Abstract
Increasing population, rapid urbanization and hasty industrialization are assisting gradual shift in human residence from rural to urban. Any city trying to become a smart and sustainable one must provide a better quality of life for its citizens and address the problem of degrading air quality and increasing pollutant emissions. The solution for these problems requires selection and decision-making among a number of candidate emission-control-alternatives that need to satiate a number of technical constraints, policy criteria and regulations. For a given pollutant and emission source, a number of control technologies are available; hence, it is coveted to choose the best combination among them to reduce emissions to a desired standard. Because of its applicability, flexibility and ease of computation in solving large-scale practical problems, dynamic programming-based approach appears to be the appropriate and feasible choice for optimal air pollution control technology selection for an urban metropolitan area. Current study develops a dynamic programming (DP) model that determines the optimal selection strategy, after defining different parameters, at a minimized total cost. The usage and applicability of the proposed model were illustrated with a representative case study of a simulated city with three major sources of pollution. It is inferred that DP is ideal for the ‘multiple sources—multiple control technologies—single air pollutant’ optimization problem.
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References
Abrams R (1975) Optimization models for regional air pollution control. In: Mathematical analysis of decision problems in ecology. Springer, Berlin, Heidelberg, pp 116–6. https://doi.org/10.1007/978-3-642-80924-8_5
Amini AA, Weymouth TE, Jain RC (1990) Using dynamic programming for solving variational problems in vision. IEEE Trans Pattern Anal Mach Intell 9:855–867. https://doi.org/10.1109/34.57681
Asif Z, Chen Z (2018) Optimization of air pollution control model for mining. Int J Civil Environ Eng 12(4):411–417. https://publications.waset.org/10008805/optimization-of-air-pollution-control-model-for-mining
Bellman RE, Dreyfus SE (1962) Applied dynamic programming. Princeton University Press, Princeton
Bellman RE, Dreyfus SE (2015) Applied dynamic programming, vol 2050. Princeton University Press, Princeton
Bernstein JA, Alexis N, Barnes C, Bernstein IL, Nel A, Peden D, Williams PB et al (2004) Health effects of air pollution. J Allergy Clin Immunol 114(5):1116–1123. https://doi.org/10.1016/j.jaci.2004.08.030
Bertsekas DP, Bertsekas DP, Bertsekas DP, Bertsekas DP (1995) Dynamic programming and optimal control, vol 1, No. 2. Athena Scientific, Belmont
Bhaskar BV, Mehta VM (2010) Atmospheric particulate pollutants and their relationship with meteorology in Ahmedabad. Aerosol Air Qual Res 10(4):301–315. https://doi.org/10.4209/aaqr.2009.10.0069
Bradley SP, Hax AC, Magnanti TL (1977) Dynamic programming. In: Applied mathematical programming [E-reader version], pp 320–362. Retrieved from http://web.mit.edu/15.053/www/AMP-Chapter-11.pdf
Caines PE (2018) Linear stochastic systems, vol 77. SIAM
Chauhan C (2007) Urbanisation in India faster than rest of the world. Hindustan Times, Retrieved from https://www.hindustantimes.com/india/urbanisation-in-india-faster-than-rest-of-the-world/story-IdmQ4BSqxEZe874AprzfnL.html
Cohen AJ, Brauer M, Burnett R, Anderson HR, Frostad J, Estep K, Feigin V et al (2017) Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the global burden of diseases study 2015. Lancet 389(10082):1907–1918. https://doi.org/10.1016/S0140-6736(17)30505-6
Cooper L, Cooper MW (2016) Introduction to dynamic programming: international series in modern applied mathematics and computer science. vol 1. Elsevier
Council on Environmental Quality (1977) Environmental quality: the eighth annual report of the council on environmental quality. U.S. Government Printing Office, Washington, D.C, p. 172. Retrieved from https://babel.hathitrust.org/cgi/pt?id=mdp.39015000421662;view=1up;seq=4
Craig KJ, De Kock DJ, Snyman JA (1999) Using CFD and mathematical optimization to investigate air pollution due to stacks. Int J Numer Meth Eng 44(4):551–565. https://doi.org/10.1002/(SICI)1097-0207(19990210)44:4<551::AID-NME519>3.0.CO;2-7
Curtis L, Rea W, Smith-Willis P, Fenyves E, Pan Y (2006) Adverse health effects of outdoor air pollutants. Environ Int 32(6):815–830. https://doi.org/10.1016/j.envint.2006.03.012
