National energy consumption structures modulate PAH source apportionment in Ethiopian soils

doi:10.21203/rs.3.rs-4715894/v1

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National energy consumption structures modulate PAH source apportionment in Ethiopian soils

https://doi.org/10.21203/rs.3.rs-4715894/v1

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Polycyclic aromatic hydrocarbons (PAHs), a ubiquitous substance in the environment, are closely associated with human energy consumption, which can be released to the environment from incomplete biomass combustion or emitted from petroleum. There is currently a knowledge gap on the relationships between energy consumption structures (ECSs) and PAH contamination, especially in African countries. This study investigated the distribution, sources, and risks of 16 prioritized PAH compounds (Σ₁₆PAHs) in Ethiopia, and explored the impact of ECSs on PAH contamination. The concentrations of Σ₁₆PAHs ranged from 12.51 to 435.67 ng/g, descending across an urban-rural gradient. Based on diagnostic ratios and positive matrix factorization (PMF) model, biomass combustion and vehicle emissions were the most important sources. By comparing the ECSs and PAH concentrations in African countries, it was found that differences in ECSs shaped the diverse patterns of sources and concentrations of PAHs in Africa. The results of incremental lifetime cancer risk assessment suggested PAHs posed a low risk to children, adolescents, and adults at some sampling sites. The Ethiopian government has been making continuous efforts to develop cleaner energy and optimize ECSs to decrease soil PAH concentrations and reduce potential risks. This study will provide support for Ethiopia to improve energy structure management.

PAHs

emission sources

energy structure

cleaner energy

health risks

Africa

Polycyclic aromatic hydrocarbons (PAHs) are a class of complex aromatic hydrocarbons with two or more fused benzene rings in various structural configurations (Song et al., 2016; Dudhagara et al., 2016; Song et al., 2019). PAHs can be released directly from petroleum products such as gasoline and can also be formed by incomplete combustion of coal, petroleum, and biomass and pyrolysis of wood or fossil fuels (Donahue et al., 2006; Ma et al., 2017; Mai et al., 2003). Due to the hydrophobicity and lipophilicity of PAHs, the soil is a natural reservoir for PAHs, which can store about 90% of PAHs in the nature environment (Esmaeili et al., 2021). Nevertheless, the presence of excessive PAHs in the soil will unavoidably result in the degradation of soil quality, and poses threats to the ecological security and human health (Chakravarty et al., 2022; Jia et al., 2017). Therefore, there is an urgent need to investigate PAH occurrence and distribution in different environmental soils to ensure the soil health and food safety.

With the development of diversification in energy, Africa pays more attention to environmental protection. Currently, developing countries contributed major PAH emissions with the variations in the emission levels. Asia and Africa were the top two contributors to global PAH emissions, accounting for 55% and 18.8% of the global emissions, respectively (Zhang and Tao, 2009). Particularly, the per capita emissions of PAHs were as high as 145g in Africa, exceeding those of Asia (70 g/cap) and South America (123 g/cap) (Shen et al., 2013). Biomass burning, encompassing both biofuel combustion and wildfires, emerged as the primary contributor of PAH emissions in Africa (Ofori et al., 2020). Combustions were prevalent in Sudan, Angola, and the Central African Republic, where extensive savanna was grown and incomplete combustions were common (Giglio et al., 2006; Zhang and Tao, 2009). The energy sector in Ethiopia heavily relied on biomass such as wood and animal dung (Mazengia, 2010). Although the demands for energy had increased significantly, the research and development for cleaner energy resources was still limited, which led to continued environmental pollution in Ethiopia, especially with respect to persistent organic pollutants such as PAHs. According to previous studies, PAH contamination was an indicator of various energy consumption structures (ECSs) (Hartmann et al., 2005; Itoh et al., 2010). However, few studies have examined the impact of ECSs on PAH contamination.

Some studies reported the concentration, temporal variation, and sources of PAHs in African countries. For instance, concentrations in the range of 489 to 5626 µg/kg were observed for Σ₁₆PAHs from soil samples of Warri, Nigeria, which were attributed to oil and biomass combustion (Parra et al., 2020). The Vingunguti industrial area, located in Dar es Salaam city, was also heavily contaminated with PAHs (Mahugija, 2015). Within the Tamale Metropolis of Ghana, heavy-duty vehicles transited to neighboring countries such as Niger, Burkina Faso, and Mali, resulting in elevated concentrations of PAHs in the soils (Obiri et al, 2011). Despite the extensive research on PAHs in Africa, studies focusing on Ethiopia remain relatively scarce. Prasse et al (2012) showed that biomass combustion and petroleum sources were responsible for PAH contamination based on the analysis of seven samples from Addis Ababa, Ethiopia, with average PAH concentrations ranging from 177 to 1640 µg/kg. Beshir et al (2023) investigated PAHs in soils from a teff-Acacia decurrens-charcoal production system in northern Ethiopia and detected Flu (22.84–24.69 ng/g dry soil), Pyr (21.99 ng/g), and Chr (21.79 ng/g). These studies have provided some preliminary data and analysis for understanding the current status of PAH pollution in Ethiopia, but studies on PAH data, source identification, and ECS impacts are still insufficient, and more investigations and analyses are urgently needed to fill the gaps in Ethiopia.

