Abstract
The objective of this research is to propose an artificial neural network (ANN) ensemble in order to estimate the hourly NO2 concentration at unsampled locations. Spatial interpolation methods and linear regression models with regularization have been compared to perform the ensemble. The study case is based on the region of the Bay of Algeciras (Spain). This area is very industrialized and presents high concentrations of traffic. Air pollution data has been collected from the monitoring network maintained by the Andalusian Government in the region. On one hand, two totally different methods have been used and compared such as inverse distance weight (IDW) and least absolute shrinkage and selection operator (LASSO) in order to generate maps of pollutant concentration values. On the other hand, an ensemble approach has been developed using the outputs of the previous models. The ensemble is based on an ANN with backpropagation learning. An experimental procedure using cross-validation has been applied in order to compare the different models based on several performance indexes (R correlation coefficient, MSE, MAE and d index of fitness) and together to Friedman test and Bonferroni correction. The results reveal that the proposed ensemble approach presents better performance than single models in general terms. A validation procedure has been conducted using a leave-one-out strategy using each monitoring station. IDW method presents an average value of R equals 0.72 and a maximum R equals 0.87, a minimum MSE equals 78.00, a minimum MAE equals 5.841 and a maximum d equals 0.913. LASSO presents an average value of R equals 0.76 and a maximum R equals 0.86, a minimum MSE equals 59.13, a minimum MAE equals 5.490 and a maximum d equals 0.900. Finally, the ANN ensemble shows an average value of R equals 0.77 and a maximum R equals 0.87, a minimum MSE equals 54.05, a minimum MAE equals 4.972 and a maximum d equals 0.915. The main objective has been to produce adequate atmospheric pollutant concentration maps and, therefore, to obtain estimations for locations that are distinct to the monitoring stations. Another objective has been to have in hand a system to produce robust measurements. This kind of system could be useful for missing data imputation and to find out reading errors (i.e. unexpected deviations or calibration problems) in some of the nodes of a network.
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References
Alimissis, A., Philippopoulos, K., Tzanis, C. G., & Deligiorgi, D. (2018). Spatial estimation of urban air pollution with the use of artificial neural network models. Atmospheric Environment, 191, 205–213. https://doi.org/10.1016/J.ATMOSENV.2018.07.058.
Aznarte, J. L. (2017). Probabilistic forecasting for extreme NO2 pollution episodes. Environmental Pollution, 229, 321–328. https://doi.org/10.1016/J.ENVPOL.2017.05.079.
Beelen, R., Hoek, G., Pebesma, E., Vienneau, D., de Hoogh, K., & Briggs, D. J. (2009). Mapping of background air pollution at a fine spatial scale across the European Union. Science of the Total Environment, 407, 1852–1867. https://doi.org/10.1016/j.scitotenv.2008.11.048.
Breiman, L. (1996). Bagging predictors. Machine Learning, 24, 123–140. https://doi.org/10.1007/BF00058655.
Burrough, P.A., McDonnell, R.A., 1998. Principles of geographical information systems. Oxford Univ. Press.
Cabaneros, S. M., Calautit, J. K., & Hughes, B. R. (2019). A review of artificial neural network models for ambient air pollution prediction. Environmental Modelling and Software, 119, 285–304. https://doi.org/10.1016/J.ENVSOFT.2019.06.014.
Contreras, L., & Ferri, E. (2016). Wind-sensitive interpolation of urban air pollution forecasts. Procedia Computer Science, 80, 313–323. https://doi.org/10.1016/j.procs.2016.05.343.
de Mesnard, L. (2013). Pollution models and inverse distance weighting: some critical remarks. Computational Geosciences, 52, 459–469. https://doi.org/10.1016/j.cageo.2012.11.002.
Dirección General de Carreteras, Ministerio de Fomento, 2017. Mapa de tráfico 2017.
Donahue, N.M., 2018. Air pollution and air quality, in: Green Chemistry. Elsevier, pp. 151–176. https://doi.org/10.1016/B978-0-12-809270-5.00007-8
Drucker, H., Cortes, C., Jackel, L. D., LeCun, Y., & Vapnik, V. (1994). Boosting and other ensemble methods. Neural Computation, 6, 1289–1301. https://doi.org/10.1162/neco.1994.6.6.1289.
Dubois, G., & Galmarini, S. (2005). Introduction to the spatial interpolation comparison (SIC) 2004 exercise and presentation of the datasets. Appied GIS, 1, 1–11. https://doi.org/10.2104/ag050009.
