Farida Lolila  This email address is being protected from spambots. You need JavaScript enabled to view it.1, Mohamed S. Mazunga1, Ntombizikhona B. Ndabeni2,3 

1 Department of Physics, University of Dar es Salaam, P.O. Box 35063, Dar es Salaam, Tanzania
2 Department of Subatomic Physics, iThemba LABS, P.O. Box 722, Somerset West 7129, South Africa
3 Department of Physics, University of Cape Town, Rondebosch, 7700, South Africa

Received: May 17, 2022
Revised: September 23, 2022
Accepted: October 8, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.4209/aaqr.220214  

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Lolila, F., Mazunga, M.S., Ndabeni, N.B. (2022). Demarcation of Pollution-Prone Areas around the Manyoni Uranium Project, Tanzania. Aerosol Air Qual. Res. 22, 220214. https://doi.org/10.4209/aaqr.220214


  • Areas prone to PM10 pollution were demarcated by AERMOD.
  • Areas with mean annual PM10 concentrations exceeding 20 µg m–3 were predicted.
  • Knowing pollution-prone areas before mining can help in protecting the environment.


This study employed PM10 source parameters and pre-processed topographical and meteorological data as input into the AERMOD atmospheric dispersion model to demarcate pollution-prone areas around the Manyoni Uranium Project. Knowing these areas before mining is an important step toward establishing efficient and effective environmental baseline data. This is because resources for collecting the data will be concentrated in areas with higher contamination potential. In this regard, AERMOD predicted that the regions suitable for pollution demarcation would be 25.55 km2, 25.85 km2, and 27.96 km2 if the prospective mine at Playa C1 operated for 5, 7, and 10 years, respectively. Within the demarcated areas, AERMOD predicted that the maximum annual ground level concentration of PM10 averaged over 5, 7, and 10 years would be 22.2 µg m–3, 22.8 µg m–3, and 25.7 µg m–3, respectively. These values are 11%, 14%, and 28.5% higher than the WHO annual limit of 20 µg m–3 for PM10. This information can help mine owners and government agencies figure out ways to protect people and the environment from the expected pollution.

Keywords: AERMOD, Uranium mining, Emission factors, Baseline data, PM10


One of the main concerns of uranium mining activities is the emission of radioactive dust into the environment. The dust can cause harmful effects to biota depending on the type and energy of radioactive decay, the duration of the exposure, activity and exposure pathway. Exposure pathways include dermal contact with contaminated dust; inhalation of radioactive dust; and ingestion of contaminated water, soils, vegetation, and food polluted by the radioactive dust. Long-term exposure to dust particles (particulate matter (PM)) has adverse health effects including cardiovascular and respiratory diseases, and cancer (Chen and Hoek, 2020; Consonni et al., 2018; Pope and Dockery, 2006). Dust particles also lower visibility, causing poor air quality, which may have a detrimental influence on the economic activities and livelihoods of the people in the vicinity of the affected area (Wu et al., 2007). Recognition of these negative effects on human health and the environment has led to the implementation of various air quality standards by many nations that limit the amount of PM discharged to the environment. For example, under normal mining operations, an eight-hour time weighted average limit of 5 mg m–3 for occupational exposures to respirable dust (i.e., dust particles that reach the deepest areas of the lungs) should not be exceeded (Nxesi, 2021; OSHA, 2021). For dust exposures other than those specified as occupational exposures, the annual and daily mass concentration of 20 µg m–3 and 50 µg m–3, respectively, for PM with aerodynamic diameter ≤ 10 µm (PM10) in ambient air should not be exceeded (WHO, 2006). For governments to enforce air quality standards and manage the quality of the environment, knowledge of PM concentrations before mining operations begin, known as baseline data, is required. The data is used as a reference to compare the environmental impacts influenced by the development, operation, and closure of the mine.

Typically, leased areas for uranium mining are large. For instance, the Mkuju River Project area in Tanzania is around 345 km2 (WNA, 2021), the Rössing mine area in Namibia is 129.79 km2 (Rössing Uranium Mine, 2022), and the Kayelekera mine area in Malawi is 55.5 km2 (Paladin Energy Ltd., 2009). Establishing baseline data for large areas can be difficult because of factors related to the demarcation of areas that require this data. This is because pollution levels needed for setting the boundary depend on source characteristics as well as meteorological and topographical parameters (Khazini et al., 2021; Huertas et al., 2010). These factors would also affect the size of the area that would be polluted when uranium mining begins (Khazini et al., 2021; Banzi et al., 2015; Huertas et al., 2010).

Several studies have found that meteorological parameters have a significant impact on air pollution (Guo et al., 2022; Cichowicz et al., 2020; Lucian et al., 2018; Czernecki et al., 2017; Tecer et al., 2008; Monn et al., 1995). Increases in relative humidity, cloudiness, and colder temperatures, for example, were observed to increase PM concentration (Czernecki et al., 2017; Tecer et al., 2008). On the other hand, Hernandez et al. (2017) discovered that an increase in relative humidity up to a threshold value (of 75%) was responsible for the increase in PM concentration. Beyond this threshold, there was a decrease in PM concentration. Furthermore, increasing wind speed disperses particulate matter more widely, reducing its concentration (and vice versa) (Cichowicz et al., 2020; Zhang et al., 2015). Wind direction has a significant impact on particulate matter concentration, with higher concentrations in downwind areas than in upwind areas at comparable distances (Kim et al., 2015; Zhang et al., 2015). When the wind direction is significantly variable, however, pollutants can be dispersed in a wide volume of air and distributed more uniformly around the pollution source, resulting in lower ground level concentrations (Godish, 2004).

As far as the topography of the area is concerned, pollutant dispersion processes across complex terrain are far more challenging than those across flat terrain because atmospheric interactions with the orography at various spatial scales have an impact on the dispersion processes (Giovannini et al., 2020). Unsafe high ground level concentrations of pollutants may be experienced over complex terrain due to pollutant impingement on the elevated terrain surfaces (Schnelle, 2003). Furthermore, air stagnation within the atmospheric boundary layer (ABL) may occur for extended periods of time in lower, valley-like areas, favoring high pollution concentrations (Toro et al., 2019; Schnelle, 2003).

