Most atmospheric pollutants emitted naturally or from human activities, have been known to have adverse effects both on human health (Bünger et al., 2012  ; Olsson et al., 1999  ) and ecosystems (Bell et al., 2004  ). They are also the cause of climate change (Doek-Rae Kim et al., 2015  ) in the form of greenhouse gas emission. Their release considerably affects air quality which becomes therefore stagnant, thick and troublesome (Sportisse, 2004  ). The pollutants with varying atmospheric lifetime are either atmospheric particulate matters or traces of gaseous molecules, and their accumulation and dispersion mainly depend on the existing sources, the meteorological conditions and local topography. In order to evaluate their rate, in-situ measurements or the use of predictive numerical models are needed. In most sub-Saharan countries air pollution is tragic. The World Health Organization (WHO) reported that, air pollution is a major environmental risk to health causing about 1.3 millions deaths in the world per year. Particularly in these countries, where the uncontrolled urban development due to the presence of several industrial plants in very small areas around which, population generally congregates for either their works or commercial purposes let them pay the heaviest price in this situation (WHO, 1987  ; WHO, 2000  ). Unfortunately the lack of air quality monitoring network in Africa has prompted the use of the numerical modeling by researchers. It has been proven that numerical models furnish acceptable results when studying atmospheric phenomena including air pollutants transportation, in predicting the outdoor spatial and temporal variations of pollutants and its behavior through mathematical algorithms that take into account atmospheric dispersion, chemical and physical processes in an attempt to approximate the concentrations of pollutants (Bin Zou, 2010  ). The aim of this work is to simulate the dispersion of the PM2.5, CO, NOx and SO2 pollutants emitted at the Logbaba thermal power plant in Douala. To estimate the concentration of these pollutants, we used American Environmental Regulatory MODel (AERMOD) that is an ideal tool to many African countries facing the problem of in-situ data collection. AERMOD has a special ability to run with minimally observed meteorological parameters. In fact, it has been reported that it only requires a single surface measurement of wind speed, air temperature at screen height and total cloud cover (Holtslag, 1983  ).
2. Materials and Methods
2.1. Pollutants Selecting
The urban atmospheric pollutants affecting the city of Douala include PM2.5, CO, NOx and SO2. They are either related to the quality of the petroleum products used in the transportation industry or the emissions from existing industrial plants.
2.1.1. Fine Particles (PM2.5)
It refers to a complex mixture of carbonaceous particles with diameter less than 2.5 mm (Debrock et al., 2009  ) and which result either from anthropogenic and natural mixture of primary and secondary aerosols (Bashurova et al. 1991  ; Spurny 1996  ; Karanasiou, 2007  ) or from the condensation of hot vapors emanating from the combustion of organic matter, biomass and liquid droplets. PM2.5 is known to have a negative impact on human health. In fact, they are a common culprit for respiratory tract irritation, increase frequency and intensity of asthmatic attacks, cardiovascular and lung diseases, even premature death (Risom et al., 2005  ; Ostro et al., 2006  ; Bräuner et al., 2007  ; Simkhovitch et al., 2008  ; IBGE-BIM, 2008  ; Liu et al., 2013  ; Al Moustafa et al., 2016  ). Moreover, they are responsible for the degradation of estates and architectural heritage (MIRA, 2007  ) through a thin layer deposition mechanism which occurs whenever their mean concentration exceeds the threshold value of 25 mg/m3 per 24 hours and 10 mg/m3 per annum (MMK, 2009  ) set by the WHO.
2.1.2. Carbon Monoxide (CO)
Carbon monoxide is both a product of incomplete hydrocarbon combustion and an important element of the atmospheric chemical process as shown in Equation (1).
Based on this equation, the reaction between CO, NO and O2 produces carbon dioxide (CO2) which is a greenhouse gas. NO2 is not only a pollutant, but also a health hazard (Clark et al., 2014  ). Consequently, CO has an adverse effect on both the central nervous system and other sensory organs. The threat of CO is so important that its accepted mean threshold value is set by WHO as low as 10 mg/m3 for 8 hours and 30 mg/m3 for an hour. An average of 8 hours is used for the evaluation of the allowed CO concentration in order to control its binding ability with haemoglobin forming carboxyhaemoglobin which is slowly released in the blood stream until an equilibrium level is reached between 6 and 8 hours (Sportisse, 2004  ).
