Geo-spatial mapping of Carbon Dioxide and Carbon Monoxide within the University of Lagos, Nigeria

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Animashaun, Alfred Alademomi, Chukwuma Okolie, Oluwatimileyin Abolaji, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3668485/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 The air quality within academic institutions in Nigeria with highly vulnerable student populations has not received adequate attention. The University of Lagos is located within the highly populated and industrialised state of Lagos, Nigeria. To assess the university’s air quality, the concentrations of Carbon Dioxide (CO 2 ) and Carbon Monoxide (CO) were mapped and evaluated. Data was collected through direct field measurements using handheld gas sensors. The analysis of ambient air quality was done by applying the Exceedance Factor (EF) method where the presence of CO and CO 2 average concentrations are classified into different categories. In addition, the USEPA Air Quality Index rating scale was used to evaluate the ambient air quality with respect to ASHRAE standards, and the pollutant concentration levels in different land use types were assessed. Regarding CO 2 , five air quality monitoring stations were found to be in the “high” category while others were in the “moderate” emission class. For CO, two stations were categorized as “moderate”, and others as “low”. The results show that CO 2 emission is substantial along road corridors in the campus. These findings are valuable to inform researchers, policy makers and other stakeholders on mitigative measures for air quality management in academic institutions. Environmental Chemistry Air Quality Index Air Pollution Carbon dioxide Carbon monoxide World Health Organisation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 I. INTRODUCTION Air is a vital requirement for the growth and survival of all living species. The life expectancy of both humans and animals within an environment is contingent upon the quality of air present (PSMAG, 2018 ). In addition to other organisms, humans require oxygen for efficient respiration in order to facilitate cellular growth and enhance metabolic processes. Air is a vital component for sustaining life, thus making it indispensable for the survival of living beings. Air is naturally safe and clean for humans and animals. Unfortunately, the process of urbanisation and various anthropogenic activities contribute to the contamination of the atmosphere, resulting in its compromised quality and posing health risks for respiration (Cavanagh et al., 2009 ). According to a report by Shola (2018), the increasing urban population in Nigeria exhibits a heightened vulnerability to both indoor and outdoor air pollution. According to Power et al. ( 2018 ), the detrimental effects of air pollution extend to human health, ecosystems, and the overall biosphere. Air pollution poses a significant concern in developing nations characterized by the presence of unregulated industries. Air pollutants such as particulate matter 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 exacerbating 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 ). According to a 2012 study conducted by the World Health Organization (WHO), it was anticipated that around 10% of the global population, equivalent to 7 million individuals, succumbed to mortality as a result 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). The elevated toxicity of exhaust nanoparticles resulting from the expansion of motor vehicle traffic and the rapid process of industrialisation is a significant factor in the occurrence of mortality related to air pollution in countries with lower and middle income levels (Mohan et al., 2013 ). The harmful impacts of air pollution on human health has been on the increase in the developing countries where power/electricity generation is mainly through fossil fuel generators used for both domestic and commercial purposes (WHO 2016, Bernstein et al., 2004). Common pollutants of global concern are carbon dioxide, carbon monoxide, nitrogen oxide, particulate matter, volatile organic compounds, hydrocarbon substances, and sulphur oxide (Bernstein et al 2004). Apart from the damages incurred by humans from air pollution, poor air quality is also a threat to any environment as it can lead to the destruction of forests, crops and lakes, dilapidation of structures and soils as well as the alteration of earth’s radiation stability (Alani, 2019). Several studies have documented the impacts of air pollution on the environment. For instance, Lorenz et al. ( 2010 ) discovered that air pollution could have negative impacts on forests and influence adverse climatic conditions. Furthermore, Gheorghe and Ion ( 2011 ) asserted that fully foliated healthy forests thrive in ecosystems where there is little or no air pollution. Several investigations have identified Nigeria as a country with rapidly deteriorating air quality (Mehta: 2013; Obanya: 2018; Ogundipe: 2018; Alani: 2019). Urbanization, industrialization, population growth, trash burning, inadequate dumpsite management, and unregulated vehicle emissions cause poor air quality (Obanya et al. 2018 ; Yusuf et al., 2013 ; WHO, 2016; Alani, 2019). With its steady transformation into a megacity, Lagos State is a prominent player in Nigeria's fast-growing economy (Alani, 2019). Lagos, like other emerging megacities, is affected by urbanisation and population increase, which affects air quality. Several researchers have examined the relationship between air quality and land use (Soneeye, 2012; Yusuf, 2013; Njoku, 2016; Obanya, 2018; Alani, 2019). Major Lagos corridors like transit, industrial estates, residential districts, dumpsites, tank farms, and others have been mapped for air quality. However, despite the high vehicular emission, emission from incinerations and burning, there is the need for more research on the spatial mapping of CO and CO 2 . Notably, incomplete combustion of carbon-containing fuels like natural gas, gasoline, or wood releases carbon monoxide (CO) from motor vehicles, power plants, wildfires, and incinerators. CO 2 comes mostly from fossil fuels. Deforestation, agricultural land clearing, and soil deterioration can also emit CO 2 , making Lagos State, especially University of Lagos campus a good study area. The University of Lagos environment is densely inhabited in Lagos State and growing in social, and commercial activities. These increases in human activity are affecting air quality, which is detrimental to the health. This study is interested in mapping CO and CO 2 concentrations at the University of Lagos. for sustainable development using Geographical Information System (GIS). II. MATERIALS AND METHODS A. Description of the Study Area The University of Lagos is an academic institution in Lagos State, Nigeria (Fig. 1 ). It is located at approximately, latitudes 6°30’N to 6°31’N and longitudes 3°25’E to 3°27’E in the Lagos Mainland Local Government Area. The main campus which is largely surrounded by the scenic view of the Lagos lagoon and is located on 802 acres (3.25 km 2 ) of land in Akoka, northeastern part of Yaba, Lagos has been chosen as the study area. The focus on the main campus is justifiable based on the level of human activities as described and for the convenience of available geo-spatial data, as well as ease of environmental data collection. B. Station Selection The siting of the monitoring stations has a profound effect on the resulting measurements and on achieving monitoring objectives. Thus, thirty-four (34) monitoring stations shown in Table 1 were set out at strategic locations within the study area such as major road intersections (for vehicular emissions), dumpsites, commercial centers, academic areas, and residential sites (see Fig. 1 ). Table 1 The measurement stations Land use type No. of stations Station ID Academic 9 9, 14, 17, 18, 20, 22, 23, 27, 29 Hospital Outdoor 1 30 Residential 3 3, 6, 31 Commercial/Industrial/Shopping 7 10, 11, 15, 25, 28, 33 Public Outdoor 1 4 Traffic 9 1, 2, 7, 8, 12, 16, 24, 32, 34 Conservation Area 4 13, 19, 21, 26 Recreational 1 5 C. Variables, Equipment and Measurement The evaluated parameters were CO and CO 2 gas which are strong variables for assessing environmental air quality. To measure the concentration levels of these gases, we adopted the use of a very simple device: calibrated hand-held solid-state gas sensor. Compu-Flow CO 2 Handheld meter, a gas monitor designed to provide continuous exposure monitoring of carbon dioxide was used to measure the CO 2 concentration levels in parts per million (ppm). Data was collected over six (6) observation windows within three days of observation. The average value for the pollution parameter readings were carefully recorded. All measurements were executed in 2-hour duplicates (i.e., repeated twice – morning peak period and afternoon off-peak period) over 3 days’ observation period across all monitoring stations. The sampling was done in-situ and each sample station was geo-referenced using Garmin GPSMAP 78SC Marine handheld GPS. The duration for exposure was set at 5 minutes at a time per station. The average values for each of the locations were computed and recorded. The acquired data was transferred to a Microsoft Excel worksheet where it was orderly sorted into rows and columns. Likewise, a point shape file was created in ESRI ArcGIS software using the GPS recorded X, Y coordinates of the monitoring stations. Subsequently, the pollutant data readings were integrated into the records of each point in the shapefile by populating the attribute table accordingly. D. Kriging Interpolation The acquired data was transferred to a Microsoft Excel worksheet and imported into ArcGIS environment where it was represented by point shapefiles. Kriging interpolation (an advanced geostatistical procedure that generates an estimated surface from a scattered set of points with values), was used to generate maps showing the spatial variation of CO and CO 2 . Kriging is a widely used Geostatistical interpolation technique (Zuo et al., 2016 ) that considers autocorrelation (the statistical relationship among measured points) (Kurakula, 2007 ). With kriging, visually appealing models can be created from data that is irregularly spaced (Wojciech, 2018 ). The application of Kriging interpolation for noise mapping has been shown in previous studies (Tsai et al., 2009 ; Rana et al., 2014; Harman et al., 2016 ). The statistical theory of kriging is well established and it can estimate errors point-by-point (Zuo et al., 2016 ). These attributes make it suitable for the present study. E. Quantitative Analysis In monitoring the air quality of any area of interest, the United States Environmental Protection Agency has designed a standardized air pollution level indicator for rating the air quality over an area. It is a rating scale for outdoor air called Air Quality Index (AQI). The lower the AQI value, the better the air quality. AQI rating A stands for Very Good (0–15), B for Good (16–31), C for Moderate (32–49), D for Poor (50–99) and E for Very poor (100 and over) (EPA, 2000 ). The air quality is determined with reference to the standard by the American Society of Heating, Refrigerating and Air Conditioning Engineers (ASHRAE). According to ASHRAE ( 2016 ), the standard for carbon dioxide is 1000ppm. $${AQI}_{POLLUTANT}= \frac{Data}{Standard} \times 