This document provides information about air quality dispersion modeling and data interpretation. It summarizes the key steps in the modeling process including: (1) using Gaussian dispersion models to calculate pollutant concentrations from point sources based on parameters like emission rates, stack characteristics, and meteorological conditions; (2) accounting for plume rise using equations that consider factors like exit velocity and temperature; and (3) interpreting the results of a case study modeling sulfur dioxide emissions from multiple stacks at an industrial facility to evaluate model predictive capability.
AIR POLLUTION CONTROL course material by Prof S S JAHAGIRDAR,NKOCET,SOLAPUR for BE (CIVIL ) students of Solapur university. Content will be also useful for SHIVAJI and PUNE university students
Air pollution measurements give important, quantitative information about ambient concentrations and deposition, but they can only describe air quality at specific locations and times, without giving clear guidance on the identification of the causes of the air quality problem.
EIAM unit 5(Assessment of Impact of development Activities on Vegetation an...GantaKalyan1
Assessment of Impact of development Activities on Vegetation and wildlife- Environmental Impact
of Deforestation. Environmental Risk Assessment and Risk management in EIA: Risk assessment and
treatment of uncertainty-key stages in performing an Environmental Risk Assessment-advantages of
Environmental Risk Assessment.
AIR POLLUTION CONTROL course material by Prof S S JAHAGIRDAR,NKOCET,SOLAPUR for BE (CIVIL ) students of Solapur university. Content will be also useful for SHIVAJI and PUNE university students
Air pollution measurements give important, quantitative information about ambient concentrations and deposition, but they can only describe air quality at specific locations and times, without giving clear guidance on the identification of the causes of the air quality problem.
EIAM unit 5(Assessment of Impact of development Activities on Vegetation an...GantaKalyan1
Assessment of Impact of development Activities on Vegetation and wildlife- Environmental Impact
of Deforestation. Environmental Risk Assessment and Risk management in EIA: Risk assessment and
treatment of uncertainty-key stages in performing an Environmental Risk Assessment-advantages of
Environmental Risk Assessment.
AIR POLLUTION CONTROL course material by Prof S S JAHAGIRDAR,NKOCET,SOLAPUR for BE (CIVIL ) students of Solapur university. Content will be also useful for SHIVAJI and PUNE university students
Meteorological Factors Influencing Air Pollution And Atmospheric Stability ...NiranjanHiremath12
1. Meteorology2.Air Pollution Meteorology3.Benefits Of Analyzing Meteorological Data
4. Meteorological Factors Influencing Air Pollution
4.1 Primary parameters
4.1.1 Wind Direction And Speed
4.1.2 Temperature inversion
4.1.3 Atmospheric Stability
4.1.4 Mixing Height or Mixing Depth
4.2 Secondary parameter
4.2.1 Precipitation
4.2.2 Humidity
4.2.3 Solar radiation
4.2.4 Visibility
5. Methods for measurement of meteorological variable
6. Lapse Rate in Air Pollution Meteorology
7. Atmospheric Stability
7.1 Super Adiabatic
7.2 Sub Adiabatic
8. Plume Behaviours
Detailed description of Environmental Impact Assessment - Historical Background - Objectives - Assessment procedure - Necessity in Water resources projects - Environmental discourse on DAM construction - Case study
The Gaussian plume model is the most common air pollution model. It is based on a simple formula that describes the three-dimensional concentration field generated by a point source under stationary meteorological and emission conditions.
Lecture note of Industrial Waste Treatment (Elective -III) as per syllabus of Solapur university for BE Civil
Prepared by
Prof S S Jahagirdar,
Associate Professor,
N K ORchid College of Engg and Tech,
Solapur
Nowadays by seeing the present scenario AIR is the essential element to live & Air Quality Index is a tool to distinguish the benefit of air quality. There are different methods to identify AQI, based on many impurities viz. PM2.5, PM10,CO were used to compare ambient air quality. By calculating AQI we define the quality level of air to be good, moderate, and hazardous as AQI is calculated by using the reference of "The United States Environmental Protection Agency" We are using thingspeak server to fetch the data into the cloud, so anyone can access the data in their respective location. We are not only focusing on stationary measurement but also on the real time value measurement of AQI. Which helps common people to access the Air Quality Index throughout the city and help them decide to stay in a cleaner air environment? Thus the foremost idea of AQI is to inform people about their air quality so they can step to defend their health.
