A Discussion on recent research in air pollution - Variability and upper bounds for maximum ground level concentrations

1969 ◽  
Vol 265 (1161) ◽  
pp. 209-220 ◽  
Keyword(s):  
Wind Speed ◽  
Upper Bound ◽  
Power Laws ◽  
Ground Level ◽  
Eddy Diffusivity ◽  
Conservation Equation ◽  
Ground Level Concentration ◽  
Mean Wind Speed ◽  
Source Level ◽  
Definition Of

The properties of the various factors which arise in the conventional formula for maximum hourly mean ground level concentration (max. g.l.c.) after fixing mean wind speed and source strength are studied with a view to assessing variability and comparing with results from the Tilbury field trial. The analysis indicates some ambiguity in defining vertical spread for dispersion from tall stacks and suggests that the formula might be more profitably rearranged. A natural rearrangement is pointed out and this leads to a simple upper bound for max. g.l.c. in an unbounded atmosphere which is only 20 % larger than the familiar result for a uniform atmosphere. This result is obtained using the diffusion equation with simple power laws for wind speed and eddy diffusivity. The general conservation equation is then considered and this leads to a specific definition of mean wind speed below source level and the indication of a general upper bound some 50 % larger than the uniform atmosphere value provided certain reasonable conditions are met. The practical implications of these results are discussed and the extra effects introduced by stable layers are pointed out.

scholarly journals Maximum crosswind integrated ground level concentration in two stability classes

MAUSAM ◽  
2021 ◽  
Vol 64 (4) ◽  
pp. 655-662
Author(s):  
M.ABDEL WAHAB ◽  
KHALED SMESSA ◽  
M. EMBABY ◽  
SAWSAN EMELSAID
Keyword(s):  
Wind Speed ◽  
Laplace Transformation ◽  
Ground Level ◽  
Eddy Diffusivity ◽  
Concentration Data ◽  
Transformation Technique ◽  
Advection Diffusion Equation ◽  
Advection Diffusion ◽  
Ground Level Concentration ◽  
Vertical Height

bl 'kks/k i= esa fu"izHkkoh vkSj vfLFkj fLFkfr;ksa esa ØkWliou lekdfyr lkanz.k ysus ds fy, nks fn’kkvksa esa vfHkogu folj.k lehdj.k ¼ADE½ dks gy fd;k x;k gSA ykIykl :ikarj.k rduhd dk mi;ksx rFkk m/okZ/kj Å¡pkbZ ij vk/kkfjr iou xfr vkSj Hkaoj folj.k’khyrk dh leh{kk djrs gq, ;g gy fudkyk x;k gSA blds lkFk gh Hkw&Lrj  vkSj vf/kdre lkanz.kksa dk Hkh vkdyu fd;k x;k gSA geus bl ekWMy esa iwokZuqekfur vkSj izsf{kr lkanz.k vk¡dM+ksa ds e/; rqyuk djus ds fy, dksiugsxu ¼MsuekdZ½ ls fy, x, vkuqHkfod vk¡dM+ksa dk mi;ksx fd;k gSA  The advection diffusion equation (ADE) is solved in two directions to obtain the crosswind integrated concentration in neutral and unstable conditions. The solution is solved using Laplace transformation technique and considering the wind speed and eddy diffusivity depending on the vertical height. Also the ground level and maximum concentrations are estimated. We use in this model empirical data from Copenhagen (Denmark) to compare between predicted and observed concentration data.

scholarly journals Assessment of wind energy potential in East Azarbaijan province of Iran (Case Stady: Sahand station)

2015 ◽  
Vol 17 (2) ◽  
pp. 418-425
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Fossil Fuels ◽  
Ground Level ◽  
Energy Potential ◽  
Wind Speeds ◽  
Mean Wind Speed ◽  
Wind Energy Potential ◽  
Seasonal Wind

