Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Analytical Study of Fuel Switching from Heavy Fuel Oil to Natural Gas in clay brick factories at Arab Abu Saed, Greater Cairo


Arab Abu Saed area in Giza governorate, south to Cairo contains more than 228 clay brick kilns represent the largest cluster of brickworks in Egypt. Burning of Heavy Fuel Oil (HFO) in such kilns is the main source of air pollution in the surrounding locations. In this study, investigation of switching the fuel used in brick kilns from (HFO) to Natural Gas (NG) is carried out and the pollution loads are assessed in both cases. In addition, two Gaussian dispersion plume models are employed to estimate the concentration of primary pollutants; PM10, SO2, and NO2 at seven locations in the vicinity of Arab Abu Saed to determine the most adversely affected locations. Statistical analysis is applied to evaluate the correlation and conformity of the results of both models. Results show that using of NG leads to a significant reduction of pollution loads of PM10, SO2 and NO2 reaches 96%, 72%, and 24% respectively. In addition, the reduction of naturally occurring radionuclides in air is analyzed. Activity concentrations of Ra-226, Th-232 and K-40 in Bq/l for HFO were measured using HPGe detector for six HFO samples. Exposure due to air submersion of naturally occurring radionuclides in the study area leads to annual equivalent dose ranged between 2.16 mSv/y (received by Uterus) and 14 mSv/y (received by skin), and average effective dose 2.65 mSv/y which represent valuable exposure.


Greater Cairo suffers from a serious air quality problem resulting from the release of different air pollutants with high emission loads. The largest contributors to the air pollution in Cairo are the industrial activities, energy production, transportation, and waste burning1,2. Most of air pollutants have significant health impact on the human being, especially PM10, SO2, and NO2. In 2013, the specialized cancer agency of the World Health Organization (WHO), the International Agency for Research on Cancer (IARC), announced that outdoor air pollution has been classified as carcinogenic to humans3. About 7 million deaths in the world per year were reported by the WHO due to air pollution4. However, the Egypt State of the Environment Report 2015 showed that the annual mean concentrations of PM10, SO2, and NO2 (148, 23, and 51 μg/m3 respectively) were complying with the Egyptian permissible limits; these concentrations were exceeding the WHO air quality standards (20, 20, 40 μg/m3)1,2,3,4,5. In June and October 2010, The Egyptian 24 h PM10 standard of 70 μg/m3 was exceeded during 91 and 96% of the sampling periods, respectively, in all studied sites in Greater Cairo. Resuspended geological dust, mobile source emissions, and open (vegetative/trash) burning were the major contributors to PM10 levels6. In addition, the mean annual concentrations of PM10 in Greater Cairo from 16 April 2014 to 29 January 2015 reveals that about 70% of the region is considered as unhealthy for sensitive groups (200–250 µg/m3 year)2,3,4,5,6,7.

Arab Abu Saed area is located approximately 40 km south to Cairo. This area contains more than 228 clay brick kilns which represent the largest cluster of brickworks in Egypt. All the brick factories are identically operated using the same old traditional Open-Hoffman kilns where fuel is combusted8. These kilns are designed to allow the movement of the combustion air and the combustion exhaust by the natural drift caused by tall stacks (60–80 m)9. The Heavy Fuel Oil (HFO) and the Natural Gas (NG) are the only available types of fuels to be used in these kilns.

