Projected Effects of Climate Change on Urban Ozone Air Quality by Using Artificial Neural Network Approach; Case Study: Tehran Metropolitan Area, Iran

Document Type : Research Article

Authors

1 Atmospheric Sciences Graduate Program, University of Nevada, Reno, USA.

2 Climate Research Institute, Atmospheric Science and Meteorological Research Center, Mashhad, Iran.

3 Department of Environmental Engineering, Faculty of Environment, University of Tehran, Tehran, Iran.

Abstract

We developed an artificial neural network as an air quality model and estimated the scope of the climate change impact on future (until 2064) summertime trends of hourly ozone concentrations at an urban air quality station in Tehran, Iran. Our developed scenarios assume that present-time emissions conditions of ozone precursors will remain constant in the future. Therefore, only the climate change impact on future ozone concentrations is investigated in this study. General Circulation Model (GCM) projections indicate more favorable climate conditions for ozone formation over the study area in the future: the surface temperature increases over all months of the year, solar radiation increases, and precipitation decreases in future summers, and summertime daily maximum temperature increases about 1.2C to 3C until 2064. In the scenario based on present-time ozone conditions in the 2012 summer without any exceedances, the summertime exceedance days of the 8-hr ozone standard are projected to increase in the future by about 4.2 days in the short term and about 12.3 days in the mid-term. Similarly, in the scenario based on present-time ozone conditions in the 2010 summer with 58 days of exceedance from the 8-hr ozone standard, exceedances are projected to increase by about 4.5 days in the short term and about 14.1 days in the mid-term. Moreover, the number of Unhealthy and Very Unhealthy days in the 8-hr Air Quality Index (AQI) is also projected to increase based on pollution scenarios of both summers.

