
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12508-X
10.1016/j.heliyon.2024.e36477
e36477
Research Article
Contribution of congested traffic flow condition to air pollution at intersections in Addis Ababa, Ethiopia
Moges Getalem Teshager a
Alemu Geteneh Teklie geteneht@kiot.edu.et
b⁎
a Department of Civil Engineering, KIoT, Wollo University, Ethiopia
b Department of Water Resource and Irrigation Engineering, KIoT, Wollo University, Ethiopia
⁎ Corresponding author. geteneht@kiot.edu.et
24 8 2024
15 9 2024
24 8 2024
10 17 e364777 12 2023
13 8 2024
16 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Currently, one of the serious problems in the world is transport-related air pollution. Air pollution from vehicles was not considered properly in the plan, design, and management system of the roads in Addis Ababa city, to solve the problem of traffic congestion. It is influenced by different factors such as road geometry, road surface type, traffic congestion, traffic characteristics, fuel type, and meteorological parameters. This study was aimed to investigate the contribution of congested traffic flow conditions on air pollution at intersection points in Addis Ababa city. To carry out the study, seven intersection points were selected in the city randomly. For the measurement technique of carbon monoxide (CO) and sulfur dioxide (SO2) gases, a portable Aeroqual series 500 instrument was exercised, and for the particular matter (PM2.5 and PM10), Light Amplification by Stimulated Emission of Radiation (LASER) was adapted. To count the number of traffic on the site, a video camera method was utilized. For the data analysis techniques, a multiple linear regression method was practiced, which is very powerful for multiple independent variables with a single dependent one. The study revealed that the level of traffic congestion at each selected intersection is at a severe level. During the congested time, as a base of the steady condition, the pollutants concentration of CO, SO2, PM2.5, and PM10 were increased by an average of 19.10, 51.61, 33.83, and 29.07 % respectively. Based on the analyzed results, when the concentration of pollutants increases, the traffic volume, percentage of heavy vehicles, green time, and approach grade are also increased. On the other hand, the concentration of pollutants decreased when the lane width and wind speed increased. Therefore, any concerned bodies in and around Addis Ababa city should take remedial measures to decrease the emission of the concentration of the pollutants. This investigation is very important for policymakers, municipalities, and planners to do any activities in the city. Researchers can also use it as a reference, for further investigations and remedial measurements.

Keywords

Air pollution
Traffic congestion
Intersections
==== Body
pmc1 Introduction

One of the most urgent problem in the world is traffic congestion, which has a detrimental economic impacts on the entire countries [1,2]. Travel time, vehicle emissions, and operating costs are the three main parameters to evaluate the impact of traffic congestion on the economic status of the societies [3]. Road capacity and construction issues, traffic incidents, weather events, socioeconomic activities, and the growth of populations in urban areas are the main causes of traffic congestion. Traffic congestion can be also affected by increasing the number of car ownerships, travel patterns, public transport operations, urban freight transports, delivery of goods, parking in the outside lane, and an imbalance between the supply and demand of the transportation system [4,5].

Air pollution is the contamination of the atmosphere with extreme quantities of pollutants like carbon monoxide (CO), carbon dioxide (CO2), sulfur oxides (SOx), nitrogen oxides (NOx), volatile organic compounds (VOC), particular matters (PM), ammonia (NH3), ozone (O3), etc [6,7]. This arises from different sources like; smoke from industry, transportation, electricity production, commercial and residential activities, agriculture, etc [8]. Respiratory infections, heart diseases, stroke, and lung cancer are some of the air pollution-related diseases. Outdoor air pollution is higher in urban areas than in rural ones, due to the increment of urbanization, the number of vehicles, and the development of economic activities [9].