2.1.3. Nitrogen Compounds
The urban atmospheric nitrogen compounds which exist in the form of nitrogen oxide (NO) and nitrogen dioxide (NO2), generally originate from the transportation industry as well as other large energy-intensive sectors such as thermal plants (Sheel et al., 2010  ; Fourmeaux et al., 2000  ). Nitrogen compounds generally cause ozone depletion following the schema proposed in Equations (2) and (3) below.
In fact, a high concentration of NOx leads to the ozone production in the troposphere (He et al., 2013  ), while a low concentration of NOx (NO) leads to the ozone depletion and NO2 production (Portmann et al., 2012  ; Arnold, 2004  ; and Seinfeld et al., 1997  ).
The chemical reactions of Equations (2) and (3) indicate the importance of atmospheric nitrogen molecules, either in the emission of greenhouse gases (CO2 and O3) or the production of air pollutants (NO2) (Aneja et al., 1996  ). Usually, the concentrations of NO in the ambient air are considered non-toxic, thus the reason why it is not subject to any restrictive standard (Fourmeaux et al., 2000  ). In the opposite, NO2 negatively affects the respiratory tract due to its high penetrating ability in the lungs (Fourmeaux et al., 2000  ). Consequently, the mean standard threshold values for NO2 are 200 mg/m3per 1 hour and 40 mg/m3 per annum.
2.1.4. Sulfur Dioxide (SO2)
Sulfur dioxide is a colorless gas with a pungent characteristic odor, emitted during the combustion of coal as well as refined diesel and gasoil. As such, urban pollution with sulfur dioxide is influenced by industrial development. This compound is generally produced by an oxidation of the sulfur impurities. The standard mean threshold values are 20 mg/m3 per 24 hours and 500 mg/m3 for 10 minutes. Many studies have shown that a good proportion of asthmatic patients experience an impact of SO2 on both their pulmonary functions and their respiratory symptoms after only a 10 minutes exposure. In addition to the effects on the respiratory system and lung functions in the form of inflammation of the respiratory tract, an increased coughing, a mucus production and chronic bronchitis, sulfur dioxide is also responsible for eyes irritation.
2.2. Description of the Study Area
Geographical location of the Logbaba thermal power plant Logbaba is an eastern neighborhood of the city of Douala, located between 5 m and 13 m above sea level within the Gulf of Guinea, and which has an access to the Atlantic Ocean (Figure 1). Douala is especially exposed to air pollution being both an industrial and a densely populated city. Although the immediate vicinity of the thermal power plant investigated in this work is relatively flat, there are still small depressions in the field which are taken into account while modeling the atmospheric dispersion of its pollutants.
Characteristics of the Source
The Logbaba thermal power plant is made of units which operate using both
Figure 1. The Logbaba thermal power plant location.
Light Fuel Oil (LFO) and Heavy Fuel Oil (HFO). Some properties of the plants associated to each of the fuel type are provided by the manufacturer of its diesel engines and are reported in Table 1.
The weak effect resulting from specific weather conditions can lead to a downwash effect in the airflow upstream or downstream depending on the wind direction with respect to the buildings. An application of the mass balance equation to the plant shows an estimated annual average emission of 80% of the total power plant load. The A0 and A1 activities for HFO and LFO respectively, were evaluated based on the NOx emission rate of 24.70 mg/m3 provided by the services of the central Logbaba thermal power station. The resulting parameters are summarized in Table 2.
Based on the assumption that all the molecules of the same type ejected from the same plant have the same activities A0 and A1, the Equation (4):
was developed by U.S. EPA, using the basic principles of emission factors (FE) and the Tribal Emission Inventory Software Solution (TEISS) model (LESS, 1996-2015  ).
where: E = Emission Rate,
A = Activity,
FE = Emission Factor,
ER = Efficiency Reduction Percentage.
2.3. Grid Construction
The grid calculation points was built by considering that 220 km2 in the city of
Table 1. Fuel characteristics used at the Logbaba thermal power plant.
Table 2. Pollutants HFO and LFO activities.