In addition to meteorological and topographical factors, pollutant emissions (also termed as emission rates or source strength) play a significant role in determining the concentration of pollutants in the ABL. For example, when atmospheric dispersion is restricted due to either meteorological or topographical factors, strong emissions contribute to high pollution concentrations, especially in the ABL (Giovannini et al., 2020; Godish, 2004). Several studies have estimated pollutant emissions and pollutant concentrations from various mines (Zhou et al., 2021; Huertas et al., 2012a; Chaulya, 2004; Chakraborty et al., 2002; Ghose and Majee, 2001), and it has been discovered that the mining activities that emit the most pollutant emissions are those that involve transportation over unpaved roads (Zhou et al., 2021; Csavina et al., 2014; Huertas et al., 2012a; Chaulya, 2004).

In this study, the source, meteorological, and topographical parameters were applied to the American Meteorological Society-Environmental Protection Agency Regulatory Model (AERMOD) to demarcate regions prone to mining dust pollution. AERMOD is an atmospheric dispersion model that uses these parameters as inputs to predict ground level concentrations of air pollutants at various distances from the source. The predicted concentrations were used to establish boundaries for which the management of the air quality and environment will be essential when uranium mining at Manyoni deposits begins. To establish these boundaries, it is necessary to quantify the dust emissions into the atmosphere, as well as its transport and deposition, which are likely to be caused by the prospective uranium mine. Since the uranium deposits in the Manyoni area are surficial deposits (Uranex NL, 2010), the extraction technique for uranium ore will most likely be open pit. Therefore, the aim of this study is to use AERMOD to demarcate areas that are likely to be polluted by the proposed uranium mining in Manyoni while limiting itself to emission inventories for open pit mines as the required input data for the model.


2.1 The Study Area

Manyoni Uranium Project area, with identified uranium resources up to 11,146 tonnes of uranium (tU) in surficial-type deposits (NEA and IAEA, 2016), is located inside the Manyoni district in central Tanzania. The Manyoni district is a semi-arid area with two seasons: dry (May–October) and rainy (November–April). The rainy season experiences low and often erratic rainfall, with a long-term average of 624 mm per year (Sawe et al., 2018; Lema and Majule, 2009). The project area has a widespread drainage system developed over weathered uranium rich granites that captures dissolved uranium seeped from underlying rocks and transports it to suitable low-lying areas referred to as playa lakes (playas) (Elisadiki and Makundi, 2015). In the project area, there are five playas, A, C (C1 and C West), E, F, and G (Fig. 1), with existing Joint Ore Reserves Committee (JORC) compliant uranium resources data that have been publicly reported. The resources (measured, indicated, and inferred) for the playas, as reported by the project operator Uranex NL, are given in Table 1 (World Distribution of Uranium Deposits (UDEPO), 2013, as cited in NEA and IAEA (2016)).

Fig. 1. The location of the Manyoni Uranium Project (Uranex NL, 2010).Fig. 1. The location of the Manyoni Uranium Project (Uranex NL, 2010).

 Table 1. Uranium Resources of the Manyoni Uranium Project (UDEPO, 2013, as cited in NEA and IAEA (2016)).

Table 1 shows that the playa with the largest uranium resource so far is C1, and it is anticipated that initial uranium mining activities will begin at this playa. Except for C1, the foreseeable source parameters suitable as AERMOD inputs were not readily available during this study. As a result, the source, topographical, and meteorological parameters from Playa C1 were used as inputs to the AERMOD. The output of the model was used to demarcate areas that are likely to be polluted when uranium mining begins on the playa.

2.2 AERMOD Air Dispersion Model

To estimate the boundary of potential areas to be polluted by particulate matter (PM10) emissions when uranium mining begins at Playa C1, version 9.8.1 of the AERMOD View™ air dispersion model was used. AERMOD is a steady-state Gaussian-based plume model that is extensively used for modelling air dispersion of emissions from surface and elevated pollution sources, both in simple and complex terrain (U.S. EPA, 2022). The model is considered accurate for modelling air dispersion up to 50 km from the emission source based on characterization of the ABL turbulence structure and scaling concepts (U.S. EPA, 2005).

AERMOD is currently one of the regulatory air dispersion models recommended by environmental authorities in the United States of America (U.S. EPA, 2022) and the Republic of South Africa (Molewa, 2014). It has been used by various researchers to predict PM10 concentrations from various emission sources in open pit mines (Huertas et al., 2012b, 2010; Jaiprakash et al., 2010). In the performance evaluation of the model for PM10 predictions for open pit mines, significant correlation coefficients (R2) of 0.832 (Huertas et al., 2010), 0.741 (Jaiprakash et al., 2010) and > 0.73 (Huertas et al., 2012b) were found between the model's results and the measured results. This indicates that AERMOD's predictions of PM10 dispersion are reliable for open pit mines.

AERMOD requires meteorological, terrain and source data as its inputs to produce pollutant concentrations at specific receptor locations as its output (U.S. EPA, 2005). Before being used by the model, the meteorological and terrain data are first pre-processed by AERMOD’s meteorological data pre-processor (AERMET) and a terrain data pre-processor (AERMAP), respectively.

2.3 Meteorological Data

To process meteorological data, AERMET requires hourly surface data and two daily measurements of upper-air data (usually taken at 0000 and 1200 Coordinated Universal Time (UTC)), which can be obtained from a weather station on-site or nearby. As a minimum requirement, surface data, measured at the Earth’s surface (technically between ground level and 10 m), should include wind speed, wind direction, dry bulb temperature and cloud cover. Similarly, upper air data, measured in different vertical layers of the atmosphere, should include wind speed, wind direction, dry bulb temperature, atmospheric pressure and relative humidity.

Based on these requirements, a ten-year surface hourly data set from 2009 to 2018 (with the local time range starting from 0000 to 2300 hours) from a nearby weather station (Dodoma Airport) was obtained from the National Oceanic and Atmospheric Administration (NOAA) global database and applied to AERMET. The surface data set from Dodoma airport station was preferred because it met the requirements and format (1-hour Integrated Surface Hourly Data (ISHD) format) suitable for AERMET processing.

