A Comparative Analysis of Air Quality and Respiratory Health in Under-Five Children from Crude Oil-Impacted Communities

Abstract

Crude oil spills create environmental hazards, leading to air pollution and respiratory health risks in under-five children due to their developing organs. This study compares ambient air quality (AAQ) and the respiratory health (RH) of under-five children in crude oil-impacted and less-impacted communities. The study involved 450 under-five children (mean age: 3 years) from three Niger Delta communities: Bodo, K-Dere, and Beeri. AAQ was measured using sensors, and RH was assessed through interviewer-administered questionnaires between July and October 2022. Mean concentrations of pollutants, including PM_2.5, PM₁₀, TVOCs, and HCHO, were consistently higher in Bodo and K-Dere (oil-impacted communities) compared to Beeri (less-impacted community), with levels frequently exceeding both WHO and national standards. These concentrations were highest near spill sites and during evening periods, highlighting localized and temporal factors influencing air pollution. Respiratory symptoms such as cough, difficulty breathing, and persistent nasal congestion were significantly more prevalent among children in oil-impacted communities. Logistic regression analysis indicated a higher likelihood of respiratory issues in these communities, with odds ratios ranging from 2.53 to 14.18 for various symptoms. Elevated air pollution from crude oil spills correlates with a higher prevalence of respiratory conditions in children from impacted communities, underscoring the need for public health interventions in these areas.

1. Introduction

Crude oil is a complex compound that is a blend of relatively volatile liquid hydrocarbons, primarily consisting of hydrogen and carbon, with traces of nitrogen, sulfur, and oxygen [1]. Crude oil is found in large reservoirs either beneath the ground or the ocean floors. The extraction of crude oil from these reservoirs through the drilling processes and its transportation through pipelines sometimes result in spillages with disastrous impacts on the ecosystem. Crude oil spills can also be a result of a sabotage of pipelines as a political statement or for economic gain [2,3].

According to the National Oil Spill Detection and Response Agency (NOSDRA), the Niger Delta experienced approximately 9300 spills between 2006 and 2015 [4,5]. It is estimated that over 550 million gallons of crude oil was released into the environment from 1958 to 2010, amounting to an average of about 11 million gallons of crude spilled yearly (approximately 42 million liters) [6]. From 2010 to 2021, over 203 barrels (over 3 million gallons) of crude were spilled [7]. One consequence of the spillage is environmental degradation, particularly affecting the local mangrove forest and animal biodiversity. For example, it was estimated that environmental degradation resulting from oil spills led to the disappearance of over 50,000 acres of local mangrove forest and loss of animal biodiversity [6]. Furthermore, a region with an estimated population of over 30 million people has been negatively impacted by the spill [8].

Ambient air quality (AAQ) can be influenced by the concentrations of air pollutants in the environment. Since crude oil is a highly volatile compound, toxic chemicals such as volatile organic compounds (VOCs) and aerosolized particulate matter (PM) are released into the environment during its degradation, for instance, through evaporation and aerosolization [9,10], thereby disrupting AAQ [10,11].

Air quality monitoring and assessment evolved considerably over the past decades, driven by advances in instrumentation and data analysis techniques. Modern analytical methods allow for continuous, real-time measurement of various pollutants and provide insights into their sources, dispersion, and impacts on human health and the environment. For example, Dewulf and colleagues highlighted the use of gas chromatography for detecting and quantifying volatile organic compounds (VOCs) in various environments [12]. Giechaskiel and Clairotte demonstrated the effectiveness of Fourier transform infrared spectroscopy (FTIR) in simultaneously monitoring multiple gaseous pollutants, such as CO₂, CO, and NOx [13].

Recent advancements include the integration of artificial intelligence (AI) and machine learning (ML) techniques, which enhance the analysis of complex air quality data and improve predictive modeling [14,15]. The development of micro-electro-mechanical systems (MEMS)-based sensors led to compact and energy-efficient devices capable of detecting various pollutants with high sensitivity [16]. The Internet of Things (IoT) facilitated the deployment of extensive sensor networks, allowing for real-time, continuous monitoring of air quality across large areas [17]. Additionally, satellite-based remote sensing technologies enabled the observation of air pollution on a global scale, providing valuable data for regions lacking ground-based monitoring infrastructure [18].

In low-resource settings, where access to advanced analytical equipment may be limited, the use of low-cost sensors, passive sampling techniques, and portable monitors offers a practical and scalable alternative for assessing air quality. For example, the United Nations Environment Programme (UNEP) highlights that low-cost sensor systems (LCS) are instrumental in filling gaps within existing air quality monitoring networks, particularly in regions lacking traditional monitoring infrastructure. These sensors have been deployed extensively in low- and middle-income countries, providing real-time data that inform policy decisions and public health initiatives [19].

An updated World Health Organization (WHO) AAQ guideline recommends a yearly average PM_2.5 concentration of no more than 5 µg/m³, and a 24 h average exposure of no more than 15 µg/m³ for between 3 and 4 days per year. Other pollutants, such as PM₁₀, NO₂, etc., with their annual concentration limits of 15 µg/m³ and 10 µg/m³, were also specified [20]. In the year 2019, an overwhelming majority, 99% of the global population, resided in areas that failed to meet the AAQ standards set by the WHO [20]. The Nigerian air quality guideline also recommends a yearly average PM_2.5 concentration of no more than 20 µg/m³, and a 24 h average exposure of no more than 40 µg/m³ [21].

Roughly 300 million children inhabit regions where the levels of ambient air pollution (AAP) exceed international limits by at least of six times [22]. Additionally, 2 billion children live in areas where the concentration of ultra-fine particulate matter surpasses the annual recommended limit of 10 µg/m³ set by the WHO [22].

Crude oil spill-related air pollutants have been found to have deleterious effects on air quality and public health [23]. In 2019, AAP was estimated to have caused approximately 4 million premature deaths worldwide [24]. Worldwide, AAQ has been severely impacted, leading to an increasing disease burden, particularly evident in middle- and low-income countries compared to developed countries [20]. About 90% of these premature deaths occurred in developing countries [24]. These air pollutants often exceed WHO air quality guidelines in many regions worldwide [25]. In the Niger Delta region of Nigeria, air quality deteriorated due to various factors, with crude oil activities playing a significant role [26]. Air pollution poses a significant environmental risk to health. Pollutants from oil spills, when inhaled, can deposit in the respiratory tract, leading to inflammation depending on the particle dose and composition. This inflammation can increase airway responsiveness to irritants, leading to bronchoconstriction [27]. Furthermore, pollutants in the respiratory tract can generate reactive oxygen species (ROS) and reduce antioxidant level, leading to oxidative stress and organ damage, resulting in various morbidities [28]. These ROS contributes to the oxidative damage of deoxyribonucleic acid (DNA), ribonucleic acid (RNA), proteins, lipids, cell membranes, and other macromolecules, as well as the formation of adducts. These result in epigenetic changes, altered gene expressions, DNA mutations, impaired protein, and other macromolecule functions [29]. Excess free radicals that are not neutralized by antioxidant defenses initiate an inflammatory response with the release of inflammatory cells and mediators (cytokines, chemokines, and adhesion molecules) that reach the systemic circulation, leading to subclinical inflammation, which not only has a negative effect on the respiratory system, but also causes systemic effects [30]. Pollutants released from spilled crude oils increase the risk of hospitalization for cardiovascular and respiratory diseases such as pneumonia, bronchitis, cough, rhinorrhoea, chronic obstructive pulmonary disease (COPD), asthma, and lung cancer [9,31]. In addition, pollutants from oil spills in general are known to cause a range of health issues, including cardiovascular and respiratory diseases, and also increased hospitalization risks due to these diseases [9].