The aim of this paper is studying the impact on the air quality due to conversion of the fuel used from HFO to NG in 176 brick kilns. The assessment is focusing on three primary air pollutants; PM10, SO2, and NO2 in seven different around locations (five towns and two monitoring stations) namely; Ikhsas, Barnasht, Tabbin, Minya, and Qibliya Towns, and Tabbin South Ambient Air Quality (AAQ) monitoring station, and Tabbin AAQ monitoring station. PM10, SO2, and NO2 are selected due to their massive negative contribution in the Greater Cairo air quality. To overcome the dilemma of carrying out detailed stack measurements for 176 stacks, specific emission factors are developed for this type of kilns based on the type of fuel. These emission factors were used to estimate the emission rates (g/s) of the target primary pollutants. Then, two Gaussian plume models, Classical GPM (CGPM) and American Environmental Regulatory Model (AERMOD), are typically used to study and understand the variation in the pollutants’ concentration and pollution loads on the surroundings and the vulnerable communities due to fuel switching. GPM requires knowledge of source characteristics, terrain and meteorology10. It has different levels and versions starting from the classical equation to the recent advanced models. It is often advised to apply such a classical model prior to using the more advanced models11,12. AERMOD is used for simulation of the dispersion of air pollutants. Because of its ability to run with minimally observed meteorological parameters, it is considered an ideal model for many African countries suffering from difficulty in collection of metrological data13. The CGPM was used to preliminary assess the pollutant concentrations at the target locations, while AERMOD was applied to verify the results through considering extra atmospheric parameters and the surrounding terrain. Also, radiation measurements were carried out for six HFO samples to investigate the dispersion of occurring radionuclides in air resulting from burning the HFO and calculate the annual average effective dose to workers or public to assess the radiological hazard.

Results and Discussions

Based on the detailed measurements and the surveyed data, the results of the emission factors’ calculations are; in case of using HFO 11380, 3115, and 979 g/1000 RB for PM10, SO2, and NO2 respectively, while for NG the emission factors are 456, 864, 744 g/1000 RB for PM10, SO2, and NO2 respectively. Comparing with a similar study in Greater Dhaka region, Bangladesh14, in which coal is the dominant fuel, results show that the coal emission factors are 9700, 4600, 4700 g/1000 RB for PM10, SO2, and NO2 respectively. Consequently, it is concluded that burning HFO causes the highest levels of PM10, while the highest levels of SO2 and NO2 are released due to using coal.

The fuel switching from HFO to NG in Arab Abu Saed brickworks cluster has resulted in a significant emission load reduction (Table 1) of primary pollutants PM10, SO2, and NO2 that reached 96%, 72%, and 24% respectively. This reduction has been resulted mainly due to the difference in the chemical composition between the HFO and NG and subsequently the combustion exhaust. NG is composed mainly of methane, where the main products of combustion are carbon dioxide and water vapor. HFO has more complex content with higher nitrogen, sulfur, and carbon ratio. Thus, combustion of HFO releases higher levels of harmful emissions, NO2, SOx and ash particles. The combustion of NG, on the other hand, releases very small amounts of sulfur dioxide and nitrogen oxides, virtually no ash or particulate matter15,16. However, the emission loads resulting from using NG is considered very low compared to the HFO loads, using the biomass fuel in the brick kilns (44 kilns) at Bang Pu area in southern Thailand17 has resulted in much fewer emission loads 71 and 363 ton/year of PM10 and SO2 respectively.

Table 1 Emission loads of primary pollutants.

Results of the dispersion models

The estimation of the highest concentration at 1-hr average of the target air pollutants was done for both scenarios of burning the HFO and NG in the brick kilns. The details of the two Gaussian dispersion models’ results are presented in Table (2), it shows that Minya town has the highest concentration of PM10, SO2, and NOx in case of burning HFO, while the lowest concentration is estimated to be at Tabbin South monitoring station if NG is used. However, Minya town is the most positively affected site of the pollution reduction resulting from the fuel switching.

Table 2 CGPM and AERMOD estimation of pollutants’ concentrations at the target locations.

The predicted 1-hr average concentrations in μg/m3 of PM10, SO2, and NOx in case of burning HFO and NG show similarity of the distribution and the locations of the high and low concentrations of the primary pollutants in both scenarios was expected due to the flat terrain of Arab Abu Saed area and the anticipated low wind speed. Comparing the estimated emissions with other studies in the same domain but using different types of fuels (Skinder et al. (2014), Kanabkaew et al. (2015), and Hoang Anh Le et al. (2010))17,18,19, revealed that using biomass fuel is the cleanest way of clay brick production. NG is considered the best alternative in case of burning fossil fuel, while HFO and coal are the worst.

