function [C,z0]=gaussianPlume(Q, u_ref, h, varargin)
%gaussianPlume Steady-state gaussian plume distribution model
%
% gaussianPlume models the dispersion of a continuous point source, i.e.
% plume, in various conditions and terrains. The output of gaussianPlume
% is a 3-dimensional matrix containing the concentrations of the emitted
% substance over a field with the first dimension (y) representing the
% cross-wind axis, the second dimension (x) the downwind distance, and
% the third dimension (z) the vertical axis. The origin is set at the
% base of the stack unless Briggs plume rise model is used in which case
% the horizontal (x axis) coordinate is offset such that x=0 marks the
% maximum rise of the plume downwind. All units are in (meters,
% grams, seconds) except where noted.
%
% Most of the equations were taken from the ISC3 User's Manual, Volume
% II, available online at
% http://www.epa.gov/scram001/userg/regmod/isc3v2.pdf
%
% The EPA now has newer software for modeling atmospheric dispersion
% including AERMOD and CALPUFF
% http://www.epa.gov/scram001/dispersion_prefrec.htm#aermod as these are
% their preferred/recommended models.
%
% C = gaussianPlume(Q) returns the steady-state Gaussian distribution
% model of a single, continuous point source emitting at a rate of Q
% grams per second for a 50m physical stack height with no calculations
% for plume rise, in rural terrain with stability class "F" in the
% Guifford-Pasquiill scale. Wind speed is assumed to be 1m/s at the stack
% tip (50m).
%
% C = gaussianPlume(Q, u_ref) sets the wind at 1m to be u_ref
%
% C = gaussianPlume(Q, u_ref, h) sets the stack height to h
%
% [C, z0] = gaussianPlume(...) returns the effective stack height in
% meters as z0.
%
% C = gaussianPlume(Q, u_ref, h, ...) allows you to set certain options.
% Options are set by 'option', pairs. The list of
% permissible options are listed below:
%
% {'h_ref', h_ref} Sets the reference height for the wind speed u_ref
% in meters. Default is 1m.
% {'stability', 'class'} Sets Guifford-Pasquill stability class to
% class 'class'. 'class' must be one of 'A', 'B', 'C', 'D', 'E',
% or 'F'. Default is 'F'.
% {'terrain', 't_type'} Sets the terrain to one of 'rural' or
% 'urban'. Default is 'urban'
% {'p', p} Sets the scaling factor for change in wind as a
% function of altitude to p. Default is 0.4.
% {'plum_rise_model', 'model'} Sets the model to use for calculation
% of plume rise. 'model' is one of 'none', 'CONCAWE',
% 'CarlsonMoses', 'Holland', or 'Briggs'. Default is 'none'.
% {'mw', mw} Sets the molecular weight of the exhaust for plume rise
% calculations. Specify in atomic mass units. Default is average
% of air, 30g/mole.
% {'amb_temp', Ta} Sets the ambient temperature in degrees Celsius.
% Default is 25C. Alternatively, you may specify {'Ta',Ta}.
% {'stack_temp', Ts} Sets the stack temperature in degrees Celsius.
% Default is 200C. Alternatively, you may specify {'Ts',Ts}.
% {'stack_diameter', ds} Sets the stack diameter (in meters). Default
% is 10m. Alternatively, you may specify {'ds',ds}.
% {'specific_heat', Cp} Sets the specific heat of the exhaust gas in
% J/degree Celsius/g. Default is 1.020 (constant pressure Cp for
% dry air). Alternatively, you may specify {'Cp',Cp}.
% {'amb_pres', Pa} Sets ambient pressure in millibars. Default is
% 1010mb. Alternatively, you may specify {'Pa',Pa}.
% {'stack_pres', Ps} Sets stack tip pressure in millibars. Default is
% 1010mb. Alternatively, you may specify {'Ps',Ps}.
% {'stack_velocity', vs} Sets the stack exit velocity in m/s. Default
% is 1m/s. Alternativelt, you may specify {'vs',vs}.
% {'lapse', eta} Sets the lapse rate for stable conditions (class E or
% F). Alternatively, you may specify {'eta',eta}.
