The study involved the collection of thirty-four rainwater samples collected directly during precipitation using a purpose-built rainwater harvesting system, ensuring minimal external contamination. Before collection, all sampling bottles were pre-sterilized, and the water was subsequently transferred into 1.5-litre plastic water sampling containers for preservation and further analysis. The fieldwork was structured by grouping the seven study sub-regions into five distinct locations: Obigbo, Komkom-Obiama, Okoloma, Egberu, and Umu Agbai-Obete ( Table 1 ). Settlements, markets, schools, hospitals, and petroleum facilities were mapped within these sub-regional locations, and samples were systematically collected. Systematic random sampling was employed to achieve an even spatial distribution across the study area, with precise coordinates and sampling station recording, ensuring a representative analysis ( Daniel, 2003 ; Teixeira & Nearing, 2019 ). The geo-referencing of all sampling points and study area location mapping was conducted using the Garmin eTrex 32x, a rugged Handheld GPS device, ensuring precise spatial documentation of sample collection sites ( Ahmed et al., 2024b ). To maintain sample integrity, all collected samples were immediately transferred to plastic containers, preserved in ice-packed coolers, and promptly transported to the laboratory. This stringent sample handling protocol prevented contamination and degradation for accurate hydrochemical analysis.

The hydrochemical analysis involved the evaluation of total petroleum hydrocarbon (TPH) content in water samples, which was conducted to determine the concentrations of both petroleum aliphatic hydrocarbons and petroleum aromatic hydrocarbons. Gas Chromatography (GC) analysis was employed using an Agilent 7820A gas chromatograph equipped with a flame ionization detector (FID) and an HP-5 fused silica capillary column (30 m × 0.32 mm ID × 0.25 μm film thickness). The purified sample extracts were analyzed with helium as the carrier gas, set at a flow rate of 1.75 mL/min and an average velocity of 29.47 cm/sec. The injection was performed in splitless mode, where 1 μL of the sample extract was introduced at an injection temperature of 300˚C. The column temperature was programmed to start at 40˚C for 1 minute, then ramped at 7˚C/min until reaching a final temperature of 320˚C. The detector temperature was maintained at 300˚C throughout the process, following the established methodology of Kim, Hong, and Won (2013) and Inyang, Aliyu, and Oyewale (2018) .

To ensure analytical accuracy, GC calibration was performed using petroleum hydrocarbon calibration standards ranging from 0.05 - 20 μg/mL, with n-hexane as the diluent. Calibration curves were generated using Agilent Chemstation chromatography software, and average response factors for each analyte were calculated. The calibration results demonstrated high linearity, with correlation coefficients (R²) ranging from 0.9846 to 0.9919, ensuring reliable quantification of hydrocarbons. Unresolved peaks were quantified using the response factor nC-15, following the approach outlined by Luan and Szelewski (2008) . The determination of TPH concentrations involved baseline integration and peak sum slicing, covering the hydrocarbon range from nC-9 to nC-36 and the unresolved complex mixture (UCM). Further hydrocarbon profiling was conducted by calculating the ratios of low molecular weight n-alkanes to high molecular weight n-alkanes and unresolved n-alkanes to resolved n-alkanes, as Inyang et al. (2018) described. This analytical protocol ensured precise quantification and characterization of petroleum hydrocarbons, providing critical insights into contamination levels in the sampled water bodies.

The dispersion analysis of targeted aerial petro-pollutants was conducted to characterize the proportional distribution and density mapping of total petroleum hydrocarbons (TPH) across the study area. This approach integrates density representation, spatial analysis and contour overlay techniques to assess the extent and intensity of hydrocarbon contamination in atmospheric water systems ( Mohamed et al., 2024 ). The analysis employed Contour and Hexbin plots to visualize pollutant distribution and highlight key contamination zones ( Hengl & Gould, 2002 ), depicting contamination density variations and effectively revealing pollution hotspots and concentration gradients across the locations and sample stations ( Antai et al., 2018 ). This approach adopts triangular or hexagonal grids, where each triangle or hexagon represents the average TPH concentration within that localized region.