Statistical analysis

Based on the results given by CGPM and AERMOD, a comparison and statistical analysis were carried out to assess the correlation and conformity between both models. Figure 1 presents the comparison between GCPM and AERMOD results of the highest 1-hr average concentrations of the target pollutants. Table (3) confirmed the correlation and the agreement of both models’ results. Consequently, it is concluded that, in case of uncomplicated (flat) surrounding terrain and low wind speed condition, using Power law scheme to calculate the plume dispersion parameters in CGPM can give accurate results compared to the advanced Gaussian Plume models like AERMOD.

Figure 1

GCPM and AERMOD results of pollutants’ concentrations (the highest1-hr average concentration).

Table 3 Standard statistical performance of CGPM and AERMOD.

Radioactivity concentration and equivalent dose

The activity concentrations (Bq/l) of Ra-226, Th-232 and K-40 for six samples of HFO are presented in Table (4). It shows that activity concentrations ranged between 1.0 to 1.9, 1.0 to 1.7 and 1.0 to 2.5 Bq/l for Ra-226, Th-232 and K-40 respectively. Although activity concentration is considered low for all radionuclides per one liter, but the daily HFO consumption of each kiln (average 5000 liter per day) leads to dispersion of a considerable activity concentration of Ra-226, Th-232 and K-40 that reach 7000, 6500, and 8000 Bq/day respectively. The calculated annual occupational exposure of Arab Abu Saeed workers is presented in Table (5), in which the annual external dose due to average concentrations of all natural radionuclides ranged between 2.16 (received by Uterus) and 14 mSv/y (received by skin). The average effective dose due to exposure to all radionuclides is 2.65 mSv/y.

Table 4 Activity concentrations (Bq/l) of HFO samples.
Table 5 Annual equivalent dose and effective dose due to average activity (mSv/y).

Materials and Methods

To assess the anticipated improvement resulting from the fuel-switching, CGPM and AERMOD are used and two scenarios are set-up. The first scenario is considering the usage of the HFO in the 176 brick kilns, and measures the pollutants’ concentrations at the target locations. The second one calculates the pollutants’ concentrations at the same locations in case of burning NG in the brick kilns. The operation of AERMOD and CGPM require obtaining specific data concerning pollutants’ emission rates, flue gas velocity, the downwind and lateral distances between the source and receptors, terrain and atmospheric data. The methodology for getting the required data relies on conducting field surveys, carrying out detailed monitoring and measurements, and the relevant previous work in the area which includes; CIDA-pilot project9.

Emission factors and Emission rates

Owing to the identical similarity of the production process of Arab Abu Saed brickworks and the difficulties of conducting real measurements for that high number of stacks, it was found that developing emission factors based on the size of production and the type of fuel would be very efficient to elicit emission rates from the stacks of all kilns. Accordingly, to calculate the emission factors, several parameters have to be determined including concentrations of pollutants, the emissions flow rates, the specific consumption of fuel, and the production capacity. Through the conducted field surveys, the amount of fuel used and the production capacity per each kiln were collected. In addition, detailed monitoring and measurements were done for a sample of two kilns per each type of fuel to determine the concentrations of air pollutants and the flow rates (summary of these measurements are presented in Table (6).

Table 6 cDetailed measurement of stack emissions for two factories burning HFO and other burning NG.

The mass emissions per unit time (g/s), unit production (g/ton of Red Brick (RB)) and unit energy used (lb/MBTU) are the basis of calculating the emission factors in terms of (g/ton RB), however this unit has been converted into another easier unit i.e. (g/1000 RB). The objective is to consider the variation of brick size by using the average density of the produced red bricks in both sets of measurements. The average density of bricks produced by burning HFO is 2.3 kg/RB, while the average density is 2.4 kg/RB if NG is burnt. The resulting Emission factors in case of using HFO are 11380, 3115, and 979 g/1000 RB for PM10, SO2, and NO2 respectively, while for NG the Emission factors are 456, 864, 744 g/1000 RB for PM10, SO2, and NO2 respectively. Thus, according to the fuel used, the emission load and the subsequent emission rate for each pollutant per each kiln stack are calculated using the following general formula:

$$Pollution\,load\,(\frac{g}{y})=Production\,size\,(\frac{No.\,of\,RB}{1000})\times Emissionfactor\,(EF)\,(\frac{g}{1000RB})$$

and the pollution load of e.g. PM10 for the cluster is estimated as follows:

$${{\rm{PM}}}_{10}=\sum _{i=1}^{n}{({\rm{Production}})}_{{\rm{i}}}\times {{\rm{EF}}}_{P{M}_{10}}$$
$$Emission\,rate\,(\frac{g}{s})=\frac{Annual\,Emission\,Load(\frac{g}{y})}{350\frac{day}{y}\times 24\frac{hr}{day}\times \frac{3600\,sec}{hr}}$$

The velocity of flue gases (m/s) is essential input for both models. The calculation depends on the measured volume flow rate (m3/s), fuel consumption rate, and the cross section area of the stack (m2). The general formula for calculating the flue gas velocity (v m/s) is;


Classical gaussian plume model (CGPM)

Conventional Gaussian plume model is commonly used for the air quality assessment and pollution analysis activities20,21. The Gaussian models are based on the solution of an advection diffusion equation derived by assuming that the wind speed and eddy diffusivity do not have spatial and temporal variations22,23. The following formula represents the concentration of a pollutant releasing from a continuous source.

$$C(x,y,z,H)=\frac{Q}{2\pi {\sigma }_{y}{\sigma }_{z}U}{e}^{(-\frac{{y}^{2}}{2{\sigma }_{y}^{2}})}\{{e}^{(-\frac{{(z-H)}^{2}}{2{\sigma }_{z}^{2}})}+{e}^{(-\frac{{(z+H)}^{2}}{2{\sigma }_{z}^{2}})}\}$$

where, C is the concentration of the pollutant at a point with coordinates x, y, z (g/m3),

x, y & z are the downwind, lateral & vertical distances from the source (m) respectively,

Q is the emission rate (g/s),

U is the downwind velocity (m/s),

H is the effective source height above the ground (m),

σy & σz are the plume dispersion parameters in the lateral direction and vertical directions respectively.

The CGPM requires the calculation of the downwind velocity at the exit height of the flue gases. Since the average stack height of the cluster is 75 meter, the downwind velocity U75 is obtained by;


where, U75 is the wind speed at 75 m height.

U10 is the wind speed at 10 m height.

hs is the physical stack height (75 m).

p is a stability class parameter.

H is the effective height above the ground (m) and is generally presented in the form;

$$\begin{array}{cc}H={h}_{s}+{\rm{\Delta }}h, & {\rm{\Delta }}h=3(\frac{w}{{U}_{75}})D\end{array}$$

where, hs is the physical stack height,

Δh is the plume rise (m),

w is the exits velocity,

D is the internal stack diameter.

σy and σz can be calculated using different schemes. However, according to22, the power law scheme was found to give accurate results in case of U75 is greater than 2 m/s.

$${\sigma }_{y}=a{x}^{b}$$
$${\sigma }_{z}=c{x}^{d}$$

where, a, b, c, and d are Brookhaven National Laboratory Parameters which are determined based on the stability classes. Table 7 presents the assumptions used in the Classical Gaussian plume model calculations of the pollutants’ concentrations at the target locations.

Table 7 CGPM Assumptions used for the calculations of the pollutants’ concentrations.


AERMOD is an advanced new generation model developed by US-EPA mainly for regulatory proposes, and it is commonly used to study the impact of new or existing industrial facilities as well. It can be applied with different types of emission sources (i.e. point, area, or volume sources) and different plume types24,25. It is recommended to be used in Arab Abu Saed case due to its ability to parameterize turbulence and handling simple or complex terrain. The Breeze AERMOD software version (7.0.58) is employed to simulate the PM10, SO2, and NO2 concentrations at the target locations. It uses Geographical Information System (GIS) based approach to define the model objects, add the terrain relief, and present the results. Consequently, in order to set up the model and run the AERMOD, there are several options required to be filled as discussed below.