Since the upper air meteorological data for the Dodoma airport station was not available, an upper air estimation tool incorporated into AERMET View™ (a meteorological pre-processor in AERMOD View™) was used to estimate the upper air data. This tool allows for pre-processing of meteorological data in AERMET without the use of actual upper air data (Thé et al., 2001).

The obtained hourly surface data and the estimated upper air data were processed in AERMET, and a wind rose diagram for the station area was generated and presented in Fig. 2. The wind rose provides a summary of the meteorological data, especially the predominant wind direction, which eventually helps us to identify the prevailing direction in which air pollutants follow. With the availability of 99.82% of wind data from a total of 63,328 hours and a 19.42% frequency of calms, it was found that the predominant wind direction was from east to west (Fig. 2), with an average speed of 4.25 m s1.

Fig. 2. The wind rose for the dates ranging from 1st January 2009 to 31st December 2018 for Dodoma Airport weather station.Fig. 2. The wind rose for the dates ranging from 1st January 2009 to 31st December 2018 for Dodoma Airport weather station.

2.4 Terrain Data

To process terrain data, AERMAP requires digital elevation data as its input (Thé et al., 2019). To fulfil this requirement, Shuttle Radar Topography Mission (SRTM 1 version 3) global terrain files with ~30 m and 1 arc-sec resolution were downloaded from an online geographical information system (WebGIS) database and applied to AERMAP.

The AERMAP and AERMET output files were fed into the AERMOD model, ready to be combined with source parameters before modelling. The source parameters required for modelling in AERMOD comprise the source location, polluting activities, and emission rates from each activity.

3 Source Data

3.1 Open Pit Location

Since AERMOD accepts rectangular pits that can be rotated (about a vertex) from North to South, a 2 km long by 1.75 km wide rectangular area with a 0° orientation from North was created from Playa C1 and used as an open pit source. To specify the source location and its orientation, AERMOD needs coordinates for a vertex that occurs in the southwest quadrant of the open pit source (the rectangular area) (Thé et al., 2019). This vertex was chosen so that a spot with the highest uranium concentration (> 10,000 ppm U3O8) at Playa C1 was placed at the centre of the rectangular area of the pit. The Universal Transverse Mercator (UTM) coordinates for this hot spot were recorded to be 36M 707,621.5E, 9,367,974.0N (Uranex NL, 2010). In this notation, 36M, 707,621.5E and 9,367,974.0N represent the UTM zone, easting and northing (in metres), respectively. Therefore, by using Google Earth drawing tools, the coordinates for the required southwestern vertex were found to be 36M 706,614.25E, 9,367,125.08N. Since the estimated depth of uranium resources is between 2 and 12 metres below the ground surface (NS Energy, 2009; Uranex NL, 2009), the pit depth was assumed to be 12 m, with a topsoil layer of 0 to 0.3 m, an overburden layer of 0.3 to 2 m, and ore depths of 2 to 12 m.

3.2 Potential Sources of Pollution in the Prospective Open Pit Uranium Mine

One of the main pollutants generated by open pit mining operations is particulate matter (Patra et al., 2016). Open pit mining operations can be considered as a series of operations including the handling of topsoil, overburden, and ore (Huertas et al., 2012a). From these operations, potential sources of dust emissions examined in this work include activities such as bulldozing, loading, transporting, and unloading of materials such as topsoil, overburden, and uranium ore. In addition, wind erosion on exposed pit surfaces was considered as another source of dust. Other sources, such as blasting and drilling of overburden or ore bodies, were not covered in this work. This is because the Manyoni uranium deposits are surficial with unconsolidated and free-digging mineralized materials. Therefore, mining at this site will most likely be done via trucking and excavation instead of blasting and drilling (Uranex NL, 2010).

3.3 Estimation of Dust Emissions to Air from Uranium Mining Operations

Since air emissions from open pit mines are affected by production levels (tonnes per year), the quantity, type, and size of equipment used, the type of material to be processed, and emission controls (if any), Eq. (1) (U.S. EPA, 1995), which takes these parameters into account, was used to estimate emission rates, ER, for the proposed mine.


In this equation, ERj denotes the emission rate of pollutant j in kilogrammes per year (kg yr1), AR is the activity rate in tonnes per hour (t h1), OP is the operating hours in hours per year (h yr1), EFj is the uncontrolled emission factor of pollutant j in kilogrammes per tonnes (kg t1), and ECj is the overall emission control efficiency for pollutant j in percentage (%). Since the Manyoni Uranium mining project site is at an exploration stage, there is a lack of real-mining emission data for modelling purposes. Therefore, instead of using real data, emission factors were used to estimate foreseeable air emissions (emission rates) to be generated when uranium mining begins. An emission factor relates the amount of a pollutant that a source releases to an activity that releases that pollutant (U.S. EPA, 1995).

To calculate the PM10 emission factors (EFPM10) for sources that release PM10, empirical Eqs. (2–6) from the USA Compilation of Air Pollutant Emission Factors (AP-42) were used (U.S. EPA, 2006a, 2006b, 1998). As shown in Table 2, Eq. (2) was used for topsoil and uranium ore bulldozing; Eq. (3) for overburden bulldozing in the loading and unloading areas; Eq. (4) for topsoil and overburden loading and unloading as well as uranium unloading; Eq. (5) for uranium ore loading and Eq. (6) for transportation by truck on unpaved roads at the mining area.


In Eqs. (2–6), EFPM10 represents the PM10 emission factor for a specific mining activity in either kilogrammes per hour (kg h1), kilogrammes per tonne (kg t1) or kilogrammes per vehicle kilometre travelled (kg VKT1). M is moisture content in percentage (%), s is silt content in %, u is wind speed in m s1 and W is mean vehicle gross mass in tonnes (t). It should be noted that Eq. (4) is only applicable under the following source conditions: 0.44%–19% silt content; 0.25%–4.8% moisture content; and 0.6 m s1–6.7 m s1 wind speed (U.S. EPA, 2006b). All of these conditions were met in this work except for moisture content, whereby the source materials had higher values of moisture content than the maximum range of 4.8%. In such a high moisture content situation, the value of 4.8% was used as proposed by Huertas et al. (2012a).