To further investigate the interrelationships among hydrochemical variables, a multivariate correlation matrix was developed to identify significant correlations between key hydrochemical parameters, including total petroleum hydrocarbons (TPH) fractions and the geographical positioning of sample stations ( Hodge & Hoque, 2011 ). By analyzing correlation coefficients, the study aimed to determine how much hydrocarbon pollutants co-occur with other environmental variables, offering insights into contamination trends and possible pollution sources ( Dale & Boulton, 2019 ). Strong correlations between hydrocarbon fractions and particular hydrochemical indicators suggested potential common contamination pathways ( Dale & Boulton, 2019 ). Also, the correlation matrix helped differentiate influences from anthropogenic hydrocarbon inputs, enhancing pollution source identification and risk assessment ( Hodge & Hoque, 2011 ). The analysis and visualization were done using Python-based geospatial and scientific visualization libraries, incorporating Matplotlib, SciPy, and Seaborn ( Müller & Meyer, 2021 ).

Integrating dispersion and correlation analyses strengthened the study’s ability to characterize pollutant behaviour, map contamination intensity, and identify underlying hydrochemical relationships within the study region ( Sadiq & Al-Sharif, 2020 ). By leveraging multiple techniques and overlap plots for pollutant distribution and correlation matrix for statistical assessment, this approach provided a comprehensive and multidimensional evaluation of hydrocarbon pollution in the Niger Delta. These findings contribute valuable empirical data for environmental monitoring, pollution control strategies, and water quality management, supporting efforts to mitigate contamination risks and promote sustainable water resource management in petroleum-producing regions ( Sadiq & Al-Sharif, 2020 ).

The study area falls within the primary oil-producing locations of the South-East senatorial district region of River State and is situated around the Oyigbo, Eleme, Tai, and Khana Local Government Areas (LGAs), however, principally located in the Oyigbo LGA, Niger Delta, Nigeria. The key reference location is situated approximately 30 kilometres Northeast of Port Harcourt. It lies within the geographic coordinates of latitude 4.9000˚N to 4.7667˚N and longitude 7.2500˚W to 7.4167˚W ( Figure 1 ). The Asa and Ndoki predominantly inhabited the area, contributing to its rich cultural and ethnic diversity ( Opu-Ogulaya, 1973 ). The region plays a strategic role in the socio-economic and political landscape of Rivers State, forming a key part of the Niger Delta region ( Obenade et al., 2020 ).

Population trends indicate substantial growth, rising from 40,407 in 1975 to 125,666 in 2015, reflecting increased urbanization and economic activities in the area ( NBS, 2006 ). The demographic expansion is largely driven by the availability of dry land and favorable terrain, which supports settlement and industrialization ( Samuel et al., 2022 ). The region experiences a tropical wet climate characterized by prolonged rainy seasons and short dry seasons. The dry season spans November to February, with December being the driest month. Heavy precipitation occurs in September, averaging 370 mm, while the annual mean rainfall is approximately 2500 mm, mostly concentrated between May and October ( Onwuka et al., 2021 ). Temperature variations remain minimal, ranging between 25˚C and 28˚C throughout the year, while relative humidity fluctuates from 80% during the rainy season to around 40% in the dry season ( Ahmed et al., 2024b ). The vegetation consists mainly of tropical rainforest along river systems, interspersed with secondary bushlands resulting from farming and fallowing activities ( Onwuka et al., 2021 ).

The target study location is a key oil-producing region and a major industrial hub, hosting several multinational petroleum firms ( Enotoriuwa et al., 2016 ). The discovery of crude oil has significantly transformed the local economy, with major petroleum infrastructure such as the Afam Power Station, Shell Okoloma Gas Plant, and Nigerian Gas Plant Plc serving as primary revenue generators ( Ahmed et al., 2024c ). However, these facilities also contribute to environmental challenges, including hydrocarbon pollution and land degradation, necessitating sustainable management strategies ( Omokpariola et al., 2022 ). The rapid urban expansion has been fueled by affordable housing relative to Port Harcourt, leading to increased migration and traffic congestion as workers commute daily to the city ( Samuel et al., 2022 ). As an industrial and commercial centre, the Oyigbo region continues to experience significant environmental pressures, highlighting the need for comprehensive pollution assessment and management strategies to mitigate the impact of petroleum-related activities on water and soil quality.