The primary concern of the world is the way how to take the mitigation measurements of the problems related to air quality from transportation activities [10]. Many environmental damages are very sensitive, especially in the developing nations with the fastest-growing economies. The damages are not included in the prices paid by the transport users for activities like accidents, noise, land use, and also pollution of air, water, soil, etc. [11]. According to the World Health Organization (WHO) report, transportation, and industries take approximately 16 % of the responsibility for the world's annual mean black carbon emissions. From these emissions, more than 40 % of NOx and PM2.5 come from road transportation activities [12].

Traffic congestion is the most visible and immediate troubling problem in Addis Ababa city. This arises from the day-to-day activities of the people in all parts of the city, especially during the morning and evening peak hours, which have a high number of vehicles. This event affects the community's health, wastes much time for driving and crossing the roads, and late the movements of the people in their day-to-day activities. This event also leads to decrease the working times of individuals, governmental, and non-governmental activities in the city. Due to this, the drivers cannot planned the traveling times accurately because of the challenges at the intersection points of the roads. This leads to generating high emissions of air pollutants in the city.

The main objective of this research was to show the effects of traffic congestion on air pollutant concentrations in Addis Ababa city, Ethiopia. This is a very important investigation to know the current status of the traffic congestion levels, to quantify the contributing factors for air pollution, and to take possible remedial measures for the identified problems in the city.

2 Methodology of the study

2.1 Description of the study area

Addis Ababa is the capital city of Ethiopia which is located in the central parts of the country. It is found in the highland regions of the country with an elevation range from 2000 to 5000m above mean sea level. The topography of the city is from rolling to hilly areas with relatively steep gradients, numerous rivers, and stream valleys. The precipitation of the city is more varying with different months and the temperature difference is up to 10 °c, depending on the elevation and prevailing wind patterns. Addis Ababa is a seat of the African Union and there are more than one hundred Embassies in the city, from different countries. Based on the 2017 census data, the population of the city was 4 million. Addis Ababa has 10 sub-cities and it holds 527 km2 of area with an estimated population density of 5,165 individuals per square kilometer.

2.2 Methods of data collection and analysis

Based on the Addis Ababa city administration road authority's office evidence, there are seventy-two signalized and thirty-six roundabout intersections in the city. By considering the limitation of budget, time, and workforce, seven controlled intersections were selected using the convenience sampling technique. The selected intersections were five signalized (Sholla, Legehar, Jemo Micheal, Africa Union, and Imperial) and two roundabout (Teklehaymanot and Abune Petros) intersections. The latitudinal and longitudinal location of the selected intersection are; Sholla (9.02°N, 38.79°E), Legehar (9.01°N, 38.75°E), Jemo Micheal (8.97°N, 38.72°E), Africa union (9°N, 38.74°E), Imperial (9.01°N, 38.764°E), Teklehaymanot (9.03°N, 38.743°E) and Abune Petros (9.03°N, 38.75°E). The selected intersections were representatives of the congested areas in the city, by considering different corridors away from other sources of air pollution like industry, agriculture, waste, etc.

The relevant data to accomplish this study were traffic (traffic count, survey, and signal data), road (lane and median width, circular lane number and width, altitude, approach grade, island diameter), ambient air quality (concentration of CO, SO2, PM2.5, and PM10), and meteorological (temperature, humidity, wind speed, and wind direction) data. These data were collected from field surveys and measurements. A traffic count survey was conducted in each selected intersection by using a video camera and then it was counted in manually. The geometric data were collected by field measurements using meter on a less traffic flow days which is Sunday for each intersection, whereas the altitude, approach grade, and distance of each section were obtained from Google Earth.

After the data were collected, analysis of the traffic volumes in the passenger car unit (PCU) and the peak hour volumes were computed. Because, determining the total traffic volume in PCU for the given time interval is very important, to convert the different classes of vehicles into a single unit, to measure the level of traffic congestion, and to show its effect on the ambient air quality. Then the peak hourly volumes of the passenger car unit and the traffic congestions were analyzed by using Signalized Intersection Design and Research Aid (SIDRA) software. Finally, compare the concentration of field measurement values with its standards which are adopted by the Ethiopian Environment Protection Authority (EEPA) and World Health Organization (WHO). The general framework of the study is briefly described in Fig. 1.Fig. 1 Graphical representation of the research methodology.