100$$ 1 Also, we adopted the Exceedance Factor (EF) analysis which is broadly understood as “the ratio of annual mean concentration of a pollutant with that of a respective standard” $$\text{Exceedance Factor }\text{= }\frac{\text{Observed annual mean concentration of criteria pollutant }}{\text{Annual standard for the respective pollutant and area class}}\text{ }$$ 2 The above formula interpretes four different threshold of air pollution as: low (EF 1.5). III. RESULTS AND DISCUSSION A. Spatial Distribution of CO Table 2 presents the daily variation of the average CO in the different land use types within the University of Lags main campus. The concentration of the CO was found to be highest at the academic areas, for both the morning and afternoon observation periods. This can be attributed to the high influx of vehicles around this area and especially within and around the faculties such as engineering where industrial machines are operational. The traffic corridors, conservation and residential areas also show significant levels of CO. The major sources for the injection of CO into the air in any environment where there is incomplete combustion of carbon are vehicular exhausts and industrial machinery. The content of this CO in the air is one of the factors responsible for respiratory problems which affect both the heart and brain. Figures 3 a and 3 b represent the average CO distribution in the study area for the morning and afternoon periods respectively. Table 2 Daily variation of average CO in the different land uses/environment types (ppm) Day Academic Commercial/ Industrial/ Shopping Conservation Area Hospital Outdoor Public Outdoors Recreational Religious Residential Traffic M1 3.50 0.20 1.00 1.00 0.00 0.00 0.00 0.50 0.00 M2 8.40 2.20 1.25 0.00 0.00 0.00 0.00 1.25 1.14 M3 1.50 1.40 4.00 0.00 0.00 0.00 2.00 0.50 0.71 Ave 4.47 1.27 2.08 0.33 0.00 0.00 0.67 0.75 0.62 A1 1.20 0.40 1.00 0.00 0.00 0.00 0.00 1.25 0.43 A2 1.35 0.30 0.63 0.00 0.00 0.00 0.00 1.00 0.71 A3 1.50 0.20 0.25 0.00 0.00 0.00 0.00 0.75 1.00 Ave 1.35 0.30 0.63 0.00 0.00 0.00 0.00 1.00 0.71 Key: M1 – Morning day 1, M2 – Morning day 2, M3 – Morning day 3; A1 – Afternoon day 1, A2 – Afternoon day 2, M3 – Afternoon day 3 From the morning observation, the CO concentration is generally low in many environments (0–2.40ppm), except at the core traffic areas and some commercial areas within the campus, e.g., Station 2 (Education Car Park − 12.67ppm), Station 1 (University Main Gate − 10.67ppm) and Station 16 (Afe-Babalola Roundabout − 8.00ppm). This pattern of CO distribution was again observed in the afternoon as the concentration levels remained high around the traffic pivots, although not as intense as observed in the morning. However, a striking observation from the afternoon map in Fig. 3 b is the sudden dip in CO levels around the main gate and its environs to about 0.5ppm. Furthermore, Fig. 4 shows the mean CO variation for the entire observation window. Summarily, the concentration level is relatively high at traffic centres, academic environments and commercial areas. In addition, aside from the presence of car parks at faculties, some of these faculties possess laboratories and workshops e.g., the Engineering workshop where sophisticated combustion engines are used, thus leading to an increase in CO levels. Also, numerous cafeterias are situated at the commercial areas and CO is a key integral of the gases emitted during the cooking process. The concentration around residential areas was relatively low. B. Spatial Distribution of CO 2 Table 3 presents the daily variation of the average CO 2 in the different land use types within the University of Lags main campus, while Figs. 5 a and 5 b represent the average CO 2 distribution in the study area for the morning and afternoon periods respectively. The morning observation for all the land use types was generally above 714ppm with traffic, religious, recreational corridors and public outdoor having high average CO 2 content above 1000ppm. The hospital outdoor environment recorded the lowest levels of CO 2 in the morning. The high atmospheric CO 2 concentration at the traffic, religious, recreational corridors and public outdoor environment is possibly due to the volume of vehicular movement in and around these corridors on campus. The concentration of CO 2 in the atmosphere on the third day was also relatively high compared to the other days for both morning and afternoon periods with public outdoor having 1401.00ppm and the hospital outdoor having 1098.00ppm in the morning and afternoon periods respectively. However, the afternoon observation period was generally above 604.00ppm with residential areas having the least. The highest average CO 2 level was observed at the conservation area (871.00ppm), traffic corridor (872.14ppm) and hospital outdoor (933.00ppm) environments. The high CO 2 level experienced at the traffic corridors can also be attributed to vehicular emissions. Conversely, the result at the hospital outdoor in this case was high, and this could be due to the high number of patients. Similarly, the observed daily variation of CO 2 level as presented in Table 3 and clearly depicted in Figs. 5 a and 5 b shows that the public outdoors which include the entrance gates and car parks on campus have the highest CO 2 concentration during the morning period while the the hospital environment has the highest CO 2 level in the afternoon. Table 3 Daily variation of average CO 2 in the different land uses (ppm) Day Academic Commercial/ Industrial/ Shopping Conservation Area Hospital Outdoor Public Outdoors Recreational Religious Residential Traffic M1 824.80 861.40 825.25 714.00 1033.00 819.00 984.00 768.50 905.14 M2 777.10 857.40 870.75 948.00 1159.00 1150.00 1108.00 1104.50 1139.71 M3 1103.00 1174.60 1030.00 1213.00 1264.00 1401.00 1237.00 1109.50 1252.14 Ave 901.63 964.47 908.67 958.33 1152 1123.33 1109.67 994.17 1098.99 A1 752.90 733.00 686.75 768.00 633.00 686.00 758.00 604.00 786.71 A2 821.05 812.20 871.00 933.00 636.50 779.50 845.00 620.50 872.14 A3 889.20 891.40 1055.25 1098.00 640.00 873.00 932.00 637.00 957.57 Ave 821.05 812.2 871 933 636.5 779.5 845 620.5 872.14 Key: M1 – Morning day 1, M2 – Morning day 2, M3 – Morning day 3; A1 – Afternoon day 1, A2 – Afternoon day 2, M3 – Afternoon day 3 Figure 6 presents the spatial variation of CO 2 concentrations across all environments throughout the entire observation duration. Overall, maximum concentrations are observed in the traffic corridors, academic and conservation environments. Again, this can be linked to the intense vehicular movements around the traffic hubs and faculty parks. Also, since CO 2 constitutes 50% of the gas emitted due to decomposition of organic wastes by microorganisms from landfills and canals, this is suspected to be the reason for the high concentrations of CO 2 noticed around the the dumpsites on campus along the International School Road. Another significant observation is the relatively low level of CO 2 around the residential environment which is most likely due to low vehicular movements around there. C. Exceedance Factor Figures 7 and 8 depict the exceedance factors of CO and CO 2 computed for each of the 34 monitoring stations. Evidently, the CO 2 level is rated high at stations 1, 8, 24 and 34, while other stations maintain the moderate level. However, stations 2 and 27 are to be monitored because a small increase in the CO2 generation in these stations will make their exceedance factor to exceed normalcy and that could negatively impact people’s health in such environment. On the other hand, CO concentration is moderate at station 1 (First Gate), 2 (Education Car Park), and 16 (Afe Babalola Roundabout), and low in all other stations. D. Relationship between Pollutant Distribution and Land Use Table 3 shows the descriptive analysis of CO concentration levels categorised by land use/environment type. The academic environment has the highest concentration of CO (7.58ppm) and a mean of 2.91ppm. The parks and laboratories where vehicular or machine exhaust is released are very prone to high degree of CO in the campus. The outcome of this continual increase in the content of CO in this vicinity could lead to severe respiratory and other health issues that are associated with the heart, hence productivity as well as learning and teaching process with both the students and staff could be hampered. Table 4 shows the descriptive analysis of CO 2 concentration levels categorised by land use/environment type, including the Air Quality Index (AQI). The traffic corridor had the highest overall mean of 1082.17ppm observed for the entire period. This indicates a higher risk of respiratory diseases to road users. Furthermore, the religious, recreational and hospital outdoor environments pose thre same risk to individuals. The residential area of the campus had the lowest concentration level of CO 2 within the observation period. Conventionally, increase in the value of AQI in any environment indicates increase in the hazardous gases, which have a negative implication on health. The values of the AQI for each of the land use types in Table 4 indicate high CO 2 pollution. This sends a red alert signal of potential health risk and the need to take immediate precautions especially in the traffic corridors of the campus. Table 3 Descriptive analysis of CO data categorised by land use Land use N Mean (ppm) 95% CI for Mean (ppm) Min (ppm) Max (ppm) AQI Rating Remark Lower Bound Upper Bound Academic 10 2.91 1.25 4.56 0.17 7.58 29.1 B Good Commercial/ Industrial/ Shopping 5 0.78 0.00 1.57 0.33 1.83 7.8 A Very Good Conservation Area 4 1.35 -2.78 5.49 0.00 5.25 13.5 A Very Good Hospital Outdoor 1 0.17 . . 0.17 0.17 1.7 A Very Good Public Outdoors 1 0.00 . . 0.00 0.00 0.0 A Very Good Recreational 1 0.00 . . 0.00 0.00 0.0 A Very Good Religious 1 0.33 . . 0.33 0.33 3.3 A Very Good Residential 4 0.88 -0.95 2.70 0.00 2.42 8.8 A Very Good Traffic 7 0.67 -0.16 1.49 0.00 2.08 6.7 A Very Good Total 34 1.38 0.73 2.04 0.00 7.58 Table 4 Descriptive analysis of CO 2 data categorised by land use/environment type Land use N Mean (ppm) 95% CI for Mean (ppm) Min (ppm) Max (ppm) AQI Rating Remark Lower Bound Upper Bound Academic 10 861.34 824.04 898.64 788.17 982.58 86.13 D Poor Commercial/ Industrial/ Shopping 5 888.33 804.46 972.20 791.50 956.17 88.83 D Poor Conservation Area 4 889.83 812.92 966.75 825.83 942.58 88.98 D Poor Hospital Outdoor 1 945.67 . . 945.67 945.67 94.57 D Poor Public Outdoors 1 894.25 . . 894.25 894.25 89.43 D Poor Recreational 1 951.42 . . 951.42 951.42 95.14 D Poor Religious 1 977.33 . . 