The proportion of respiratory symptoms associated with crude oil spills has been found to be greater in the exposed group compared to the non-exposed group. For example, according to follow-up research on the prestige oil disaster and its respiratory health (RH) effects, those who were exposed were more likely to experience lower respiratory tract symptoms such as wheezing, difficulty in breathing, coughing, and producing phlegm (RR 1.4, 95%CI 1.0–2.0). In comparison to those who were symptom-free, there was an increase in the risk of developing chronic respiratory symptoms (e.g., wheeze, shortness of breath, cough, and phlegm) with increasing exposure levels: RR: 1.7 (95%CI 0.9–3.1) and 3.3 (95%CI 1.8–6.0) for those who were moderately or severely exposed, respectively [32].

According to research in the Niger Delta area of Nigeria by Ordinioha and Sawyerr, the oil spillage that occurred in Etiama Nembe in Bayelsa State, Nigeria, resulted in the release of 2500 barrels of crude oil (about 400,000 litres) into the farms, forests, and waterbodies, contaminating about 20 hectares of land [33]. Due to its high volatility, spilled crude oil forms strong pungent fumes and mist, which, when inhaled, results in respiratory health consequences among the residents that were exposed [34].

By decreasing the level of air pollution, countries can lessen the burden of disease from stroke, cardiovascular diseases, lung cancers, and both acute and chronic respiratory diseases, including asthma [20]. Despite government protocols on oil spill response and efforts by organizations such as the WHO and United Nations Children’s Fund (UNICEF) to prevent, manage, and control AAP and its impact on respiratory health, particularly among children under five, crude oil spills and their pollutants remain a major public health concern. There is lack of data in Nigeria, especially in the Niger Delta area, comparing air quality and respiratory health outcomes in oil-polluted and less oil-polluted communities. While studies have been conducted on oil spills in the Niger Delta, very few research works have been carried out on the health impacts of crude oil spills in Nigeria. Moreover, a lesser number of studies have been carried out on its consequences on child health, specifically the RH of children under the age of five in Nigeria, particularly in the Niger Delta region. The population of under-five children is large, and they have a greater propensity of developing respiratory health issues than adults due to their vulnerability, more emphasizing the need for this study. Studies have been conducted on the exposure to polycyclic aromatic hydrocarbons (PAHs) [29] and the adverse health effects linked to crude oil spills [35]. However, the interrelation between crude oil spills, air quality, and the RH of U5 children in the Niger Delta and employing different methods to determine this association is lacking. There is an increasing need for more studies to bridge this literature gap, raise awareness about the effects of crude oil spills on inhaled air quality and their resulting impact on the RH of children under five years old, and develop effective control measures, hence the significance of this study.

2. Materials and Methods

2.1. Study Area

The study was conducted in three communities in Ogoniland, namely Bodo, and Kegbara Dere (K-Dere), which are considered oil-impacted communities in the Gokana Local Government Area (LGA), as well as Beeri, a less oil-impacted community in Khana LGA, Rivers State, Nigeria. Ogoniland is part of the Niger Delta, located within the southern region of Nigeria. The Niger Delta is fan-shaped, covering approximately 70,000 km² of land mass, and serves as the drainage basin for the Niger and Benue rivers into the Atlantic Ocean [6,36]. It is generally believed that all communities in the Niger Delta area of Nigeria are affected by crude oil spills, but to varying degrees [37]. According to information obtained from the inhabitants of these communities, there are no oil pipelines in Beeri and no direct oil spills, although spilled oil from other communities may affect it, hence its classification as less impacted.

2.2. Study Design and Sample Size Estimation

A comparative cross-sectional study design was employed, with data collected using a pre-tested, semi-structured interviewer-administered questionnaire. The questionnaires were administered to eligible parents or guardians of under-five children. A multistage sampling method was applied in selecting the study respondents from the different communities. Stage one: stratification of communities in the Niger Delta region according to their crude oil experience in the last five years into impacted and non-impacted communities. Stage two: Selection of study communities from the list of eligible impacted communities in the Niger Delta region. The Ogoni communities of Bodo and K-Dere were selected from the list. Beeri in Khana LGA in Ogoniland was chosen as a comparable non-impacted community based on the HYPREP map of polluted and non-polluted communities in Ogoniland. Stage three: selection of households from selected communities. Stage four: Selection of eligible child–parent/guardian pairs in the study households using a systematic sampling method, with the houses in the communities as a sampling unit. If more than one household was present in a house, a sampling balloting method was used to select only one household. Qualitative information from the questionnaire was analyzed and published elsewhere [10]. The concentrations of air pollutants were measured with various equipment. Formaldehyde (HCHO), total volatile organic compounds (TVOCs), PM_2.5, and PM₁₀ were measured using a portable, easy-to-use multiple air quality monitor (HLW-100).

The HLW-100 air quality monitor (Guangzhou Janjuan Electronic Technology Co., Ltd., Janjuan brand, Guangzhou, China), employs electrochemical sensors for detecting HCHO, with a detection range of 0–5 mg/m³ and an accuracy of ±0.03 mg/m³, enabling precise monitoring. To measure TVOCs, the monitor uses a metal oxide semiconductor (MOS) sensor, capable of detecting a wide variety of volatile pollutants, including benzene, toluene, and xylene, with a detection range of 0–10 mg/m³ and an accuracy of ±10% of the measured value. This makes it suitable for assessing VOC concentrations in diverse settings. Both PM_2.5 and PM₁₀ concentrations were measured using an optical laser scattering sensor, which provides high sensitivity to particles as small as 0.3 microns. The detection range for PM_2.5 and PM₁₀ was 0–500 µg/m³, with an accuracy of ±10–15%, accounting for environmental factors such as humidity and airflow.

Hydrogen sulfide (H₂S) and carbon monoxide (CO) were measured with a Bosean BH-4S portable multiple gas detector (Henan Bosean Co., Ltd., Zhengzhou, China), for combustible gasses, with an accuracy of ≤±5% full scale (F.S) and a response time of ≤30 s. Wind speed was measured using the Hyelec Ms6252a anemometer, (MGL International Group, Taipei, Taiwan) with an accuracy of ±(2.0% reading + 50). All sensors were new and factory-calibrated before use. These equipments were all sourced and hired from (International Energy Services Limited (IESL), Port Harcourt, Rivers State, Nigeria). A total of 450 participants were recruited for the study, with 150 participants per community, in accordance with the sample size calculation described by Whitley and Ball [38]. Children were selected from each community through random sampling to ensure that the sample was representative of the entire community population of children under 5 years of age.

