J. Nig. Soc. Phys. Sci. 6 (2024) 1898 Journal of the Nigerian Society of Physical Sciences Spatial distribution and policy implications of the exhaust emissions of two-stroke motorcycle taxis: a case study of southwestern state in Nigeria P. K. Alimoa, G. Lartey-Youngb, S. Agyemanc, T. Y. Akintunded, E. Kyere-Gyeaboure, F. Krampahf, A. Awomutib,g, O. Oderindeh,∗, A. O. Agbejai, O. G. Afolabij aCollege of Transportation Engineering, Tongji University, 4800 Cao’an Road, Shanghai, P.R. China bUNEP-Institute of Environment and Sustainable Development (IESD), Tongji University, 1239 Siping Road, Shanghai, 200092, P.R. China cDepartment of Civil Engineering, Sunyani Technical University, Sunyani, Ghana dDepartment of Sociology, School of Public Administration, Hohai University, Jiangning Campus, Nanjing, P.R. China eDepartment of Geography and Resource Development, University of Ghana, Legon, Ghana fDepartment of Environmental and Safety Engineering, University of Mines and Technology, Tarkwa, Ghana gState Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, P.R. China hDepartment of Chemistry, Faculty of Natural and Applied Sciences, Lead City University, Ibadan, Nigeria iDepartment of Sustainable Forest Management, Forestry Research Institute of Nigeria, PMB 5054, Ibadan, Nigeria jDepartment of Works and Physical Planning, Babcock University, Ilisan-Remo, Ogun State, Nigeria Abstract Two-stroke motorcycles emit harmful exhaust fumes because of incomplete combustion. Although they constitute the main fleet of motorcycle taxis in sub-Saharan Africa, monitoring, spatial assessment, and regulation are weak, leaving dire health consequences in cities. This study collected motorcycle raw exhaust emissions of 1,950 two-stroke petrol-driven motorcycle taxis, otherwise called okada, in Ogun State, Nigeria, using an idle mode test approach under 10 minutes and employed correlations, hierarchical multiple linear regression models, and spatial analysis. It was found that carbon monoxide (CO) and hydrocarbons (HC) were the most highly concentrated, and the latter were beyond allowable limits. The concentration of CO was found to be at the minimum of 0.00 % and the highest being at 6.40% (an average of 1.05%), while the HC concentration was reported at the minimum of 18.00 ppm and the highest at 15446 ppm (an average of 3560 ppm). Notably, Kriging interpolation analysis indicated that cumulative effects due to the clustering and operations of motorcycle taxis could increase these concentrations over time, extending their long-term impacts. Given the severe effects of these emissions on health and the wider environment, a DPSIR policy framework is proposed to regulate two-stroke motorcycle taxis in sub-Saharan Africa. DOI:10.46481/jnsps.2024.1898 Keywords: Motorcycle taxi, Motorcycle emission, Two-stroke engines, Idle mode test, Spatial analysis Article History : Received: 11 November 2023 Received in revised form: 25 March 2024 Accepted for publication: 27 March 2024 Published: 03 April 2024 © 2024 The Author(s). Published by the Nigerian Society of Physical Sciences under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Communicated by: Muyiwa Orosun 1 https://nsps.org.ng https://creativecommons.org/licenses/by/4.0 Alimo et al. / J. Nig. Soc. Phys. Sci. 6 (2024) 1898 2 1. Introduction Air pollution is a leading threat to human health glob- ally, causing approximately 4.2 million premature deaths [1, 2]. Transportation contributes 23% of total carbon emissions [3]. In sub-Saharan Africa, two-stroke motorcycle taxis, preferen- tially called ‘Okada’, constitute a major source of traffic pollu- tion [4, 5]. Most motorcycle taxis are composed of two-stroke powered engines banned in some countries outside sub-Saharan Africa due to their high carbon emission rates and incomplete combustion [6, 7]. However, they are preferred for first-and- last-mile transport in at least 27 countries [8–10], generating income for the unemployed youth [11–14]. Therefore, how to regulate the increasing demand for two-stroke motorcycle taxis amid their health consequences remains a significant pol- icy concern. The engine capacities of motorcycles are a distinguishing feature [15]. Chen et al. [16] observed that two-stroke engines have elevated emissions of hydrocarbons (HC) and low nitrogen oxides (NOx). Similarly, two-stroke motorcycles make more ozone (O3) than other motorcycles. Tsai et al. [17] investigated the carbon monoxide (CO), total hydrocarbon (THC), and NOx emissions from seven new and twelve in-use motorcycles with and without catalysts. They observed that in-use motorcycles exhibited higher CO and HC and lower NOx emissions than the new ones. Additionally, volatile organic compounds (VOCs) at varying operation modes were reported to emit more during decelerating and idling modes [17, 18]. However, there is little empirical evidence of the propor- tions of chemical compositions and the spatial distribution of two-stroke motorcycle exhaust emissions in sub-Saharan African cities. This knowledge gap makes it challenging for policymakers to propose enforcement and environmental mit- igation measures to curb the situation. Despite the more haz- ardous health implications of two-stroke motorcycle emissions, road-based and simulated emission tests have extensively fo- cused on cars due to their higher city densities. In addition, all exhaust emission limit values proposed by developed countries (Euro I-IV) have primarily focused on four-stroke powered engines, as two-stroke engines are now considered obsolete. However, these two-stroke engines are still largely depended upon in Africa. Chan et al. [19] re- ported that motorcycles emit twelve times more total hydrocar- bons (THCs) and CO than cars. In contrast, the emission pro- files (CO, HC, NOx, and CO2) of motorcycles, when correlated to passenger cars, revealed that emissions of NOx from mo- torcycles were elevated [20, 21]. The higher emissions of mo- torcycles are caused by their incomplete fuel combustion and poor emission control technologies [22–24]. Benzene