Keywords

Main Subjects


Alibak, A.H., Khodarahmi, M., Fayyazsanavi, P., Alizadeh, S.M., Hadi, A.J., & Aminzadehsarikhanbeglou, E. (2022). Simulation the adsorption capacity of polyvinyl alcohol/carboxymethyl cellulose based hydrogels towards methylene blue in aqueous solutions using cascade correlation neural network (CCNN) technique. J. Clean. Prod, 337, 130509. https://doi.org/10.1016/j.jclepro.2022.130509.
Arhami, M., Kamali, N., & Rajabi, M.M. (2013). Predicting hourly air pollutant levels using artificial neural networks coupled with uncertainty analysis by Monte Carlo simulations, Environ. Sci. Pollut. Res, 20, 4777–4789. https://doi.org/10.1007/s11356-012-1451-6.
Atash, F. (2007). The deterioration of urban environments in developing countries: Mitigating the air pollution crisis in Tehran, Iran. Cities, 24, 399–409. https://doi.org/10.1016/j.cities.2007.04.001.
Beale, M.H., Hagan, M.T., & Demuth, H.B. (2012). Neural Network ToolboxTM User’s Guide, in: R2012a, The MathWorks, Inc., 3 Apple Hill Drive Natick, MA 01760-2098, Www.Mathworks.Com.
Bell, M.L., Goldberg, R., Hogrefe, C., Kinney, P.L., Knowlton, K., Lynn, B., Rosenthal, J., Rosenzweig, C., & Patz, J.A. (2007). Climate change, ambient ozone, and health in 50 US cities. Clim. Change, 82, 61–76. https://doi.org/10.1007/s10584-006-9166-7.
Camalier, L., Cox, W., & Dolwick, P. (2007). The effects of meteorology on ozone in urban areas and their use in assessing ozone trends. Atmos. Environ., 41, 7127–7137. https://doi.org/10.1016/j.atmosenv.2007.04.061.
Chaloulakou, A., Saisana, M., & Spyrellis, N. (2003). Comparative assessment of neural networks and regression models for forecasting summertime ozone in Athens. Sci. Total Environ., 313, 1–13. https://doi.org/10.1016/S0048-9697(03)00335-8.
Comrie, A.C. (1997). Comparing Neural Networks and Regression Models for Ozone Forecasting. J. Air Waste Manag. Assoc., 47, 653–663. https://doi.org/10.1080/10473289.1997.10463925.
Cox, W.M., & Chu, S. H. (1996). Assessment of interannual ozone variation in urban areas from a climatological perspective. Atmos. Environ., 30, 2615–2625. https://doi.org/10.1016/1352-2310(95)00346-0.
Dawson, J.P., Adams, P.J., & Pandis, S.N. (2007). Sensitivity of ozone to summertime climate in the eastern USA: A modeling case study. Atmos. Environ., 41, 1494–1511. https://doi.org/10.1016/j.atmosenv.2006.10.033.
Dawson, J.P., Racherla, P.N., Lynn, B.H., Adams, P.J., & Pandis, S.N. (2009). Impacts of climate change on regional and urban air quality in the eastern United States: Role of meteorology., J. Geophys. Res. Atmospheres, 114. https://doi.org/10.1029/2008JD009849.
Ebi, K.L., & McGregor, G. (2008). Climate Change, Tropospheric Ozone and Particulate Matter, and Health, Impacts. Environ. Health Perspect., 116, 1449–1455. https://doi.org/10.1289/ehp.11463.
Ephrath, J.E., Goudriaan, J., & Marani, A. (1996). Modelling diurnal patterns of air temperature, radiation wind speed, and relative humidity by equations from daily characteristics. Agric. Syst., 51, 377–393. https://doi.org/10.1016/0308-521X(95)00068-G.
Fuentes, J.D., Lerdau, M., Atkinson, R., Baldocchi, D., Bottenheim, J.W., Ciccioli, P., Lamb, B., Geron, C., Gu, L., Guenther, A., Sharkey, T.D., & Stockwell, W. (2000). Biogenic Hydrocarbons in the Atmospheric Boundary Layer: A Review. Bull. Am. Meteorol. Soc., 81, 1537–1575.
Gardner, M.W., & Dorling, S.R. (1998). Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmos. Environ., 32, 2627–2636. https://doi.org/10.1016/S1352-2310(97)00447-0.
Gibson, P.B., Perkins-Kirkpatrick, S.E., & Renwick, J.A. (2016). Projected changes in synoptic weather patterns over New Zealand examined through self-organizing maps. International Journal of Climatology, 36, 3934–3948. https://doi.org/10.1002/joc.4604.
