The Bassa Industrial Zone is located in Douala 3 in the Wouri Division of the Littoral Region of Cameroon. It hosts a variety of industrial activities, including manufacturing, chemical production, and transportation and has the highest level of industrial activities in Cameroon ( Asaah et al., 2006 ). The area (coordinates at the outlet are 4˚01'40.64''N and 9˚44'10.13''E, altitudes < 100 m) is in the low coastal areas of Cameroon. The climate is of the tropical monsoon type, hot and humid, with two distinct seasons: a rainy season from mid-March to mid-November and a dry season from mid-November to mid-March. Temperatures range from 18˚C to 34˚C, with annual average rainfall of about 4000 mm ( Charleine et al., 2024 ).

The geology of the site is characterized by sedimentary rock from the tertiary to quaternary sediments ( Asaah et al., 2006 ) and area consists dominantly of sand stones with a few intercalations of limestone and shale.

Soil samples were collected from 17 sampling locations within the month of April. At each location, 10 sub-samples were randomly collected using a Manual stainless steel auger to a depth of 0 - 15 cm. The 10 sub-samples were thoroughly mixed and bulked to form a composite sample representing that sampling location. Three control composite soil samples (Mean value reported) were collected from uncontaminated sites few kilometres from the industrial zone to provide a baseline against which the experimental samples were compared. This allowed for the identification of any variations in heavy metal concentrations that may be attributed to contamination rather than natural variability. In addition, all control sites were confirmed to have sandy clay loam soils, similar in texture, drainage properties, and parent material to the soils within the industrial zone. This similarity minimizes variability due to natural soil characteristics and strengthens confidence in the background metal concentrations used for pollution assessment. Purslane (Portulaca oleracea) plant samples were also collected around the soil sampling points. Plant samples of locations with similar environment were bulk giving a total of 4 composite plant samples. These samples were further separated to collect the leaves. All sampling sites were geo-located with a global positioning system receiver. Figure 1 shows the distribution of sampling points. The soil and leaf samples were sent to the laboratory for analysis. Soil samples were air-dried and ground to pass through a 2 mm sieve and were further fine ground to pass through a 0.5 mm sieve for organic carbon analysis. Plant samples were cleaned and oven dried at 60˚C for 48 hours and ground to a fine powder. Soil samples were extracted using aqua regia (a mixture of hydrochloric and nitric acids) solution ( ISO 11466, 1995 ) and the plant samples with concentrated nitric acid ( Jones & Case, 1990 ). Heavy metals in the soil digest (Cd, Pb, As, Cu, Cr, Ni) and plant digest (Cd, Pb, As, Cu, Cr) were measured by Inductively Coupled Plasma Optical Emission Spectroscopy (ICP - OES). Soil pH in water was determined in a 1:2.5 (w/v) soil:water suspension, organic carbon by chromic acid digestion and spectrophotometric method ( Heanes, 1984 ) and the particle size distribution by the hydrometer method ( Bouyoucos, 1951 ). Soil organic matter (SOM) was calculated by multiplying organic carbon (OC) by 1.724 ( $\text{SOM}\left(\%\right)=\text{OC}\left(\%\right)\times 1.724$) as described by Allison (1965) .

The present study adopted a strict QC program. Four external reference soil and plant samples, one certified soil sample from international soil exchange program and one standard plant reference sample from National Institute of Standards and Technology (NIST) were also included in every batch. The Instrument was calibrated with certified standards. All chemicals used were of analytical reagent grade with high purity, and all solutions were prepared using deionised water.

The assessment of HMs in soil was evaluated by the following quantitative indices: contamination factor (CF), pollution load index (PLI), ecological risk factor (Er), potential ecological risk index (PERI) and geoaccumulation index (I_geo) ( Mugoša et al., 2016 ).

