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Monitoring Air Quality in Urban Centres: New Insights 1

from Douala, Cameroon 2

3 Air quality progressively deteriorates as urbanization, motorization and 4 economic activities increase. Aerosol particles smaller than 2.5 µm (PM2.5), a 5 widespread form of pollution is an emergent threat to human health, the 6 environment, quality of life, and the world’s climate. The composition of these 7 particles is an important aspect of interest not only related to possible health 8 and environmental effects of the elemental content but the elemental 9 determination which also adds valuable information for source apportionment. 10 This study investigates and evaluates the level of PM2.5 in the town of Douala. 11 Monitoring was conducted over a one month period at three locations; two sites 12 in industrial zones and a residential zone. The particles were collected using a 13 cyclone that separates the PM2.5 from the air stream and impacts them on 14 polycarbonate filters which were changed every 24-hour sampling period. The 15 samples were analyzed for particulate mass concentration, black carbon (BC) 16 and trace elements. Trace element analysis was done by EDXRF. Cl, K, Ca, Ti, 17 Mn, Fe, Ni, Cu, Zn, Br, Sr, and Pb were identified and quantified for samples. 18 Local meteorology was used to study variations in PM2.5 mass concentrations. 19 Possible sources for the pollutants were also investigated. The mean particle 20 mass concentration was 252 ± 130.8μg/m

3 while BC attained a maximum of 21

6.993μg/m3. The influence of leaded gasoline was inferred while combustion 22

and road traffic were identified as the major anthropogenic activities emitting 23 particulate matter. Trends in meteorological parameters were influenced by 24 thunderstorms. Sea spray was identified as another major contributor to aerosol 25 PM in this city. The industrial zones had higher concentrations. This study 26 highlights high pollution levels in the city of Douala, with potential important 27 impacts on the health of the regional population. 28 29 Keywords: PM2.5, Air Quality, Aerosol, Mass Concentration, EDXRF 30

31 32 Introduction 33

34 With the recent upsurge in globalization and industrialization coupled with 35

increasing pressures of global climate change, deforestation and shifts in land use 36 pattern, air pollution is emerging as one of the major factors influencing human 37 health, agriculture and natural ecosystems (Agrawal et al., 2003; Kuan et al., 38

2017; Ana et al., 2018). Air quality of is of particular concern due to the 39 degradation of human health attributed to it. Air borne particles that play pivotal 40

roles in human health and atmospheric processes such as fog and cloud formation, 41 visibility, solar radiation and precipitation, acidification of clouds, rain and fog, 42

and climate change as a whole are of particular interest (Goldoni et al., 2006; 43 Dawson et al., 2007; Kawata et al., 2007; Donaldson et al., 2009; Kelly and 44 Fussell, 2011; Tiwari et al., 2012; Amodio et al., 2013). Several epidemiological 45 studies have indicated a strong association between high concentrations of 46 inhalable particles and increased mortality and morbidity (Russell and Brunekreef, 47

2009; Bell et al., 2009; Kuan et al., 2017; Cao et al., 2018; Ariundelger et al., 48

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2020) especially in urban centers where urbanization, motorization, and rapid 1 economic growth rapidly deteriorate the quality of the air (Boman et al., 2009). 2

The size of particles affect their ability to penetrate the human respiratory system 3 causing adverse health effects (Schwarze et al., 2006; Seinfield and Pandis, 2006; 4 Kaonga and Ebenso, 2011). Exposure to fine particles especially PM2.5 which are 5 aerosol particles with aerodynamic diameter ≤ 2.5µm can cause short and long 6 term effects such as decreased lung function, alterations in tissue and lung 7

structure, increased respiratory symptoms, alterations in the respiratory tract and 8 even premature death (Tsai et al., 2003; Katsouyanni et al., 2009; Samet et al., 9 2012; Kuan et al., 2017). Increased toxicity and PM carcinogenicity have been 10 strongly associated with the elemental composition of PM and the presence of 11 polycyclic aromatic hydrocarbons (PAHs) in the finer particles. According to 12

Huggins (2004) and Linak (2000), some of these elements are essential to maintain 13

metabolic activities in the human body. However, at higher concentrations, they 14 can lead to poisoning. More attention is required for “Heavy metals”, usually 15

referred to as metallic chemical elements having a relatively high density and toxic 16 or poisonous at low concentrations (Hg, Cd, As, Cr, Ni and Pb). In addition, trace 17 metals are proven to be useful tracers and are extensively used to identify sources 18

of emissions to be targeted by the emission reduction policies (Gotschi et al., 19 2005; Querol et al., 2006; Querol et al. 2007; Viana et al. 2007; Jeong et al. 2008). 20

According to WHO (2005), Forster (2007) and Tai et al. (2012), climatic 21

conditions have direct effects on the concentration, dispersion and life time of 22 aerosol particles in the atmosphere. The dynamics of the atmosphere and the 23

meteorological conditions play a vital role in governing the fate of air pollutants. 24 Lecoeur et al. (2012) states that concentrations of PM are strongly dependent on 25 meteorological conditions of which temperature, wind speed, humidity, rain rate 26

and mixing height are the variables that impact PM concentrations the most. In this 27

study, the relationship between ambient PM concentration and meteorological 28 factors, such as temperature, rainfall, wind speed and direction and relative 29 humidity is statistically analyzed. 30