977.33 977.33 97.73 D Poor Residential 4 807.33 653.19 961.48 725.33 915.08 80.73 D Poor Traffic 7 985.57 872.87 1098.27 725.42 1082.17 98.56 D Poor Total 34 897.39 865.30 929.48 725.33 1082.17 Table 5 presents the Pearson’s correlation (r) between mean CO levels in different land uses/environment types. The CO concentration level in the conservation area is positively correlated with religious area. The CO levels at other land use types within the university community are all positively correlated except for hospital outdoor and traffic corridor which are strongly negatively correlated. The hospital outdoor environment is also negatively correlated with residential, religious and commercial/industrial/shopping areas. The academic area is also negatively correlated with conservation and religious areas. Table 6 presents the Pearson’s correlation (r) between mean CO 2 levels in different land uses/environment types. The academic environment is strongly correlated with the commercial/industrial/shopping area. The same strong correlation is observed for public outdoor and religious/residence area, and between the traffic corridor and religious, recreational, and residential area. These areas are where vehicular movements and human activities are largely observed leading to high concentration of CO 2 in the atmosphere. Other areas where the concentration of CO 2 can impact adversely are commercial and religious areas, commercial and residential areas, conservation area and hospital outdoor, public outdoor and residential areas. Table 5 Pearson’s correlation (r) between mean CO levels in different land uses Land use Academic Commercial/ Industrial/ Shopping Conservation Area Hospital Outdoor Public Outdoors Recreational Religious Residential Traffic Academic 1.00 0.75 -0.06 0.10 . a . a -0.24 0.35 0.35 Commercial/ Industrial/ Shopping 0.75 1.00 0.51 -0.34 . a . a 0.36 0.27 0.57 Conservation Area -0.06 0.51 1.00 -0.13 . a . a .965 ** -0.43 0.00 Hospital Outdoor 0.10 -0.34 -0.13 1.00 . a . a -0.20 -0.53 -0.80 Public Outdoors . a . a . a . a . a . a . a . a . a Recreational . a . a . a . a . a . a . a . a . a Religious -0.24 0.36 .965 ** -0.20 . a . a 1.00 -0.53 0.06 Residential 0.35 0.27 -0.43 -0.53 . a . a -0.53 1.00 0.40 Traffic 0.35 0.57 0.00 -0.80 . a . a 0.06 0.40 1.00 **. Correlation is significant at the 0.01 level (2-tailed); a. Cannot be computed because at least one of the variables is constant. Table 6 Pearson’s correlation (r) between mean CO 2 levels in different land uses Academic Commercial/ Industrial/ Shopping Conservation Area Hospital Outdoor Public Outdoors Recreational Religious Residential Traffic Academic 1.00 .967 ** 0.75 0.80 0.50 0.76 0.72 0.49 0.71 Commercial/ Industrial/ Shopping .967 ** 1.00 0.74 0.78 0.70 .885 * .868 * 0.69 .854 * Conservation Area 0.75 0.74 1.00 .880 * 0.27 0.61 0.61 0.34 0.65 Hospital Outdoor 0.80 0.78 .880 * 1.00 0.28 0.74 0.62 0.45 0.74 Public Outdoors 0.50 0.70 0.27 0.28 1.00 .847 * .921 ** .944 ** .845 * Recreational 0.76 .885 * 0.61 0.74 .847 * 1.00 .960 ** .926 ** .992 ** Religious 0.72 .868 * 0.61 0.62 .921 ** .960 ** 1.00 .930 ** .970 ** Residential 0.49 0.69 0.34 0.45 .944 ** .926 ** .930 ** 1.00 .931 ** Traffic 0.71 .854 * 0.65 0.74 .845 * .992 ** .970 ** .931 ** 1.00 **. Correlation is significant at the 0.01 level (2-tailed); *. Correlation is significant at the 0.05 level (2-tailed). IV. CONCLUSION The low concentration of CO and CO 2 were observed in the residential areas. On the other hand, the high concentrations observed at the traffic cores can be linked to the massive vehicular movements around those places. This implies that if appropriate measures are not implemented to abate the presence of these pollutants, those who spend long hours along roadsides in these areas could be highly vulnerable to the risk of having respiratory problems. Also, the eastern part of the school which has more roads manifested higher CO and CO 2 levels compared to the western end. In addition, the concentration of CO 2 is generally high around the faculties and eateries and this could be due to the presence of laboratories that depend basically electricity generating plants as well as cooking. Also, the substantial concentration of CO 2 noticed around the dumpsite and is suspected to be because of CO 2 which constitutes a high proportion of the gas emitted from decomposition of wastes by microorganisms. The AQI ratings as well as the computed exceedance factor prove that the University’s air quality with respect to CO 2 is generally poor while that of CO is satisfactory. The air quality in terms of CO 2 generally failed to conform with the ASHRAE standards while that of CO slightly conforms. Furthermore, the correlation between the mean CO and CO 2 levels in different land uses reveals that the content of CO and CO 2 in a particular region can impact its concentration level in another land use type. Overall, the study shows that CO and CO 2 are mainly concentrated around traffic pivots, laboratories, eateries, and industrial areas, and are generally low in the residential areas of the university. This study has exposed some common channels of environmental pollution and its effects on the students, staff and residents of the university. The sources include the use of electricity generating plants, diesel generators, vehicular emissions, combustion engines from laboratories, cooking by food vendors, and the presence of dumpsites. The university is exposed to significantly higher levels of CO 2 than it is deemed appropriate for healthy living. Hence, immediate and definite measures must be implemented to lessen this menace of air pollution currently being experienced. With the knowledge of air pollution implications to the health of any society, it is recommended that the school’s environmental policies should be reviewed, extensive awareness/sensitization should be done, improved traffic control must be ensured, existing legislations should be strictly enforced, and permanent air pollution monitoring stations should be established at strategic places on the campus. This study aimed to evaluate the dynamics in concentration levels of CO and CO 2 within the University of Lagos with respect to international standards. The assessment from the generated maps, tables and charts was limited by the number of measurement stations, and the inability to measure simultaneously at all stations. The duration of observation was also limited due to pecuniary and logistics constraints. Nevertheless, the findings serve as a knowledge base to give a better understanding of air quality within the university environment. A long-term continuous monitoring of pollutant concentrations via the use of autonomous monitoring equipment to assess the dynamics of air pollutants in the study area should be considered for subsequent studies. Declarations The authors declare no competing interests. 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Ambient particulate air pollution and acute lower respiratory infections: a systematic review and implications for estimating the global burden of disease. Air Quality, Atmosphere & Health , 6(1), 69-83. Mohan D, Thiyagarajan D, Murthy PB. Toxicity of exhaust nanoparticles. Afr J Pharm Pharmacol [Internet]. 2013 Feb 22 [cited 2018 Jun 18];7(7):318-31 Available from: http://www.academicjournals.org/article/ article1380800058_Mohan%20et%20al.pdf Njoku, K. L., Rumide, T. J., Akinola, M. O., Adesuyi, A. A., and Jolaoso, A. O. (2016). Ambient air quality monitoring in metropolitan city of Lagos, Nigeria. Journal of Applied Sciences and Environmental Management , 20(1), 178-185. Obanya, H. E., Amaeze, N. H., Togunde, O., and Otitoloju, A. A. (2018). Air Pollution Monitoring Around Residential and Transportation Sector Locations in Lagos Mainland. Journal of Health and Pollution , 8(19), 180903. Ogundipe, A. A, Akinyemi, O. and Ogundipe, O. M. (2018). Energy Access: Pathway to Attaining Sustainable Development in Africa. International Journal of Energy Economics and Policy, 8(6), 371-381 Odumosu, O., Nelson-Twakor, E. N., and Ajala, A. O. (1999). Demographic Effects of Regional Differentials in Education Policies in Nigeria. In International Seminar on'Educational Strategies, Families, and Population Dynamics Olowoporoku, D., Hayes, E., Longhurst, J., and Parkhurst, G. (2011). Improving road transport-related air quality in England through joint working between Environmental Health Officers and Transport Planners. Local Environment , 16(7), 603-618. Pham, L., Molden, N., Boyle, S., Johnson, K., and Jung, H. (2019). Development of a standard testing method for vehicle cabin air quality index. SAE Int. J. Commer. Veh ., 12(2). Power, A. L., Tennant, R. K., Jones, R. T., Tang, Y., Du, J., Worsley, A. T., & Love, J. (2018). Monitoring impacts of urbanisation and industrialisation on air quality in the Anthropocene using urban pond sediments. Frontiers in Earth Science, 6, 131. Rana R., Chou C.T., Bulusu N., Kanhere S., Hu W., Ear-phone: A context-aware noise mapping using smart phones. Pervasive and Mobile Computing, 2015, 17(A), 1–22. https://doi.org/10.1016/j.pmcj.2014.02.001 MAG. (2018). Silent Killer: In London, Air Pollution has become A matter of Life and Death. Retrieved from https://psmag.com/environment/air-pollution-is-killing-london, On 22/4/2019. Soneye, A. S. (2012). Concentrations of green house gases(GHGs) around tankfarms and petroleum tankers depots, Lagos, Nigeria. Journal of Geography and Regional Planning , 5(4), 108-114. Tsai K.T., Lin M.D., and Chen, Y.H., Noise mapping in urban environments: A Taiwan study. Applied Acoustics, 2009, 70, 964–972. University of Lagos 2019, About Us [Official Website]. viewed 21 January 2019, USEPA. (2019). Overview of Greenhouse Gases . Retrieved from https://www.epa.gov/ghgemissions/overview-greenhouse-gases Vanguard. (2019). Air pollution: Nigeria ranks 4th deadliest globally. Retrieved from https://www.vanguardngr.com/2018/09/air-pollution-nigeria-ranks-4th-deadliest-globally/, On 22/4/2019. Wojciech M., Kriging Method Optimization for the Process of DTM Creation Based on Huge Data Sets Obtained from MBESs. Geosciences, 2018, 8, 433, DOI: 10.3390/geosciences8120433. World Health Organisation. (2016). Ambient air pollution: A global assessment of exposure and burden of disease . Geneva, Switzerland: WHO. WHO. (2010-2012). Global Health Estimates Technical Paper WHO/HIS/HSI/GHE/2014.7. 2014 . Retrieved from http://www. WHO. (2014, March 25). 7 million premature deaths annually linked to air pollution . Retrieved from World Health Organization: https://www.who.int/news/item/25-03-2014-7-million-premature-deaths-annually-linked-to-air-pollution WHO global urban ambient air pollution database (update 2016) [Internet]. Geneva, Switzerland: World Health Organization: c2018 [cited 16 May 2020]. Available from: http://www.who.int/phe/health_topics/ outdoorair/databases/cities/en/ Yusuf, K. A., Oluwole, S., Abdusalam, I. O., and Adewusi, G. R. (2013). Spatial patterns of urban air pollution in an industrial estate, Lagos, Nigeria. International Journal of Engineering Inventions , 2(4), 1-9. Zuo J., Xia H., Liu S., Qiao Y., Mapping Urban Environmental Noise Using Smartphones. Sensors, 2016, 16, 1692, DOI: 10.3390/s16101692. 