Contamination factor (CF): This represents the ratio of an individual metal value to the background values in soil. It is calculated as: $\text{CF}=C_{\text{metal}}/C_{\text{background}}$. The CF value monitors the HM enrichment in soil over a period of time. A CF value ≥ 6 represents high contamination, 3 ≤ CF < 6, is considerable, 1 ≤ CF < 3 is moderate, and CF < 1 shows low contamination of a given metal ( Ferreira et al., 2022 ).

Geoaccumulation index (I_geo): The geoaccumulation index (I_geo) matrix could be applied to assess soil pollution. I_geo values for soils are calculated as: $I_{geo}={\text{Log}}_2\left(C_n/1.5B_n\right)$, where C_n is the soil concentration of metal n; and B_n is the background geochemical value of element n. In this case, B_n is the control value. The classification used to estimate the pollution level of the calculated Geo-accumulation index (I_geo) is: I_geo value ≤ 0 (unpolluted), I_geo values between 0 and 1(unpolluted-moderately polluted), I_geo values between 1 and 2 (moderately polluted), I_geo values between 2 and 3 (moderately-strongly polluted), I_geo value between 3 and 4 (strongly polluted), I_geo value between 4 and 5 (strongly-extremely polluted), I_geo value > 5 (extremely pollution) ( Okoro et al., 2020 ).

Pollution load index (PLI): Quality of soil is evaluated via the pollution load index. It is defined as the n^th root of the metal multiplications of the contamination

factor ( $\text{PLI}=\left[{\left({\text{CF}}_1\times {\text{CF}}_2\times {\text{CF}}_3\times \cdots \times {\text{CF}}_n\right)}^{\frac{1}{n}}\right]$). This index classifies the soil into

three categories, which are: Polluted (PLI > 1), Baseline levels of pollution (PLI = 1) and, Not polluted (PLI < 1) ( Ferreira et al., 2022 ).

Ecological risk factor (Er): It presents the ecological risk of a chemical element on soil studied. The equation was suggested by Hakanson (1980) . It is expressed as: $E_r={\text{CF}}_i\times T_i$, where Ti is the toxic-response factor for a given substance, and CF is the contamination factor. The Ti values of these HMs by Håkanson are such that: Cd is 30; As is 10; Cu, Pb and Ni is 5; and Cr is 2. This index classifies the chemical element in five categories: Er < 40, low; 40 Er < 80, moderate; 80 Er < 160, considerable; 160 Er < 320, high; and Er > 320, very high ( Ferreira et al., 2022 ).

The potential ecological risk index (PERI): It is determined by the sum of the risk factors ( $\text{PERI}=\sum E_r$). To describe the PERI, the following terminology were used: PERI < 150, low risk; 150 < PERI < 300, moderate; 300 < PERI < 600, considerable; PERI ≥ 600, very high ( Ferreira et al., 2022 ).

The soil and plant data were analysed statistically using Microsoft Excel 2016 and JMP 16 Statistical Software (SAS Institute). Heavy metal levels in the soil samples were examined through descriptive statistics (mean, median, standard deviation, minimum, and maximum values), and their variability was assessed using the coefficient of variation (CV). Less than 20% CV is regarded as low variability, between 20 and 50% CV is regarded as moderate variability, and between 50 and 100% CV is regarded as high variability. Any CV above 100% is regarded as very high variability ( Phil-Eze, 2010 ; Nanganoa et al., 2020 ). Pearson’s correlation was conducted to determine the key parameters influencing soil chemistry, with statistical significance set at a p-value less than 0.05. Factor analysis was carried out to describe the degree of association and identify possible sources of metals in the soil samples. The principal components were extracted with eigenvalues >1 through a varimax rotation.

