Urban population growth has caused the need for the increase in means of transport, especially in the absence of an organized system of public transport. The motorcycle taxis, expanding in several West African capitals provide cross-country mobility of the populations. These social facts contribute to enhance the level of air pollution in most urban areas in particular in Benin West Africa   .
The theme of air pollution has been widely explored in Benin   -  . Several studies have analysed the issue of air pollution related to road traffic in Cotonou and the exposure of motorcycle taxi drivers to Benin. These air pollution studies focus primarily on measuring the air concentration of carbon monoxide (CO), sulphide dioxide (SO2) on the one hand, and the study of perceptions and exposure of populations, on the other hand. The research reveals that in Benin the level of gases (CO, SO2) is considerably higher than the environmental standards in force, especially on the roads  . In addition, there has few works on fine particles such Particulate Matter (PM2.5). In 2018, a comparative study of PM2.5 concentrations in the cities of Cotonou and Abidjan shows that the concentration of PM2.5 is three times higher than the concentrations recommended by the World Health Organization. In addition, there is a significant difference in carbon aerosols between the Abidjan and Cotonou circulation sites. The average OC/EC ratio averages 4.0 for Cotonou and 2.0 for Abidjan, which clearly indicates the higher contribution of emissions from two-wheeled motorcycles in Cotonou compared to Abidjan  . For two decades, Benin's main cities have been experiencing an explosion in the number of motorized vehicles whose average age is very high as a result of urbanization. These second-hand vehicles imported from Europe or America, as well as motorcycles, have become the most used means of transport in Benin    . Thus, land transport is the primary source of ambient air pollution by exhaust gases followed by industrial activities and other anthropogenic activities   . It is obvious that the roads in Cotonou are places at high risk of exposure of the population. The exposure of the population to air pollution on the roads is more intense according to schedules. Indeed, the level of pollutant in the air is higher on working days than on weekends. Similarly, the level of particle concentration is higher on high traffic than on low traffic  . In addition, the occupation of the roadside for various economic activities is common practice in Cotonou. The exposure of urban populations to fine particles (PM2.5) is therefore a current public health problem in Benin, particularly in Cotonou.
Although few in West Africa, particularly in Benin, studies on PM2.5 concentrations in outdoor air show that factors such as season, intensity/density of road traffic, wind, temperature. For example, the study by Wu, McNaughton  assessed the vertical distribution of PM2.5 and found that road traffic pollution has a small influence on ambient air exposure in the homes of participants in the area study. However, considering individual exposure and factors such as wind and traffic density, exposure to PM2.5 becomes important  . A study in Canada of PM2.5, NO2 and Black Carbon concentrations in vehicles in circulation showed that PM2.5 and NO2 concentrations in vehicles exceeded the level of regional concentrations. In addition, PM2.5 and Black Carbon have higher concentrations inside vehicles than outdoors (>15%); this according to the traffic density  . A study in Turkey has shown that the daily values of PM 10 and PM2.5 exceed the recommendations of the European Union and especially in winter  . The same results were found in Marseille but only on PM2.5  .
Exposure to PM2.5 causes many health risks. After finding non-standard concentrations of PM2.5 in the air, one study found that chronic exposure to PM2.5 is associated with mortality in the eastern and central United States  . Similarly, Dziubanek and Spychala  have confirmed a link between life expectancy and long-term inhalation of PM10 with a higher risk in women. This result was reiterated by another study that focuses on the development of cardiovascular diseases  . Fuertes and Bracher  specify, from the paralleling of air quality and spirometric data, that long-term exposure to NO2 and PM2.5 contributes to weakening pulmonary function in the less of 15 years while identifying asthmatics as the most at risk population which has been corroborated by another study on health effects  .
Indeed, the roadsides are places where many sellers settle to perform their daily activities. The bottleneck of markets and the desire to ensure proximity with customers are factors that perpetuate the phenomenon of occupation of the roadside. The finding is that crossroads and roadsides are favorite sites for sellers and shops selling all kinds of items. The various health risks to residents living near road traffic are a major public health concern in Benin’s socio-economic context.