Simulation of Height of Stack Pile using SCREEN3 module for Particulate Matte...IJERA Editor
This study is regarding the air pollution in selected areas near to port (beside stack yards of port) interested in particulate matter pollution. In this study, the amount of air pollution due to particulates is analyzed. The amount of air pollution is estimated using SCREEN 3 Methodology. In this study, SCREEN 3 methodology is a predefined software tool which can be used to estimate particulate matter pollution levels at different source release heights, terrain heights and at particular receptor height. The results obtained are reported and finally concluded that to avoid the pollution in the selected area, it is better to construct a periphery along the sides of stack yard (source of pollution).
AIR POLLUTION CONTROL course material by Prof S S JAHAGIRDAR,NKOCET,SOLAPUR for BE (CIVIL ) students of Solapur university. Content will be also useful for SHIVAJI and PUNE university students
Meteorological Factors Influencing Air Pollution And Atmospheric Stability ...NiranjanHiremath12
1. Meteorology2.Air Pollution Meteorology3.Benefits Of Analyzing Meteorological Data
4. Meteorological Factors Influencing Air Pollution
4.1 Primary parameters
4.1.1 Wind Direction And Speed
4.1.2 Temperature inversion
4.1.3 Atmospheric Stability
4.1.4 Mixing Height or Mixing Depth
4.2 Secondary parameter
4.2.1 Precipitation
4.2.2 Humidity
4.2.3 Solar radiation
4.2.4 Visibility
5. Methods for measurement of meteorological variable
6. Lapse Rate in Air Pollution Meteorology
7. Atmospheric Stability
7.1 Super Adiabatic
7.2 Sub Adiabatic
8. Plume Behaviours
Detailed description of Environmental Impact Assessment - Historical Background - Objectives - Assessment procedure - Necessity in Water resources projects - Environmental discourse on DAM construction - Case study
The Gaussian plume model is the most common air pollution model. It is based on a simple formula that describes the three-dimensional concentration field generated by a point source under stationary meteorological and emission conditions.
Lecture note of Industrial Waste Treatment (Elective -III) as per syllabus of Solapur university for BE Civil
Prepared by
Prof S S Jahagirdar,
Associate Professor,
N K ORchid College of Engg and Tech,
Solapur
Nowadays by seeing the present scenario AIR is the essential element to live & Air Quality Index is a tool to distinguish the benefit of air quality. There are different methods to identify AQI, based on many impurities viz. PM2.5, PM10,CO were used to compare ambient air quality. By calculating AQI we define the quality level of air to be good, moderate, and hazardous as AQI is calculated by using the reference of "The United States Environmental Protection Agency" We are using thingspeak server to fetch the data into the cloud, so anyone can access the data in their respective location. We are not only focusing on stationary measurement but also on the real time value measurement of AQI. Which helps common people to access the Air Quality Index throughout the city and help them decide to stay in a cleaner air environment? Thus the foremost idea of AQI is to inform people about their air quality so they can step to defend their health.
Simulation of Height of Stack Pile using SCREEN3 module for Particulate Matte...IJERA Editor
This study is regarding the air pollution in selected areas near to port (beside stack yards of port) interested in particulate matter pollution. In this study, the amount of air pollution due to particulates is analyzed. The amount of air pollution is estimated using SCREEN 3 Methodology. In this study, SCREEN 3 methodology is a predefined software tool which can be used to estimate particulate matter pollution levels at different source release heights, terrain heights and at particular receptor height. The results obtained are reported and finally concluded that to avoid the pollution in the selected area, it is better to construct a periphery along the sides of stack yard (source of pollution).
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Methods for assessment of a cooling tower plume sizeIJERA Editor
Currently, several methods exist for assessment of the total size of a cooling tower plume, which is created in the space above the evaporation cooling systems. Practically all the available methods, however, allow only qualitative assessment of this size. With the development of moisture recovery systems, there is a need to quantify the cited phenomenon, particularly to allow for assessment of MRE systems. The contribution for this reason discusses the compilation of a simple mathematical model on whose basis the cited quantification may be done. At the same time, it has also been proven that not even one of the methods applied to date can give correct results
Sensitivity Analysis Study Considering the Selection of Appropriate Land-Use ...BREEZE Software
This paper investigates the relationship between surface characteristics and air dispersion impacts as well how these relationships are addressed by regulatory agencies. Furthermore, this paper presents case studies of how the evaluation of surface characteristics can play a significant role in regulatory review of air dispersion modeling.