To estimate emission rates ERPM10 for the prospective uranium mine from the potential sources of pollution mentioned earlier in Section 3.2, Eq. (1) was used after three important steps. The first step was to estimate EFPM10, OP, and AR using suitable (project pre-feasibility) data. The second step was to search for overall emission control efficiencies for each pollutant, ECjs, used in open pit mining, which was obtained from Australia’s National Pollutant Inventory manual (NPI, 2012). The third step was to convert the values obtained in the previous steps to appropriate units and substitute them in Eq. (1) to obtain ERPM10 in grammes per second (g s1) units. All values obtained in these steps were recorded in Table 2. In addition, the emission rate for exposed pit surfaces was also estimated, though in a separate way. This is because emission factor equation for this category could not be found in AP-42. Therefore, a direct estimation of the emission rate for suspended particulate matter (ERTSP) developed by Chaulya (2006) was done using Eq. (7) and later converted to ERPM10. The conversion was done by borrowing experiences from open pit coal mining, which show that 50% of total suspended particulate matter (TSP) emissions from exposed coal stockpiles are considered as PM10 emissions (U.S. EPA (1998) as cited in NPI (2012)). The same assumption was used to convert ERTSP to ERPM10 for exposed pit surfaces in uranium mining.


In Eq. (7), M is moisture content in %, s is silt content in %, u is wind speed in m s1, and a is area of the source in square kilometres (km2). The obtained emission rate was also recorded in Table 2.

Table 2. Estimated emission factors and rates from planned uranium mining activities at Playa C1.

Assuming that the prospective mine will be operating for 5 to 10 years, the obtained emission rates and meteorological (AERMET output) and topographical (AERMAP output) factors were fed into AERMOD to estimate dust dispersion over three-time intervals. The time intervals ranging from 2009 to 2013, 2009 to 2015, and 2009 to 2018 were selected to simulate 5, 7, and 10 years of dust dispersion, respectively. The modelling produced ground level concentrations of PM10 at various distances away from the centre of the prospective mine (the hotspot).


4.1 The Demarcated Area

Isopleths of predicted annual ground level concentrations (AGLC) of PM10 (without background), due to emissions from all the modelled sources, averaged over 5 years (2009–2013), 7 years (2009–2015), and 10 years (2009–2018), are presented in Figs. 3(a), 3(b) and 3(c), respectively. Fig. 3(d) depicts the maximum AGLC predicted by the model for the 5, 7, and 10 years. All geographical coordinates in this section, including Fig. 3, are in UTM zone 36M. From the concentration-isopleths in Fig. 3, it can be seen that the dispersion patterns of pollutants (PM10) for the three modelling time intervals are similar. As expected, the pollutant dispersion extended westward (i.e., West-South-West) because the prevailing wind was blowing from the East (Fig. 2). In addition, the coloured isopleths show that the mean AGLC of PM10 varies from a minimum mean value of 2 µg m–3 (shown in the violet region) to maximum mean values greater than 20 µg m–3 (shown in the orange region).

Fig. 3. Isopleths (a–c) and a graph (d) showing AGLC of PM10 averaged over different modelling periods.Fig. 3. Isopleths (a–c) and a graph (d) showing AGLC of PM10 averaged over different modelling periods.

The model predicted that the maximum mean AGLC would be 22.2 µg m–3, 22.8 µg m–3, and 25.7 µg m–3 for the modelling periods of 5, 7, and 10 years, respectively (Fig. 3(d)). These maximum mean AGLCs are 11%, 14% and 28.5% higher than the recommended WHO annual limit of 20 µg m–3 of PM10 for the 5, 7, and 10 years, respectively, and would all be experienced at the same location with UTM coordinates 36M 705,689.65E, 9,367,456.36N. Since this study was aimed at demarcating areas that require baseline data, it is important to note that the minimum mean value of AGLC (2 µg m–3) was a cut-off below which concentrations were considered insignificant for setting boundaries for the establishment of the data. The cut-off concentration of 2 µg m–3 was chosen so that it equals 10% of the recommended WHO annual PM10 limit of 20 µg m–3.

To depict the relationship between AGLC and distance, several data transect lines P1P2 as shown in Fig. 3, were drawn on the isopleths-surface starting from the centre of the prospective mine (P1) to a maximum dispersal distance in the downwind direction (P2), i.e., towards the West (including West-Northwest (WNW) and West-South-West (WSW)). The downwind direction was preferred because it is well known that pollutants, after being released, are mostly transported downwind rather than upwind before being deposited on the ground (IAEA, 2001). In addition, the ground level concentrations of air pollutants are usually highest in this direction (Abdel-Rahman, 2011). For all modelling intervals, it was found that transects in the WSW direction (Fig. 3) passed through the maximum mean AGLC of PM10 predicted by the model. Fig. 4, obtained from the WSW transects, illustrates that when we move away from the centre of the prospective mine the mean AGLC of PM10 increases, reaching maximum at about 2000 m then decreases to minimum at about 8000 m. The distance of the maximum mean AGLC was influenced by the meteorological, topographical, and source parameters (used as input to AERMOD). In addition to these parameters, the decrease in mean AGLC downwind could be caused by the mixing of pollutants (PM10) with less polluted air due to mechanical turbulence, advection, diffusion, convection, and displacement processes in the atmosphere (Godish, 2004). It was also found that the values of the maximum mean AGLC obtained from the transect lines were in good agreement (equal) with those recorded by the model as Max. in Fig. 3, i.e., 22.2 µg m–3, 22.8 µg m–3, and 25.7 µg m–3 for the years 2009–2013, 2009–2015, and 2009–2018, respectively.

Moreover, Fig. 4 shows that the locations where the mean AGLC are higher than the recommended WHO annual limit of 20 µg m–3 for PM10 would be between 1600 m and 2500 m. This advocates for baseline as well as operational monitoring of dust concentration along this downwind distance so as to protect humans and the environment from the harmful effects of PM10 pollution. The operational monitoring would also help to check compliance with national environmental protection regulations.

