A novel bagging ensemble approach for predicting summertime ground-level ozone concentration

References

• Abdul-Wahab, S. A., C. S. Bakheit, and S. M. Al-Alawi. 2005. Principal component and multiple regression analysis in modelling of ground-level ozone and factors affecting its concentrations. Environ. Model. Softw. 20:1263–1271. doi:10.1016/j.envsoft.2004.09.001.
   Web of Science ®Google Scholar
• Adame, J. A., and J. G. Sole. 2013. Surface ozone variations at a rural area in the northeast of the Iberian Peninsula. Atmos. Pollut. Res. 4:130–141. doi:10.5094/APR.2013.013.
   Web of Science ®Google Scholar
• Adam-Poupart, A., A. Brand, M. Fournier, M. Jerrett, and A. Smargiassi. 2014. Spatiotemporal modeling of ozone levels in quebec (canada): a comparison of kriging, land-use regression (lur), and combined bayesian maximum entropy-lur approaches. Environ. Health Perspect. 122:970–976. doi: 10.1289/ehp.1306566.
   PubMed Web of Science ®Google Scholar
• Al Abri, E. S., E. A. Edirisinghe, A. Nawadha, and U. Kingdom. 2015. Modelling ground-level ozone concentration using ensemble learning algorithms. Int. Conf. Data Min. (DMIN). Steer. Comm. World Congr. Comput. Sci. Comput. Eng. Appl. Comput 148–154.
   Google Scholar
• Anav, A., A. De Marco, C. Proietti, A. Alessandri, A. Dell’Aquila, I. Cionni, P. Friedlingstein, D. Khvorostyanov, L. Menut, E. Paoletti, P. Sicard, S. Sitch, and M. Vitale. 2015. Comparing concentration-based (aot40) and stomatal uptake (pody) metrics for ozone risk assessment to european forests. Glob. Chang. Biol. 22:1608–1627. doi: 10.1111/gcb.13138.
   Web of Science ®Google Scholar
• Astitha, M., H. Luo, S. T. Rao, C. Hogrefe, R. Mathur, and N. Kumar. 2017. Dynamic evaluation of two decades of WRF-CMAQ ozone simulations over the contiguous United States. Atmos. Environ. 164:102–116. doi:10.1016/j.atmosenv.2017.05.020.
   Web of Science ®Google Scholar
• Awang, N. R., N. A. Ramli, A. S. Yahaya, and M. Elbayoumi. 2015. Multivariate methods to predict ground level ozone during daytime, nighttime, and critical conversion time in urban areas. Atmos. Pollut. Res. 6:726–734. doi:10.5094/APR.2015.081.
   Web of Science ®Google Scholar
• Barrero, M. A., J. O. Grimalt, and L. Cantón. 2006. Prediction of daily ozone concentration maxima in the urban atmosphere. Chemom. Intell. Lab. Syst. 80:67–76. doi:10.1016/j.chemolab.2005.07.003.
   Web of Science ®Google Scholar
• Beli, D. S. 2006. Global warming and greenhouse gases. Physics, chemistry and Technology, Facta Universitatis, 4, 45–55.
   Google Scholar
• Boynard, A., M. Beekmann, G. Foret, A. Ung, S. Szopa, C. Schmechtig, and A. Coman. 2011. An ensemble assessment of regional ozone model uncertainty with an explicit error representation. Atmos. Environ. 45:784–793. doi:10.1016/j.atmosenv.2010.08.006.
   Web of Science ®Google Scholar
• Brankov, E., R. F. Henry, K. L. Civerolo, W. Hao, S. T. Rao, P. K. Misra, R. Bloxam, and N. Reid. 2003. Assessing the effects of transboundary ozone pollution between Ontario, Canada and New York, USA. Environ. Pollut. 123:403–411. doi:10.1016/S0269-7491(03)00017-4.
   PubMed Web of Science ®Google Scholar
• Calatayud, V., Diéguez, J.J., Sicard, P., Schaub, M., De Marco, A. Testing approaches for calculating stomatal ozone fluxes from passive samplers. Sci. Total Environ. 572:56–67. doi: 10.1016/j.scitotenv.2016.07.155.