AERMOD requires comprehensive meteorological information covering two types of data, one is a surface profile (typically available from the ground weather stations) and the other is the upper surface data (for parameters such as Monin-Obukhov length, albedo, bowen ratio etc. based on radiosonde observations). The Wind rose of the Cairo region based on the annual meteorological data (Fig. 2) was developed in the AERMOD required format. The height of the anemometer instrument is assumed 10 m. The wind rose shows the wind speeds range between 3 to 8 m/s in most periods of the year. Approximately half a year the wind speed reaches 5.14 m/s. The highest frequency wind direction was the northeast.

Figure 2

Wind rose of Cairo region.


For setting up the geographical location (latitude and longitude) of the sources and receptors in this model, the Universal Transverse Mercator (UTM) in the World Geodetic system (WGS1984) was used with zone 36 in the northern hemisphere (for Cairo, Egypt). Most of the terrain in and around the cluster is flat, although the terrain on the north and east is moderately complex (hilly) and of heights up to 145 m on the east. Since most of the terrain in the immediate vicinity are less than 10 times the average stack height, terrain do not have a considerable effect on the ambient path of the pollutants released from the stacks of the brick kilns23,24,25,26.

The sources of pollutant emissions (176 kiln stacks) were defined on the airshed map using their latitude and longitude. The worst case scenario was designed where pollutants were considered to be released in a constant rate throughout the year without any emission variations.

Receptor options

Seven target receptors were selected and defined on the airshed map. The receptors were placed in the surrounding vulnerable communities and represented different categories such as urban town areas, farmlands, desert areas, and mixed land use areas, as well. The locations of these receptors are; Ikhsas town, Barnasht town, Tabbin town, Minya town, Qibliya town, Tabbin South AAQ station, and Tabbin AAQ station.

Output options

Two scenarios were set-up to assess the impact of the fuel switching in Arab Abu Saed brickworks clusters; the first one considered the burning of the HFO, while the second scenario showed the result of the fuel switching to NG. In both scenarios, the 1-hr average concentrations of PM10, SO2, and NO2 were predicted in the target receptors. Based on the results of afore-elucidated calculation treatments and the survey outputs, Table (8) represents a sample of the input data used in both models, CGPM and AERMOD, for five brick kilns. These data are calculated for the entire cluster (i.e. 176 brick kilns) in order to obtain the substantial environmental impact of the fuel switching.

Table 8 Sample of input data used for the calculation of the annual average concentrations of the primary pollutants.

Statistical analysis

Different statistical parameters were used to analyze the correlation and agreement between both models. These parameters are Normalized Mean Square Error (NMSE), Fractional Bias (FB), Correlation Coefficient (COR), and Factor of two (FAC2)27,28.

$${\rm{Correlation}}\,{\rm{Coefficient}}\,({\rm{COR}})=\frac{1}{{N}_{m}}\sum _{i=1}^{{N}_{m}}\,({C}_{pi}-\overline{{C}_{p}})\times \frac{({C}_{oi}-\overline{{C}_{o}})}{({\sigma }_{p}{\sigma }_{o})}$$
$${\rm{Factor}}\,{\rm{of}}\,{\rm{Two}}\,({\rm{FAC}}2)=0.5\le \frac{{{\rm{C}}}_{{\rm{p}}}}{{{\rm{C}}}_{{\rm{o}}}}\le 2.0$$

In this case, the CGPM results are considered the predicted results (Cp = CCGPM/Q), while the observed results are the outputs of the AERMOD (Co = CAERMOD/Q). σp and σo are the standard deviations of predicted \(\overline{{C}_{p}}\) and observed \(\overline{{C}_{o}}\) normalized crosswind integrated concentration respectively. Over bars indicate the average over all measurements. For perfect-correlated models, NMSE and FB should be equal or close to zero, while COR and FAC2 should be equal or close to one.

