1 Introduction

A crucial environmental issue that has received a lot of attention recently is the continued and possibly quickening rise in global temperatures. The enhanced greenhouse effect brought on by the buildup of greenhouse gases (GHGs) in the Earth's atmosphere is widely blamed for this phenomena, also known as global warming and decimation of human health [1, 2]. Due to their significant heat-trapping abilities, carbon dioxide (CO2) and methane (CH4) have emerged as two of these gases' main contributions to this warming trend. For determining the degree of their contribution to global warming and evaluating potential environmental effects, it is essential to accurately determine the atmospheric concentrations of CO2 and CH4 [3]. Climate change in Nigeria is evident through warmer weather, heavier rainfall, and increased risk of flooding [4]. For example, in 2012, flooding wreaked havoc on 28 out of the country’s 36 states, resulting in thousands of deaths, the destruction of property worth billions of Naira, 18,000 injuries, and the displacement of over 21 million people [5]. Human-generated greenhouse gases, specifically carbon dioxide (CO2) and methane (CH4), have the most profound impact on Earth’s net radiative forcing, making them significant contributors to climate change. The detrimental effects associated with anthropogenic air pollution have raised concerns globally. High concentrations of gases and particulates such as CO2, CH4, and hydrocarbons pollute the atmosphere, posing risks to both the environment and human health [6,7,8]. Hence, effective control measures are crucial to address air pollution, which has become a significant environmental concern in the modern era. The combustion of fossil fuels introduces pollutants like methane (CH4), carbon monoxide (CO), sulphur hexafluoride (SF6), sulphur oxides (SOx), volatile organic compounds, nitrogen oxides (NOx), and other atmospheric gases like carbon dioxide, methane, water vapor, nitrous oxide, chlorofluorocarbons (CFCs), and hydrofluorocarbons (HFCs) into the atmosphere [9]. The increased use of fossil fuels has resulted in the presence of these pollutants at levels that impact both the environment and society According to the Intergovernmental Panel on Climate Change, human activities significantly contribute to global warming by emitting greenhouse gases (GHGs), particularly CO2 and CH4. Since pre-industrial times, CO2 and CH4 concentrations have risen by 40% and 150% respectively. The main causes of these increases are fossil fuel emissions and net emissions from land use change [9]. Measurements taken in May 2013 at Una Loa, Hawaii, detected CO2 levels exceeding 400 ppm on several occasions [10]. The sources and sinks of CH4 remain uncertain, but they have garnered increased attention [11, 12]. In India, agriculture and waste are two primary sources of CH4 emissions. While numerous sources and sinks of CH4 have been identified, their individual contributions to atmospheric CH4 remain unclear [12, 13]. The production of electricity is responsible for fifty percent of all CO2 equivalent emissions in India [13]. To improve emission reduction strategies, it is recommended to focus on measuring and understanding the most uncertain sources of emissions. For example, research is needed to study CH4 and N2O emissions from various types of flooded rice conditions in Asia. Identifying rice cultivars, nutrient management strategies, water management techniques, and cultural practices that can minimize CH4 emissions while maintaining output and reducing N2O emissions requires extensive research. Methane, the most abundant organic trace gas in the atmosphere (CH4), plays a significant role as a greenhouse gas due to its chemical properties and atmospheric interactions. Natural gas primarily consists of methane and is generated and released into the atmosphere through biological processes in anaerobic conditions. Methane in the atmosphere absorbs infrared radiation from the Earth, preventing its escape into space. Methane concentrations have doubled or tripled in response to human activities and continue to rise. Changes in natural methane sources in the past have contributed to trends in atmospheric methane levels observed in ice cores. Due to anthropogenic contributions, greenhouse gases such as carbon dioxide are present in the atmosphere. The concentration of CO2 is closely linked to the biogeochemical carbon cycle, involving exchanges with terrestrial, oceanic, and marine biospheres. Estimates suggest that the biosphere and oceans have absorbed a similar amount of carbon dioxide released into the atmosphere over the years [14, 15]. Monitoring changes in carbon emissions in response to regional, governmental, and international factors requires long-term, continuous urban measurements. This requires comprehensive knowledge of air transport and accurate evaluation of emission factors. Additionally, atmospheric gas measurements taken at higher altitudes, several hundred meters above the ground, provide insight into local and regional fluxes with a range of approximately 500–1000 km, enabling a clear distinction between local and regional fluxes [16]. Extensive CO2 measurements have been conducted in urban environments, focusing on diurnal fluctuations, their relationship with the planetary boundary layer, and emissions [17, 18]. This study aims to measure the concentrations of atmospheric methane, carbon dioxide, and meteorological parameters in two major states, as well as examine the correlation between the gas concentrations and the parameters.