In this study, the concentration, spatial distribution, and sources of PAHs in Ethiopian soils were investigated and the connections between PAH emissions and ECSs were established. In addition, a health risk assessment was conducted. The objective of this study was to provide insights for the next stage of pollution control, offer guidance for the optimization of ECSs, and facilitate the balanced development of energy consumption and environmental protection in Ethiopia.

2.1 Study area and sampling

The terrain of Ethiopia is dominated by mountain plateaus, most of which belong to the Ethiopian plateau, with an average elevation of 3,000 meters. The annual temperature varies modestly in Ethiopia, which primarily changes with altitude. In low-altitude regions, the climate is characterized by intense heat and aridity, while the highland areas experience a pleasant and rainy climate. The study area is concentrated in the central region of Ethiopia, with altitudes ranging from 1560–2960 m, spanning 3 latitudes (6 ~ 8º) and 3 longitudes (38º~ 40º) (Fig. 1). This region is one of the most densely populated areas in Ethiopia with a large number of towns and cities with a concentrated population, complex transportation routes, and active economic activities such as agriculture, manufacturing, and services. The region is representative in the study of PAH pollution status in Ethiopia.

Thirty-three soil samples were collected from the top 10 cm of the sampling sites that were selected based on a combination of distance uniformity, potential impacts of human activities, and typical land use types (Fig. 1). The sampling route started from the city of Nazret, passing through the towns of Asera, Birebilo, etc. to Dodola, west to Awasa, then north to Lake Sara and Lake Zwai, and finally to the vicinity of the last sampling site, Mojo. Samples were sealed and stored in clean brown glass bottles at 4℃ during transportation to protect from light in accordance with the requirements of HJ / T 166 (TSSEM 2004).

2.2 Analytical procedures

The analysis included the measurements of 16 priority PAHs, as specified by the US EPA: naphthalene (Nap), anthracene (Ant), acenaphthylene (Acy), fluorene (Flu), phenanthrene (Phe), pyrene (Pyr), benzo[a]anthracene (Baa), acenaphthene(Acp), fluoranthene(Flt), chrysene (Chr), benzo[a]pyrene (Bap), Benzo[b]fluoranthene (Bbf), benzo[k]fluoranthene (Bkf), Benzo[g,h,i]perylene (Bgp), dibenzo[a,h]anthracene (Daa), and indeno[1,2,3-cd]pyrene (Inp). The analysis started with sample treatment. Soil samples, weighing 1.0 g, were combined with anhydrous sodium sulfate and subjected to accelerated solvent extraction (ASE). Each sample received a spike of ANA-d10 and PHE-d10 before being extracted with 210 ml of methylene chloride over 48 hours. The conditions for the solvent extraction included a solvent mixture of 1:1 n-hexane and dichloromethane, heating to 120°C for 6 minutes, applying an extraction pressure of 1500 psi for 8 minutes, and purging with nitrogen for 100 seconds. This extraction procedure was repeated three times. The resulting extract was concentrated to 1 ml using rotary evaporation at a speed of 65 rpm. The concentrated extract underwent fractionation and purification using a Supelco Florisil SPE column, which was eluted with n-hexane and a 3:7 (v/v) mixture of methylene chloride/hexane to isolate PAHs. Finally, the eluted extract was concentrated to 1 ml under a gentle nitrogen stream (Liu et al., 2020; Ma et al., 2021; Song et al., 2019).

PAHs in soil samples were determined by gas chromatography mass spectrometry (GC-MS). The PAHs were compared with the standard substance mass spectrum, retention time, and fragment ion mass-to-charge ratio, and quantified by internal standards. The retention time of the target PAHs was analyzed more than three times and the average was used. The retention time of the PAHs in the sample was all within the range. PAHs with a relative abundance higher than 30% in the target mass spectrum were identified. Based on the peak area of the quantitative ion, the internal standard method was used to quantitatively analyze the PAHs.

For quality control and quality assurance (QC/QA), parallel samples, matrix spikes, and procedural blanks were analyzed. Recovery indicators monitored sample preparation and matrix effects. Quality control samples were tested for every 20 duplicate samples. The limits of detection (LOD) and quantification (LOQ) were calculated as three and ten times the signal-to-noise ratios (S/N) in spiked samples, respectively. The LOD for target PAHs ranged between 0.22 and 0.91 ng/g, while the LOQ ranged from 0.72 to 3.02 ng/g. PAH recovery rates were from 96.87 ± 13.24% to 105.19 ± 6.71%. The recovery rate for the indicator Phe was 86.93 ± 7.31%, and for Acp, it was 88.79 ± 9.34%.

