10.1080/01944360902952295Ma D, Zhang Z (2016) Contaminant dispersion prediction and source estimation with integrated Gaussian-machine learning network model for point source emission in atmosphere. J Hazard Mater 311:237–245. https://doi.org/10.1016/j.jhazmat.2016.03.022Madu C, Kuei N, Lee P (2017) Urban sustainability management: a deep learning perspective. Sustain Cities Soc 30:1–17. https://doi.org/10.1016/j.scs.2016.12.012Mellos K (1988) Theory of eco-development. In: Perspectives on ecology. Palgrave Macmillan, London. https://doi.org/10.1007/978-1-349-19598-5_4Ni XY, Huang H, Du WP (2017) Relevance analysis and short-term prediction of PM2.5 concentrations in Beijing based on multi-source data. Atmos Environ 150:146–161. https://doi.org/10.1016/j.atmosenv.2016.11.054Oprea M, Dragomir E, Popescu M, Mihalache S (2016) Particulate matter air pollutants forecasting using inductive learning approach. Rev Chim 67:2075–2081Paas B, Stienen J, Vorländer M, Schneider Ch (2017) Modelling of urban near-road atmospheric PM concentrations using an artificial neural network approach with acoustic data input. Environments 4:1–25. https://doi.org/10.3390/environments4020026Pandey G, Zhang B, Jian L (2013) Predicting submicron air pollution indicators: a machine learning approach. Environ Sci Proc Impacts 15:996–1005. https://doi.org/10.1039/c3em30890aPeng H, Lima A, Teakles A, Jin J, Cannon A, Hsieh W (2017) Evaluating hourly air quality forecasting in Canada with nonlinear updatable machine learning methods. Air Qual Atmos Health 10:195–211. https://doi.org/10.1007/s11869-016-0414-3Pérez-Ortíz M, de La Paz-Marín M, Gutiérrez PA, Hervás-Martínez C (2014) Classification of EU countries’ progress towards sustainable development based on ordinal regression techniques. Knowl Based Syst 66:178–189. https://doi.org/10.1016/j.knosys.2014.04.041Phillis Y, Kouikoglou V, Verdugo C (2017) Urban sustainability assessment and ranking of cities. Comput Environ Urban 64:254–265. https://doi.org/10.1016/j.compenvurbsys.2017.03.002Saeed S, Hussain L, Awan I, Idris A (2017) Comparative analysis of different statistical methods for prediction of PM2.5 and PM10 concentrations in advance for several hours. Int J Comput Sci Netw Secur 17:45–52Sayegh A, Munir S, Habeebullah T (2014) Comparing the performance of statistical models for predicting PM10 concentrations. Aerosol Air Qual Res 14:653–665. https://doi.org/10.4209/aaqr.2013.07.0259Shaban K, Kadri A, Rezk E (2016) Urban air pollution monitoring system with forecasting models. IEEE Sens J 16:2598–2606. https://doi.org/10.1109/JSEN.2016.2514378Sierra B (2006) Aprendizaje automático conceptos básicos y avanzados Aspectos prácticos utilizando el software Weka. Madrid Pearson Prentice Hall, MadridSingh K, Gupta S, Rai P (2013) Identifying pollution sources and predicting urban air quality using ensemble learning methods. Atmos Environ 80:426–437. https://doi.org/10.1016/j.atmosenv.2013.08.023Song L, Pang S, Longley I, Olivares G, Sarrafzadeh A (2014) Spatio-temporal PM2.5 prediction by spatial data aided incremental support vector regression. In: International joint conference on neural networks. IEEE, Beijing, pp 623–630. https://doi.org/10.1109/IJCNN.2014.6889521Souza R, Coelho G, da Silva A, Pozza S (2015) Using ensembles of artificial neural networks to improve PM10 forecasts. Chem Eng Trans 43:2161–2166. https://doi.org/10.3303/CET1543361Suárez A, García PJ, Riesgo P, del Coz JJ, Iglesias-Rodríguez FJ (2011) Application of an SVM-based regression model to the air quality study at local scale in the Avilés urban area (Spain). Math Comput Model 54:453–1466. https://doi.org/10.1016/j.mcm.2011.04.017Tamas