2 Materials and methods

2.1 Study area

Lagos State covers an approximate area of 3,577 km2, with its administrative center located in Ikeja. It is situated between latitudes 6°22' N and 6°52' N and longitudes 2°42' E and 3°42' E There are various seasons in Nigeria and it metrological parameters vary from one location to another [20]. The issue of seasonal variation in Nigeria can not be over stretched The state is bordered by the Guinea Coast to the south, the Republic of Benin to the west, and Ogun State to the north and east. Its climate is tropical, with an average annual rainfall of 1532 mm. The average temperature throughout the year is 27 °C. The predominant vegetation consists of wetland swamp forests, tropical swamp forests, freshwaters, and mangroves. Due to high urbanization and industrial expansion, Lagos has a population exceeding 9.3 million people, making it one of the most densely populated areas on the planet [21].Ibadan is situated in southwest Nigeria, at coordinates 7340 degrees north of the equator and 3530 degrees east of the Greenwich meridian. It is the fourth most populous city in Nigeria and the second largest in Africa, with an estimated population of four million residents [22]. Ibadan is located 345 km southwest of Nigeria's federal capital, Abuja, and 128 km northeast of Lagos. The city is divided into eleven contiguous local government areas, further subdivided into six urban space areas, namely Moniya, Egbeda, Akinyele, Ona-Ara, Ido, Oluyole, and Lagelu, as well as five urban areas including South-West Ibadan, Ibadan North, Ibadan North-West, and Ibadan North-East. Similar to many other Nigerian cities, Ibadan developed spontaneously without the aid of master planning [22].

Figure 1 depicts the various research fields, and Table 1 provides the coordinates of the sampling locations.

Fig. 1
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The various sampling locations

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Table 1 Coordinates of the sample collection sites
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2.2 Instrumentations

The KP826 multi-combination gas detector was utilized in this study. This device is specifically designed for monitoring and detecting gas leaks. The KP826 gas detector is constructed using high-strength engineering polymers, providing both durability and a comfortable feel. It exhibits excellent resistance to water, dust, and explosions. The detection ranges of the KP826 for CH4 (Methane), CO2 (Carbon Dioxide), and NH3 (Ammonia) are as follows: 0–100% LEL for CH4, 0–5000 ppm for CO2, and 0–100 ppm for NH3. The KP826 can operate effectively within a wide range of environmental conditions, including temperatures ranging from −20 °C to 50 °C, relative humidity below 95% RH (without condensation). Its dimensions are 68 mm x 33 mm x 124 mm, with a weight of 400 g (without an explosion-proof mark).

For measuring wind speed, the Benetech GM816 pocket wind speed and air temperature tester was used. This instrument is known for its cutting-edge design and reliable performance. It offers a measuring range of 0 to 30 m/s, 1.4 to 90 km/h, and 0 to 5860 ft/min, as well as 0 to 65 mph (or 0 to 55 sea miles per hour) with an accuracy of 5%. The wind temperature can be measured within a range of -10 to 45 degrees Celsius (14 to 113 degrees Fahrenheit) with a resolution of 0.2 degrees Celsius and 0.2 m per second. The device features a Beaufort wind scale with frozen tips, automatic cutoff after one minute of inactivity, low-voltage warning, and an LCD display with backlight. It provides the maximum, average, and current values. The power is supplied by a 3.0 V CR2032 Lithium battery that lasts for four hours. For measuring relative humidity and temperature, the HTC-2 indoor and outdoor thermometer handbook was utilized. This device provides readings for both indoor and outdoor temperatures, as well as relative humidity. The temperature ranges recorded are as follows: −10 to 50 °C (14 to 122 °F) for indoor temperature and −58 to + 158 degrees Fahrenheit (−50 to + 70 degrees Celsius) for outdoor temperature. The temperature display precision is 0.1 °C with an accuracy of ± 1 °C. The relative humidity range is 20–99% with a humidity display precision of 1% RH and an error of ± 5%RH. The operating voltage is 1.5 V, and the device consumes very little power.