emis- sions were observed to have been emitted at low proportions for cars fitted with catalytic converters compared to motorcy- cles [25, 26]. However, Benzene emissions were found to be very low for cars and other vehicles without a catalytic con- verter [19, 27]. These indications suggest that policies target- ∗Corresponding author Tel. No.: +234-803-280-1872. Email address: oderinde.olayinka@lcu.edu.ng (O. Oderinde ) ing controlling and reducing two-stroke motorcycles’ emissions must be centered around high empirical data. This empirical study was conducted in Ogun State in south- western Nigeria to address these research gaps. In Ogun State, two-stroke motorcycles constitute 33% of vehicular traf- fic alone. The high preference for motorcycle taxis has rendered it challenging for regulation and impossible for an outright ban [13, 28, 29]. The calls for motorcycle bans have been premised on their susceptibility to crashes rather than their emissions and environmental/health hazards [30]. Besides, it has been found that outright bans on motorcycles can lead to increased private car ownership, which also has adverse effects on the environ- ment [31]. Thus, rather than discussing bans, knowing the hot spots and finding regulatory solutions is more productive. Notably, Nigeria does not follow the standards of the Eu- ropean Union (EU) or the United States but has its local ve- hicular standards [32]. The operating requirements applicable to these motorcycles, as set out in Section 98 of the National Road Traffic Regulations, 2012 (Act, 2007), specify that motor- cycles with engines between 100 and 200cc shall carry no more than one passenger, with the rider required to wear a crash hel- met, while restriction of carrying cargo is gazetted. These reg- ulations lack enforcement, making motorcycle taxi operations significantly contribute to several environmental consequences. An empirical investigation will help improve transport policies in Ogun State toward controlling motorcycle emissions. Given the increasing number of two-stroke motorcycles in Ogun State, motorcycle taxis were hypothesized to significantly contribute to emissions in the study area [33, 34]. The second hypothesis posits that fuel consumption has a significant rela- tionship with exhaust emission rates. Thirdly, it was hypoth- esized that CO and O2 have a significant predictive effect on CO2. The fourth hypothesis posits that towns with higher mo- torcycle densities are more likely to be hot spots of CO, O2, CO2, and HC than those with smaller densities. Therefore, using an idle mode test involving 1950 two-stoke petrol-driven motorcycle taxis in Ogun State, Nigeria, the raw exhaust of tail-pipe emissions are reported by answering the following questions: • What are the temporal and spatial dynamics of motorcy- cle emissions and their extent across Ogun State? • Which policy interventions can promote efficient motor- cycle usage and lower emission rates? The contributions of this study to the literature are as follows. This study adds to the growing literature on motorcycle taxi emission in sub-Saharan Africa by using direct exhaust emis- sion and idle mode tests for the first time in motorcycle studies from sub-Saharan Africa. This new dataset helped to identify the primary pollutants and the major hot spots of emissions in a whole state, making it easy for policymakers to track. Addi- tionally, this study proposes a policy framework for controlling motorcycle emissions in the study area, which other urban areas in sub-Saharan Africa can adopt. The ensuing part of this study is structured as follows. Sec- tion 2 explains the idle mode test, the formulation of related 2 Alimo et al. / J. Nig. Soc. Phys. Sci. 6 (2024) 1898 3 assumptions, and the statistical and spatial analyses. Section 3 presents and discusses the key results. Section 3 has a proposed policy regulation framework. Section 5 has the conclusion and also details the future research areas. 2. Materials and methodology 2.1. Study area and sampling locations The study area was Ogun State, which is one of Nigeria’s thirty-six states and is situated in the Southwest part. It shares borders with Lagos State (the commercial nerve center of the country) and the Atlantic Ocean on the south, Oyo and Osun States on the north, Ondo State on the east, and the Republic of Benin on the west. Ogun State is home to the highest num- ber of industries in Nigeria and has the longest stretch of road connecting Lagos to other parts of the country [35, 36]. The sampling locations within the state were catego- rized under the Local Government Areas (LGAs), including Abeokuta North, Abeokuta South, Odeda, Egbado (Yewa) North, Egbado (Yewa) South, Ewekoro, Ifo, Ijebu North, Ijebu Ode, Ijebu East, Ijebu North-East, Ikenne, Imeko-Afon, Remo North, Odogbolu, Obafemi Owode, Sagamu, and Ado-Odo/Ota as shown in Figure 1. 2.2. Monitoring and data collection Raw exhaust emissions from motorcycle taxis were sam- pled using a hand-held KANE Automotive 4-Gas Analyzer (Model 4-2) (Figure 2a-d). The selection of this analyzer was not only due to its porta- bility, lightweight, and ease of use with its long-lasting battery (battery run time (>4 h from a full charge with the pump run- ning) but also due to its ability to log up to 500 readings in its memory. The instrument was programmed to detect and measure carbon dioxide, CO2 with an accuracy of ± 0.5% vol- ume of reading at a resolution of 0.1% and 0-16% range and 25% over-range), oxygen, O2 (with an accuracy of ± 0.1% vol- ume of reading at a resolution of 0.01%, 0-21% range and 25% over-range), hydrocarbons, HC (with an accuracy of ± 12 parts per million volume (ppm vol.) of reading at a resolution of 1 ppm, 0-5000 ppm range and 10000 ppm over-range) and carbon monoxide, CO (with accuracy of ± 0.06% volume of reading at a resolution of 0.01%, 0-10% range and 20% over-range). Lambda, ? (at a resolution of 0.001 and a range of 0.8-1.2) was calculated per equation (1) [37, 38]. λ = [CO2]+[ CO 2 ]+[O2]−[ NO 2 ]+   HCV 4 × 3.5 3.5+ [CO] [CO2] − OCV 