Fig. 1

Before going to the measurements, calibration of the instrument was a mandatory task. This was held by preparing the equipment, accessing the calibration menu, and selecting ZERO CAL (resetting the zero point of the monitor). The data were measured during the dry season's, Within the two months (October and November). The measurement was done within 15-min intervals on the consecutive working days from 7:30 a.m. to 6:00 p.m. and the vertical position of the instrument was at 1.5 m above the ground. The minimum, average, and maximum measured data from field surveys like traffic, road, air pollutant concentration, and meteorological parameters are briefly discussed in Table 1.Table 1 The minimum, average, and maximum values of the measured data.

Table 1Data description	Minimum	Average	Maximum	
Traffic data	Traffic volume (PCU/15 min)	173	432	873	
Green time (Second)	0	40	75	
Percentage of heavy vehicles (%)	2	12	30	
Road data	Approach grade (%)	2	3.5	6	
Lane width (Meter)	3	3.3	4	
Air pollutant concentrations	Carbon monoxide (μg/m3)	1007	6790.19	12,010	
Sulfur dioxide (μg/m3)	16	150.67	460	
Particular matter 2.5 (μg/m3)	33	148.87	328	
Particular matter (μg/m3)	52	202.92	383	
Meteorological data	Temperature (oc)	18	21	27	
Humidity (%)	38	43	53	
Wind Speed (Km/hr)	5	7	10	
Type of control (if signalized = 1, otherwise = 0)	0	0.714	1	
Time of day	Time of day (if morning = 1, otherwise = 0)	0	0.5	1	
Time of day (if afternoon = 1, otherwise = 0)	0	0.5	1	
Day of week	Tuesday (if Tuesday = 1, otherwise = 0)	0	0.333	1	
Wednesday (if Wednesday = 1, otherwise = 0)	0	0.333	1	
Thursday (if Thursday = 1, otherwise = 0)	0	0.333	1	
Wind direction	East north (if East north = 1, otherwise = 0)	0	0.343	1	
East south (if East south = 1, otherwise = 0)	0	0.429	1	
West north (if West north = 1, otherwise = 0)	0	0.1	1	
West south (if west south = 1, otherwise = 0)	0	0.114	1	

One of the most well-known data analysis technique is multiple regration method. This is a broad class of data analysis methods that includes linear and nonlinear regressions with many explanatory variables. In this study, the multiple linear regression model was preferred, because it can identify both outliers and estimates the relative influence of two or more independent variables on the dependent one. This leads to a more accurate and precise understanding of the association of each factor with the outcome [13]. The multiple linear regression model analysis is based on assumptions [14,15]. Those assumptions are; each independent and dependent variables are relatively linear, the independent variables have not a high range of correlations with the others, residuals should normally be distributed with a mean of 0 and variance σ, residuals should have a constant variance along with the fitted value (homoscedasticity). The multiple linear regression analysis technique is calculated by the formulas described in Equation (1).(1) Y=β0+β1X1+β2X2+β3X3+β4X4+…+βnXn

in this study, the selected pollutants were CO, SO2, PM2.5, and PM2.5 to conduct for roadside ambient air qualities. Because those are more dangerous to society's health, and there was also a limitation of instruments availability for the measurements of other pollutants. Those parameters were also utilized to show the effect of traffic congestion among the most common air pollutants. The maximum increased percentage of the selected pollutants such as; CO, SO2, PM2.5, and PM10 formulas are described from Equations (2), (3), (4)).Theincreasepercentage(%)ofmaximumCO,SO2,PM2.5,PM10