W, Notton G, Paoli C, Nivet M, Voyant C (2016) Hybridization of air quality forecasting models using machine learning and clustering: an original approach to detect pollutant peaks. Aerosol Air Qual Res 16:405–416. https://doi.org/10.4209/aaqr.2015.03.0193Toumi O, Le Gallo J, Ben Rejeb J (2017) Assessment of Latin American sustainability. Renew Sustain Energy Rev 78:878–885. https://doi.org/10.1016/j.rser.2017.05.013Tzima F, Mitkas P, Voukantsis D, Karatzas K (2011) Sparse episode identification in environmental datasets: the case of air quality assessment. Expert Syst Appl 38:5019–5027. https://doi.org/10.1016/j.eswa.2010.09.148United Nations, Department of Economic and Social Affairs (2019) World urbanization prospects The 2018 Revision. New York. https://doi.org/10.18356/b9e995fe-enWang B (2019) Applying machine-learning methods based on causality analysis to determine air quality in China. Pol J Environ Stud 28:3877–3885. https://doi.org/10.15244/pjoes/99639Wang X, Xiao Z (2017) Regional eco-efficiency prediction with support vector spatial dynamic MIDAS. J Clean Prod 161:165–177. https://doi.org/10.1016/j.jclepro.2017.05.077Wang W, Men C, Lu W (2008) Online prediction model based on support vector machine. Neurocomputing 71:550–558. https://doi.org/10.1016/j.neucom.2007.07.020WCED (1987) Report of the world commission on environment and development: our common future: report of the world commission on environment and development. WCED, Oslo. https://doi.org/10.1080/07488008808408783Weizhen H, Zhengqiang L, Yuhuan Z, Hua X, Ying Z, Kaitao L, Donghui L, Peng W, Yan M (2014) Using support vector regression to predict PM10 and PM2.5. In: IOP conference series: earth and environmental science, vol 17. IOP. https://doi.org/10.1088/1755-1315/17/1/012268WHO (2016) OMS | La OMS publica estimaciones nacionales sobre la exposición a la contaminación del aire y sus repercusiones para la salud. WHO. http://www.who.int/mediacentre/news/releases/2016/air-pollution-estimates/es/. Accesed 26 Nov 2018Yeganeh N, Shafie MP, Rashidi Y, Kamalan H (2012) Prediction of CO concentrations based on a hybrid partial least square and support vector machine model. Atmos Environ 55:357–365. https://doi.org/10.1016/j.atmosenv.2012.02.092Zalakeviciute R, Bastidas M, Buenaño A, Rybarczyk Y (2020) A traffic-based method to predict and map urban air quality. Appl Sci. https://doi.org/10.3390/app10062035Zeng L, Guo J, Wang B, Lv J, Wang Q (2019) Analyzing sustainability of Chinese coal cities using a decision tree modeling approach. Resour Policy 64:101501. https://doi.org/10.1016/j.resourpol.2019.101501Zhan Y, Luo Y, Deng X, Grieneisen M, Zhang M, Di B (2018) Spatiotemporal prediction of daily ambient ozone levels across China using random forest for human exposure assessment. Environ Pollut 233:464–473. https://doi.org/10.1016/j.envpol.2017.10.029Zhang Y, Huan Q (2006) Research on the evaluation of sustainable development in Cangzhou city based on neural-network-AHP. In: Proceedings of the fifth international conference on machine learning and cybernetics, vol 2006. pp 3144–3147. https://doi.org/10.1109/ICMLC.2006.258407Zhang Y, Shang W, Wu Y (2009) Research on sustainable development based on neural network. In: 2009 Chinese control and decision conference. IEEE, pp 3273–3276. https://doi.org/10.1109/CCDC.2009.5192476Zhou Y, Chang F-J, Chang L-Ch, Kao I-F, Wang YS (2019) Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts. J Clean Prod 209:134–145. https://doi.org/10.1016/j.jclepro.2018.10.24

Machine learning for estimation of building energy consumption and performance:a review

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