<p>Today&#39;s world requires a change in how the use of different types of energy. With declining reserves of fossil fuels for renewable energies is of course the best alternative. Among the renewable energy from the wind can be considered one of the best forms of energy can be introduced. Accordingly, most countries are trying to identify areas with potential to benefit from this resource.</p> <p>The aim of this study was to assess the potential wind power in Sahand station of Iran country. Hourly measured long term wind speed data of Sahand during the period of 2000-2013 have been statistically analyzed. In this study the wind speed frequency distribution of location was found by using Weibull distribution function. The wind energy potential of the location has been studied based on the Weibull mode. The results of this study show that mean wind speed measured at 10 m above ground level is determined as 5.16 m/s for the studied period. This speed increases by, respectively, 34.78 % and 41.21 %, when it is extrapolated to 40 and 60 m hub height.</p> <div> <p>Long term seasonal wind speeds were found to be relatively higher during the period from January to September. At the other hand, higher wind speeds were observed between the period between 06:00 and 18:00 in the day. These periods feet well with annual and daily periods of maximum demand of electricity, respectively.&nbsp;</p> </div> <p>&nbsp;</p>

scholarly journals APPRAISAL OF THE WIND POTENTIAL AS AN ALTERNATIVE SOURCE OF ENERGY IN KASHERE, GOMBE STATE, NIGERIA.

2020 ◽  
Vol 4 (3) ◽  
pp. 446-452
Author(s):  
Michael Ohakwere-Eze ◽  
A. A. Udo ◽  
C. V. Nosike ◽  
A. Alhassan ◽  
M. K. Adamu
Keyword(s):  
Wind Speed ◽  
Rainy Season ◽  
Ground Level ◽  
Technical Solution ◽  
Alternative Source ◽  
Distribution Method ◽  
K Value ◽  
Wind Potential ◽  
Mean Wind Speed ◽  
The Mean

The sources of energy we use in our day-day activities contributes significantly to the alarming global warming which the world is currently experiencing. A technical solution to the menace of an environmental friendly, sustainable and reliable energy is the peak of this research. Wind speed data from 2014 to 2017 measured at a height of 2 m were analyzed using the Weibull’s distribution method. The results show that all through the studied years and seasons, the mean wind speed distribution for the rainy season is significantly stable as seen from the K-values. However, the dry season has the highest K-value of 2.08 signifying more stable winds during the season. The monthly averages, computed for height of 60 m above ground level ranges between 2.15 m/s and 6.42 m/s with the maximum wind speed in June while the minimum wind speed occurred in September. This implies that the wind velocity of the study area tends to be lower during the end of the rainy season. Nevertheless, the deviation in the mean wind speed was not significant, as such wind energy can serve as a reliable energy source for the area hence could be harvested

A post-processing module based on Cressman’s analysis to improve the Wind Energy Simulation Toolkit mapping system

2018 ◽  
Vol 43 (3) ◽  
pp. 277-298
Author(s):  
Yoandy Alonso ◽  
Yosvany Martinez ◽  
Alfredo Roque ◽  
Wei Yu ◽  
Israel Borrajero
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Ground Level ◽  
Energy Simulation ◽  
Post Processing ◽  
Processing Scheme ◽  
Mapping System ◽  
Mean Wind Speed ◽  
Processing Module ◽  
Wind Resource

In this work, a post-processing module based on Cressman’s method of objective analysis is added to the Wind Energy Simulation Toolkit in order to improve the accuracy of the numerical wind atlas of Cuba. Mean wind speed surface observations at 35 meteorological stations and mean wind speed observations at 10, 30, 50, and 100 m height above ground level collected at a network of 58 observation towers are assimilated in the Cressman analysis. Furthermore, the 3-year numerical wind atlas generated for the same period of time is considered as the first guess for the Cressman method. A new wind atlas of Cuba is generated and verified using observation records at 32 meteorological stations and 10 observation towers distributed over the country. In addition, the capability of the new post-processing scheme to adding information on the temporal variability of the wind resource is explored.