2 × ([CO2]+[CO])( 1+ HCV 4 − OCV 2 ) ×([CO2]+[CO]+(K1×[HC])) ,(1) where CO, CO2, and O2 are measured in percentage volume (% vol.), and HC is measured in ppm vol. K1 is the HC conver- sion factor expressed in ppm vol. equivalent to normal hexane (C6H14). The value is given as 6.0×10−4 according to equation 1. HCV denotes the hydrogen-carbon atomic ratio of the fuel (minimal value is 1.7261), and OCV denotes the oxygen-carbon atomic ratio of the fuel (minimal value is 0.0176) [39]. The test was conducted on a total of 1950 motorcycles, which were done in eighteen out of the twenty LGAs in the state, as presented in Table 1. Before each measurement round, the motorcycle taxis were allowed to travel 50 m from their stations, and the ‘No–Load Short Test,’ commonly referred to as ‘idle mode tests,’ was performed on each motorcycle taxi. The idle mode test ap- proach has been recently reported in similar studies as effective in collecting emission data since motorcycles are not required to move at constant load, mimicking stationary equipment [39– 41]. The exhaust probe of the sampling instrument was in- serted into the motorcycle’s exhaust pipe end and clamped to the tail end to avoid falling off (Figure 2d). Measurements were recorded in (%) volume for CO2, CO, and O2 concen- trations and ppm for HC. Each measurement round lasted 10 minutes. All recorded data is automatically stored in the instru- ment’s memory drive for later download. After each round of measurements, the sampling analyzer was calibrated to ‘zero’ by exposing the probes to ambient conditions while ensuring that the exhaust probe tips were clean of any dirt or debris. All samples and testing events were undertaken in November 2020- February 2021, coinciding with Nigeria’s dry season. There- fore, during all testing, the air temperature was between 31 and 40 ◦C, and the relative humidity was between 45 and 60%. Sampling events were conducted in triplicate for each motor- cycle taxi within the sampling period to determine statistical variations in the datasets. 2.3. Statistical analysis The normality of the obtained datasets was checked using Kolmogorov-Smirnov test (p > 0.05). “Using a correlation ma- trix, the associations between roadworthiness (RW), CO, CO2, O2, HC, motorcycle model, and city of registration were cal- culated. Correlation coefficients were elucidated by comparing small (r = 0.10), medium (r = 0.30), or large (r = 0.50)′′ [42]. Hierarchical multiple linear regression models were used to de- termine the predictive effect of CO, O2, and motorcycle models on the CO2 of total motorcycles investigated in this study. The city of registration, RW, and HC were excluded because they had no predictive effect on CO2 based on the stepwise approach adopted for the regression analysis. “Model 1 explored the pre- dictive effect of O2 on CO2. Model 2 explored the enhanced forecasting value of CO attributes to model 1. Model 3 showed the added predictive effect of the motorcycle model on model 2. The regression model’s effect sizes and p-values are reported as an overall fit by ‘adjusted R2’ statistics, while R-change and F-test show the importance of adjustments in model fit. The re- gression coefficient values in the models (β) were interpreted as β = 0.1 indicating a small, β = 0.3 a medium, and β = 0.5 a large effect [42]. For all analyses, the significance level was set at p < 0.05. Data standardization was done by dividing original values by their standard deviation. The standardized scale for each variable was indicated to enable easy comparison on a similar scale. This reduced multicollinearity and helped deal with sig- nificant differences in data (noise) since not all were measured 3 Alimo et al. / J. Nig. Soc. Phys. Sci. 6 (2024) 1898 4 Table 1. Distribution of motorcycles tested from the sampled LGAs. LGA Samples of motorcycles tested Abeokuta North 158 Abeokuta South 147 Ado Odo/Ota 126 Ewekoro 14 Ifo 122 Ijebu East 4 Ijebu North 254 Ijebu North-East 19 Ijebu Ode 717 Ikenne 28 Imeko Afon 10 Obafemi Owode 18 Odeda 9 Odogbolu 33 Remo North 52 Sagamu 228 Yewa North 5 Yewa South 6 Table 2. Motorcycle models tested in the study area. Model Frequency Percentages (%) Bajaj 130 6.7 Hague Suzuki 90 4.6 Jincheng 1489 76.4 Lifan 167 8.6 Qlink 17 0.9 Sinoki Supra 57 2.9 Total 1950 100 Table 3. Mean, Standard Deviation, and Correlation Matrix (N = 1950). Variable M Std. D 1 2 3 4 5 6 7 RW .86 .344 1 -.529∗∗ .147∗∗ .123∗∗ -.646∗∗ .043 -.067∗∗ CO 1.6062 .99564 1 -.257∗∗ -.245∗∗ .385∗∗ -.105∗∗ -.064∗∗ CO2 3.6002 1.27684 1 -.664∗∗ -.076∗∗ .093∗∗ .004 O2 14.9303 1.86508 1 -.125∗∗ -.005 .047∗ HC 1.6097 1.0 1 -.042 .260∗∗ Motorcycle Model 3.01 .835 1 .024 City 4.39 1.938 1 Note: p-value significant at **p<0.01, *p<0.05 (two tailed); M/Std.D: Mean/Standard Deviation; RW: Pass/Fail; CO: CO2; O2; HC; Model; City. on the same scale. The technique also has common applicabil- ity, especially in a regression analysis, to standardize predictor variables and to determine those with higher effects while con- trolling for variations in scale. Data standardization, statistical correlation, and regression analysis were performed on Minitab 19.0 and IBM SPSS Version 26. Furthermore, it is noteworthy to state that the RW of each sampled motorcycle is arrived at based on the vehicular exhaust emission standard limit set by the Ogun State Environmental Protection Agency (OGEPA) that the CO limit should be 4.0%, while HC is set at 6000 ppm for 2- and 3-stroke vehicles. Any of the sampled motorcycles whose exhaust reading is within the state’s set standard limit is a certified pass (roadworthy, RW). In contrast, those whose reading exceeds the limit are certified fail (non-roadworthy, NRW). 