Feng, X., Li, Q., Zhu, Y., Hou, J., Jin, L., & Wang, J. (2015). Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation. Atmospheric Environment, 107, 118–128. https://doi.org/10.1016/j.atmosenv.2015.02.030.
García, E.M., Rodríguez, M.L.M., Jiménez-Come, M.J., Espinosa, F.T., Domínguez, I.T., 2011. Prediction of peak concentrations of PM10 in the area of Campo de Gibraltar (Spain) using classification models. pp. 203–212. https://doi.org/10.1007/978-3-642-19644-7_22
Gómez-Losada, Á., Santos, F. M., Gibert, K., & Pires, J. C. M. (2019). A data science approach for spatiotemporal modelling of low and resident air pollution in Madrid (Spain): implications for epidemiological studies. Computers, Environment and Urban Systems, 75, 1–11. https://doi.org/10.1016/J.COMPENVURBSYS.2018.12.005.
Gong, G., Mattevada, S., & O’Bryant, S. E. (2014). Comparison of the accuracy of kriging and IDW interpolations in estimating groundwater arsenic concentrations in Texas. Environmental Research, 130, 59–69. https://doi.org/10.1016/J.ENVRES.2013.12.005.
González-Enrique, J., Ruiz-Aguilar, J.J., Moscoso-López, J.A., Van Roode, S., Urda, D., Turias, I.J., 2019a. A genetic algorithm and neural network stacking ensemble approach to improve NO2 level estimations. Springer International Publishing, pp. 856–867. https://doi.org/10.1007/978-3-030-20521-8_70
González-Enrique, J., Turias, I. J., Ruiz-Aguilar, J. J., Moscoso-López, J. A., & Franco, L. (2019b). Spatial and meteorological relevance in NO2 estimations: a case study in the Bay of Algeciras (Spain). Stochastic Environmental Research and Risk Assessment, 33, 801–815. https://doi.org/10.1007/s00477-018-01644-0.
González-Enrique, J., Turias, I. J., Ruiz-Aguilar, J. J., Moscoso-López, J. A., Jerez-Aragonés, J., & Franco, L. (2019c). Estimation of NO2 concentration values in a monitoring sensor network using a fusion approach. Fresenius Environmental Bulletin, 28, 681–686.
He, J., & Christakos, G. (2018). Space-time PM2.5 mapping in the severe haze region of Jing-Jin-Ji (China) using a synthetic approach. Environmental Pollution, 240, 319–329. https://doi.org/10.1016/J.ENVPOL.2018.04.092.
Healey, S. P., Cohen, W. B., Yang, Z., Kenneth Brewer, C., Brooks, E. B., Gorelick, N., Hernandez, A. J., Huang, C., Joseph Hughes, M., Kennedy, R. E., Loveland, T. R., Moisen, G. G., Schroeder, T. A., Stehman, S. V., Vogelmann, J. E., Woodcock, C. E., Yang, L., & Zhu, Z. (2018). Mapping forest change using stacked generalization: an ensemble approach. Remote Sensing of Environment, 204, 717–728. https://doi.org/10.1016/J.RSE.2017.09.029.
Hengl, T., 2009. A practical guide to geostatistical mapping, JCR Scientific and Technical Research Series.
Hengl, T., Minasny, B., & Gould, M. (2009). A geostatistical analysis of geostatistics. Scientometrics, 80, 491–514. https://doi.org/10.1007/s11192-009-0073-3.
Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2, 359–366. https://doi.org/10.1016/0893-6080(89)90020-8.
Jiang, X., Zou, B., Feng, H., Tang, J., Tu, Y., & Zhao, X. (2019). Spatial distribution mapping of Hg contamination in subclass agricultural soils using GIS enhanced multiple linear regression. Journal of Geochemical Exploration, 196, 1–7. https://doi.org/10.1016/J.GEXPLO.2018.10.002.
Lehmann, E.L., Casella, G., 1998. Theory of point estimation, 2nd edi. ed.
Li, J., Heap, A.D., 2008. A review of spatial interpolation methods for environmental scientists, Record 200. ed.
Li, J., & Heap, A. D. (2011). A review of comparative studies of spatial interpolation methods in environmental sciences: performance and impact factors. Ecological Informatics, 6, 228–241. https://doi.org/10.1016/j.ecoinf.2010.12.003.
Li, J., & Heap, A. D. (2014). Spatial interpolation methods applied in the environmental sciences: a review. Environmental Modelling and Software, 53, 173–189. https://doi.org/10.1016/j.envsoft.2013.12.008.