Radiation measurement

The aim of the radiation measurement is to assess the radiological impact of burning the HFO in the brick kilns and determine the annual effective equivalent dose to the workers due to the exposure to the resulting radionuclides.

The activity concentrations

Six HFO samples were collected randomly from different Brick kilns to measure and calculate the activity concentrations (Bq/l) of Ra-226, Th-232 and K-40. Each sample was packed in plastic cylindrical container; closed tightly and kept for about one month to attain secular equilibrium between Ra −226 and its progenies before exposing to gamma spectroscopy. The γ-spectroscopy used was P-type coaxial (HPGe) with 30% relative efficiency and 2.1 keV FWHM at 1.33 MeV (associated with electronic components), and connected to multi-channel analyzer (MCA). GENE 2000 software program was used to analyze the peak areas of gamma spectra29.

Activity concentration (A) in Bq/l of each radionuclide in the sample was calculated by using the count rates detected by the HPGe for 24 hours and subtracting the background counts under the selected photo peaks using the following equation;

$${\boldsymbol{A}}=\frac{{{\boldsymbol{N}}}_{{\boldsymbol{c}}}}{{\boldsymbol{\varepsilon }}\times {{\boldsymbol{I}}}_{{\boldsymbol{\gamma }}}\times (\frac{{\boldsymbol{m}}}{{\boldsymbol{\rho }}})}$$

where, Nc is the net count rate and equals to (Gross counts per second from the samples - background counts per second),

ε is the efficiency of the detector for the specific energy,

Iγ is the intensity of the gamma ray,

m is sample mass in gm,

ρ is the density of the HFO sample in gm/l.

External equivalent and effective organ doses

By using the activity concentration of different radionuclides, the external dose to an organ/tissue Ht (mSv/y) can be determined using the specific external dose coefficients for each organ/tissue; ht (Sv.m3/sec.Bq), that were calculated by Eckerman and Ryman30 through DFEXT code; Federal Guidance Report No. 12.

$${H}_{T}=A\times T\times 3600(sec/hr)\times {h}_{t}\times 1000$$

where, T is the exposure time (5 day/week *50 week/year for workers),

For the remainder tissues, the committed equivalent dose Hrem can be calculated using the external dose coefficient of remainder tissues hrem which is given 0.2 Sv.m3/sec.Bq. The effective dose (E) is the sum of the weighted equivalent doses in all tissues and organs of the body, and can be calculated as:

$$E=\sum _{T}{H}_{T}{W}_{T}$$

where, WT is the weighting factor of tissue T used to represent the relative contribution of the equivalent dose in a tissue or organ to the total radiation hazard resulting to the body. WT is found in ICRP-6031.


The main source of air pollution in Arab Abu Saed Brickworks cluster is burning the fuel in the brick kilns. Switching the used fuel from HFO to NG has resulted in a significant load reduction of air pollutants. By calculating the emission rates of PM10, SO2, and NO2, it is found that fuel switching has led to substantial reductions in sulphur dioxide and particulate (respirable) concentrations in the ambient, while Nitrogen dioxide concentrations have reduced but to a slightly lesser extent. The load reduction of PM10, SO2, and NO2 has reached to 96%, 72%, and 24% respectively and this will considerably reduce exposure levels in the near vicinity and bring in health benefits to the nearby population. We can conclude that the reduction in exposure level and the health benefits will occur due to fuels change overall other industries sources and mobile sources rather than brick kilns only Two Gaussian plume models, CGPM and AERMOD, were used to assess the concentration of PM10, SO2, and NO2 at seven target locations in case of using HFO and NG scenarios. The results of both models are correlated, in a good conformity, and are factor of 2. Therefore, it is concluded that, in case of uncomplicated (flat) surrounding terrain and low wind speed condition, using Power law scheme to calculate the plume dispersion parameters in CGPM can give accurate results compared to the advanced Gaussian Plume models like AERMOD. The assessment of pollutants’ concentrations indicated that Minya and Qibliya towns are the most vulnerable locations affected by the pollution resulting from Arab Abu Saed Brickworks. The radiation measurement of burning HFO in brick kilns showed that, the average concentrations of Ra-226, Th-232 and K-40 per each kiln reached 7000, 6500, and 8000 Bq/day respectively. However, the average concentrations of naturally occurring radionuclides were considerable high; the average dose due to external exposure to different organs or tissues was 2.65 mSv/y. This value within the international permissible values for workers rather than public and it represents valuable exposure that must be taken into considerations.


