Mapping air quality zones for coastal urban centers

References

• 40CFR81. 1991. Designation of areas for air quality planning purposes. Washington, DC: USEPA.
   Google Scholar
• Acker, J.G., and G. Leptoukh. 2007. Online analysis enhances use of NASA Earth science data. Eos Trans. Am. Geophys. Union 88:4. doi:10.1029/2007EO020003
   Google Scholar
• Al-Awadhi, J.M. 2014. Measurement of air pollution in Kuwait City using passive samplers. Atmos. Climate Sci. 4:253–71. doi:10.4236/acs.2014.42028
   Google Scholar
• Al-Mutairi, N., A. Abahussain, and A. El-Battay. 2014. Spatial and temporal characterizations of water quality in Kuwait Bay. Mar. Pollut. Bull. 83:127–31. doi:10.1016/j.marpolbul.2014.04.009
   PubMed Web of Science ®Google Scholar
• Al-Rashidi, M.S., V. Nassehi, and R.J. Wakeman. 2005. Investigation of the efficiency of existing air pollution monitoring sites in the state of Kuwait. Environ. Pollut. 138:219–29. doi:10.1016/j.envpol.2005.04.006
   PubMed Web of Science ®Google Scholar
• Alberghi, S., A. Maurizi, and F. Tampier. 2002. Relationship between the vertical velocity skewness and kurtosis observed during sea-breeze convection. J. Appl. Meteorol. Climatol. 41:885–889. doi:10.1175/1520-0450(2002)041<0885:RBTVVS>2.0.CO;2
   Web of Science ®Google Scholar
• Amjad, M., Q. Zafar, F. Khan, and M. M. Sheikh. 2015. Evaluation of weather research and forecasting model for the assessment of wind resource over Gharo, Pakistan. Int. J. Climatol. 35:12. doi:10.1002/joc.2015.35.issue-8
   Web of Science ®Google Scholar
• Boersma, K.F., H.J. Eskes, R.J. Dirksen, R.J. van der A, J.P. Veefkind, P. Stammes, V. Huijnen, Q.L. Kleipool, M. Sneep, J. Claas, J. Leitão, A. Richter, Y. Zhou, D. Brunner. 2011. An improved tropospheric NO₂ column retrieval algorithm for the ozone monitoring instrument. Atmos. Meas. Technol. 4:1905–28. doi:10.5194/amt-4-1905-2011
   Google Scholar
• Breivogel, M., S.S. Griswold, A. Hasegawa, and J.R. Taylor. 1961. Air pollution-potential advisory service for industrial zoning cases. J. Air Pollut. Control Assoc. 11:327–35. doi:10.1080/00022470.1961.10468006
   Google Scholar
• Carlisle, T. 2010. Kuwaiti pupils lead air quality protests. The National, May 2. http://www.thenational.ae/business/kuwaiti-pupils-lead-air-quality-protests (accessed February 3, 2017).
   Google Scholar
• Carr, D., and W. Yan. 2012. Federal environmental policy and local industrial diversification: The case of the Clean Air Act. Regional Stud. 46:639–49. doi:10.1080/00343404.2010.509131
   Web of Science ®Google Scholar
• Central Statistic Bureau. 2012. Population. In Annual statistical abstract 2011, 48th ed., chpa. 3. Kuwait City, Kuwait: State of Kuwait Central Statistic Bureau.
   Google Scholar
• Chi-Chun, H., H. Yu-Wei, C. Yu-Hsien, and K. Chia-Hao. 2008. Bayesian classification for bed posture detection based on kurtosis and skewness estimation. 10th International Conference on e-Health Networking, Applications and Services, 2008, HealthCom 2008, 165–68.