 Fig. 4. A graph (depicted from a transect line P1P2 across the isopleths in Fig. 3) showing variations of the mean AGLC of PM10 with downwind distance along the WSW direction.Fig. 4. A graph (depicted from a transect line P1P2 across the isopleths in Fig. 3) showing variations of the mean AGLC of PM10 with downwind distance along the WSW direction.

To obtain the size of the area that would be affected by pollution when uranium mining begins, a tool for measuring areas, found in AERMOD View™, was used in two steps. Firstly, the isopleth with the minimum AGLC (i.e., 2 µg m–3 cut-off concentration), shown by a violet colour in Fig. 3, was chosen. As mentioned earlier, the minimum concentration of 2 µg m–3 was considered significant for setting boundaries for the establishment of baseline data. Secondly, using a computer mouse and an in-built ruler, the area enclosed by the outer perimeter of the chosen isopleth was measured by clicking on a start point, then on a series of points along the perimeter towards an end point (i.e., the starting point). When the end point was clicked, the measured area was presented as a hatching on the map along with its value (Fig. 5(a)). The resulting area included all the mean AGLC of PM10 from 2 µg m–3 to the maximum concentration (of the modelling period) in all directions: upwind, downwind, crosswind and sideways (Fig. 5(a)). Based on these steps, it was found that if the prospective mine at Playa C1 runs for 5, 7, and 10 years, dust pollution will affect areas of 25.55 km2, 25.85 km2, and 27.96 km2, respectively (Fig. 5(b)). The demarcated areas include two administrative wards: Manyoni Mjini to the south and Mkwese to the north of the Manyoni district (Fig. 5(c)). About 70% of each of the demarcated areas lies in the Manyoni Mjini ward, with a population density of 156.23 km2, and the remaining 30% lies in the Mkwese ward, with a population density of 14.52 km2. The population sizes used to calculate these densities were obtained from the 2012 population census reported by the NBS and OCGS (2013).

Within the demarcated areas, there are simulated areas shown by orange colour in Fig. 5 (also in Fig. 3), with AGLC greater or equal to the recommended WHO annual limit of 20 µg m–3 for PM10. The areas covered 0.15 km2, 0.23 km2, and 0.71 km2 for the modelling periods of 5, 7, and 10 years, respectively. This suggests that, when uranium mining begins at Playa C1, additional pollution control measures should be taken to reduce dust emissions into the air and ground level concentrations of PM10 in these areas. Moreover, these areas should be highly considered when selecting sampling locations for the establishment of baseline data as they contain the expected maximum AGLC of PM10. The baseline data can be compared to operational monitoring, data that will be collected during mining, to ensure the safety of the public's health and compliance with environmental protection regulations.

A similar study in Iran by Khazini et al. (2021) used AERMOD to predict that pollution emissions from the Sungun open pit copper mine would affect an area within a 14 km radius (~616 km2), implying that the mine posed a significant threat to the environment, including the Arasbaran forests. In Colombia, Huertas et al. (2010) used AERMOD to delineate areas impacted by open pit coal mining and used the information to define high, medium, low, and moderate pollution-source areas for the mining zone in Cesar Department. In Tanzania, Banzi et al. (2015) used AERMOD to mark a 1300 km2 area that they thought would be polluted by the future Mkuju River Project uranium mine. They then used this area to establish baseline data on the concentrations of heavy metals and natural radioactivity (Banzi et al., 2017a, 2017b, 2016, 2015). Banzi et al. (2015) did some good work, but our study builds on it by providing a more detailed methodology for using AERMOD to identify locations in the Manyoni uranium deposit that are likely to be polluted by future uranium mining.

 Fig. 5. The demarcated area where mining pollution is feasible.Fig. 5. The demarcated area where mining pollution is feasible.

4.2 The Influence of the Daily PM10 Pollution on the Demarcated Area

Since annual averages were used to identify areas that are likely to be polluted by PM10 (Figs. 3 and 5), more analysis was done to find out how daily PM10 concentrations affect these areas. This was performed by examining the number of counts and zones where PM10 concentrations could exceed the WHO (2006) daily limit of 50 µg m–3 over the course of 5, 7, and 10 years, as shown in Figs. 6(a), 6(b), and 6(c), respectively. The daily exceedance count ranges were 1–64, 2–88, and 4–124, covering a total of 12.56 km2, 13.27 km2, and 11.69 km2 zones, for the 5, 7, and 10 years, respectively. Fig. 6(d) shows that these zones are within 49.2%, 51.3%, and 41.8% of the demarcated areas derived from annual averages in Figs. 3(a), 3(b), and 3(c). This means that the areas that have been demarcated include all of the regions where uranium mining is likely to cause significant PM10 pollution.

Fig 6. Isopleths indicating the number of counts exceeding the WHO daily guideline in (a) 5 years (b) 7 years (c) 10 years (d) Comparison of areas exceeding the WHO daily guideline with the demarcated area.Fig 6. Isopleths indicating the number of counts exceeding the WHO daily guideline in (a) 5 years (b) 7 years (c) 10 years (d) Comparison of areas exceeding the WHO daily guideline with the demarcated area.

4.3 Seasonal Variations in PM10 Concentrations

The ground level concentrations of PM10 from 2009 to 2018 (representing other time periods) were assessed to determine the impact of seasonal variation in PM10 concentrations. It was found that the seasonal averages of PM10 concentrations were higher in the dry season (May–October) than in the rainy season (November–April). The concentrations were lower during the rainy season due to wet deposition by rainfall. Moreover, peak values were found in June, followed by July, the coldest months of the year (Fig. 7). Outapa and Ivanovitch (2019), Kliengchuay et al. (2018), and Mkoma and Mjemah (2011) reported comparable results, which showed that the average PM10 concentrations were higher in the dry season than in the rainy season.

Fig. 7. Monthly average of PM10 concentration during the dry and rainy seasons.Fig. 7. Monthly average of PM10 concentration during the dry and rainy seasons.