2.4. Spatial analysis Geostatistical analysis revealed the Spatio-temporal dynam- ics of motorcycle taxi emissions over the study area. The Getis– Ord Gi*spatial analysis and Kriging interpolation approaches were employed in the ESRI [43] ArcGIS 10.8 environment. Getis–Ord Gi* spatial analysis using the Hot Spot Analysis 4 Alimo et al. / J. Nig. Soc. Phys. Sci. 6 (2024) 1898 5 Figure 1. Study area and sampling locations. Table 4. Hierarchical multiple regression models exploring CO2, O2, CO, and Motorcycle Models in Abeokuta Metropolis (N = 1950). Parameters B SE B p Adjusted R2 ∆F p∆F Model 1 0.440 1534.81 0.001 Constant 10.386* 0.175 .000 O2 -0.454* 0.012 -0.664 .000 Model 2 0.628 978.98 0.001 Constant 12.42* 0.157 .000 O2 -.529* 0.010 -0.773 .000 CO -572* 0.18 -0.446 Model 3 0.629 9.338 0.002 Constant 12.203* 0.172 .000 O2 -0.528* 0.010 -0.772 .000 CO -0.566* 0.018 -0.441 .000 Motorcycle Model .0.65* 0.021 0.042 .002 Note: Constant: CO2 Model 1: Constant, O2 Model 2: Constant, O2, CO Model 3: Constant, O2, CO, Motorcycle Model P<0.05 function tool uses spatial association of high and low emis- sion values from motorcycles to identify the spatial association among neighboring sampled locations within a particular area (Equations (2)–(4)). This effectively identified spatial clusters of high and low emission values observed through hotspots and coldspots for measured parameters over the study area. The tool considers each sampled emission within the context of the neighboring sampled emission. At the same time, the local sum for each location is compared proportionally to the sum of all neighboring features. The z-scores and p-values that are gen- erated for features show whether there are high-value or low- value clusters in space. Hence, a high-emission location may 5 Alimo et al. / J. Nig. Soc. Phys. Sci. 6 (2024) 1898 6 Figure 2. (a-b) KANE Automotive 4-Gas Analyzer (Model 4-2) used; Photographs of (c) typical motorcycle taxis terminus (d) field exhaust emission test of the sampled motorcycles. not be a statistically significant hotspot [44]. The tool has been adopted in related studies and found to be suitable for this case study [45–47]. G∗i= ∑n j=1 wi.jxj −X ∑n j=1 wi.j s √ [ n ∑n j=1 w2 i.j−( ∑n j=1 wi.j)2 ] n−1 , (2) where xj is the emission parameter value for feature j, wi.j is the spatial weight between feature i and j, n is equal to the total number of sampled locations: X = ∑n j=1 x j n . (3) S = √∑n j=1 x2 j n −(X) 2 . (4) Kriging is a geostatistical interpolation method that uses statistical models to establish statistical relationships among measured points and can produce prediction surfaces between sampled locations [48, 49]. Kriging uses the distance or di- rection between sampled locations to establish a spatial rela- tionship that explains differences in the predicted surface. The Kriging tool fits a scientific operation (Equation 5) to a speci- fied number of points, or all points within a defined radius, to identify the output value for each location and finally assigns weights to the surrounding measured points to predict an un- measured location. Ẑ (so) = N∑ i=1 λiZ (S i) , (5) where Z(S i) denotes the calculated value at the ith location, λi is an unknown weight for the calculated value at the ith loca- tion, so is the prediction location, and N denotes the number of calculated values. The weight, λi, is based on the distance between the mea- sured points and the location to be predicted, as well as the overall spatial layout of the measured points. The study em- ployed the spherical kriging model where there is a progressive decrease in spatial autocorrelation (equivalent to an increase in semivariance) to a distance where autocorrelation is zero. Us- ing kriging to predict a surface based on measured points across a location has been extensively used [48–52]. 6 Alimo et al. / J. Nig. Soc. Phys. Sci. 6 (2024) 1898 7 Figure 3. Spatial distribution of motorcycle emissions across the study area (a) CO2, (b) CO, (c) O2, (d) Hydrocarbons (HC). Figure 4. Recorded max and min CO2 levels. 7 Alimo et al. / J. Nig. Soc. Phys. Sci. 6 (2024) 1898 8 Figure 5. Max and Min CO Levels. Figure 6. Max and Min O2 Levels. Table 5. Semi-variogram and model parameters. Motorcycle emission parameter Model type Nugget (C0) Partial sill (C) Sill (C0 + C) Nugget/sill ratio (%) Spatial class Range CO2 Spherical 0.992 0.794 1.787 55.55 Moderate 2.88 CO Spherical 0.164 0.690 0.855 19.21 Strong 4.047 O2 Spherical 1.309 1.617 2.927 44.74 Moderate 3.428 HC Spherical 953350 3165300 4118650 23.15 Strong 4.278 8 Alimo et al. / J. Nig. Soc. Phys. Sci. 6 (2024) 1898 9 Figure 7. Max and Min HC Levels. Figure 8. Hot and cold analysis of motorcycle emissions across the study area (a). CO2, (b). CO, (c). O2 and (d) Hydrocarbons (HC). 2.5. Policy analysis and resolution The second objective of this study is to find a policy frame- work that can help address the pollution created by motorcycles and enhance public health. The policy framework is essential because it is the only way to control emissions. Accordingly, a DPSIR framework (Drivers, Pressures, State, Impacts, Re- 9 Alimo et al. / J. Nig. Soc. Phys. Sci. 6 (2024) 1898 10 Figure 9. Roadworthiness of motorcycles across study areas. sponses) [53, 54] was employed to analyze the case of emis- sions associated with motorcycle taxis in Ogun State and pro- pose policy interventions. The influencing elements linked with motorcycle usage are called Drivers. The effects of motorcycle taxi activities on the environment are the Pressure. States de- pict the current condition of the environment, economy, and society due to external forces. Impacts originate from partic- ular consequences detected in the socio-economic or political environment of the state. Responses illustrate recommended policies that could resolve prevailing conditions by intervening with Pressures, States, and Impacts. 