  1. 1.

    Egypt State of Environment (2015).

  2. 2.

    Moawad, B. M, Youssief, A. & Madkour, K. Modeling and Monitoring of Air Quality in Greater Cairo Region, Egypt Using Landsat-8 Images, HYSPLIT and GIS Based Analysis. Climate Change Research at Universities pp 37–54 (2017).

  3. 3.

    WHO International Agency for Research on Cancer (IARC) Press release no. 223 (2013).

  4. 4.

    WHO ambient (outdoor) air quality database Summary results, update 2018 (2018).

  5. 5.

    WHO Air quality guidelines for particulate matter, ozone, nitrogen dioxide and sulfur dioxide, Global update 2005, Summary of Risk Assessment (2005).

  6. 6.

    Lowenthal, D. H., Gertler, A. W. & Labib, M. W. Particulate matter source apportionment in Cairo: recent measurements and comparison with previous studies. Int. J. Environ. Sci. Technol. 11, 657–670 (2014).

    CAS  Article  Google Scholar 

  7. 7.

    WHO Ambient air pollution database (2016).

  8. 8.

    Ibrahim, M. & Ali, A. A. M. Practical case study for life cycle assessment of the Egyptian brick industry: A comparative analysis of the Japanese industry. Science Direct (2017).

  9. 9.

    The Egyptian Brick Factory Fuel-Switch Project, GHG Clean Projects Registry Ref. No. 0561-8286 (2010).

  10. 10.

    Kumar, A., Patil, R. S., Dikshit, A. K. & Kumar, R. Application of WRF Model for Air Quality Modelling and AERMOD – A Survey. Aerosol and Air Quality Research 17, 1925–1937 (2017).

    Article  Google Scholar 

  11. 11.

    Ahmed, S. & Hossain, I. Applicability of air pollution modeling in a cluster of brickfields in Bangladesh. Chemical Engineering Research Bulletin 12(2008), 28–34 (2008).

    Google Scholar 

  12. 12.

    Demirarslan, K. O., Çetin, D. S. & Karademir, A. Evaluation of three pollutant dispersion models for the environmental assessment of a district in Kocaeli, Turkey. Global NEST Journal, 19 (2017).

  13. 13.

    Mbiaké, R. et al. Atmospheric Dispersion Modeling of the Emissions from the Logbaba Thermal Power Plant, Douala-Cameroon. Open Journal of Air Pollution 6, 117–134, (2017).

    Article  Google Scholar 

  14. 14.

    Guttikunda, S. K., Begum, B. A. & Wadud, Z. Particulate pollution from brick kiln clusters in the Greater Dhaka region, Bangladesh. Air Quality Atmosphere & Health (2014).

  15. 15.

    Morvay, Z. K. & Gvozdenac, D. D. Fuels, combustion, and environmental impact, Fundamentals for analysis and calculation of energy and environmental performance – Applied Industrial Energy and Environmental Management (2008).

  16. 16.

    Wielgosiński, G. Pollutant Formation in Combustion Processes. Advances in Chemical Engineering, Dr. Zeeshan Nawaz (Ed.), ISBN: 978-953-51-0392-9 (2012).

  17. 17.

    Kanabkaew, T. & Buasing, K. Assessment of air pollution concentrations from brick kilns using an atmospheric dispersion model. Transactions on Ecology and The Environment, 198 (2015).

  18. 18.

    Skinder, B. M., Pandit, A. K., Sheikh, A. Q. & Ganai, B. A. Brick kilns: Cause of Atmospheric Pollution. Pollution Effects & Control 2(2) (2014).