   PubMed Web of Science ®Google Scholar
• Cannon, A. J., E. R. Lord. 2011. Forecasting summertime surface-level ozone concentrations in the lower fraser valley of British Columbia: An ensemble neural network approach. J. Air Waste Manag. Assoc. 50: 322–339.. doi:10.1080/10473289.2000.10464024.
   Google Scholar
• Dawson, J. P., P. J. Adams, and S. N. Pandis. 2007. Sensitivity of ozone to summertime climate in the eastern USA: A modeling case study. Atmos. Environ. 41:1494–1511. doi:10.1016/j.atmosenv.2006.10.033.
   Web of Science ®Google Scholar
• Dietterich, T. G. 2000. Ensemble methods in machine learning. Mult. Classif. Syst. 1857:1–15. doi:10.1007/3-540-45014-9.
   Google Scholar
• Erdal, H., and İ. Karahanoğlu. 2016. Bagging ensemble models for bank profitability: An emprical research on Turkish development and investment banks. Appl. Soft Comput. J. 49:861–867. doi:10.1016/j.asoc.2016.09.010.
   Web of Science ®Google Scholar
• Finlayson-pitts, B.J., and J.N. Pitts, 2000. Chemistry of the Upper and Lower Atmosphere. Theory, experiments, and applications, in: Academic Press. p. 969. doi: 10.1016/B978-0-12-257060-5.50027-7
   Google Scholar
• Friedman, J. H. 2002. Stochastic gradient boosting. Comput. Stat. Data Anal. 38:367–378. doi:10.1016/S0167-9473(01)00065-2.
   Web of Science ®Google Scholar
• Gabusi, V., E. Pisoni, and M. Volta. 2005. Transboundary pollution and local emission impact in tropospheric ozone accumulation processes: Control strategy modelling assessment. Proc. 44th IEEE Conf. Decis. Control 7404–7409. doi:10.1109/CDC.2005.1583356.
   Google Scholar
• Gao, M., L. Yin, and J. Ning. 2018. Artificial neural network model for ozone concentration estimation and Monte Carlo analysis. Atmos. Environ. 184:129–139. doi:10.1016/j.atmosenv.2018.03.027.
   Web of Science ®Google Scholar
• Ghorani-Azam, A., B. Riahi-Zanjani, and M. Balali-Mood. 2016. Effects of air pollution on human health and practical measures for prevention in Iran. J. Res. Med. Sci. 21:65. doi:10.4103/1735-1995.189646.
   PubMed Web of Science ®Google Scholar
• Gong, B., and J. Ordieres-Meré. 2016. Prediction of daily maximum ozone threshold exceedances by preprocessing and ensemble artificial intelligence techniques: Case study of Hong Kong. Environ. Model. Softw. 84:290–303. doi:10.1016/j.envsoft.2016.06.020.
   Web of Science ®Google Scholar
• Gorai, A. K., F. Tuluri, P. B. Tchounwou, and S. Ambinakudige. 2015. Influence of local meteorology and NO(2) conditions on ground-level ozone concentrations in the eastern part of Texas, USA. Air Qual. Atmos. Health 8:81–96. doi:10.1007/s11869-014-0276-5.
   PubMed Web of Science ®Google Scholar
• Green, I. R. A., and D. Stephenson. 1986. Criteria for comparison of single event models. Hydrol. Sci. J. 31:395–411. doi:10.1080/02626668609491056.
   Web of Science ®Google Scholar
• Gupta, M., and M. Mohan. 2015. Validation of WRF/Chem model and sensitivity of chemical mechanisms to ozone simulation over megacity Delhi. Atmos. Environ. 122:220–229. doi:10.1016/j.atmosenv.2015.09.039.
   Web of Science ®Google Scholar
• Hacer, Y. A. M., E. Aykut, E. Halil, and E. Hamit. 2015. Optimizing the monthly crude oil price forecasting accuracy via bagging ensemble models. J. Econ. Int. Financ 7:127–136. doi:10.5897/JEIF2014.0629.
   Google Scholar
• Hoshyaripour, G., G. Brasseur, M. F. Andrade, M. Gavidia-Calderón, I. Bouarar, and R. Y. Ynoue. 2016. Prediction of ground-level ozone concentration in São Paulo, Brazil: Deterministic versus statistic models. Atmos. Environ. 145:365–375. doi:10.1016/j.atmosenv.2016.09.061.
   Web of Science ®Google Scholar
• Javanmardi, P., P. Morovati, M. Farhadi, S. Geravandi, Y. O. Khaniabadi, K. A. Angali, A. M. Taiwo, P. Sicard, G. Goudarzi, A. Valipour, A. De Marco, B. Rastegarimehr, and M. J. Mohammadi. 2018. Monitoring the impact of ambient ozone on human health using time series analysis and air quality model approaches. Fresenius Environ. Bull. 27:533–544.
   Web of Science ®Google Scholar
• Jellinek, H. H. G. 1973. Reactions of linear polymers with nitrogen dioxide and sulfur dioxide. Text. Res. J. 43:557–560. doi:10.1177/004051757304301001.
   Web of Science ®Google Scholar
• Jenkin, M.E., and K.C. Clemitshaw. 2002. Chapter 11 ozone and other secondary photochemical pollutants: chemical processes governing their formation in the planetary boundary layer. Dev. Environ. Sci. 1:285–338. doi: 10.1016/S1474-8177(02)80014-6.
   Google Scholar
• Jerrett, M., A. Arain, P. Kanaroglou, B. Beckerman, D. Potoglou, T. Sahsuvaroglu, J. Morrison, and C. Giovis. 2004. A review and evaluation of intraurban air pollution exposure models. J. Expo. Anal. Environ. Epidemiol. 15:185. doi:10.1038/sj.jea.7500388.
   Google Scholar
• Jiang, Z., B. Mao, X. Meng, X. Du, S. Liu, and S. Li. 2010. An air quality forecast model based on the BP neural network of the samples self-organization clustering. 2010 Sixth Int. Conf. Natural Comput. 1523–1527. doi:10.1109/ICNC.2010.5582643.
   Google Scholar
• Karlický, J., P. Huszár, and T. Halenka. 2017. Validation of gas phase chemistry in the wrf-chem model over europe. Adv. Sci. Res. 14:181-186. doi: 10.5194/asr-14-181-2017.
   Google Scholar
• Lee, D. S., M. R. Holland, and N. Falla. 1996. The potential impact of ozone on materials in the. U.K. Atmos. Environ. 30:1053–1065. doi:10.1016/1352-2310(95)00407-6.
   Web of Science ®Google Scholar
• Lefohn, A. S., C. S. Malley, L. Smith, B. Wells, M. Hazucha, H. Simon, V. Naik, G. Mills, M. G. Schultz, E. Paoletti, et al. 2018. Tropospheric ozone assessment report: Global ozone metrics for climate change, human health, and crop/ecosystem research. Elem. Sci. Anth. 6:28. doi:10.1525/elementa.279.
   Web of Science ®Google Scholar
• Liu, H., S. Liu, B. Xue, Z. Lv, Z. Meng, X. Yang, T. Xue, Q. Yu, and K. He. 2018. Ground-level ozone pollution and its health impacts in China. Atmos. Environ. 173:223–230. doi:10.1016/j.atmosenv.2017.11.014.
   Web of Science ®Google Scholar
• Lu, W. Z., and D. Wang. 2014a. Learning machines: Rationale and application in ground-level ozone prediction. Appl. Soft Comput. J. 24:135–141. doi:10.1016/j.asoc.2014.07.008.
   Web of Science ®Google Scholar
• Lu, W.-Z., and D. Wang. 2014b. Learning machines: Rationale and application in ground-level ozone prediction. Appl. Soft Comput. 24:135–141. doi:10.1016/j.asoc.2014.07.008.
   Web of Science ®Google Scholar
• Mishra, D., and P. Goyal. 2016. Neuro-Fuzzy approach to forecasting Ozone Episodes over the urban area of Delhi, India. Environ. Technol. Innov. 5:83–94. doi:10.1016/j.eti.2016.01.001.
   Web of Science ®Google Scholar
• Nash, J. E., and J. V. Sutcliffe. 1970. River flow forecasting through conceptual models part I — A discussion of principles. J. Hydrol. 10:282–290. doi:10.1016/0022-1694(70)90255-6.
   Google Scholar
• Nawahda, A. 2016. An assessment of adding value of traffic information and other attributes as part of its classifiers in a data mining tool set for predicting surface ozone levels. Process Saf. Environ. Prot. 99:149–158. doi:10.1016/j.psep.2015.11.004.
   Web of Science ®Google Scholar
• Pal, S., T. R. Lee, S. Phelps, and S. F. J. De Wekker. 2014. Impact of atmospheric boundary layer depth variability and wind reversal on the diurnal variability of aerosol concentration at a valley site. Sci. Total Environ. 496:424–434. doi:10.1016/j.scitotenv.2014.07.067.
   PubMed Web of Science ®Google Scholar
• Pineda Rojas, A. L., L. E. Venegas, and N. A. Mazzeo. 2016. Uncertainty of modelled urban peak O3concentrations and its sensitivity to input data perturbations based on the Monte Carlo analysis. Atmos. Environ. 141:422–429. doi:10.1016/j.atmosenv.2016.07.020.
   Web of Science ®Google Scholar
• Proietti, C., A. Anav, A. De Marco, P. Sicard, and M. Vitale. 2016. A multi-sites analysis on the ozone effects on Gross Primary Production of European forests. Sci. Total Environ. 556:1–11. doi:10.1016/j.scitotenv.2016.02.187.
   PubMed Web of Science ®Google Scholar
• Sicard, P., A. Anav, A. De Marco, and E. Paoletti. 2017. Projected global ground-level ozone impacts on vegetation under different emission and climate scenarios. Atmos. Chem. Phys. 17:12177–12196. doi:10.5194/acp-17-12177-2017.
   Web of Science ®Google Scholar
• Sicard, P., R. Serra, and P. Rossello. 2016. Spatiotemporal trends in ground-level ozone concentrations and metrics in France over the time period 1999–2012. Environ. Res. 149:122–144. doi:10.1016/j.envres.2016.05.014.
   PubMed Web of Science ®Google Scholar
• Singh, K. P., S. Gupta, and P. Rai. 2013. Identifying pollution sources and predicting urban air quality using ensemble learning methods. Atmos. Environ. 80:426–437. doi:10.1016/j.atmosenv.2013.08.023.
   Web of Science ®Google Scholar
• Swanson, T.J., and J. Chapman 2018. Ozone Toxicity. Treasure Island, FL: StatPearls Publications.
   Google Scholar
• Tan, K. C., H. San Lim, and M. Z. M. Jafri. 2016. Prediction of column ozone concentrations using multiple regression analysis and principal component analysis techniques: A case study in peninsular Malaysia. Atmos. Pollut. Res. 7:533–546. doi:10.1016/j.apr.2016.01.002.
   Web of Science ®Google Scholar
• Thi, H., E. E. Kwon, K. Kim, S. Kumar, S. Chambers, P. Kumar, C. Kang, S. Cho, J. Oh, and R. J. C. Brown. 2017. Factors regulating the distribution of O 3 and NO x at two mountainous sites in Seoul, Korea. Atmos. Pollut. Res. 8:328–337. doi:10.1016/j.apr.2016.10.003.
   Web of Science ®Google Scholar
• Tong, N. Y. O., D. Y. C. Leung, and C.-H. Liu. 2011. A review on ozone evolution and its relationship with boundary layer characteristics in urban environments. Water, Air, Soil Pollut 214:13–36. doi:10.1007/s11270-010-0438-5.
   Web of Science ®Google Scholar
• Wang, J., and G. Song. 2018. A deep spatial-temporal ensemble model for air quality prediction. Neurocomputing. doi:10.1016/j.neucom.2018.06.049.
   PubMed Web of Science ®Google Scholar
• Watson, AY., RR. Bates, and D. Kennedy. 1998. Air pollution, the automobile, and public health. Washington, DC: National Academies Press (US. Atmospheric Transport and Dispersion of Air Pollutants Associated with Vehicular Emissions). https://www.ncbi.nlm.nih.gov/books/NBK218142/.
   Google Scholar
• Willmott, C. J. 1981. On the validation of models. Phys. Geogr. 2:184–194. doi:10.1080/02723646.1981.10642213.
   Google Scholar
• Xiao, Y., J. Wu, Z. Lin, and X. Zhao. 2018. A deep learning-based multi-model ensemble method for cancer prediction. Comput. Methods Programs Biomed. 153:1–9. doi:10.1016/j.cmpb.2017.09.005.
   PubMed Web of Science ®Google Scholar