(2) =(Congestedmaximum−Steadymaximum)*100Steadymaximum

Theincreasepercentage(%)ofmaximumCO,SO2,PM2.5,PM10

(3) =(Congestedaverage−Steadyaverage)*100Steadyaverage

Theincreasepercentage(%)ofmaximumCO,SO2,PM2.5,PM10

(4) =(Congestedminimum−Steadyminimum)*100Steadyminimum

3 Result and discussion

3.1 Traffic congestion analysis of the study area

Traffic congestion level at the intersection point of the road can be analyzed with different methods, but in this research, SIDRA software was selected. The utilized input parameters for SIDRA intersection software are road data (number of lanes, lane and median width, approach grade, and distance), traffic data (peak hour volume which is the maximum hourly volume by summing up the four consecutive 15 min in PCU, percentage of vehicle which is the ratio of total heavy vehicle volume to the total traffic volume in PCU times 100 % and peak hour factor is the ratio of peak hour volume to 4 times the peak of 15-min volumes within the peak hour) for signalized intersection phases. But for round intersections, a circular lane number, lane width, and island diameter were inserted into the software.

Finally, the SIDRA software generates the level of services (LOS) as well as the volume-to-capacity ratio (V/C) of the selected approaches at each intersection of the roads. The result of this analysis is shown in Table 2 and it is also briefly discussed in the supplementary material 1. Based on the Nashua Regional Planning Commission level of traffic congestion identifications, each selected intersection has a severe congestion level since each intersection has F level of services and volume to capacity ratio is greater than one (V/C > 1) which may result in the pollution of ambient air quality due to road transportation [16].Table 2 Levels of traffic congestion from the selected intersections.

Table 2No	Name of intersection	Type of intersection	Level of service	Volume to capacity ratio	Level of traffic congestion	
1.	Africa union	Signalized	F	1.936	Severe Congestion	
2	Jemo Michael	Signalized	F	2.748	Severe Congestion	
3	Legehar	Signalized	F	1.88	Severe Congestion	
4	Imperial	Signalized	F	1.703	Severe Congestion	
5	Sholla	Signalized	F	1.584	Severe Congestion	
6	Teklehaymanot	Roundabout	F	1.581	Severe Congestion	
7	Abune Petros	Roundabout	F	1.373	Severe Congestion	

3.2 Contribution of congested traffic flow on air pollution

The percentage of increases from the concentrations of the pollutants at maximum, minimum, and average levels were computed. This is very important to understand the effect of traffic congestion on air pollution. The activities were done by separating the measured air pollutants concentration with different traffic flow conditions by filtering the data at congested and steady states. The estimated minimum, average, and maximum concentrations of CO, SO2, PM2.5, and PM10 were briefly described in Table 3.Table 3 The contribution of traffic congestion to air pollution as a base of steady condition.

Table 3Air pollutants	Traffic flow conditions	Minimum	Average	Maximum	
(μg/m3)	(μg/m3)	(μg/m3)	
CO	Congested	1,521	7,382	12,010	
Steady	1,007	6,198.38	9,101	
Percentage of increases (%)	51.04	19.1	31.97	
SO2	Congested	20	181.57	460	
Steady	16	119.76	420	
Percentage of increases (%)	25	51.61	9.5	
PM2.5	Congested	39	170.4	328	
Steady	33	127.33	239	
Percentage of increases (%)	18.18	33.83	37.24	
PM10	Congested	61	228.67	383	
Steady	52	177.17	311	
Percentage of increases (%)	17.31	29.07	23.15	

The increment percentage of air pollutants concentration of minimum, average, and maximum values were; for CO (51.04, 19.10, and 31.97 %), SO2 (25, 51.61, and 9.5 %), PM2.5 (18.18, 33.83, and 37.24 %) and PM10 (17.31, 29.07 and 23.15 %) respectively. In the ambient air records during site measurements, the average concentrations of CO, SO2, PM2.5, and PM10 for congested and steady state traffic flow conditions were (7,382 & 6,198.38), (181.57 & 119.76), (170.40 &127.33) and (228.67 & 177.17) μg/m3 respectively. From the analyzed data, the increased average air pollutants concentration during congested and steady state traffic flow conditions for CO, SO2, PM2.5, and PM10 were 19.10, 51.61, 33.83, and 29.07 % respectively.