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3668485","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":253347913,"identity":"9ecbf29f-bf64-421e-96d4-78092a5c0a4d","order_by":0,"name":"Musa B. Animashaun","email":"","orcid":"","institution":"Department of Surveying and Geoinformatics, Faculty of Engineering, University of Lagos, Lagos, Nigeria","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Musa","middleName":"B.","lastName":"Animashaun","suffix":""},{"id":253347914,"identity":"617bec6c-1f7e-4f8d-86e2-ef944ad7a82f","order_by":1,"name":"Alfred Alademomi","email":"","orcid":"","institution":"Department of Surveying and Geoinformatics, Faculty of Engineering, University of Lagos, Lagos, Nigeria","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Alfred","middleName":"","lastName":"Alademomi","suffix":""},{"id":253347915,"identity":"273a19e9-ee77-49cb-929d-f784cca3b0d7","order_by":2,"name":"Chukwuma Okolie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYFCCA2xAIoGBj5mB8QGQxcNHtBY2ZgZmA5AWNiKsgWoBMiRgXLyAv/HwsccFFWlybOzMxyq/5tjJsDEwP3x0A48WiQPH0o1nnMkxZmNmS7stuy0Z6DA2Y+McPFoMGM6YSfO2VSS2MfOY3ZbcxgzUwsMmTaQW/m/FktvqidaSA7KFjfHjtsOEtQD9kibNcyYN5BdjacZtx3lADLx+4Z9x+Jg0T0WyHD//4Ycff26rtudnb374GJ8WoDUINjMPmMSnHGxNA4LN+IOQ6lEwCkbBKBiRAAA45TzkpLaXWAAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Surveying and Geoinformatics, Faculty of Engineering, University of Lagos, Lagos, Nigeria","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Chukwuma","middleName":"","lastName":"Okolie","suffix":""},{"id":253347916,"identity":"2e7e4aac-94fb-4bd2-9e06-35a246f9a3ce","order_by":3,"name":"Oluwatimileyin Abolaji","email":"","orcid":"","institution":"Department of Surveying and Geoinformatics, Faculty of Engineering, University of Lagos, Lagos, Nigeria","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Oluwatimileyin","middleName":"","lastName":"Abolaji","suffix":""},{"id":253347917,"identity":"a2a9381d-f47b-4c9d-97ef-7795385bc7c3","order_by":4,"name":"Babatunde Ojegbile","email":"","orcid":"","institution":"Department of Surveying and Geoinformatics, Faculty of Engineering, University of Lagos, Lagos, Nigeria","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Babatunde","middleName":"","lastName":"Ojegbile","suffix":""},{"id":253347918,"identity":"1e581266-7a4d-4aa0-a81c-72a366c043ef","order_by":5,"name":"Olagoke Daramola","email":"","orcid":"","institution":"Department of Surveying and Geoinformatics, Faculty of Engineering, University of Lagos, Lagos, Nigeria","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Olagoke","middleName":"","lastName":"Daramola","suffix":""},{"id":253347919,"identity":"53970882-b90d-49f1-805f-794ef5d71082","order_by":6,"name":"Nehemiah Alozie","email":"","orcid":"","institution":"Department of Mechanical Engineering, Faculty of Engineering, University of Lagos, Lagos, Nigeria","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nehemiah","middleName":"","lastName":"Alozie","suffix":""},{"id":253347920,"identity":"f3a6701f-1bf0-4446-a3ee-c5e88073381e","order_by":7,"name":"Abdulwaheed Tella","email":"","orcid":"","institution":"Division of Earth, Environment and Space, Foresight Institute of Research and Translation, Ibadan, Nigeria","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Abdulwaheed","middleName":"","lastName":"Tella","suffix":""}],"badges":[],"createdAt":"2023-11-26 17:55:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3668485/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3668485/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":47226683,"identity":"a5b3615a-e811-43a1-8bc3-e2975031ac05","added_by":"auto","created_at":"2023-11-28 19:29:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3048804,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of measurement stations in the study area\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3668485/v1/fd7b2db1f4a016a3f6c5ad45.png"},{"id":47226664,"identity":"3935c5ee-ab40-4134-bc21-37f0d92df115","added_by":"auto","created_at":"2023-11-28 19:29:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":39002,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow diagram of the assessment methodology\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3668485/v1/4edb340b898d77bdc989f944.png"},{"id":47226682,"identity":"67f9c18b-8f3f-41ab-9181-d472d8912e9e","added_by":"auto","created_at":"2023-11-28 19:29:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":821564,"visible":true,"origin":"","legend":"\u003cp\u003eMap showing the spatial variation of mean CO during the morning and afternoon periods of observation – (a) morning (b) afternoon\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3668485/v1/8a4b4c9d94aeef8864bc807b.png"},{"id":47226657,"identity":"be344abf-8507-4d90-8f23-7381bae35b1c","added_by":"auto","created_at":"2023-11-28 19:29:50","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":182054,"visible":true,"origin":"","legend":"\u003cp\u003eMap showing the spatial variation of mean CO for the duration of study\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3668485/v1/07a3da47f33f8a9830433cc9.jpg"},{"id":47226687,"identity":"c1941bb9-b137-44be-96f9-744698c0f042","added_by":"auto","created_at":"2023-11-28 19:29:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":851195,"visible":true,"origin":"","legend":"\u003cp\u003eMap showing the spatial variation of mean CO\u003csub\u003e2\u003c/sub\u003e at the two periods – (a) morning (b) afternoon\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3668485/v1/7e1853ee7d08f192a5893ffa.png"},{"id":47226684,"identity":"e4193418-7086-4aa4-b6f7-c63b69e7722e","added_by":"auto","created_at":"2023-11-28 19:29:51","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":296050,"visible":true,"origin":"","legend":"\u003cp\u003eMap showing the spatial variation of mean CO\u003csub\u003e2\u003c/sub\u003e for the duration of study\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3668485/v1/b9f5ba2d5c39c338cda9d5a6.jpg"},{"id":47226685,"identity":"f7695575-7138-42f3-a13b-f8b4af87ea10","added_by":"auto","created_at":"2023-11-28 19:29:51","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":24032,"visible":true,"origin":"","legend":"\u003cp\u003eCO Exceedance Factor per monitoring station\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-3668485/v1/decce448489a37c4bd11d557.png"},{"id":47227870,"identity":"c2e81d38-b497-46b6-ae91-c34ad4f6107e","added_by":"auto","created_at":"2023-11-28 19:37:51","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":29256,"visible":true,"origin":"","legend":"\u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e Exceedance Factor per monitoring station\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-3668485/v1/83dcce87ebcccf1cd8f38768.png"},{"id":47228496,"identity":"e78924dd-4056-456b-8f08-1331d18afe1a","added_by":"auto","created_at":"2023-11-28 19:45:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4239394,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3668485/v1/b006faba-f05e-4660-a67d-ba2aba69a1e6.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003e\u003cstrong\u003eGeo-spatial mapping of Carbon Dioxide and Carbon Monoxide within the University of Lagos, Nigeria\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"I. INTRODUCTION","content":"\u003cp\u003eAir is a vital requirement for the growth and survival of all living species. The life expectancy of both humans and animals within an environment is contingent upon the quality of air present (PSMAG, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In addition to other organisms, humans require oxygen for efficient respiration in order to facilitate cellular growth and enhance metabolic processes. Air is a vital component for sustaining life, thus making it indispensable for the survival of living beings. Air is naturally safe and clean for humans and animals. Unfortunately, the process of urbanisation and various anthropogenic activities contribute to the contamination of the atmosphere, resulting in its compromised quality and posing health risks for respiration (Cavanagh et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). According to a report by Shola (2018), the increasing urban population in Nigeria exhibits a heightened vulnerability to both indoor and outdoor air pollution.\u003c/p\u003e \u003cp\u003eAccording to Power et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), the detrimental effects of air pollution extend to human health, ecosystems, and the overall biosphere. Air pollution poses a significant concern in developing nations characterized by the presence of unregulated industries. Air pollutants such as particulate matter 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 exacerbating 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=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). According to a 2012 study conducted by the World Health Organization (WHO), it was anticipated that around 10% of the global population, equivalent to 7\u0026nbsp;million individuals, succumbed to mortality as a result of air pollution (WHO, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). 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, 2016). The elevated toxicity of exhaust nanoparticles resulting from the expansion of motor vehicle traffic and the rapid process of industrialisation is a significant factor in the occurrence of mortality related to air pollution in countries with lower and middle income levels (Mohan et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe harmful impacts of air pollution on human health has been on the increase in the developing countries where power/electricity generation is mainly through fossil fuel generators used for both domestic and commercial purposes (WHO 2016, Bernstein et al., 2004). Common pollutants of global concern are carbon dioxide, carbon monoxide, nitrogen oxide, particulate matter, volatile organic compounds, hydrocarbon substances, and sulphur oxide (Bernstein et al 2004). Apart from the damages incurred by humans from air pollution, poor air quality is also a threat to any environment as it can lead to the destruction of forests, crops and lakes, dilapidation of structures and soils as well as the alteration of earth\u0026rsquo;s radiation stability (Alani, 2019). Several studies have documented the impacts of air pollution on the environment. For instance, Lorenz et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) discovered that air pollution could have negative impacts on forests and influence adverse climatic conditions. Furthermore, Gheorghe and Ion (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) asserted that fully foliated healthy forests thrive in ecosystems where there is little or no air pollution.\u003c/p\u003e \u003cp\u003eSeveral investigations have identified Nigeria as a country with rapidly deteriorating air quality (Mehta: 2013; Obanya: 2018; Ogundipe: 2018; Alani: 2019). Urbanization, industrialization, population growth, trash burning, inadequate dumpsite management, and unregulated vehicle emissions cause poor air quality (Obanya et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yusuf et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; WHO, 2016; Alani, 2019). With its steady transformation into a megacity, Lagos State is a prominent player in Nigeria's fast-growing economy (Alani, 2019). Lagos, like other emerging megacities, is affected by urbanisation and population increase, which affects air quality. Several researchers have examined the relationship between air quality and land use (Soneeye, 2012; Yusuf, 2013; Njoku, 2016; Obanya, 2018; Alani, 2019). Major Lagos corridors like transit, industrial estates, residential districts, dumpsites, tank farms, and others have been mapped for air quality. However, despite the high vehicular emission, emission from incinerations and burning, there is the need for more research on the spatial mapping of CO and CO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eNotably, incomplete combustion of carbon-containing fuels like natural gas, gasoline, or wood releases carbon monoxide (CO) from motor vehicles, power plants, wildfires, and incinerators. CO\u003csub\u003e2\u003c/sub\u003e comes mostly from fossil fuels. Deforestation, agricultural land clearing, and soil deterioration can also emit CO\u003csub\u003e2\u003c/sub\u003e, making Lagos State, especially University of Lagos campus a good study area. The University of Lagos environment is densely inhabited in Lagos State and growing in social, and commercial activities. These increases in human activity are affecting air quality, which is detrimental to the health. This study is interested in mapping CO and CO\u003csub\u003e2\u003c/sub\u003e concentrations at the University of Lagos. for sustainable development using Geographical Information System (GIS).