Gryparis, A., Forsberg, B., Katsouyanni, K., Analitis, A., Touloumi, G., Schwartz, J., Samoli, E., Medina, S., Anderson, H.R., Niciu, E.M., Wichmann, H.-E., Kriz, B., Kosnik, M., Skorkovsky, J., Vonk, J.M., & Dörtbudak, Z. (2004). Acute Effects of Ozone on Mortality from the “Air Pollution and Health A Europian Approach” Project. Am. J. Respir. Crit. Care Med., 170, 1080–1087. https://doi.org/10.1164/rccm.200403-333OC.
Guenther, A., Geron, C., Pierce, T., Lamb, B., Harley, P., & Fall, R. (2000). Natural emissions of non-methane volatile organic compounds, carbon monoxide, and oxides of nitrogen from North America. Atmos. Environ., 34, 2205–2230. https://doi.org/10.1016/S1352-2310(99)00465-3.
Halek, F., Kavouci, A., & Montehaie, H. (2004). Role of motor-vehicles and trend of air borne particulate in the Great Tehran area, Iran. Int. J. Environ. Health Res., 14, 307–313. https://doi.org/10.1080/09603120410001725649.
Hessami, M., Gachon, P., Ouarda, T.B.M.J., & St-Hilaire, A. (2008). Automated regression-based statistical downscaling tool. Environ. Model. Softw., 23, 813–834. https://doi.org/10.1016/j.envsoft.2007.10.004.
Holloway, T., Spak, S.N., Barker, D., Bretl, M., Moberg, C., Hayhoe, K., Van Dorn, J., & Wuebbles, D. (2008). Change in ozone air pollution over Chicago associated with global climate change. J. Geophys. Res. Atmospheres, 113. https://doi.org/10.1029/2007JD009775.
Hosseinpoor, A.R., Forouzanfar, M.H., Yunesian, M., Asghari, F., Naieni, K.H., & Farhood, D. (2005). Air pollution and hospitalization due to angina pectoris in Tehran, Iran: A time-series study. Environ. Res., 99, 126–131. https://doi.org/10.1016/j.envres.2004.12.004.
Hoveidi, H., Aslemand, A., Vahidi, H., & Akhavan, F. (2013). Cost Emission of Pm10 on Human Health Due to the Solid Waste Disposal Scenarios, Case Study; Tehran, Iran. J. Earth Sci. Clim. Change., https://doi.org/10.4172/2157-7617.1000139.
IPCC, (2021). Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−32, doi:10.1017/9781009157896.001.
IPCC, (2007). Summary for Policymakers. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
Jacob, D.J., & Winner, D.A. (2009). Effect of climate change on air quality. Atmos. Environ., 43, 51–63. https://doi.org/10.1016/j.atmosenv.2008.09.051.
Jacobson, M.Z. (2005). Fundamentals of Atmospheric Modeling, 2nd ed. Cambridge University Press, Cambridge. https://doi.org/10.1017/CBO9781139165389.
Khansalari, S., Ghobadi, N., Bidokhti, A., & Fazel-Rastgar, F. (2020). Statistical classification of synoptic weather patterns associated with Tehran air pollution. J. Air Pollut. Health, 5, 43–62. https://doi.org/10.18502/japh.v5i1.2858.
Lee, B. S., & Wang, J. L. (2006). Concentration variation of isoprene and its implications for peak ozone concentration. Atmos. Environ., 40, 5486–5495. https://doi.org/10.1016/j.atmosenv.2006.03.035.
Leibensperger, E.M., Mickley, L.J., & Jacob, D.J. (2008). Sensitivity of US air quality to mid-latitude cyclone frequency and implications of 1980–2006 climate change. Atmospheric Chem. Phys., 8, 7075–7086. https://doi.org/10.5194/acp-8-7075-2008.
Liao, H., Chen, W.-T., & Seinfeld, J.H. (2006). Role of climate change in global predictions of future tropospheric ozone and aerosols. J. Geophys. Res. Atmospheres, 111. https://doi.org/10.1029/2005JD006852.
Lioubimtseva, E., & Henebry, G.M. (2009). Climate and environmental change in arid Central Asia: Impacts, vulnerability, and adaptations. J. Arid Environ., 73, 963–977. https://doi.org/10.1016/j.jaridenv.2009.04.022.
Liu, S.C., Trainer, M., Fehsenfeld, F.C., Parrish, D.D., Williams, E.J., Fahey, D.W., Hübler, G., & Murphy, P.C. (1987). Ozone production in the rural troposphere and the implications for regional and global ozone distributions. J. Geophys. Res. Atmospheres, 92, 4191–4207. https://doi.org/10.1029/JD092iD04p04191.
Lynn, B.H., Druyan, L., Hogrefe, C., Dudhia, J., Rosenzweig, C., Goldberg, R., Rind, D., Healy, R., Rosenthal, J., & Kinney, P. (2004). Sensitivity of present and future surface temperatures to precipitation characteristics. Clim. Res., 28, 53–65. https://doi.org/10.3354/cr028053.