In this case in Cotonou, Benin, crossroads are points of high traffic. The density of traffic on the intersections is indeed more important. Are road locations such as intersections areas where the PM2.5 exposure level is increased? Are people who work on the roadside more exposed according to traffic intensity schedule? The interest of this work is to study the concentrations of PM2.5 and the risk of exposure of the occupants near the roads according to the road locations and traffic intensity schedule in the city of Cotonou.
2. Materials and Methods
2.1. Study Areas
The study took place in Cotonou, economic capital of Benin republic. Located in the department of “Littoral”, this city is heart of economic activities of people living in Cotonou and areas nears. The measurements were done along one of the main traffic road through the downtown (Figure 1). The traffic on this road is very high throughout the day because it serves the largest market and the port in the city. Two crossroads R1 and R2 been considered along this road. For each crossroad, two points were chosen before and after to measure the exposure of PM2.5 outside the crossroad. Figure 1 illustrates the points of measure chart along the road.
2.2. Study Site and Data Collection
PM2.5 concentrations were measured with Quest 3M, a handy portable device that continuously measures different air quality parameters with the desired measurement time. Measurements were carried out in dry season and rainy seasons. For each season the measurements were made over two months either one week per month. For each week measurements were carried out from Monday to Saturday from 7 am to 9 pm. This time slice was chosen because it determines the period of significant human activity in the study area. The device was placed at 1.5 meters on floor and calibrated to record the data at 5 minutes interval. Measurements were carried out in January and February 2018 (dry season) and June and July 2018 (rainy season).
2.3. Statistical Analysis
The average concentrations of PM2.5 on hourly basis at each point were graphically represented using GraphPad software Prism 6 and Ms-Excel 2016. The Generalized Linear Mixed Model was used to identify factors influencing the level
Figure 1. Different points of measure along the road.
of PM2.5 concentrations along road traffic. The dependent variable was concentration of PM2.5. The explicative variables were location (L) and season (S). L was dichotomized in crossroads and outside roundabout. S was dichotomized in dry and rainy season. Statistical analyzes were performed using package lme4 in R3.5.1 and p < 0.05 was considered significant.
3.1. Hourly Variations of PM2.5 Concentrations
In the rain season the PM2.5 hourly concentrations ranged between 400 μg/m3 and 500 μg/m3while in the dry season hourly PM2.5 concentrations recorded ranged between 100 μg/m3 and 300 μg/m3. It can also be observed in Figure 2 and Figure 3 that from 7 am to 9 pm the variations were remarkable in the dry season compared to the rainy season. In dry season and rainy season, peak value (respectively 244.45 ± 37.19 μg/m3 and 468.08 ± 14.71 μg/m3) was observed between 7 am and 9 am. From 10 am, a continuous decrease in the average hourly concentration was observed. Then, starting at 6 pm, the PM2.5 concentration levels begin to rise again, reaching a peak between 7 pm and 9 pm respectively 177.88 ± 53.65 μg/m3 and 466.07 ± 12.87 μg/m3.
3.2. Concentrations of PM2.5 According along the Road
Levels of PM2.5 concentrations were appeared to be higher in the rainy season compared to dry season (see Figure 4 and Figure 5). At crossroads, the average of PM2.5 concentration was 463.25 ± 66.21 μg/m3 in the rainy season against 232.75 ± 97.29 μg/m3 in the dry season (p < 0.001). At outside of crossroads, the average of PM2.5 concentration was 264.75 ± 50.97 μg/m3 in the rainy season against 123.31 ± 63.79 μg/m3 in the dry season (p < 0.001).
Whether in the dry season or the rainy season, the average concentrations of PM2.5 were seemed to be more important at the crossroads than outside the crossroads. This was confirmed through the Generalized Linear Mixed Model of analysis (GLMM).
Figure 2. Hourly concentrations of PM2.5 in dry season from 8 am to 9 pm.
Figure 3. Hourly concentrations of PM2.5 in rainy season from 7 am to 9 pm.
Figure 4. Concentrations of PM2.5 along the road in January and February (dry season).
Figure 5. Concentrations of PM2.5 along the road in June and July (rainy season).