The hexbin and contour overlay plots provide a spatial and geometric representation of TPH concentration levels at different sampling stations within the Obigbo location ( Figures 2(a)-(d) ). The Contour Plot uses color gradients to depict concentration variations, while the Hexbin highlights measured sample densities. High PAH concentrations ( Figure 2(a) ), indicated by red/maroon zones, were observed at stations such as OYB 5, OYB 9, and OYB 4, corresponding to Umuebele Market, Shell Facility, and Atata Market, which are linked to petroleum spills, vehicular emissions, and industrial discharges ( Ahmed et al., 2024a ). Low PAH concentrations, represented by blue zones, were recorded at OYB 3 (Main Market), where reduced contamination suggests lower hydrocarbon inputs or natural attenuation (Li & Wong, 2018). The combined plot refines observations by characterizing pollution hotspots with dense hexagonal bins. These findings highlight the influence of industrial and commercial activities in PAH distribution, with vehicular emissions and petroleum residues as likely contributors ( Mason & Brubaker, 2019 ; Nadim & Hossain, 2021 ).

The plot for C8 to C16 TAHs ( Figure 2(b) ) indicates contamination trends across the area. Higher concentrations were detected at OYB 4 (Atata Market), OYB 9 (Shell Facility), and OYB 7 & OYB 6 (CSS Umuakpahu & CSS Umudinor), which are areas associated with fuel residues, vehicular emissions, and industrial activities ( Nadim & Hossain, 2021 ). Lower concentrations were present at OYB 3 (Main Market) and OYB 1 (Health Center), likely due to natural dispersion or minimal hydrocarbon sources. The volatility of C8 to C16 hydrocarbons suggests that while these compounds degrade faster, their presence in residential and commercial zones poses air quality and water contamination risks ( Cohen & Coyle, 2019 ).

The C17 to C24 fraction ( Figure 2(c) ) represents semi-volatile hydrocarbons with longer environmental persistence. The plot highlights elevated levels at OYB 4 (Atata Market), OYB 9 (Shell Facility), and OYB 7 & OYB 6 (CSS Umuakpahu & CSS Umudinor), suggesting persistent contamination from industrial and vehicular sources. Lower concentrations were observed at OYB 3 (Main Market), OYB 1 (Health Center), and OYB 2 (Timber Market), implying reduced hydrocarbon input ( Hwang & Park, 2021 ).

Given their lower degradation rates, C17 to C24 contribute to long-term soil and water pollution, emphasizing needs for sustained monitoring. The heavy C25 to C40 is highly persistent, with a strong affinity for water and soil, leading to long-term contamination risks ( Nadim & Hossain, 2021 ). Figure 2(d) reveals high levels at OYB 4 (Atata Market), OYB 9 (Shell Facility), and OYB 5 (Umuebele Market), confirming the influence of petroleum residues and industrial emissions. Lower levels were found at OYB 3 (Main Market), OYB 1 (Health Center), and OYB 2 (Timber Market), implying reduced pollutants. The Hexbin Overlay enhances visualization by representing each station contamination value, highlighting localized pollution severity. Due to their low volatility, C25 to C40 hydrocarbons persist in industrial locations, increasing ecological and human health risks ( George et al., 2023 ).