Air pollution is a global environmental challenge that has continued to receive worldwide attention despite the recent decline in concentration of atmospheric pollutants following stringent environmental protection regulations. The major source of this pollution remains fossil fuels; hence the urgent need for cleaner energy sources. This study presents a review of the models applied in monitoring ambient air quality. The primary aim of air pollution modeling is to identify and quantitatively characterize pollutant emission at its source and subsequent dispersion through the atmosphere, subject to meteorological conditions, physical and chemical transformations. The common models and model assumptions for modeling air pollution and quality were critically reviewed and analyzed in this work for application in both forecasting and estimation of air pollutants on the basis of considered causes and in air quality assessment and air pollution control.
Implication of Applying CALPUFF to Demonstrate Compliance with the Regional ...BREEZE Software
In this paper, a case study of applying the CALPUFF modeling for regional haze analysis is conducted for an industrial source with potential impact on a nearby Class I area. Results for a base case are discussed and influences of several variables on modeling results are evaluated.
Temporal trends of spatial correlation within the PM10 time series of the Air...Florencia Parravicini
We analyse the temporal variations which can be observed within time series of variogram parameters (nugget, sill and range) of daily air quality data (PM10) over a ten years time frame.
Atmospheric turbulent layer simulation for cfd unsteady inlet conditionsStephane Meteodyn
The aim of this work is to bridge the gap between experimental approaches in wind tunnel testing and numerical computations, in the field of structural design against strong winds. This paper focuses on the generation of an unsteady flow field, representative of a natural wind field, but still compatible with CFD inlet requirements. A simple and “naïve” procedure is explained, and the results are successfully compared to some standards.
Numerical Study Of Flue Gas Flow In A Multi Cyclone SeparatorIJERA Editor
The removal of harmful particulate matter from power plant flue gas is of critical importance to the environment and its inhabitants. The present work illustrates the use of multi-cyclone separators to remove the particulate matter from the bulk of the gas exhausted to the atmosphere. The method has potential to replace conventional systems like electrostatic precipitator due to inherent low power requirement and low maintenance. A parametric model may be employed to design the system based on the requirement of the power station. The present work describes the simulation of flue gas flow through a cyclonic separator. A Finite volume approach has been used and the pressure-velocity coupling is resolved using the SIMPLE algorithm. Discrete phase model is used to inject solid particles from inlet. In this numerical analysis a cluster of four cyclonic separators are considered. Comparisons are made between the available experimental results and the computational work for validation of the numerical models and schemes employed in the work. The separation efficiency and particle trajectories are shown and found comparable to similar cases from literature. The experimental results correlate well for the model under consideration.
Atmospheric Correction of Remote Sensing Data_RamaRao.pptxssusercd49c0
Atmospheric correction of remote sensing data. This PPT describes development of a region sensitive atmospheric correction method for hyperspectral image processing
Climate Change All over the World .pptxsairaanwer024
Climate change refers to significant and lasting changes in the average weather patterns over periods ranging from decades to millions of years. It encompasses both global warming driven by human emissions of greenhouse gases and the resulting large-scale shifts in weather patterns. While climate change is a natural phenomenon, human activities, particularly since the Industrial Revolution, have accelerated its pace and intensity
WRI’s brand new “Food Service Playbook for Promoting Sustainable Food Choices” gives food service operators the very latest strategies for creating dining environments that empower consumers to choose sustainable, plant-rich dishes. This research builds off our first guide for food service, now with industry experience and insights from nearly 350 academic trials.
Willie Nelson Net Worth: A Journey Through Music, Movies, and Business Venturesgreendigital
Willie Nelson is a name that resonates within the world of music and entertainment. Known for his unique voice, and masterful guitar skills. and an extraordinary career spanning several decades. Nelson has become a legend in the country music scene. But, his influence extends far beyond the realm of music. with ventures in acting, writing, activism, and business. This comprehensive article delves into Willie Nelson net worth. exploring the various facets of his career that have contributed to his large fortune.