\u003c/p\u003e"},{"header":"II. MATERIALS AND METHODS","content":"\u003cp\u003e\u003cstrong\u003eA. Description of the Study Area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe University of Lagos is an academic institution in Lagos State, Nigeria (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). It is located at approximately, latitudes 6\u0026deg;30\u0026rsquo;N to 6\u0026deg;31\u0026rsquo;N and longitudes 3\u0026deg;25\u0026rsquo;E to 3\u0026deg;27\u0026rsquo;E in the Lagos Mainland Local Government Area. The main campus which is largely surrounded by the scenic view of the Lagos lagoon and is located on 802 acres (3.25 km\u003csup\u003e2\u003c/sup\u003e) of land in Akoka, northeastern part of Yaba, Lagos has been chosen as the study area. The focus on the main campus is justifiable based on the level of human activities as described and for the convenience of available geo-spatial data, as well as ease of environmental data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB. Station Selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe siting of the monitoring stations has a profound effect on the resulting measurements and on achieving monitoring objectives. Thus, thirty-four (34) monitoring stations shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e were set out at strategic locations within the study area such as major road intersections (for vehicular emissions), dumpsites, commercial centers, academic areas, and residential sites (see Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eThe measurement stations\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLand use type\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNo. of stations\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStation ID\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAcademic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9, 14, 17, 18, 20, 22, 23, 27, 29\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHospital Outdoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResidential\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3, 6, 31\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCommercial/Industrial/Shopping\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10, 11, 15, 25, 28, 33\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePublic Outdoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTraffic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1, 2, 7, 8, 12, 16, 24, 32, 34\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConservation Area\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13, 19, 21, 26\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRecreational\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eC. Variables, Equipment and Measurement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe evaluated parameters were CO and CO\u003csub\u003e2\u003c/sub\u003e gas which are strong variables for assessing environmental air quality. To measure the concentration levels of these gases, we adopted the use of a very simple device: calibrated hand-held solid-state gas sensor. Compu-Flow CO\u003csub\u003e2\u003c/sub\u003e Handheld meter, a gas monitor designed to provide continuous exposure monitoring of carbon dioxide was used to measure the CO\u003csub\u003e2\u003c/sub\u003e concentration levels in parts per million (ppm). Data was collected over six (6) observation windows within three days of observation. The average value for the pollution parameter readings were carefully recorded. All measurements were executed in 2-hour duplicates (i.e., repeated twice \u0026ndash; morning peak period and afternoon off-peak period) over 3 days\u0026rsquo; observation period across all monitoring stations. The sampling was done in-situ and each sample station was geo-referenced using Garmin GPSMAP 78SC Marine handheld GPS. The duration for exposure was set at 5 minutes at a time per station. The average values for each of the locations were computed and recorded.\u003c/p\u003e\n\u003cp\u003eThe acquired data was transferred to a Microsoft Excel worksheet where it was orderly sorted into rows and columns. Likewise, a point shape file was created in ESRI ArcGIS software using the GPS recorded X, Y coordinates of the monitoring stations. Subsequently, the pollutant data readings were integrated into the records of each point in the shapefile by populating the attribute table accordingly.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD. Kriging Interpolation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe acquired data was transferred to a Microsoft Excel worksheet and imported into ArcGIS environment where it was represented by point shapefiles. Kriging interpolation (an advanced geostatistical procedure that generates an estimated surface from a scattered set of points with values), was used to generate maps showing the spatial variation of CO and CO\u003csub\u003e2\u003c/sub\u003e. Kriging is a widely used Geostatistical interpolation technique (Zuo et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e) that considers autocorrelation (the statistical relationship among measured points) (Kurakula, \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). With kriging, visually appealing models can be created from data that is irregularly spaced (Wojciech, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). The application of Kriging interpolation for noise mapping has been shown in previous studies (Tsai et al., \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; Rana et al., 2014; Harman et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). The statistical theory of kriging is well established and it can estimate errors point-by-point (Zuo et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). These attributes make it suitable for the present study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE. Quantitative Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn monitoring the air quality of any area of interest, the United States Environmental Protection Agency has designed a standardized air pollution level indicator for rating the air quality over an area. It is a rating scale for outdoor air called Air Quality Index (AQI). The lower the AQI value, the better the air quality. AQI rating A stands for Very Good (0\u0026ndash;15), B for Good (16\u0026ndash;31), C for Moderate (32\u0026ndash;49), D for Poor (50\u0026ndash;99) and E for Very poor (100 and over) (EPA, \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e). The air quality is determined with reference to the standard by the American Society of Heating, Refrigerating and Air Conditioning Engineers (ASHRAE). According to ASHRAE (\u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e), the standard for carbon dioxide is 1000ppm.\u003c/p\u003e\n\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ1\" class=\"mathdisplay\"\u003e$${AQI}_{POLLUTANT}= \\frac{Data}{Standard} \\times 100$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eAlso, we adopted the Exceedance Factor (EF) analysis which is broadly understood as \u0026ldquo;the ratio of annual mean concentration of a pollutant with that of a respective standard\u0026rdquo;\u003c/p\u003e\n\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ2\" class=\"mathdisplay\"\u003e$$\\text{Exceedance Factor }\\text{= }\\frac{\\text{Observed annual mean concentration of criteria pollutant }}{\\text{Annual standard for the respective pollutant and area class}}\\text{ }$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eThe above formula interpretes four different threshold of air pollution as: low (EF\u0026thinsp;\u0026lt;\u0026thinsp;0.5), moderate (EF is between 0.5-1.0), high (EF is between 1.0-1.5), and critical (EF\u0026thinsp;\u0026gt;\u0026thinsp;1.5).\u003c/p\u003e"},{"header":"III. RESULTS AND DISCUSSION","content":"\u003cp\u003e\u003cstrong\u003eA. Spatial Distribution of CO\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the daily variation of the average CO in the different land use types within the University of Lags main campus. The concentration of the CO was found to be highest at the academic areas, for both the morning and afternoon observation periods. This can be attributed to the high influx of vehicles around this area and especially within and around the faculties such as engineering where industrial machines are operational. The traffic corridors, conservation and residential areas also show significant levels of CO. The major sources for the injection of CO into the air in any environment where there is incomplete combustion of carbon are vehicular exhausts and industrial machinery. The content of this CO in the air is one of the factors responsible for respiratory problems which affect both the heart and brain.\u003c/p\u003e\n\u003cp\u003eFigures \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea and \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb represent the average CO distribution in the study area for the morning and afternoon periods respectively.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDaily variation of average CO in the different land uses/environment types (ppm)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDay\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAcademic\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCommercial/ Industrial/ Shopping\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eConservation Area\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHospital Outdoor\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePublic Outdoors\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRecreational\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eReligious\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eResidential\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTraffic\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eM1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eM2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.14\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eM3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.71\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAve\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.62\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.43\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.71\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAve\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.71\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"10\"\u003eKey: M1 \u0026ndash; Morning day 1, M2 \u0026ndash; Morning day 2, M3 \u0026ndash; Morning day 3; A1 \u0026ndash; Afternoon day 1, A2 \u0026ndash; Afternoon day 2, M3 \u0026ndash; Afternoon day 3\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFrom the morning observation, the CO concentration is generally low in many environments (0\u0026ndash;2.40ppm), except at the core traffic areas and some commercial areas within the campus, e.g., Station 2 (Education Car Park \u0026minus;\u0026thinsp;12.67ppm), Station 1 (University Main Gate \u0026minus;\u0026thinsp;10.67ppm) and Station 16 (Afe-Babalola Roundabout \u0026minus;\u0026thinsp;8.00ppm). This pattern of CO distribution was again observed in the afternoon as the concentration levels remained high around the traffic pivots, although not as intense as observed in the morning. However, a striking observation from the afternoon map in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb is the sudden dip in CO levels around the main gate and its environs to about 0.5ppm. Furthermore, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows the mean CO variation for the entire observation window. Summarily, the concentration level is relatively high at traffic centres, academic environments and commercial areas. In addition, aside from the presence of car parks at faculties, some of these faculties possess laboratories and workshops e.g., the Engineering workshop where sophisticated combustion engines are used, thus leading to an increase in CO levels. Also, numerous cafeterias are situated at the commercial areas and CO is a key integral of the gases emitted during the cooking process. The concentration around residential areas was relatively low.