2.3 Source identification

Diagnostic ratios (DRs) were used as a qualitative analysis of PAH sources, and the positive matrix factorization (PMF) model was used to calculate the proportion of source contributions. The DRs of PAHs, employed as source apportionment indices, provided an important tool for the identification of PAH pollution emission sources (Jiang et al., 2009; Katsoyiannis et al., 2007; Li et al., 2022; Nim et al., 2023). The ratios of Baa/(Baa + Chr), Flt/(Flt + Pyr), Inp/(Inp + Bgp), Flt/Pyr, Ant/(Ant + Phe), Bap/Bgp, Phe/Ant, and PAH(4)/PAH(5 + 6) were also calculated. In comparison to other ratios, the Flt/(Flt + Pyr) and Inp/(Inp + Bgp) ratios were considered more conservative (Mali et al., 2022; Pang et al., 2022). These two ratios were prioritized to investigate the sources of PAHs. Generally, higher values of these two ratios indicated a larger relative contribution from pyrogenic sources (coal and biomass combustion), while lower ratios suggested a relatively higher input from petrogenic sources.

The PMF model decomposed data into matrices of factor contributions and factor profiles (Karakas et al., 2017) to identify represented source types based on measured source profile information and emission inventories (Paatero and Tapper, 1994, 1993). In PMF, the factor analytic model was calculated through the following matrix relationship (Eq. 1):

\(\:X=GF+E\) (Eq. 1)

or component form (Eq. 2):

\(\:{X}_{ij}={\sum\:}_{k=1}^{p}{G}_{ik}\bullet\:{F}_{kj}+{e}_{ij}\) (Eq. 2)

where \(\:{X}_{ij}\) denotes the concentration of species \(\:j\) in the sample \(\:i\), which is factored into matrix \(\:G\) (factor or source contribution matrix) and matrix \(\:F\) (source composition matrix), along with a residue matrix \(\:F\), \(\:p\) is the number of factors to be resolved, \(\:{G}_{ik}\:\)represents the relative contribution of sample \(\:i\) to the source \(\:k\), \(\:{F}_{kj}\) indicates the content of species \(\:j\) in the factor component spectrum \(\:k\), and \(\:{e}_{ij}\) represents the residual of species \(\:j\) in sample \(\:i\). The solution was obtained by a weighted least-square fitting in PMF, which involved utilizing the known standard deviation for each value in matrix \(\:X\) to determine the weights of the residuals in matrix \(\:E\). In matrix \(\:F\), each row represents the 'profile' for a specific source, and the corresponding column of \(\:G\) indicates the quantity of this emission present in the samples.

The determination of \(\:{X}_{ij}\) was achieved by evaluating the \(\:Q\) value, where \(\:{X}_{ij}\) was selected based on minimizing the \(\:Q\) value. The expression for the \(\:Q\) value is provided in Eq. 3.

\(\:Q={\sum\:}_{i=1}^{n}{\sum\:}_{j=1}^{m}{\left[\frac{{e}_{ij}}{{\mu\:}_{ij}}\right]}^{2}\) (Eq. 3)

where \(\:m\) represents the number of samples, \(\:n\) denotes the number of species, and \(\:{\mu\:}_{ij}\) represents the uncertainty of species \(\:j\) in sample \(\:i\). The data analysis in this investigation utilized the EPA Positive Matrix Factorization (PMF) 3.0 model.

2.4 Incremental lifetime cancer risk (ILCR) model

To assess the human health risks of PAHs in Ethiopia, the Incremental lifetime cancer risk (ILCR) model was used. The ILCR model incorporates various exposure pathways, including inhalation, ingestion, and dermal contact (Krajčovičová & Eschenroeder, 2007; Ernyasih, 2023; Tannenbaum et al., 2003). Guidelines released by the USEPA in 1991 state that a cancer incidence rate of 1 in a million (ILCR = 10^− 6) is an acceptable threshold. Special attention is warranted when the estimated ILCR exceeds the threshold (Means, 1989). Consequently, cancer risk values less than 10^− 6 indicate negligible risks, 10^-6-10^-4 suggest low health risks, and more than 10^− 4 interpret as high risks that require special attention (Pierce & Preston, 2000; Sawut et al., 2018). As ILCR_inhalation associated with exposure to soil PAHs was significantly lower than the threshold of 10^− 6, the health risks posed by inhalation could be considered negligible. Our investigation was thus mainly focused on the pathways of ingestion and dermal contact. The equations for calculating the potential cancer risks were based on the USEPA standard models (Means, 1989; USEPA, 1991; Zhang et al., 2016) as shown in Eqs. 4–6:

\(\:{ILCR}_{ingestion}=\:\frac{{TEQ}_{Bap}\times\:({CSF}_{ingestion}\times\:\sqrt[3]{BW/70})\times\:{IR}_{ingestion}\times\:EF\times\:ED}{BW\times\:AT\times\:{10}^{6}}\) (Eq. 4)