In the Douala-Bassa industrial zone, the SOM contents ranged from 1.22 to 2.99%, with a mean value of 1.86%. The soil pH ranged from 5.43 to 7.87, with an average value of 7.07, indicating that the soil in this area is neutral ( Table 1 ). These soils have higher pH values as compared to the control site which was strongly acidic (4.76). This increase in soil pH might be due to HM such as Ni and Cr in the soil, making them more alkaline ( Angon et al., 2024 ). The average EC of the soils was 148.16 µs/cm, ranging from 57.8 to 295 µs/cm and was also higher than that of the control (28.20 µs/cm). Electrical conductivity (EC) in heavy metal contaminated soils is generally higher compared to clean soil due to the increased presence of charged ions from the heavy metals, which readily conduct electricity when dissolved in soil moisture ( Gomaa et al., 2020 ; Mboga et al., 2025 ). According to the USDA textural diagram, the studied soils had sandy clay loam texture with the exception of sample points S10 and S13 that were sandy loam ( Figure 2 ). The predominant fraction is sand with an average value of 70.97%. The silt fraction is low with an average of 5.66%, while the clay fraction has an average of 23.37%. No variation in the content of sand, silt, and clay was observed with control site as compared to the study site, and so, the textures of the soils were similar and fall within the sandy clay loam textural class. Soils of this textural class are typically characterized by high porosity and permeability, which can promote the migration of heavy metals into the surrounding environment ( Makuleke & Ngole-Jeme, 2020 ).

As shown in Table 1 , the average concentrations of the HMs (in mg kg⁻¹: Cd 0.65; Pb 292.65; As 16.87; Cr 52.48, Cu 25.37, and Ni 14.74) were higher than their control values with accumulations of 622.22%; 49501.69%; 85.79%; 100.92%; 806.07%; and 383.28% respectively. Pb is the largest accumulated element in this soil, with maximum value of 1491.28 mg∙kg⁻¹ and the metal order in the soil was Pb > Cr > Cu > As > Ni > Cd. Tian et al. (2020) also reported high accumulation of Pb in sediments from the coastal areas of the Bohai Sea. The CV value of Cd (215.38%) and Pb (132.75%) were very high. Cu had a high CV (81.28%), while As (28.39%), Cr (37.94%), and Ni (49.39%) had moderate CVs in the soils. The high coefficient of variation observed in the spatial distribution of heavy metals likely reflects the heterogeneity in industrial pollution sources, with different industries contributing pollutants in distinct ways. Such variability is commonly associated with localized or point-source pollution resulting from anthropogenic activities. However, this study is limited by its focus on a single sampling season and the surface soil, which may not capture seasonal or vertical variability in metal concentrations.

An attempt was made to compare Cd, Pb, As, Cr, Cu, and Ni concentrations in the soils against Soil quality guidelines for Environmental Health for total metal concentrations for industrial land use ( CCME, 2007 ). The highest concentrations of Cd and Ni in this study were within the maximum allowable Limits. However, the concentration of Pb, As, Cr, and Cu in sample location S8, including Pb and As for locations S4, S5, and S11; and As for the remaining locations (except for S10 and S13) were above the Soil Quality Guidelines for environmental health for industrial land use ( Table 2 ) hence, soils were polluted and dangerous to human health and the environment ( CCME, 2007 ; Bermudez et al., 2010 ).

Thus, assessing HM pollution in these soils cannot be done solely based on the concentrations of HM. Other quantitative pollution and ecological risk indices are needed in assessing the contamination from these chemical elements in soils.

The CF results for each element is shown in Table 3 . The highest CF values were obtained for Pb (very high contamination) with a mean CF of 497.27. Very high contamination for Cd and Cu ( Table 3 ) were observed. However, for Cd, only location S1 (22.34), S2 (27.03), S3 (11.19), and S8 (61.15) were very highly contaminated, S10 (1.58), and S13 (1.03) were moderately contaminated and the other locations had low contamination (CF < 1). Copper (Cu) showed considerable contamination in sample locations S12 (3.19), S14 (4.24), S16 (3.64), and S17 (4.05). The mean CF values suggested that there is moderate metal contamination (1 ≤ CF < 3) for As and Cr except for location S8 with considerable As (3.13) and Cr (4.33) contaminations. Ni recorded a considerable enrichment. However, very high contamination (CF value ≥ 6) was obtained in S1, S2, S6-S8, and S15. The PLI values exceeded 1 in all the locations, indicating the presence of heavy metal pollution in the entire study area with location S8 recording the highest PLI value ( Figure 3 ). The heavy metal enrichment particular Pb in the Douala Bassa industrial zone is primarily attributed to industrial activities that release heavy metals into the environment ( Asaah et al., 2006 ).