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB. Spatial Distribution of CO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the daily variation of the average CO\u003csub\u003e2\u003c/sub\u003e in the different land use types within the University of Lags main campus, while Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea and \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb represent the average CO\u003csub\u003e2\u003c/sub\u003e distribution in the study area for the morning and afternoon periods respectively. The morning observation for all the land use types was generally above 714ppm with traffic, religious, recreational corridors and public outdoor having high average CO\u003csub\u003e2\u003c/sub\u003e content above 1000ppm. The hospital outdoor environment recorded the lowest levels of CO\u003csub\u003e2\u003c/sub\u003e in the morning. The high atmospheric CO\u003csub\u003e2\u003c/sub\u003e concentration at the traffic, religious, recreational corridors and public outdoor environment is possibly due to the volume of vehicular movement in and around these corridors on campus. The concentration of CO\u003csub\u003e2\u003c/sub\u003e in the atmosphere on the third day was also relatively high compared to the other days for both morning and afternoon periods with public outdoor having 1401.00ppm and the hospital outdoor having 1098.00ppm in the morning and afternoon periods respectively.\u003c/p\u003e\n\u003cp\u003eHowever, the afternoon observation period was generally above 604.00ppm with residential areas having the least. The highest average CO\u003csub\u003e2\u003c/sub\u003e level was observed at the conservation area (871.00ppm), traffic corridor (872.14ppm) and hospital outdoor (933.00ppm) environments. The high CO\u003csub\u003e2\u003c/sub\u003e level experienced at the traffic corridors can also be attributed to vehicular emissions. Conversely, the result at the hospital outdoor in this case was high, and this could be due to the high number of patients. Similarly, the observed daily variation of CO\u003csub\u003e2\u003c/sub\u003e level as presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and clearly depicted in Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea and \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb shows that the public outdoors which include the entrance gates and car parks on campus have the highest CO\u003csub\u003e2\u003c/sub\u003e concentration during the morning period while the the hospital environment has the highest CO\u003csub\u003e2\u003c/sub\u003e level in the afternoon.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDaily variation of average CO\u003csub\u003e2\u003c/sub\u003e in the different land uses (ppm)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDay\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAcademic\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCommercial/ Industrial/ Shopping\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eConservation Area\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHospital Outdoor\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePublic Outdoors\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRecreational\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eReligious\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eResidential\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTraffic\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eM1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e824.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e861.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e825.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e714.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1033.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e819.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e984.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e768.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e905.14\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eM2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e777.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e857.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e870.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e948.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1159.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1150.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1108.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1104.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1139.71\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eM3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1103.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1174.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1030.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1213.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1264.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1401.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1237.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1109.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1252.14\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAve\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e901.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e964.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e908.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e958.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1152\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1123.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1109.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e994.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1098.99\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e752.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e733.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e686.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e768.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e633.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e686.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e758.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e604.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e786.71\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e821.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e812.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e871.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e933.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e636.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e779.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e845.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e620.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e872.14\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e889.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e891.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1055.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1098.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e640.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e873.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e932.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e637.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e957.57\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAve\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e821.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e812.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e871\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e933\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e636.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e779.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e845\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e620.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e872.14\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"10\"\u003eKey: M1 \u0026ndash; Morning day 1, M2 \u0026ndash; Morning day 2, M3 \u0026ndash; Morning day 3; A1 \u0026ndash; Afternoon day 1, A2 \u0026ndash; Afternoon day 2, M3 \u0026ndash; Afternoon day 3\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e presents the spatial variation of CO\u003csub\u003e2\u003c/sub\u003e concentrations across all environments throughout the entire observation duration. Overall, maximum concentrations are observed in the traffic corridors, academic and conservation environments. Again, this can be linked to the intense vehicular movements around the traffic hubs and faculty parks. Also, since CO\u003csub\u003e2\u003c/sub\u003e constitutes 50% of the gas emitted due to decomposition of organic wastes by microorganisms from landfills and canals, this is suspected to be the reason for the high concentrations of CO\u003csub\u003e2\u003c/sub\u003e noticed around the the dumpsites on campus along the International School Road. Another significant observation is the relatively low level of CO\u003csub\u003e2\u003c/sub\u003e around the residential environment which is most likely due to low vehicular movements around there.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC. Exceedance Factor\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigures \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e depict the exceedance factors of CO and CO\u003csub\u003e2\u003c/sub\u003e computed for each of the 34 monitoring stations. Evidently, the CO\u003csub\u003e2\u003c/sub\u003e level is rated high at stations 1, 8, 24 and 34, while other stations maintain the moderate level. However, stations 2 and 27 are to be monitored because a small increase in the CO2 generation in these stations will make their exceedance factor to exceed normalcy and that could negatively impact people\u0026rsquo;s health in such environment. On the other hand, CO concentration is moderate at station 1 (First Gate), 2 (Education Car Park), and 16 (Afe Babalola Roundabout), and low in all other stations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD. Relationship between Pollutant Distribution and Land Use\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the descriptive analysis of CO concentration levels categorised by land use/environment type. The academic environment has the highest concentration of CO (7.58ppm) and a mean of 2.91ppm. The parks and laboratories where vehicular or machine exhaust is released are very prone to high degree of CO in the campus. The outcome of this continual increase in the content of CO in this vicinity could lead to severe respiratory and other health issues that are associated with the heart, hence productivity as well as learning and teaching process with both the students and staff could be hampered.