CANARINA:
DISPER:
Commands Algorithms I Algorithms II Algorithms III Algorithms IV Algorithms V Emissions
The mathematical model that the software uses provides options to model emissions from a wide range of sources that might be present at industrial areas and urban areas. The model is analogous to ISC3 from EPA. The basis of the model is the straight-line, steady-state Gaussian plume equation, which is used to model simple point source emissions from stacks, roads, storage piles and conveyor belts. Emission sources are categorized into three basic types of sources: point sources, line sources and area sources. The algorithms used to model each of these source types are described in detail in the following sections. The DISPER dispersion model accepts meteorological data records to define the conditions for plume rise and transport. The model estimates the concentration value for each source and receptor combination and calculates user-selected averages.
Point source emissions
The model uses a steady-state Gaussian plume equation to model emissions from point sources, such as stacks.
The Gaussian Equation
The model for stacks uses the steady-state Gaussian plume equation for a continuous elevated source. For each source, the origin of the stack coordinate system is placed at the ground surface at the base of the stack. The x axis is positive in the downwind direction, the y axis is crosswind (normal) to the x axis and the z axis extends vertically. The fixed receptor locations are converted to each source's coordinate system. The hourly concentrations calculated for each source at each receptor are summed to obtain the total concentration produced at each receptor by the combined source emissions.
For a Gaussian plume, the hourly concentration at downwind distance x (meters) and crosswind distance y (meters) is given by:
c =(Q K V D/2 pi us sigy sigz) exp[-0.5(y/sigy)2] (1)
where:
Q= pollutant emission rate (mass per unit time)
K= a scaling coefficient to convert calculated concentrations to desired units (default value of 1 x 106 for Q in g/s and concentration in µg/m3)
V= vertical term (See Section 1.1.6)
D= decay term (See Section 1.1.7)
sigy,sigz= standard deviation of lateral and vertical concentration distribution (m) (See Section 1.1.5)
us= mean wind speed (m/s) at release height (See Section 1.1.3)
Downwind and Crosswind Distances
The model uses a Cartesian receptor network. All receptor points are converted to Cartesian (X,Y) coordinates prior to performing the dispersion calculations. In the Cartesian coordinate system, the X axis is positive to the east of the user-specified origin and the Y axis is positive to the north. The user must define the location of each source with respect to the origin of the grid using Cartesian coordinates. If the X and Y coordinates of the source are X(S) and Y(S), the downwind distance x to the receptor, along the direction of plume travel, is given by:
x=-[X(R)-X(S)]sin(WD)-[Y(R)-Y(S)]cos(WD) (2)
where WD is the direction from which the wind is blowing. The downwind distance is used in calculating the distance-dependent plume rise and the dispersion parameters. The crosswind distance y to the receptor from the plume centerline is given by:
y=-[X(R)-X(S)]cos(WD)-[Y(R)-Y(S)]sin(WD) (3)
Wind Speed Profile
The wind power law is used to adjust the observed wind speed, uref, from a reference measurement height, zref, to the stack or release height, hs. The stack height wind speed, us, is used in the Gaussian plume equation. The power law equation is of the form:
us=uref(hs/zref)p (4)
where p is the wind profile exponent. Values of p may be provided by the user as a function of stability category and wind speed class. Default values are as follows:
|
Stability Category |
Rural Exponent |
Urban Exponent |
|
A |
0.07 |
0.15 |
|
B |
0.07 |
0.15 |
|
C |
0.10 |
0.20 |
|
D |
0.15 |
0.25 |
|
E |
0.35 |
0.30 |
|
F |
0.55 |
0.30 |
The stack height wind speed, us, is not allowed to be less than 1.0 m/s.
Algorithms I - Algorithms II - Algorithms III - Algorithms IV - Algorithms V
Canarina Algoritmos Numéricos, S.L.
Environmental software · environmental software solutions
Canary Islands, Spain
e-mail: Contact us
European Union · network on Pollution
Member of MAPO: European network on Marine Pollution.
Project funded by the European Commission
through the 6th Framework Programme for Research and Development
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DISPER software solutions: This application has been used in great number of environmental reports, air pollution courses and air pollution studies in the last years. We currently have users in more than 10 countries. Air monitoring reports in Chile, Uruguay and Argentina:
Chile: particulates PM10 monitoring - SOx pollutant monitoring - SO2 air monitoring - ozone air monitoring - air monitoring in Arica - CH4 air monitoring in Antofagasta - NH3 air monitoring in Calama - CH4 air monitoring in Chillán - CH3 air monitoring in Coihaique - Cl2 air monitoring in Concepción - CH4 air monitoring in Copiapo - Cl2 air monitoring in Iquique - CH4 air monitoring in La Serena - SO4 air monitoring in Los Ángeles - CH4 air monitoring in Mejillones - SO4 air monitoring in Pucón - Cl2 air monitoring in Punete Alto - SO4 air monitoring in Puerto Montt - Cl2 air monitoring in Puerto Varas - CH3 air monitoring in Punta Arenas - SO4 air monitoring in San Bernardo - Cl2 air monitoring in Santiago - Cl2 air monitoring in Talcahuano - CH4 air monitoring in Talca - Cl2 air monitoring in Temuco - NH3 air monitoring in Valdivia - NH3 air monitoring in Valparaíso - CH3 air monitoring in Viña del Mar - NO2 air monitoring - NH3 emission monitoring - particles PM2.5 monitoring
Uruguay: particulate PM10 air monitoring - aerosol monitoring - NOx pollutants monitoring - PM10 air monitoring in Artigas - HNO3 air monitoring in Canelones - Cl2 air monitoring in Colonia del Sacramento - SO4 air monitoring in Durazno - HNO3 air monitoring in Florida - NH3 air monitoring in Fray Bentos - Cl2 air monitoring in Las Piedras - NH3 air monitoring in Maldonado - PM10 air monitoring in Melo - air monitoring in Mercedes - Cl2 air monitoring in Minas - NH3 air monitoring in Montevideo - HNO3 air monitoring in Nueva Palmira - CH4 air monitoring in Paysandú - PM10 air monitoring in Punta del Este - SO4 air monitoring in Rivera - Cl2 air monitoring in Rocha - NH3 air monitoring in Salto - Cl2 air monitoring in San José - PM10 air monitoring in Treinta y Tres - NH3 air monitoring in Trinidad - microbiological air monitoring - particle PM2.5 air monitoring - stack gas emissions monitoring
Argentina:
PM10 air monitoring particulate matter air monitoring - ambient microdust monitoring - bioaerosol air monitoring - HNO3 air monitoring in Buenos Aires - CH4 air monitoring in Córdoba - CH4 air monitoring in Rosario - NH3 air monitoring in suelo Mendoza - CH3 air monitoring in La Plata - NH3 air monitoring in San Miguel de Tucumán - Cl2 air monitoring in Mar del Plata - CH3 air monitoring in Salta - air monitoring in Santa Fe - air monitoring in San Juan - HNO3 air monitoring in Resistencia - Cl2 air monitoring in Neuquen - HNO3 air monitoring in Santiago del Estero - CH4 air monitoring in Corrientes - HNO3 air monitoring in Bahia Blanca - PM2.5 air monitoring - diffusive Cl2 simulation - dust air monitoring
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