In Cameroon, Douala is of particular interest for air pollution studies because 31

of its dense population coupled with intense industrial and commercial activities 32 (Tchotsoua, 2007; Kemajou et al., 2007). However, there has been limited air 33 pollution research in this city. 34

With focus on PM2.5, spatial analysis and temporal variations of air pollution 35 in areas of different development typology in Douala, the economic capital of 36

Cameroon was studied. Detailed measurements of air pollution (PM2.5) were 37 carried out at street sites in 2 industrial zones (1 semi industrial interspersed with 38 residential and commercial neighborhoods, and 1 absolute industrial zone) and a 39

residential neighborhood all of which were centrally located and paved 40 neighborhoods. 41 42 Roadmap of Write-up 43

44 This write up is presented in 4 sections as follows: 45

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It begins with an Introduction which is followed by a Literature Review of 1 key concepts. After the literature review, the methods used are examined in the 2

Methodology section which is tailed by Results and Discussion. The Conclusion 3 then ends the write up. The Reference list immediately follows the conclusion. 4 Acknowledgement is the last but not the list item in the write-up. 5

6

7 Literature Review 8 9

Airborne particulate matter (PM) is a complex mixture of thousands of 10 organic and inorganic species that emerge from a wide range of natural and 11 anthropogenic sources (Seinfeld and Pandis, 2006). 12

Some particulates occur naturally, originating from volcanoes, dust storms, 13

forest and grassland fires, living vegetation and sea spray. Human activities, such 14 as the burning of fossil fuels in vehicles (Omidvarborna et al., 2015), stubble 15

burning, power plants, wet cooling towers in cooling systems and various 16 industrial processes, also generate significant amounts of particulates. Coal 17 combustion in developing countries is the primary method for heating homes and 18

supplying energy. Because salt spray over the oceans is the overwhelmingly most 19 common form of particulate in the atmosphere, anthropogenic aerosols currently 20 account for about 10% of the total mass of aerosols in our atmosphere. 21

According to Seinfeld and Pandis (2006); Brook et al. (2010); Heal et al. 22 (2012) and Manisalidis et al., 2020, air pollutants may either be emitted directly 23

into the atmosphere (primary pollutants) or formed within the atmosphere itself 24 through chemical reactions and physical processes (secondary pollutants). Heal et 25 al. (2012), further classifies them as inhalable, thoracic and respirable dust 26

fractions depending on the depth of penetration of the particle into the respiratory 27

system. The inhalable dust fraction with a size threshold of about 50µm can be 28 captured by inhalation in the nasal cavity (easily filtered by cilia or mucus). The 29 dust fraction reaching all the way to the lungs (lower respiratory tract) is the 30

thoracic dust fraction. These are particles ≤ 10 but > 2.5µm (Boman et al., 2010; 31

Fortoul et al., 2012; Manisalidis et al., 2020), and the fine dust that can penetrate 32 even further into the bronchioles and alveoli (gas exchange region) is known as the 33 respirable dust fraction. These particles are not ejected by breathing out, coughing, 34 or expulsion by mucus, and approximates a particle size threshold of about 3.5 – 35 4µm. In effect, they are < 2.5µm (WHO, 2003; Boman et al., 2010; Fortoul et al., 36

2012; Heal et al., 2012), and are capable of reaching the gas exchange surfaces of 37 the alveoli (Heal et al., 2012). 38

39 Effects of Particulate Matter 40 41 Health Effects 42

Particulates are the deadliest form of air pollution and of particular importance 43

when human health is concerned due to their ability to penetrate deep into the 44 alveolar region of the lungs and blood streams unfiltered, causing permanent DNA 45 mutations, heart attacks, respiratory disease, and premature death (Boman et al., 46

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2010; US EPA, 2010; Fortoul et al., 2012 ). Inhalation of PM in the atmosphere 1 can directly or indirectly lead to or deteriorate various symptoms/diseases by 2

triggering inflammation in the smaller airways leading to the exacerbation of 3 asthma and bronchitis (Brook et al., 2004; Samet and Krewski, 2003 Ann et al., 4 2018 Manisalidis et al., 2020). Other health symptoms include hay fever, 5 increased respiratory symptoms, pulmonary inflammation, reduced lung function, 6 and cardiovascular diseases. PM can reduce lung functioning and can cause or 7

aggravate respiratory conditions, and increase the long term risk of lung cancer or 8 other lung disease such as emphysema, bronchiectasis, pulmonary fibrosis, and 9 cystic lungs (Samet and Krewski, 2003; Begum et al., 2007; Kuan et al., 2017; 10 Lindsay and Xioaohong, 2018). As stated by Samet and Krewski (2003) PM2.5 11 leads to high plaque deposits in arteries, causing vascular inflammation and 12

atherosclerosis - a hardening of the arteries that reduces elasticity, which can lead 13

to heart attacks and other cardiovascular problems. Air pollutants have been linked 14 with endothelial dysfunction and vasoconstriction, increased blood pressure (BP), 15

prothrombotic and coagulant changes, systemic inflammatory and oxidative stress 16 responses, autonomic imbalance and arrhythmias, and the progression of 17 atherosclerosis (Brook et al., 2010). Samet and Krewski (2003) suggest that the 18

mechanisms of formation (e.g. nucleation, coagulation and condensation for the 19 finer fraction or the more abrasive formation systems of the coarser fraction) may 20 also have implications for respiratory related health effects. 21