2.3 Sampling method

Two states were selected for the study, and in each state, four different sample sites were chosen: farmland, a dumpsite, an airport, and a residential area. To measure the concentrations of CO2 and CH4, the KP826 Multi-Combination Gas Detector was utilized. The Clock/Humidity HTC-2 instrument was used to determine the temperature and relative humidity. Prior to data collection, all instruments were carefully calibrated to ensure accuracy and quality assurance. Readings were collected at each sampling site three times, with each collection period lasting 5 to 15 min. These readings were taken at intervals of 2 to 3 h over a period of 3 months. Additionally, hourly average meteorological data were gathered and recorded at each sample site. Hourly measurements of carbon dioxide and methane concentrations were also recorded, and the mean concentrations were employed for statistical analysis, such as linear regression.

2.4 Quality control

Accurate monitoring of greenhouse gas levels is essential for maintaining a predefined standard. To ensure precision, a hand-held portable gas monitoring device and a calibrated hand-held meteorological monitoring device were employed to sample the study area for 5 to 15 min. These devices were carefully calibrated according to established standards. Time-based quantitative data was then collected from diverse sources using the KP826 gas monitoring device.

2.5 Statistical analysis

Statistical analysis of carbon dioxide, methane, and meteorological parameters was conducted using SPSS version 26 for Windows, a widely used software in social sciences. This tool has proven to be efficient in assessing the statistical impact of experimental analysis and has played a pivotal role in our analysis process. Temporal variation of greenhouse gases in the sampling area was determined through the use of ANOVA. Additionally, PCA (Principal Component Analysis) was employed to examine the levels of variation based on sampling locations and the measured concentrations of greenhouse gases. Correlation analysis was utilized to assess the similarities between air quality data and meteorological parameters in the sampling areas. Finally, a box plot was generated to visualize the distribution of meteorological parameters.

3 Results and discussion

Table 2 compares the mean concentrations of greenhouse gases at different sampling locations in Lagos. The results indicate no spatial variation in CO2 and CH4, except for variations observed between Residential and other locations, as well as Farmland and Airport. However, these variations were not statistically significant (p > 0.05). Farmland had the highest mean carbon dioxide measurement (751.78 ± 58.16 ppm), likely due to activities such as livestock farming, agricultural soils, and rice production. On the other hand, the lowest mean carbon dioxide concentration was recorded in the Residential area (717.67 ± 86.66 ppm), where emissions from activities contributing to greenhouse gas production are typically lower. The highest mean CH4 concentration was recorded at the dumpsite (0.45 ± 0.13). Improper waste disposal, poorly managed landfills, and open dumping of waste can lead to the release of toxic gases like methane (CH4) into the atmosphere, contributing to air pollution and groundwater contamination through leachate. The lowest mean CH4 concentration was recorded in the residential area (0.14 ± 0.26 ppm), where activities that contribute to greenhouse gas emissions are less prevalent. The mean concentrations of carbon dioxide at the Airport and Dumpsite were recorded at (743.78 ± 113.82 ppm) and (732.56 ± 95.62 ppm), respectively. For CH4, the mean concentrations at the Airport and Farmland were (0.36 ± 0.25 ppm) and (0.30 ± 0.29 ppm), respectively. Greenhouse gases (GHGs) are gases that can absorb infrared light, leading to the trapping of heat in the atmosphere [23]. These gases directly contribute to the warming of the atmosphere and play a significant role in the greenhouse effect, which causes global warming. Human activities, such as the use of fossil fuels, deforestation, the use of synthetic fertilizers, and intensive farming, are major contributors to the greenhouse effect. Other sources include the respiration of plants and animals, as well as extraterrestrial factors such as solar radiation, volcanic eruptions, and meteor events. Efforts to reduce greenhouse gas emissions require comprehensive mitigation strategies with strict criteria for industrialized nations [24]. Increasing energy efficiency in businesses, communities, and homes, as well as implementing measures like rewetting peatlands and using organic soil for farming, can prevent carbon loss to the atmosphere [25, 26]. Net emissions from agriculture and forestry can be reduced through dietary adjustments, improved crop and forest management, and sustainable transportation methods [27]. Local actors, including cities, regions, corporations, public–private partnerships, and community organizations, play crucial roles in developing regional and municipal policies to combat climate change [28, 29]. Effective mitigation requires collaboration and network development across various levels of government and between the public and private sectors [30]. Given that cities are significant emitters of greenhouse gases, they can contribute to successful mitigation efforts [31].