The ecological risk factor (Er) ( Table 3 ) was compatible with the contamination factor. Similar observation was also made by do Nascimento Monte et al. (2021) . For example, Cd, only the locations (S1, S2, S3, and S8) with very high CF values (CF ≥ 6) presented serious ecological risk (E_r ≥ 320). The Er values for As and Cr varied from 8.80 to 31.30 and 2.29 to 8.66 respectively, indicating low ecological risk from these metals ( Table 3 ). Ni also presented low ecological risk except for location S8 (Er = 58.77) which showed moderate ecological risk. Lead (Pb) posed a very high ecological risk in comparison to any of the other elements ( Table 3 ). However, Pb at S14 (93.9) had considerable ecological risk while S12 (162.64), S13 (172.06), S16 (243.6), and S17 (298) showed high ecological risk and was also consistent with the conclusions relative to the contamination factor. Results for PERI are presented in Figure 3 . Locations S12, S13, and S14 had moderate ecological risk (150 < PERI < 300), S16 and S17 presented high potential ecological risk (300 < PERI < 600), and the rest of the sites showed significantly high ecological risk (PERI ≥ 600). The primary contributors to the PERI are the most toxic elements: Pb, followed by Cd and then Cu. These elevated concentrations of heavy metals, particularly Pb, Cd, and Cu, detected in the industrial zone pose serious ecological and human health risks at both local and regional scales. Locally, the accumulation of these metals in soils can degrade soil quality, reduce microbial biodiversity, and impair plant health and productivity. This, in turn, affects terrestrial food chains and may lead to bioaccumulation in herbivores and higher trophic levels. Cd, for instance, is known for its high bioavailability and toxicity to plants and soil organisms, potentially disrupting local ecosystems and agricultural viability in adjacent areas. Regionally, the risk extends through runoff and atmospheric deposition, enabling the spread of contaminants to surrounding farmland, surface waters, and residential areas. This increases the likelihood of human exposure through contaminated food crops, water sources, and air, especially in communities located near the industrial operations. The accumulation of Pb for example in soil around industries poses serious health risks to humans, particularly through ingestion and inhalation, which leads to neurological and reproductive damage ( Moya & Phillips, 2014 ). It also harms ecosystems by disrupting plant growth, contaminating the food chain, and negatively affecting soil organisms ( Collin et al., 2022 ). Health risks posed by Cd include kidney and bone damage, cancer, and cardiovascular issues. It is especially dangerous to vulnerable populations such as children and pregnant women. Ecologically, Cd contaminates soil and disrupts plant growth, soil organisms, and the food chain, causing long-term harm to ecosystems ( Mugoša et al., 2016 ). Cu is an essential trace element for both humans and plants, but in excess in soil, can pose serious health risks, including liver and kidney damage, gastrointestinal issues, and neurological effects, particularly in vulnerable populations. Ecologically, excessive copper in soil can harm plant growth, reduce biodiversity, and disrupt soil ecosystems, including soil microorganisms and invertebrates. Copper can also bio-accumulate through the food chain, affecting animals and humans ( Afzal & Mahreen, 2024 ). The observed correlation between high metal concentrations and elevated electrical conductivity (EC) also indicates increased ion loading, which may further stress aquatic and terrestrial ecosystems in the broader region.