The Total PAH across the Komkom-Obiama location, as shown in Figure 3(a) , was represented using a contour and hexbin overlay visualization, which revealed distinct spatial variations in contamination levels. High PAH concentrations were recorded at OYB 10 (Konko Market) and OYB 12 (Lekuma Township), indicating strong influences from petroleum spills, vehicular emissions, and industrial activities ( Cohen & Coyle, 2019 ). Lower PAH concentrations were observed at OYB 13 (Komkom Township), OYB 14 (Obiama Township), and OYB 11 (CSS Komkom), suggesting reduced contamination or possible natural attenuation ( Li & Wong, 2018 ). The contour interpolation further highlighted an increasing concentration gradient in high-traffic and industrial zones, emphasizing the necessity for remediation and pollution control strategies ( Liu & Liu, 2020 ). The C8 - C16 fraction of TAH, depicted in Figure 3(b) , exhibited elevated concentrations at OYB 13 (Komkom Township), likely due to petroleum-related activities, fuel emissions, and industrial processes ( Nadim & Hossain, 2021 ). Conversely, lower concentrations were recorded at OYB 12 (Lekuma Township) and OYB 14 (Obiama Township), suggesting lower contamination exposure. The high volatility of the C8 - C16 fraction may explain its wider dispersion, requiring a monitoring approach that considers airborne transport and infiltration into surface waters ( Cohen & Coyle, 2019 ).

The C17 - C24 fraction, including Pristane and Phytane, exhibited more persistent environmental presence, as shown in Figure 3(c) . High concentrations were recorded at OYB 13 (Komkom Township), OYB 11 (CSS Komkom), and OYB 10 (Konko Market), indicating long-term retention and reduced degradation of these hydrocarbons ( Häggblom & Bossert, 2021 ). Lower concentrations were noted at OYB 12 (Lekuma Township) and OYB 14 (Obiama Township), reinforcing the role of dispersion in these areas ( Siciliano & Enkiri, 2019 ). The semi-volatile nature of this fraction highlights its potential for soil adsorption and secondary contamination, which calls for further sediment and water quality assessments. The C25 - C40 fraction, which consists of heavier hydrocarbons, demonstrated localized contamination patterns with limited mobility, as illustrated in Figure 3(d) . Strong concentration zones were identified at OYB 13 (Komkom Township), OYB 11 (CSS Komkom), and OYB 10 (Konko Market), where petroleum residues and industrial discharge appear to be dominant contamination sources.

Lower concentrations were observed at OYB 12 (Lekuma Township) and OYB 14 (Obiama Township), indicating less contamination or enhanced degradation mechanisms in these zones ( Ghosh & Sinha, 2020 ). Due to their high persistence and strong soil-binding capacity, C25 - C40 hydrocarbons pose long-term contamination risks, necessitating targeted remediation efforts focused on hydrocarbon sequestration in soil and sediments ( Fang & Zhao, 2021 ). The systematic classification and visualization of TPH fractions at the Komkom-Obiama Axis provided critical insights into contamination distribution and potential environmental risks. The observed higher PAH and TAH (C8 - C40) concentrations in industrial and commercial areas underline the influence of petroleum activities,

fuel combustion, and industrial emissions ( Zhang & Zhang, 2019 ; Cohen & Coyle, 2019 ). Meanwhile, lower contamination levels near residential areas and community service locations may indicate dispersion effects, natural attenuation, or limited hydrocarbon inputs ( Häggblom & Bossert, 2021 ). The study revealed that lighter hydrocarbons (C8 - C16) exhibit wider dispersion, whereas heavier fractions (C25 - C40) demonstrate localized persistence, necessitating tailored mitigation strategies.

The Total PAH concentration across the Okoloma location ( Figure 4(a) ) revealed distinct spatial variations in contamination levels. High PAH concentrations were recorded at OYB 17 (Ayama Township) and OYB 20 (Okoloma Gas Plant), indicating strong influences from petroleum spills, gas flaring, and industrial emissions.

Lower PAH concentrations were observed at OYB 19 (Obumku Township) and OYB 16 (Umuosi Market), suggesting reduced contamination or possible natural attenuation. The contour interpolation further highlighted an increasing concentration gradient in high-traffic and industrial zones, emphasizing the necessity for pollution control strategies. The C8 - C16 fraction of TAH, depicted in Figure 4(b) , exhibited elevated concentrations at OYB 16 (Umuosi Market), OYB 20 (Okoloma Gas Plant), and OYB 21 (Afam Power Plant) due to petroleum-related activities, fuel emissions, and industrial processes ( Rapaport & Guers, 2019 ). Conversely, lower concentrations were recorded at OYB 15 (Okoloma Market) and OYB 17 (Ayama Township), suggesting lower contamination exposure. The high volatility of the C8 - C16 fraction may explain its wider dispersion, requiring a monitoring approach considering airborne transport and infiltration into surface waters ( Rapaport & Guers, 2019 ).

