Spatial Distribution of Energy Stations and Monitoring of Air Quality in Port Harcourt, Rivers State, Nigeria.

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Barinedum Valentine Kponi, Chike Enyinda, MeeluBari Barinua Kpang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4541872/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examines the geospatial locations of energy stations within Port-Harcourt Aba expressway to ascertain their locations if it conforms to standard practice. It also took into cognizance the in-situ monitoring of the various air pollutants within the vicinity of the energy stations. The study location is in Obio/Akpor and Port Harcourt city Local Government Area, Rivers State. The result of the proximity analysis reveals that the energy stations do not fall within the standard of 15metres specified by DPR (Department of Petroleum Resources). The various pollutants examined are PM 10, PM 2.5, carbon monoxide (Co), Nitrogen Dioxide (No2), ground level ozone (O3), and sulphur dioxide (S02). The result reveals that their concentrations values fall within the permissible limit as specified by NESREA except (C02) which is the primary pollutant was extremely highly than the permissible limit of 400 (ppm). This can be attributed to the influx of vehicles that buys fuel from the energy stations. The concentration of (C02) poses a serious health effect on the pump attendants due to their constant inhaling of the pollutants. The study concludes that energy stations should be cited at the approved DPR standard of (15metres) and actionable measures should be put in place to aid in the reduction of CO2 within the energy stations. Finally, the study recommends that there should be strict compliance to DPR standard in the siting of energy stations. Geospatial Analysis Energy station locations Ambient Air Quality Figures Figure 1 Figure 2 Introduction Air pollution can directly or indirectly affect human health, causing physical discomfort and leading to disease or even death. Studies have shown that when the human body is exposed to highly polluted air for a long time, the mortality rate increases. (Tharby, R 2002 ). With rapid development of the economy and booming population growth, an enormous number of resources (e.g, energy, water, and food) is required in our society to sustain our activities. As a result, various kinds of pollution have been produced. Among the various pollution problems, air pollution has caused major concern over the world due to its widespread nature, damage to our environment and potential health risk to humans. Although concern has been raised regarding the emission of air pollutants from anthropogenic sources, our society still relies heavily on fossil fuels for various applications such as electricity generation, transportation, industrial and domestic heating, and so on. An obvious result of this is the deterioration of air quality, particularly in developing countries. Air pollution has become a public concerned problem in modern metropolises. (Seinfeld, 1986 ). Air pollution may be defined as any atmospheric condition in which certain substances are present in such concentrations that they can produce harmful effects on man and his environment. Unpolluted air on the other hand is a vital requirement for the growth and survival of all living species. The life expectancy of both man and animal species within a giving environment is reliant on the quality of air present (PSMAG, 2018). Of worthy to note, humans require oxygen for efficient respiration in other to facilitate and enhance metabolic activities. Air is a vital component for the sustenance of all life forms on earth, thereby, making air an indispensable tool for the survival of man. A good air is supposed to be free and safe for human inhalation. Unfortunately, due to urbanization, and the introduction of several anthropogenic activities such as the sales of premium motor spirit (PMS), which has contributed to the contamination of the ambient outdoor air quality thereby posing serious health risks concern (Cavanagh et al., 2009 ). According to a report by Shola (2018), the increasing urban population in Nigeria and her dependence on PMS exhibits a heightened vulnerability human health. As stated by Power et al. (2018), the detrimental effects of air pollution negatively affect human health, ecosystems, and the overall biosphere. Air pollution poses a significant concern in developing nations characterized by the presence of unregulated industries such as filling stations. Air pollutants such as particulate matter (PM 10 & PM 2.5), carbon monoxide (Co), Nitrogen Dioxide (No2), ground level ozone (O3), and sulfur dioxide (S02) have detrimental effects on the well-being and physiological stability of people and other organisms. Moreover, the World Health Organization (WHO) has consistently emphasized the role of air pollution in the prevalence and intensifying diseases such as tuberculosis, asthma, cancer, as well as respiratory and dermatological ailments. These health conditions have resulted in a higher mortality rate compared to AIDS, as reported by Mehta et al. ( 2013 ). In a study conducted in 2012 by the World Health Organization (WHO), it was stated that around 10% of the global population, equivalent to 7 million individuals, succumbed to mortality because of air pollution (WHO, 2014). In 2019, the global yearly mortality rate reached 2.9 million individuals, a statistic predominantly observed in developing nations, accounting for almost 85% of the total deaths (World Health Organization, 2016 ). Of worthy to note, one of the major contributors to air pollution is fumes generated from vehicles at various filling stations at the purchase of PMS, gases releases from the nozzles from pumps at filling stations etc which affect air quality and, in most cases, leads to mortality (Mehta: 2013; Obanya: 2018; Ogundipe: 2018; Alani: 2019) listed emission of fumes from vehicles as a major pollutant of air quality in filling stations which causes difficulty in breathing, wheezing, and sneezing. Notably, this is as result of the concentration of gases that has engulfed the filling station environment. This problem is synonymous across all filling stations. As stated by Ashmore, ( 2005 ), it is also suspected that industrialization and the presence of various business centres that emits carbons through from their daily activities could be responsible for the indiscriminate emissions of the obnoxious gases. This in return could pose a serious health effect to the staff and those doing business in the filling station vicinity. To ascertain the air quality within the various energy stations, the distances of the locations of the energy stations were examined to verify if it falls within the permissible limit as specified by DPR (Department of Petroleum Resources). Materials and Methods Study Area The study area is situated within Port Harcourt metropolis which is made up of Obio-Akpor LGA and PHALGA. It is situated between 4°42 and 4°52' North and between latitudes 6°53' and 7°08' East. It is in the Niger Delta region. Port Harcourt is often regarded as the garden city of Nigeria, it was established by the British Colonial administration under Lord Lugard to meet the pressing economic needs of the Europeans. The study area is bounded by Obio-Akpor to the West, Eleme to the East, Oyigbo to the North and Degema LGA to the South. See (fig 3.1 ). The study area is highly characterized by the presence of filling stations across the study area. Due to the urbanization in the study area, it is characterized by several filling stations and with associated business centres which is likely to influence the air quality in these areas. It is characterized by alternate wet and dry season. (Hoje, 1972) with annual total rainfall of between 160mm and 294mm; relative humility of over 90% and mean temperature of 27 o c. The detailed geology of the area has been described by Allen (1965), Rayment (1965), short and stauble (1967). The local geology of the area consists of the stratified sediments starting with Benin Formation underlain by the Miocene Agbada formation and under compacted Akata formation respectively (Short and Stauble, 1967). The Benin formation consists of massive highly porous sands and gravels of fluviatile origin. Agbata formation is also of Eocene-recent in age, and it consists of an admixture of inter-bedded sands, which are fluviatile coastal, fluvio-marine and shale in origin. Akata formation is also predominately shale or clay but is relatively under compacted. (Ehirim et al, 2009). The mean maximum temperature all the year round in the study area is 30 o C. The dry season months of February, March, and April record the highest mean diurnal temperature for the period of ten years (1985-1994), the month of March recorded 36 o C while February recorded 35 O c1 O c. The temperature within the Study area is 33 O c. Fig 3.1: Study Area. Geospatial locations of filling stations within the study area Type of data/Method of Data Collection Data used for this study is the primary data. The primary data is the data that is gotten directly from the field through direct field measurements. This involves the use of field instruments to directly collect the sample for investigation. The various coordinate points were also collected with the use of hand-held GPS (Global Positioning System) device. The distances from the major road to the filling station locations were collected in the entire study area across the various filling stations sampled with a hand-held tape. Also, the air quality data were examined to ascertain their concentration levels in the atmosphere. The following pollutants were examined to ascertain their presence and concentrations. They are PM 10, PM 2.5, carbon monoxide (Co), Nitrogen Dioxide (No2), ground level ozone (O3), and sulfur dioxide (S02). The air quality reader (AERO-QUAL 500 SERIES) was utilized in the data collection. The data was collected at different times of the day; 10am, 12pm and 5pm respectively. This is done in accordance with the methods specified by Francis Tuluri, Amit Kr. Gorai, Aaron James (2007). The air quality instruments is specified below. PLATE 3.1 : AIR QUALITY READER. Table 3.1: Coordinate Locations of sample energy stations. S/N Sample Places. Distance(s) Metres Coordinate points Longitude Latitude 1 NNPC Filling Station 2 7.039777 4.856064 2 TOTAL Filling Station 3 7.048483 4.845983 3 CONOIL Filling Station 2.5 7.027405 4.837277 4 AP Filling Station 0.2 7.002662 4.832237 5 MRS Filling Station 0.1 7.009077 4.815741 6 Forte Oil filling station 2 7.00816 4.800162 7 Evergreen Field Oil 0.05 7.004037 4.793289 8 Mobil Filing Station 0.5 7.009077 4.785041 9 Stage Oil filling station 0.05 7.016866 4.765338 DPR Standard (Decree) 15 (Metres) Source : Researchers field