3. Results and discussion 3.1. Statistical results Datasets were non-uniformly distributed across the study areas. The majority of the datasets attained skewness (> 0) in most of the sampled regions. The datasets also displayed a typ- ical leptokurtic (K > 3) and platykurtic (K < 3) distribution across the study area. The descriptive analysis of the indica- tors based on the mean (M) and standard deviation (S D) result shows that RW had a mean score of (0.86 ± 0.344). Other indi- cator scores were CO (1.61 ± 0.99), CO2 (3.6 ± 1.28), and O2 (14.93 ± 1.87). The highly uneven distribution in the datasets could be associated with the sampling approach, distribution, and location of motorcycle taxis. A correlation matrix was used to appraise the associations between RW, CO, CO2, O2, HC, Motorcycle Model, and sam- pling LGAs where the motorcycles were registered. Motorcy- cle model refers to the brand of motorcycles used in the study area. These brands are presented in Table 2. Notably, there is no apparent reason for the predictive effects of the Motorcy- cle model except that the frequency may play a role since the Jincheng brand is mainly used and has the most impact com- pared to other brands. The evidence in Table 3a shows that RW was negatively associated with CO (r = −0.53; p < 0.01), suggesting that when CO increases, there is a reduction in RW. In addition, RW was negatively associated with HC (r = −0.65; p < 0.01), indicating that when HC increases, there is a reduction in RW. Similarly, RW was negatively related to the city of motorcycle registration (r = −0.07; p < 0.01), with a clear indication that the city of motorcycle registration is a determinant of RW (note: city and RW are captured as a categorical variable such that the statistical direction is not measured on a scale). However, there was a notable positive association between RW and CO2 (r = 0.15; p < 0.01), indicating that when CO2 increases, RW is likely to increase. The results further show a positive association between RW and O2 (r = 0.12; p < 0.01), suggesting an increase in RW when O2 increases. While ex- ploring the correlation of CO with other indicators besides RW, CO was negatively correlated with CO2 (r = −0.26; p < 0.01), O2 (r = −0.25; p < 0.01), Motorcycle Model (r = −0.11; p < 0.01), and city of motorcycle registration (r = −0.06; p < 0.01), suggesting that when CO increases, there is a reduction in CO2 and O2 which are further a reflection of motorcycle mod- els and city of registration. Meanwhile, CO and HC were posi- tively correlated (r = 0.39; p < 0.01), suggesting that when CO increases, there is a further increase in HC. The CO2 indicator had a negative correlation to O2 (r = −0.66; p < 0.01) and HC (r = −0.08; p < 0.01), which means when CO2 increases, there is a reduction in O2 and HC. CO2 was positively associated with Motorcycle Models (r = 0.09; p < 0.01), suggesting that de- pending on the motorcycle model, there is a possible increase in CO2. The hierarchical multiple linear regression (Model 1) in 10 Alimo et al. / J. Nig. Soc. Phys. Sci. 6 (2024) 1898 11 Figure 10. Kriging interpolation of motorcycle emissions across the study area (a) CO2, (b). CO, (c) O2 (d) Hydrocarbons (HC). Table 4 showed the statistical significance of the dependent variable, CO2, and the predictor (O2) (adjusted R2 = 0.44; p < 0.001) for the total sample of the motorcycles explored in the study. Model 2 appraises the added effect of CO, which pro- pounded added predictive effects of about 18.7% for the total motorcycle sample (adjusted R2 = 0.628, p < 0.001). The result from (Model 1) also supports O2 as an important pre- dictor of CO2 for the total motorcycle sample (β = −0.664; p < 0.001), indicating that with every 1 SD increase in O2, there is a 0.664 SD decrease in CO2. The result from (Model 2) also supports CO as a predictor of CO2 (β = −0.446; p < 0.001), indicating that with every 1 SD increase in CO, there is a 0.466 SD reduction in CO2. Interestingly, while adding the motorcycle models as Model 3, the added effect of motorcycle model features on Model 2 proposed an added predictive effect of about 0.002% for the total motorcycle sample (adjusted R2 = 0.629; p < 0.001). Also, Model 3 supports the motorcycle model as a predictor of CO2 (β = 0.004; p < 0.002), highlighting that when there is a 1 SD change in the motorcycle model, there is a 0.004 increase in CO2. Overall, the results show that CO, O2, and motorcycle models explained about 63% of the variance explained in CO2 emissions (adjusted R2 = 0.629; p < 0.001). 3.2. Spatial distribution of emissions The general extent of CO2, CO, O2, and HC distribution across the study area is shown in Figure 3. CO2 represents the ‘desirable’ end-product produced when fuel is combusted. The ideal (optimal) value of CO2 for a per- fectly working engine is about 15.5% [55]. However, functional issues such as air/fuel imbalances, misfires (i.e., the release of unburned fuel), engine mechanical problems, or sample dilu- tion could cause decreases in CO2. Therefore, high CO2 read- ings show that the engine operates optimally, exhibiting high combustion efficiency [39, 56]. The CO2 levels over the study area were heavily distributed from the central to southeast- ern and southwestern regions, mainly within Shagamu, Ikenne, Abeokuta North and South, Ijebu North and North-East, Ifo, Ado-Odo/Ota, and Ijebu Ode (Figure 3a) and Figure 4. The high distributions could be associated with the fre- quency of use of motorcycles. LGAs such as Abeokuta South & North are densely populated, with about 80% of residents relying on motorcycles and triclyes (2-stroke engines) daily. The highest CO2 concentration was measured at 9.5% in Ado- Odo/Ota, in the southwestern region, where 90 bikes were sam- pled, while the lowest CO2 concentration was 0.9% in Ijebu North, in the southeastern region, where 276 motorcycles were sampled. However, the high CO2 emissions across the state could contribute cumulatively to carbon stock additions and en- 11 Alimo et al. / J. Nig. Soc. Phys. Sci. 6 (2024) 1898 12 Figure 11. Spatial trend of motorcycles across study areas. vironmental effects within the study area [57]. CO is produced due to variations in fuel-to-air supply, re- sulting in incomplete combustion. High CO readings indi- cate that the engine is experiencing a fuel load, insufficient air, or both (i.e., rich air/fuel mixture). The highest CO level of 6.4% was recorded in Ijebu Ode, which was above the com- pliance limit of OGEPA and EURO IV standards (See Figure 5). OGEPA [58] has a 4.0% emission standard limit of CO for 