2.3. Data Analysis

The collected data were coded, cleaned, and analyzed with the Statistical Package for Social Sciences (SPSS 25.0, IBM, Armonk, NY, USA). Kolmogorov–Smirnov test was used to test for normality, which showed a significant deviation from the normal distribution. Descriptive statistics were presented in the form of frequencies, percentages, mean, median, and standard deviation. They are presented in tables and charts. Chi-square test and bivariate logistic regression with the odds ratio (OR) were used for hypothesis testing, assessing the pattern of respiratory infections between communities, and significance was detected when p ≤ 0.05 with a 95% confidence interval. Analysis of variance (ANOVA) was used in the analysis of the air quality and meteorology data. A Tukey’s post hoc test for multiple comparisons was also used to assess significant differences between mean concentrations of air pollutants. The mean concentrations of the air quality parameters were compared with both WHO and Nigeria’s acceptable limits.

2.4. Ethical Approval

The protocol for this study was approved by the Ethical Review Board of the University of Port Harcourt, Port Harcourt, Rivers State, Nigeria, with permission number UPH/CEREMAD/REC/MM79/031, and was conducted in accordance with the principles outlined in the Declaration of Helsinki. The decision was made on 24 August 2021. Participation in the study was voluntary and research subjects were referred to anonymously throughout the study. Written consent was obtained from all the participants before data collection, and they were informed of their right to withdraw from the study at any time. The safety of all participants was ensured.

3. Results

Socio-Demographic Characteristics

The descriptive statistics of the under-five children sampled in Bodo, K-Dere, and Beeri in this study are presented in

Table 1. Socio-demographic characteristics of the participants (under-five children) in Bodo, K-Dere, and Beeri.

Variables	Bodo n = 150		K-Dere n = 150		Beeri n = 150
	n	%	n	%	N	%
Children
Age
1	15	10.00	17	11.33	14	9.33
2	24	16.00	43	28.67	25	16.67
3	58	38.67	40	26.67	45	30.00
4	53	35.33	50	33.33	66	44.00
Median (IQR)	3 (2–4)			3 (2.5–4)		3 (2–4)
Gender
Male	80	53.33	87	58.00	91	60.67
Female	70	46.67	63	42.00	59	39.33
Number of Siblings
None	4	2.67	25	16.67	3	2.00
1–3	98	65.33	99	66.00	118	78.67
≥4	48	32.00	26	17.33	29	19.33
Median (IQR)	2 (2.5–3)			2 (1.5–3.5)		3 (2–4)
Birth Position
First	23	15.33	40	26.67	44	29.33
Second	34	22.67	42	28.00	53	35.33
Third	43	28.67	41	27.33	26	17.33
Fourth	25	16.67	13	8.67	16	10.67
Fifth	17	11.33	10	6.67	6	4.00
Sixth	5	3.33	2	1.33	5	3.33
Seventh	2	1.33	2	1.33	0	0.0
Eight	1	0.67	1	0.67	0	0.0
Number of years resident in your community
1	15	10.00	16	10.67	14	9.33
2	30	20.00	52	34.67	25	16.67
3	58	38.67	40	26.67	44	29.33
4	47	31.33	42	28.00	67	44.67
Median (IQR)	3 (2–4)			3 (2.5–4)		3 (2–4)

. The majority (90%) of the children were from 2 to 4 years old. The percentage of males in each community was as follows: 53% in Bodo, 58% in K-Dere, and 61% in Beeri.

In Bodo, approximately 70% of the respondents resided in the community for 3 years and above, about 54% resided in K-Dere for the same duration, and roughly 74% resided in Beeri for 3 years and above.

Table 2. Socio-demographic characteristics of the respondents (parent/guardian) in Bodo, K-Dere, and Beeri.

Variables	Bodo n = 150		K-Dere n = 150		Beeri n = 150
	n	%	n	%	n	%
Parent/Guardian
Father’s educational level
None	6	4.00	5	3.33	0	0.0
Primary	31	20.67	24	16.00	5	3.33
Secondary	45	30.00	55	36.67	80	53.33
Tertiary	28	18.67	50	33.33	38	25.33
Post-graduate	40	26.67	16	10.67	27	18.00
Mother’s educational level
None	8	5.33	10	6.67	0	0.0
Primary	32	21.33	45	30.00	11	7.33
Secondary	50	33.33	40	26.67	97	64.67
Tertiary	33	22.00	42	28.00	12	8.00
Post-graduate	27	18.00	13	8.67	30	20.00
Father’s occupation
Professional	38	25.33	12	8.00	11	7.33
Civil servant	30	20.00	38	25.33	54	36.00
Fishing/farming	59	39.33	32	21.33	31	20.67
Artisans	23	15.33	68	45.33	54	36.00
Mother’s occupation
Professional	21	14.00	13	8.67	6	4.00
Civil servant	47	31.33	24	16.00	32	21.33
Fishing/farming	64	42.67	53	35.33	49	32.67
Artisans	18	12.00	59	39.33	63	42.00

presents descriptive statistics of the sampled parents/guardians of the children under five years old in Bodo, K-Dere, and Beeri.

Only a small proportion in Bodo, K-Dere, and Beeri, respectively (father/mother: 4%/5%, 3%/7%, and 0/0), of all the sampled parents/guardian sampled had no formal education, while the majority (19%/22%, 33%/28%, and 25%/8%) completed Nigeria’s compulsory basic education (tertiary education). The majority of the sampled parents/guardians were employed in blue-collar occupations, for example, parents/guardians who were civil servants (father/mother: 20%/31%, 25%/16%, and 36%/21%).

Table 3. Mean concentrations and standard deviations of crude oil spill-related air pollutants and meteorological data of Bodo and Beeri.