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows the descriptive analysis of CO\u003csub\u003e2\u003c/sub\u003e concentration levels categorised by land use/environment type, including the Air Quality Index (AQI). The traffic corridor had the highest overall mean of 1082.17ppm observed for the entire period. This indicates a higher risk of respiratory diseases to road users. Furthermore, the religious, recreational and hospital outdoor environments pose thre same risk to individuals. The residential area of the campus had the lowest concentration level of CO\u003csub\u003e2\u003c/sub\u003e within the observation period. Conventionally, increase in the value of AQI in any environment indicates increase in the hazardous gases, which have a negative implication on health. The values of the AQI for each of the land use types in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e indicate high CO\u003csub\u003e2\u003c/sub\u003e pollution. This sends a red alert signal of potential health risk and the need to take immediate precautions especially in the traffic corridors of the campus.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDescriptive analysis of CO data categorised by land use\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eLand use\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMean (ppm)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e95% CI for Mean (ppm)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMin (ppm)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMax (ppm)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAQI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eRating\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eRemark\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLower Bound\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eUpper Bound\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAcademic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e29.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGood\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCommercial/ Industrial/ Shopping\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVery Good\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConservation Area\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-2.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVery Good\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHospital Outdoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVery Good\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePublic Outdoors\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVery Good\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRecreational\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVery Good\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReligious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVery Good\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResidential\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVery Good\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTraffic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVery Good\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDescriptive analysis of CO\u003csub\u003e2\u003c/sub\u003e data categorised by land use/environment type\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eLand use\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMean (ppm)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e95% CI for Mean (ppm)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMin (ppm)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMax (ppm)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAQI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eRating\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eRemark\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLower Bound\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eUpper Bound\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAcademic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e861.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e824.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e898.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e788.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e982.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e86.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCommercial/ Industrial/ Shopping\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e888.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e804.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e972.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e791.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e956.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e88.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConservation Area\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e889.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e812.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e966.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e825.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e942.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e88.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHospital Outdoor\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e945.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e945.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e945.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e94.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePublic Outdoors\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e894.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e894.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e894.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e89.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRecreational\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e951.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e951.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e951.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e95.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReligious\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e977.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e977.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e977.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e97.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResidential\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e807.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e653.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e961.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e725.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e915.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e80.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTraffic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e985.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e872.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1098.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e725.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1082.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e98.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePoor\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e897.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e865.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e929.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e725.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1082.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e presents the Pearson\u0026rsquo;s correlation (r) between mean CO levels in different land uses/environment types. The CO concentration level in the conservation area is positively correlated with religious area. The CO levels at other land use types within the university community are all positively correlated except for hospital outdoor and traffic corridor which are strongly negatively correlated. The hospital outdoor environment is also negatively correlated with residential, religious and commercial/industrial/shopping areas. The academic area is also negatively correlated with conservation and religious areas.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e presents the Pearson\u0026rsquo;s correlation (r) between mean CO\u003csub\u003e2\u003c/sub\u003e levels in different land uses/environment types. The academic environment is strongly correlated with the commercial/industrial/shopping area. The same strong correlation is observed for public outdoor and religious/residence area, and between the traffic corridor and religious, recreational, and residential area. These areas are where vehicular movements and human activities are largely observed leading to high concentration of CO\u003csub\u003e2\u003c/sub\u003e in the atmosphere. Other areas where the concentration of CO\u003csub\u003e2\u003c/sub\u003e can impact adversely are commercial and religious areas, commercial and residential areas, conservation area and hospital outdoor, public outdoor and residential areas.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePearson\u0026rsquo;s correlation (r) between mean CO levels in different land uses\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLand use\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAcademic\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCommercial/ Industrial/ Shopping\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eConservation Area\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHospital Outdoor\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePublic Outdoors\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRecreational\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eReligious\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eResidential\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTraffic\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAcademic\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.35\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCommercial/ Industrial/ Shopping\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.57\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eConservation Area\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.965\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHospital Outdoor\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.80\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePublic Outdoors\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eRecreational\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eReligious\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.965\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eResidential\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.40\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTraffic\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003e**. Correlation is significant at the 0.01 level (2-tailed); a. Cannot be computed because at least one of the variables is constant.