The effect of congested traffic flow is higher than from the steady conditions, for the estimated CO, SO2, PM2.5, and PM10 concentration's as it described briefly in Table 3 and Fig. 2(a–d). In congested traffic flow conditions, there are a large number of vehicles, and it also leads to spending more time rather than the steady traffic flow conditions, especially at the signalized intersections, with higher acceleration, and deceleration movements. In addition, most of the vehicular ages are very oldies which can contribute to more emissions in the congested traffic flow conditions. This event leads to increase the emission of air pollutants. However, the effect is not only by traffic data but also by geometric and meteorological factors.Fig. 2 Concentrations of CO, SO2, PM2.5, and PM10 at congested vs. steady traffic flow conditions.

Fig. 2

3.3 Regression analysis and models for CO, SO2, PM2.5, and PM10 air pollutant parameters

Before performing the regression analysis, checking the assumptions is very essential. The assumption results for CO are discussed briefly in this section.

3.3.1 Assumptions of multiple linear regression model for CO

Assumption 1 There is a linear relationship between the dependent and independent variables.

From this research, when the traffic volume increases, the concentration of CO is also increased. As the wind speed decreases, the concentration of CO is decreased. This result shows that the relationship between the dependent and independent variables are linear. Therefore, this satisfies assumption 1. This relationship is briefly discussed in Fig. 3(a–c).Fig. 3 The relationships between CO concentration with traffic volume, green time, and wind speed.

Fig. 3

Assumption 2 The independent variables are not too highly correlated with each other.

From this research, the correlation coefficient among the independent variables was lower than 0.8. Therefore, this result indicates that each independent variable is not highly correlated. The general correlation coefficients among the independent variables are briefly discussed in Table 4.Table 4 Correlation coefficients among the independent variables.

Table 4Independent variables	Traffic volume	% of HV	green time	Humidity	Temperature	Wind speed	Altitude	Approach ch grade	
Traffic volume	1								
% of HV	0.014	1							
Green time	0.532	0.11	1						
Humidity	−0.133	−0.209	0.136	1					
Temperature	−0.235	−0.109	−0.011	0.125	1				
Wind speed	−0.122	−0.225	−0.113	−0.179	−0.089	1			
Altitude	−0.52	−0.493	−0.678	0.105	−0.062	0.327	1		
Approach Grade	0.143	−0.411	−0.322	−0.009	−0.084	0.085	0.409	1	
Approach distance	0.118	0.603	0.061	−0.395	0.119	−0.105	−0.602	−0.516	
Number of lane	0.083	0.011	0.282	−0.409	0.265	−0.192	−0.222	−0.227	
Lane width	−0.056	0.112	−0.041	−0.178	−0.295	0.588	0.068	−0.13	

Assumption 3 Residuals should be normally distributed with a mean of 0 and variance σ.

The concentration of CO with its frequency is a bell-shaped normal distribution graph and from the normal probability plot, the points are approximately a straight line, as it described in Fig. 4(a–b). This shows that the residual is almost normally distributed and it satisfied the assumption.Fig. 4 The concentration of CO with normal density and standardized normal probability plot.

Fig. 4

Assumption 4 Residuals should have constant variance with fitted values (homoscedasticity).

This was checked by plotting the residual Vs fitted (predicted) values. The computed result indicates that the standardized residuals are concentrated around zero which implies that the variance is homogenous, as briefly described in Fig. 5. Therefore, this result also satisfied the assumption.Fig. 5 Residual vs fitted value of CO.

Fig. 5

Assumption 5 Check the outliers using a box plot.

It is advised, to avoid the outlier data from the analysis because it leads to bias in the computed results. Based on the calculated results, there is no outlier for CO. Therefore, in this test the assumption is satisfied as discussed in Fig. 6.Fig. 6 Identified the outlier data from the box plot of CO concentration.