The health risk from particulates is a function of the size and concentration of 22 the dose inhaled, the surface area and the chemical composition (Donaldson and 23

Tran, 2002; Noel de Nevers, 2000; WHO, 2006; Heal et al., 2012; Ann et al., 24 2018; Manisalidis et al., 2020). 25

Associations between exposure to PM, pregnancy and neo-natal outcomes 26

have been reviewed by WHO (WHO, 2005) and others (Heal et al., 2012). 27

Research suggests health impacts of air pollution in the early stages of human 28 development (Samet and Krewski, 2003; WHO, 2006). In one study, low ambient 29 air pollution concentrations were associated with adverse pregnancy outcomes 30

including low birth weight, preterm birth, and intrauterine growth retardation (Liu 31

et al., 2003). In another study, particles were found to be positively associated with 32 first hospitalizations due to respiratory disease in early childhood (Yang et al., 33 2003). 34 35 Effects on Vegetation 36

The effects of PM are not only limited to human health but extend widely to 37 plant productivity and survival (Woo et al., 2007). Kuwar et al. (2018) states that 38 air pollution can directly affect plants via leaves. Although plants are very 39

important to maintain ecosystem health, they may, however, be severely affected 40 by PM pollution (Agbaire and Esiefarienrhe, 2009; Shweta, 2012; Rai, 2013; 41 Panda and Rai, 2015). According to Liu and Ding, 2008, particulate air pollution 42 can directly affect plants via leaves or indirectly via soil acidification. When 43

exposed to airborne pollutants, most plants experience physiological changes 44 before exhibiting visible damage to leaves (Cotrozzi et al., 2017). Foliar surface of 45 plants acts as a sink for PM deposition and through their deposition they show 46

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specific morphological, physiological, and biochemical responses inducing 1 structural and functional changes (Panda and Rai, 2015; Garrec, 2020). Hogan 2

(2010) explains that PM can also clog stomata of plants interfering with 3 photosynthesis functions. In this manner high PM concentrations in the 4 atmosphere can lead to growth stunting or mortality in some plant species. 5

6 Meteorological Implications 7

8 According to Tai et al. (2012), Lecoeur et al. (2012) and Li et al. (2020), the 9

fate of air pollutants is influenced by the movements and characteristics of the air 10 mass into which they are emitted. The measurements of wind speed and direction, 11 temperature, humidity, rainfall and solar radiation are important parameters used 12

in the study of air quality and can assist in furthering understanding of the 13

chemical reactions that occur in the atmosphere (Lecoeur et al., 2012; Wei Dai et 14 al., 2012). 15

As stated by Tai et al. (2010) and Li et al. (2020), PM2.5 concentrations 16 depend on meteorological conditions, suggesting that climate change could have 17 significant effects on PM2.5 air quality and vice versa. PM is comprised of many 18

different species, and meteorology can have complex effects on total PM 19 concentrations due to its impacts on individual species (Dawson et al., 2007; Zhou 20 et al., 2020). Aerosol SO4

2- concentrations depend on the temperature-dependent 21

oxidation of SO2 in both the gas and aqueous phases, and sunlight intensity 22 (Seinfeld and Pandis, 2006 and Dawson et al., 2007; Zhou et al., 2020). On the 23

other hand, concentrations of semi-volatile NO3- and organic aerosols are 24

temperature and relative humidity dependent; they can also vary with the amount 25 of oxidants present, which is linked to photolysis rates and, therefore, cloud cover 26

(Dawson et al., 2007). Tai et al. (2010), Dawson et al. (2007) and Tsigaridis and 27

Kanakidou (2007) report that SO42-

concentrations are expected to increase with 28 increasing temperature due to faster SO2 oxidation, but semi-volatile components 29 such as NO3

- and organics are expected to decrease as they shift from the particle 30

phase to the gas phase at higher temperature. 31

Higher relative humidity (RH) promotes the formation of ammonium nitrate, 32 but an increase in precipitation causes a decrease in all PM2.5 components through 33 scavenging and since all species have wet deposition as a sink, precipitation is 34 expected to have a significant effect on aerosol concentrations (Dawson et al., 35 2007). During a study by Dawson et al. (2007), it was observed that the changes 36

in PM2.5 resulted even in areas with little or no base-case precipitation indicating 37 that changes in precipitation in upwind areas affected PM2.5 concentrations in 38 downwind areas. Seinfeld and Pandis (2006) conclude that changes in absolute 39

humidity have the largest effects on concentrations of ammonium nitrate aerosol 40 with concentrations increasing with increased absolute humidity and that increases 41 in humidity shift the equilibrium of the ammonia-nitric acid system toward the 42 aerosol phase, resulting in higher concentrations of ammonium nitrate aerosol. 43