2 and 3-wheeled vehicles. The lowest CO recorded during the study was 0.01 % in Imeko-Afon. CO levels were above the OGEPA CO limits in most studied areas except for Remo North, Imeko-Afon, Ijebu East, and Egbado (Yewa) South, where concentrations were within the OGEPA standards. The spatial distribution of CO levels indicated a high concentra- tion from the central to southeastern and southwestern areas of the study area comprising Shagamu, Ikenne, Abeokuta North and South, Ijebu North and North-East, Ifo, Ado-Odo/Ota, and Ijebu Ode (Figure 3b). The CO level was highest at 6.4 % in Ijebu Ode, in the southeastern part of Ogun State, where 548 motorcycles were sampled. The level of CO in Ijebu Ode was above the Ogun State Environmental Protection Agency’s com- pliance limit. Contrary to high CO2 levels, the lowest CO levels of 0.0% were recorded in Abeokuta North & South. The low lev- els could be attributed to the high economic status of resi- dents, which allows for regular maintenance of the motorcy- cles, thereby resulting in lowered emissions. At the same time, law enforcement agencies’ compliance with vehicular and road- worthiness regulations is also strictly monitored. The LGAs that did not comply with the Ogun State Environmental Protec- tion Agency’s CO emission standard were found in the study area’s central, southwestern, and southeastern areas. Although CO levels in the study region were below regulation limits, their large extent and distribution are concerning. Recent re- search has proven that motorcycle taxi drivers are particularly susceptible to respiratory health effects from these emissions [59]. Previous studies have also established some correlations between drivers’ behavior and emissions. Huang [60] found that under high speeding conditions, CO emissions are high. Although the speed of such association is not established in this study, it is proposed in future research. The highest O2 level of 17.92% (Figure 6) was recorded in Ikenne and Shagamu. The OGEPA has no emission standard limits of O2 for 2 and 3-wheeled vehicles. The lowest O2 level 12 Alimo et al. / J. Nig. Soc. Phys. Sci. 6 (2024) 1898 13 Figure 12. Semi-variograms of motorcycle emissions across the study area. of 1% was recorded in Abeokuta North. All the LGAs of Ogun State recorded an average O2 level of more than 14%. In terms of their spatial distribution, O2 levels were highly distributed in the central to southeastern and southwestern areas of Ogun state, comprising Shagamu, Ikenne, Abeokuta North and South, Ijebu North and North-East, Ifo, Ado-Odo/Ota, and Ijebu Ode 13 Alimo et al. / J. Nig. Soc. Phys. Sci. 6 (2024) 1898 14 (Figure 3c). HC emission data represents the amount of unburned fuel emitted from an engine’s exhaust pipe, often measured as ppm. The HC reading of a perfectly working engine, i.e., in the ideal state where the air-to-fuel (A/F) ratio is 14.7:1, should be ap- proximately 0-120 ppm [61]. However, the OGEPA has recom- mended an emission standard of 6000 ppm for motorcycles and tricycles. The highest HC level recorded was 15,446 ppm in Ijebu Ode, above the OGEPA and EURO IV vehicle standards. In comparison, the lowest HC level recorded during the study was 18 ppm in Abeokuta South (Figure 7). Overall, the LGAs did not comply with the OGEPA limit except for the Imeko-Afon LGA, which recorded a maximum HC of 4,860 ppm. HC spatial distribution was heavily clus- tered in the southeast and southwest regions of the research area, comprising Shagamu, Ikenne, Abeokuta North, Abeokuta South, Ifo, Ado-Odo/Ota, Ijebu North, Ijebu North-East, and Ijebu Ode (Figure 3d). High HC recordings could be related to relatively old motorcycles operating in the study area. The preference for such old motorcycles by users or owners could be due to their initial lower purchase cost for commercial and private use. HC concentrations above the maximum limit can harm human health [62]. 3.3. Hotspot and coldspot analysis Cluster analysis of high and lower concentration zones en- ables researchers to assess the contribution of source emissions from a particular area and how they influence cumulative emis- sion generation. These can be demonstrated by hotspot (high emission zones) and coldspot (low emission zones) analysis. Regions with significant clusters, which could be described as dense sources of emissions contributing to the overall effect in the study area, were observed by hot spot and cold spot anal- ysis. CO2 was found to have significant hot spot areas with a 99% confidence interval across Ado-Odo/Ota, Abeokuta South, Shagamu, Odogbolu, Ijebu Ode, Ijebu North, and Obafemi- Owode LGA. Only one significant coldspot with a 99% con- fidence interval was observed in Shagamu. Overall, hot spots and coldspots with 90% confidence intervals were distributed within the study area’s central, southeastern, and southwestern parts (Figure 8a). These areas are triggers for implementing emission con- trol measures and monitoring programs. Continuous monitor- ing of CO2 emissions from these hotspots could provide essen- tial baseline data for motorcycle emission regulations. Sim- ilarly, CO-significant hotspot areas with a 99% confidence in- terval were clustered in Abeokuta South. In comparison, signif- icant cold spots with a 99% confidence interval were clustered around Ijebu North, Ijebu North-East, and Odogbolu. There were also coldspots with 90% confidence intervals in Shagamu and Ado-Odo/Ota LGAs in the study area’s central, southeast- ern, and southwestern parts (Figure 8b). O2 was found to have significant hot spot areas with a 99% confidence interval clustered in Abeokuta South. In compari- son, significant cold spots with a 99% confidence interval were clustered around Ijebu North, Ijebu North-East, and Odogbolu. Coldspots with 90% confidence intervals were in Shagamu and Ado-Odo/Ota LGAs in Ogun State’s central, southeastern, and southwestern parts (Figure 8c). For HC concentrations, hot spots were significantly observed at 99% in the southeast and northeast areas of the study area, mainly across Odogbolu