Fig. 6

The assumption status of SO2, PM2.5, and PM10, are briefly discussed in supplementary material 2. Since all five assumptions were satisfied, a multiple linear regression model was selected and applied to the analysis techniques.

Different methods are utilized to know the reliability of the estimated data, but in this research, the selected technique was coefficient of determination (R2). Coefficients of determination (R2 and adjusted R2) are used to estimate how well the regression model fits with the given data. Adjusted R2 provides a more honest association between dependent and independent variables than R2. The probability of F ratio (P > F) indicates the statistical significance of the overall regression models, while the probability of t-test (P > t) shows the statistical significance of the independent variables, and the coefficient indicates, how much the dependent variable varies with the independent one. This is done by holding all other independent variables as a constant. Root mean square error (MSE) shows the average distance of the estimations from the mean or standard deviation of the regression. If the P-value is less than 0.05 ((p≥|t|) <0.05) for a 95 % confidence interval or a 5 % significance level, the coefficients are statistically significant to apply the regression model.

From the result, the adjusted coefficient of determination R2 were 85.5, 89.6, 89.7, and 82.5 % for CO, SO2, PM2.5, and PM10 respectively. This shows that the regression relationship is very strong since the variance is explained by a linear relationship between the dependent and the predicted variables. Generally, the linear regression model of CO, SO2, PM2.5, and PM10 air pollutant concentration is well-fitted. Since P > F is equal to 0 which is less than 0.05, the model is statistically significant.

Increasing the traffic volume, percentage of heavy vehicles, green time, temperature, and approach grade were positively associated with CO, SO2, PM2.5, and PM10 as discussed in Table 3. When the traffic volume increases by one unit, the air pollutant concentrations of CO, SO2, PM2.5, and PM10 were increased by 0.41, 6.8, 0.75, and 0.08 respectively, by holding all other independent variables as constant. In the other case, when the percentage of heavy vehicles increased by one unit, the air pollutant concentration of CO, SO2, PM2.5, and PM10 were increased by 88, 3.77, 2.59, and 2.8 respectively, by holding all other independent variables also as constant. This is true that the increment of heavy vehicles in daily traffic volume causes to increase the concentration of CO, SO2, PM2.5, and PM10 variables.

When the green time increases by one unit, the air pollutants concentration of CO, SO2, PM2.5, and PM10 were increased by 266, 11.17, 2.96, and 6.14 respectively, by considering all other independent variables as constants due to the large number of vehicles releasing gas at a higher acceleration rates. When the temperature increases by one unit, the air pollutants concentration of CO, SO2, PM2.5, and PM10 were also increased by 166.16, 39.45, 5.06, and 7.16, by controlling other independent variables as constant. This event was also described by Refs. [[17], [18], [19]]. For particular mater 10, the air pollutant concentration increases by 19.94, when the approach grade increases by one unit, with a consideration of all other independent variables as constant. This is happened, due to the increment of the road grade which creates a high load on the vehicle, and it leads to the production of more PM10. This effect is also discussed in the same manner by Ref. [20].

Wind speed and lane width were negatively associated with CO, SO2, PM2.5, and PM10 emissions. When the wind speed increases by one unit, the concentrations of CO, SO2, PM2.5, and PM10 are decreased by 399, 7.6, 8.63, and 16.06 respectively. This relationship was also described by Ref. [21]. When the lane width increased by one unit, the concentrations of CO, SO2, PM2.5, and PM10 were decreased by 3110, 204, 43.94, and 80 respectively, by holding all other independent variables as constant. This is due to the density of the vehicles decreasing as the lane width increases, which may reduce the emissions. This indicates that when the lane width increases, the drivers have the chance to navigate at a higher speed, which in turn reduces the CO, SO2, PM2.5, and PM10 emissions in the environment. The overall estimated concentration results of CO, SO2, PM2.5, and PM10 are briefly discussed in Table 5.Table 5 Overall estimated concentration results of CO, SO2, PM2.5, and PM10 parameters.