\(\:{ILCR}_{demal}=\:\frac{{TEQ}_{Bap}\times\:({CSF}_{demal}\times\:\sqrt[3]{BW/70})\times\:SA\times\:AF\times\:ABS\times\:EF\times\:ED}{BW\times\:AT\times\:{10}^{6}}\) (Eq. 5)

\(\:{TEQ}_{Bap}=\sum\:{TEF}_{i}\times\:{C}_{i}\) (Eq. 6)

where \(\:{ILCR}_{ingestion}\) represents the cancer risk associated with inadvertent soil ingestion, \(\:{ILCR}_{demal}\) signifies the cancer risk attributed to dermal contact with the soil, \(\:{TEQ}_{Bap}\) represents the sum of conversion concentrations of PAHs in soils based on the toxic equivalency factor (\(\:TEF\)) for Bap (mg·kg^− 1) (Nisbet and LaGoy, 1992), \(\:{C}_{i}\) represents the measured concentration of PAHs, \(\:{TEF}_{i}\) is the toxic equivalency factor for PAHs (Table S1), \(\:CSF\) is the slope factor for carcinogens (mg·kg^− 1·d^− 1), determined from the carcinogenic potency of Bap, \(\:BW\) refers to body weight, \(\:{ILCR}_{ingestion}\) is the human soil ingestion efficiencies, \(\:EF\) represents the exposure frequency, \(\:ED\) is the duration of exposure, \(\:SA\) is the dermal surface area, \(\:AF\) is the dermal adherence factor, \(\:ABS\) is the dermal absorption factor, and \(\:AT\) is the average lifespan (Table S2). The \(\:{CSF}_{ingestion}\) and \(\:{CSF}_{demal}\) for Bap were 7.3 and 25 mg·kg^− 1·d^− 1, respectively (Knafla et al., 2006). In this study, the population was categorized into three groups according to age: children (0–10 years), adolescents (11–18 years), and adults (19–70 years).

3.1 Concentration and spatial distribution of PAHs

The total concentrations of PAHs (Σ₁₆PAHs) ranged from 12.51 to 435.67 ng/g, with an average of 279.41 ng/g, which was higher than that in Heilongjiang, China (209 ng/g) (Ma et al., 2013), while lower than that in the Awotan, Nigeria (2212 ng/g) and Jamshedpur, India (5867 ng/g) (Ambade et al., 2023; Parra et al., 2020) as well the Ethiopian capital Addis Ababa (907 ng/g) (Prasse et al., 2012) (Table S3). According to the categorization proposed for Polish agricultural soils (Maliszewska-Kordybach, 1996), PAH contamination levels in soils were classified as no contaminated (< 200 ng/g), mildly contaminated (200–600 ng/g), moderately contaminated (600–1000 ng/g), and seriously contaminated (> 1000 ng/g). Based on the above categorization, the Ethiopia soils were classified as mildly contaminated in this study. The highest concentration of 16 PAH congeners was Phe, with a median concentration of 101.91 ng/g, followed by Flu and Acy, with concentrations of 60.08 ng/g and 52.87 ng/g, respectively (Table 1). The 3-ring PAHs accounted for the highest proportion (77.25%), followed by the 4-ring PAHs (10.66%), and the 6-ring PAHs accounted for the smallest proportion (0.75%). Low-ring PAHs (2–3 rings) accounted for 85.11% of the total in the surface soils of the study area (Fig. 3c).

The concentrations of PAHs varied widely across land use types. Table S4 shows the average PAH concentration was 322.58 ng/g in construction land, 306.19 ng/g in transportation land, and 235.25 ng/g in agriculture/forest/grass land. It was worth noting that construction land had the highest PAH values, which was likely affected by the surrounding human activities. PAHs in transportation land could come from activities such as road asphalt paving and vehicle emissions. The PAH concentrations in farmland, forestland, and grassland were significantly lower than those of construction land and transportation land.

The coefficient of variation (CV) reflected the degree of variation in the spatial distribution of PAHs (Abdi, 2010; Jalilibal et al., 2021). The CV values of PAHs ranged from 0.28 to 1.32, with an average value of 0.53, which indicated that the study area was significantly affected by external factors. The high degree of variation was attributed to the fact that some of the sampling sites were around the main traffic roads and towns with various anthropogenic PAH sources. Various PAHs released by human activities were spatially heterogeneous (Gong et al., 2022; Wang et al., 2023). These substances diffused through various media and were deposited in different locations under the influence of airflow (Mu et al., 2018; White et al., 2005), resulting in significant variability of PAH distribution in the soils. Besides, significant differences in CV values between different land use types were observed. The CV values of construction, land transportation land, and forest/grassland ranged from 0.33 to 0.72, 0.28 to 1.07, and 0.29 to 1.32, respectively. These indicated that different land use types had varying degrees of impact on the PAH congener concentrations in the environment.