T-D	T (°C)	R	Noise (dB)	Wind Speed (m/s)	SO2 (µg/m3)	NO2 (µg/m3)	CO (µg/m3)	H2S (µg/m3)	PM2.5 (µg/m3)	PM10 (µg/m3)	TVOC (µg/m3)	HCHO (µg/m3)
M_0 m	29.50 ± 1.02	85.56 ± 3.11	68.07 ± 10.85	25.76 ± 3.67	0.00 ± 00	0.47 ± 0.49	2.74 ± 1.43	0.00 ± 00	21.26 ± 6.24	38.86 ± 8.37	0.76 ± 10.07	0.010 ± 0.01
A_0 m	30.32 ± 1.10	84.07 ± 3.11	70.48 ± 10.85	25.82 ± 3.67	0.00 ± 00	0.55 ± 0.53	2.43 ± 1.58	0.00 ± 00	19.76 ± 6.18	39.50 ± 8.40	0.76 ± 10.07	0.011 ± 0.01
E_0 m	29.97 ± 1.24	85.16 ± 3.11	66.98 ± 10.85	25.73 ± 3.67	0.00 ± 00	0.69 ± 0.49	3.09 ± 1.27	0.00 ± 00	27.26 ± 6.18	43.64 ± 8.54	0.76 ± 10.07	0.012 ± 0.01
M_50 m	30.79 ± 1.06	84.86 ± 3.26	66.60 ± 7.27	26.05 ± 3.05	0.00 ± 00	0.29 ± 0.37	1.94 ± 1.09	0.00 ± 00	15.79 ± 5.09	29.98 ± 4.60	0.05 ± 0.06	0.011 ± 0.01
A_50 m	30.61 ± 0.98	84.41 ± 3.26	69.01 ± 7.27	26.11 ± 3.05	0.00 ± 00	0.39 ± 0.39	2.14 ± 1.38	0.00 ± 00	15.86 ± 5.16	31.12 ± 4.53	0.05 ± 0.06	0.011 ± 0.00
E_50 m	30.56 ± 1.13	84.66 ± 3.23	65.82 ± 7.30	25.98 ± 3.08	0.00 ± 00	0.49 ± 0.38	1.95 ± 0.97	0.00 ± 00	17.71 ± 4.97	32.78 ± 4.64	0.05 ± 0.06	0.011 ± 0.00
M_100 m	30.57 ± 1.12	89.41 ± 1.93	73.55 ±4.85	25.25 ± 3.12	0.00 ± 00	0.03 ± 0.13	0.49 ± 0.76	0.00 ± 00	11.49 ± 3.03	12.72 ± 5.79	0.34 ± 0.49	0.007 ± 0.00
A_100 m	30.48 ± 0.82	89.37 ± 1.95	74.85 ± 4.90	25.25 ± 3.14	0.00 ± 00	0.01 ± 0.01	0.29 ± 0.42	0.00 ± 00	11.26 ± 2.33	13.12 ± 4.82	0.35 ± 0.50	0.007 ± 0.00
E_100 m	30.16 ± 1.11	89.39 ± 1.95	72.55 ± 4.90	25.16 ± 3.14	0.00 ± 00	0.00 ± 0.01	0.23 ± 0.35	0.00 ± 00	12.05 ± 2.20	15.83 ± 5.37	0.35 ± 0.50	0.007 ± 0.00
Beeri-Control	30.48 ± 0.55	82.00 ± 1.33	74.81 ± 4.95	18.90 ± 1.13	0.00 ± 00	0.00 ± 00	0.00 ± 00	0.00 ± 00	6.47 ± 4.64	11.95 ± 5.49	0.00 ± 0.00	0.00 ± 0.00
Nat’l Limit					80	80	10	6969.3	20	60		24.56
WHO Limit			53		40	10	7	0.03	5	15	0–0.05	0.814

T-D = time distance, T = temperature, R = relative humidity, M = morning, A = afternoon, and E = evening.

compares the mean AAQ parameters measured at 0 m, 50 m, and 100 m from the pollution site in Bodo with those in Beeri. Similarly,

Table 4. Mean concentrations and standard deviations of air quality parameters for K-Dere and Beeri.

T-D	T (°C)	R	Noise (dB)	Wind Speed (m/s)	SO2 (µg/m3)	NO2 (µg/m3)	CO (µg/m3)	H2S (µg/m3)	PM2.5 (µg/m3)	PM10 (µg/m3)	TVOC (µg/m3)	HCHO (µg/m3)
M_0 m	31.50 ± 1.09	86.48 ± 3.90	55.59 ± 2.13	25.78 ± 2.04	0.00 ± 00	0.73 ± 0.54	3.19 ± 1.69	0.001 ± 0.00	14.60 ± 4.52	38.52 ± 15.57	0.02 ± 0.15	0.004 ± 0.01
A_0 m	32.32 ± 1.16	84.99 ± 3.90	58.00 ± 2.13	25.84 ± 2.04	0.00 ± 00	0.76 ± 0.60	2.88 ± 1.77	0.001 ± 0.00	13.10 ± 4.44	39.17 ± 15.59	0.02 ± 0.02	0.005 ± 0.01
E_0 m	32.04 ± 1.29	86.08 ± 3.90	54.50 ± 2.13	25.75 ± 2.04	0.00 ± 00	0.89 ± 0.65	3.50 ± 1.63	0.001 ± 0.00	20.60 ± 4.44	43.31 ± 15.66	0.02 ± 0.02	0.006 ± 0.01
M_50 m	30.98 ± 0.55	84.15 ± 3.15	50.90 ± 4.46	26.09 ± 2.75	0.00 ± 00	0.58 ± 0.44	2.15 ± 0.96	0.001 ± 0.00	12.79 ± 6.86	26.64 ± 10.21	0.01 ±0.01	0.004 ± 0.01
A_50 m	31.24 ± 0.99	83.70 ± 3.15	53.31 ± 4.46	26.15 ± 2.75	0.00 ± 00	0.63 ± 0.52	2.27 ± 1.39	0.001 ± 0.00	12.86 ± 6.91	27.79 ± 10.18	0.01 ± 0.01	0.004 ± 0.01
E_50 m	30.93 ± 1.03	84.05 ± 3.15	50.26 ± 4.46	26.06 ± 2.75	0.00 ± 00	0.79 ± 0.58	2.26 ± 0.70	0.001 ± 0.00	14.86 ± 6.80	29.43 ± 10.20	0.01 ± 0.01	0.004 ± 0.01
M_100 m	30.74 ± 0.59	91.69 ± 3.71	49.50 ± 6.90	25.12 ± 2.91	0.00 ± 00	0.12 ± 0.15	1.16 ± 0.77	0.001 ± 0.00	7.86 ± 4.27	18.26 ± 7.28	0.00 ± 0.00	0.003 ± 0.00
A_100 m	31.13 ± 0.94	91.64 ± 3.71	50.75 ± 6.90	45.86 ± 31.64	0.00 ± 00	0.12 ± 0.15	0.74 ± 0.65	0.001 ± 0.00	7.93 ± 4.26	19.12 ± 7.18	0.00 ± 0.00	0.004 ± 0.00
E_100 m	30.88 ± 1.16	91.66 ± 3.71	48.45 ± 6.90	25.09 ± 2.91	0.00 ± 00	0.11 ± 0.15	0.61 ± 0.61	0.001 ± 0.00	8.71 ± 4.19	21.83 ± 7.54	0.00 ± 0.00	0.004 ± 0.00
Beeri-Control	30.48 ± 0.55	82.00 ± 1.33	74.81 ± 4.95	18.90 ± 1.13	0.00 ± 00	0.00 ± 0.00	0.00 ± 0.00	0.00 ± 0.00	6.47 ± 4.64	11.95 ± 5.49	0.00 ± 0.00	0.00 ± 0.00
Nat’l Limit					80	80	10	6969.3	20	60		24.56
WHO Limit			53		40	10	7	0.03	5	15	0–0.05	0.814

T-D = time distance, T = temperature, R = relative humidity, M = morning, A = afternoon, E = evening.

compares the mean AAQ parameters measured in K-Dere with those in Beeri. The mean concentration of measured pollutants was higher in the oil-impacted communities (Bodo and K-Dere) compared to the less oil-impacted community (Beeri). These concentrations were highest in the evenings and at the spill sites, and sometimes exceeded the WHO guidelines and the national limits, especially for PM_2.5. Additionally, concentrations decreased with increasing distance away from the spill site at most sampling points.