Ma, J., Ding, Y., Cheng, J. C. P., Jiang, F., & Wan, Z. (2019a). A temporal-spatial interpolation and extrapolation method based on geographic long short-term memory neural network for PM2.5. Journal of Cleaner Production, 237, 117729. https://doi.org/10.1016/J.JCLEPRO.2019.117729.
Ma, X., Longley, I., Gao, J., Kachhara, A., & Salmond, J. (2019b). A site-optimised multi-scale GIS based land use regression model for simulating local scale patterns in air pollution. Science of the Total Environment, 685, 134–149. https://doi.org/10.1016/J.SCITOTENV.2019.05.408.
Martín, M. L., Turias, I. J., González, F. J., Galindo, P. L., Trujillo, F. J., Puntonet, C. G., & Gorriz, J. M. (2008). Prediction of CO maximum ground level concentrations in the Bay of Algeciras, Spain using artificial neural networks. Chemosphere, 70, 1190–1195. https://doi.org/10.1016/j.chemosphere.2007.08.039.
Matheron, G., 1965. Les variables régionalisées et leur estimation, une application de la théorie de fonctions aléatoires aux sciences de la nature. Masson.
Mora, C., Frazier, A. G., Longman, R. J., Dacks, R. S., Walton, M. M., Tong, E. J., Sanchez, J. J., Kaiser, L. R., Stender, Y. O., Anderson, J. M., Ambrosino, C. M., Fernandez-Silva, I., Giuseffi, L. M., & Giambelluca, T. W. (2013). The projected timing of climate departure from recent variability. Nature, 502, 183–187. https://doi.org/10.1038/nature12540.
Muñoz, E., Martín, M. L., Turias, I. J., Jimenez-Come, M. J., & Trujillo, F. J. (2014). Prediction of PM10 and SO2 exceedances to control air pollution in the Bay of Algeciras, Spain. Stochastic Environmental Research and Risk Assessment, 28, 1409–1420. https://doi.org/10.1007/s00477-013-0827-6.
Naughton, O., Donnelly, A., Nolan, P., Pilla, F., Misstear, B. D., & Broderick, B. (2018). A land use regression model for explaining spatial variation in air pollution levels using a wind sector based approach. Science of the Total Environment, 630, 1324–1334. https://doi.org/10.1016/J.SCITOTENV.2018.02.317.
Piccini, C., Marchetti, A., Rivieccio, R., & Napoli, R. (2018). Multinomial logistic regression with soil diagnostic features and land surface parameters for soil mapping of Latium (Central Italy). Geoderma. https://doi.org/10.1016/J.GEODERMA.2018.09.037.
Qi, Y., Li, Q., Karimian, H., & Liu, D. (2019). A hybrid model for spatiotemporal forecasting of PM2.5 based on graph convolutional neural network and long short-term memory. Science of the Total Environment, 664, 1–10. https://doi.org/10.1016/J.SCITOTENV.2019.01.333.
Requia, W. J., Coull, B. A., & Koutrakis, P. (2019). Evaluation of predictive capabilities of ordinary geostatistical interpolation, hybrid interpolation, and machine learning methods for estimating PM2.5 constituents over space. Environmental Research, 175, 421–433. https://doi.org/10.1016/J.ENVRES.2019.05.025.
Rumelhart, D., Hinton, G., & Williams, R. (1986). Learning internal representations by error propagation, in: Parallel Distributed Processing (pp. 318–362). Cambridge: MIT Press.
Russo, A., & Soares, A. O. (2014). Hybrid model for urban air pollution forecasting: a stochastic spatio-temporal approach. Mathematical Geoscience, 46, 75–93. https://doi.org/10.1007/s11004-013-9483-0.
Sellier, Y., Galineau, J., Hulin, A., Caini, F., Marquis, N., Navel, V., Bottagisi, S., Giorgis-Allemand, L., Jacquier, C., Slama, R., & Lepeule, J. (2014). Health effects of ambient air pollution: do different methods for estimating exposure lead to different results? Environment International, 66, 165–173. https://doi.org/10.1016/J.ENVINT.2014.02.001.
Shahbazi, H., Karimi, S., Hosseini, V., Yazgi, D., & Torbatian, S. (2018). A novel regression imputation framework for Tehran air pollution monitoring network using outputs from WRF and CAMx models. Atmospheric Environment, 187, 24–33. https://doi.org/10.1016/J.ATMOSENV.2018.05.055.