The C17 - C24 fraction, including Pristane and Phytane, exhibited more persistent environmental presence, as shown in Figure 4(c) . High concentrations were recorded at OYB 16 (Umuosi Market), OYB 19 (Obumku Township), OYB 20 (Okoloma Gas Plant), and OYB 21 (Afam Power Plant), indicating long-term retention and reduced degradation of these hydrocarbons ( Lima & Mata, 2021 ). Lower concentrations were noted at OYB 15 (Okoloma Market) and OYB 17 (Ayama Township), reinforcing the role of natural attenuation and dispersion in these areas. The semi-volatile nature of this fraction highlights its potential for soil adsorption and secondary contamination, which calls for further sediment and water quality assessments ( Chen & Zhang, 2020 ).

The C25 - C40 fraction, which consists of heavier hydrocarbons, demonstrated localized contamination patterns with limited mobility ( Figure 4(d) ). Strong concentration zones were identified at OYB 16 (Umuosi Market), OYB 19 (Obumku Township), and OYB 21 (Afam Power Plant), where petroleum residues and industrial discharge appear to be dominant contamination sources. Lower concentrations were observed at OYB 17 (Ayama Township) and OYB 20 (Okoloma Gas Plant), indicating less contamination in these zones ( Chen & Zhang, 2020 ). Due to their high persistence and strong soil-binding capacity, C25 - C40 hydrocarbons pose long-term contamination risks, necessitating targeted efforts focused on hydrocarbon sequestration in soil and sediments ( Chen & Zhang, 2020 ).

The Total PAH ( Figure 5(a) ) revealed distinct spatial variations in contamination levels across the Egberu. High PAH concentrations were observed at OYB 23 (Ndoki Market) and OYB 24 (CSS Ndoki), indicating strong influences from commercial activities, petroleum contamination, and vehicular emissions ( González & Aroca, 2021 ). Lower PAH was detected at OYB 25 (Afam-Uku Township) and OYB 26 (Ndoki Township), suggesting lower contamination exposure or effective natural attenuation mechanisms ( Crump & Smith, 2020 ). The colour interpretation of the contour and hexbin overlay revealed that red/maroon regions correspond to high PAH concentrations, while blue regions indicate lower PAH contamination. The neutral/white areas represent moderate contamination levels, forming transitional zones between highly contaminated and less impacted areas. The hexbin overlay further reinforces these findings, as hexagonal bins with darker red intensities confirm measured high PAH levels in key industrial and commercial zones ( Hwang & Kim, 2020 ; Frolova & Guo, 2020 ).

The C8 - C16 fraction of TAH ( Figure 5(b) ) exhibited spatial variability across the Egberu location, with elevated contamination in commercial and industrial locations. Higher C8 - C16 concentrations were recorded at OYB 22 (Ndoki Health Centre), OYB 23 (Ndoki Market), and OYB 24 (CSS Ndoki), likely due to petroleum-related contamination, vehicular emissions, and commercial activities ( Rapaport & Guers, 2019 ). In contrast, lower concentrations were noted at OYB 25 (Afam-Uku Township), OYB 26 (Ndoki Township), and OYB 27 (Afam-Nta

Township), implying natural dispersion or reduced contamination input. The C8 - C16 fraction is more volatile and degrades faster than heavier hydrocarbons; however, its presence in high-traffic areas raises concerns about airborne and soil contamination risks ( Rapaport & Guers, 2019 ).