For example, in K-Dere, the mean concentrations of PM_2.5, PM₁₀, NO₂, CO, TVOC, and HCHO were highest in the evening, with a mean value of 20.60 µg/m³, 43.31 µg/m³, 0.89 µg/m³, and 3.50 µg/m³, respectively. The detectable levels of H₂S, TVOC, and HCHO were negligible in K-Dere. Additionally, there were no detectable levels of SO₂ concentration in Bodo and K-Dere. TVOC exceeded the WHO-acceptable limits in Bodo.

Figure 1 and Figure 2 show a graphical presentation of the mean concentrations of air pollutants in Bodo with respect to time of collecting the air quality sample and the distance from the spill site. It compares the mean AAQ parameters measured in Bodo with those in Beeri.

Figure 3 and Figure 4 show a graphical presentation of the mean concentrations of air pollutants in K-Dere with respect to time of collecting the air quality sample and the distance from the spill site. It compares the mean AAQ parameters measured in K-Dere with those in Beeri.

Table 5. Comparing mean concentration between Bodo and Beeri.

	Time_Distance	PM2.5	p-Value 95%CI	PM10	p-Value (95%CI)	TVOC	p-Value (95%CI)
Beeri (less oil impacted area)	Bodo, morning_0 m	−14.796 * (0.893)	0.000 (−17.95 to −11.65)	−30.040 * (1.628)	0.000 (−35.78 to −24.30)	−0.741 * (0.097)	<0.001 (−1.040 to −0.481)
	Bodo afternoon_0 m	−13.296 * (0.893)	0.001 (−16.45 to −10.15)	−30.683 * (1.628)	0.001 (−36.43 to −24.94)	−0.742 * (0.097)	0.001 (−1.042 to −0.482)
	Bodo, evening_0 m	−20.796 * (0.893)	0.001 (−23.95 to −17.65)	−34.826 * (1.628)	0.001 (−40.57 to −29.08)	−0.743 * (0.097)	0.001 (−1.042 to −0.483)
	Bodo, morning_50 m	−9.320 * (0.893)	0.001 (−12.47 to −6.17)	−21.159 * (1.628)	0.001 (−26.90 to −15.42)	−0.033 (0.097)	1.000 (−0.332 to 0.227)
	Bodo, afternoon_50 m	−9.392 * (0.893)	0.001 (−12.54 to −6.24)	−22.302 * (1.628)	0.001 (−28.05 to −16.56)	−0.033 (0.097)	1.000 (−0.333 to 0.227)
	Bodo, evening_50 m	−11.392 * (0.893)	0.001 (−14.54 to −8.24)	−23.945 * (1.628)	0.001 (−29.69 to −18.20)	−0.034 (0.097)	1.000 (−0.333 to 0.226)
	Bodo, morning_100 m	−4.725 * (0.893)	0.000 (−7.88 to −1.57)	−3.445	0.828 (−9.19 to 2.30)	−0.328 (0.097)	0.002 (−0.627 to −0.068)
	Bodo, afternoon_100 m	−4.796 * (0.893)	0.001 (−7.95 to −1.65)	−4.302	0.449 (−10.05 to 1.44)	−0.328 (0.097)	0.002 (−0.627 to −0.068)
	Bodo, evening_100 m	−5.582 * (0.893)	0.001 (−8.73 to −2.43)	−7.017 * (1.628)	0.003 (−12.76 to −1.27)	−0.328 (0.097)	0.002 (−0.627 to −0.068)

* The mean difference is significant at p < 0.05.

and

Table 6. Comparison of Mean (standard error) concentrations of air quality parameters measured between K-Dere and Beeri and their p-values (95%CI).

	Time_Distance	PM2.5	p-Value (95%CI)	PM10	p-Value (95%CI)	TVOC	p-Value (95%CI)
Beeri (less oil-impacted area)	K-Dere, morning_0 m	−8.130 * (0.893)	0.001 (−11.28 to −4.98)	−29.707 * (1.628)	0.001 (−35.45 to −23.96)	−0.001 (0.097)	1.000 (−0.299 to 0.260)
	K-Dere afternoon_0 m	−6.630 * (0.893)	0.001 (−9.78 to −3.48)	−30.350 * (1.628)	0.001 (−36.09 to −24.61)	−0.001 (0.097)	1.000 (−0.300 to 0.259)
	K-Dere, evening_0 m	−14.30 * (0.893)	0.001 (−17.28 to −10.98)	−34.493 * (1.628)	0.001 (−40.24 to −28.75)	0.008 (0.097)	1.000 (−0.300 to 0.259)
	K-Dere, morning_50 m	−6.320 * (0.893)	0.001 (−9.47 to −3.17)	−17.826 * (1.628)	0.001 (−23.57 to −12.08)	0.008 (0.097)	1.000 (−0.291 to 0.268)
	K-Dere, afternoon_50 m	−6.392 * (0.893)	0.001 (−9.54 to −3.24)	−18.969 * (1.628)	0.001 (−24.71 to −13.22)	0.007 (0.097)	1.000 (−0.291 to 0.268)
	K-Dere, evening_50 m	−8.392 * (0.893)	0.001 (−11.54 to −5.24)	−20.612 * (1.628)	0.001 (−26.36 to −14.87)	0.007 (0.097)	1.000 (−0.292 to 0.267)
	K-Dere, morning_100 m	−1392 (0.893)	0.989 (−4.54 to 1.76)	−9.445 * (1.628)	0.001 (−15.19 to −3.70)	0.018 (0.097)	1.000 (−0.281 to 0.278)
	K-Dere, afternoon_100 m	−1.463 (0.893)	0.982 (−4.61 to 1.69)	−10.302 * (1.628)	0.001 (−16.05 to −4.56)	0.019 (0.097)	1.000 (−0.281 to 0.279)
	K-Dere, evening_100 m	−2.249 (0.893)	0.545 (−5.40 to 0.90)	−13.017 * (1.628)	0.001 (−18.76 to −7.27)	0.019 (0.097)	1.000 (−0.281 to 0.279)

* The mean difference is significant at 0.05 level.

show a one-way ANOVA that reveals the statistically significant difference in the air quality parameters between at least two groups (F(between groups df, within groups df) = [F-value], p = [p-value]).

[PM_2.5 (F:57.529, df (between groups—18; within groups—863) = [F = 57.529], p < 0.001].

[PM₁₀ (F:79.919, df (between groups—18; within groups—863) = [F = 79.919], p < 0.001].

[TVOC (F: 17.693, df (between groups—18; within groups—863) = [F = 17.693], p < 0.001].

A Tukey’s post hoc test for multiple comparisons revealed statistically significant differences in the mean values of AAP, including PM_2.5, PM₁₀, and TVOC, measured in Beeri compared to Bodo, and between Beeri and K-Dere at various distances and times, as shown in Table 5 and Table 6. Significant differences were also observed between morning, afternoon, and evening measurements, as well as at 0 m, 50 m, and 100 m distances, with PM_2.5 significantly higher in Bodo compared to Beeri at these different times and distances (p < 0.05). Similar findings were observed for PM₁₀ (p < 0.05), except for air quality readings at 100 m in the morning and afternoon. TVOC showed statistically significant differences only at 0 m, indicating that TVOC was significantly higher in Bodo than in Beeri at the spill site throughout different times (p < 0.05). No statistically significant difference in TVOC was observed between K-Dere and Beeri.

Respiratory symptoms in Bodo, K-Dere and Beeri

Parents who reported respiratory symptoms among the under-five children studied are shown in Figure 1.

Figure 5 indicates that Beeri had the lowest number of reported respiratory symptoms, except for fever, compared to Bodo and K-Dere. Cough at night, persistent stuffy nostrils, sleep disturbance due to troubled breathing, ear pain, itchy nostrils, and fast breathing were found to be higher in K-Dere, while cough, frequent sneezing, fast breathing at rest, and itchy eyes were higher in Bodo.

The pattern of distribution of respiratory infections between Bodo and Beeri was assessed using chi-square (χ) analysis and bivariate logistic regression with an odds ratio (OR) and 95% confidence interval (CI), as shown in

Table 7. Comparison between respiratory symptoms in Bodo and Beeri.

Variables	Bodo n = 150		Beeri n = 150		χ2 (p-Value)	Odds Ratio (OR) (95%CI)	p-Value
	n	%	n	%
Cough
Yes	113	75.33	82	54.67	14.08 (0.001) *	2.53	0.001 *
No	37	24.67	68	45.33		(1.55–4.13)
Coughing at night
Yes	96	64.00	42	28.00	39.13 (0.001) *	4.57	0.001 *
No	54	36.00	108	72.00		(2.81–7.44)
Chest pain when coughing
Yes	55	36.67	27	18.00	13.16 (0.001) *	2.64	0.002 *
No	95	63.33	123	82.00		(1.55–4.49)
Difficulty in breathing/fast breathing
Yes	95	63.33	33	22.00	54.19 (0.001) *	6.32	0.001 *
No	55	36.67	117	78.00		(3.81–10.67)
Persistent stuffy (blocked) nostrils
Yes	107	71.33	32	21.33	79.65 (0.001) *	9.18	0.001 *
No	43	28.67	118	78.67		(5.41–15.54)
Itchy nostrils
Yes	117	78.00	30	20.00	94.08 (0.001) *	14.18	0.001 *
No	33	22.00	120	80.00		(8.13–24.73)
Frequent sneezing
Yes	115	76.67	35	23.33	85.33 (0.001) *	10.79	0.001 *
No	35	23.33	115	76.67		(6.32–18.44)
Fever
Yes	81	54.00	95	63.33	2.69 (0.101)	0.68	0.101
No	69	46.00	55	36.67		(0.43–1.08)
Itchy throat
Yes	78	52.00	26	17.33	39.79 (0.001) *	5.17	0.001 *
No	72	48.00	124	82.67		(3.04–8.78)
Itching of the eyes
Yes	118	78.67	30	20.00	103.27 (0.001) *	14.75	0.001 *
No	32	21.33	120	80.00		(8.43–25.80)
Whistling sound from the chest
Yes	52	34.67	29	19.33	8.95 (0.003) *	2.21	0.001 *
No	98	65.33	121	80.67		(1.31–3.75)
Fast breathing even at rest
Yes	77	51.33	26	17.33	38.46 (0.001) *	5.03	0.001 *
No	73	48.67	124	82.67		(2.96–8.54)
Ear pain
Yes	64	42.67	26	17.33	22.92 (0.001) *	0.04	0.001 *
No	86	57.33	124	82.67		(0.02–0.067)
Sleep disturbance due to troubled breathing
Yes	77	51.33	24	16.00	41.93 (0.001) *	5.54	0.001 *
No	73	48.67	126	84.00		(3.22–9.52)

* Statistically significant (p < 0.05); χ² = chi-square.

.

Respondents residing in the oil-impacted community (Bodo) had a statistically significantly higher proportion (OR) for all respiratory symptoms except ear pain compared to those living in a less oil-impacted community (Beeri). For example, cough (OR: 2.53, 95%CI: 1.55–4.13, p = 0.001), cough at night (OR: 4.57, 95%CI: 2.81–7.44, p = 0.001), chest pain when coughing (OR: 2.64, 95%CI: 1.55–4.49, p = 0.001), and difficulty in breathing/fast breathing (OR: 6.32, 95%CI: 3.81–10.67, p = 0.001) all had higher odds in Bodo. There was no statistically significant difference for fever between the two communities.

A statistically significantly higher proportion (OR) of all respiratory symptoms including ear pain was observed in the oil-impacted community of K-Dere compared to observations from the less oil-impacted community of Beeri as shown in

Table 8. Comparison of respiratory symptoms between K-Dere and Beeri.

Variables	K-Dere n = 150		Beeri n = 150		χ2 (p-Value)	Odds Ratio (OR) (95%CI)	p-Value
	n	%	n	%
Cough
Yes	110	73.33	82	54.67	11.34 (0.001) *	2.28	0.001 *
No	40	26.67	68	45.33		(1.40–3.70)
Coughing at night
Yes	102	68.00	42	28.00	48.07 (0.001) *	5.54	0.001 *
No	48	32.00	108	72.00		(3.33–8.96)
Chest pain when coughing
Yes	61	40.67	27	18.00	18.58 (0.001) *	3.12	0.001 *
No	89	59.33	123	82.00		(1.84–5.29)
Difficulty in breathing/fast breathing
Yes	102	68.00	33	22.00	64.12 (0.001) *	7.53	0.001 *
No	48	32.00	117	78.00		(4.49–12.63)
Persistent stuffy (blocked) nostrils
Yes	119	79.33	32	21.33	100.92 (0.001) *	14.16	0.001 *
No	31	20.67	118	78.67		(8.16–24.67)
Itchy nostrils
Yes	124	82.67	30	20.00	117.89 (0.001) *	19.08	0.001 *
No	26	17.33	120	80.00		(10.66–34.14)
Frequent sneezing
Yes	103	68.67	35	23.33	62.05 (0.001) *	7.20	0.001 *
No	47	31.33	115	76.67		(4.32–12.02)
Fever
Yes	91	60.67	95	63.33	0.22 (0.634)	0.89	0.721
No	59	39.33	55	36.67		(0.56–1.42)
Itchy throat
Yes	96	64.00	26	17.33	67.69 (0.001) *	8.47	0.001 *
No	54	36.00	124	82.67		(4.98–14.53)
Itching of the eyes
Yes	110	73.33	30	20.00	85.71 (0.001) *	11.0	0.001 *
No	40	26.67	120	80.00		(6.41–18.87)
Whistling sound from the chest
Yes	59	39.33	29	19.33	14.47 (0.003) *	2.71	0.001 *
No	91	60.67	121	80.67		(1.60–4.55)
Fast breathing even at rest
Yes	72	48.00	26	17.33	32.06 (0.001) *	4.40	0.001 *
No	78	52.00	124	82.67		(2.59–7.48)
Ear pain
Yes	68	45.33	26	17.33	27.32 (0.001) *	3.90	0.001 *
No	82	54.67	124	82.67		(2.33–6.71)
Sleep disturbance due to troubled breathing
Yes	79	52.67	24	16.00	44.72 (0.001) *	5.84	0.001 *
No	71	47.33	126	84.00		(3.39–10.04)

* Statistically significant (p < 0.05); χ² = chi-square.

. There was no statistically significant difference in fever symptoms in K-Dere compared to Beeri as shown in Table 8.

4. Discussion

This study compared the air quality parameters in two oil-impacted communities with one less oil-impacted community in the Niger Delta region of Nigeria. Additionally, it compared the respiratory health symptoms of under-five children residing in these communities. Our results indicate that both Bodo and K-Dere exhibited higher mean concentrations of ambient air pollutants compared to Beeri.

According to Nriagu and colleagues, the entire Niger Delta region is affected by crude oil activities, directly or indirectly, due to continuous crude oil exploration, a major source of income for Nigeria [37]. Our measurements reveal that the mean concentrations of some of the pollutants exceeded WHO recommended levels, including PM_2.5, TVOC, and PM₁₀ [20]. These mean concentrations, specifically PM_2.5, also exceeded the national limits in the morning in Bodo community and in the evening in K-Dere community [21]. Pollutants found in the air from oil spills may arise from various processes, such as degradation, combustion, and mechanical disruption of spilled oil on water and land surfaces. The exceedance of WHO limits suggests that the degradation and combustion of spilled oil may affect the AAQ of the oil-impacted communities. The mechanical disruption of the water surface and the breaking waves results in the eruption and ejection of spilt oil in the form of aerosolized PM containing these pollutants [39,40].

Our findings align with previous studies indicating that the combustion of oil releases pollutants such as carbon black, CO₂, NO, heavy metals, and soot into the atmosphere [3,10,41]. Osaiyuwu and Ugbebor observed poor AAQ in oil-producing communities of the Niger Delta due to pollutants such as PM_2.5, CO, and nitrogen oxides (NOx) exceeding WHO recommendations [42].

The significantly higher levels of AAP measured in oil-polluted communities compared to less oil-polluted communities may also be influenced by meteorological factors. For instance, during the morning hours, low atmospheric temperature and stable atmospheric boundary layers, which represent the area of the atmosphere directly in contact with the Earth’s surface, remain relatively stable. This stability leads to a lower height of the surface thermal inversion and reduced friction velocity. Consequently, the mixing and dilution of air pollutants released from various sources are inhibited [43]. This same phenomenon occurs again in the evenings as temperature decreases and the winds slow down, leading to a reduction in friction velocity and the height of the thermal inversion. Consequently, this results in an increase in the concentration of pollutants in the air. In contrast, during the afternoon period, the height of the atmospheric boundary layer is high, providing a larger volume of air for the mixing and dilution of air pollutants. As a result, the concentration of air pollutants decreases [43,44]. In addition, the concentration of air pollutants is typically highest at spill sites due to the presence of surface thermal inversion. This phenomenon reduces the volume of air available for mixing and diluting local emissions from the spill sites, leading to increased pollutant concentrations. Temperature inversion, another meteorological phenomenon, further exacerbates this issue by trapping cool air and air pollutants close to the Earth’s surface. This inhibits their upward movement due to the capping effect of warm air above, resulting in a decline in air quality in the region and contributing to associated respiratory and cardiovascular effects [44].

The Niger Delta region of Nigeria has a humid tropical climate, characterized by moderately high temperatures, high relative humidity, and low wind speeds, particularly during the early morning and evening hours [45,46]. These meteorological conditions contribute to the formation of stable atmospheric layers and surface temperature inversions, which limit the vertical dispersion of air pollutants and promote their accumulation near ground level.

The mean concentrations of these pollutants were observed to be higher at the spill sites and gradually decreased with increasing distance from the spill site. This suggests that under-five children residing in close proximity to the spill sites may face greater exposure to pollutants, potentially increasing their susceptibility to respiratory diseases. Under-five children are generally more vulnerable to air pollution due to their developing respiratory systems, higher breathing rates per body weight, and the fact that they spend time outdoors, often playing close to the ground where pollutant concentrations tend to be higher. In the Niger Delta area, this vulnerability may be further exacerbated by certain environmental and socio-cultural factors. For example, in many communities, children often accompany caregivers to farms or markets near polluted areas and may play in open spaces close to contaminated water bodies or soil. Therefore, future studies should investigate this aspect to better understand the health risks associated with living near oil spills. In the study in Taean, Korea, under-five children residing close to the Hebei Spirit oil spills were found to be exposed to higher concentrations of the measured toxicants [47]. This suggests that air pollutant concentrations were indeed higher around the oil spill sites. The respiratory symptoms assessed were cough, dry cough at night, chest pain while coughing, difficulty breathing/fast breathing, persistent stuffy (blocked) nostrils, itchy nostrils, frequent sneezing, fever, itchy throat, itchy eyes, whistling sound from the chest, fast breathing even at rest, ear pain, and sleep disturbances due to troubled breathing. The majority of these symptoms exhibited significant differences when comparing participants’ regions.

According to Jung and colleagues, children highly exposed to the Hebei Spirit oil spill exhibited higher respiratory symptoms compared to those less exposed [47]. Similarly, a study on the Prestige oil disaster found that individuals exposed to the spill experienced lower respiratory tract symptoms such as wheezing, dyspnoea, coughing, and sputum production (RR 1.4, 95%CI 1.0–2.0) [32]. Compared to previous studies in other oil spill-affected regions such as Korea [48] and Spain [32], our study differs in several methodological and contextual aspects. For instance, the Hebei Spirit oil spill study [48] and the Prestige oil disaster study [32] were conducted as post-spill assessments, with data collected months or even years after the spill events. These studies often relied on retrospective exposure assessment methods, including models and proximity-based estimates, due to the lack of real-time monitoring data at the time of the incidents. In contrast, our study involved the direct measurement of ambient air quality at multiple distances and times of day, providing a real-time representation of pollutant exposure. While the majority of the crude oil spills in the Niger Delta region were due to intentional causes, including poor maintenance of pipelines and storage tanks, vandalism, illegal drilling, and refining of crude oil [49], other recorded spills, such as the Deepwater Horizon oil spill [50], Prestige oil spill [32], Hebei Spirit oil spill [48], etc., were accidental in nature, such as explosions and natural causes, e.g., storms destroying a tanker in the case of the Prestige oil spill [32]. Furthermore, the Niger Delta context is distinct in that oil pollution is not limited to isolated incidents but occurs continuously and pervasively, often with limited cleanup or remediation. These differences in study timing, exposure assessment methods, and environmental conditions may influence pollutant levels and associated health outcomes. Nonetheless, the consistency in findings across these diverse contexts strengthens the evidence linking crude oil pollution to adverse respiratory health effects in children.