Douala out of a total of 1000 km2 is occupied by buildings and industries, justifying the need of additional points. In order to provide an optimal resolution in the vicinity of the source where the spatial concentration gradient is the highest, the mesh size was narrowed and stretched while walking away from the thermal power station.
The calculation grid points were further divided into two sectors. The first consists of the points found within 1 km of the plant and described by a meshing system of 100 m × 100 m unit. The second consists of the points extending from 1 to 3.1 km after the plant and described by a meshing system of 150 m × 150 m unit. Furthermore, two additional calculation points also known as point receivers were added for the representation of the concentrations in sensitive areas of the modeling domain, especially at the crossroads of Ndokoti located at 2.29 km west-north-west and the District Hospital of Logbaba located in the western part of the Logbaba thermal power plant.
2.4. Meteorological Data
The temperature, speed and direction of the wind soil data used for the simulation below 10 meters were the 5-year meteorological data (2008-2012). They were provided by National Oceanic Atmospheric Administration (NOAA) and prepared in conformity with the standard procedure for AERMET (MDDEP, 2005  ). In the case of wind parameters, the NOAA data were compared to those collected and supplied by the Agency for Aviation Safety in Africa and Madagascar (ASECNA), located at approximately 4.84 km (in a straight line) North-East and 39.06˚ of the thermal power station. The wind rose data derived from both NOAA and ASECNA showed a good agreement. For the investigated period, the wind rose is presented in Figure 2. It shows that the dominant winds blow west-south-west (WSW), south
Figure 2. Wind rose derived from both ASECNA and NOAA data for the period of 2008-2012.
west (SW) and west (W) with frequencies of respectively 6.63%, 8.71% and of 9.34%.
The main directions showed by the wind rose are consistent with the geographical location of the city of Douala with respect to the coastal side which directs the air movement. Therefore, the strong percentage of weak winds is found as 48.19% and can be explained by the geographical position of the city of Douala below the level of the gulf of Guinea. Moreover, the direction and intensities of the wind help explaining the dispersion of the pollutant as shown by the distribution of the iso-concentrations.
3. Modeling and Results
3.1. AERMOD Model
AERMOD is a near field and steady-state guideline model in that it assumes that concentrations at all distances during a modelled hour are governed by a set of hourly meteorological inputs, which are held constant (Cimorelli et al., 1998  ). Using available meteorological data and similarity theory scaling relationships associated with the geographical location of the Logbaba thermal power plant, we used AERMOD to construct hourly gridded vertical profiles of required meteorological variables. These variables include wind speed, wind direction, as well as vertical and horizontal turbulences, which are used to calculate plume rise, transport and dispersion of each plume as described by Perry (Perry et al., 2004  ).
The AERMOD modelling system consists of the model itself (AERMOD) and two stand-alone input data pre-processors: the meteorological pre-processor (AERMET) and the terrain pre-processor (AERMAP), described in Venkatram (Venkatram, 2008  ). AERMET is used to provide meteorological data for organizing into a format suitable for use by the AERMOD, while AERMET is important for simulating the structure of the atmospheric boundary layer to estimate its depth and the dispersion of pollutants around the power plant. The meteorological data are used in conjunction with the surface characteristics such as roughness of the soil, small scale variation of surface topology, reflectivity (albedo) that describes the effect of the surface on the net incoming solar radiation (EPA, 2008  ), and Bowen’s ratio (β) that determines the moisture quantity available for evaporation.
In this study, the AERMET program is performed by recording only one year of in-situ available meterological parameters with the data provided in Table 3 and the surface data provided in the AERMOD meteorological pre-processor AERMET view version 8.2.0
The five-year meteorological parameters necessary for stability was first generated using AERMET followed by the calculation of the friction velocity (u) that appears in the Convective Boundary Layer (Panofsky et al., 1984  ), the Monin-Obukhov length (L) used to evaluate the Stable Boundary Layer (Venkatram, 1980  ), the convective velocity scale (w) that takes into account the portion of the turbulence in the Convective Boundry Layer (Moen et al., 1988  ) and finally evaluates temperature scale (q), mixed layer heights (zi) and the surface heat flux (H) that are the Planetary Boundary Layer parameters.