4.4 The Influence of Pollution Sources on PM10 Concentration

The pollution sources included in this research (Section 3.2) were grouped into 4 parts and analysed to determine their impact on the previously found AGLC of PM10 shown in Fig. 3. As shown in Fig. 8, transportation on unpaved roads had the highest AGLC: 13.5 µg m–3 (2009–2013), 14.0 µg m–3 (2009–2015), and 16.1 µg m–3 (2009–2018); followed by bulldozing at 5.7 µg m–3 (2009–2013), 5.6 µg m–3 (2009–2015), and 6.1 µg m–3 (2009–2018); then loading and unloading: 3.1 µg m–3 (2009–2013), 3.2 µg m–3 (2009–2015), and 3.4 µg m–3 (2009–2018). Wind erosion on the exposed pit surface had the lowest AGLC: 0.032 µg m–3 (2009–2013), 0.032 µg m–3 (2009–2015), and 0.036 µg m–3 (2009–2018). Our results are in line with those that show that road transportation is the main dust pollution source in open pit mines (Zhou et al., 2021; Csavina et al., 2014; Huertas et al., 2012a; Chaulya, 2004). Therefore, when uranium mining begins, suitable dust control measures should be taken to reduce dust emissions from transportation on unpaved roads and other sources.

 Fig. 8 The impact of various pollution sources on PM10 concentrations.Fig. 8 The impact of various pollution sources on PM10 concentrations.


The present study was designed to use the AERMOD dispersion model to predict the size of the areas prone to pollution in the vicinity of a prospective uranium mine at Playa C1 in the Manyoni Project. The expected pollution would likely affect areas of 25.55 km2, 25.85 km2, and 27.96 km2 if the prospective mine operated for 5, 7, and 10 years, respectively. Within these areas, there were small regions located in the downwind direction away from the mine, with a mean AGLC of PM10 higher than the recommended WHO annual limit of 20 µg m–3. The availability of these regions requires baseline and operational monitoring of dust concentration in and around these areas in order to safeguard people and the environment from the negative effects of PM10 pollution. In addition, when the size of the areas that are likely to be polluted is known before baseline data is collected, the resources set aside for data collection can be used more efficiently because they will not be spent on areas that do not pose a significant pollution risk.


The authors would like to thank the Dar es Salaam University College of Education for funding this research, and Lakes Environmental Software for supplying the AERMOD View™ modelling software. We are also grateful to Mr. Ryoba Chacha from Uranex Tanzania Limited for supplying necessary information on source parameters used for modelling; Dr. Ismail Makundi from University of Dar es Salaam and Dr. Firmi Banzi from Tanzania Atomic Energy Commission for improving the manuscript. We are also grateful to the late Prof. Peter Msaki for his constructive discussion and valuable guidance in the initial stage of this work.


  1. Abdel-Rahman, A.A. (2011). On the dispersion models and atmospheric dispersion. Int. J. Glob. Warm. 3, 257–273. https://doi.org/10.1504/IJGW.2011.043422

  2. Banzi, F.P., Msaki, P.K., Mohammed, N.K. (2015). Distribution of heavy metals in soils in the vicinity of the proposed Mkuju uranium mine in Tanzania. Environ. Pollut. 4, 42–50. https://doi.org/​10.5539/ep.v4n3p42

  3. Banzi, F.P., Msaki, P.K., Mohammed, N.K. (2016). Natural radioactivity in water and its potential human health risk in the vicinity of Mkuju River Uranium Project in Tanzania. Int. J. Environ. Prot. Policy 4, 111–119. https://doi.org/10.11648/j.ijepp.20160405.11

  4. Banzi, F., Msaki, P., Mohammed, N. (2017a). Assessment of radioactivity of 226Ra, 232Th and 40K in soil and plants for estimation of transfer factors and effective dose around Mkuju River Project, Tanzania. Min. Miner. Depos. 11, 93–100. https://doi.org/10.15407/mining11.03.093

  5. Banzi, F.P., Msaki, P.K., Mohammed, N.K. (2017b). Assessment of natural radioactivity in soil and its contribution to population exposure in the vicinity of Mkuju River Uranium Project in Tanzania. Expert Opin. Environ. Biol. 2017, 1–10. https://doi.org/10.4172/2325-9655.1000140

  6. Chakraborty, M.K., Ahmad, M., Singh, R.S., Pal, D., Bandopadhyay, C., Chaulya, S.K. (2002). Determination of the emission rate from various opencast mining operations. Environ. Model. Softw. 17, 467–480. https://doi.org/10.1016/S1364-8152(02)00010-5

  7. Chaulya, S.K. (2004). Assessment and management of air quality for an opencast coal mining area. J. Environ. Manage. 70, 1–14. https://doi.org/10.1016/j.jenvman.2003.09.018

  8. Chaulya, S.K. (2006). Emission rate formulae for surface iron ore mining activities. Environ. Model. Assess. 11, 361–370. https://doi.org/10.1007/s10666-005-9026-2

  9. Chen, J., Hoek, G. (2020). Long-term exposure to PM and all-cause and cause-specific mortality: A systematic review and meta-analysis. Environ. Int. 143, 105974. https://doi.org/10.1016/j.​envint.2020.105974

  10. Cichowicz, R., Wielgosiński, G., Fetter, W. (2020). Effect of wind speed on the level of particulate matter PM10 concentration in atmospheric air during winter season in vicinity of large combustion plant. J. Atmos. Chem. 77, 35–48. https://doi.org/10.1007/s10874-020-09401-w

  11. Consonni, D., Carugno, M., De Matteis, S., Nordio, F., Randi, G., Bazzano, M., Caporaso, N.E., Tucker, M.A., Bertazzi, P.A., Pesatori, A.C., Lubin, J.H., Landi, M.T. (2018). Outdoor particulate matter (PM10) exposure and lung cancer risk in the EAGLE study. PLoS One 13, 1–20. https://doi.org/​10.1371/journal.pone.0203539

  12. Csavina, J., Field, J., Félix, O., Corral-Avitia, A.Y., Sáez, A.E., Betterton, E.A. (2014). Effect of wind speed and relative humidity on atmospheric dust concentrations in semi-arid climates. Sci. Total Environ. 487, 82–90. https://doi.org/10.1016/j.scitotenv.2014.03.138

  13. Czernecki, B., Półrolniczak, M., Kolendowicz, L., Marosz, M., Kendzierski, S., Pilguj, N. (2017). Influence of the atmospheric conditions on PM10 concentrations in Poznań, Poland. J. Atmos. Chem. 74, 115–139. https://doi.org/10.1007/s10874-016-9345-5

  14. Elisadiki, J., Makundi, I. (2015). Establishment of background radiation dose rate in the vicinity of the proposed Manyoni uranium project, Singida. Tanz. J. Sci. 41, 19–26.