The result of Generalized Linear Mixed Model showed that there was high significant difference between concentrations of PM2.5 at crossroads compared to outside of crossroads on the one hand and on the other hand, there was high significant difference between concentrations of PM2.5 in the dry season compared to the rainy season (see Table 1).
Table 1. Result of generalized linear mixed model.
The purpose of this study was to study the concentrations of PM2.5 and the risk of exposure of the occupants near the roads according to the road locations and traffic intensity schedule in the city of Cotonou. After the collection, processing, and analysis of the data, it appears that:
- In the rain season the PM2.5 hourly concentrations ranged between 400 μg/m3 and 500 μg/m3;
- In the dry season hourly PM2.5 concentrations recorded ranged between 100 μg/m3 and 300 μg/m3;
- There was high significant difference between exposition of PM2.5 in the dry season compared to the rainy season (p-value = 0.000, α = 5%);
- There was high significant difference between exposition of PM2.5 at crossroads compared to outside of crossroads (p-value = 0.000, α = 5%);
- PM2.5 concentrations highest level is observed in the schedules 7 am to 9 am and 7 pm to 9 pm.
Thus, the objectives of this study were achieved, looking the main results.
4.1. Strengths and Limitations of Study
Measurements of the concentration of PM2.5 in air were made using suitable devices for measuring the concentration of air pollutants. The measurements were carried out by personnel trained in the handling of these devices. The use of the same devices when collecting data on the various targeted points, the meticulous verification of the data entered have made it possible to minimize the risk of erroneous data. In addition, the use of the statistical model Generalized Linear Mixed Model, to determine a predictive model of the concentration level of PM2.5 based on road locations and the season is one of the main strengths of this study.
4.2. Factors Influencing PM2.5 Concentration of Air
This study highlights factors that influence air PM2.5 concentrations, including the season, the time of day, and road locations.
The level of PM2.5 concentrations in the rainy season is significantly above the level in the dry season. In 2008 and 2009, through a study conducted in the city of Eskişehir, central Turkey, they found that concentrations of all the pollutants such nitrogen dioxide (NO2), sulfur dioxide (SO2), particulate matter (PM2.5 and PM10) showed a seasonal pattern increasing in winter period, except for ozone (O3) having higher concentrations in summer season  . The similarity between the results is due to the fact that winter corresponds to rainy season in tropical areas. The high concentrations observed in the rainy season can be explained by the wet conditions. Indeed, moisture does not facilitate the dispersion of particles.
Further results, showed that others factors taking count of daily characteristics like winds. Indeed, authors highlighted that adding temporally-averaged vertical air pollution data had a small effect on residential ambient exposures for their population; however, greater effects were observed when individual days were considered (e.g., winds were off the highways)  .
In urban areas such as the city of Cotonou, the intensity of road traffic varies according to peak hours. These are schedules that coincide with the hours of work start and the hours of closure. This explains the two peak periods of PM2.5 concentrations, especially in the morning between 7 am and 9 am and in the evening between 7 pm and 9 pm. The traffic flow at these times of the day is intense because of shoreline travel to their place of professional activity, and schools, for example. In addition, the fact that official working hours in Benin are from 8 am to 6:30 pm, reinforces the density of road traffic observed at these times of the day.
In addition, the crossroads are rallying points of the main roads of the city, so it is obvious that it represents locations where road traffic is particularly dense compared to other urban roads. The high PM2.5 concentrations measured at the roundabout site are thought to be due to these characteristics of the crossroads of the city of Cotonou.
Locations as crossroads are areas where the level of exposure PM2.5 is highest on road traffics. This research highlights significant difference exists between exposition to PM2.5 at crossroads compared to outside of crossroads, particularly rainy season. Further research should be realized to better understand factors influencing population exposure to PM2.5 in African urban areas.
“Le financement de cette étude a été accordé par le Centre de Recherches pour le Développement International (CRDI) dans le cadre du projet” Chairepol “(projet CRDI 107347) de la Communauté de pratique en écosanté pour l’Afrique de l’Ouest et du Centre (CoPES-AOC)/Chaire ECOSANTE”.
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