Follow us on: Pinterest
Introduction
Willie Nelson net worth is a testament to his enduring influence and success in many fields. Born on April 29, 1933, in Abbott, Texas. Nelson's journey from a humble beginning to becoming one of the most iconic figures in American music is nothing short of inspirational. His net worth, which estimated to be around $25 million as of 2024. reflects a career that is as diverse as it is prolific.
Early Life and Musical Beginnings
Humble Origins
Willie Hugh Nelson was born during the Great Depression. a time of significant economic hardship in the United States. Raised by his grandparents. Nelson found solace and inspiration in music from an early age. His grandmother taught him to play the guitar. setting the stage for what would become an illustrious career.
First Steps in Music
Nelson's initial foray into the music industry was fraught with challenges. He moved to Nashville, Tennessee, to pursue his dreams, but success did not come . Working as a songwriter, Nelson penned hits for other artists. which helped him gain a foothold in the competitive music scene. His songwriting skills contributed to his early earnings. laying the foundation for his net worth.
Rise to Stardom
Breakthrough Albums
The 1970s marked a turning point in Willie Nelson's career. His albums "Shotgun Willie" (1973), "Red Headed Stranger" (1975). and "Stardust" (1978) received critical acclaim and commercial success. These albums not only solidified his position in the country music genre. but also introduced his music to a broader audience. The success of these albums played a crucial role in boosting Willie Nelson net worth.
Iconic Songs
Willie Nelson net worth is also attributed to his extensive catalog of hit songs. Tracks like "Blue Eyes Crying in the Rain," "On the Road Again," and "Always on My Mind" have become timeless classics. These songs have not only earned Nelson large royalties but have also ensured his continued relevance in the music industry.
Acting and Film Career
Hollywood Ventures
In addition to his music career, Willie Nelson has also made a mark in Hollywood. His distinctive personality and on-screen presence have landed him roles in several films and television shows. Notable appearances include roles in "The Electric Horseman" (1979), "Honeysuckle Rose" (1980), and "Barbarosa" (1982). These acting gigs have added a significant amount to Willie Nelson net worth.
Television Appearances
Nelson's char
Prevalence of Toxoplasma gondii infection in domestic animals in District Ban...Open Access Research Paper
Toxoplasma gondii is an intracellular zoonotic protozoan parasite, infect both humans and animals population worldwide. It can also cause abortion and inborn disease in humans and livestock population. In the present study total of 313 domestic animals were screened for Toxoplasma gondii infection. Of which 45 cows, 55 buffalos, 68 goats, 60 sheep and 85 shaver chicken were tested. Among these 40 (88.88%) cows were negative and 05 (11.12%) were positive. Similarly 55 (92.72%) buffalos were negative and 04 (07.28%) were positive. In goats 68 (98.52%) were negative and 01 (01.48%) was recorded positive. In sheep and shaver chicken the infection were not recorded.
Artificial Reefs by Kuddle Life Foundation - May 2024punit537210
Situated in Pondicherry, India, Kuddle Life Foundation is a charitable, non-profit and non-governmental organization (NGO) dedicated to improving the living standards of coastal communities and simultaneously placing a strong emphasis on the protection of marine ecosystems.
One of the key areas we work in is Artificial Reefs. This presentation captures our journey so far and our learnings. We hope you get as excited about marine conservation and artificial reefs as we are.
Please visit our website: https://kuddlelife.org
Our Instagram channel:
@kuddlelifefoundation
Our Linkedin Page:
https://www.linkedin.com/company/kuddlelifefoundation/
and write to us if you have any questions:
info@kuddlelife.org
UNDERSTANDING WHAT GREEN WASHING IS!.pdfJulietMogola
Many companies today use green washing to lure the public into thinking they are conserving the environment but in real sense they are doing more harm. There have been such several cases from very big companies here in Kenya and also globally. This ranges from various sectors from manufacturing and goes to consumer products. Educating people on greenwashing will enable people to make better choices based on their analysis and not on what they see on marketing sites.