  15. Ghose, M.K., Majee, S.R. (2001). Air pollution caused by opencast mining and its abatement measures in India. J. Environ. Manage. 63, 193–202. https://doi.org/10.1006/jema.2001.0434

  16. Giovannini, L., Ferrero, E., Karl, T., Rotach, M.W., Staquet, C., Trini Castelli, S., Zardi, D. (2020). Atmospheric pollutant dispersion over complex terrain: Challenges and needs for improving air quality measurements and modeling. Atmosphere 11, 646. https://doi.org/10.3390/​atmos11060646

  17. Godish, T. (2004). Air Quality, 4th ed. Lewis Publishers, Boca Raton.

  18. Guo, Q., Wu, D., Yu, C., Wang, T., Ji, M., Wang, X. (2022). Impacts of meteorological parameters on the occurrence of air pollution episodes in the Sichuan basin. J. Environ. Sci. 114, 308–321. https://doi.org/10.1016/j.jes.2021.09.006

  19. Hernandez, G., Berry, T.A., Wallis, S., Poyner, D. (2017). Temperature and humidity effects on particulate matter concentrations in a sub-tropical climate during winter. Int. Proc. Chem. Biol. Environ. Eng. 102, 41–49.

  20. Huertas, J.I., Izquierdo, S., Gonza´lez, E.D. (2010). Modeling dispersion of PM10 and PST in the Cesar Department mining region, Colombia by using ISC and AERMOD, in: POWER2010, ASMEDC, Chicago, Illinois, USA, pp. 97–112. https://doi.org/10.1115/POWER2010-27356

  21. Huertas, J.I., Camacho, D.A., Huertas, M.E. (2012a). Standardized emissions inventory methodology for open-pit mining areas. Environ. Sci. Pollut. Res. 19, 2784–2794. https://doi.org/10.1007/​s11356-012-0778-3

  22. Huertas, J.I., Huertas, M.E., Izquierdo, S., González, E.D. (2012b). Air quality impact assessment of multiple open pit coal mines in northern Colombia. J. Environ. Manage. 93, 121–129. https://doi.org/10.1016/j.jenvman.2011.08.007

  23. International Atomic Energy Agency (IAEA) (2001). Generic models for use in assessing the impact of discharges of radioactive substances to the environment. IAEA, Vienna.

  24. Jaiprakash, Singh, G., Pal, A.K. (2010). Air pollution dispersion modeling performance for mining complex. Environ. We Int. J. Sci. Technol. 5, 205–222.

  25. Khazini, L., Dehkharghanian, M.E., Vaezihir, A. (2021). Dispersion and modeling discussion of aerosol air pollution caused during mining and processing of open-cast mines. Int. J. Environ. Sci. Technol. 19, 913–924. https://doi.org/10.1007/s13762-021-03225-1

  26. Kim, K.H., Lee, S.B., Woo, D., Bae, G.N. (2015). Influence of wind direction and speed on the transport of particle-bound PAHs in a roadway environment. Atmos. Pollut. Res. 6, 1024–1034. https://doi.org/10.1016/j.apr.2015.05.007

  27. Kliengchuay, W., Cooper Meeyai, A., Worakhunpiset, S., Tantrakarnapa, K. (2018). Relationships between meteorological parameters and particulate matter in Mae Hong Son province, Thailand. Int. J. Environ. Res. Public Health 15, 2801. https://doi.org/10.3390/ijerph15122801

  28. Lema, M.A., Majule, A.E. (2009). Impacts of climate change, variability and adaptation strategies on agriculture in semi arid areas of Tanzania: The case of Manyoni district in Singida region, Tanzania. Afr. J. Environ. Sci. Technol. 3, 206–218. https://doi.org/10.5897/AJEST09.099

  29. Lucian, S., Iulian, I., Pavel, I., Alina, L., Marius-Mihai, C., Silviu, G., Cătălin-Răzvan, T. (2018). The influence of weather conditions and local climate on particulate matter (PM10) concentration in metropolitan area of Iasi, Romania. Present Environ. Sustain. Dev. 12, 47–69. https://doi.org/​10.2478/pesd-2018-0029

  30. Mkoma, S.L., Mjemah, I.C. (2011). Influence of meteorology on ambient air quality in Morogoro, Tanzania. Int. J. Environ. Sci. 1, 1107–1115.

  31. Molewa, B.E.E. (2014). National environmental management: Air quality act, 2004 (Act no. 39 of 2004) - Regulations regarding air dispersion modelling. Government Gazette Republic of South Africa, Republic of South Africa.

  32. Monn, Ch., Braendli, O., Schaeppi, G., Schindler, Ch., Ackermann-Liebrich, U., Leuenberger, Ph. (1995). Particulate matter < 10 μm (PM10) and total suspended particulates (TSP) in urban, rural and alpine air in Switzerland. Atmos. Environ. 29, 2565–2573. = https://doi.org/10.1016/1352-2310(95)94999-U

  33. National Bureau of Statistics (NBS) and Office of Chief Government Statistician Zanzibar (OCGS) (2013). 2012 population and housing census: Population distribution by administrative areas. The United Republic of Tanzania. (accessed 23 November 2021).

  34. National Pollutant Inventory (NPI) (2012). Emission estimation technique manual for mining, Version 3.1. Department of Sustainability, Environment, Water, Population and Communities, Canberra, Australian. 