Mejia, J., Wilcox, E., Rayne, S., & Mosadegh, E. (2018). Final report: Vehicle Miles Traveled Review, https://doi.org/10.13140/RG.2.2.29814.52807.
Millstein, D.E., & Harley, R.A. (2009). Impact of climate change on photochemical air pollution in Southern California. Atmospheric Chem. Phys., 9, 3745–3754. https://doi.org/10.5194/acp-9-3745-2009.
Mosadegh, E. (2013). Modeling the Regional Effects of Climate Change on Future Urban Air Quality (With Special Reference to Future Ozone Concentrations in Tehran, Iran), Univ. Tehran, Iran. DOI: 10.13140/RG.2.2.23815.32165.
Mosadegh, E., & Babaeian, I. (2022a). Projection of Temperature and Precipitation for 2020-2100 Using Post-processing of General Circulation Models Output and Artificial Neural Network Approach, Case Study: Tehran and Alborz Provinces. Iranian Journal of Geophysics. https://doi.org/10.30499/ijg.2022.311104.1370.
Mosadegh, E., & Babaeian, I. (2022b). Quantifying the Range of Uncertainty in GCM Projections for Future Solar Radiation, Temperature and Precipitation under Global Warming Effect in Dushan-Tappeh Station, Tehran, Iran. Iranian Journal of Geophysics. https://doi.org/10.30499/ijg.2022.310958.1369.
Mosadegh, E., Mejia, J., Wilcox, E.M., & Rayne, S. (2018). Vehicle Miles Travel (VMT) trends over Lake Tahoe area and its effect on Nitrogen Deposition, A23M-3068.
Mosadegh, E., & Nolin, A.W. (2020). Estimating Arctic sea ice surface roughness by using back propagation neural network, C014-0005.
Mosadegh, E., & Nolin, A.W. (2022). A New Data Processing System for Generating Sea Ice Surface Roughness Products from the Multi-Angle Imaging SpectroRadiometer (MISR) Imagery. Remote Sens., 14, 4979. https://doi.org/10.3390/rs14194979.
Mott, J.A., Mannino, D.M., Alverson, C.J., Kiyu, A., Hashim, J., Lee, T., Falter, K., & Redd, S.C. (2005). Cardiorespiratory hospitalizations associated with smoke exposure during the 1997, Southeast Asian forest fires. Int. J. Hyg. Environ. Health, 208, 75–85. https://doi.org/10.1016/j.ijheh.2005.01.018.
Murazaki, K., & Hess, P. (2006). How does climate change contribute to surface ozone change over the United States. J. Geophys. Res. Atmospheres, 111. https://doi.org/10.1029/2005JD005873.
Narumi, D., Kondo, A., & Shimoda, Y. (2009). The effect of the increase in urban temperature on the concentration of photochemical oxidants. Atmos. Environ., 43, 2348–2359. https://doi.org/10.1016/j.atmosenv.2009.01.028.
Nejatishahidin, N., Fayyazsanavi, P., & Kosecka, J. (2022). Object Pose Estimation using Mid-level Visual Representations (No. arXiv:2203.01449). arXiv. https://doi.org/10.48550/arXiv.2203.01449.
Niska, H., Hiltunen, T., Karppinen, A., Ruuskanen, J., & Kolehmainen, M. (2004). Evolving the neural network model for forecasting air pollution time series, Eng. Appl. Artif. Intell., Intelligent Control and Signal Processing, 17, 159–167. https://doi.org/10.1016/j.engappai.2004.02.002.
Nunnari, G., Nucifora, A.F.M., & Randieri, C. (1998). The application of neural techniques to the modelling of time-series of atmospheric pollution data. Ecol. Model., 111, 187–205. https://doi.org/10.1016/S0304-3800(98)00118-5.
Ordóñez, C., Mathis, H., Furger, M., Henne, S., Hüglin, C., Staehelin, J., & Prévôt, A.S.H. (2005). Changes of daily surface ozone maxima in Switzerland in all seasons from 1992 to 2002 and discussion of summer 2003. Atmospheric Chem. Phys., 5, 1187–1203. https://doi.org/10.5194/acp-5-1187-2005.
Orru, H., Andersson, C., Ebi, K.L., Langner, J., Åström, C., & Forsberg, B. (2013). Impact of climate change on ozone-related mortality and morbidity in Europe. Eur. Respir. J., 41, 285–294. https://doi.org/10.1183/09031936.00210411.
Racherla, P.N., & Adams, P.J. (2006). Sensitivity of global tropospheric ozone and fine particulate matter concentrations to climate change. J. Geophys. Res. Atmospheres, 111. https://doi.org/10.1029/2005JD006939.