Work, (2024) The table depicts the various coordinate locations of the nine (9) filling stations in the study area. Field measurements were recorded for the various distances in metres to ascertain the locations of the various filling stations away from the major roads in the study area. DPR is the sole regulatory agency that spearheads the activities of filling stations in Nigeria. They are the regulatory agency saddled with the responsibility of siting of filling stations across Nigeria. Furthermore, the latitude and longitude of the various filling stations were also recorded accordingly. As specified by DPR, (2016), all filling stations must be situated within the distance of 15metres from the road. Out of the identified energy stations, three (3) major marketers were used in the study to ascertain the quality of air. However, the various distances across the nine (9) energy stations was used in the study to also verify their distances if it falls within the permissible limit as specified by DPR. Method of Data Analysis The various air quality data from different stations was investigated to ascertain the station that has the highest pollutants concentration using descriptive statistics (mean, and standard deviation). The result was further used to ascertain if it falls within NESREA (National Environmental Standards and Regulations Enforcement Agency), permissible limit for out-door air quality. while the one-way analysis of variance (ANOVA) and probability level set at (P < 0.05) was considered to indicate statistical significance. This was done to ascertain if statistically, there exist a significant variation in the air quality across the filling stations within the study area. Fig 3.2: 15 metres Buffer of filling station locations Source: Researcher’s GIS/Remote sensing analysis, (2024) The map presented in (Fig 3.2) is a 15 metres buffer analysis of the various filling station locations in the study area. From the result of the analysis as presented in the map above, it is obvious that all the filling stations did not adhere to the DPR standard (decree) which states that the actual distance from the main road to the location of any filling station must be 15 metres. Interestingly, the petroleum filling station amendment decree no. 37 of 1977 safety rules and regulations stipulate site inspection by DPR of proposed filling station, among other things, issue report on the following basic requirements: - (i )Size of the proposed land site. (ii) Whether site lie within pipeline or electricity high tension cable Right of Way (ROW). (iii) Distance from the edge of the road to the nearest pump (not less than 15 meters). (iv) The number of petrol stations within 2km stretch of the site on both sides of the road will not be more than four, including the one under consideration. (v) The distance between an existing station and the proposed one will not be less than 400 (four hundred) meters. (vi) The drainage from the site will not go into a stream or river. (vii) In some instances where site is along Federal Highway, a letter of consent from the Federal Highway is required. (viii) DPR guided/supervised EIA study of the site by DPR accredited consultant. This study is concerned with regulation number (iii) which state that: a) The distance from the edge of the road to the nearest pump will not be less than 15 meters. Presentation of Air Quality Results Across Major Filling Stations Table 3.2: NNPC FILLING STATION pollutants S.I Unit Morning Afternoon Evening Mean Standard Deviation NESREA Standard Co (ppm) 0.08 0.6 0.10 0.26 0.294618 0.06 SO2 (ppm) 0.06 0.9 0.8 0.586667 0.458839 0.05 03 (ppm) 0.03 0.023 0.32 0.124333 0.169488 0.06 PM2.5 (ppm) 0.006 0.009 0.004 0.006333 0.002517 0.2 PM10 (ppm) 0.004 0.009 0.003 0.005333 0.003215 0.4 CO2 (ppm) 994 1025 933 984 46.80812 400 Source: Researchers Filed Work, (2024) The result above reveals the various parameters and the associated results as presented in table (3.1). Six parameters were investigated in the different times of the day (10am, 1pm and 5pm). This was done to ascertain the time that has the highest concentration of the gas. Cabon monoxide was absent in the morning while sulfur dioxide was minute with a value of 0.01, ground level ozone was absent also. Pm2.5 has a value of 0.008 while PM10 has a value of 0.010. However, carbon-dioxide was present with a value of 1077. In the afternoon, both Cabon monoxide, sulfur dioxide and ozone were absent while PM2.5 and PM10 has a value of 0.002 and 0.001 which is lesser than the value in the morning and carbon-dioxide was 1025. In the evening, carbon monoxide was 1.8 while sulfur-dioxide was absent. Also, ozone was not present while PM2.5 and PM10 has same reading of 0.003 respectively. Quite noticeable, carbon dioxide has a value of 1099. This is a clear indication that carbon is more concentrated in the evening than any other time of the day within NNPC filling station. Table 3.3: TOTAL FILLING STATION pollutants S.I Unit Morning Afternoon Evening Mean Standard Deviation NESREA Standard Co (ppm) 0.8 0.4 0.6 0.6 0.2 0.06 SO2 (ppm) 0.6 0.9 0.6 0.7 0.173205 0.05 03 (ppm) 0.03 0.023 0.032 0.028333 0.004726 0.06 PM2.5 (ppm) 0.002 0.001 0.003 0.002 0.001 0.2 PM10 (ppm) 0.003 0.006 0.009 0.006 0.003 0.4 CO2 (ppm) 994 1025 933 984 46.80812 400 Source: Researchers Filed Work, (2024) The result presented in table (3.2) is the air quality result of Total Filling State. Six air quality parameters were tested at different times of the day (10am, 1pm and 5pm). This was done to ascertain if there are presence of any of them and in what quantity. However, it was discovered that Carbon monoxide (CO) which is a colourless, odourless gas which also results from incomplete combustion and is emitted by a wide variety of combustion sources, including motor vehicles was present with a value of 0.8 (ppm) while sulfur dioxide was absent, however, ground level ozone was present with a value of 0.03 (ppm). Remarkably, it was discovered that PM2.5 has a value of 0.002 while PM10 has a value of 0.001 while carbon-dioxide was present with a value of 1051. In the afternoon, both Cabon monoxide, sulphur-dioxide was absent. Ground level Ozone was present with a value of 0.012 while PM2.5 and PM10 has a value of 0.002 and 0.002 respectively. However, carbon-monoxide was 998. In the evening, carbon monoxide was 1.0 while sulphur-dioxide was 0.1 while ground level ozone was absent. Coincidentally, PM2.5 and PM10 has same reading of 0.002. Carbon monoxide (CO) is a colourless, odourless gas which results from the incomplete combustion and is emitted by a wide variety of combustion sources, including motor vehicles etc. has a value of 1016 which is far higher than the morning and afternoon. Table 3.4: CONOIL FILLING STATION pollutants S.I Unit Morning Afternoon Evening Mean Standard Deviation NESREA Standard Co (ppm) 0.6 0.2 1.8 0.866667 0.832666 0.06 SO2 (ppm) 0.9 0.6 0.5 0.666667 0.208167 0.05 03 (ppm) 0.000 0.00 0.000 0 0 0.06 PM2.5 (ppm) 0.008 0.002 0.003 0.004333 0.003215 0.2 PM10 (ppm) 0.010 0.012 0.019 0.013667 0.004726 0.4 CO2 (ppm) 1077 1025 1099 1067 38 400 Source: Researchers Filed Work, (2024 The table above shows the results of the air quality as presented in table (3.3). air quality data was collected from Mobil filling station along Port Harcourt Aba express way. However, it was discovered that Carbon monoxide was present with a value of 56.4, sulfur oxide was present with a value of 4.5 while ozone was absent. PM2.5 has a value of 0.008 while PM10 has a value of 0.007. Carbon dioxide has a value of 1121. In the afternoon, carbon monoxide has decline with a value of 37.5 and sulphur-oxide was totally absent alongside ozone. PM2.5 and PM10 was read at the same value of 0.006 and 0.008 respectively. Carbon dioxide was present with a value of 989. In the evening, both carbon monoxide, sulfur oxide and ozone were absent. This could be because of reduced or absent of social activities. PM2.5 and PM10 was 0.006 and 0.012. Carbon oxide was present with a value of 976 which is lower than the morning and afternoon session. This is possible because of the reduced activities within the filling station during that time of the day. Permissibility of air quality as specified by NESREA. Table 3.4: Standard For Air Quality from Industrial Sources/Operations S/N POLLUTANTS Maximum Permissible Limits (ppm) 1 Co 0.06 2 SO2 0.05 3 03 0.06 4 PM2.5 0.2 5 PM10 0.4 6 CO2 400 Source: NESREA, (2020). The table depicts the air quality permissible limit as specified by NESREA (National Environmental Standards and Regulations Enforcement Agency). In comparison to the measured air quality within the major filling stations along Port Harcourt Aba Express way. As specified by NESREA, Carbon monoxide (CO) which is a colourless, odourless gas which also results from incomplete combustion and is emitted by a wide variety of combustion sources, including motor vehicles should not exceed 0.06 (ppm). Also, Sulfur dioxide (SO 2 ) which is an colourless gas with a sharp odour. It is produced from the burning of fossil fuels (coal and oil) and the smelting of mineral ores that contain sulfur, NESREA specified that its concentration should not exceed 0.05 (ppm), furthermore, Ozone at ground level, not to be confused with the ozone layer in the upper atmosphere which is one of the major constituents of photochemical smog and it is formed through the reaction with gases in the presence of sunlight should not exceed 0.06 (ppm). Particulate matter (PM) PM 2.5 and PM 10 which is a common proxy indicator for air pollution. There is strong evidence for the negative health impacts associated with exposure to this pollutant. The major components of PM are sulfates, nitrates, ammonia, sodium chloride, black carbon, mineral dust, and water. NESREA specified that its concentrations should not exceed 0.2 and 0.4 (ppm) respectively. Finally, Carbon dioxide which is an important chemical needed for the survival of all life forms in the environment was also analysed to ascertain its concentration. The permissible limit as specified by NESREA is 400 (ppm). At high level, this pollutant becomes dangerous to human. Findings Carbon dioxide is an important greenhouse gas, it is a by-product of the burning of fossil fuel with a half-life of 50-200 years and global warming potential (Gattuso, and Hansson 2011). CO2 at high levels above permissible limits may result in environmental hazards such as ocean acidification. According to NESREA, the minimum permissible limit for CO2 is 400 (ppm), once it goes higher, it becomes hazardous. The values of CO2 recorded across the different sample locations were higher than the standard given by NESREA (2020). The highest concentration of CO2 at 1521 ppm was obtained at Coinoil filling station. The high rate of CO2 emission in the study area is as a result burning of