   Google Scholar
• Chusai, C., K. Manomaiphiboon, P. Saiyasitpanich, and S. Thepanondh. 2012. NO2 and SO2 dispersion modeling and relative roles of emission sources over Map Ta Phut industrial area, Thailand. J. Air Waste Manage. Assoc. 62:932–45. doi:10.1080/10962247.2012.687704
   PubMed Web of Science ®Google Scholar
• CIA. 2015. The World Factbook—Kuwait. CIA. https://www.cia.gov/library/publications/resources/the-world-factbook/geos/ku.html (accessed February 3, 2017).
   Google Scholar
• Cortes, C., and V. Vapnik. 1995. Support-vector networks. Machine Learning 20:273–97. doi:10.1007/BF00994018
   Web of Science ®Google Scholar
• Cox, N.J. 2010. Speaking Stata: The limits of sample skewness and kurtosis. Stata J. 10:482–95.
   Web of Science ®Google Scholar
• Cristelli, M., A. Zaccaria, and L. Pietronero. 2012. Universal relation between skewness and kurtosis in complex dynamics. Phys. Rev. E 85:066108. doi:10.1103/PhysRevE.85.066108
   Web of Science ®Google Scholar
• Crosilla, F., D. Macorig, M. Scaioni, I. Sebastianutti, and D. Visintini. 2013. LiDAR data filtering and classification by skewness and kurtosis iterative analysis of multiple point cloud data categories. Appl. Geomatics 5:225–40. doi:10.1007/s12518-013-0113-9
   Google Scholar
• Crosman, E.T., and J.D. Horel. 2010. Sea and lake breezes: A review of numerical studies. Boundary-Layer Meteorol. 137:1–29. doi:10.1007/s10546-010-9517-9
   Web of Science ®Google Scholar
• Cuxart, J., M.A. Jiménez, M. Telišman Prtenjak, and B. Grisogono. 2014. Study of a Sea-breeze case through momentum, temperature, and turbulence budgets. J. Appl. Meteorol. Climatol. 53:2589–609. doi:10.1175/JAMC-D-14-0007.1
   Web of Science ®Google Scholar
• CYGM. 2010. Clean air activity plan (2010–2013). Republic of Turkey Ministry of Environment and Urbanization. http://www.cygm.gov.tr/CYGM/Files/EylemPlan/Temiz_Hava_Eylem_Plani.pdf (accessed February 3, 2017).
   Google Scholar
• Elangasinghe, M.A. N. Singhal, K.N. Dirks, J.A. Salmond, and S. Samarasinghe. 2014. Complex time series analysis of PM10 and PM2.5 for a coastal site using artificial neural network modelling and k-means clustering. Atmos. Environ. 94, 106–116. doi:10.1016/j.atmosenv.2014.04.051
   Web of Science ®Google Scholar
• European Community. 2008. Directive 2008/50/EC of the European Parliament and of the Council of 21 May 2008 on ambient air quality and cleaner air for Europe EU. http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32008L0050&from=en (accessed February 3, 2017).
   Google Scholar
• Feng, X., Q. Li, Y. Zhu, J. Hou, L. Jin, and J. Wang. 2015. Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation. Atmos. Environ. 107:118–28. doi:10.1016/j.atmosenv.2015.02.030
   Web of Science ®Google Scholar
• Fiore, A.M., V. Naik, and E.M. Leibensperger. 2015. Air quality and climate connections. J. Air Waste Manage. Assoc. 65:645–85. doi:10.1080/10962247.2015.1040526
   PubMed Web of Science ®Google Scholar
• Freeman, B., S. Faisal, and S. Al-Yakoob. 2013. Identification of air quality zones for Kuwait. Kuwait City, Kuwait: UNDP.
   Google Scholar
• Gamas, J., R. Dodder, D. Loughlin, and C. Gage. 2015. Role of future scenarios in understanding deep uncertainty in long-term air quality management. J. Air Waste Manage. Assoc. 65:1327–40. doi:10.1080/10962247.2015.1084783
   PubMed Web of Science ®Google Scholar
• Geohive. 2015. Population of the entire world, yearly, 1950–2100. http://www.geohive.com/earth/his_history3.aspx (accessed February 3, 2017).