and Ijebu-Ode, Ikenne, Ijebu North, and Shagamu. The hotspot sta- tus of these areas could be ascribed to the higher usage of mo- torcycles in these areas, as well as low compliance with road- worthiness regulations of the State Vehicle Monitoring Agency. A few hotspot areas were observed in Ijebu East, while 90% of significant hotspot zones were observed across a few portions of Ikenne. Cold spots were heavily distributed in the western portion of the study area. A significant 99% coldspots were observed across Abeokuta North and South, Egbado (Yewa) North, Ewekoro, and Egbado (Yewa) South. Relatively 90% significant coldspot zones were observed across Ado-Odo/Ota (Figure 8d). The spatial extent of the RW of motorcycles across the study area was significant (Figure 9), as most operational motorcycles passed the OGEPA Exhaust Emissions compliance limits. Few motorcycles in the study area’s northwest, central, and south- ern areas failed to meet OGEPA compliance limits. Therefore, it could be inferred that these old motorcycles contributed to a high generation of emissions and clustering across the study area. From an urban planning perspective, the spatial distribu- tion of these related emissions could reveal important criteria for functional zone planning. It is evident from the observa- tions that areas with high clustering are highly motorcycle taxi- dependent zones. Such hotspot areas could be decongested with motorcycles by introducing alternative modes of commuting. Further, these hotspot zones could support creating more effective policy interventions or enhancing the compliance per- formance of existing instruments. For example, identified hotspot zones could receive emission reduction schemes to im- prove air quality. In contrast, ultra-low emission zone standards could be set for these areas. However, vehicles not meeting the criteria must pay high daily fees before operating in these areas. With the increased proliferation of motorcycle use in the study area, such novel measures for motorcycle operation/use could significantly reduce emissions and potential public health ef- fects. Identifying such hot and cold spots can direct the design of targeted regulatory policies to reduce emissions in high- concentration areas, such as a ban on importing secondhand motorcycles and instituting carbon emission credit schemes across the LGAs. 3.4. Spatial interpolation and mapping Kriging analysis revealed spatial patterns and interdepen- dences in datasets at unsampled locations of the study areas, as shown in Figure 10 and Figure 11. The spatial interferences of the predicted motorcycle emis- sions were studied through model parameters for the best-fit semi-variograms shown in Table 5. The parameters compris- ing the nugget effect (Co), the sill (Co + C), and the range of influence for each parameter were adopted. The extent of auto- correlation amongst the sampling units was associated with their spatial dependencies. The nugget values were derived 14 Alimo et al. / J. Nig. Soc. Phys. Sci. 6 (2024) 1898 15 Figure 13. A DPSIR framework for motorcycle emission control in Ogun State, Nigeria. from measurements of accuracy in variation of properties that could not be observed within the sample range and are an indi- cator of continuity at close distances. The sill value represented the peak of the fixed semi-variogram model. The “nugget-to- sill ratio (N/S)” was then associated with the spatial dependence of emission parameters (CO2, CO, HC, O2). At the same time, the range of the semi-variogram was represented by the average distance through which most of the semivariance parameters reached their highest value. The spatially dependent variables were therefore grouped as randomly spatially dependent if the N/S was >75%. This ratio is relatively spatially dependent if it is between 25% and 75% while strongly spatially dependent if < 25% [63]. The highest levels of CO2 were between 3.875 and 5.858, and the lowest levels were between 0.993 and 3.015. The max- imum concentrations were predicted to be generated by LGAs 15 Alimo et al. / J. Nig. Soc. Phys. Sci. 6 (2024) 1898 16 within the study area’s central north and south portions. They comprised Abeokuta North and South, Ijebu North, North-East and East, and parts of Shagamu, Ifo, and Oba Ode (Figure 10a), similar to the original datasets obtained. This observation fur- ther strengthens the case that motorcycles in the northern por- tions of the study area could be a major source for compliance studies. For CO, predicted concentrations across unsampled regions in the study area ranged from a maximum of 2.054- 3.831 to a minimum of 0.447-1.283. The maximum concentra- tions were predicted to be generated from the northwest por- tions of the study area, comprising Abeokuta North and South, Odeda, Obafemi-Owode, Remo-North, and Ipokia in the south- west (Figure 10b). Similarly, for O2, the maximum predicted concentration in unsampled areas ranged from 15.4-17.09, majorly in the north- western (Imeko-Afon) and relative central portions of the study area. However, minimum predicted concentrations ranged from 0.372-14.081, primarily centrally distributed in the study area (Figure 10c). The maximum HC predicted concentration at unsampled locations in the study area ranged from 5,060.7- 7,708.57 in the southwest areas, while minimum concentrations ranged from 446.28-2,782.94. Concentrations were evenly dis- tributed between the north-central and southeast portions of the study area (Figure 10d). From Table 5, the spatial dependencies of predicted emis- sions revealed a general moderate to strong association among the emission parameters. The spatial variabilities at unsampled sources of neighboring areas ranged from 2.880-4.278 meters for CO2, CO, O2, and HC, respectively. Although CO2 and O2 emissions indicated an even distribution (Figure 12a and 12c), their dependencies were only moderate (N/S = 25-75%). This could imply that variations in the observations were possibly as- sociated with the influence of external emissions sources in the study