Table 1

Summary of PAH concentrations (ng/g)
Substance (rings)	Mean	Median	SD
Nap (2)	21.99	16.98	14.17
Acy (3)	4.82	5.85	2.37
Acp (3)	50.75	52.87	21.79
Flu (3)	55.28	60.08	31.09
Phe (3)	98.23	101.91	46.83
Ant (3)	6.73	7.25	3.91
Flt (4)	16.75	15.82	8.48
Pyr (4)	7.87	6.97	4.21
Baa (4)	1.66	1.36	0.91
Chr (4)	3.54	3.24	1.77
Bbf (5)	2.83	2.48	1.37
Bkf (5)	0.57	0.35	0.52
Bap (5)	5.84	5.27	3.4
Daa (5)	0.47	0.43	0.16
Inp (6)	1.09	0.94	0.57
Bgp (6)	1.01	0.76	1.05
Σ₁₆PAHs	279.41	286.25	142.61

Figure 2 shows the spatial distribution of the main PAH congeners, including Acy, Flu, and Phe. The highest PAH concentration (435.67 ng/g) was recorded at site T10, which was located in a densely populated area with industrial facilities, housing complexes, and extensive road network. In the study area, the concentrations of PAHs showed the trends of south > north and east > west (Fig. 2). PAHs exhibited spatial distribution patterns associated with energy consumption and population density. As shown in Fig. 2 and Figure S1, the southeastern part of the study area had many cities and towns with high population density, while the population and human activity were low in the northwestern part.

3.2 PAH concentrations across an urban-rural gradient

The changes in soil PAH concentrations across an urban-rural gradient, including distances from urban centers (DU), towns (DT), rural areas (DR) and elevation, were derived. Measured results showed PAH concentrations in the urban soils (0–4-km sites) that were larger than those in rural areas (25–35 km sites), with mean values of 319.56 ng/g and 240.70 ng/g, respectively (Fig. 3b). As the distance from the urban area increased, PAHs exhibited a gradual decreasing trend. However, a slight increase was observed at a distance of 15–20 km from the urban area (Fig. 3b). This suggested that suburban industrial activities have had a greater impact. The primary cause for the higher standard deviation of PAH concentration may be attributed to the aggregation and uneven distribution of human activities. As depicted in Fig. 3a, a negative correlation was noted between PAH concentrations and Nap, Chr, Bbf, Inp, and Bgp, indicating more frequent wood combustion and vehicle emissions significantly contributed to the occurrence of PAHs (Bai et al., 2020; Cavalcante et al., 2017; Kumar et al., 2016). Correlations with PAHs were not significant for DT and DR, which were often accompanied by more complex human activities (Bucala, 2014; Li et al., 2016), such as biomass burning and specific agricultural activities, which might lead to elevated local PAH concentrations. In addition, specific characteristics of the sampling sites, such as proximity to point sources (e.g., factories, intersections), prevailing wind directions, and the nature of local activity, might significantly influence the observed correlations (Jiang et al., 2009; Siudek, 2023). Furthermore, the correlations between different elevations and different ring PAHs showed inconsistent relationships, resulting from the occurrence characteristics and migration behavior of PAHs in different media. For example, the transport flux proportional relationship of low-ring PAHs in multiple media was: water-atmosphere > atmosphere-water > atmosphere > soil (Ma et al., 2022; Wang et al., 2009), while high-ring PAHs mostly stayed in the particle phase (Ho et al., 2009), which explained why high ring PAH concentrations were negatively correlated with altitudes.

3.3 Source identification

According to the previous studies, Flt/(Flt + Pyr) < 0.4 indicated petroleum source, while values within the range of 0.4–0.5 suggested the combustion of liquid fossil fuels, and values above 0.5 indicate a large contribution from pyrogenic sources. An Inp/(Inp + Bgp) < 0.2 indicated input from petrogenic sources whereas values between 0.2–0.5 suggested inputs from petroleum combustion. Besides, the Inp/(Inp + Bgp) > 0.5 was indicative of biomass and coal combustion (Akodu et al., 2023; Gao et al., 2018; Jautzy et al., 2015; Yang et al., 2023). As shown in Table S5 and Figure S2a, 96% of the samples had Flt/(Flt + Pyr) ratios exceeding 0.5, and the Inp/(Inp + Bgp) ratio of Ethiopia varied from 0.35 to 0.65 with 80% of the samples having values above 0.5, suggesting that biomass and coal combustion were the sources responsible for the presence of PAHs. From Figure S2b, it was observed that the Inp/(Inp + Bgp) and Flt/(Flt + Pyr) ratios exhibited an upward fluctuating trend from north to south. This trend was attributed to the higher urbanization and population density in the southeastern part of the study area, leading to increased activities related to biomass and coal combustion.