Table 5
Variables	Carbon monoxide, CO (R-square = 0.8654, Prob > F = 0.000)	Sulfur dioxide, SO2 (R-square = 0.9045, Prob > F = 0.000)	Particular matter, PM2.5 (R-square = 0.9134, Prob > F = 0.000)	Particular matter, PM10 (R-square = 0.8528, Prob > F = 0.000)	
Coef	Std. Err.	p≥|t|	Coef	Std. Err.	p≥|t|	Coef	Std. Err.	p≥|t|	Coef	Std. Err.	p≥|t|	
Traffic volume	6.8006	0.82712	0.000	0.405274	0.04133	0.000	0.754906	0.01566	0.000	0.0778	0.23082	0.001	
% of Heavy vehicle	87.830	20.1142	0.000	3.765981	0.76898	0.000	2.593477	0.8435	0.003	2.807147	1.24059	0.027	
Green time	266.08	29.0235	0.000	11.16648	0.62204	0.000	2.963997	0.95721	0.003	6.142149	1.4086	0.000	
Humidity	−13.66	34.5201	0.693										
Temperature	166.16	62.3353	0.008	6.520592	2.72468	0.018	5.05544	1.89050	0.009	7.15696	2.78199	0.012	
Wind speed	−399.13	87.1295	0.000	−7.6038	3.48040	0.030	−8.62559	4.2376	0.046	−16.0558	6.23589	0.012	
Altitude	−74.75	10.2142	0.000	5.524353	0.31182	0.000	1.849366	0.55399	0.001	3.704088	0.81573	0.000	
Lane width	−3110.6	372.805	0.000	−204.64	10.8363	0.000	−43.9097	13.9509	0.002	−80.3214	20.5298	0.000	
Grade				39.44844	5.52363	0.000	7.549157	2.94306	0.012	19.94423	4.33089	0.000	
Approach distance				0.696789	0.05029	0.000	0.282902	0.06998	0.000	0.452918	0.10298	0.000	
Type of control (if signalized = 1, otherwise = 0)	−23763	2929.64	0.000										
Tuesday (if Tuesday = 1, otherwise = 0)				−4.77693	7.54677	0.528	−8.74257	6.64236	0.192	−9.9827	9.77465	0.311	
Wednesday (if Wednesday = 1, otherwise = 0)				−20.4673	7.59882	0.008	−9.51817	6.68433	0.152	−0.51765	9.83641	0.958	
Time of day (if morning = 1, otherwise = 0)	106.71	176.74	0.547	−14.6059	6.93937	0.037	−3.99144	7.08205	0.575	−16.8505	10.4217	0.110	
East north (if East north = 1, otherwise = 0)	−3109.5	541.056	0.000										
East south (if East south = 1, otherwise = 0)	−1149.7	436.267	0.009	170.7471	14.0054	0.000	84.17505	9.31710	0.000	82.23279	13.7107	0.000	
_cons	203077	27698.7	0.000	−13389.1	797.449	0.000	−4287.98	1345.03	0.002	−8602.61	1979.30	0.000	

Finally, by using Table 5, the models were developed for future conditions to determine the concentration of CO, SO2, PM 2.5, and PM10 from different factors. This were done by using Stata software. These models will be useful for policymakers, transportation planners, road agencies, government, and designers of road networks to minimize air pollution from traffic congestion and also it can be used as a stepping-stone for further research works. The developed models are briefly discussed from Equations (5), (6), (7), (8)).1. The developed models for CO concentration

(5) CO = 203,077.3 + 6.8 TV + 87.83 % of HV + 266.08 GT + 166.16 T- 399.13 WS – 74.75 AL – 3110.64 LW – 23763.03 DS – 3109.55 DEN – 1149.67 DES

2. The developed models for SO2 concentration

(6) SO2 = −13,389.05 + 0.405 TV + 3.766 % of HV + 11.166 GT + 6.52 T- 7.604 WS + 5.524 AL + 39.448 AG + 0.697 AD – 204.64 LW – 20.467 DW - 14.606 DM + 170.747 DES