Sharma, N., Taneja, S., Sagar, V., & Bhatt, A. (2018). Forecasting air pollution load in Delhi using data analysis tools. Procedia Comput. Sci., 132, 1077–1085. https://doi.org/10.1016/J.PROCS.2018.05.023.
Shepard, D. (1968). A two-dimensional interpolation function for irregularly-spaced data (pp. 517–524). New York: Proceedings of the 1968 ACM National Conference. https://doi.org/10.1145/800186.810616.
Sun, J., & Li, H. (2008). Listed companies’ financial distress prediction based on weighted majority voting combination of multiple classifiers. Expert Systems with Applications, 35, 818–827. https://doi.org/10.1016/j.eswa.2007.07.045.
Tadić, J. M., Ilić, V., & Biraud, S. (2015). Examination of geostatistical and machine-learning techniques as interpolators in anisotropic atmospheric environments. Atmospheric Environment, 111, 28–38. https://doi.org/10.1016/J.ATMOSENV.2015.03.063.
Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B:Statistical Methodology, 58, 267–288.
Tibshirani, R. (2011). Regression shrinkage and selection via the lasso: a retrospective. Journal of the Royal Statistical Society. Series B:Statistical Methodology, 73, 273–282. https://doi.org/10.1111/j.1467-9868.2011.00771.x.
Ting, K. M., & Witten, I. H. (1999). Issues in stacked generalization. Journal of Artificial Intelligence Research, 10, 271–289.
Turias, I. J., González, F. J., Martín, M. L., & Galindo, P. L. (2006). A competitive neural network approach for meteorological situation clustering. Atmospheric Environment, 40, 532–541. https://doi.org/10.1016/j.atmosenv.2005.09.065.
Turias, I. J., González, F. J., Martin, M. L., & Galindo, P. L. (2008). Prediction models of CO, SPM and SO2 concentrations in the Campo de Gibraltar Region, Spain: a multiple comparison strategy. Environmental Monitoring and Assessment, 143, 131–146. https://doi.org/10.1007/s10661-007-9963-0.
Turias, I. J., Jerez, J. M., Franco, L., Mesa, H., Ruiz-Aguilar, J. J., Moscoso, J. A., & Jiménez-Come, M. J. (2017). Prediction of carbon monoxide (CO) atmospheric pollution concentrations using meteorological variables. WIT Transactions on Ecology and the Environment, 211(9), 137–145. https://doi.org/10.2495/AIR170141.
Van Roode, S., Ruiz-Aguilar, J. J., González-Enrique, J., Moscoso-López, J. A., & Turias, I. J. (2018). Using geostatistical modelling for analysis of air pollution and its relation with road traffic in Bay of Algeciras (Spain). XIII Congreso de Ingeniería del Transporte (CIT). Gijón.
Van Roode, S., Ruiz-Aguilar, J.J., González-Enrique, J., Turias, I.J., 2020. A hybrid approach for short-term NO2 forecasting: case study of Bay of Algeciras (Spain) 190–198. https://doi.org/10.1007/978-3-030-20055-8_18
Wang, J., & Song, G. (2018). A deep spatial-temporal ensemble model for air quality prediction. Neurocomputing, 314, 198–206. https://doi.org/10.1016/j.neucom.2018.06.049.
Willmott, C. J. (1981). On the validation of models. Physical Geography, 2, 184–194.
Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5, 241–259. https://doi.org/10.1016/S0893-6080(05)80023-1.
Woźniak, M., Graña, M., & Corchado, E. (2014). A survey of multiple classifier systems as hybrid systems. Information Fusion, 16, 3–17. https://doi.org/10.1016/j.inffus.2013.04.006.
Yu, H., Russell, A., Mulholland, J., Odman, T., Hu, Y., Chang, H. H., & Kumar, N. (2018). Cross-comparison and evaluation of air pollution field estimation methods. Atmospheric Environment, 179, 49–60. https://doi.org/10.1016/J.ATMOSENV.2018.01.045.
Acknowledgements
This work is part of the research project TIN2014-58516-C2-2-R supported by (MICINN Ministerio de Economía y Competitividad-Spain). The data have been kindly provided by Junta de Andalucía (Spain).
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Van Roode, S., Ruiz-Aguilar, J.J., González-Enrique, J. et al. An artificial neural network ensemble approach to generate air pollution maps. Environ Monit Assess 191, 727 (2019). https://doi.org/10.1007/s10661-019-7901-6
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DOI: https://doi.org/10.1007/s10661-019-7901-6