   Google Scholar
• Ghannam, K., and M. El-Fadel. 2013a. Emissions characterization and regulatory compliance at an industrial complex: An integrated MM5/CALPUFF approach. Atmos. Environ. 69:156–69. doi:10.1016/j.atmosenv.2012.12.022
   Web of Science ®Google Scholar
• Ghannam, K., and M. El-Fadel. 2013b. A framework for emissions source apportionment in industrial areas: MM5/CALPUFF in a near-field application. J. Air Waste Manage. Assoc. 63:190–204. doi:10.1080/10962247.2012.739982
   Web of Science ®Google Scholar
• Grell, G.A., J. Dudhia, and D. Stauffer. 1994. A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5). Penn State/NCAR. http://nldr.library.ucar.edu/repository/collections/TECH-NOTE-000-000-000-214 (accessed January 3, 2017).
   Google Scholar
• Gsella, A., A.D. Meij, A. Kerschbaumer, E. Reimer, P. Thunis, and C. Cuvelier. 2014. Evaluation of MM5, WRF and TRAMPER meteorology over the complex terrain of the Po Valley, Italy. Atmos. Environ. 89:797–806. doi:10.1016/j.atmosenv.2014.03.019
   Web of Science ®Google Scholar
• Hao, J., S. Wang, B. Liu, and K. He. 2000. Designation of acid rain and SO2 control zones and control policies in China. J. Environ. Sci. Health Part A 35:1901–14. doi:10.1080/10934520009377085
   Web of Science ®Google Scholar
• Henmi, T. 2004. Statistical studies of mesoscale forecast models MM5 and WRF, 40. Army Research Laboratory. http://www.dtic.mil/dtic/tr/fulltext/u2/a428276.pdf (accessed February 3, 2017).
   Google Scholar
• Henschel, S., X. Querol, R. Atkinson, M. Pandolfi, A. Zeka, A.L. Tertre, A. Analitis, K. Katsouyanni, O. Chanel, M. Pascal, C. Bouland, D. Haluza, S. Medina, and P.G. Goodman. 2013. Ambient air SO2 patterns in 6 European cities. Atmos. Environ. 79:236–47. doi:10.1016/j.atmosenv.2013.06.008
   Web of Science ®Google Scholar
• Holland, W.D., A. Hasegawa, J.R. Taylor, and E.K. Kauper. 1960. Industrial zoning as a means of controlling area source air pollution. J. Air Pollut. Control Assoc. 10:147–74. doi:10.1080/00022470.1960.10467914
   PubMedGoogle Scholar
• Holman, C., R. Harrison, and X. Querol. 2015. Review of the efficacy of low emission zones to improve urban air quality in European cities. Atmos. Environ. 111:161–69. doi:10.1016/j.atmosenv.2015.04.009
   Web of Science ®Google Scholar
• Indumati, S., R.B. Oza, Y.S. Mayya, V.D.Puranik, and H.S. Kushwaha. 2009. Dispersion of pollutants over land–water–land interface: Study using CALPUFF model. Atmos. Environ. 43:473–78. doi:10.1016/j.atmosenv.2008.09.030
   Web of Science ®Google Scholar
• Irwin, J.S. 2014. A suggested method for dispersion model evaluation. J. Air Waste Manage. Assoc. 64:255–64. doi:10.1080/10962247.2013.833147
   Web of Science ®Google Scholar
• KAMCO. 2013. State of Kuwait: Economic & financial facts. KIPCO Asset Management Company KSC. http://www.kamconline.com/Temp/Reports/48218661-476e-43f1-8d12-ecc124e90e93.pdf (accessed February 3, 2017).