1. Air Quality Dispersion Modeling and
Data Interpretation
S.K. Goya I
Scientist
Air Pollution Control Division, NEERI, Nagpur - 440 020
Preamble
Air pollution models are used for simulation of the transport and diffusion
of various pollutants (SPM, S02, NOx, CO etc.) being released into atmosphere
as a result of industrial combustion processes, domestic fuel burning during
cooking and vehicular movement. The models are extensively applied in
regulation and urban planning for impact assessment of existing or new sources,
forecasting of pollution episodes, evaluation of control strategies and design of air
quality surveillance programs.
Several mathematical models have been developed to determine ambient
air pollutant concentrations by assuming certain steady state conditions in
atmospheric boundary layer, and to account for various types of sources and
topographic conditions in the immediate vicinity of the air pollution sources.
These models vary from simple empirical relations to very complex numerical
solutions. Besides, complexity of models also varies with the scale, viz. from
micro climatic changes to air pollution forecasting or even global climatic change.
Time and space scales are used in air pollution dispersion and can be
described in terms of four geographical subdivisions: site specific (local),
regional, national and global. These form a reasonable classification scheme for
horizontal spatial and time scales of air quality models. At the lowest subdivision,
site specific or local situations involve considerations such as emissions, source
characteristics, initial plume rise, initial phase of mixing, local terrain and initial
transport. At higher spatial resolutions, the site-specific category is concerned
with interacting plumes from sources separated by 10-20 km. The regional scales
range from an urban area or large industrial complex to a region where urban
areas are represented as point sources in the air quality models.
2. The determination of time scales from the model application perspective
depends on the effects of the pollutant, the regulatory standards and the
variability of emissions and meteorology. The air quality model calculates
pollution concentrations at pre-selected times at locations called grid points,
which are referred to as receptor locations. The times and grid points are
required model inputs. The outcome of the AQM is highly dependent on the
availability of the meteorological input data.
The air pathway processes that control the fate of pollutants from sources
to receptor are transport, diffusion, transformation, and removal. Because of the
complexity of these processes, as well as the complications introduced by terrain
and the pollutants themselves, there exists a large and diverse family of air
quality models. Broadly, the air quality models can be categorized in to three
categories: empirical, semi-empirical and numerical. These posses a variety of
characteristics that can be used to further refine model classification. These
characteristics result from the ambient meteorological and topographical
conditions, the time and space scales inherent in the model application, the
mathematical procedures used to solve the system of equations, and the
pollutants and reaction mechanisms required to solve a particular air pollution
problem.
Gaussian Dispersion Models
Dispersion models are formulated from the fundamental differential
equations governing the conservation of species. Dispersion models are more
appropriate for the prediction of air quality because the models consider the
point-by-point transport, dispersion, generation and removal of pollutant species,
and provide for spatial and temporal variation of these processes, over
reasonable distances covering air sheds of size, 1-50 km.
Most of the models available use Gaussian plume equation (normal
distribution of pollutant concentration in vertical and horizontal directions within
the plume) dispersion calculations for all practical purposes from continuous point
217
3. sources. Fig. 1 depicts the dispersion in the horizontal and vertical directions
about the centerline of a plume from an elevated source.
z
Fig. 1 : Coordinate System Showing Gaussain Distribution
in the Horizontal and Vertical
For a continuous emission source with 0,0, H co-ordinates, the concentration
at a given point (x, y, z) can be calculated using Gaussian dispersion equation:
C(x,y,z,H) =
I n Uo g7
y z
exp
v v y
exp (z-H?l . _ f - ( z + H?
V 2 a
z ,
+ exp - ( 1 )
however, for determining concentration on ground (z = 0), the above equation
reduces to
C(x,y,0/H)=
71 UO..O-
exp M
-
7 + — j
2a f Iv y J l ^ z ;
-- (2)
where,
C - predicted ground level concentration at a receptor located at x meters
downwind and y meters cross wind of the source stack, pg/m3
Q - mass emission rate of pollutant, g/s
4. u - wind velocity at the plume center line height or at effective stack height,
m/s
H - plume center-line height or effective stack height
y - crosswind distance from the plume center line to the receptor, m
ay and az - Pasquill crosswind and vertical dispersion coefficients
The C, u, ay and oz refer to the same average period of meteorological
conditions. Based on empirical data, the dispersion coefficients (ay and az) are a
function of the atmospheric stability class (Table 1).