in activities that goes on within the environment, the high influx of vehicles buying fuel and the fumes from their exhaust pipes, also from business generators, heavy duty trucks etc. This result agrees with the works of Tse and Oguama (2014) who also record high levels of CO2 concentration in their study. Nitrogen dioxide is an important environmental pollutant. It is introduced into the air through gas stoves; it causes photochernical smog at high concentrations as well as other health effects such pulmonary edema and hemorrhage (Searl, 2004). This is because of the high rate of vehicular emissions from the cars and motor bikes which are constantly on the move. Indeed, motor vehicles produce more pollution than any other single human activities (Ukemenam, 2014). Tse et al (2014) also recorded high concentrations of CO around commercial areas in their study and at filling stations. Carbon monoxide is a colorless, odourless, and tasteless gas toxic gas which is produced from the incomplete combustion of fossil fuel in generators and automobiles. Exposures to carbon monoxide may cause significant damage to the heart and central nervous system (Kampa, and Castanas, 2008). Pollution due to traffic constitutes 90-95% of the ambient CO levels pose a serious threat to human health (Uyigue and Agho, 2007). The risk of the fuel pump attendants who inhale this gas in a daily basis is quite high and may have long term negative health consequences. The highest concentration of SO2 was recorded as the various sampled energy stations with a value of 0.9 (ppm) and it is highly concentrated in the afternoon. This is because of emission from the fleet of cars that come in and go out to purchase fuel. This finding is in tandem with the results of Tse et al (2014) who also recorded high levels of SO2 in filling stations in their study. Sulphur dioxide (SO2) is an environmental pollutant and the main component of acid decomposition. SO2 is emitted directly into the atmosphere from sources such as coal and oil power plants, oil refineries smelters, generators and automobiles and can remain suspended for days allowing wide distribution of the pollutant (Ukernenam, 2014). Excess concentration of S02 can lead to respiratory problems, severe headache, irritating lungs, and damage to vegetation. It can also cause increased rate of corrosion of Iron, Zinc, Steel, and aluminum (Anderson, 2005). Also, they were presence of particulate matter (PM2.5 & PM 10) at the various energy stations. Although their values were within the permissible limit as specified by NESREA. Other particulates recorded lower concentration of pollutants in many of the samples. PM2.5 is of a more serious health concern since smaller particles can travel more deeply into our lungs and cause more harmful effects. This result also agrees with the findings of Tse et al, (2014) that compared the indoor and outdoor air quality as it relates to particulate matter within business environment and residential areas and found the concentration of particulates to be higher outdoor. The air quality within the study area can be said to be poor with high concentration of pollutants that deter the quality of air within the various filling stations. Air quality monitoring helps to assess the quality of air within an environment. It is an activity that must be carried out continuously at intervals; not just for the sake of measurement but to ensure that steps are taken to reduce the act of air pollution to the barest minimum. Particulate matter is the sum of all solid and liquid particles suspended. Atmospheric suspended particles which cause impairment of visibility (Dayan and Levy, 2005). The reduction of visibility is caused by buildup of the atmospheric particles. Particulates are the deadliest form of air pollution due to their ability to penetrate deep into the lungs and blood streams unfiltered, causing permanent DNA mutations, heart attacks, asthma, cough catarrh, chronic bronchitis, and premature death. Conclusion This study has reveals that, majority of the filling stations along the Port-Harcourt ABA express way is not situated in 15 metres away from the major road as specified by DPR. This is a death trap for commuters as any activity that result to fire outbreak will cause a lot of harm and loss of lives and properties. The study also concludes the air quality within the various energy stations is polluted, but amongst other pollutants as observed, CO2 is the major air pollutants with values higher than NESREA standards for outdoor air quality. Declarations Author Contribution Kponi, barinedum valentine wrote the manuscript and produced the associated maps. Enyinda, Chike took the field data for each of the energy stations and their coordinate locations. Kpang, Meelubari Barinua produced the various tables, he also carried out a thorough reading of the entire work and corrections was made by him where necessary. Acknowledgement We acknowledge the support of Mrs. Precious N. Barinedum-Kponi for her field assistance and Dr. I.C Ezekwe for his encouragement . Also, we wish to thank Dr. Uchege Friday for his guidance. References Abaje IB, Bello Y, Ahmad SA (2020) A review of air quality and concentrations of air pollutants in Nigeria. J Appl Sci Environ Manage 24(2):373–379 Alghodhaifi H, Lakshmanan S (2020) May). Simulation-based model for surrogate safety measures analysis in automated vehicle-pedestrian conflict on an urban environment. Autonomous Systems: Sensors, Processing, and Security for Vehicles and Infrastructure 2020, vol 11415. 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S (2014) Global status report on noncommunicable diseases 2010 World Health Organization (2016) World Health Statistics 2016 [OP]: Monitoring Health for the Sustainable Development Goals (SDGs). World Health Organization Plate Plate 3.1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files floatimage2.jpeg PLATE 3.1: AIR QUALITY READER. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4541872","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":317918578,"identity":"27a49ed5-dd40-4aef-9d6d-c59ef50d3665","order_by":0,"name":"Barinedum Valentine Kponi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYBACAyA+wMDAzMDAA+baAAnGxgOkaEkDaWkgqIUBoYXhMJjEq8Wc/ezDwwUV1olre84efFxQcN5ubfthoC01NtG4tFj2pBscnnEmPXHb2b5k4xkGt5O3nUkEajmWltuAy2EH0hgO87YdTtx2nsdMmgeoxewAUAtjw2HcWs4/A2r5B9dyLtns/EMCWm6AbGkAajnbA9JywM7sBgFbLGcAbeE5lm687cy5ZGMeg+QEsxtAWxLw+MWcP435M0+Ntey2M7kHH/P8sbM3O5/+8MGHGhucWpAAJGISwSoTCCtHaLEnTvEoGAWjYBSMJAAAAsBlwt4YJwcAAAAASUVORK5CYII=","orcid":"","institution":"University of Port Harcourt","correspondingAuthor":true,"prefix":"","firstName":"Barinedum","middleName":"Valentine","lastName":"Kponi","suffix":""},{"id":317918579,"identity":"e593577a-7767-4019-9615-2db2418d4e07","order_by":1,"name":"Chike Enyinda","email":"","orcid":"","institution":"Rivers State University","correspondingAuthor":false,"prefix":"","firstName":"Chike","middleName":"","lastName":"Enyinda","suffix":""},{"id":317918582,"identity":"b8da9420-4e2c-484c-b819-454bd92e4847","order_by":2,"name":"MeeluBari Barinua Kpang","email":"","orcid":"","institution":"University of Port Harcourt","correspondingAuthor":false,"prefix":"","firstName":"MeeluBari","middleName":"Barinua","lastName":"Kpang","suffix":""}],"badges":[],"createdAt":"2024-06-06 17:24:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4541872/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4541872/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59485219,"identity":"3adf49bd-5cfb-409c-9cdc-e661cbb83217","added_by":"auto","created_at":"2024-07-02 10:55:19","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":248543,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e3.1: Study Area.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeospatial locations of filling stations within the study area\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4541872/v1/74897634f0c25f4343df4963.jpeg"},{"id":59485221,"identity":"222b9c5f-0f9a-4fb2-ab0f-61ba2238bbff","added_by":"auto","created_at":"2024-07-02 10:55:19","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":240086,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e3.2: 15 metres Buffer of filling station locations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Researcher’s GIS/Remote sensing analysis, (2024)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4541872/v1/ecc8525a0206c51570171a8e.jpeg"},{"id":62141344,"identity":"65293de9-2dea-4ad3-90b8-7f3acb8efe66","added_by":"auto","created_at":"2024-08-09 17:20:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1069013,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4541872/v1/d7dc16aa-8b8a-469f-87ac-366dc1f8974c.pdf"},{"id":59485220,"identity":"249f86fb-ec9c-410e-917f-abc386d6f831","added_by":"auto","created_at":"2024-07-02 10:55:19","extension":"jpeg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":159623,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePLATE 3.1\u003c/strong\u003e: AIR QUALITY READER.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4541872/v1/9384495ed7af27fc7b6efd7e.jpeg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatial Distribution of Energy Stations and Monitoring of Air Quality in Port Harcourt, Rivers State, Nigeria.","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAir pollution can directly or indirectly affect human health, causing physical discomfort and leading to disease or even death. Studies have shown that when the human body is exposed to highly polluted air for a long time, the mortality rate increases. (Tharby, R \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). With rapid development of the economy and booming population growth, an enormous number of resources (e.g, energy, water, and food) is required in our society to sustain our activities. As a result, various kinds of pollution have been produced. Among the various pollution problems, air pollution has caused major concern over the world due to its widespread nature, damage to our environment and potential health risk to humans. Although concern has been raised regarding the emission of air pollutants from anthropogenic sources, our society still relies heavily on fossil fuels for various applications such as electricity generation, transportation, industrial and domestic heating, and so on. An obvious result of this is the deterioration of air quality, particularly in developing countries. Air pollution has become a public concerned problem in modern metropolises. (Seinfeld, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). Air pollution may be defined as any atmospheric condition in which certain substances are present in such concentrations that they can produce harmful effects on man and his environment. Unpolluted air on the other hand is a vital requirement for the growth and