Some studies demonstrated the significant impact of inhaled air quality on human health, with particular emphasis on under-five children due to their unique vulnerabilities [51,52]. The findings of this study further corroborate this evidence, indicating that respiratory health in children is adversely affected in communities impacted by oil spills. Children’s lungs and immune systems are still developing and they inhale more air per kilogram of body weight than adults, making them particularly susceptible to the harmful effects of air pollutants [53]. Yakubu highlighted the consequences associated with human exposure to particle pollution from crude oil exploration and exploitation processes in Nigeria. Petroleum industries, particularly in the Niger Delta, are identified as major sources of air pollution, contributing to a range of health challenges including difficulty in breathing, lung cancer, and asthma [54].

A high proportion of the study participants exhibited wheezing; a symptom associated with asthma. Michel and colleagues, in their study, stated that exposure to environmental pollutants in early childhood could lead to the symptoms of asthma in early childhood and later in life [55]. Furthermore, exposure to atmospheric VOCs can lead to the formation of ozone, which in turn can trigger acute and chronic IgE-mediated inflammation, including asthmatic crisis [56]. Additionally, oil-related irritants and toxicants have been implicated in damaging the bronchial epithelial structure, inducing oxidative stress, increasing cytokines and leukotrienes-mediated inflammation, promoting growth factor secretion, and enhancing bronchial hyper-responsiveness [53]. These symptoms include cough, wheezing, catarrh, throat irritation, dyspnoea, chest pain, and eye irritation, to mention but a few. These symptoms were similar and common among those who were exposed to different oil spill incidents. Findings from this study also reveal that the respondents lived in areas with visible oil pollution and therefore regularly suffered direct exposure to oil in their environment. This is similar to the reports from Nriagu and colleagues [37]. Numerous studies reveal similar methods of assessing the RH of its respondents. Zock and colleagues as well as Jung and colleagues used questionnaires in assessing the RH of their study participants [47,57]. This method was also employed in this study.

Children under the age of five living in oil-impacted communities in the Niger Delta can regularly be exposed to harmful toxicants through inhalation, ingestion, and contact, posing significant risks to their respiratory system and overall well-being. Addressing this environmental and health hazard requires a collaborative effort aimed at ensuring the safety of these children and the provision of clean air. Effective strategies to mitigate the adverse effects of exposure to toxicants in oil-impacted communities should include advocacy, raising awareness, education, and fostering community participation to preserve the environment and safeguard residents’ health. Government intervention is crucial, necessitating the implementation and enforcement of regulatory laws regarding oil spills. Additionally, providing basic amenities, promoting personal hygiene practices, ensuring comprehensive immunization coverage to boost children’s immunity, and facilitating prompt treatment of respiratory symptoms and diseases are essential measures to protect the well-being of community members.

While several public health interventions have been suggested, their feasibility and potential effectiveness must be considered within the context of the Niger Delta. For instance, community-based education and awareness campaigns are relatively low in cost and could be implemented through local health workers, schools, and faith-based organizations [58]. These efforts could increase awareness about pollutant exposure risks and promote protective behaviors such as limiting outdoor activities for children during periods of visible pollution [59]. Strengthening regulatory enforcement may be more challenging due to governance issues and resource limitations, but focused enforcement in highly polluted communities could bring about localized improvements. Improving access to healthcare services and expanding immunization coverage are realistic goals that align with national health priorities and could help reduce the severity of respiratory conditions. Although our study did not quantitatively assess the impact of these interventions, the observed relationship between higher pollutant concentrations and increased respiratory symptoms suggests that reducing pollutant exposure through even modest interventions could lead to measurable improvements in children’s respiratory health. These context-specific strategies should be further explored through implementation research and community engagement.

While this study may be limited by the use of questionnaires alone to assess respiratory health outcomes of children under five, which could introduce recall bias, it is important to consider the context of limited healthcare infrastructure in the Niger Delta for gathering respiratory health data [60]. Furthermore, conducting invasive testing on children under five for scientific research raises serious ethical concerns due to their vulnerability. A cross-sectional design was used in this study. This design is quick, easy, and cheap to perform with no loss to follow up as participants were questioned once; however, this design limits the ability to establish a temporal or causal relationships between exposure and outcome variables, therefore, only associations, not causation, can be inferred [61]. The utilization of a multistage sampling method could also introduce certain biases, such as the selection bias and non-response bias. These were therefore tackled by randomizing the selection of the strata and subunits at each stage and incorporating a 10% non-response rate during the sample size calculation. The monitoring period was limited to four months (July to October 2022), which may not capture seasonal variations in air quality. Additionally, while monitoring was conducted at three distances (0 m, 50 m, and 100 m) from spill sites to reflect exposure gradients, a wider spatial coverage across different community zones would provide a more detailed understanding of pollutant dispersion. The study also possesses significant strengths. Notably, it has a comparative nature and directly measures AAQ at various times of the day. By comparing the air quality and respiratory health of children under five exposed to air pollutants from oil spills with those less exposed in a community less impacted by oil, the study sheds light on the relationship between environmental factors and human health, particularly respiratory health in young children. This underscores the urgency to address this issue, especially in developing countries such as Nigeria, which are heavily reliant on crude oil as a primary source of foreign earnings. We recommend future research extend monitoring over a full year to capture seasonal influences on pollutant levels. It should compare mean concentrations of air pollutants between oil-impacted communities and regions unaffected by oil spills. There is also need for future cohort studies or intervention trials to be conducted. Uncontrolled factors such as household cooking fuel, indoor air conditions, and secondhand smoke exposure may have influenced respiratory health outcomes, and their absence represents a limitation in the study design. Future studies should also consider gathering data on potential confounding variables and adjusting for them in their statistical analyses.

5. Conclusions

The air quality in the oil-impacted communities assessed was markedly poorer compared to less-impacted communities. Furthermore, the likelihood of under-five children experiencing respiratory symptoms was significantly higher in the oil-impacted communities investigated, compared to those less impacted by oil pollution, across most of the respiratory symptoms assessed. As recommendations, we advocate for health education initiatives, timely oil spill clean-up efforts, improved regulatory control measures, and prompt treatment of respiratory diseases in these communities.