Based on I_geo, the greatest contribution to heavy metal contamination were Pb and Cd with respectively 15 and 1 out of the 17 samples being extremely contaminated (I_geo > 5) ( Figure 4 ). Heavily to extremely contamination level (4 ≤ I_geo ≤ 5) were obtained from Pb (S12 and S13), Cd (S2) and Cu (S8) ( Figure 4 ). Uncontaminated to moderately contaminated class resulted from As and Cr. The Douala Bassa industrial zone was also moderately contaminated by Ni though with low ecological risk. I_geo also showed that, the main contaminants were Pb, followed by Cd and Cu. Asaah et al. (2006) , who studied heavy metal concentrations and distribution in surface soils of Bassa industrial zone 1 of Douala, Cameroon, indicated that, the area was highly polluted in metals like Pb, Zn, Cu, Co, Sb, and to a lesser extent in Ag, Cd, Fe, Mn, Mo, and Ni. Metals like Cr and As showed moderate pollution.

The Pearson correlation coefficients between the six heavy metals and other soil properties are listed in Table 4 . Inter element relationships can provide interesting information on heavy metal sources and pathways. Cadmium (Cd) showed a significant positive correlation with As, Cr, Cu, and Ni (r² = 0.5868 - 0.8951), with correlations at the 0.01 significance level for As, Cu, and Ni, and at the 0.05 level for Cr ( Table 4 ). This suggests that; these metals are likely influenced by common factors and may originate from similar or related sources. Such relationships point to a multi-metal contamination scenario, where a single pollution source contributes multiple heavy metals to the environment. Additionally, the strong correlations indicate that Cd, As, Cr, Cu, and Ni may exhibit similar environmental behaviour, particularly in terms of transport and mobility in soil. The concentration of Pb did not show a significant correlation with other heavy metals ( Table 4 ), which may indicate that Pb originates from different sources or follows distinct environmental pathways. Unlike more mobile metals such as Cd and Ni, Pb tends to be less mobile and more strongly bound to soil particles, potentially leading to a different spatial distribution. Additionally, Pb contamination may reflect historical pollution, in contrast to the more recent industrial inputs associated with other metals ( Modrzewska & Wyszkowski, 2014 ). EC was also significantly correlated with Cd, Cr, Cu and Ni suggesting that these metal ions contributed to the elevated EC observed in the industrial zone compared to the control site. This relationship implies that industrial discharges not only increased heavy metal concentrations but also enhanced the ionic strength of soil solutions, resulting in higher electrical conductivity. Okonokhua & Orhue (2024) also reported a positive significant correlation between HMs and EC. Soil pH and EC were also significantly correlated ( Table 4 ).

Factor analysis with Varimax rotation was applied to the experimental data to effectively identify the potential sources (lithogenic or anthropogenic) and gain a deeper understanding of the relationships between the metals in the soils and other soil properties ( Akoto et al., 2023 ). The PCA revealed that only two components had eigenvalues greater than 1, accounting for 74.4% of the total variance ( Table 5 ). The first component (F1), explaining 52.07% of the variance, had high loadings for Cd, As, Cr, Cu, and Ni, indicating a strong association and likely a common anthropogenic source such as industrial effluents. In contrast, Pb showed a weak association with this factor, suggesting it may originate from a different source or exhibit distinct environmental behaviour, consistent with its lack of significant correlation with other metals. The different industries present in MAGZI, BASSA industrial zone are metal transformation, Wood, Paint, Printing, Water and Energy industries coupled with transportation.

*Correlation is significant at the 0.05 level. ^**at the 0.01 level.

NB: High positive factor loading is shown in bold.

The bioaccumulation tendency of purslane for heavy metals (Cd, Pb, As, Cr and Cu) in the Bassa industrial zone was investigated. Plant sample 1 (PS1) was composite leaf sample from location S1 to S4; Plant sample 2 (PS2) from S5 to S12; Plant sample 3 (PS3) from locations S13 and S14; and plant sample 4 (PS4) from locations S15 to S17. The average heavy metal content in the leaf samples and their corresponding mean soil values are shown in Table 6 . These values are reported in dry weight.