These parameters were transferred to the interface with AERMOD, and used in conjunction with the similarity expressions to calculate different parameters such as the vertical profiles of wind speed (u), the lateral and vertical turbulent fluctuations (Fv, Fw), the potential temperature gradient (dq/dz) and the potential temperature (q). AERMAP uses gridded terrain data supplied in the Digital Elevation Model (DEM) format (USGS, 1994  ) for the computation of the representative influence heights (hc) of each receptor and source (EPA, 2004  ). The pre-processor computes both the terrain and critical hill height values for each receptor used as input into the AERMOD.
3.2. Pollution Dispersion Modeling Maintaining
The dispersion of PM2.5 , CO, NOx and SO2 was modeled under normal operating conditions in terms of pollutant release. Assuming that the power plant is operational all year round, the emission rates under these conditions were calculated as average of the measurements taken during the year.
Table 3. Type of land used.
The iso-concentration curves are plotted for an hour, one day and one year. These curves were derived from the model computation for each hour. The concentration of a given pollutant was considered at all grid points while only the highest of the 43,824-hour (05 years) measurements of the broadcasting time was retained. The iso-concentration time curve at each grid point was then generated. These concentrations are not necessarily produced at the same time and do not represent the effect of emissions at a specific time; only cases of extreme pollution were taken into account during the 5-year study period. On the Figures 3-5 are plotted the iso-concentration curves of the pollutants computed over an hour, one day and one year respectively.
Figure 3. Hourly iso-concentration curves of PM2.5, CO, NOx and SO2 respectively.
Figure 4. Daily iso-concentration curves of PM2.5, CO, NOx and SO2 respectively.
Figure 5. Annual iso-concentration curves of PM2.5, CO, NOx and SO2 respectively rate to 2008-2012 period.
Following, daily iso-concentration curves were derived from the model computation at each grid point followed by a calculation of their average over a day. Only the highest average of the 1826-day study period was retained. Figure 4 shows the daily iso-concentration curves of the pollutants.
The process used to draw the annual iso-concentration curves was slightly different. In fact, the AERMOD model computes the average of all hourly concentrations modeled over the five years (43,824 hours), and then connects the points of same concentration. In this case, no maximum value is retained to calculate the annual average at a given point. Figure 5 shows the annual iso-concentration curves of pollutants.
3.3. Evaluation of Each Pollutant Concentration
The concentrations at the point of maximal impact obtained by AERMOD and shown in Table 4 for each contaminant are compared with the reference air quality values provided by the World Health Organization (WHO). LV and CV denote the WHO limit value in ( µg/m3 ) and the calculated value in ( µg/m3 ), and RC denotes the respect of criteria.
In the case of sulfur dioxide, the air quality limit value of the concentration below an hour is computed from Equation (5) (Leduc, 2004  ), where T is the period expressed in hour and C1 hour is the maximum concentration over one hour.
The Relative Concentration CR
A better appreciation of the air quality with respect to each pollutant was done
Table 4. Comparison between the calculated and the WHO limit value concentrations.
by plotting the relative concentration (CR), defined by Equation (6) as the ratio of the concentrations calculated by AERMOD (Ccal = Cc) and the reference values provided by WHO (CWHO = CL).
Among all the curves that could be plotted for the pollutants studied in this work, taking into account the lifetime and their dispersion in the air, we selected and show in Figures 6-8 the curves of the pollutants that have a real effect on the healthy cause of their relative concentration RC that is greater than one at the immediate vicinity of the thermal plant.
In these plots, the variation indicates the polluting effect of each species. In this context, CR > 1 indicates a highly polluting species. Beyond the distance of 330 m where the first houses are located, the air quality is quasi normal (CR < 1) with respect to the species investigated. On the other hand, NOx annual rates are 3.6 and 1.5 times higher at the top of the thermal power and at the distance of 300 m respectively.
For a better appreciation of the dispersion we opt to determine the period of the day of high air pollution due to the presence of the Logbaba thermal plant. For that, we first selected the 50 hours of highest pollution out of the 43,824 hours analyzed by AERMOD. That was followed by their distribution according to their time of occurrence during the day. The results of this analysis such as presented in Figure 9(a) show that about 30 hours over the 50 hours of the highest pollution lie between 7:00 and 9:00 while the remain 20 hours representing the weak pollution peak appears first between 1:00 and 2:00 and secondly between 18:00 and 22:00. Furthermore, a plot of the curves showing the wind speed variations with the period of the day given in Figure 9(b) indicates a good correlation between the periods of high wind speeds and the periods of almost no pollution. Such observation is justifiable by the phenomenon of pollutant dispersion caused by air transportation.