Dreyfus S (2002) Richard Bellman on the birth of dynamic programming. Oper Res 50(1):48–51. https://doi.org/10.1287/opre.50.1.48.17791
Environmental Cooperation Office, Global Environment Bureau, Ministry of Environment, Japan (1998) Air pollution control technology manual (Copyright 2005). Retrieved from https://www.env.go.jp/earth/coop/coop/document/01-apctme/contents.html
Galil Z, Park K (1992) Dynamic programming with convexity, concavity and sparsity. Theoret Comput Sci 92(1):49–76. https://doi.org/10.1016/0304-3975(92)90135-3
Goswami E, Larson T, Lumley T, Liu LJS (2002) Spatial characteristics of fine particulate matter: identifying representative monitoring locations in Seattle, Washington. J Air Waste Manag Assoc 52(3):324–333. https://doi.org/10.1080/10473289.2002.10470778
Goswami P, Baruah J (2011) Urban air pollution: process identification, impact analysis and evaluation of forecast potential. Meteorol Atmos Phys 110(3–4):103–122. https://doi.org/10.1007/s00703-010-0105-9
Goyal P, Sidhartha (2003) Present scenario of air quality in Delhi: a case study of CNG implementation. Atmos Environ 37(38):5423–5431. https://doi.org/10.1016/j.atmosenv.2003.09.005
Guttikunda SK, Gurjar BR (2012) Role of meteorology in seasonality of air pollution in megacity Delhi, India. Environ Monit Assess 184(5):3199–3211. https://doi.org/10.1007/s10661-011-2182-8
Guttikunda SK, Nishadh KA, Gota S, Singh P, Chanda A, Jawahar P, Asundi J (2019) Air quality, emissions, and source contributions analysis for the Greater Bengaluru region of India. Atmos Pollut Res 10(3):941–953. https://doi.org/10.1016/j.apr.2019.01.002
Haith DA (1982) Environmental systems optimization, p 115
He YJ, Chen DZ (2008) Hybrid particle swarm optimization algorithm for mixed-integer nonlinear programming. J Zhejiang Univ (Eng Sci) 42(5):747–751. https://doi.org/10.3785/j.issn.1008-973X.2008.05.005
Howard DB, Thé J, Soria R, Fann N, Schaeffer R, Saphores JDM (2019) Health benefits and control costs of tightening particulate matter emissions standards for coal power plants-the case of Northeast Brazil. Environ Int 124:420–430. https://doi.org/10.1016/j.envint.2019.01.029
Huan L, Kebin H (2012) Traffic optimization: a new way for air pollution control in China’s urban areas. https://doi.org/10.1021/es301778b
Jiang Y, Jiang ZP (2015) Global adaptive dynamic programming for continuous-time nonlinear systems. IEEE Trans Autom Control 60(11):2917–2929. https://doi.org/10.1109/TAC.2015.2414811
Kafkes A (2017) Demystifying dynamic programming. Retrieved from https://medium.freecodecamp.org/demystifying-dynamic-programming-3efafb8d4296
Kanada M, Fujita T, Fujii M, Ohnishi S (2013) The long-term impacts of air pollution control policy: historical links between municipal actions and industrial energy efficiency in Kawasaki City, Japan. J Clean Prod 58:92–101. https://doi.org/10.1016/j.jclepro.2013.04.015
Kanaroglou PS, Jerrett M, Morrison J, Beckerman B, Arain MA, Gilbert NL, Brook JR (2005) Establishing an air pollution monitoring network for intra-urban population exposure assessment: a location-allocation approach. Atmos Environ 35t, 39(13):2399–2409. https://doi.org/10.1016/j.atmosenv.2004.06.049
Kelman J, Stedinger JR, Cooper LA, Hsu E, Yuan SQ (1990) Sampling stochastic dynamic programming applied to reservoir operation. Water Resour Res 26(3):447–454. https://doi.org/10.1029/WR026i003p00447
Kondili E (2005) Review of optimization models in the pollution prevention and control. In: Computer aided chemical engineering, vol 20. Elsevier, pp 1627–1632. https://doi.org/10.1016/S1570-7946(05)80113-0
Kukkonen J, Härkönen J, Karppinen A, Pohjola M, Pietarila H, Koskentalo T (2001) A semi-empirical model for urban PM10 concentrations, and its evaluation against data from an urban measurement network. Atmos Environ 35(26):4433–4442. https://doi.org/10.1016/S1352-2310(01)00254-0
Kumar P, Morawska L, Martani C, Biskos G, Neophytou M, Di Sabatino S, Britter R et al (2015) The rise of low-cost sensing for managing air pollution in cities. Environ Int 75:199–205. https://doi.org/10.1016/j.envint.2014.11.019
Kumar R, Joseph AE (2006) Air pollution concentrations of PM2.5, PM10 and NO2 at ambient and kerbsite and their correlation in Metro City–Mumbai. Environ Monit Assess 119(1–3):191–199. https://doi.org/10.1007/s10661-005-9022-7
Landrigan PJ (2017) Air pollution and health. Lancet Public Health 2(1):e4–e5. https://doi.org/10.1016/S2468-2667(16)30023-8
Lebret E, Briggs D, Van Reeuwijk H, Fischer P, Smallbone K, Harssema H, Elliott P et al (2000). Small area variations in ambient NO2 concentrations in four European areas. Atmos Environ 34(2):177–185. https://doi.org/10.1016/S1352-2310(99)00292-7