In the purslane leaf samples, the concentrations of Cd ranged from 0.82 to 2.08 mg kg⁻¹, Pb (3.07 to 8.74 mg kg⁻¹), As (0.07 to 0.46 mg kg⁻¹), Cr (1.28 to 5.41 mg kg⁻¹), and Cu (13.65 to 41.67 mg kg⁻¹). The HMs in plant samples were in the order Cu > Pb > Cd > Cr > As for PS1 and in the order Cu > Pb > Cr > Cd > As in PS2, PS3, and PS4. While the highest Cd value was measured in PS1 (2.08 mg kg⁻¹), the lowest values was measured in PS3 (0.82 mg kg⁻¹). Based on available data, the typical range of HMs in plants is as follows: Cadmium (Cd) usually between 0.01 - 0.2 mg kg⁻¹, Lead (Pb) ranges from 5 - 10 mg kg⁻¹, Arsenic (As) falls between 1 - 1.5 mg kg⁻¹, Copper (Cu) is around 5 - 30 mg kg⁻¹, and Chromium (Cr) is typically between 0.1 - 0.5 mg kg⁻¹ ( Shi et al., 2023 ). According to these values, the Cd concentrations in this study site were higher than the normal range in plants ( Table 6 ) ( Kacálková et al., 2014 ). Erkoç and Çolak-Esetlili (2024) , in another study, obtained 1.12 mg kg⁻¹ as their highest value of Cd in purslane leaf samples when 5 mg Cd per kg of CdSO₄ treatment was applied in soil pot experiment in greenhouses, followed by the applications of 20 mg kg⁻¹ Cd (0.79 mg kg⁻¹) and 10 mg kg⁻¹ Cd (0.60 mg kg⁻¹). Their study indicated that Cd accumulation in purslane leaves occurs up to a certain value irrespective of Cd content in the soil medium. According to literature, Pb and As values were within the normal range of this metals in plants ( Table 6 ) ( Osma et al., 2014 ). Despite high Pb in soil, purslane did not absorb significant amounts of Pb. Other studies have indicated that this plant fails to remove Pb when it is exposed to Pb along other heavy metals ( Wei et al., 2005 ; Javed et al., 2018 ). The Pb concentrations in purslane from various locations in Istanbul were also found to be under 10 mg kg⁻¹ ( Osma et al., 2014 ). In comparison to the typical levels of heavy metals found in plants, Cr exceeded the normal range of Cr in plants and was within the maximum toxicity threshold (Cr: 5 - 0.5 mg kg⁻¹) with PS4 (5,41 mg kg⁻¹) slightly higher than the upper limit ( Shi et al., 2023 ). Only PS1 had Cu value above the normal range. Except for Cu in PS1 (41.67 mg kg⁻¹), the Cu levels were consistent with the findings of Osma et al. (2014) , where Cu concentrations in both wild-type and cultivated purslane grown at various sites in Istanbul ranged from 2 to 20 µg Cu/g. Though purslane is known to be a hyper accumulator of Cr (VI), the amount of Cr accumulated by the plant was very low as compared to report by Alyazouri et al., 2020 . This low uptake of Cr might be as a result of soil pH that has a major effect on the geochemical properties of chromium by affecting the chemical speciation of the metal and the adsorption ability of the soil. In neutral-alkaline soils, like in the case of our study area, Cr (VI) exists as sparingly soluble calcium chromate thus reduce uptake by the plant. Most studies have shown that, the highest HMs accumulation was in Purslane root ( Yousefi et al., 2023 ). Therefore, soil Neutral to alkaline pH, may have slowed movement of heavy metals to the upper extremities of the plant ( Yousefi et al., 2023 ). Nonetheless, the use of composite plant samples masked localized differences in uptake, and ecological and human health risk estimates.