   Google Scholar
• Karaca, F. 2012. Determination of air quality zones in Turkey. J. Air Waste Manage. Assoc. 62:408–19. doi:10.1080/10473289.2012.655883
   Web of Science ®Google Scholar
• Kimbrough, E.S., R.W. Baldauf, and N. Watkins. 2013. Seasonal and diurnal analysis of NO2 concentrations from a long-duration study conducted in Las Vegas, Nevada. J. Air Waste Manage. Assoc. 63:934–42. doi:10.1080/10962247.2013.795919
   Web of Science ®Google Scholar
• Leavitt, J.M. 1960. Meteorological considerations in air quality planning. J. Air Pollut. Control Assoc. 10:246–50.
   Google Scholar
• Lee, S.-M., M. Princevac, S. Mitsutomi, and J. Cassmassi. 2009. MM5 simulations for air quality modeling: An application to a coastal area with complex terrain. Atmos. Environ. 43:447–57. doi:10.1016/j.atmosenv.2008.07.067
   Web of Science ®Google Scholar
• Levy, I., Y. Mahrer, and U. Dayan. 2009. Coastal and synoptic recirculation affecting air pollutants dispersion: A numerical study. Atmos. Environ. 43:1991–99. doi:10.1016/j.atmosenv.2009.01.017
   Web of Science ®Google Scholar
• Lozano, R.L., M.A. Hernández-Ceballos, J.F. Rodrigo, E.G.S. Miguel, M. Casas-Ruiz, R. García-Tenorio, and J.P. Bolívar. 2013. Mesoscale behavior of 7Be and 210Pb in superficial air along the Gulf of Cadiz (south of Iberian Peninsula). Atmos. Environ. 80:75–84. doi:10.1016/j.atmosenv.2013.07.050
   Web of Science ®Google Scholar
• Lu, R., and R.P. Turco. 1995. Air pollutant transport in a coastal environment—II. Three-dimensional simulations over Los Angeles basin. Atmos. Environ. 29:1499–518. doi:10.1016/1352-2310(95)00015-Q
   Web of Science ®Google Scholar
• Lu, R., and R.P. Turco. 1996. Ozone distributions over the Los Angeles basin: Three-dimensional simulations with the smog model. Atmos. Environ. 30:4155–76. doi:10.1016/1352-2310(96)00153-7
   Web of Science ®Google Scholar
• Lu, W.Z., and W.J. Wang. 2005. Potential assessment of the “support vector machine” method in forecasting ambient air pollutant trends. Chemosphere 59:693–701. doi:10.1016/j.chemosphere.2004.10.032
   PubMed Web of Science ®Google Scholar
• Luna, A.S., M.L.L. Paredes, G.C.G. de Oliveira, and S.M. Corrêa. 2014. Prediction of ozone concentration in tropospheric levels using artificial neural networks and support vector machine at Rio de Janeiro, Brazil. Atmos. Environ. 98:98–104. doi:10.1016/j.atmosenv.2014.08.060
   Web of Science ®Google Scholar
• Mangia, C., I. Schipa, A. Tanzarella, D. Conte, G.P. Marra, M.M. Miglietta, and U. Rizza. 2010. A numerical study of the effect of sea breeze circulation on photochemical pollution over a highly industrialized peninsula. Meteorol. Appl. 17:19–31. doi:10.1002/met.147
   Web of Science ®Google Scholar
• Martins, L.R. 1965. Significance of skewness and kurtosis in environmental interpretation. J. Sedimentary Res. 35:768–770.doi:10.1306/74D7135C-2B21-11D7-8648000102C1865D
   Google Scholar
• Miao, Y., X.-M. Hu, S. Liu, T. Qian, M. Xue, Y. Zheng, and S. Wang. 2015. Seasonal variation of local atmospheric circulations and boundary layer structure in the Beijing–Tianjin–Hebei region and implications for air quality. J. Adv. Modeling Earth Systems. doi:10.1002/2015MS000522
   Web of Science ®Google Scholar
• Mizak, C., S. Campbell, K. Sopkin, S. Gilbert, M. Luther, and N. Poor. 2007. Effect of shoreline meteorological measurements on NOAA Buoy model prediction of coastal air–sea gas transfer. Atmos. Environ. 41:4304–9. doi:10.1016/j.atmosenv.2006.08.061
   Web of Science ®Google Scholar
• Moazami, S., R. Noori, B.J. Amiri, B. Yeganeh, S. Partani, and S. Safavi. 2016. Reliable prediction of carbon monoxide using developed support vector machine. Atmos. Pollut. Res. 7:412–18. doi:10.1016/j.apr.2015.10.022
   Web of Science ®Google Scholar
• Narassiguin, A., M. Bibimoune, H. Elghazel, and A. Aussem. 2016. An extensive empirical comparison of ensemble learning methods for binary classification. Pattern Anal. Appl. 19:1093–28. doi:10.1007/s10044-016-0553-z
   Web of Science ®Google Scholar
• National Institute of Standards and Technology. 2016. NIST/SEMATECH e-Handbook of Statistical Methods. NIST. http://www.itl.nist.gov/div898/handbook/ (accessed February 3, 2017).
   Google Scholar
• National Oceanic and Atmospheric Administration. 2013. National coastal population report—population trends from 1970 to 2020. NOAA State of the Coast Report Series. NOAA. http://oceanservice.noaa.gov/facts/coastal-population-report.pdf (accessed February 3, 2017).
   Google Scholar
• National Oceanic and Atmospheric Administration. 2015. GSHHG—A global self-consistent, hierarchical, high-resolution geography database. National Oceanic and Atmospheric Administration/National Centers for Environmental Information. https://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html (accessed February 3, 2017).
   Google Scholar
• OICA. 2014. Total world vehicles in use. International Organization of Motor Vehicle Manufacturers. http://www.oica.net/category/vehicles-in-use
   Google Scholar
• OMI. 2012. Ozone Monitoring Instrument (OMI) Data User’s Guide, 66. NASA. https://disc.gsfc.nasa.gov/Aura/data-holdings/additional/documentation/README.OMI_DUG.pdf (accessed February 3, 2017).
   Google Scholar
• Palisade. 2013. @RISK Users Guide: Risk Analysis and Simulation Add-In for Microsoft® Excel. Ithaca, NY: Palisade Software.
   Google Scholar
• Panin, G.N., and T. Foken. 2005. Air–sea interaction including a shallow and coastal zone. J. Atmos. Ocean Sci. 10:289–305. doi:10.1080/17417530600787227
   Google Scholar
• Pereira, M.C., R.C. Santos, and M.C.M. Alvim-Ferraz. 2007. Air quality improvements using European environment policies: A case study of SO2 in a coastal region in Portugal. J. Toxicol. Environ. Health A 70:347–51. doi:10.1080/15287390600884990
   PubMed Web of Science ®Google Scholar
• Rivera-González, L.O., Z. Zhang, B.N. Sánchez, K. Zhang, D.G. Brown, L. Rojas-Bracho, A. Osornio-Vargas, F. Vadillo-Ortega, and M.S. O’Neill. 2015. An assessment of air pollutant exposure methods in Mexico City, Mexico. J. Air Waste Manage. Assoc. 65, 581–591. doi:10.1080/10962247.2015.1020974
   PubMed Web of Science ®Google Scholar
• Scire, J.S., F.R. Robe, M.E. Fernau, and R.J. Yamartino. 2000. A User’s Guide for the CALMET Meteorological Model (Version 5). Earth Tech, Inc. http://www.src.com/calpuff/download/CALMET_UsersGuide.pdf (accessed February 3, 2017).
   Google Scholar
• Seo, J.S., and S. Lee. 2011. Higher-order moments for musical genre classification. Signal Process. 91:2154–57. doi:10.1016/j.sigpro.2011.03.019
   Web of Science ®Google Scholar
• Skamarock, W.C., J.B. Klemp, J. Dudhia, D.O. Gill, D.M. Barker, M.G. Duda, X.-Y. Huang, W. Wang, and J.G. Powers. 2008. A Description of the Advanced Research WRF Version 3, 125. Boulder, CO: National Center for Atmospheric Research.