survival of all living species. The life expectancy of both man and animal species within a giving environment is reliant on the quality of air present (PSMAG, 2018). Of worthy to note, humans require oxygen for efficient respiration in other to facilitate and enhance metabolic activities. Air is a vital component for the sustenance of all life forms on earth, thereby, making air an indispensable tool for the survival of man. A good air is supposed to be free and safe for human inhalation. Unfortunately, due to urbanization, and the introduction of several anthropogenic activities such as the sales of premium motor spirit (PMS), which has contributed to the contamination of the ambient outdoor air quality thereby posing serious health risks concern (Cavanagh et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). According to a report by Shola (2018), the increasing urban population in Nigeria and her dependence on PMS exhibits a heightened vulnerability human health. As stated by Power et al. (2018), the detrimental effects of air pollution negatively affect human health, ecosystems, and the overall biosphere. Air pollution poses a significant concern in developing nations characterized by the presence of unregulated industries such as filling stations. Air pollutants such as particulate matter (PM 10 \u0026amp; PM 2.5), carbon monoxide (Co), Nitrogen Dioxide (No2), ground level ozone (O3), and sulfur dioxide (S02) have detrimental effects on the well-being and physiological stability of people and other organisms. Moreover, the World Health Organization (WHO) has consistently emphasized the role of air pollution in the prevalence and intensifying diseases such as tuberculosis, asthma, cancer, as well as respiratory and dermatological ailments. These health conditions have resulted in a higher mortality rate compared to AIDS, as reported by Mehta et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn a study conducted in 2012 by the World Health Organization (WHO), it was stated that around 10% of the global population, equivalent to 7\u0026nbsp;million individuals, succumbed to mortality because of air pollution (WHO, 2014). In 2019, the global yearly mortality rate reached 2.9\u0026nbsp;million individuals, a statistic predominantly observed in developing nations, accounting for almost 85% of the total deaths (World Health Organization, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Of worthy to note, one of the major contributors to air pollution is fumes generated from vehicles at various filling stations at the purchase of PMS, gases releases from the nozzles from pumps at filling stations etc which affect air quality and, in most cases, leads to mortality (Mehta: 2013; Obanya: 2018; Ogundipe: 2018; Alani: 2019) listed emission of fumes from vehicles as a major pollutant of air quality in filling stations which causes difficulty in breathing, wheezing, and sneezing. Notably, this is as result of the concentration of gases that has engulfed the filling station environment. This problem is synonymous across all filling stations. As stated by Ashmore, (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), it is also suspected that industrialization and the presence of various business centres that emits carbons through from their daily activities could be responsible for the indiscriminate emissions of the obnoxious gases. This in return could pose a serious health effect to the staff and those doing business in the filling station vicinity. To ascertain the air quality within the various energy stations, the distances of the locations of the energy stations were examined to verify if it falls within the permissible limit as specified by DPR (Department of Petroleum Resources).\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study area is situated within Port Harcourt metropolis which is made up of Obio-Akpor LGA and PHALGA. It is situated between 4\u0026deg;42 and 4\u0026deg;52\u0026apos; North and between latitudes 6\u0026deg;53\u0026apos; and 7\u0026deg;08\u0026apos; East. It is in the Niger Delta region. Port Harcourt is often regarded as the garden city of Nigeria, it was established by the British Colonial administration under Lord Lugard to meet the pressing economic needs of the Europeans. The study area is bounded by Obio-Akpor to the West, Eleme to the East, Oyigbo to the North and Degema LGA to the South. \u0026nbsp;See \u003cstrong\u003e(fig 3.1\u003c/strong\u003e). The study area is highly characterized by the presence of filling stations across the study area. Due to the urbanization in the study area, it is characterized by several filling stations and with associated business centres which is likely to influence the air quality in these areas. \u0026nbsp;It is characterized by alternate wet and dry season. (Hoje, 1972) with annual total rainfall of between 160mm and 294mm; relative humility of over 90% and mean temperature of 27\u003csup\u003eo\u003c/sup\u003ec. The detailed geology of the area has been described by Allen (1965), Rayment (1965), short and stauble (1967). The local geology of the area consists of the stratified sediments starting with Benin Formation underlain by the Miocene Agbada formation and under compacted Akata formation respectively (Short and Stauble, 1967). The Benin formation consists of massive highly porous sands and gravels of fluviatile origin. Agbata formation is also of Eocene-recent in age, and it consists of an admixture of inter-bedded sands, which are fluviatile coastal, fluvio-marine and shale in origin. Akata formation is also predominately shale or clay but is relatively under compacted. (Ehirim et al, 2009).\u003c/p\u003e\n\u003cp\u003eThe mean maximum temperature all the year round in the study area is 30\u003csup\u003eo\u003c/sup\u003eC. The dry season months of February, March, and April record the highest mean diurnal temperature for the period of ten years (1985-1994), the month of March recorded 36\u003csup\u003eo\u003c/sup\u003eC while February recorded 35\u003csup\u003eO\u003c/sup\u003ec1\u003csup\u003eO\u003c/sup\u003ec. The temperature within the Study area is 33\u003csup\u003eO\u003c/sup\u003ec.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig 3.1: Study Area.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeospatial locations of filling stations within the study area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eType of data/Method of Data Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData used for this study is the primary data. The primary data is the data that is gotten directly from the field through direct field measurements. This involves the use of field instruments to directly collect the sample for investigation. The various coordinate points were also collected with the use of hand-held GPS (Global Positioning System) device. The distances from the major road to the filling station locations were collected in the entire study area across the various filling stations sampled with a hand-held tape. Also, the\u0026nbsp;air quality data were examined to ascertain their concentration levels in the atmosphere. The following pollutants were examined to ascertain their presence and concentrations. They are\u0026nbsp;PM 10, PM 2.5, carbon monoxide (Co), Nitrogen Dioxide (No2), ground level ozone (O3), and sulfur dioxide (S02). The air quality reader (AERO-QUAL 500 SERIES) was utilized in the data collection. The data was collected at different times of the day; 10am, 12pm and 5pm respectively. This is done in accordance with the methods specified by\u0026nbsp;Francis Tuluri, Amit Kr. Gorai, Aaron James\u003csup\u003e\u0026nbsp;\u003c/sup\u003e(2007). The air quality instruments is specified below.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePLATE 3.1\u003c/strong\u003e: AIR QUALITY READER.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.1:\u003c/strong\u003e Coordinate Locations of sample energy stations.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"578\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.304498269896193%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eS/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.083044982698965%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSample Places.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.916955017301039%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDistance(s)\u003c/p\u003e\n \u003cp\u003eMetres\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.69550173010381%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Coordinate points\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.20746887966805%\" valign=\"top\"\u003e\n \u003cp\u003eLongitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.79253112033195%\" valign=\"top\"\u003e\n \u003cp\u003eLatitude\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.304498269896193%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.083044982698965%\" valign=\"top\"\u003e\n \u003cp\u003eNNPC Filling Station\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.916955017301039%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.93425605536332%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.039777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.761245674740483%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.856064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.304498269896193%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.083044982698965%\" valign=\"top\"\u003e\n \u003cp\u003eTOTAL Filling Station\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.916955017301039%\" valign=\"top\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.93425605536332%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.048483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.761245674740483%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.845983\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.304498269896193%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.083044982698965%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;CONOIL Filling Station\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.916955017301039%\" valign=\"top\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.93425605536332%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.027405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.761245674740483%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.837277\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.304498269896193%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.083044982698965%\" valign=\"top\"\u003e\n \u003cp\u003eAP Filling Station\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.916955017301039%\" valign=\"top\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.93425605536332%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.002662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.761245674740483%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.832237\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.304498269896193%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.083044982698965%\" valign=\"top\"\u003e\n \u003cp\u003eMRS Filling Station\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.916955017301039%\" valign=\"top\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.93425605536332%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.009077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.761245674740483%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.815741\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.304498269896193%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.083044982698965%\" valign=\"top\"\u003e\n \u003cp\u003eForte Oil filling station\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.916955017301039%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.93425605536332%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.00816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.761245674740483%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.800162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.304498269896193%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.083044982698965%\" valign=\"top\"\u003e\n \u003cp\u003eEvergreen Field Oil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.916955017301039%\" valign=\"top\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.93425605536332%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.004037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.761245674740483%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.793289\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.304498269896193%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.083044982698965%\" valign=\"top\"\u003e\n \u003cp\u003eMobil Filing Station\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.916955017301039%\" valign=\"top\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.93425605536332%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.009077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.761245674740483%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.785041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.304498269896193%\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.083044982698965%\" valign=\"top\"\u003e\n \u003cp\u003eStage Oil filling station \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.916955017301039%\" valign=\"top\"\u003e\n \u003cp\u003e0.05\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.93425605536332%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.016866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.761245674740483%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.765338\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.38754325259516%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; DPR Standard (Decree)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.916955017301039%\" valign=\"top\"\u003e\n \u003cp\u003e15 (Metres)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.69550173010381%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e: Researchers field Work, (2024)\u003c/p\u003e\n\u003cp\u003eThe table depicts the various coordinate locations of the nine (9) filling stations in the study area. Field measurements were recorded for the various distances in metres to ascertain the locations of the various filling stations away from the major roads in the study area. DPR is the sole regulatory agency that spearheads the activities of filling stations in Nigeria. They are the regulatory agency saddled with the responsibility of siting of filling stations across Nigeria. Furthermore, the latitude and longitude of the various filling stations were also recorded accordingly. As specified by DPR, (2016), all filling stations must be situated within the distance of 15metres from the road. Out of the identified energy stations, three (3) major marketers were used in the study to ascertain the quality of air. However, the various distances across the nine (9) energy stations was used in the study to also verify their distances if it falls within the permissible limit as specified by DPR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod of Data Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe various air quality data from different stations was investigated to ascertain the station that has the highest pollutants concentration using\u0026nbsp;descriptive statistics (mean, and standard deviation). The result was further used to ascertain if it falls within NESREA\u0026nbsp;(National Environmental Standards and Regulations Enforcement Agency),\u0026nbsp;permissible limit for out-door air quality. while the one-way analysis of variance (ANOVA) and probability level set at (P \u0026lt; 0.05) was considered to indicate statistical significance. This was done to ascertain if statistically, there exist a significant variation in the air quality across the filling stations within the study area.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig 3.2:\u0026nbsp;\u003c/strong\u003e15 metres Buffer of filling station locations\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource:\u0026nbsp;\u003c/strong\u003eResearcher\u0026rsquo;s GIS/Remote sensing analysis, (2024)\u003c/p\u003e\n\u003cp\u003eThe map presented in (Fig 3.2) is a 15 metres buffer analysis of the various filling station locations in the study area. From the result of the analysis as presented in the map above, it is obvious that all the filling stations did not adhere to the DPR standard (decree) which states that the actual distance from the main road to the location of any filling station must be 15 metres. Interestingly, the petroleum filling station amendment decree no. 37 of 1977 safety rules and regulations stipulate site inspection by DPR of proposed filling station, among other things, issue report on the following basic requirements: -\u003c/p\u003e\n\u003cp\u003e(i )Size of the proposed land site.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(ii) Whether site lie within pipeline or electricity high tension cable Right of Way (ROW).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(iii) Distance from the edge of the road to the nearest pump (not less than 15 meters).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(iv) The number of petrol stations within 2km stretch of the site on both sides of the road will not be more than four, including the one under consideration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(v) The distance between an existing station and the proposed one will not be less than 400 (four hundred) meters.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(vi) The drainage from the site will not go into a stream or river.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(vii) In some instances where site is along Federal Highway, a letter of consent from the Federal Highway is required.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(viii) DPR guided/supervised EIA study of the site by DPR accredited consultant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study is concerned with regulation number (iii) which state that:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ea) The distance from the edge of the road to the nearest pump will not be less than 15 meters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePresentation of Air Quality Results Across Major Filling Stations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003eTable 3.2: NNPC FILLING STATION\u0026nbsp;\u003c/strong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"597\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.40033500837521%\" valign=\"top\"\u003e\n \u003cp\u003epollutants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.217755443886098%\" valign=\"top\"\u003e\n \u003cp\u003eS.I Unit\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.39530988274707%\" valign=\"top\"\u003e\n \u003cp\u003eMorning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.23283082077052%\" valign=\"top\"\u003e\n \u003cp\u003eAfternoon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.39530988274707%\" valign=\"top\"\u003e\n \u003cp\u003eEvening\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.56281407035176%\" valign=\"top\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.90284757118928%\" valign=\"top\"\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.892797319932999%\" valign=\"top\"\u003e\n \u003cp\u003eNESREA\u003c/p\u003e\n \u003cp\u003eStandard\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.40033500837521%\" valign=\"top\"\u003e\n \u003cp\u003eCo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.217755443886098%\" valign=\"top\"\u003e\n \u003cp\u003e(ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.39530988274707%\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.23283082077052%\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.39530988274707%\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.56281407035176%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.90284757118928%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.294618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.892797319932999%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.40033500837521%\" valign=\"top\"\u003e\n \u003cp\u003eSO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.217755443886098%\" valign=\"top\"\u003e\n \u003cp\u003e(ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.39530988274707%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.23283082077052%\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.39530988274707%\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.56281407035176%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.586667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.90284757118928%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.458839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.892797319932999%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.40033500837521%\" valign=\"top\"\u003e\n \u003cp\u003e03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.217755443886098%\" valign=\"top\"\u003e\n \u003cp\u003e(ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.39530988274707%\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.23283082077052%\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.39530988274707%\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.56281407035176%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.124333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.90284757118928%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.169488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.892797319932999%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.40033500837521%\" valign=\"top\"\u003e\n \u003cp\u003ePM2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.217755443886098%\" valign=\"top\"\u003e\n \u003cp\u003e(ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.39530988274707%\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.23283082077052%\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.39530988274707%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.56281407035176%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.006333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.90284757118928%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.002517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.892797319932999%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.40033500837521%\" valign=\"top\"\u003e\n \u003cp\u003ePM10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.217755443886098%\" valign=\"top\"\u003e\n \u003cp\u003e(ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.39530988274707%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.23283082077052%\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.39530988274707%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.56281407035176%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.005333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.90284757118928%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.003215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.892797319932999%\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.40033500837521%\" valign=\"top\"\u003e\n \u003cp\u003eCO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.217755443886098%\" valign=\"top\"\u003e\n \u003cp\u003e(ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.39530988274707%\"\u003e\n \u003cp\u003e994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.23283082077052%\"\u003e\n \u003cp\u003e1025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.39530988274707%\"\u003e\n \u003cp\u003e933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.56281407035176%\" valign=\"bottom\"\u003e\n \u003cp\u003e984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.90284757118928%\" valign=\"bottom\"\u003e\n \u003cp\u003e46.80812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.892797319932999%\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eSource: Researchers Filed Work, (2024)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe result above reveals the various parameters and the associated results as presented in table (3.1). Six parameters were investigated in the different times of the day (10am, 1pm and 5pm). This was done to ascertain the time that has the highest concentration of the gas. Cabon monoxide was absent in the morning while sulfur dioxide was minute with a value of 0.01, ground level ozone was absent also. Pm2.5 has a value of 0.008 while PM10 has a value of 0.010. However, carbon-dioxide was present with a value of 1077. In the afternoon, both Cabon monoxide, sulfur dioxide and ozone were absent while PM2.5 and PM10 has a value of 0.002 and 0.001 which is lesser than the value in the morning and carbon-dioxide was 1025. In the evening, carbon monoxide was 1.8 while sulfur-dioxide was absent. Also, ozone was not present while PM2.5 and PM10 has same reading of 0.003 respectively. Quite noticeable, carbon dioxide has a value of 1099. This is a clear indication that carbon is more concentrated in the evening than any other time of the day within NNPC filling station.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.3: TOTAL FILLING STATION\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"626\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.535031847133759%\" valign=\"top\"\u003e\n \u003cp\u003epollutants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.35031847133758%\" valign=\"top\"\u003e\n \u003cp\u003eS.I Unit\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\" valign=\"top\"\u003e\n \u003cp\u003eMorning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171974522292993%\" valign=\"top\"\u003e\n \u003cp\u003eAfternoon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.579617834394904%\" valign=\"top\"\u003e\n \u003cp\u003eEvening\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" valign=\"top\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.853503184713375%\" valign=\"top\"\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.464968152866241%\" valign=\"top\"\u003e\n \u003cp\u003eNESREA\u003c/p\u003e\n \u003cp\u003eStandard\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.535031847133759%\" valign=\"top\"\u003e\n \u003cp\u003eCo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.35031847133758%\" valign=\"top\"\u003e\n \u003cp\u003e(ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\" valign=\"top\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171974522292993%\" valign=\"top\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.579617834394904%\" valign=\"top\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.853503184713375%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.464968152866241%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.535031847133759%\" valign=\"top\"\u003e\n \u003cp\u003eSO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.35031847133758%\" valign=\"top\"\u003e\n \u003cp\u003e(ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\" valign=\"top\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171974522292993%\" valign=\"top\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.579617834394904%\" valign=\"top\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.853503184713375%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.173205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.464968152866241%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.535031847133759%\" valign=\"top\"\u003e\n \u003cp\u003e03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.35031847133758%\" valign=\"top\"\u003e\n \u003cp\u003e(ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\" valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171974522292993%\" valign=\"top\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.579617834394904%\" valign=\"top\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.028333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.853503184713375%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.004726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.464968152866241%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.535031847133759%\" valign=\"top\"\u003e\n \u003cp\u003ePM2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.35031847133758%\" valign=\"top\"\u003e\n \u003cp\u003e(ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171974522292993%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.579617834394904%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.853503184713375%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.464968152866241%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.535031847133759%\" valign=\"top\"\u003e\n \u003cp\u003ePM10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.35031847133758%\" valign=\"top\"\u003e\n \u003cp\u003e(ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171974522292993%\" valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.579617834394904%\" valign=\"top\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.853503184713375%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.464968152866241%\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.535031847133759%\" valign=\"top\"\u003e\n \u003cp\u003eCO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.35031847133758%\" valign=\"top\"\u003e\n \u003cp\u003e(ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.305732484076433%\" valign=\"top\"\u003e\n \u003cp\u003e994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.171974522292993%\" valign=\"top\"\u003e\n \u003cp\u003e1025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.579617834394904%\" valign=\"top\"\u003e\n \u003cp\u003e933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.738853503184714%\" valign=\"bottom\"\u003e\n \u003cp\u003e984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.853503184713375%\" valign=\"bottom\"\u003e\n \u003cp\u003e46.80812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.464968152866241%\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Researchers Filed Work, (2024)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe result presented in table (3.2) is the air quality result of Total Filling State. Six air quality parameters were tested at different times of the day (10am, 1pm and 5pm). This was done to ascertain if there are presence of any of them and in what quantity. However, it was discovered that Carbon monoxide (CO) which is a colourless, odourless gas which also results from incomplete combustion and is emitted by a wide variety of combustion sources, including motor vehicles was present with a value of 0.8 (ppm) while sulfur dioxide was absent, however, ground level ozone was present with a value of 0.03 (ppm). Remarkably, it was discovered that PM2.5 has a value of 0.002 while PM10 has a value of 0.001 while carbon-dioxide was present with a value of 1051. In the afternoon, both Cabon monoxide, sulphur-dioxide was absent. Ground level Ozone was present with a value of 0.012 while PM2.5 and PM10 has a value of 0.002 and 0.002 respectively. However, carbon-monoxide was 998. In the evening, carbon monoxide was 1.0 while sulphur-dioxide was 0.1 while ground level ozone was absent. Coincidentally, PM2.5 and PM10 has same reading of 0.002. \u0026nbsp;Carbon monoxide (CO) is a colourless, odourless gas which results from the incomplete combustion and is emitted by a wide variety of combustion sources, including motor vehicles etc. has a value of 1016 which is far higher than the morning and afternoon.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.4: CONOIL FILLING STATION\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"627\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.556618819776714%\" valign=\"top\"\u003e\n \u003cp\u003epollutants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.36682615629984%\" valign=\"top\"\u003e\n \u003cp\u003eS.I Unit\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.64274322169059%\" valign=\"top\"\u003e\n \u003cp\u003eMorning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.354066985645932%\" valign=\"top\"\u003e\n \u003cp\u003eAfternoon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.91866028708134%\" valign=\"top\"\u003e\n \u003cp\u003eEvening\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.64274322169059%\" valign=\"top\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.035087719298245%\" valign=\"top\"\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.483253588516746%\" valign=\"top\"\u003e\n \u003cp\u003eNESREA\u003c/p\u003e\n \u003cp\u003eStandard\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.556618819776714%\" valign=\"top\"\u003e\n \u003cp\u003eCo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.36682615629984%\" valign=\"top\"\u003e\n \u003cp\u003e(ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.64274322169059%\" valign=\"top\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.354066985645932%\" valign=\"top\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.91866028708134%\" valign=\"top\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.64274322169059%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.866667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.035087719298245%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.832666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.483253588516746%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.556618819776714%\" valign=\"top\"\u003e\n \u003cp\u003eSO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.36682615629984%\" valign=\"top\"\u003e\n \u003cp\u003e(ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.64274322169059%\" valign=\"top\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.354066985645932%\" valign=\"top\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.91866028708134%\" valign=\"top\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.64274322169059%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.666667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.035087719298245%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.208167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.483253588516746%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.556618819776714%\" valign=\"top\"\u003e\n \u003cp\u003e03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.36682615629984%\" valign=\"top\"\u003e\n \u003cp\u003e(ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.64274322169059%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.354066985645932%\" valign=\"top\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.91866028708134%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.64274322169059%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.035087719298245%\" valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.483253588516746%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.556618819776714%\" valign=\"top\"\u003e\n \u003cp\u003ePM2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.36682615629984%\" valign=\"top\"\u003e\n \u003cp\u003e(ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.64274322169059%\" valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.354066985645932%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.91866028708134%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.64274322169059%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.004333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.035087719298245%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.003215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.483253588516746%\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.556618819776714%\" valign=\"top\"\u003e\n \u003cp\u003ePM10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.36682615629984%\" valign=\"top\"\u003e\n \u003cp\u003e(ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.64274322169059%\" valign=\"top\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.354066985645932%\" valign=\"top\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.91866028708134%\" valign=\"top\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.64274322169059%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.013667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.035087719298245%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.004726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.483253588516746%\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.556618819776714%\" valign=\"top\"\u003e\n \u003cp\u003eCO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.36682615629984%\" valign=\"top\"\u003e\n \u003cp\u003e(ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.64274322169059%\" valign=\"top\"\u003e\n \u003cp\u003e1077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.354066985645932%\" valign=\"top\"\u003e\n \u003cp\u003e1025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.91866028708134%\" valign=\"top\"\u003e\n \u003cp\u003e1099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.64274322169059%\" valign=\"bottom\"\u003e\n \u003cp\u003e1067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.035087719298245%\" valign=\"bottom\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.483253588516746%\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Researchers Filed Work, (2024\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe table above shows the results of the air quality as presented in table (3.3). air quality data was collected from Mobil filling station along Port Harcourt Aba express way. However, it was discovered that Carbon monoxide was present with a value of 56.4, sulfur oxide was present with a value of 4.5 while ozone was absent. PM2.5 has a value of 0.008 while PM10 has a value of 0.007. Carbon dioxide has a value of 1121. In the afternoon, carbon monoxide has decline with a value of 37.5 and sulphur-oxide was totally absent alongside ozone. PM2.5 and PM10 was read at the same value of 0.006 and 0.008 respectively. Carbon dioxide was present with a value of 989. In the evening, both carbon monoxide, sulfur oxide and ozone were absent. This could be because of reduced or absent of social activities. PM2.5 and PM10 was 0.006 and 0.012. Carbon oxide was present with a value of 976 which is lower than the morning and afternoon session. This is possible because of the reduced activities within the filling station during that time of the day.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePermissibility of air quality as specified by NESREA.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003eTable 3.4: Standard For Air Quality from Industrial Sources/Operations\u0026nbsp;\u003c/strong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"474\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003eS/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.037974683544306%\" valign=\"top\"\u003e\n \u003cp\u003ePOLLUTANTS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.835443037974684%\" valign=\"top\"\u003e\n \u003cp\u003eMaximum Permissible Limits (ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.037974683544306%\" valign=\"top\"\u003e\n \u003cp\u003eCo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.835443037974684%\" valign=\"top\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.037974683544306%\" valign=\"top\"\u003e\n \u003cp\u003eSO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.835443037974684%\" valign=\"top\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.037974683544306%\" valign=\"top\"\u003e\n \u003cp\u003e03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.835443037974684%\" valign=\"top\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.037974683544306%\" valign=\"top\"\u003e\n \u003cp\u003ePM2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.835443037974684%\" valign=\"top\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.037974683544306%\" valign=\"top\"\u003e\n \u003cp\u003ePM10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.835443037974684%\" valign=\"top\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.126582278481013%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"43.037974683544306%\" valign=\"top\"\u003e\n \u003cp\u003eCO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.835443037974684%\" valign=\"top\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSource: NESREA, (2020).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe table depicts the air quality permissible limit as specified by NESREA (National Environmental Standards and Regulations Enforcement Agency). In comparison to the measured air quality within the major filling stations along Port Harcourt Aba Express way. As specified by NESREA, Carbon monoxide (CO) which is a colourless, odourless gas which also results from incomplete combustion and is emitted by a wide variety of combustion sources, including motor vehicles should not exceed 0.06 (ppm). Also, \u003cstrong\u003eSulfur dioxide (SO\u003csub\u003e2\u003c/sub\u003e) which\u003c/strong\u003e is an colourless gas with a sharp odour. It is produced from the burning of fossil fuels (coal and oil) and the smelting of mineral ores that contain sulfur, NESREA specified that its concentration should not exceed 0.05 (ppm), furthermore, Ozone at ground level, \u0026nbsp;not to be confused with the ozone layer in the upper atmosphere \u0026nbsp;which is one of the major constituents of photochemical smog and it is formed through the reaction with gases in the presence of sunlight should not exceed 0.06 (ppm). Particulate matter (PM) PM 2.5 and PM 10 which is a common proxy indicator for air pollution. There is strong evidence for the negative health impacts associated with exposure to this pollutant. The major components of PM are sulfates, nitrates, ammonia, sodium chloride, black carbon, mineral dust, and water. NESREA specified that its concentrations should not exceed 0.2 and 0.4 (ppm) respectively. Finally, Carbon dioxide which is an important chemical needed for the survival of all life forms in the environment was also analysed to ascertain its concentration. The permissible limit as specified by NESREA is 400 (ppm). At high level, this pollutant becomes dangerous to human.\u003c/p\u003e"},{"header":"Findings","content":"\u003cp\u003eCarbon dioxide is an important greenhouse gas, it is a by-product of the burning of fossil fuel with a half-life of 50-200 years and global warming potential (Gattuso, and Hansson 2011). CO2 at high levels above permissible limits may result in environmental hazards such as ocean acidification. According to NESREA, the minimum permissible limit for CO2 is 400 (ppm), once it goes higher, it becomes hazardous. The values of CO2 recorded across the different sample locations were higher than the standard given by NESREA (2020). The highest concentration of CO2 at 1521 ppm was obtained at Coinoil filling station. The high rate of CO2 emission in the study area is as a result burning of in activities that goes on within the environment, the high influx of vehicles buying fuel and the fumes from their exhaust pipes, also from business generators, heavy duty trucks etc. This result agrees with the works of Tse and Oguama (2014) who also record high levels of CO2 concentration in their study. Nitrogen dioxide is an important environmental pollutant. It is introduced into the air through gas stoves; it causes photochernical smog at high concentrations as well as other health effects such pulmonary edema and hemorrhage (Searl, 2004). This is because of the high rate of vehicular emissions from the cars and motor bikes which are constantly on the move. Indeed, motor vehicles produce more pollution than any other single human activities (Ukemenam, 2014). Tse et al (2014) also recorded high concentrations of CO around commercial areas in their study and at filling stations. Carbon monoxide is a colorless, odourless, and tasteless gas toxic gas which is produced from the incomplete combustion of fossil fuel in generators and automobiles. Exposures to carbon monoxide may cause significant damage to the heart and central nervous system (Kampa, and Castanas, 2008). Pollution due to traffic constitutes 90-95% of the ambient CO levels pose a serious threat to human health (Uyigue and Agho, 2007). The risk of the fuel pump attendants who inhale this gas in a daily basis is quite high and may have long term negative health consequences. The highest concentration of SO2 was recorded as the various sampled energy stations with a value of 0.9 (ppm) and it is highly concentrated in the afternoon. This is because of emission from the fleet of cars that come in and go out to purchase fuel. This finding is in tandem with the results of Tse et al (2014) who also recorded high levels of SO2 in filling stations in their study. Sulphur dioxide (SO2) is an environmental pollutant and the main component of acid decomposition. SO2 is emitted directly into the atmosphere from sources such as coal and oil power plants, oil refineries smelters, generators and automobiles and can remain suspended for days allowing wide distribution of the pollutant (Ukernenam, 2014). Excess concentration of S02 can lead to respiratory problems, severe headache, irritating lungs, and damage to vegetation. It can also cause increased rate of corrosion of Iron, Zinc, Steel, and aluminum (Anderson, 2005). Also, they were presence of particulate matter (PM2.5 \u0026amp; PM 10) at the various energy stations. Although their values were within the permissible limit as specified by NESREA. Other particulates recorded lower concentration of pollutants in many of the samples. PM2.5 is of a more serious health concern since smaller particles can travel more deeply into our lungs and cause more harmful effects. This result also agrees with the findings of Tse et al, (2014) that compared the indoor and outdoor air quality as it relates to particulate matter within business environment and residential areas and found the concentration of particulates to be higher outdoor. The air quality within the study area can be said to be poor with high concentration of pollutants that deter the quality of air within the various filling stations. \u0026nbsp;Air quality monitoring helps to assess the quality of air within an environment. It is an activity that must be carried out continuously at intervals; not just for the sake of measurement but to ensure that steps are taken to reduce the act of air pollution to the barest minimum. Particulate matter is the sum of all solid and liquid particles suspended. Atmospheric suspended particles which cause impairment of visibility (Dayan and Levy, 2005). The reduction of visibility is caused by buildup of the atmospheric particles. Particulates are the deadliest form of air pollution due to their ability to penetrate deep into the lungs and blood streams unfiltered, causing permanent DNA mutations, heart attacks, asthma, cough catarrh, chronic bronchitis, and premature death.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study has reveals that, majority of the filling stations along the Port-Harcourt ABA express way is not situated in 15 metres away from the major road as specified by DPR. This is a death trap for commuters as any activity that result to fire outbreak will cause a lot of harm and loss of lives and properties. The study also concludes the air quality within the various energy stations is polluted, but amongst other pollutants as observed, CO2 is the major air pollutants with values higher than NESREA standards for outdoor air quality.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eKponi, barinedum valentine wrote the manuscript and produced the associated maps. Enyinda, Chike took the field data for each of the energy stations and their coordinate locations. Kpang, Meelubari Barinua produced the various tables, he also carried out a thorough reading of the entire work and corrections was made by him where necessary.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe acknowledge the support of Mrs. Precious N. Barinedum-Kponi for her field assistance and Dr. I.C Ezekwe for his encouragement . Also, we wish to thank Dr. Uchege Friday for his guidance.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbaje IB, Bello Y, Ahmad SA (2020) A review of air quality and concentrations of air pollutants in Nigeria. J Appl Sci Environ Manage 24(2):373\u0026ndash;379\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlghodhaifi H, Lakshmanan S (2020) May). Simulation-based model for surrogate safety measures analysis in automated vehicle-pedestrian conflict on an urban environment. Autonomous Systems: Sensors, Processing, and Security for Vehicles and Infrastructure 2020, vol 11415. SPIE, pp 8\u0026ndash;21\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnderson AK (2005) Affective influences on the attentional dynamics supporting awareness. J Exp Psychol Gen 134(2):258\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAshmore MR (2005) Assessing the future global impacts of ozone on vegetation. Plant Cell Environ 28(8):949\u0026ndash;964\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCavanagh JAE, Zawar-Reza P, Wilson JG (2009) Spatial attenuation of ambient particulate matter air pollution within an urbanised native forest patch. Urban Forestry Urban Green 8(1):21\u0026ndash;30\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDayan U, Levy I (2005) The influence of meteorological conditions and atmospheric circulation types on PM10 and visibility in Tel Aviv. J Appl Meteorol Climatology 44(5):606\u0026ndash;619\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrancis Tuluri AKG, James A Hotspot Analysis For Examining The Association Between Spatial Air Pollutants And Asthma In New York State, USA Using Kernel Density Estimation (KDE)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGattuso JP, Kirkwood W, Barry JP, Cox E, Gazeau F, Hansson L, Brewer PG (2014) Free-ocean CO 2 enrichment (FOCE) systems: present status and future developments. Biogeosciences 11(15):4057\u0026ndash;4075\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKampa M, Castanas E (2008) Human health effects of air pollution. Environ Pollut 151(2):362\u0026ndash;367\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMehta SB, Cornell D, Fan X, Gregory A (2013) Bullying climate and school engagement in ninth-grade students. J Sch Health 83(1):45\u0026ndash;52\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eObanya HE, Amaeze NH, Togunde O, Otitoloju AA (2018) Air pollution monitoring around residential and transportation sector locations in Lagos Mainland. J Health Pollution 8(19):180903\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSearl A (2004) A review of the acute and long term impacts of exposure to nitrogen dioxide in the United Kingdom. Edinburgh: Inst Occup Med, 1\u0026ndash;196\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeinfeld JH (1986) ES\u0026amp;T books: atmospheric chemistry and physics of air pollution. Environ Sci Technol 20(9):863\u0026ndash;863\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTse AC, Oguama AC (2014) Air quality in parts of the University of Port Harcourt, rivers state. Scientia Africana, \u003cem\u003e13\u003c/em\u003e(1)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTharby R (2002) Catching gasoline and diesel adulteration, vol 24743. The World Bank, pp 1\u0026ndash;4\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO, G. S (2014) Global status report on noncommunicable diseases 2010\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization (2016) World Health Statistics 2016 [OP]: Monitoring Health for the Sustainable Development Goals (SDGs). World Health Organization\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Plate","content":"\u003cp\u003ePlate 3.1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Geospatial Analysis, Energy station locations, Ambient Air Quality","lastPublishedDoi":"10.21203/rs.3.rs-4541872/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4541872/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the geospatial locations of energy stations within Port-Harcourt Aba expressway to ascertain their locations if it conforms to standard practice. It also took into cognizance the in-situ monitoring of the various air pollutants within the vicinity of the energy stations. The study location is in Obio/Akpor and Port Harcourt city Local Government Area, Rivers State. The result of the proximity analysis reveals that the energy stations do not fall within the standard of 15metres specified by DPR (Department of Petroleum Resources). The various pollutants examined are PM 10, PM 2.5, carbon monoxide (Co), Nitrogen Dioxide (No2), ground level ozone (O3), and sulphur dioxide (S02). The result reveals that their concentrations values fall within the permissible limit as specified by NESREA except (C02) which is the primary pollutant was extremely highly than the permissible limit of 400 (ppm). This can be attributed to the influx of vehicles that buys fuel from the energy stations. The concentration of (C02) poses a serious health effect on the pump attendants due to their constant inhaling of the pollutants. The study concludes that energy stations should be cited at the approved DPR standard of (15metres) and actionable measures should be put in place to aid in the reduction of CO2 within the energy stations. Finally, the study recommends that there should be strict compliance to DPR standard in the siting of energy stations.\u003c/p\u003e","manuscriptTitle":"Spatial Distribution of Energy Stations and Monitoring of Air Quality in Port Harcourt, Rivers State, Nigeria.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-02 10:55:14","doi":"10.21203/rs.3.rs-4541872/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a7947f71-a865-49b6-ac6a-7ff2caf80357","owner":[],"postedDate":"July 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-09T17:12:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-02 10:55:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4541872","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4541872","identity":"rs-4541872","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}