scholarly journals Using dispersion modeling for ground level concentration

MAUSAM ◽  
2021 ◽  
Vol 62 (2) ◽  
pp. 239-244
Author(s):  
KHALEDS.M. ESSA ◽  
FAWZIA MUBARAK
Keyword(s):  
Exact Solution ◽  
Short Range ◽  
Meteorological Data ◽  
Ground Level ◽  
Eddy Diffusivity ◽  
Dispersion Modeling ◽  
Mixing Height ◽  
Advection Diffusion Equation ◽  
Advection Diffusion ◽  
Ground Level Concentration

A short range model calculating ground-level concentration from elevated sources is estimated, which realized a Fickian-type formula. Taking the source and mixing height are functions of the wind velocity and eddy diffusivity profiles. The model estimated with an exact solution of the advection diffusion equation is compared with experimental ground level concentrations using meteorological data collected near the ground.

Observational Estimates of the Horizontal Eddy Diffusivity and Mixing Length in the Low-Level Region of Intense Hurricanes

2012 ◽  
Vol 69 (4) ◽  
pp. 1306-1316 ◽  
Author(s):  
Jun A. Zhang ◽  
Michael T. Montgomery
Keyword(s):  
Wind Speed ◽  
Momentum Flux ◽  
Numerical Models ◽  
Eddy Diffusivity ◽  
Small Scale ◽  
Mixing Length ◽  
Low Level ◽  
Horizontal Mixing ◽  
Mean Wind Speed ◽  
Horizontal Momentum

Abstract This study examines further the characteristics of turbulent flow in the low-level region of intense hurricanes using in situ aircraft observations. The data analyzed here are the flight-level data collected by research aircraft that penetrated the eyewalls of category-5 Hurricane Hugo (1989), category-4 Hurricane Allen (1980), and category-5 Hurricane David (1979) between 1 km and the sea surface. Estimates of horizontal eddy momentum flux, horizontal eddy diffusivity, and horizontal mixing length are obtained. It is found that the horizontal momentum flux and horizontal diffusivity increase with increasing wind speed. The horizontal mixing length increases slightly with wind speed also, but the mixing length is not significantly dependent on the wind speed. The magnitude of the horizontal momentum flux is found to be comparable to that of the vertical momentum flux, indicating that horizontal mixing by turbulence becomes nonnegligible in the hurricane boundary layer, especially in the eyewall region. Within the context of simple K theory, the results suggest that the average horizontal eddy diffusivity and mixing length are approximately 1500 m2 s−1 and 750 m, respectively, at about 500 m in the eyewall region corresponding to the mean wind speed of approximately 52 m s−1. It is recalled also that the mixing length is a virtual scale in numerical models and is quantitatively smaller than the energy-containing scale of turbulent eddies. The distinction between these two scales is a useful reminder for the modeling community on the representation of small-scale turbulence in hurricanes.

scholarly journals Ground-level Concentration of Ozone in Ambient Air in Chittagong (Bangladesh) City

2012 ◽  
Vol 47 (1) ◽  
pp. 83-88
Author(s):  
BA Begum ◽  
G Saroar ◽  
M Nasiruddin ◽  
SK Biswas
Keyword(s):  
Wind Speed ◽  
Ozone Concentration ◽  
Ground Level ◽  
Ambient Air ◽  
Quality Standard ◽  
Annual Average ◽  
Ground Level Ozone ◽  
Low Wind Speed ◽  
Ground Level Concentration ◽  
Ambient Air Quality Standard

The distribution of the ground-level ozone concentration in Chittagong city was continuously monitored at air monitoring station in Chittagong city during period of December 2006 to December 2007. The results of this study have revealed that the ground-level ozone concentration in Chittagong city varied from season to season. The highest ground-level ozone concentration was found in winter. The groundlevel ozone concentration has also a clear diurnal cycle - with higher values in the daytime and notably becomes zero at night depending on season. Meteorological conditions are known to influence the formation and dispersion of ground-level ozone concentration. At temperature lower than 20°C, the concentration of ozone becomes high where as at high temperature (> 30°C), the concentration becomes low. It has also been found that at low wind speed, the ozone concentration is high and at high wind speed, the concentration becomes low due to dispersion. The results also establish that the during the study periods, the ozone concentration was below the Bangladesh National Ambient Air Quality Standard (BNAAQS) of 80 ppb (annual average). DOI: http://dx.doi.org/10.3329/bjsir.v47i1.10729 Bangladesh J. Sci. Ind. Res. 47(1), 83-88, 2012