Table 1: Stability Categories for Dispersion Coefficients
Surface
Wind speed
at a height of
10 m (m/s)
Day Incoming Solar Radiation
(Insolation)
NightSurface
Wind speed
at a height of
10 m (m/s)
Strong Moderate Slight
>1/2 Cloud
Cover
Thinly overcast or
< 1/2 Cloud Cover
< 2 A A-B B — —
2-3 A-B B C E F
3-5 B B-C c D E
5-6 C C-D D D D
>6 C D D D D
Sky Cover
Solar Elevation Angle (degree)
Sky Cover
> 60 < 60 but > 35 < 35 but > 15
4/8 or less or any amount
of high thin clouds
Strong Moderate Slight
5/8 to 7/8 middle clouds
(7000-16000 foot base)
Moderate Slight Slight
5/8 to 7/8 low clouds
(less than 7000 foot base)
Slight Slight Slight
Insolation categories are determined using sky cover and solar elevation information.
The neutral class (D) should be assumed for all overcast conditions during day or night.
Coefficients; ay and crz can be calculated according to stability class from
the Figs. 2 & 3 respectively, as a function of downwind distance (x).
219
5. 10000
1000
100
100 1000 10000
Distance Downwind, meters
100000
Fig. 2 : Pasquill-Gifford Horizontal Dispersion Parameter, (sigma y)
as Functions of Pasquill Stability Class & Downwind Distance
10000
1000
100
100 1000 10000
Distance Downwind, meters
100000
Fig. 3 : Pasquill-Gifford Vertical Dispersion Parameter, (sigma z) as Functions of
Pasquill Stability Class and Downwind Distance
6. Plume Rise
Plume rise is calculated during the estimation of GLCs of emitted
pollutants, which are emitted from the stacks with significant gas velocities and
elevated temperatures. The resulting vertical momentum and thermal buoyancy
are modeled in the plume rise. The maximum GLC is related to the inverse of the
square of the final plume height.
Using the Briggs method for calculating the plume rise above the stack
height as a function of the downwind distance (X) from the stack, the critical
downwind distance (X*) is defined by;
X* = 14 Fb5/8
for Fb < 55m
X* = 34 Fb
2/5
for Fb > 55m
Where, Fb is the empirical buoyancy flux parameter determined by using
an empirical equation as:
Fb = g vs rs
2
(Ts-Ta)/Ts
Where, Fb is in meters and
g = acceleration of gravity, 9.8 m/s2
Vs = stack gas exit velocity, m/s
r 2
's = stack exit radius (or equivalent radius), m
Ts = gas exit temperature, °K
Ta = ambient temperature at stack exit, °K
The plume rise for unstable or neutral atmospheric conditions behaves
according to a "2/3 law", when h is the stack height (m) and H is the plume height
(m). With u as the average wind speed (m/s) at h, for x > 3.5x*, the final plume
rise (m) is empirically determined by;
H = 1.6 Fb
1/3
u"1
(3.5 x*)2/3
For x* < 3.5x*, the atmospheric turbulence comes into play and the
formula becomes;
H = 1.6 Fb
1/3
u"1
x2/3
These equations can be used for buoyant plumes (Ts >Ta) as well as for
jets - that is non-buoyant plumes with Ts s Ta. However, these semi empirical
formulas have a high degree of uncertainty.
001
7. Air Pollution Modeling and Input Data Requirement
Number of air pollution models suggested/recommended by international
agencies like USEPA are available for predicting ground level concentration of
pollutants from a single source or multiple sources at desired receptor locations.
Simple screening models, i.e. PTMAX (Point Maximum) require only point
source characteristics (like pollutant emission rate, stack height, stack tip
diameter, flue gas exit temperature and velocity). The model computes short-term
(1 hr average) maximum ground level concentrations (GLCs) and the distance of
maximum GLC occurrence from a single source, as a function of wind speed and
stability class. The model does not require meteorological data input.
Another screening model, PTDIS (Point Distance) also computes short
term (1 hr average) maximum GLCs and the distance of maximum GLC
occurrence from a single source for a given set of meteorological conditions like
wind speed, temperature, mixing height and stability class for that hour.