Table 2 Mean comparison of greenhouse gases in different sampling sites in Lagos
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Table 3 presents the mean comparison of greenhouse gases at different sampling sites in Ibadan. The results indicate no spatial variation in CO2, while CH4 shows significant spatial variation, with the residential area significantly lower than other sampling sites. The highest mean CO2 concentration was recorded at the farmland (765.11 ± 122.90 ppm), while the lowest mean CO2 concentration was observed at the Airport (655.33 ± 93.04 ppm). For CH4, the highest mean concentration was found at the Airport (0.43 ± 0.23), while the lowest mean concentration was recorded in the residential area (0.08 ± 0.13 ppm). The dumpsite and the residential area had CO2 mean concentrations of (683.67 ± 51.32 ppm) and (676.44 ± 157.75 ppm), respectively. The farmland and dumpsite had CH4 mean concentrations of (0.32 ± 0.29 ppm) and (0.30 ± 0.15 ppm), respectively. Many developed and developing countries are confronted with the challenge of reducing greenhouse gas emissions or facing significant risks associated with global warming, such as droughts, floods, sea level rise, food insecurity, and an increasing number of extreme events. Climate change is expected to have wide-ranging impacts on ecosystems, societies, human livelihoods, food security, human health, and overall well-being. These impacts can manifest as temperature changes and shifts in the seasonal patterns of rainfall, among other factors. According to the Intergovernmental Panel on Climate Change [32], a temperature increase between 1.4 °C and 5.8 °C by 2100 will have severe adverse effects on the socioeconomic well-being of people. In Nigeria, some of the effects include increased rainfall amounts and frequency, coastal flooding, and a higher risk of more frequent 100-year floods [33]. Africa is considered one of the most vulnerable continents to the effects of climate change [32]. Like other African countries, Nigeria is grappling with the consequences of climate change and is assessing the associated costs. Nigerians are directly experiencing the impacts of climate change on their socioeconomic lives, ranging from desertification and drought in the north to gully erosion and flooding in the south. There is growing evidence in the literature that the business environment in Nigeria has been transformed due to climate change. For instance, there has been an increase in health risks [34], a decline in agricultural productivity [35], reduced access to quality education, decreased rainfall in already arid regions of northern Nigeria, and an escalation of conflicts and insecurity. All these factors significantly affect the quality of life and the standard of living of the Nigerian population.

Table 3 Mean comparison of greenhouse gases at different sampling sites in Ibadan
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Table 4 presents the results of the multivariate linear regression (MLR) analysis, which examines the relationship between meteorological parameters and CO2 levels while controlling for sampling sites in Lagos. The findings indicate that none of the variables included in the analysis were able to explain the variations in CO2 values effectively. The model suggests that the selected explanatory variables collectively account for only 8% of the CO2 levels observed in the location. Specifically, the analysis reveals a negative relationship estimate between temperature and humidity with carbon dioxide (CO2) concentration values in Lagos, with respective p-values of 0.728 and 0.327. On the other hand, wind speed shows a positive relationship estimate with CO2 concentration values in Lagos, with a p-value of 0.260. Carbon dioxide (CO2) is a prominent greenhouse gas that has garnered significant attention from the scientific community due to its role in climate change [36]. The increasing levels of CO2 in the atmosphere and their implications for global climate change have made it a topic of global importance with wide-ranging political and economic implications [37]. The impact of CO2 on global warming has raised concerns worldwide. The combustion of fuels in the power, transportation, and residential sectors leads to CO2 emissions, as well as the release of various short-lived air pollutants. These emissions collectively contribute to climate change and have adverse effects on human health resulting from global energy consumption [38]. Considering the influence of meteorological factors, such as temperature, relative humidity, and windspeed, on CO2 emissions is essential, as these factors can vary over different time periods, ranging from minutes to seasons.