Finally, mixing and dilution influence PM concentrations, so wind speed and 44 mixing height are expected to have an impact as well. According to Tai et al. 45 (2009), if the air is calm and pollutants cannot disperse then the concentration of 46

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these pollutants will build up. Conversely, if a strong, turbulent wind is blowing 1 any pollution generated will be rapidly dispersed into the atmosphere resulting in 2

lower pollutant concentrations in the air. Wind speed changes affect all species 3 that comprise PM2.5, with increases in wind speed generally leading to decreases in 4 PM2.5 concentrations, and decreases in wind speed generally leading to increases in 5 PM2.5 (Dawson et al., 2007; Ya-Gao et al., 2019). According to Brook et al., 6 (2010), increases in wind speed leads to changes in advection and transport 7

resulting in decreases in PM2.5 concentrations. 8 9 Objectives 10 11

This study seeks to investigate and evaluate the level of PM in the industrial 12

town of Douala in Cameroon. The objectives of the study were to analyse and 13

quantify PM2.5 in terms of mass and mass concentration and the elemental 14 composition of the PM found in Douala. The study also focused on determining 15

the influence of meteorological parameters on PM2.5 concentration in Douala, 16 embarked on apportioning sources to the different PM components that would be 17 identified, and analyzing the variation of fine particles with meteorological 18

parameters, and determine the sources of the PM2.5 in the area. 19

20

21 Methodology 22 23 Sample Collection 24

25 Sampling was done using an electrically powered cyclonic sampler (Casella 26

Group Ltd) capable of collecting PM in PM2.5 – 10 size fractions. The cyclone 27

separates the PM2.5 particles from the air stream and impacts them on 28 polycarbonate filters. Operating at a constant volumetric flow rate of 3L/min, 29 PM2.5 particles were collected for a period of 1 month. The flow through the 30

cyclone was kept constant by a critical orifice (accuracy of ± 15%) to maintain a 31

particle cut off diameter of 2.5µm. Purposefully, samples were collected at 3 sites 32 in the industrial town of Douala (Figure 1); Site 1, the Bonaberi industrial zone 33 (longitude 09

o74” East and latitude 04

o09” North), Site 2, the Bassa industrial zone 34

(longitude 09o68” East and latitude 04

o07” North) and Site 3, the Bonamoussadi 35

residential zone (longitude 09o75” East and latitude 04

o04” North). At site 1, the 36

sampler was placed at a height of 8.5m above ground with the intake nozzle placed 37 1.5m above the platform on which the sampler was placed. The nozzle was placed 38 such that airflow was unobstructed (at all sites) about 200m away from the 39

industrial area and 5m away from the road. The Bassa site saw similar sampling 40 conditions. The sampler was placed facing the industrial zone but away from the 41 road 100m away. It was placed 8.0m above ground and 80m away from the 42 railway. At site 3 the sampler was placed on the flat roof 5m above ground with 43

the nozzle position 2m above the roof and 100m away from the roadside. The 44 sampler was placed such that air flow was unobstructed. Effective sampling time 45 was ± 24hours. 46

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Particle collection was done on 25mm diameter Teflon filters with a pore size 1 of 0.4µm. The polycarbonate filters were initially preconditioned at 45% relative 2

humidity and 20◦C before they were weighed and placed in Petri dishes. After 3

sampling, the filters were again placed and stored in the same dishes. An OHAUS 4 Adventurer Pro electronic analytical microbalance with a sensitivity of 0.0001 mg 5 was used for gravimetric determination of the sampled particle mass. The set up 6 was mounted at each location for 9 sampling days. Since the same setup was used 7

during the whole campaign the different samples can be compared with each other 8 without being influenced by possible differences in sampling flow. 9 10 Figure 1. Map of Study Area showing Sampling Sites in Douala 11

12 Source: Fieldwork, 2012. 13 14 Sample Analysis 15

The samples were analyzed for PM mass concentration, elemental 16 composition and black carbon. A principal component analysis (PCA) was 17 conducted to apportion PM sources. 18 19 PM Mass Concentration 20

Gravimetric analysis was performed to determine the mass concentration of 21 the sample aerosol. 22

The total volume of air sampled was determined from the volumetric flow 23

rate of 3L/min and sampling time in minutes. The concentration of PM2.5 in the 24 ambient air is computed as total mass of collected particles divided by the volume 25 of air sampled in actual conditions. The concentrations are expressed in 26 micrograms per actual cubic meter (μg/m

3). The equation governing the 27

gravimetric analysis is given below: 28 29

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𝐶𝑃𝑀 =𝑀

𝑉

Where, 1 CPM = PM Mass Concentration 2

M = Net mass of the particulate matter collected on the sample filter 3 V = The volume of air sampled 4

5 Elemental Analysis 6

Samples obtained were analyzed for elemental composition by Energy 7

Dispersive X-Ray Fluorescence (EDXRF) spectrometry (Van Grieken and 8 Markowicz, 1993). The spectrometer based on a three-axial geometry consisted of 9 a Siemens Mo-anode X-ray tube with a Mo secondary target that facilitated a good 10 signal to background ratio giving low detection limits (Table 2), and a Kevex Si 11