% {'reflection', true} enables ground reflection whereas
% {'reflection', false} disables ground reflection
% {'deposition', true} models dry deposition whereas
% {'deposition', false} disables dry deposition modeling
% {'term_velocity', vt} Sets terminal/settling velocity in m/s.
% Default is settling velocity for PM2.5, 0.5cm/s. Synonyms
% include {'settling_velocity', vt} or simply {'vt', vt}.
% {'X', x} Sets sampling points at the downwind distances. x is a
% vector specifying the downwind distances to evaluate for C in
% meters. Default is 1 to 5km in 100m resolution.
% {'Y', y} Same as above except for the y-axis (cross-wind)
% {'Z', z} Same as above except for the vertical axis
%
% Example
% ------------------------------
% C=gaussianPlume(500, 5, 50, 'h_ref', 10, 'reflection', false, ...
% 'deposition', false, 'amb_pres', 1103, 'amb_temp', 22);
% Computes the steady-state Gaussian-distribution of an emission at a
% rate of 500g/s with average wind speed of 5m/s at 10m from a
% physical stack height of 50m. No plume rise, reflection, nor
% deposition will be modeled. The data will be sampled in 100m
% resolution across the downwind and crosswind axes from 1 to 5km and
% 1 to 1km in the vertical axis.
% Check for missing arguments. Set to empty so we don't get undefined
% errors.
if(nargin<2)
u_ref=[];
end
if(nargin<3)
h=[];
end
if(isempty(u_ref))
% Default wind speed is 1m/s
u_ref=1;
end
if(isempty(h))
% Default stack height is 50m
h=50;
end
% Set default values
stability='F'; % Guifford-Pasquill stability class
plume_rise_model='none'; % Plume rise models: 'none', 'CONCAWE', 'Briggs'
reflection=false; % Model ground reflection?
deposition=false; % Model dry deposition?
terrain='urban'; % Terrain type, 'urban' or 'rural'
p=0.4; % Wind speed variation as a function of altitude, from 0.07 to 0.6
mw=30; % Average molecular weight of the efflux, typically air (g/mol)
Ta=25; % Ambient temperature (degrees C)
Ts=200; % Stack tip temperature (degrees C)
ds=10; % Stack tip diameter (meters)
Cp=1.020; % Specific heat of efflux (J/g degrees Kelvin). 1.020 is for air (http://www.efunda.com/materials/common_matl/show_gas.cfm?MatlName=Air0C)
Pa=1010; % Ambient pressure (millibars = 100 N/m^2 = 100 [kg*m/s^2]/m^2 = 1e5 [g/m/s^2])
Ps=1010; % Stack tip pressure (millibar)
x_grid=[1 100:100:5e3]; % Downwind distance of each sampled point (m)
y_grid=[1 100:100:4e3]; % Crosswind distance of each sampled point (m)
z_grid=[1 100:100:1e3]; % Altitude above ground level of each sampled point (m)
vt=0.005; % Settling (terminal) velocity in m/s
vs=1.5; % Stack exit velocity (m/s)
h_ref=1; % Altitude above ground level of measured windspeed (u_ref) (in m)
eta=0.035; % Lapse rate (degrees C/m)
% The rest of the arguments are set by optional 'string' 'value' pairs
for argnum=1:2:length(varargin)
switch(varargin{argnum})
case 'stability'
stability=varargin{argnum+1};
case 'plume_rise_model'
plume_rise_model=varargin{argnum+1};
case 'reflection'
reflection=varargin{argnum+1};
case 'deposition'
deposition=varargin{argnum+1};
case 'terrain'
terrain=varargin{argnum+1};
case 'p'
p=varargin{argnum+1};
case 'mw'
mw=varargin{argnum+1};
case 'amb_temp'
Ta=varargin{argnum+1};
case 'Ta'
Ta=varargin{argnum+1};
case 'stack_temp'
Ts=varargin{argnum+1};
case 'Ts'
Ts=varargin{argnum+1};
case 'stack_diameter'
ds=varargin{argnum+1};
case 'ds'
ds=varargin{argnum+1};
case 'specific_heat'
Cp=varargin{argnum+1};
case 'Cp'
Cp=varargin{argnum+1};
case 'amb_pres'
Pa=varargin{argnum+1};
case 'Pa'
Pa=varargin{argnum+1};
case 'stack_pres'
Ps=varargin{argnum+1};
case 'Ps'
Ps=varargin{argnum+1};
case 'X'
x_grid=varargin{argnum+1};
case 'Y'
y_grid=varargin{argnum+1};
case 'Z'
z_grid=varargin{argnum+1};
case 'term_velocity'
vt=varargin{argnum+1};
case 'vt'
vt=varargin{argnum+1};
case 'settling_velocity'
vt=varargin{argnum+1};
case 'stack_velocity'
vs=varargin{argnum+1};
case 'vs'
vs=varargin{argnum+1};
case 'h_ref'
h_ref=varargin{argnum+1};
case 'lapse'
eta=varargin{argnum+1};
case 'eta'
eta=varargin{argnum+1};
otherwise
warn('gaussianPlume:args', ['Unrecognized option: ', varargin{argnum}]);
end
end
% Setup some physical constants
Rc=8.3144e3; % Universal gas constant in mJ/mol/K = [g*m^2/s^2/mol/K]
gc=9.80616; % Graviational acceleration, m/s^2
% For internal calculations, convert stack and ambient temperatures
% into degrees Kelvin
Ta=Ta+273;
Ts=Ts+273;
% Force x_grid, y_grid, and z_grid to be row vectors
if(size(x_grid, 1)==1)
x_grid=x_grid';
end
if(size(y_grid, 1)==1)
y_grid=y_grid';
end
if(size(z_grid, 1)==1)
z_grid=z_grid';
end
% Determine wind velocity (us) at tip of stack using power law for wind
% speed measured at height h_ref (m) to be u_ref (m/s)
us=((h/h_ref)^p)*u_ref; % in m/s
% ISC3 manual warns of issues with us<1m/s
if(us<1)
warning('gaussianPlume:badus','Stack height wind speed, u_s, is %.3fm/s which is less than 1m/s.', us);
end
% Compute mass flow using ideal gas law assumption
% PV=nRT (ideal gas law) <==> n=PV/RT
% m_dot=[m^2]*[m/s]*[g/m/s^2]*[g/mol]/[(mJ/mol/K)*K]
% =[m^3][1/s]*[g/m/s^2]*[g/mol]/[g*m^2/s^2/mol]
% =[g/s]
m_dot=pi*(ds/2)^2*vs*(Ps*1e5)*mw/(Rc*Ts); % g/s
% Now compute heat flux
% Qh=[g/s]*[J/(g*degK)]*degK
% =[J/s]
Qh=m_dot*Cp*(Ts-Ta); % J/s
% Compute plume rise
switch(plume_rise_model)
case 'none'
% The rise is then set to zero
plume_rise=0;
x_f=0;
case 'CONCAWE'
% TODO: Verify
plume_rise=4.71*(Qh^0.44/us^0.694);
x_f=0;
case 'Holland'
% TODO: Verify
plume_rise=vs*ds/us*(1.5+0.01*Qh/(vs*ds));
x_f=0;
case 'CarlsonMoses'
% TODO: Verify
plume_rise=0.029*vs*ds/us+2.62*(Qh^0.5/us);
x_f=0;
case 'Briggs'
% Compute buoyancy and momentum factors
% Fb=[m/s^2]*[m/s]*[m^2]*[degK]/[degK]
% =[m^4]/[s^3]
Fb=gc*vs*ds^2*(Ts-Ta)/(4*Ts);
% Fm=[m^2/s^2]*[m^2]*[degK]/[degK]
% =[m^4]/[s^2]
Fm=vs^2*ds^2*Ta/(4*Ts);
% Compute stability parameter if stable
if(stability=='E' || stability=='F')
% s = [m/s^2]*[degK/m]/[degK]
% = [1/s^2]
s=gc*eta/Ta; % 1/s^2
% Can't perform unit analysis, factor 0.019... is unknown
dTc=0.019582*Ts*vs*sqrt(s);
% Check if buoyancy dominated (thermal) or momentum (kinetic)
if( (Ts-Ta)>=dTc)
% Buoyancy dominates
x_f=2.0715*us/sqrt(s);
plume_rise=2.6*(Fb/(us*s))^(1/3);
else
x_f=0;
% Note that we also evaluate unstable/neutral and select
% the lower value
plume_rise=min(1.5*(Fm/(us*sqrt(s)))^(1/3), 3*ds*vs/us);
end
else
% Unstable or neutral
if(Fb<55)
% Check for buoyancy dominated or momentum
dTc=0.0297*Ts*vs^(1/3)/ds^(2/3);
if( (Ts-Ta)>=dTc )
% Buoyancy dominated
x_f=49*Fb^(5/8);
plume_rise=21.425*Fb^(3/4)/us;
else
% Momentum dominated
x_f=0;
plume_rise=3*ds*vs/us;
% Check
if(vs/us<=4)
warning('gaussianPlume:Briggs', 'Momentum rise model selected but may not be accurate due to low stack exit velocity');
end
end
else
% Brigg's equations seem to diverage at Fb==55
dTc=0.00575*Ts*vs^(2/3)/ds^(1/3);
if( (Ts-Ta)>=dTc )
% Offset horizontal downwind distance for distance to
% max height (rise)
x_f=119*Fb^(2/5);
plume_rise=38.71*Fb^(3/5)/us;
else
x_f=0;
plume_rise=3*ds*vs/us;
if(vs/us<=4)
warning('gaussianPlume:Briggs', 'Momentum rise model selected but may not be accurate due to low stack exit velocity');
end
end
end
end
otherwise
warning('gaussianPlume:riseModel', ['Unrecognized plume rise model specified: ', plume_rise_model]);
plume_rise=0;
x_f=0;
end
% Set effective stack height
z0=h+plume_rise;
% Offset the downwind direction to take into account distance to max rise
% TODO: Substitute appropriate equations for distance less than final rise
% per ISC3v2 manual for buoyancy dominated conditions
x_grid=x_grid-x_f;
% Compute the dispersion coefficients
switch(terrain)
case 'rural'
% Pasquill-Gifford curves
switch(stability)
case 'A'
% [c, d] coefficients
coeffs_y=[24.1670, 2.5334];
% [x a b] matrix
coeffs_z=[0.10 122.800 0.94470;
0.16 158.080 1.05420;
0.21 170.220 1.09320;
0.26 179.520 1.12620;
0.31 217.410 1.26440;
0.41 258.890 1.40940;
0.51 346.750 1.72830;
3.11 453.850 2.11660;
inf nan nan];
case 'B'
coeffs_y=[18.3330, 1.8096];
coeffs_z=[0.20 90.673 0.93198;
0.40 98.483 0.98332;
inf 109.300 1.09710];
case 'C'
coeffs_y=[12.5000, 1.0857];
coeffs_z=[inf 61.141 0.91465];
case 'D'
coeffs_y=[8.3330, 0.72382];
coeffs_z=[0.31 34.459 0.86974;
1.01 32.093 0.81066;
3.01 32.093 0.64403;
10.01 33.504 0.60486;
30.00 36.650 0.56589;
inf 44.053 0.51179];
case 'E'
coeffs_y=[6.2500, 0.54287];
coeffs_z=[0.10 24.260 0.83660;
0.31 23.331 0.81956;
1.01 21.628 0.75660;
2.01 21.628 0.63077;
4.01 22.534 0.57154;
10.01 24.703 0.50527;
20.01 26.970 0.46713;
40.00 35.420 0.37615;
inf 47.618 0.29592];
case 'F'
coeffs_y=[4.1667, 0.36191];
coeffs_z=[0.21 15.209 0.81558;
0.71 14.457 0.78407;
1.01 13.953 0.68465;
2.01 13.953 0.63227;
3.01 14.823 0.54503;
7.01 16.187 0.46490;
15.01 17.836 0.41507;
30.01 22.651 0.32681;
60.00 27.074 0.27436;
inf 34.219 0.21716];
otherwise
error('gaussianPlume:stability', ['Unknown stability class ', stability]);
end
% Construct sigma_y vector along the x-axis
% Note that x should be in kilometers (x_grid/1.e3)
sigma_y=465.11628.*(x_grid./1e3).*tan(0.017453293.*(coeffs_y(1)-coeffs_y(2).*log(x_grid./1e3))); % m
% Construct sigma_z vector along the x-axis
prev_boundary=0; % in km
% Pre-allocate (should be same size as x_grid since all tables end
% with 'inf')
sigma_z=nan(size(x_grid));
for section=1:size(coeffs_z, 1)
idx=find(prev_boundary<=(x_grid./1e3) & (x_grid./1e3)5e3 for stability classes A-C
switch(stability)
case {'a', 'b', 'c'}
sigma_y(sigma_y>5e3)=5e3;
end
%sigma_z=shiftdim(sigma_z, -1);
case 'urban'
% Pasquill-Gifford with urban fit (McElroy-Pooler)
switch(stability)
case 'A'
coeffs_y=0.32;
coeffs_z=[0.24 1 0.001 0.5];
case 'B'
coeffs_y=0.32;
coeffs_z=[0.24 1 0.001 0.5];
case 'C'
coeffs_y=0.22;
coeffs_z=[0.20 1 0 0];
case 'D'
coeffs_y=0.16;
coeffs_z=[0.14 1 0.0003 -0.5];
case 'E'
coeffs_y=0.11;
coeffs_z=[0.08 1 0.0015 -0.5];
case 'F'
coeffs_y=0.11;
coeffs_z=[0.08 1 0.0015 -0.5];
otherwise
error('gaussianPlume:stability', ['Unrecognized stability class ', stability]);
end
% Construct sigma_y along x-axis
sigma_y=coeffs_y(1).*x_grid.*(1+0.0004.*x_grid).^(-0.5);
sigma_y=shiftdim(sigma_y, -1);
% Construct sigma_z along x-axis
sigma_z=coeffs_z(1).*x_grid.*(coeffs_z(2)+coeffs_z(3).*x_grid).^coeffs_z(4);
sigma_z=shiftdim(sigma_z, -1);
otherwise
error('gaussianPlume:terrain', ['Unrecognized terrain option ', terrain]);
end
% Now we have a choice when we compute Gaussian distribution. We
% can either save memory and use a for-loop, or speed the code up
% at the expense of more memory requirement. Since memory is cheap
% these days, we'll go ahead and optimize for speed in favor of
% size.
% Now replicate sigma vectors into 3D matrices
sigma_y=repmat(sigma_y, [length(y_grid) 1 length(z_grid)]);
sigma_z=repmat(sigma_z, [length(y_grid) 1 length(z_grid)]);
% Create wind matrix (scaled according to altitude)
u_matrix=((z_grid./h_ref).^p).*u_ref; % [m/s]
% Move over into z-dimension
u_matrix=shiftdim(u_matrix, -2);
u_matrix=repmat(u_matrix, [length(y_grid) length(x_grid) 1]);
% At this point, we will scale x_grid/y_grid/z_grid into full 3-D matrices
% Because we now manipulate them, first save their original lengths
sx=size(x_grid, 1);
sy=size(y_grid, 1);
sz=size(z_grid, 1);
x_grid=repmat(shiftdim(x_grid, -1), [sy 1 sz]);
y_grid=repmat(y_grid, [1 sx sz]);
z_grid=repmat(shiftdim(z_grid, -2), [sy sx 1]);
% Setup deposition
if(deposition)
% Note here we had to massage x_grid into full 3-D matrix
dz_dep=vt.*x_grid./u_matrix;
else
dz_dep=zeros(size(x_grid));
end
if(reflection)
% Reflection scales everything by 2
r=2;
else
r=1;
end
% Now compute steady-state Gaussian dispersion
% (Note everything inside the exp() becomes unitless)
% C=[g/s]/[m/s*m*m]=[g/m^3]
C=r.*Q./ ...
(2.*pi.*u_matrix.*sigma_y.*sigma_z).*exp( ...
(-y_grid.^2)./(2.*sigma_y.^2) - ((z_grid-z0-dz_dep).^2)./(2.*sigma_z.^2));