Four factors/sources were identified by quantitative analysis using the PMF model in Ethiopia (Fig. 4). Factor 1 was strongly characterized by 3-ring and 4-ring PAHs (e.g. Ane, Fla), accounting for 56%, which were typically produced during coal combustion (Ma et al., 2020; Wang et al., 2018, Li et al., 2006). Therefore, Factor 1 was identified as the coal combustion contribution. Factor 2 has been identified as a source of petroleum, with high concentrations for Daa and Nap and small amounts of Bbf and Bkf, which was identified as a source of petroleum leaks. Nap is prevalent in petrogenic origins like carbon and crude oil and spills of petroleum and BbF and BkF have been identified as markers of incomplete combustion of fossil fuels (Liu et al., 2009; Wang et al., 2016). Factor 3 was responsible for 21.7% of all factor contributions and was characterized by Inp, Bgp, Bbf, Bkf, and Pyr. According to the previous studies, high-ring PAHs, including Bbf, Bkf, and Bgp, were mostly formed by vehicle emissions, while Inp primarily arose from the combustion of gasoline (Li et al., 2023; Wu et al., 2021; Xu et al., 2014). Consequently, Factor 3 indicated an origin of vehicle emissions and the combustion of gasoline. Factor 4, contributing to 49.2% of the total PAHs, was composed predominately of Flu, Phe, and Nap. Wood combustion was a significant source of Flu emissions (Ma et al., 2020; Rajeev et al., 2023), while burning straw also generated a considerable quantity of Nap (Lai et al., 2009). It was proposed that Bkf, Bbf, and Flu represented three of the most common species in biomass burning (Masclet et al., 1995), and this source profile in the soils was primarily obtained from biomass burning.

In conclusion, four sources were successfully identified based on the PMF results: 1) coal combustion sources (19.5%), 2) petrogenic sources (9.6%), 3) vehicle emissions (21.7%), and 4) biomass combustion (49.2%). Hence, pyrolytic sources, including coal/biomass/gasoline combustion, were considered the main sources of PAHs released into the environment, which was consistent with the diagnostic ratio method described above.

3.4 Risk assessment and recommendations

The results of cancer health risk assessment showed that the total cancer risks for children, adolescents, and adults ranged from 9.90×10^− 7 to 1.44×10^− 5, 9.81×10^− 7 to 1.43×10^− 5, and 1.49×10^− 6 to 2.17×10^− 5, with average values of 6.29×10^− 6, 6.23×10^− 6, and 9.46×10^− 6, respectively (Table 2). The ILCR values in these three population groups were at the 10^− 6 level, which was slightly above the safety threshold, indicating a potential level of health risk of concern. Moreover, cancer risks exhibited variations across the distinct pathways of exposure. The risks were higher for dermal exposure than ingestion in all groups. Under both exposure pathways, the ILCR values for adults were higher than those of adolescents and children. This study revealed that the exposure duration (ED) had the most significant impact on cancer risk. In this study, the ED for adults was 4–7 times higher than that for children, which could be a key reason for the substantially higher cancer risk in adults. Soil ingestion rate (IR_ingestion), exposure frequency (EF), and body weight (BW) were also crucial influencing factors in health risk assessment. It was worth noting that the ingestion risk of children was greater than adolescents due to their heightened sensitivity and their tendency to engage in more frequent hand-to-mouth activities, making them prone to ingesting contaminated soil easily. For individual PAH, the cancer risk levels did not surpass the safety threshold except for Bap (Table S6). Bap contributed the most to the carcinogenic risk (80.03%), followed by Daa (6.49%). This was primarily due to the higher toxic equivalency factors of Bap and Daa, which also accounted for a significant proportion of the total TEQ_Bap (43.6% and 38.2%, respectively).

Table 2

Cancer risk due to PAH exposure via soils in the study area
		ILCR(Ingestion)	ILCR(Dermal)	ILCR(Sum)
Child	Minimum	4.41\(\:\times\:\)10^-7	5.49\(\:\times\:\)10^-7	9.90\(\:\times\:\)10^-7
	Maximum	6.42\(\:\times\:\)10^-6	8.01\(\:\times\:\)10^-6	1.44\(\:\times\:\)10^-5
	Average	2.80\(\:\times\:\)10^-6	3.49\(\:\times\:\)10^-6	6.29\(\:\times\:\)10^-6
Adolescent	Minimum	2.81\(\:\times\:\)10^-7	7.00\(\:\times\:\)10^-7	9.81\(\:\times\:\)10^-7
	Maximum	4.09\(\:\times\:\)10^-6	1.02\(\:\times\:\)10^-5	1.43\(\:\times\:\)10^-5
	Average	1.78\(\:\times\:\)10^-6	4.45\(\:\times\:\)10^-6	6.23\(\:\times\:\)10^-6
Adult	Minimum	5.37\(\:\times\:\)10^-7	9.53\(\:\times\:\)10^-7	1.49\(\:\times\:\)10^-6
	Maximum	7.82\(\:\times\:\)10^-6	1.38\(\:\times\:\)10^-5	2.17\(\:\times\:\)10^-5
	Average	3.41\(\:\times\:\)10^-6	6.06\(\:\times\:\)10^-6	9.46\(\:\times\:\)10^-6