3. The developed models for PM2.5 concentration

(7) PM2.5 = −4287.98 + 0.075 TV+2.593 % of HV +2.964 GT–8.63 WS + 5.055 T + 1.849 AL + 7.549 AG + 0.283 AD – 43.91 LW + 84.175 DES

4. The developed models for PM10 concentration

(8) PM10 = −8602.612 + 0.08 TV + 2.807 % of HV + 6.142 GT–16.056 WS + 7.157 T + 3.704 AL +19.944 AG + 0.4529 AD – 80.321 LW + 82.232 DES

where:- TV = traffic volume

- HV = % of heavy vehicle (%)

- GT = Green Time of the signal of the selected approach (second)

- T = temperature (°C)

- WS= Wind Speed (Km/hr)

- AL = Altitude (Meter)

- AG = Approach Grade between the consecutive major intersections (Meter)

- AD = Approach distance between the consecutive major intersections (Meter)

- LW = lane width (Meter)

- D = Dummy variable

- DS = D = 1 if signalized while D = 0 if roundabout

- DEN = D = 1 if East North wind direction while D = 0 for others

- DES = D = 1 if East south wind direction while D = 0 for others

- DM = D = 1 if morning while D = 0 if afternoon

The developed models of the pollutants give fitted results with the measured values. The results of the developed model output with the measured values from Stata software for CO, SO2, PM2.5, and PM10 are briefly discussed in Fig. 7(a–d).Fig. 7 The measured and modeled concentration results of CO, SO2, PM2.5, and PM10.

Fig. 7

4 Conclusion

This research was conducted at seven selected intersections in Addis Ababa, Ethiopia. The study was focused, on investigating the contribution of congested traffic flow conditions on air pollution at intersection points in the selected city. The first task, of the study was to understand the level of traffic congestion at the selected intersections. Based on the volume-to-capacity ratio results, all the selected intersections have higher congestion levels. The increment concentrations of the pollutants of CO, SO2, PM2.5, and PM10 at maximum, average, and minimum levels are (31.97, 19.10 & 51.04 %), (9.5, 51.61 & 25 %), (37.24, 33.83, & 18.18 %) and (23.15, 29.07 & 17.31 %) respectively. Based on the analyzed results, the concentration of the pollutants was mainly influenced by traffic data, geometric and weather condition parameters. The concentration of pollutants was increased when the traffic volume, percentage of heavy vehicles, green time, and grade of the approaches were also increased. On the other hand, the concentration was decreased when the lane width and wind speed were also increased.

This research is very important for road agencies, policymakers, transportation planners, municipalities, and other governmental and none governmental organizations. This is also used as a reference point, for planning, identifying, and designing stages of road networks while minimizing air pollution due to traffic congestion from road transportation systems.

From this study, some recommendations were proposed, to reduce the effects of the traffic congestion problems in the city. The concerned bodies should take care of time allocation, especially for heavy vehicles, improving the networks, and applying rapid bus transit systems. Additionally, modern roundabouts channelized approach, yield control on all entries, counter-clockwise circulation of the vehicles around the central island, and an appropriate geometric curvature to inspire slow speeds through the intersections are very important parameters to reduce air pollution in the city. Expanding the railway transport system within the city is also very important to reduce air pollution, due to congestion time from vehicles on the road. Designing and applying signal timing from the sequence intersection of the traffic flow is also another solution to reduce congestion and concentrations of air pollutants.

Data availability

The data used to support the results of this study are included in the article.

CRediT authorship contribution statement

Getalem Teshager Moges: Writing – original draft, Software, Project administration, Methodology, Investigation, Formal analysis, Conceptualization. Geteneh Teklie Alemu: Writing – review & editing, Visualization, Validation, Supervision, Data curation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

The following is the Supplementary data to this article:Multimedia component 1

Multimedia component 1

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e36477.
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