The C17 - C24 fraction of TAH, depicted in Figure 5(c) , exhibited a more persistent environmental presence across the study area. Higher concentrations were recorded at OYB 22 (Ndoki Health Centre), OYB 23 (Ndoki Market), and OYB 24 (CSS Ndoki), indicating long-term retention and reduced degradation of these hydrocarbons ( Kumar & Singh, 2018 ; Lima & Mata, 2021 ). Meanwhile, lower concentrations were observed at OYB 25 (Afam-Uku Township), OYB 26 (Ndoki Township), and OYB 27 (Afam-Nta Township), reinforcing the role of natural attenuation and dispersion in these areas ( Chen & Zhang, 2020 ). The C25 - C40 fraction of TAH, as illustrated in Figure 5(d) , demonstrated localized contamination patterns with limited mobility across the study area. Strong concentration zones were identified at OYB 22 (Ndoki Health Centre), OYB 23 (Ndoki Market), and OYB 24 (CSS Ndoki), where petroleum residues and industrial discharges appeared to be the dominant contamination sources. Lower concentrations were observed at OYB 25 (Afam-Uku Township), OYB 26 (Ndoki Township), and OYB 27 (Afam-Nta Township), suggesting reduced contamination exposure ( Crump & Smith, 2020 ).

The Total PAH concentration, as shown in Figure 6(a) , revealed distinct spatial variations in contamination levels across the Umu Agbai-Obete Axis. High PAH was observed at OYB 31 (Okpontu Township) and OYB 33 (Marihun Township), suggesting strong influences from industrial discharges, petroleum contamination, and commercial activities ( Creegan & Gillett, 2020 ). In contrast, lower PAH concentrations were recorded at OYB 28 (Ban-Lori Market) and OYB 32 (Azuagu Township), indicating lower contamination exposure. The colour interpretation revealed that red/maroon regions correspond to high PAH, while blue regions indicate lower PAH levels ( Yunker & MacDonald, 2019 ). The neutral/white areas represent moderate levels, forming transitional zones between highly contaminated and less impacted areas. The hexbin overlay further reinforces these findings, where hexagonal bins with darker red intensities confirm measured high PAH levels in key industrial and commercial zones ( Lima & Mata, 2021 ).

The C8 - C16 fraction of TAH, represented in Figure 6(b) , exhibited spatial variability across the Umu Agbai-Obete Axis, with elevated contamination levels in commercial and industrial locations ( Ruan & Liu, 2020 ). Higher C8 - C16 concentrations were recorded at OYB 28 (Ban-Lori Market) and OYB 29 (Obete Township), likely due to petroleum-related contamination, vehicular emissions, and market activities ( Creegan & Gillett, 2020 ). In contrast, lower concentrations were noted at OYB 31 (Okpontu Township) and OYB 34 (Azumini Township), implying natural dispersion or reduced contamination input. The colour-coded interpretation indicated that red/maroon regions correspond to higher concentrations, while blue regions indicate areas of lower contamination ( Yunker & MacDonald, 2019 ). The C8 - C16 fraction is more volatile and degrades faster than heavier hydrocarbons; however, its presence in high-traffic areas raises concerns about airborne and soil contamination risks ( González & O’Brien, 2021 ).

The C17 - C24 fraction of TAH, depicted in Figure 6(c) , exhibited a more persistent environmental presence across the area. Higher concentrations were recorded at OYB 28 (Ban-Lori Market), OYB 29 (Obete Township), and OYB 31 (Okpontu Township), indicating long-term retention and reduced degradation of these hydrocarbons ( Ruan & Liu, 2020 ). Meanwhile, lower concentrations were observed at OYB 30 (Umu Agbai Township), OYB 33 (Marihun Township), and OYB 34 (Azumini Township), reinforcing the role of natural attenuation and dispersion in these areas. The C25 - C40 fraction of TAH, as illustrated in Figure 6(d) ,

demonstrated localized contamination patterns with limited mobility across the study area ( Wang & Chen, 2020 ). Strong concentration zones were identified at OYB 28 (Ban-Lori Market), OYB 29 (Obete Township), and OYB 34 (Azumini Township), where petroleum residues and industrial emissions appeared to be the dominant contamination sources ( Wang & Chen, 2020 ). Lower concentrations were observed at OYB 31 (Okpontu Township), OYB 30 (Umu Agbai Township), and OYB 33 (Marihun Township), suggesting reduced contamination exposure or enhanced degradation mechanisms. The color-coded spatial representation indicated that red/maroon regions corresponded to higher concentrations, while blue regions represented lower contamination levels ( Lee & Park, 2021 ). The C25 - C40 hydrocarbons are highly persistent, tending to bind to soil and sediments, posing a long-term risk of environmental contamination that requires targeted soil and groundwater remediation strategies ( Wang & Chen, 2020 ).