These receptors are located at respectively the source which is the origin, the first
Figure 6. Hourly and annual NOx curves of relative concentration RC, respectively.
residences at 330 m west, the Logababa district hospital at 770 m west and the Ndokoti crossroads at 2.29 km west-north-west of the thermal power station. These points represent the locations where a few hundred people can be found all day long. The variation of the pollutant concentrations at those locations is given in Table 5.
An examination of the iso-concentration plots can highlight a number of interesting facts. Indeed, Figures 4-6 which respectively represent the iso-concentration curves of the pollutants for an hour, one day and one year indicate that their dispersion is strongly oriented in areas west-south-west, southwest and west, in good agreement with the dominant winds as shown by the wind rose in Figure 3. Additionally, the highest annual iso-concentrations occur at a distance of 330 meters east of the thermal power plant. Moreover, a quick decrease of the concentration was observed with the distance from the plant. Finally, a drop of at least 35% of the annual concentrations was observed with respect to the highest annual concentration after the nearest residences to the thermal power plant. However, the concentration decreased by over 85% in the direction which is less affected by the emissions from the plant.
An evaluation of the pollution with respect to time and concentration indicates that all the investigated species show some polluting effect in time, in particular NOx which is most often emitted from the Logbaba plant. A comparison
Figure 7. Hourly and 8 hours CO curves of relative concentration CR, respectively.
Figure 8. Hourly and annual PM2.5 curves of relative concentration CR, respectively.
Table 5. Concentration of the pollutants in µg/m3 at particular locations.
Figure 9. (a) Variation of the pollutant; and (b) Wind speed distribution over 24 hours. Frequency over 24 hours.
of the pollution periods and the wind distribution shows that periods of strong pollution are in fact associated to winds of negligible speed. The mean air velocity is 2.03 m/s during its active period found between 9:00 and 18:00, with a maximum of 3.52 m/s.
4. Conclusions and Recommendations
The AERMOD atmospheric dispersion model is shown to be appropriate in the description of the atmospheric pollutants dispersion from the Logbaba thermal power plant. The resulting concentrations of PM2.5, CO2 , NOx and SO2 were relatively higher near the power plant and consistently decreased as a function of the inverse of the distance from the source. This study shows that NOx is a dominant pollutant owing to its strong and permanent concentration in the atmosphere. However, the reliability of our results can be improved by carrying out further investigations with in-situ measurements at the level of the Logbaba thermal power plant. Such investigation will improve the model by enabling the comparison of the output values with those simulated by AERMOD.
A further investigation could address the harmful effects of these pollutants on human health in the region, in particular NOx as its coexistence with haze can reduce the environmental capacity for SO2; this would lead to a rapid conversion of SO2 to sulfate, because NO2 and SO2 have a synergistic effect when they react, for example on the surface of mineral (He et al., 2014  ). Indeed, occupational exposure to mineral dust is a significant cause of disability, morbidity and mortality. It is well established that pneumoconiosis (the fibrotic reaction of pulmonary tissue to retained dust) is one of the major debilitating outcomes of such exposure. Other health problems including chronic bronchitis and emphysema (Omland et al., 2014  ), pleural fibrosis (Rangelov et al., 2014  ), and certain cancers (Alazzam et al., 2010  ), as well as increased risk of tuberculosis (Salvi et al., 2009  ) are also associated with exposure to mineral dusts. In developing countries, people affected by these diseases can go undiagnosed and untreated. Moreover, a lack of awareness of such problem results in a lack of effective preventive measures. An investigation of these issues will therefore enable the decision makers to formulate effective policies for both the environment and public health protection. This will lead to social benefit by reducing respiratory diseases, and enable savings in the health care costs associated with air pollution.
This work was conducted under the UNESCO?HP Brain Gain initiative. The authors express their gratitude to AirMet Sciences and Dr. J. Thé of Lakes Environmental who provided AERMOD.
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