Many advanced models like MPTER (Multiple Point Sources with Terrain
Adjustment) and Industrial Source Complex - Short Term (ISCST) model, in
addition to source characteristics need hourly meteorological data. These models
can take up more than 100 point sources and GLCs at several hundred receptor
locations for different averaging periods up to 24 hours. These models are also
based on gaussian dispersion and are widely used for the prediction of GLCs in
EIA and regional air pollution modeling. The details of these models are well
documented and also available on the internet. Further, more sophisticated
models are developed for specific applications, e.g. coastal region, valley
situation etc.
Data input requirements for one of the widely used model, ISCST are
discussed here briefly. The following data pertaining to source characteristics,
meteorological parameters and receptor network are required as input to the
model:
8. 1. Source data
• Emission rate, (g/s)
• Stack height, (m)
• Stack top inner diameter, (m)
• Stack gas exit velocity, (m/s)
• Stack gas exit temperature, (°K)
2. Hourly meteorological data for the simulation period
• Wind speed, (m/s)
• Wind direction (degree)
• Pasquill-Giffford stability class (1 to 6)
• Mixing height, (m)
• Ambient temperature, (°K)
3. Receptor data
• Scaled receptor coordinates (x, y), (m)
• Gridded receptor coordinates generated by the model, (m)
• Height of receptor, (m)
During computations, the model considers the following options
1. Gradual Plume Rise
2. Stack-tip down wash
3. Buoyancy-induced dispersion
4. Calm processing routine
5. Missing data processing routine
6. Default wind profile exponents
7. Default vertical potential temperature gradients
8. Uniform terrain
9. No wet / dry depletion / deposition
223
9. 10. Use upper bound concentration estimates for sources influenced by
building down wash from super-squat buildings
Data Interpretation - A Case Study
Most of the air pollution models being used by USEPA and other
regulatory agencies are based on gaussian dispersion, which involves number of
assumptions and uncertainties leading to the belief that the gaussian models
normally over / under predict the ground level concentration of pollutants than the
actual concentration levels7
. The order of difference between predicted and
actual concentrations may even extend up to 80 times or even higher in some
extreme cases; however, 4-6 times variation is quite common. In order to
minimize such variations, various parameters, which have a significant bearing
on the model output results, are discussed here. A careful evaluation of these
parameters shall certainly help in understanding the predictive capability of the air
quality model and the results obtained.
Model Input Data
Source Characteristics
A total S02 emission rate of 800 kg/hr from seven major stacks in a typical
refinery is considered. Details of the individual stacks with respect to physical
height, top inside diameter, exit gas temperature and velocity along with S02
emission rate is given in Table 2. All the stack characteristics differ in one or the
other parameters.
10. Table 2 : Stack Details and SO2 Emission Rate
Stack
No.
Stack
Coordinates
(m)
Stack
Height
(m)
Stack
Diameter
(m)
Stack
Tempe-
rature
(°K)
Exit
Gas
Velocity
(m/s)
so2
Emission
Rate (g/s)
Stack
No.
X Y
Stack
Height
(m)
Stack
Diameter
(m)
Stack
Tempe-
rature
(°K)
Exit
Gas
Velocity
(m/s)
so2
Emission
Rate (g/s)
1 0 0 70 1.9 573 13.0 70.0
2 -600 -600 100 2.4 673 8.0 12.0
3 -200 -900 100 2.1 483 7.0 55.0
4 + 1230 -560 60 1.0 573 2.0 27.0
5 -600 -200 70 3.1 463 10.0 15.0
6 -250 -600 100 5.0 483 2.0 18.0
7 -240 -800 70 3.5 463 10.0 25.0
Out of 7 stacks, 3 are of 100 m, 3 of 70 m and one is of 60 m height. Stack
diameter varies from 1.9 to 5.0 m and exit gas velocity ranges between 2 & 13
m/s. The spatial distribution of the stacks is shown in Fig. 4.