  19. 19.

    Le, H. A. & Oanh, N. T. K. Integrated assessment of brick kiln emission impacts on air quality. Environmental Monitoring and Assessment (2010).

  20. 20.

    Visscher, A. D. Air Dispersion Modeling, foundation and Application, (John Wiley & Sons, USA, 2014).

  21. 21.

    Juodis, L., Filistovič, V., Maceika, E. & Remeikis, V. Analytical dispersion model for the chain of primary and secondary air pollutants released from point source. Atmospheric Environment 128, 216–226 (2016).

    ADS  CAS  Article  Google Scholar 

  22. 22.

    Essa, K. S. M., Ghobrial, R. A. R., Mina, A. N. & Higazy, M. Calculation of Crosswind Integrated Concentration by Using Different Dispersion Schemes. MAUSAM 62(1), 51–60 (2011).

    Google Scholar 

  23. 23.

    Hoogha, K. et al. Comparing land use regression and dispersion modeling to assess residential exposure to ambient air pollution for epidemiological studies. Environmental International 73, 382–392 (2014).

    Article  Google Scholar 

  24. 24.

    Gibson, M. D., Kundu, S. & Satish, M. Dispersion model evaluation of PM2.5, NOX and SO2 from point and major line sources in Nova Scotia, Canada using AERMOD Gaussian plume air dispersion model. Atmospheric Pollution Research 4, 157–167 (2013).

    CAS  Article  Google Scholar 

  25. 25.

    EPA, AERMOD Implementation Guide, U.S. Environmental Protection Agency Office of Air Quality Planning and Standards, EPA-454/B-16-013, December (2016).

  26. 26.

    Abiye, O. E. et al. Atmospheric dispersion modeling of uncontrolled gaseous pollutants (SO2 and NOX) emission from a scrap-iron recycling factory in Ile-Ife, Southwest Nigeria. Cogent Environmental Science 2, 1275413 (2016).

    Article  Google Scholar 

  27. 27.

    Leelőssy, Á. et al. Dispersion modeling of air pollutants in the atmosphere: a review. Cent. Eur. J. Geosci. 6(3), 257–278 (2014).

    ADS  Google Scholar 

  28. 28.

    Hanna, S. R., Briggs, G. A., Rayford, P. & Hosker, Jr. Handbook in Atmospheric Diffusion. U.S. Dept. of Energy report COE/TIC-11223, Washington, D. C (1982).

  29. 29.

    Mubarak, F. & Hafez, H. S. Dose Assessment Due to Natural Radioactivity in Sand, Soil and Water of Some Egyptian Beaches. African Journal of Basic & Applied Sciences 8(2), 110–121, (2016).

    CAS  Article  Google Scholar 

  30. 30.

    Eckerman, K. F. & Ryman, J. C. External Exposure to Radionuclides in Air, Water, and Soil. Federal Guidance Report No. 12, EPA Report 402-R-93–081 (Washington, DC, 1993).

  31. 31.

    ICRP International Commission on Radiological Protection. The Recommendations of the Commission. ICRP Publication 60, Annals of the ICRP, 21(1–3) (1991a) (Pergamon Press, New York). 3, 295–302 (1990).

Download references


Special thanks to, Environmental consultants, Dr. Rahul Datar and Dr. Samir A. El Mowafi for their valuable and appreciated technical input and support

Author information




Mamdouh Higazy carried out the measurements and calculations, and collected samples and data. Khaled S.M. Essa and Fawzia Mubarak analyzed data and wrote the main manuscript text, El-Sayed M. El-Sayed, Abdelsattar M. Sallam, and Mona S. Talaat revised the methodology and results.

Corresponding author

Correspondence to Fawzia Mubarak.

Ethics declarations

Competing Interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Higazy, M., Essa, K.S.M., Mubarak, F. et al. Analytical Study of Fuel Switching from Heavy Fuel Oil to Natural Gas in clay brick factories at Arab Abu Saed, Greater Cairo. Sci Rep 9, 10081 (2019).

Download citation


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing