title: Mapping air quality zones for coastal urban centers.
abstract: This study presents a new method that incorporates modern air dispersion models allowing local terrain and land-sea breeze effects to be considered along with political and natural boundaries for more accurate mapping of air quality zones (AQZs) for coastal urban centers. This method uses local coastal wind patterns and key urban air pollution sources in each zone to more accurately calculate air pollutant concentration statistics. The new approach distributes virtual air pollution sources within each small grid cell of an area of interest and analyzes a puff dispersion model for a full year's worth of 1-hr prognostic weather data. The difference of wind patterns in coastal and inland areas creates significantly different skewness (S) and kurtosis (K) statistics for the annually averaged pollutant concentrations at ground level receptor points for each grid cell. Plotting the S-K data highlights grouping of sources predominantly impacted by coastal winds versus inland winds. The application of the new method is demonstrated through a case study for the nation of Kuwait by developing new AQZs to support local air management programs. The zone boundaries established by the S-K method were validated by comparing MM5 and WRF prognostic meteorological weather data used in the air dispersion modeling, a support vector machine classifier was trained to compare results with the graphical classification method, and final zones were compared with data collected from Earth observation satellites to confirm locations of high-exposure-risk areas. The resulting AQZs are more accurate and support efficient management strategies for air quality compliance targets effected by local coastal microclimates.
  
  
  
  A novel method to determine air quality zones in coastal urban areas is introduced using skewness (S) and kurtosis (K) statistics calculated from grid concentrations results of air dispersion models. The method identifies land-sea breeze effects that can be used to manage local air quality in areas of similar microclimates.
authors.affiliation: a School of Engineering , University of Guelph , Guelph , Ontario , Canada.
authors.forename: Brian
authors.initials: B
authors.lastname: Freeman
authors.affiliation: a School of Engineering , University of Guelph , Guelph , Ontario , Canada.
authors.forename: Bahram
authors.initials: B
authors.lastname: Gharabaghi
authors.affiliation: a School of Engineering , University of Guelph , Guelph , Ontario , Canada.|b Lakes Environmental , Waterloo , Ontario , Canada.
authors.forename: Jesse
authors.initials: J
authors.lastname: Thé
authors.affiliation: b Lakes Environmental , Waterloo , Ontario , Canada.
authors.forename: Mohammad
authors.initials: M
authors.lastname: Munshed
authors.affiliation: c Kuwait Integrated Environmental Management Systems , United Nations Development Program , Kuwait City , Kuwait.
authors.forename: Shah
authors.initials: S
authors.lastname: Faisal
authors.affiliation: d Department of Air Quality and Follow Up , Kuwait Environment Public Authority , Kuwait City , Kuwait.
authors.forename: Meshal
authors.initials: M
authors.lastname: Abdullah
authors.affiliation: d Department of Air Quality and Follow Up , Kuwait Environment Public Authority , Kuwait City , Kuwait.
authors.forename: Athari
authors.initials: A
authors.lastname: Al Aseed
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D000397:Air Pollution / Q000032:analysis*; D001493:Bathing Beaches; D002947:Cities; D004784:Environmental Monitoring / Q000379:methods*; D062305:Geographic Mapping; D015233:Models, Statistical; D008962:Models, Theoretical; D014919:Wind
chemical_list: D000393:Air Pollutants