Rahnama, M., & Noury, M. (2008). Developing of Halil River Rainfall-Runoff Model, Using Conjunction of Wavelet Transform and Artificial Neural Networks. Res. J. Environ. Sci., 2, 385–392. https://doi.org/10.3923/rjes.2008.385.392.
Schlink, U., Dorling, S., Pelikan, E., Nunnari, G., Cawley, G., Junninen, H., Greig, A., Foxall, R., Eben, K., Chatterton, T., Vondracek, J., Richter, M., Dostal, M., Bertucco, L., Kolehmainen, M., & Doyle, M. (2003). A rigorous inter-comparison of ground-level ozone predictions. Atmos. Environ., 37, 3237–3253. https://doi.org/10.1016/S1352-2310(03)00330-3.
Seinfeld, J.H., & Pandis, S.N. (2006). Atmospheric chemistry and physics: from air pollution to climate change, 2nd ed. Wiley, Hoboken, N.J.
Semenov, M., & Barrow, E. (2002). LARS-WG A Stochastic Weather Generator for Use in Climate Impact Studies.
Semenov, M., & Stratonovitch, P. (2010). Use of multi-model ensembles from global climate models for assessment of climate change impacts. Clim. Res., 41, 1–14. https://doi.org/10.3354/cr00836.
Semenov, M.A. (2007). Development of high-resolution UKCIP02-based climate change scenarios in the UK. Agric. For. Meteorol, 144, 127–138. https://doi.org/10.1016/j.agrformet.2007.02.003.
Sillman, S. (1999). The relation between ozone, NOx and hydrocarbons in urban and polluted rural environments. Atmos. Environ., 33, 1821–1845. https://doi.org/10.1016/S1352-2310(98)00345-8.
Sillman, S., & Samson, P.J. (1995). Impact of temperature on oxidant photochemistry in urban, polluted rural and remote environments. J. Geophys. Res. Atmospheres, 100, 11497–11508. https://doi.org/10.1029/94JD02146.
Sousa, S.I.V., Martins, F.G., Alvim-Ferraz, M.C.M., & Pereira, M.C. (2007). Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations. Environ. Model. Softw., 22, 97–103. https://doi.org/10.1016/j.envsoft.2005.12.002.
Spitters, C.J.T., Toussaint, H.A.J.M., & Goudriaan, J. (1986). Separating the diffuse and direct component of global radiation and its implications for modeling canopy photosynthesis Part I. Components of incoming radiation. Agric. For. Meteorol., 38, 217–229. https://doi.org/10.1016/0168-1923(86)90060-2.
Steiner, A.L., Tonse, S., Cohen, R.C., Goldstein, A.H., & Harley, R.A. (2006). Influence of future climate and emissions on regional air quality in California. J. Geophys. Res. Atmospheres, 111. https://doi.org/10.1029/2005JD006935.
Stott, P.A., & Kettleborough, J.A. (2002). Origins and estimates of uncertainty in predictions of twenty-first century temperature rise. Nature, 416, 723–726. https://doi.org/10.1038/416723a.
Varotsos, K.V., Tombrou, M., & Giannakopoulos, C. (2013). Statistical estimations of the number of future ozone exceedances due to climate change in Europe. J. Geophys. Res. Atmospheres, 118, 6080–6099. https://doi.org/10.1002/jgrd.50451.
Webster, M.D., Babiker, M., Mayer, M., Reilly, J.M., Harnisch, J., Hyman, R., Sarofim, M.C., & Wang, C. (2002). Uncertainty in emissions projections for climate models. Atmos. Environ., 36, 3659–3670. https://doi.org/10.1016/S1352-2310(02)00245-5.
Wilby, R., Charles, S., Zorita, E., Timbal, B., Whetton, P., & Mearns, L. (2004). Guidelines For Use of Climate Scenarios Developed From Statistical Downscaling Methods, Support. Mater., Intergov. Penel Clim. Change.
Wilks, D.S., & Wilby, R.L. (1999). The weather generation game: a review of stochastic weather models. Prog. Phys. Geogr. Earth Environ, 23, 329–357. https://doi.org/10.1177/030913339902300302.
Wise, E.K. (2009). Climate-based sensitivity of air quality to climate change scenarios for the southwestern United States. Int. J. Climatol., 29, 87–97. https://doi.org/10.1002/joc.1713.
Zarghami, M., Abdi, A., Babaeian, I., Hassanzadeh, Y., & Kanani, R. (2011). Impacts of climate change on runoffs in East Azerbaijan, Iran. Glob. Planet. Change, 78, 137–146. https://doi.org/10.1016/j.gloplacha.2011.06.003.