3.5 Possible impact of energy consumption structures (ECSs) on PAHs

3.5.1 Energy structure and cleaner energy in Ethiopia

Ethiopia stands among the countries globally facing energy shortages as a net importer. The four primary energy sources in Ethiopia are biomass, oil, hydro, and coal (IEA-Ethiopia, 2021). Based on the official government report (MoWIE-Ethiopia, 2018), Ethiopia’s total energy consumption increased from 41,902 kilotons of oil equivalent (ktoe) in 2017 to 47,249 ktoe in 2022 (Enerdata-Ethiopia, 2022). Traditional fuels (charcoal, firewood, dung cakes, and agricultural residues) are the main consumed energy, accounting for approximately 87% of total energy consumption (Enerdata-Ethiopia, 2022). Individuals in rural areas predominantly relied on fuelwood and tree residues for lighting and cooking due to the restricted availability of national electricity. Modern fuels constituted roughly 13% of the overall energy consumption in 2018. Within this category, hydrocarbon products accounted for 80%, (comprising 25% light petroleum products, 47% heavy petroleum products, and 8% coal), with electricity contributing the remaining 15% (Figure S3 (a) (MoWIE-Ethiopia, 2018). Dependence on traditional energy sources has raised concerns of environmental pollution of PAHs in Ethiopia, especially the release of large amounts of PAHs from biomass burning. In this respect, Ethiopia has been actively engaging in substantial efforts to advance the development of cleaner energy technologies over the past two decades, focusing on policies related to energy production, deployment, and electrification (MOFED-Ethiopia, 2015; NPCE, 2017; Mengistu et al., 2015). Ethiopia possesses abundant cleaner energy resources, including water, wind, solar, and geothermal energy. However, the development share of these resources was less than 5% as of 2023 (ITA-Ethiopia, 2023). The Ethiopian government is actively working towards fully harnessing these energy sources and plans to increase the utilization rate of cleaner energy to 30% in the next 10 years (ITA-Ethiopia, 2023). Hence, emphasizing the prioritization of cleaner energy is crucial not just for curbing fossil fuel emissions but also for alleviating the adverse impacts of PAH pollution on both the environment and public health.

3.5.2 Implications from energy structure in Africa

Africa is a large and diverse continent, with each of its 54 countries having a very different degree of economic and energy sector development. Differentiating the source contribution and contamination of accumulated regional PAHs on the continental scale is critical for the development of strategies and socioeconomic implications in the context of diverse ECSs. The average PAH concentration of 279.41 ng/g in Ethiopia was much lower than those reported in the other regions of Africa, such as Cape Town, South African (Avg.Σ₁₆PAHs = 3980 ng/g), Niger Delta, Nigeria (Avg.Σ₁₆PAHs = 1456 ng/g) and El-Tabbin, Egypt (Avg.Σ₁₆PAHs = 2557 ng/g) (Bandowe and Nkansah, 2016; Mahugija, 2015; Mungai et al., 2019). The notable discrepancy in the concentrations of PAHs between Ethiopia and other African regions may be broadly attributed to differences in the energy consumption within these areas. Overall energy consumption per capita in Ethiopia was reported to be 0.40 tons of oil equivalent (toe), which was significantly lower than the average the Sub-Saharan energy consumption of 0.80 toe (Nyasha et al., 2018). In 2018–2020, the average consumption of fossil fuels (coal, gas, and oil) in South Africa was approximately 311 million tones of oil equivalent (Mtoe), which was only 24 Mtoe in Ethiopia (Fig. 5, SRWE, 2022). Correspondingly, the concentrations of PAHs in South African soils were also very high, registering an average of 3980 ng/g (Andong Omores, 2016).

Regarding the source apportionment of PAHs, coal combustion and biomass combustion were 41% and 7% in South Africa, respectively (Fig. 5, Kimemia & Annegarn, 2011), whereas in Ethiopia, the contributions were 19% and 49%, respectively. This disparity revealed the differences in ECSs across different regions. Historically, South Africa predominantly relied on coal consumption (Menyah and Wolde-Rufael, 2010; Roy et al., 2023). In 2021, African coal consumption was 1,000 million tons of oil equivalent. South Africa is the biggest consumer of coal in the continent, accounting for 83.84% of total coal consumption in Africa (WECD, 2021). However, according to the Ethiopian National Bureau of Statistics, the largest share of energy consumption (87%) in Ethiopia was dominated by traditional fuels (charcoal, fuel wood, dung cakes, and agricultural residues). This concurred with the predominance of biomass combustion as the source of PAHs in Ethiopia.

