Improved Simulation of Advection and Dispersion of Urban Air Toxics

EPA Grant Number: R827929
Title: Improved Simulation of Advection and Dispersion of Urban Air Toxics
Investigators: Walcek, Chris
Institution: The State University of New York at Albany
EPA Project Officer: Chung, Serena
Project Period: December 1, 1999 through December 1, 2002 (Extended to August 2, 2004)
Project Amount: $347,991
RFA: Urban Air Toxics (1999) RFA Text |  Recipients Lists
Research Category: Air Quality and Air Toxics , Air


Currently, concentrations of toxic or harmful pollutants downwind of pollution emission areas are evaluated using Gaussian Plume or puff dispersion models. While these models are based on sound scientific principles, the circumstances over which they can be validly and accurately applied are somewhat limited due to assumptions about the steady-state nature of the wind and turbulence properties of the atmosphere that is transporting and diffusing the pollutants. The goal of this research effort is to develop more accurate methods of calculating the (1) transport, and (2) diffusion of air pollutants downwind of pollution emission areas.


(A) Transport: One factor which makes calculation of the transport of pollutants difficult to precisely quantify is the extremely high temporal and spatial variability of wind speeds and directions which transport pollutants. Many simple Gaussian plume models assume that wind speed and directions are constant for periods of up to an hour or so, and spatially uniform throughout the depth of the planetary boundary layer (PBL) and horizontally uniform out to distances 10-20 km downwind of emissions areas. Unfortunately, many observations show extremely high variability of wind speeds and directions both vertically and horizontally.

It is possible to quantify transport of pollutants under variable-flow conditions using both Lagrangian or Eulerian approaches. Lagrangian approaches essentially follow a particular puff of pollution as it moves through the flowing atmosphere. As long as the wind speeds and directions do not change appreciably over the vertical and horizontal extent of the dispersing puff, which is relatively small initially, such an approach is extremely accurate. Unfortunately, turbulence in the planetary boundary layer disperses puffs of pollution to appreciable sizes within a relatively short time. It is not unusual to find pollution vertically dispersed throughout the entire depth of the PBL within 20 minutes to an hour following emission. When puffs become this large, there is an appreciable change in the wind speeds and directions, especially in the vertical, across the puff dimensions. Therefore the top portions of the puff will blow in one direction, while the base portions of the puff will move with different wind speeds and directions. Under these shearing conditions, the concept that a given parcel moves with the mean motion of the parcel center of mass becomes vague, and appreciable errors can result when calculating concentrations in the vicinity of the shearing puff.

Eulerian approaches to quantifying pollution transport subdivide the atmosphere into a fixed grid of calculation points where the fundamental advective tendency equation is explicitly solved. Pollution concentrations are calculated throughout a 2-D or 3-D grid with relatively high time resolution. Unfortunately, computer storage limitations restrict the size of individual cells in a Eulerian grid about 1/10th - 1/100th the horizontal domain size considered. Therefore if one is interested in quantifying transport of pollutants out to distances 30 km from an emission point, an Eulerian grid with a resolution of 300-3000 m is usually constructed. Individual Apuffs@ of pollution will be smaller than this grid size for an appreciable fraction of the time while they are being transported out to the 30 km range. It is well known that existing Eulerian transport algorithms contain appreciable errors known as Anumerical diffusion@ when calculating transport of features that are not resolved by more than several grid cells.

We have developed an extremely accurate numerical advection algorithm that removes 50-80% of the numerical diffusion that is inherent in even the highest-order accurate Eulerian advection algorithms (Walcek & Aleksic, 1998, Atmospheric Environment 32, p3863; Walcek, 2000, Journal of Geophysical Research, 105, in press). Thus one of the major shortcomings of Eulerian advection calculations near pollution emission sources is largely overcome. It is now possible to use Eulerian approaches to accurately quantify pollution dispersion while the puffs of pollution are relatively small. For this research effort, we will devise a fine-resolution 3-D Eulerian grid, and when used with our low-diffusive advection algorithm, we can reliably investigate the transport and diffusion downwind of pollution sources.

One goal of this study is to further develop and refine our highly accurate and computationally efficient algorithm for simulating the advection of poorly-resolved point sources of toxic pollution in urban environments. Our advection scheme is absolutely monotonic, mass conserving, and is capable of advecting poorly-resolved features with errors that are appreciably smaller than the best algorithms used today. The innovative feature of this algorithm that enhances its accuracy is the use of a minor flux adjustment near local extremes of a tracer distribution to reduce numerical diffusion away from sharp peaks of pollution. During this research effort, we will investigate a similar flux adjustment technique around sharp gradients to further improve the accuracy of this scheme.

B: Diffusion: There is a complex interaction between advection of pollutants by larger-scale winds and smaller-scale dispersion of pollutants by turbulent diffusion. As turbulent diffusion mixes pollutants, the pollutants Aexperience@ different wind speeds and directions that can deform an air parcel in complex manners that cannot be explained with simple dispersion models.

This highly accurate numerical advection algorithm described above will be used within an urban-scale 3-dimensional model of the PBL to simulate the transport and shear-induced diffusion of point sources of pollutants in urban areas. It is hypothesized that small amounts of vertical shear of the horizontal wind direction coupled with small amounts of isotropic turbulence will induce substantial horizontal dispersion that is currently poorly understood and simulated by urban-scale dispersion models which assume Auniform@ horizontal dispersion coefficients without recognizing that there is a preferential direction of horizontal dispersion aligned with the vertical wind shear vector.

Expected Results:

The results of this research effort will be a highly accurate numerical advection algorithm for use in many applications, as well as a more thorough understanding of dispersion within a PBL containing wind shear. Methods and algorithms developed by this project could be used by other models to provide more accurate exposure and risk assessments of toxic pollutants, and could also be used to improve the accuracy of source-apportionment investigations.

Publications and Presentations:

Publications have been submitted on this project: View all 16 publications for this project

Journal Articles:

Journal Articles have been submitted on this project: View all 3 journal articles for this project

Supplemental Keywords:

ambient air, atmosphere, ozone, acid deposition, tropospheric, precipitation, chemical transport, exposure, VOC, oxidants, sulfates, environmental chemistry, physics, engineering, mathematics, modeling, general circulation models, climate models., RFA, Scientific Discipline, Air, air toxics, Environmental Chemistry, Chemistry, climate change, tropospheric ozone, fate and transport, urban air toxics, urban air, Lagrangian approach, air pollutants, plumes, stratospheric ozone, air quality models, ozone, VOCs, climate variations, urban air pollutants, air pollution models, circulation model, atmospheric pollutant loads, Volatile Organic Compounds (VOCs), air quality, climate variability, dispersion modeling

Progress and Final Reports:

  • 2000 Progress Report
  • 2001 Progress Report
  • 2002 Progress Report
  • 2003
  • Final Report