Mabahwi NAB, Leh OLH, Omar D (2014) Human health and wellbeing: Human health effect of air pollution. Procedia-Soc Behav Sci 153:221–229. https://doi.org/10.1016/j.sbspro.2014.10.056
Owoade KO, Hopke PK, Olise FS, Ogundele LT, Fawole OG, Olaniyi BH, Bashiru MI et al (2015) Chemical compositions and source identification of particulate matter (PM2.5 and PM2.5–10) from a scrap iron and steel smelting industry along the Ife–Ibadan highway, Nigeria. Atmos Pollut Res 6(1):107–119. https://doi.org/10.5094/APR.2015.013
Parvin M, Grammas GW (1976) Optimization models for environmental pollution control: a synthesis. J Environ Econ Manage 3(2):113–128. https://doi.org/10.1016/0095-0696(76)90026-7
Plumlee GS, Ziegler TL (1999) Environmental geochemistry. Treatise on Geochemistry
Poor HV (1984) Backward forward and backward-forward dynamic programming models under commutativity conditions. In: Proceedings of 23rd IEEE conference decision control, pp 1081–1086. https://doi.org/10.1109/CDC.1984.272179
Powell WB (2007) Approximate dynamic programming: solving the curses of dimensionality, vol 703. Wiley
Puterman ML (2014) Markov decision processes: discrete stochastic dynamic programming. Wiley, New York
Ross SM (2014) Introduction to stochastic dynamic programming. Academic Press
Rust J (1997) Using randomization to break the curse of dimensionality. Econometrica: J Econometric Soc, 487–516. https://doi.org/10.2307/2171751
Sakawa M, Sawaragi Y (1975) Multiple-criteria optimization of pollution control model. Int J Syst Sci 6(8):741–748. https://doi.org/10.1080/00207727508941858
Schnelle KB Jr, Dunn RF, Ternes ME (2015) Air pollution control technology handbook. CRC Press
Shaban HI, Elkamel A, Gharbi R (1997) An optimization model for air pollution control decision making. Environ Model Softw 12(1):51–58. https://doi.org/10.1016/S1364-8152(96)00008-4
Shao M, Tang X, Zhang Y, Li W (2006) City clusters in China: air and surface water pollution. Front Ecol Environ 4(7):353–361. https://doi.org/10.1890/1540-9295(2006)004[0353:CCICAA]2.0.CO;2
Showalter WE, Halpin DW (2008) Dynamic programming approach to optimization of site remediation. J Constr Eng Manage 3(10):820–827. https://doi.org/10.1061/(ASCE)0733-9364(2008)134:10(820)
Shukla K, Srivastava PK, Banerjee T, Aneja VP (2017) Trend and variability of atmospheric ozone over middle Indo-Gangetic Plain: impacts of seasonality and precursor gases. Environ Sci Pollut Res 24(1):164–179. https://doi.org/10.1007/s11356-016-7738-2
Si J, Barto AG, Powell WB, Wunsch D (eds) (2004) Handbook of learning and approximate dynamic programming, vol 2. Wiley, New York
Trijonis JC, Peng TK, McRae GJ, Lees L (1976) Emissions and air quality trends in the South Coast Air Basin. Retrieved from https://authors.library.caltech.edu/25765/
United Nations (2018) 2018 Revision of world urbanization prospects. Retrieved from https://www.un.org/development/desa/en/news/population/2018-revision-of-world-urbanization-prospects.html
Wang KM (1981) Optimization of an air pollution control model by linear programming. J Chin Inst Eng 4(1):1–11. https://doi.org/10.1080/02533839.1981.9676662
White DJ (1969) Dynamic programming, vol 1. Oliver & Boyd, Edinburgh
World Health Organization (WHO), Department of Public Health, Environmental and Social Determinants of Health (PHE) (2014) Healthy environments, Healthy People (Press Release). Retrieved from https://www.who.int/phe/eNews_63.pdf
Yadav R, Sahu LK, Beig G, Jaaffrey SNA (2016) Role of long-range transport and local meteorology in seasonal variation of surface ozone and its precursors at an urban site in India. Atmos Res 176:96–107. https://doi.org/10.1016/j.atmosres.2016.02.018
Zeger SL, Thomas D, Dominici F, Samet JM, Schwartz J, Dockery D, Cohen A (2000) Exposure measurement error in time-series studies of air pollution: concepts and consequences. Environ Health Perspect 108(5):419–426. https://doi.org/10.1289/ehp.00108419
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Shiva Kumar, G., Sharma, A., Shukla, K., Nema, A.K. (2020). Dynamic Programming-Based Decision-Making Model for Selecting Optimal Air Pollution Control Technologies for an Urban Setting. In: Ahmed, S., Abbas, S., Zia, H. (eds) Smart Cities—Opportunities and Challenges. Lecture Notes in Civil Engineering, vol 58. Springer, Singapore. https://doi.org/10.1007/978-981-15-2545-2_58
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