The plots in Figure 7 represent the Hexbin density and spatial distribution of TPH concentrations across different sample stations. Warm colors (red, orange, yellow) indicate higher TPH concentrations, suggesting areas with significant contamination, whereas cool colors (blue, purple) indicate lower TPH levels ( Nguyen & Tran, 2020 ). Gradient transition in contours helps identify hotspots of high contamination and diffusion trends, indicating potential hydrocarbon migration zones and natural attenuation zones where contamination is gradually reducing in regions with uneven TPH spread ( Creegan & Gillett, 2020 ).

Following the Hexbin density representation of the previously presented sub-regional locations, the study area was divided into hexagonal grids, where each hexagon represented the average TPH concentration within that localized region ( Nguyen & Tran, 2020 ). The darker hexagons indicate higher contamination zones, while lighter hexagons indicate less contamination. This hexagonal density visualization helps identify contamination clusters, which could guide mitigation priorities and distinguish areas of local variability ( Lee & Park, 2021 ). The Hexbin density comparison across the study area shows that the Western and Central locations and sample stations have dense dark hexagons, signifying persistent contamination, potentially from a continuous atmospheric medium of industrial and commercial petro-pollutants discharge ( Nguyen & Tran, 2020 ). The Southern and Eastern sections, where lighter hexagons dominate, suggest lower contamination levels, possibly due to natural attenuation or dilution through atmospheric movement.

Contour overlay and spatial representation depict each black-circled point representing an actual sample station where TPH concentrations were measured. The colour of each point is directly linked to its measured TPH concentration, using the same scale as the contour and Hexbin map ( Hwang & Kim, 2020 ). This allows for a direct comparison between actual measured values and interpolated estimations. Contaminant concentrations and characterization across the study area show that the Northern, Central, and North-Western sections, mainly Obigbo and Okoloma, exhibit higher TPH concentration patterns, as indicated by yellow/orange contours. This suggests long-term hydrocarbon impact from potential sources like vehicular exhaust, industrial discharge, or gas flaring activities ( Lee & Park, 2021 ). The Eastern and South-Eastern sections, mainly Umu Agbai and Obete, represent locations of a gradual decrease in TPH levels (purple/blue transition), representing less contamination and suggesting dispersion of contaminants due to distance from source regions, atmospheric conditions, or proximity to natural pollution sinks like forests ( Wang & Chen, 2020 ).

The integrated analysis and characterization show that the highest TPH concentrations are found in regions where red or orange contours overlap with dark hexagons, and sample stations indicate high values, reinforcing the accuracy of contamination hot zones. The potential migration zones are the areas with fewer sample points but steep concentration gradients, like regions where the contour colour changes abruptly. These suggest that petro-pollutant movement is occurring, likely due to wind flow or atmospheric moisture ( Yunker & MacDonald, 2019 ). Lower TPH zones indicated by the blue or purple reflect areas less impacted by contamination, likely areas of atmospheric dilution, or natural processes mitigating the contamination levels.