(Li) detector. According to Boman et al (2005) and Gatari et al (2006), the 12 spectrometer was operated at 50kV and 25mA and the samples were analyzed for 13 1000s. Sample elemental spectra were obtained and evaluated by a quantitative x-14 ray analysis system (QXAS) software provided by the International Atomic 15

Energy Agency (IAEA) laboratories, Seibersdorf, Austria. Atmospheric 16 concentrations and detection limits (DLs) were determined and several of the 17 elements below detection limits were excluded from analysis. 18

19 Black Carbon Determination 20

BC concentrations were determined by a black smoke detector model FH 21

621-N (ESM Emberline, Erlangen, Germany). In this device the samples were 22 illuminated by red light emitting diodes and the reflected light then detected and 23

given as a voltage reading. These voltage readings were later recalculated into BC 24

concentrations. 25

26 Principal Component Analysis 27

Factor analysis with Principal Components as extraction method known as 28 Principal Component Analysis (PCA) in air quality studies is widely used to 29 provide information on PM or gaseous pollutant sources (Querol et al., 2001; 30 Amodio et al., 2010). The PCA technique identifies components that explain the 31

common variation pattern of the included variables (elements, and BC) based on 32 the principle that elements with similar concentration patterns most likely originate 33 from a common source. The components are identified by the PCA, and possible 34 sources of the components ascribed. Values < 0.3 have low loading, those between 35 0.3 and 0.6 are considered to have moderate loadings, those with 0.8 are said to be 36

moderately high loadings and values > 0.8 are considered as high loadings 37

(Boman et al., 2009). 38

39 Meteorological Conditions 40

Since PM2.5 concentrations are strongly dependent on meteorological 41 conditions, it is important to investigate the relationships between PM2.5 and 42 meteorological parameters (Lecoeur et al., 2012). The meteorological information 43

during the measurement campaign was obtained from the Douala Meteorological 44

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centre which records such data for the entire city. The website 1 http:/wunderground.com was also valuable in getting certain daily variations for 2

some climate parameters. 3

4 5 Results and Discussion 6 7

Mass and Mass Concentration 8 9

The mean mass and mass concentration of PM collected from the 3 sites is as 10 shown on Table 1. 11 12

13

14 15

16 17 18

19 20 21

22 23

24 25 26

27

28 29 30

31

32 33 34 35 36

37 38 39

40 The cumulative mean concentration for all sites is 252 ± 130.8µg/m

3 which is 41

relatively higher than the WHO Guideline of 25 µg/m3 for PM2.5 (24-hour mean), 42

as stated by Heal et al. (2012). The daily variation in particle mass concentration 43

of PM2.5 in Douala is clearly shown in Figure 2 below. 44

Table 1(b). Mass and Mass Concentration for Bonaberi Mass

(g)

Mass

Concentration (µg/m3)

Mean 0.0012 275

Standard Dev. 0.000856 197

Range 0 – 0.0025 0 - 579

Median 0.00135 309

Source: Fieldwork, 2012.

Table 1(c). Mass and Mass Concentration for Bassa Mass

(g)

Mass

Concentration (µg/m3)

Mean 0.00115 271

Standard Dev. 0.000475 109

Range 0.0003 – 0.002 77 - 463

Median 0.00115 271

Source: Fieldwork, 2012.

Table 1(a). Mass and Mass Concentration for Bonamoussadi Mass

(g)

Mass

Concentration (µg/m3)

Mean 0.0009 209

Standard Dev. 0.000327 70

Range 0.0003 – 0.0014 77 - 317

Median 0.001 224

Source: Fieldwork, 2012.

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Figure 2. Daily Variations in Particle Mass Concentration of PM2.5 in Douala 1

2 Source: Fieldwork, 2012. 3 4

The huge population of the city, the wide range of commercial and industrial 5 activities, variety of old and new, maintained and poorly maintained vehicles, 6 large scale two wheel activities, and the dependence of the population on biomass 7

significantly contribute to the high value of PM2.5 mass concentration. Open 8 burning and the high dependence on fuel wood and charcoal for domestic and 9

commercial purposes significantly contributes the high BC concentrations for all 10 sampling sites. 11 12

Elemental Analysis 13

14 EDXRF was run for 22 elements, and a total of 12 elements were identified 15

(above detection limits as shown in Table 2), and quantified in most of the 16

samples; Cl, K, Ca, Ti, Mn, Fe, Ni, Cu, Zn, Br, Sr, and Pb (Table 2). 17

18 Table 2. Detection Limits (DL) in µg/m

3 for the Analysed Elements 19

Elt Cl K Ca Ti Mn Fe Ni Cu Zn Br Sr Pb

DL

0.1

2

0.0

24459

0.0

17763

0.0

07277

0.0

02214

0.0

02029

0.0

02

0.0

0159

0.0

01724

0.0

011

0.0

011

0.0

01299

NB: Three axial geometry with Mo secondary target. X-ray tube operated with 50kV and 25mA. Lifetime of 1000s. A

collection time of 24h.