area. Notably, the research area (State) has the highest concentration of manufacturing industries in Nigeria [35, 64], resulting in substantial industrial activity. However, a highly uneven CO and HC emissions distribu- tion across the study area (Figure 12b and 12d) revealed a solid spatial interdependence among the parameters. The lower (N/S < 25%) could confirm that CO and HC emission variations across the study area were majorly due to inherent/functional aspects of the motorcycle, with less impact or none at all due to external/ambient sources. These inherent aspects could be related to the low purchasing power of the operators (many live on < US $1 per day) [65–67], thereby making most of them prefer to use cheap, fairly-used (imported), sometimes substan- dard spare parts and engine oils for maintenance to maximize profits. These often have ultimate effects resulting in discor- dance in ignition timing, worn-out valves and piston rings, and faulty carburetors [39], hence the observed high CO and HC emissions. 4. Proposed policy and regulatory intervention The findings indicate that CO and CO2 emissions were above OGEPA emission standards. Weak regulatory regimes are one of motorcycle-related public health and traffic prob- lems in sub-Saharan African countries such as Ghana, Togo, Benin, and Cameroon, with known public health implications [8, 9, 68–70]. The public health implications of this situation cannot be overemphasized. The ultimate solution is to strengthen the regulatory frame- work in Ogun State. In line with the second objective of this study, reliance on imported, fairly-used, substandard spare parts and engine oils would require remediation. Additionally, an inter-institutional regulatory framework would be necessary to implement policies effectively. Accordingly, through a DPSIR framework (Figure 13), this study proposes policy interventions for the local governments in Ogun State. The driving forces for motorcycle ridership, such as em- ployment for drivers, easy accessibility of motorcycles, and laxity of emission standard enforcement, pose three significant pressures in Ogun State: greenhouse gas emissions, possible road crashes, and traffic congestion. The proposed responses in- clude roadworthy audits of motorcycles, enforcement of emis- sion standards for private and commercial motorcycles, strict enforcement of the National Traffic Regulation, streamlining lo- cal motorcycle assembly businesses, regular engagement with motorcycle taxi driver associations, commuter health checks, and enhanced rapid transit to address the resulting impacts of respiratory diseases, body pains and weakness, and grave envi- ronmental concerns. Local government institutions, including the Bureau of Transportation, the Ogun State Environmental Protection Agency, and the Federal Driver’s License Authority, would re- quire enhanced collaboration to regulate motorcycle emissions. However, regular engagement of the Commercial Motorcycle Riders Association of Nigeria and commuters is imperative to make these recommendations effective. These policies could also be used in other countries in sub-Saharan Africa, where there is a high demand for motorcycle transport and a lot of dangerous pollution. Integrating electric motorcycles (e-bikes) into the main transport modes, mixed with improved regulatory/control framework and existing road infrastructure, remains a sustain- able alternative. For example, it was estimated that in Uganda, electric motorcycles have the prospects of reducing CO2, CO, NOx, and HC emissions by 36%, 90%, 58%, and 99%, re- spectively [24]. As a result, huge benefits may accrue in Ogun State due to the development of electric motorbike paths. Such policy shift, however, is incredibly reliant on Nigeria’s ability to produce and deliver power. It was already found in Bo- gotá and Santiago (Latin America) that electric vehicles pow- ered with renewable energy have significantly reduced transport emissions [71]. Finally, charging infrastructure for motorcycles would be necessary. Perhaps this merits more investigation in the future. 5. Conclusion This study reported comprehensive results of field emis- sion monitoring from two-stroke commercial motorcycles, also 16 Alimo et al. / J. Nig. Soc. Phys. Sci. 6 (2024) 1898 17 known as ’okada,’ from Ogun State, Nigeria. Particular obser- vations were related to the uneven spatial distribution of emis- sions (HC, CO2, CO, and O2) across the study area, with a high distribution of measured parameters occurring in the northeast- ern and central portions of the state and a moderate distribu- tion in its southern and western portions. Critical LGAs where motorcycles contributed to high emission concentrations were Shagamu, Ikenne, Abeokuta North, Abeokuta South, Ifo, Ado- Odo/Ota, Ijebu North, Ijebu North-East, and Ijebu Ode. The results also corresponded to field observations where LGAs with high economic activity tended to have a high dis- tribution of emissions, unlike areas with less economic activ- ity. The application of statistical and interpolation analysis re- vealed a high correlation between emission parameters, CO, and HC and their dominance in terms of concentrations over the study area. This key finding is partly attributed to the study area’s characteristic operational functions and booming motorcycle activities. Recommendations to contribute towards resolving motorcycle emissions concerns in Nigeria were for- mulated through a DPSIR analysis to guide plausible future decision-making by policymakers. Since motorcycle taxis operate in at least 27 sub-Saharan African countries and predominantly use two-stroke engines, the results can significantly contribute to similar studies within the African sub-region on reducing road transport emissions and their long-term effects on air pollution and health. For example, the spatial concentrations of HC, CO2, CO, and O2 in dense settlements and the central business districts would require investigation in Africa’s large cities. 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