Petro-pollutant atmospheric contamination, particularly through rainfall deposition, is a significant environmental concern in industrial and high-traffic regions ( Lee & Park, 2021 ). The analyzed Total Petroleum Hydrocarbons (TPH), including Total Aliphatic Hydrocarbons (TAH) and Total Polycyclic Aromatic Hydrocarbons (TPAH), serve as indicators of airborne pollution from fossil fuel combustion, transportation, gas flaring, and industrial emissions ( Wang & Chen, 2020 ). The geochemical and geospatial correlation presents a comparative analysis of petro-pollutant fractions in rainwater samples collected across the five sub-regional locations (Obigbo, Komkom-Obiama, Okoloma, Egberu, and Umu Agbai-Obete) and thirty-four sample stations of the study region. This analysis evaluates the correlation between TPH, TAH, and TPAH across all study locations. It analyzes spatial variations in hydrocarbon pollutant deposition using latitude and longitude data and compares contamination trends between different locations to identify potential emission sources and high-risk zones. For all the locations ( Figures 8(a)-(e) ), the strong positive correlation (close to +1) between TPH and TAH indicates that aliphatic hydrocarbons are the dominant fraction of petroleum hydrocarbons in the independent location and sample stations.

Aliphatic hydrocarbons originate primarily from vehicular exhausts, industrial emissions, and fuel combustion, contributing significantly to atmospheric hydrocarbon pollution ( Hwang & Kim, 2020 ). The dominance of TAH over TPAH suggests that a major source of airborne petroleum hydrocarbons in the different locations is fresh fuel combustion emissions rather than long-term industrial soot or heavy hydrocarbon residues ( Yunker & MacDonald, 2019 ). Aliphatic hydrocarbons are more volatile and can be transported over long distances before being scavenged by rain ( Schauer & Cass, 2019 ), explaining their widespread presence. This finding implies that air quality monitoring and emission control measures should focus on reducing transportation-related emissions, as they contribute significantly to hydrocarbon contamination in rainfall. The correlation matrix across the study region ( Figure 8(f) ) reveals consistent trends across the different locations. A strong positive correlation between TPH and TA H (>0.9) suggests that aliphatic hydrocarbons dominate petroleum hydrocarbon contamination in rainwater. In contrast, the moderate to weak correlation between TPH and TPAH indicates that PAHs originate from distinct sources and behave differently in the atmosphere ( Lee & Park, 2021 ). The spatial correlations with latitude and longitude show regional variations in contamination trends and present the comparative deductions concerning the source, transport mechanisms and the pollutants’ fate, reaffirming that higher pollution levels are concentrated in western and Northern locations ( Table 2 ).

All locations exhibit a strong TPH-TAH correlation (>0.9), confirming that aliphatic hydrocarbons dominate hydrocarbon pollution across all locations. TPH-TPAH correlation varies between weak to moderate (~0.2 - 0.4), indicating diverse PAH sources, including gas flaring and biomass combustion ( Feng & Zhang, 2021 ). Higher PAH levels in Okoloma and Umu Agbai-Obete suggest greater contributions from gas flaring and incomplete combustion processes ( Hwang & Kim, 2020 ).

Hydrocarbon deposition trends by Latitude and Longitude provide further insights into spatial hydrocarbon deposition patterns across the study locations. For Longitude vs. TPH/TAH, the negative correlation in most locations suggests that hydrocarbon contamination is higher in the Western and Northern parts of the study area. This trend is strongest in Obigbo and Komkom-Obiama, indicating primary pollution sources in these regions. Latitude vs. TPH/TAH presents a positive correlation in most locations, indicating higher hydrocarbon concentrations in the Northern region. Okoloma and Egberu show the strongest positive correlation, suggesting potential long-range transport of pollutants. These findings emphasize that local and regional emission sources contribute to hydrocarbon pollution, with Western and Northern locations experiencing the highest levels.

PAHs in rainwater pose carcinogenic and mutagenic risks, especially in communities relying on rainfall for water supply ( Hwang & Kim, 2020 ). Inhalation of airborne hydrocarbons from rainfall-contaminated surfaces can contribute to respiratory diseases and chronic health conditions ( Nguyen & Tran, 2020 ). Since the TPH-TAH correlation is highest across all locations, transportation emissions remain a primary contributor to hydrocarbon pollution. The higher PAH levels in Okoloma and Obigbo suggest a need for stricter enforcement of industrial emission standards and gas flaring regulations. There is a need for comprehensive air monitoring programs to track hydrocarbon pollution trends and implement evidence-based mitigation strategies.