Source: Fieldwork, 2012. 20 21 Full campaign concentration means with standard deviations in µg/m

3 for the 22

analysed elements and the PM2.5 mass concentrations at the three sites including 23 BC concentrations in µg/m

3 are given in Table 2. Reported elemental 24

concentrations are those above DL and blank filter concentrations. N is the number 25 of samples with concentration above DL. DL for analysed elements ranged from 26

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0.0011µg/m3 for Br and Sr to 0.12µg/m

3 for Cl as shown on table 3 which shows 1

the average elemental concentrations for the entire city while figure 3 shows how 2

the elemental concentration varied at different sites. 3 4 Table 3. Average Elemental Concentrations for the Entire City 5

Parameter Range Average

(µg/m3)

Average

(ng/m3)

N

(Max = 24)

Particle mass

concentration

(µg/m3)

0 – 578.7 251.4 ± 133.7 251400 ±

133700 24

Black Carbon 0 – 7.0 1.9 ± 1.815 1900 ± 1815 21

Cl 0.131 - 0.760 0.347 ± 0.189 347 ± 189 9

K 0.048 - 0.507 0.164 ± 0.103 167 ± 103 19

Ca 0.027 - 0.810 0.190 ± 0.195 190 ± 195 23

Ti 0.008 - 0.085 0.029 ± 0.020 29 ± 20 18

Mn 0.003 - 0.126 0.017 ± 0.033 17 ± 33 13

Fe 0.035 - 0.917 0.248 ± 0.216 248 ± 216 24

Ni 0.003 - 0.009 0.005 ± 0.002 5 ± 2 4

Cu 0.002 - 0.013 0.005 ± 0.003 5 ± 3 17

Zn 0.004 - 1.777 0.296 ± 0.469 296 ± 469 24

Br 0.017 - 0.040 0.022 ± 0.005 22 ± 5 24

Sr 0.001 - 0.022 0.004 ± 0.005 4 ± 5 15

Pb 0.007 - 2.075 0.292 ± 0.48 292 ± 480 24 Source: Fieldwork, 2012. 6 7 Figure 3: Variation of Elemental Concentration at Different Sites 8

9 10

For Bonaberi, Ca, Fe, Zn, Br and Pb were present in all samples while for 11 Bassa only Fe, Zn, Br and Pb were identified in all samples. Cl and Ni have the 12 lowest frequencies of occurrence with Cl appearing only once in Bonamoussadi, 13

Ni appearing twice in Bonamoussadi, once in Bonaberi and Bassa with the highest 14 concentration recorded in Bassa. 15

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All concentrations but for Fe, Zn and Pb have concentrations < 0.3µg/m3 for 1

Bonaberi. But for Cl, all other elements have concentrations < 0.3µg/m3 for 2

Bonamoussadi and Bassa. Ca presents relatively high concentrations in Bonaberi 3 than for the other zones most probably due to the presence of the cement industry 4 which is present in Bonaberi and absent in the other zones. 5

While the concentration of Br is relatively low for the other zones, 6 Bonamoussadi presents a higher Br concentration. The high Br concentration 7

could be explained by a high input of particles from biomass burning, since Br can 8 be tracer element for pyrogenic biomass burning. 9

The possible contribution of Br from sea spray or some combustion source 10 especially vehicle exhaust cannot be excluded (Boman et al., 2009). Apart from 11 Cl, K and Ca which are higher in Bonamoussadi than in Bassa, Bonamoussadi 12

generally has lowest concentrations. It can then be concluded that Bonaberi has 13

highest elemental concentrations followed by Bassa then by Bonamoussadi. The 14 industrial zones therefore contain more PM2.5 than the residential zone. 15

The lone appearance of Cl in Bonamoussadi indicates there is no local source 16 in that locality. The high Cl concentration could either be from a sporadic source 17 or blown in by the wind. PM concentrations here seem to be higher than for 18

Bonamoussadi. Ca, Fe, Zn and Pb have highest concentrations as can be seen in 19 Figure 3. Unlike in Bonamoussadi, Ca has peaks higher than the 0.3 line which 20 can be explained by the presence of the cement industry in Bonaberi. Cl also 21

presents concentrations higher than the other sites. 22 The relatively high concentration of Pb especially in Bonaberi can be 23

accounted for by the presence of fuel from unofficial markets (locally known as 24 “fungeh”). Leaded paint is also being scrapped from walls and spread in both old 25 and new construction sites and also at the shipyard (eye witness account). Chronic 26

exposure to low concentrations of Pb is very dangerous and can lead to reduction 27

of intelligence, increased blood pressure and a range of behavioral and 28 developmental effects (WHO, 2005). 29

The results show a large variation in sample mass, BC concentration as well 30

as in the concentrations of the analyzed elements. A look at the individual samples 31

from the different sites (Figure 4) signifies that the variation of the different 32 constituents does not show the same pattern. This adds to the picture of different 33 sources for different pollutants and to the influence of meteorology on PM 34 concentration. The different patterns are as shown in the Figure 4a-c. 35

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Figure 4(a). Individual Element Pattern for Elements Found in PM2.5 in 1 Bonamoussadi 2

3 4 Figure 4(b). Individual Element Pattern for Elements Found in PM2.5 in Bonaberi 5

6 7

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Figure 4(b). Individual Element Pattern for Elements Found in PM2.5 in Bass 1

2 3 Black Carbon (BC) 4

Very high concentrations of BC are recorded for Bonaberi, high 5

concentrations for Bonamoussadi and averagely high concentrations for Bassa. 6 The mean concentration of Bonaberi, 3.17 ± 2.376 µg/m3 as opposed to 1.42 7

±0.459 µg/m3 for Bonamoussadi and 1.10 ± 1.566 µg/m3 for Bassa is relatively 8 very high. The sampling site for Bonaberi is the gateway to and away from the 9

Wouri Bridge hence very dense traffic for most parts of the day hence emissions 10 from vehicle exhaust can explain the high BC concentration. The industrial zone 11 also has very high dependence on fossil fuel for energy production. Bonamoussadi 12

concentrations can be due to the burning of biomass for domestic purposes while 13

vehicle emissions cannot be ignored. Traffic also accounts for that of the Bassa 14 area which is a gateway from Douala to Yaoundé with high traffic at Ndokotti 15 Market. Open burning and the high dependence on fuel wood and charcoal 16

burning for domestic and commercial purposes significantly contribute the high 17 BC concentrations for all the areas. 18

19 Factor Analysis 20 21

Three components were extracted by the analysis explaining a total of 76.8% 22

variance for the whole data set as shown on Table 4. The first component is 23 characterized by high loadings of Fe, K, Zn, and moderately high loadings for Cu, 24 Cl, Sr, Br, Ti, and Ca. This component accounts for 50.5 % of the total variance 25 obtained. The component could be interpreted as a combination of geological 26

(soil/crust) origin and combustion aerosols. There is a combination of high and 27 moderately high loadings for elements which are derived from these sources. K 28 and Fe for example have high loading and indicate soil originated aerosols, Ti and 29

Ca have moderately high loadings and indicate soil originated aerosols while Zn 30 with High loading indicate industrial activity (Boman et al., 2009) especially 31 metallurgical activities (Heal et al.,2012). The moderately high Cl and Br loadings 32 also indicate industrial combustion. Zn and Fe could also be emitted from 33

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lubricating oil additives (used in two-stroke engine where engine oil is mixed with 1 fuel), combustion of impure fuel, vehicle engine and brake tire wear. 2

3 Table 4. Component Matrix for Source Apportionment for Elements in Air 4 Samples from Douala 5

Elements

Components

1 2 3

Cl .754 -.021 -.518

K .882 .367 -.130

Ca .668 .538 .141

Ti .721 .050 .517

Mn .345 .748 .289

Fe .946 -.207 -.063

Cu .779 .147 -.191

Zn .818 -.158 -.150

Br .727 -.357 .168

Sr .753 -.242 -.358

Pb .394 -.730 .394

BC .450 -.049 .581

6

The first component has high loading for all detected elements but for Mn, Pb 7

and BC. Zn compounds are extensively employed as lubricants, antioxidants and 8

as detergent/dispersant improvers for lubricating oils (Begum et al., 2007). The 9 high and moderately high loadings are of more mixed character, possibly 10

representing both soil and combustion origin. To confirm this mixed character, 11 Fe, K, and Ca are characteristic to road dust attributed to diesel vehicle emissions. 12 The industrial and petrochemical waste managing companies that incinerate most 13 of these wastes could also be a significant source for these elements in the 14

atmosphere. 15 The second component is characterized by moderate loading from Mn, and 16

this component accounts for 15.0% of the total variance obtained. Moderate 17 loadings of Mn bear sign of aerosol from the metallurgical industry (Heal et al., 18 2012). This could be confirmed by the moderate loading of Ca in this component. 19

Other activities generating Mn include vehicle engine, brake and tyre wear, and 20

during combustion of impure fuel (particularly coal), and fuel and lubricating oil 21

additives (Heal et al., 2012). Nonetheless, Mn and Ca are typical mineral elements 22 and consequently, this factor could be associated with the crustal fraction of PM2.5 23 (Heal et al., 2005). The presence of these elements in PM2.5 could mainly be a 24 result of local and regional dust re-suspension by wind, convection and other 25 natural processes. However, if the emissions from incomplete and complete 26

combustion of biomass have different sources, this could explain the appearance in 27 two different components in the analysis. 28

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The third component had moderate loading for BC accounting for 11.4% of 1 the obtained variance. BC is a good marker for combustion – derived component 2

of airborne particles. It is also a good marker for traffic-related PM pollution (Heal 3 et al., 2012). 4

PCA for individual sampling sites showed same results as above implying 5 that the individual sites have similar sources of particulate pollution. 6

Ni which was not included in the PCA due to its being below detection limits 7

for most samples mainly comes from dust and re-suspended soil particles. It is also 8 a tracer for the combustion of heavy fuel oil (Boman et al., 2010). 9

10 Influence of Meteorology on PM Concentration 11 12

Diurnal temperatures varied between 23◦C and 31

◦C and relative humidity 13

between 81 and 95%. Winds in Douala were westerly, showing a diurnal pattern 14 with gusts of up to 10m/s during daytime, while evenings and nights were 15

generally calmer with very stable atmospheric conditions. 16 Scattered plots showing variation of meteorological variables with the 17

elemental concentration for the sampling period are given in the Figure 5. 18

19 Figure 5. Variation of PM Mass Concentration with Meteorological Parameters: 20 (A) with Temperature, (B) with Rainfall, (C) with Wind Speed and (D) with 21

Relative Humidity 22

23

24

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1 2

3 4

Trends for the plot for temperature against mass concentration (A) show that 5

as temperature increases in Bonaberi, the mass concentration reduces while mass 6

concentration increases with increase in temperature for both Bonamoussadi and 7

Bassa. These variations can be explained by the chemical species present in the 8 PM2.5. The mass concentration reduction with increasing temperature for Bonaberi 9

could be explained using the species present in the PM2.5. This trend may indicate 10 the dominance of nitrates in this area since nitrates are semi-volatile. It could also 11 indicate the dominance of primary particulates in this region (Seinfeld and Pandis, 12

2007). 13 For Bonamoussadi and Bassa, mass concentration increases with increasing 14

temperature indicating the probable presence of sulphates and dust. According to 15 Seinfeld and Pandis (2007), the oxidation of SO2 to SO4

2- is temperature 16

dependent and increases as temperature increases thus increasing the aerosol 17

concentration. This also indicates that these two areas are dominated by secondary 18 particulates. From literature, one of the precursor gases for the formation of 19

secondary particulates is SO2 thus confirming the above assertion. On another 20 hand, these variations can be explained by wind speeds and direction. The wind 21

blows away from Bonaberi hence transporting away a bulk of the mass of particles 22 with it, whereas for Bonamoussadi and Bassa, the wind blows to their direction 23 bringing in more particles from the upwind. This confirms the theories of Brook et 24 al. (2004), Dawson et al. (2007) and Tai et al. (2009). 25

Mass concentration of PM2.5 shows a slight decrease with increasing rainfall 26

for Bassa. A slight increase is noticed for Bonaberi and a sharp increase for 27

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Bonamoussadi. This can be explained by the nature of rainfall which is 1 predominantly thunderstorms. Depending on the strength of the wind during the 2

storm, the process of wet deposition can be counteracted and the particles blown 3 away in the wind faster than they are deposited or washed out by the rain. In this 4 case, the particles are blown downwind and mass concentration can then increase 5 in the downwind area with increasing rainfall. This confirms Dawson et al. 6 (2007)’s conclusion that changes in precipitation in upwind areas affect PM2.5 7

concentrations in downwind areas and also explains the slight increase in PM mass 8 concentration in Bonaberi as rainfall increases. The degree of change could be 9 affected by the aerosol concentration of the upwind area. 10

PM mass concentration increases with increasing wind speed increases in all 11 the locations contradicting the theories of Dawson et al. (2007) and Tai et al. 12

(2009) which states that if the air is calm and pollutants cannot disperse then the 13

concentration of these pollutants will build up. Conversely, if a strong, turbulent 14 wind is blowing any pollution generated will be rapidly dispersed into the 15

atmosphere resulting in lower pollutant concentrations in the air. This 16 contradicting behaviour could be explained by the thunderstorm effect. However, 17 it should be noted that the month of November which should be dry recorded quite 18

some rainfall which could affect the behaviour of aerosols. In the dry season, there 19 is the general tendency of particles to build up. And if suddenly strong winds and 20 heavy rainfall come to play, patterns could be altered. 21

PM2.5 mass concentration generally decreases as relative humidity increases. 22 According to Dawson et al. (2007) this trend can be explained by the different 23

species in the PM2.5. Nitrates are semi-volatile thus, when relative humidity and 24 temperature increase, they are taken out of the atmosphere by volatilisation hence 25 reducing the mass concentration of PM2.5. This trend could be an indication that 26

the PM2.5 in these areas is dominated by primary particulates. Tsigaridis and 27

Kanakidou (2007) states that nitrates shift from particle phase to gas phase as 28 temperature and relative humidity increase. 29

30 31

Conclusion 32 33

PM2.5 for the city of Douala was collected over a 24hr sampling period and 34 analysed for mass, mass concentration and elemental composition. The cumulative 35 mass concentration of 252 ± 130.8µg/m

3 which is critically higher than the WHO 36

standard of 25µg/m3 was recorded. Cl, K, Ca, Ti, Mn, Fe, Ni, Cu, Zn, Br, Sr, and 37

Pb were identified in the PM collected. The high concentration of Pb indicated the 38 use of leaded fuels and/or paints. The industrial zones exhibited higher PM 39

concentrations than the residential areas. Meteorological parameters greatly 40 influenced the PM concentration. However, the “thunderstorm effect” 41 counteracted the normal variation of PM2.5 with wind speed and precipitation. 42

Diurnal patterns in concentrations suggested a common PM source. From 43

PCA it was concluded that anthropogenic activities like traffic, biomass burning 44 and industrial activities were the major PM sources. Old fleet and poorly 45 maintained vehicles exacerbate the situation. Despite the high emission from these 46

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sources, natural sources such as sea spray and windblown dust significantly 1 contribute to the PM load of this city. 2

Conclusively, the results presented in this study indicate that the majority of 3 the Douala inhabitants are exposed to high air pollution levels in their everyday 4 life. 5 6 7

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