   Google Scholar
• Strawa, A.W., R.B. Chatfield, M. Legg, B. Scarnato, and R. Esswein. 2013. Improving retrievals of regional fine particulate matter concentrations from Moderate Resolution Imaging Spectroradiometer (MODIS) and Ozone Monitoring Instrument (OMI) multisatellite observations. J. Air Waste Manage. Assoc. 63:1434–46. doi:10.1080/10962247.2013.822838
   Web of Science ®Google Scholar
• Suresh, T., and E. Desa. 2005. Seasonal variations of aerosol over Dona Paula, a coastal site on the west coast of India. Atmos. Environ. 39:3471–80. doi:10.1016/j.atmosenv.2005.01.063
   Web of Science ®Google Scholar
• Tsai, H.-H., C.-S. Yuan, C.-H. Hung, C. Lin, and Y.-C. Lin. 2011. Influence of sea–land breezes on the tempospatial distribution of atmospheric aerosols over coastal region. J. Air Waste Manage. Assoc. 61:358–376. doi:10.3155/1047-3289.61.4.358
   Web of Science ®Google Scholar
• U.S. Environmental Protection Agency. 1990. Clean Air Act, 42 U.S. Code § 7511—Classifications and attainment dates. USEPA. https://www.law.cornell.edu/uscode/text/42/7511 (accessed February 3, 2017).
   Google Scholar
• U.S. Environmental Protection Agency. 2014. Preferred/recommended models—CALPUFF. Support Center for Regulatory Atmospheric Modeling (SCRAM), USEPA. http://www.epa.gov/ttn/scram/dispersion_prefrec.htm#calpuff (accessed February 3, 2017).
   Google Scholar
• U.S. Environmental Protection Agency. 2015. AirData interactive map. USEPA. http://www.epa.gov/airdata/ad_maps.html (accessed February 3, 2017).
   Google Scholar
• U.S. Environmental Protection Agency. 2016. National area and county-level multi-pollutant information, Green Book nonattainment areas. USEPA. https://www.epa.gov/green-book/green-book-national-area-and-county-level-multi-pollutant-information (accessed February 3, 2017).
   Google Scholar
• U.S. Geological Survey. 2000. Index of /srtm/version2_1/SRTM3/Africa. USGS.
   Google Scholar
• U.S. Geological Survey. 2008. Global land cover characterization, 23 June 2008. U.S. Department of the Interior/U.S. Geological Survey. https://lta.cr.usgs.gov/GLCC (accessed March 17, 2017).
   Google Scholar
• Weiss, L., J. Thé, B. Gharabaghi, E.A. Stainsby, and J.G. Winter. 2014. A new dust transport approach to quantify anthropogenic sources of atmospheric PM10 deposition on lakes. Atmos. Environ. 96:380–92. doi:10.1016/j.atmosenv.2014.07.060
   Web of Science ®Google Scholar
• Wu, M., D. Wu, Q. Fan, B.M. Wang, H.W. Li, and S.J. Fan. 2013. Observational studies of the meteorological characteristics associated with poor air quality over the Pearl River Delta in China. Atmos. Chem. Phys. 13:10755–10766. doi:10.5194/acp-13-10755-2013
   Google Scholar
• Yang, J.Y., W.F. Ip, C.M. Vong, and P.K. Wong. 2011. Effect of choice of kernel in support vector machines on ambient air pollution forecasting. Proceedings 2011 International Conference on System Science and Engineering, 552–57. doi:10.5194/acp-13-10755-2013
   Google Scholar
• Zhu, M., and B.W. Atkinson. 2004. Observed and modelled climatology of the land–sea breeze circulation over the Persian Gulf. Int. J. Climatol. 24:883–905. doi:10.1002/(ISSN)1097-0088
   Web of Science ®Google Scholar