  35. NS Energy (2009). Uranex commences final stage of pre-feasibility study at Manyoni Uranium Project in Tanzania.  (accessed 25 May 2021).

  36. Nuclear Energy Agency (NEA) and International Atomic Energy Agency (IAEA) (2016). Uranium 2016: Resources, production and demand. OECD, Paris.

  37. Nxesi, T.W. (2021). Occupational health and safety act, 1993 - Regulations for hazardous chemical agents, 2021. Government Gazette, Republic of South Africa.

  38. Occupational Safety and Health Administration (OSHA) (2021). Permissible exposure limits, OSHA annotated table Z-3. (accessed 21 December 2021).

  39. Outapa, P., Ivanovitch, K. (2019). The effect of seasonal variation and meteorological data on PM10 concentrations in Northern Thailand. Int. J. GEOMATE 16, 46–53. https://doi.org/10.21660/​2019.56.4558

  40. Paladin Energy Ltd. (2009) Paladin Energy Ltd annual report 2009. Paladin Energy Ltd., Australia. (accessed 8 July 2022).

  41. Patra, A.K., Gautam, S., Kumar, P. (2016). Emissions and human health impact of particulate matter from surface mining operation - A review. Environ. Technol. Innovation 5, 233–249. https://doi.org/10.1016/j.eti.2016.04.002

  42. Pope, C.A., Dockery, D.W. (2006). Health effects of fine particulate air pollution: Lines that connect. J. Air Waste Manage. Assoc. 56, 709–742. https://doi.org/10.1080/10473289.2006.10464485

  43. Rössing Uranium Mine (2022). History and location of Rössing. (accessed 25 July 2022).

  44. Sawe, J., Mung’ong’o, C.G., Kimaro, G.F. (2018). The impacts of climate change and variability on crop farming systems in Semi-Arid Central Tanzania: The case of Manyoni district in Singida region. Afr. J. Environ. Sci. Technol. 12, 323–334. https://doi.org/10.5897/AJEST2018.2481

  45. Schnelle, K.B. (2003). Atmospheric diffusion modeling, in: Meyers, R.A. (Ed.), Encyclopedia of physical science and technology, 3rd ed, Academic Press, New York, pp. 679–705. https://doi.org/10.1016/B0-12-227410-5/00036-3

  46. Tecer, L.H., Süren, P., Alagha, O., Karaca, F., Tuncel, G. (2008). Effect of meteorological parameters on fine and coarse particulate matter mass concentration in a coal-mining area in Zonguldak, Turkey. J. Air Waste Manage. Assoc. 58, 543–552. https://doi.org/10.3155/1047-3289.58.4.543

  47. Thé, J.L., Lee, R., Brode, R.W. (2001). Worldwide data quality effects on PBL short-range regulatory air dispersion models. Presented at the 7th International conference on harmonization within atmospheric dispersion modelling for regulatory purposes, Belgirate, Italy.

  48. Thé, J.L., Thé, C.L., Johnson, M.A. (2019). AERMOD View™ user guide. Lakes Environmental Software, Ontario.

  49. Toro, A.R., Kvakić, M., Klaić, Z.B., Koračin, D., Morales S, R.G.E., Leiva G, M.A. (2019). Exploring atmospheric stagnation during a severe particulate matter air pollution episode over complex terrain in Santiago, Chile. Environ. Pollut. 244, 705–714. https://doi.org/10.1016/j.envpol.​2018.10.067

  50. U.S. Environmental Protection Agency (U.S. EPA) (1995). Introduction, in: AP-42, Compilation of air pollutant emissions factors, U.S. EPA, Research Triangle Park NC, pp. 1–10.

  51. U.S. Environmental Protection Agency (U.S. EPA) (1998). 11.9 Western surface coal mining, in: AP-42, Compilation of air pollutant emissions factors, U.S. EPA, Research Triangle Park NC, pp. 1–14.

  52. U.S. Environmental Protection Agency (U.S. EPA) (2005). Revision to the guideline on air quality models: Adoption of a preferred general purpose (flat and complex terrain) dispersion model and other revisions. Final Rule. Fed. Regist. 70, 68218–68261. 

  53. U.S. Environmental Protection Agency (U.S. EPA) (2006a). 13.2.2 Unpaved roads, in: AP-42, Compilation of air pollutant emissions factors, U.S. EPA, Research Triangle Park NC, pp. 1–20.

  54. U.S. Environmental Protection Agency (U.S. EPA) (2006b). 13.2.4 Aggregate handling and storage piles, in: AP-42, Compilation of air pollutant emissions factors, U.S. EPA, Research Triangle Park NC, pp. 1–6.

  55. U.S. Environmental Protection Agency (U.S. EPA) (2022). Air quality dispersion modeling - Preferred and recommended models. (accessed 6 July 2022).

  56. Uranex NL (2009). Commencement of final stage of Manyoni pre-feasibility study. Uranex NL, Melbourne.

  57. Uranex NL (2010). Report for the quarter ended 31st December 2009. Uranex NL, Melbourne.

  58. World Health Organization (WHO) (2006). Air quality guidelines: Global update 2005: Particulate matter, ozone, nitrogen dioxide and sulfur dioxide. WHO Regional Office for Europe, Copenhagen.

  59. World Nuclear Association (WNA) (2021). Uranium in Africa. (accessed 8 July 2022).

  60. Wu, D., Xueyan, B., Deng, X., Li, F., Tan, H., Guolian, L., Jian, H. (2007). Effect of atmospheric haze on the deterioration of visibility over the Pearl River Delta. J. Meteor. Res. 21, 215–223.

  61. Zhang, H., Wang, Y., Hu, J., Ying, Q., Hu, X.M. (2015). Relationships between meteorological parameters and criteria air pollutants in three megacities in China. Environ. Res. 140, 242–254. https://doi.org/10.1016/j.envres.2015.04.004

  62. Zhou, L., Li, Z., Meng, L., Li, T., Domingos Raimundo Lopes, N. (2021). Environmental risk assessment for PM2.5 pollution from non-point sources in the mining area based on multi-source superposition and diffusion. Sustainability 13, 6619. https://doi.org/10.3390/su13126619 

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