-10 -8 -6 -4 -2 0 2 4 6 8 10
10
8
! ^
1 1
J
1 / l
' ' • 1 •
A
1
i.
i i i
i i i
i i i
11
1
L
1
—I—
i
i
1
I
L _
1
- 10
- 8
6
i X 1 1
T
i i i
1 T 1
1
r
i
i
1
- 6
E
X
4 / ~l 1 -L 1 0- 1 L j . L X - 4
c I I I i i I
.2'5ro
2 •
1 1
1 1
1
1
1 1 1
1 1 1
i
i
i
i
i
1 T• 2
>- 1 1 1 1
'(0,0)1 i i i
Ol
c
o
ra
0
1 1
1 |
(- -
1
1
-1- _ * — 1 -
• 1
1 1 1
- )—
i
i
-i -
i
i
- I
i
i I
- 0
01
u
c
ra*•>
u>
Q
-2 •
1 1
1 1
1
1
1 1 1
1 1 1
i
i
i
i
I j
I /
- -201
u
c
ra*•>
u>
Q -4 •
-6 •
V -t — i -
1 1
1
iV i
- -+ -
1
1
1
1 1 1
1 1 1
1 1 1
- i —
i
i
i
-i -
i
i
i
- h- 4 -
1 /
/ l
• -4
• -6
-8 -
-10 -
i i
"I rs.
1 1
1 L-
1
T
• • •hT:
1 1 1
I r i
i i i
— 1 —
i
r
—J—
i /
i
1
r
i
• • 1 —
• -8
• -10
10 -8 -6 -4 -2 0 2 4 6 8 10
Distance along X-axis in Km
Figure^. : Stack Locations and Study Area
11. Meteorological Data
Hourly surface (wind speed, wind direction & temperature) and upper air
(mixing height & stability class) meteorological data is either generated for the
specific study region or is collected from the nearby meteorological station.
Minimum data required is for one day, to calculate 24 hourly pollutant
concentrations. In this study, one month met data is averaged for the winter
season.
Receptor Data
Ground level concentrations of pollutant can be predicted within the
defined study/impact zone (e.g. 10 km X 10 km) of the emission sources. The
study area can be divided into any suitable square grid size (e.g. 400 mX400 m).
Prediction of Air Quality
The S02 concentration levels in ambient air are then predicted for the
given emission sources under the given meteorological scenario using ISCST
(Version 3) model. The model predicts pollutant concentration at the center of the
each grid. However, first few highest GLCs can be tabulated separately, as given
in Table 3.
Table 3 : Predicted First Five Highest GLCs of S02
Rank
Concentration
(l^g/m3
)
Direction
(Degree)
Distance
(Km)
1 32 124° 1.4
2 31 117° 1.8
3 31 98° 2.8
4 31 99° 2.4
5 30 97° 3.2
Direction and distance are with respect to major source
Maximum GLC of 32 [jg/m3
is predicted occurring at a distance of 1.4 km
from the major emission source in south-east direction, which is much lower that
the CPCB Standards of 80 pg/m3
for mixed use area category.
Depending upon the user's requirement, the pollutant concentrations can
be computed for different averaging periods ranging from 1 hour to 24 hours, and
for the whole simulation period. In the present case study, diurnal variation in the
predicted 8 hourly concentration at selected receptors is presented in Table 4,
which indicates occurrence of maximum GLC during 8-16 hrs at a distance of
about 1.4 km from the major source in SE direction. No concentration is predicted
in the W direction, as the wind was from N-W sector.
99R
12.
13. Table 4 : Diurnal Variation in S02 Cone, at Selected Receptors
Period
(hrs)
S02 Concentration at Receptors
Period
(hrs)
0.4,-0.8
(Km)
1.2,-0.8
(Km)
- 1.6, 0.0
(Km)
-0.4,-1.2
(Km)
0 - 8 0.032 0.85 0.00 0.027
8 - 1 6 41.28 91.78 0.00 28.38
1 6 - 2 4 0.11 1.97 0.00 39.38
0 - 2 4 13.81 31.53 0.00 22.59
Further, S02 concentration isopleths can be drawn (using a graphical
package, SURFER) to show the spatial coverage of pollutant conc. depending
upon the prevalence of wind direction in the study region, as shown in Fig. 5.
X-axis : Distance in East Direction (Km);
Y-axis : Distance in North Direction (Km);
Z-axis : S02 Concentration (pg/m3
)
Fig. 5 : Predicted Iso-Concentration Zones of S 0 2
227