The profile of energy consumption sources is quite different for countries where crude oil is the main source of fossil fuel energy consumption (e.g., Algeria, 41%; Nigeria, 41%; Egypt, 44%; and Angola, 54%, SRWE, 2022). In Nigeria, crude oil was the main source of energy (Osueke & Ezugwu, 2011), and the pollution signature of soil PAHs observed over the Niger Delta was believed to be contaminated from various oil-related human activities such as oil exploration, refinery, and exploitation (Iwegbue et al., 2016; Ekanem et al.,2019). It was also reported the ECSs in Egypt relied on three main sources, including oil, natural gas, and hydroelectricity generated by large dams on the Nile River (Eshra, 2022). A previous study revealed that petroleum constituted the primary source of PAH contamination in Egyptian samples, accounting for 34% (Havelcová et al., 2014), which was only 10% in Ethiopia.

Additionally, a notable consistency in the proportional distribution of PAH congeners across different energy structures was observed. In this study, the highest concentrations were observed for Phe, Flu, Nap, and Bbf, which were primarily formed during the combustion of biomass, aligning with the fact that biomass constituted the primary energy source in Ethiopia. In the studies conducted in Egypt, Nigeria, and South Africa, the highest concentrations of individual PAHs were identified as Nap, Ace, and Flu, corresponding to petroleum and coal sources. Above all, the source apportionment and concentrations of PAHs were not only consistent with previous research on PAH emission inventories across the African continent (Ofori et al., 2020; Zhang and Tao, 2009), but also highlighted the potential impacts of regionally varied ECSs.

It is necessary to acknowledge the uncertainties and limitations of this work. Although the selected sites were intended to represent typical characteristics of pollution patterns across the country, we obtained a limited number of samples and historical data. Differences in environmental factors, industrial activities, and population densities across different regions of Ethiopia may introduce uncertainty in representing the results of the study for the whole country. Furthermore, the lack of data on annual and seasonal dynamics may be a limitation in elucidating the seasonal patterns of energy usage and shifts in ECSs, such as the increased demand for coal and wood combustion during winter heating accompanied by increased PAH emissions. The study relied on existing government energy consumption data and previous studies, and differences in the accuracy and consistency of the various data sources could lead to potential uncertainty in the results.

The occurrence, distribution, sources, impacts of the ECSs, and human health risks of PAHs were investigated in Ethiopia, Africa. The total concentration of the PAHs (Σ₁₆ PAHs) ranged from 12.51 to 435.67 ng/g, with an average of 279.49 ng/g in soils. The average PAH concentration in construction land was the highest at 322.58 ng/g, followed by transportation land at 306.19 ng/g. Agriculture, forest, and grassland had the lowest PAHs with an average concentration of 235.25 ng/g. The differences in PAH levels were attributed to human activities and vehicle emissions. Notably, two predominant contributors of PAHs, biomass combustion and vehicle emissions, were identified through diagnostic ratios and PMF model, which were consistent with the results that PAH concentrations were higher in construction and transportation land. The relative consistency between changes in soil PAH concentrations and ECSs was examined in Africa. The PAH pollution in different areas was highly correlated with the consumption of fossil fuels, the transformation of economic structure, and the development of cleaner technologies, which were the more significant variables affecting the levels of PAH contamination. The transformation of ECSs promoted the widespread use of cleaner and sustainable energy resources, which helped reduce the dependence on traditional fossil energy and environmental pollution, and achieve sustainable development. The average total cancer risks of soil PAHs for children, adolescents, and adults near the critical threshold of 10^− 6 using the ILCR model, indicating low health risks. The carcinogenic risks associated with individual PAHs, excluding Bap, fell within acceptable ranges. Dermal contact was identified as the primary exposure pathway. The results emphasized the need for ongoing monitoring and targeted intervention to protect the environment as well as human health.

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.

Author Contribution

X.S. and Q.W. conducted the investigation and data curation, and drafted the original manuscript. W.Y. contributed to investigation and data curation, and reviewed and edited the manuscript. S.S. conceptualized the study, designed the methodology, and reviewed and edited the manuscript. F.L. and G.C. provided critical review and editing. S.Y. and C.S. curated the data. H.H. contributed to investigation and validation. All authors reviewed the manuscript.

Acknowledgements

This work was supported by the Chinese National Natural Science Foundation (Nos. 42371485, 41761144053, 42030707, 42107420), the International Partnership Program of the Chinese Academy of Sciences (No. 121311KYSB20190029), and the National Key R&D Program of China (No. 2023YFC3804902).

Data Availability

Data is provided within the manuscript or supplementary information files

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National energy consumption structures modulate PAH source apportionment in Ethiopian soils

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Abstract

Figures

1 Introduction

2 Materials and methods

2.1 Study area and sampling

2.2 Analytical procedures

2.3 Source identification

2.4 Incremental lifetime cancer risk (ILCR) model

3 Results and discussion

3.1 Concentration and spatial distribution of PAHs

3.2 PAH concentrations across an urban-rural gradient

3.3 Source identification

3.4 Risk assessment and recommendations

3.5 Possible impact of energy consumption structures (ECSs) on PAHs

3.5.1 Energy structure and cleaner energy in Ethiopia

3.5.2 Implications from energy structure in Africa

4 Conclusion

Declarations

Declaration of competing interest

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Acknowledgements

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