Experimental and Theoretical Investigations into Chimney Emissions

1975 ◽  
Vol 189 (1) ◽  
pp. 33-43 ◽  
Author(s):  
D. J. Moore
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Atmospheric Stability ◽  
Ground Level ◽  
Turbulent Velocity ◽  
Good Representation ◽  
Plume Dispersion ◽  
Ground Level Concentration ◽  
Simple Boundary ◽  
Theoretical Investigations

The simple ‘conical’ model of plume dispersion from an elevated source of effective height H (m) which indicates that the maximum ground-level concentration Cm (units m-3) is proportional to Q (rate of emission, units s-1) (σZ/ày)/ U (wind speed, ms-1) X H2 assumes that the vertical (σz) and cross-wind (σ y) spreads of plume material are similar functions of distance downwind For time average values of Cm of duration about 1 h, the length scale of the turbulence responsible for the cross-wind spread is, in general, much greater than that responsible for the vertical spread. This length *** l is restricted either by the depth h of the boundary layer or the height above the ground. In this case (σz /σy) in the expression for Cm must be replaced by (Some representative vertical turbulent velocity * l)/(Some representative cross-wind turbulent velocity X H) ’*** In conditions of strong thermal convection and light winds the turbulent vertical velocities are effectively independent of the wind speed and so the form of the first expression for Cm will change both with wind speed, atmospheric stability and the height of the plume in relation to the top of the boundary layer. Simple boundary-layer models for ‘convective’ and ‘windy’ conditions are shown to lead to equations for predicting Cm which are similar to those previously shown by the author to give a good representation of the ground-level concentrations in all categories of wind speed and stability observed on 2500 separate occasions in the Tilbury-Northfleet plume rise and dispersion experiment. The application of these expressions to other locations and sizes of plant is discussed.

Prediction of Atmospheric Turbulence Characteristics for Surface Boundary Layer using Empirical Spectral Methods

2020 ◽  
Author(s):  
Yagya Dutta Dwivedi ◽  
Vasishta Bhargava Nukala ◽  
Satya Prasad Maddula ◽  
Kiran Nair
Keyword(s):  
Boundary Layer ◽  
Time Series ◽  
Surface Roughness ◽  
Wind Speed ◽  
Atmospheric Turbulence ◽  
Surface Boundary ◽  
Low Frequencies ◽  
Surface Boundary Layer ◽  
Power Spectral ◽  
Mean Wind Speed

Abstract Atmospheric turbulence is an unsteady phenomenon found in nature and plays significance role in predicting natural events and life prediction of structures. In this work, turbulence in surface boundary layer has been studied through empirical methods. Computer simulation of Von Karman, Kaimal methods were evaluated for different surface roughness and for low (1%), medium (10%) and high (50%) turbulence intensities. Instantaneous values of one minute time series for longitudinal turbulent wind at mean wind speed of 12 m/s using both spectra showed strong correlation in validation trends. Influence of integral length scales on turbulence kinetic energy production at different heights is illustrated. Time series for mean wind speed of 12 m/s with surface roughness value of 0.05 m have shown that variance for longitudinal, lateral and vertical velocity components were different and found to be anisotropic. Wind speed power spectral density from Davenport and Simiu profiles have also been calculated at surface roughness of 0.05 m and compared with k−1 and k−3 slopes for Kolmogorov k−5/3 law in inertial sub-range and k−7 in viscous dissipation range. At high frequencies, logarithmic slope of Kolmogorov −5/3rd law agreed well with Davenport, Harris, Simiu and Solari spectra than at low frequencies.

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