Table 4 Multivariate linear regression (MLR) relating meteorological parameters and CO2 controlling for sampling sites in Lagos
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Table 5 presents the results of the multivariate linear regression (MLR) analysis, which explores the relationship between meteorological parameters and CO2 levels. The findings indicate that none of the variables included in the analysis were found to be significant predictors of CO2 levels in Ibadan. Specifically, the analysis reveals that temperature has a negative influence on CO2 levels in Ibadan, with a p-value of 0.599. On the other hand, humidity and wind speed show a positive influence on CO2 levels in Ibadan, with respective p-values of 0.152 and 0.936. The model suggests that the selected explanatory variables account for only 20% of the variations observed in the CO2 levels across the locations. It is worth noting that meteorological factors have a significant impact on the measured concentrations of CO2, as highlighted in previous studies [39, 40]. Globally, the increase in greenhouse gas concentrations, leading to rising temperatures in the atmosphere, has resulted in a decrease in human comfort [41]. Carbon dioxide (CO2), as a prominent greenhouse gas, receives considerable attention as a fundamental component of the Earth's climate system. For example, studies have shown that the doubling of carbon dioxide has led to increased temperatures in many regions, particularly in the tropics [42]. Additionally, some analyses have suggested an inverse relationship between CO2 and wind speed [43]. One limitation of these analyses is the presence of scattered measurements and potential outliers, which can obscure the underlying concentration patterns. However, sensitivity analysis has revealed the significant role played by meteorological variables, particularly wind speed, in influencing CO2 concentrations. These findings highlight the importance of better integrating emission reduction efforts at the local urban level with current and anticipated weather conditions [44]. In Nigeria, the dry seasons are characterized by increased atmospheric temperatures, which have been associated with higher particulate concentrations [45]. However, different studies have reported contrasting effects of atmospheric temperature on particulate matter concentrations [46]. High relative humidity, typically indicating heavy rainfall, has been linked to the scavenging of atmospheric pollutants, leading to lower air pollutant concentrations [47]. The inverse relationship between relative humidity and CO and particulates suggests that an increase in ambient relative humidity is associated with a decrease in air pollutant levels [48]. Overall, the complex relationship between meteorological parameters and CO2 levels necessitates further research and improved understanding to effectively address emissions and mitigate the impacts of climate change.

Table 5 Multivariate linear regression (MLR) relating meteorological parameters and CO2 controlling for sampling sites in Ibadan
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Table 6 presents the results of the multivariate linear regression (MLR) analysis, examining the relationship between meteorological parameters and CH4 levels while controlling for sampling sites in Lagos. The findings indicate that wind speed and certain sampling sites were significant predictors of CH4 in Lagos. The analysis reveals that wind speed has a negative influence on the level of CH4 in Lagos, with a p-value of 0.03. Furthermore, the Farmland and dumpsites exhibit significantly higher CH4 levels compared to the Residential site. On the other hand, temperature and relative humidity positively influence the level of CH4 in Lagos, with respective p-values of 0.305 and 0.109.

Table 6 Multivariate linear regression (MLR) relating meteorological parameters and CH4 controlling for sampling sites in Lagos
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The model suggests that the selected explanatory variables account for 35% of the variations observed in the CH4 levels across the locations. Methane (CH4) is one of the primary greenhouse gases, and understanding its sources and sinks on land remains challenging. Previous studies have predominantly focused on CH4 emissions from wetlands/peatlands, agricultural feed lots/lagoons, and municipal waste sites [11, 49, 50]. These studies suggest that terrestrial vegetation may contribute to CH4 flux, ranging from 62 to 236 Tg year1, accounting for 12% to 45% of global terrestrial emissions [51]. Recent discussions have emerged regarding the role of plants in greenhouse gas mitigation techniques, particularly the proposed plant route of aerobic CH4 generation by various authors [11, 51,52,53] influence the atmospheric methane. In this study, we investigated the effects of agricultural crops, specifically corn and soybean, on the CH4 budget at both the plant and landscape scales. Our hypothesis is that corn and soybean cultivation in the United States (US) significantly impacts atmospheric CH4 during the growing season. We aim to gain a better understanding of the underlying mechanisms considering their widespread spatial distribution, high net primary productivity, and the substantial nutrient inputs required to support these systems [US Department of Agriculture]. These high level CH4 emphasize the complexity of CH4 dynamics and the need for further research to elucidate the specific factors driving CH4 emissions and develop effective mitigation strategies.

Table 7 displays the results of the multivariate linear regression (MLR) analysis, investigating the relationship between meteorological parameters and CH4 levels while controlling for sampling sites in Ibadan. The findings indicate that humidity and certain sampling sites were significant predictors of CH4 in Ibadan. The analysis reveals that humidity has a positive influence on the level of CH4 in Ibadan, with a p-value of 0.002. Furthermore, temperature and wind speed also demonstrate a positive influence on CH4 levels in Ibadan, although with less statistical significance, with respective p-values of 0.062 and 0.920. It is noteworthy that Farmland and Airport sites reported significantly higher CH4 levels compared to the Residential site. The model suggests that the selected explanatory variables account for 56% of the variations observed in the CH4 levels across the locations. With increasing temperatures during the spring and summer seasons, there is an associated rise in methane gas emissions, making temperature a key factor in methane emissions. It is important to consider the spatial distribution of natural and human methane emission sources, such as wetlands, rice fields, landfills, and livestock breeding, as they contribute to the release of this gas throughout the year. Regions with high temperatures and low humidity experience a decrease in atmospheric methane due to diminished OH concentrations. According to (Sweeney et al. [54] methane emissions are temperature-dependent, and a 10 °C temperature rise within the range can result in a doubling of methane concentration. Seasonal variations in methane in North America from 2012 to 2018 are influenced by the seasonality of production resources (wetlands, rice fields, and biomass burning) and the atmospheric reactions associated with this gas [55]. There are correlations between temperature variables and CH4 emissions, where variations in mean air temperature explain a significant portion of the variation in CH4 ebullition and total flux. Laboratory experiments have also shown that higher temperatures positively impact CH4 production in sediments, as methanogenesis is highly temperature-dependent [56,57,58]. The increase in relative humidity resulting from heavy rainfall enhances the adsorption of water vapor onto particles, potentially contributing to lower concentrations of particulate matter during the wet season. This phenomenon causes particles to settle and deposit in a dry state [46]. This study quantitatively analyzed the concentration of greenhouse gases (GHGs) and thermal comfort using multivariate linear regression.

Table 7 Multivariate linear regression (MLR) relating meteorological parameters and CH4 controlling for sampling sites in Ibadan
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When comparing the two study areas, it is evident that the mean concentration of CO2 in Lagos is generally higher than in Ibadan, except for the Farmland site. This difference can be attributed to the rapid urbanization and industrialization experienced by Lagos, making it one of the most densely populated places globally, with approximately 9.3 million people as per the 2006 Census [21]. In terms of methane (CH4) mean concentration, the residential area and dumpsite in Lagos exhibit higher levels compared to Ibadan. On the other hand, the farmland and Airport sample sites in Ibadan report higher CH4 mean concentrations than those in Lagos. This discrepancy may be due to Ibadan's status as the second-largest city in Africa, with an estimated population of around four million, and as the fourth-most populous city in Nigeria [22]. Additionally, activities related to CH4 emissions in Ibadan's farmland might contribute to a higher mean concentration in that area, surpassing the levels observed in Lagos farmland, as indicated in Table 8. These observations highlight the distinct characteristics and emission patterns of CO2 and CH4 in Lagos and Ibadan, emphasizing the influence of urbanization, industrial activities, and population density on greenhouse gas concentrations in these areas.

Table 8 Mean Concentration comparison between Sample sites
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Table 9 provides a comparison of the CO2 mean concentrations between Ibadan, Lagos, and other studies. This study reported the highest mean concentration of CO2 when compared to the findings of other studies in Table 9. The elevated levels observed in this study may be attributed to the continuous annual increase in greenhouse gas emissions. Among the studies listed, the second-highest mean concentration of CO2 was recorded in Pakistan waste [59]. Similar mean concentrations were also reported in Roe, Italy [60] and the farmland sample site in Ibadan [61]. Additionally, the waste sample site in Ibadan [61], the motorpark sample site in Ibadan [62], and Phoenix, USA [63] exhibited comparable mean concentrations. Conversely, the lowest mean concentration was documented in Lagos, specifically in Ikorodu [64]. These comparisons highlight the variations in CO2 mean concentrations across different locations and studies, underscoring the importance of local factors, emissions sources, and specific environmental conditions in influencing CO2 levels.

Table 9 CO2 Mean Concentration comparison between samples sites and other studies
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Table 10 presents a comparison of the CH4 mean concentrations between Ibadan, Lagos, and other studies. This study documented the highest mean concentration of CH4 when compared to the findings of other studies in Table 10. The second-highest mean concentration was observed at the farmland sample site in Ibadan [61]. Similarly, the mean concentrations reported in the urban area of Seoul, Korea [65], and the waste sample site in Pakistan [59] were found to be comparable. These comparisons highlight the variations in CH4 mean concentrations across different locations and studies, indicating the influence of local factors, emission sources, and specific environmental conditions. The elevated CH4 levels recorded in this study and in similar locations emphasize the importance of monitoring and addressing methane emissions to mitigate their potential impact on climate change and air quality.

Table 10 CH4 Mean Concentration comparison between sample sites, samples sites and other studies
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Figure 2 illustrates the positive relationships between temperature, wind speed, and humidity with CH4 concentrations. The scatter plot matrix presents a collection of scatter plots, which visually depict the bivariate correlations between different variables. This matrix allows for the exploration of multiple associations within a single chart, as each scatter plot represents the relationship between a pair of variables. The link between CH4 and climate change has received significant attention due to the observed synchronization between meteorological variables and CH4 concentrations. This raises questions about whether increasing CH4 emissions contribute to the onset of climate change or if they rapidly respond to rising atmospheric temperatures. Understanding the relationship between CH4 and meteorological characteristics is crucial in comprehending the impact of temperature, relative humidity, and seasonal fluctuations on ambient air quality, as investigated by Jayamurugan et al. [66] in a coastal metropolitan region.

Fig. 2
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Scatter plot matrix showing relationship between meteorological parameters and CH4 in Ibadan

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Based on Fig. 3, humidity exhibits a positive correlation with CO2, while temperature and wind speed show negative correlations. Scatterplot matrices are useful for identifying linear relationships between multiple variables. They provide a clear visualization of the associations between different pairs of variables within a single set of charts. This study examined the influence of meteorological factors, including wind speed, air temperature, and relative humidity, on methane concentrations. The urban area under investigation is experiencing a significant influx of people from rural regions. Meteorological data plays a crucial role in determining air quality in urban environments [67, 68].

Fig. 3
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Scatter plot matrix showing the relationship between meteorological parameters and CO2 in Ibadan

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Based on Fig. 4, it can be observed that temperature and humidity exhibit a negative correlation with CO2, while wind speed shows a positive correlation. Scatter plots, which plot data points on horizontal and vertical axes, are useful in displaying the relationship, if any, between observed quantities or variables. They provide a visual representation of the level of association between different variables in statistical analysis. In this context, relative humidity, temperature, and wind speed and direction are considered important factors as they can influence the dispersion process, removal mechanisms, and formation of atmospheric particles. These factors play a significant role in determining the concentrations of air pollutants [67, 69].

Fig. 4
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Scatter plot matrix showing the relationship between meteorological parameters and CO2 in Lagos

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According to Fig. 5, it is evident that temperature and humidity exhibit a positive correlation with CH4, while wind speed demonstrates a negative correlation. The scatter plot matrix was employed to examine the association between variations in greenhouse gas (GHG) concentration and air temperature. By plotting data points on the matrix, it becomes possible to analyze the relationship between these variables and gain insights into their interplay.

Fig. 5
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Scatter plot matrix showing relationship between meteorological parameters and CH4 in Lagos

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Figure 6 presents the Box-Violin plot, which compares the temperature across different sampling sites in Lagos. The plot reveals that there is no significant variation (p > 0.05) in temperature among the four sampling sites in Lagos. In a similar study conducted by Dangal et al. [70], they found a low methane concentration in China and Mongolia during May, followed by a high concentration in September. They attributed this increase in methane to higher temperatures during the period from May to September.

Fig. 6
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Box-Violin plot showing the comparison of Temperature over different sampling sites in Lagos

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Figure 7 presents the Box-Violin plot, comparing the temperature across different sampling sites in Ibadan. The plot indicates that there is no significant difference (p > 0.05) in the temperature among the various sampling sites in Ibadan. Furthermore, the rise in Earth's surface temperature is primarily attributed to human activities, as opposed to natural factors. This increase in temperature leads to the emission of infrared radiation from the heated atmosphere back towards the Earth's surface. It is important to note that the greenhouse effect, which is caused by human activity, is distinct from greenhouse warming, where glass panes trap heat by enclosing heated air [71].

Fig. 7
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Box-Violin plot showing comparison of Temperature over different sampling sites in Ibadan

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Figure 8 displays the Box-Violin plot, comparing the humidity across different sampling sites in Lagos. The plot indicates that there is no statistically significant difference in humidity among the various sampling sites in Lagos (p > 0.05). In tropical regions, such as Lagos, humidity plays a crucial role in the dilution process that combines CO2 and CH4 in the atmosphere [72]. Relative humidity represents the percentage of water vapor present in the atmosphere at a given temperature. Greenhouses, for instance, are known for their high temperatures and humidity levels because warmer air has the capacity to hold more moisture. The presence of humidity is important in greenhouses as it creates a favorable environment for plant growth [72].

Fig. 8
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Box-Violin plot showing the comparison of Humidity over different sampling sites in Lagos

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Figure 9 presents the Box-Violin plot, comparing the humidity across different sampling sites in Ibadan. The plot indicates that there is no significant difference in humidity among the various sampling sites in Ibadan (p > 0.05). Relative humidity (RH) represents the percentage of water vapor in a given volume of air [73], and it has a direct impact on atmospheric visibility. Changes in one weather variable can potentially result in alterations in other variables [74].

Fig. 9
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Box-Violin plot showing comparison of Humidity over different sampling sites in Ibadan

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Based on Fig. 10, there are no noticeable variations in wind speed across the different sampling sites in Lagos (p > 0.05). Although wind speed is routinely measured, further research is needed to understand how pollutants interact with it. In this study, hourly readings of CO2 and CH4 were collected over a nine-year period in a rural area. Wind speed is associated with pollution events, both locally and globally, as demonstrated by previous studies [19, 75]. Exponential functions have been utilized by researchers in the past to examine and parameterize the direct correlations between CO2 concentrations and wind speed [18, 76].

Fig. 10
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Box-Violin plot showing the comparison of wind speed over different sampling sites in Lagos

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Based on Fig. 11, there is no significant difference in wind speed among the sampling sites in Ibadan (p > 0.05). Wind speed, along with operational time and concentration and pressure differentials, can influence the mass emission process by altering the dynamics of the emitting surface. Previous research has shown that the rate of water evaporation in soil columns increased with higher wind speeds [77].

Fig. 11
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Box-Violin plot showing the comparison of wind speed over different sampling sites in Ibadan

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4 Conclusion

Significant amounts of greenhouse gas emissions are produced by cities, farms, airports, and landfills. Massive CO2 and CH4 emissions have had significant effects and ramifications that fuel climate change. This research provides a nugget of information on the issues, consequences, and mitigation strategies related to lowering and regulating CO2 and CH4 emissions in Lagos and Ibadan, Nigeria. Mean Concentration in Lagos was higher than that of Ibadan in all sample Sites except Farmland, Lagos CH4 mean concentration in residential area and dumpsite was higher than that of Ibadan, while Ibadan CH4 mean concentration in farmland and Airport sample sites was higher than that of Lagos. In Ibadan, the relationship between temperature, wind speed, and humidity was positive for CH4 but negative for CO2, while the relationship between humidity and CO2 was positive in Ibadan. In Lagos, the relationship between temperature and humidity and CO2 was negative, whereas the relationship between wind speed and CO2 was positive. The relationship between temperature and humidity and CH4 was also positive, whereas the relationship between wind speed and CH4 was negative. Cities, farms, airports, and landfills contribute significantly to greenhouse gas emissions, particularly CO2 and CH4, which have profound implications for climate change. This study sheds light on the issues, consequences, and potential mitigation strategies associated with reducing and regulating CO2 and CH4 emissions in Lagos and Ibadan, Nigeria. The mean concentration of CO2 was generally higher in Lagos compared to Ibadan, except for the farmland site. In terms of CH4, Lagos exhibited higher mean concentrations in residential areas and dumpsites compared to Ibadan, while Ibadan had higher mean concentrations in farmland and airport sample sites compared to Lagos. In Ibadan, a positive relationship was observed between temperature, wind speed, and humidity with CH4 levels, while a negative relationship was observed between these meteorological variables and CO2 levels. Additionally, a positive relationship was found between humidity and CO2 in Ibadan. In Lagos, a negative association was observed between temperature and humidity with CO2 levels, while wind speed showed a positive association with CO2. Regarding CH4, a positive relationship was observed with temperature and humidity, while wind speed exhibited a negative relationship.These findings provided valuable insights into the dynamics of CO2 and CH4 emissions in the study areas and highlighted the importance of considering meteorological factors in emissions control and reduction strategies. Complex modelling methods that incorporate atmospheric measurements and data on human activities are needed to distinguish between natural and anthropogenic sources and to identify the individual industries that contribute to emissions. A challenging task is measuring the contribution of diverse sources and developing accurate emissions inventories.