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab7\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePearson\u0026rsquo;s correlation (r) between mean CO\u003csub\u003e2\u003c/sub\u003e levels in different land uses\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAcademic\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCommercial/ Industrial/ Shopping\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eConservation Area\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHospital Outdoor\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePublic Outdoors\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRecreational\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eReligious\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eResidential\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTraffic\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAcademic\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.967\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.71\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCommercial/ Industrial/ Shopping\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.967\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.885\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.868\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.854\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eConservation Area\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.880\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.65\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHospital Outdoor\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.880\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.74\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePublic Outdoors\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.847\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.921\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.944\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.845\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eRecreational\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.885\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.847\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.960\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.926\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.992\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eReligious\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.868\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.921\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.960\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.930\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.970\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eResidential\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.944\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.926\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.930\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.931\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTraffic\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.854\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.845\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.992\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.970\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.931\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003e**. Correlation is significant at the 0.01 level (2-tailed); *. Correlation is significant at the 0.05 level (2-tailed).\u003c/em\u003e\u003c/p\u003e"},{"header":"IV. CONCLUSION","content":"\u003cp\u003eThe low concentration of CO and CO\u003csub\u003e2\u003c/sub\u003e were observed in the residential areas. On the other hand, the high concentrations observed at the traffic cores can be linked to the massive vehicular movements around those places. This implies that if appropriate measures are not implemented to abate the presence of these pollutants, those who spend long hours along roadsides in these areas could be highly vulnerable to the risk of having respiratory problems. Also, the eastern part of the school which has more roads manifested higher CO and CO\u003csub\u003e2\u003c/sub\u003e levels compared to the western end. In addition, the concentration of CO\u003csub\u003e2\u003c/sub\u003e is generally high around the faculties and eateries and this could be due to the presence of laboratories that depend basically electricity generating plants as well as cooking. Also, the substantial concentration of CO\u003csub\u003e2\u003c/sub\u003e noticed around the dumpsite and is suspected to be because of CO\u003csub\u003e2\u003c/sub\u003e which constitutes a high proportion of the gas emitted from decomposition of wastes by microorganisms. The AQI ratings as well as the computed exceedance factor prove that the University\u0026rsquo;s air quality with respect to CO\u003csub\u003e2\u003c/sub\u003e is generally poor while that of CO is satisfactory.\u003c/p\u003e \u003cp\u003eThe air quality in terms of CO\u003csub\u003e2\u003c/sub\u003e generally failed to conform with the ASHRAE standards while that of CO slightly conforms. Furthermore, the correlation between the mean CO and CO\u003csub\u003e2\u003c/sub\u003e levels in different land uses reveals that the content of CO and CO\u003csub\u003e2\u003c/sub\u003e in a particular region can impact its concentration level in another land use type. Overall, the study shows that CO and CO\u003csub\u003e2\u003c/sub\u003e are mainly concentrated around traffic pivots, laboratories, eateries, and industrial areas, and are generally low in the residential areas of the university. This study has exposed some common channels of environmental pollution and its effects on the students, staff and residents of the university. The sources include the use of electricity generating plants, diesel generators, vehicular emissions, combustion engines from laboratories, cooking by food vendors, and the presence of dumpsites.\u003c/p\u003e \u003cp\u003eThe university is exposed to significantly higher levels of CO\u003csub\u003e2\u003c/sub\u003e than it is deemed appropriate for healthy living. Hence, immediate and definite measures must be implemented to lessen this menace of air pollution currently being experienced. With the knowledge of air pollution implications to the health of any society, it is recommended that the school\u0026rsquo;s environmental policies should be reviewed, extensive awareness/sensitization should be done, improved traffic control must be ensured, existing legislations should be strictly enforced, and permanent air pollution monitoring stations should be established at strategic places on the campus.\u003c/p\u003e \u003cp\u003eThis study aimed to evaluate the dynamics in concentration levels of CO and CO\u003csub\u003e2\u003c/sub\u003e within the University of Lagos with respect to international standards. The assessment from the generated maps, tables and charts was limited by the number of measurement stations, and the inability to measure simultaneously at all stations. The duration of observation was also limited due to pecuniary and logistics constraints. Nevertheless, the findings serve as a knowledge base to give a better understanding of air quality within the university environment. A long-term continuous monitoring of pollutant concentrations via the use of autonomous monitoring equipment to assess the dynamics of air pollutants in the study area should be considered for subsequent studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eACKNOWLEDGEMENTS\u003c/h2\u003e \u003cp\u003eOur sincere gratitude goes to Prof Alabi Soneye of the Department of Geography, University of Lagos for the provision of gas sensors which were used to acquire field data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdeniran, E. 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In \u003cem\u003eInternational Seminar on\u0026apos;Educational Strategies, Families, and Population Dynamics\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eOlowoporoku, D., Hayes, E., Longhurst, J., and Parkhurst, G. (2011). Improving road transport-related air quality in England through joint working between Environmental Health Officers and Transport Planners. \u003cem\u003eLocal Environment\u003c/em\u003e, 16(7), 603-618.\u003c/li\u003e\n\u003cli\u003ePham, L., Molden, N., Boyle, S., Johnson, K., and Jung, H. (2019). Development of a standard testing method for vehicle cabin air quality index. \u003cem\u003eSAE Int. J. Commer. Veh\u003c/em\u003e., 12(2).\u003c/li\u003e\n\u003cli\u003ePower, A. L., Tennant, R. K., Jones, R. T., Tang, Y., Du, J., Worsley, A. T., \u0026amp; Love, J. (2018). Monitoring impacts of urbanisation and industrialisation on air quality in the Anthropocene using urban pond sediments. 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Applied Acoustics, 2009, 70, 964\u0026ndash;972. \u003c/li\u003e\n\u003cli\u003eUniversity of Lagos 2019, About Us [Official Website]. viewed 21 January 2019, \u0026lt;https://unilag.edu.ng/about-us/\u0026gt;\u003c/li\u003e\n\u003cli\u003eUSEPA. (2019). \u003cem\u003eOverview of Greenhouse Gases\u003c/em\u003e. Retrieved from https://www.epa.gov/ghgemissions/overview-greenhouse-gases\u003c/li\u003e\n\u003cli\u003eVanguard. (2019). Air pollution: Nigeria ranks 4th deadliest globally. Retrieved from https://www.vanguardngr.com/2018/09/air-pollution-nigeria-ranks-4th-deadliest-globally/, On 22/4/2019. \u003c/li\u003e\n\u003cli\u003eWojciech M., Kriging Method Optimization for the Process of DTM Creation Based on Huge Data Sets Obtained from MBESs. Geosciences, 2018, 8, 433, DOI: 10.3390/geosciences8120433.\u003c/li\u003e\n\u003cli\u003eWorld Health Organisation. (2016). \u003cem\u003eAmbient air pollution: A global assessment of exposure and burden of disease\u003c/em\u003e. Geneva, Switzerland: WHO.\u003c/li\u003e\n\u003cli\u003eWHO. (2010-2012). \u003cem\u003eGlobal Health Estimates Technical Paper WHO/HIS/HSI/GHE/2014.7. 2014\u003c/em\u003e. Retrieved from http://www.\u003c/li\u003e\n\u003cli\u003eWHO. (2014, March 25). \u003cem\u003e7 million premature deaths annually linked to air pollution\u003c/em\u003e. Retrieved from World Health Organization: https://www.who.int/news/item/25-03-2014-7-million-premature-deaths-annually-linked-to-air-pollution\u003c/li\u003e\n\u003cli\u003eWHO global urban ambient air pollution database (update 2016) [Internet]. Geneva, Switzerland: World Health Organization: c2018 [cited 16 May 2020]. Available from: http://www.who.int/phe/health_topics/ outdoorair/databases/cities/en/\u003c/li\u003e\n\u003cli\u003eYusuf, K. A., Oluwole, S., Abdusalam, I. O., and Adewusi, G. R. (2013). Spatial patterns of urban air pollution in an industrial estate, Lagos, Nigeria. \u003cem\u003eInternational Journal of Engineering\u003c/em\u003e \u003cem\u003eInventions\u003c/em\u003e, 2(4), 1-9.\u003c/li\u003e\n\u003cli\u003eZuo J., Xia H., Liu S., Qiao Y., Mapping Urban Environmental Noise Using Smartphones. Sensors, 2016, 16, 1692, DOI: 10.3390/s16101692.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Lagos","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":"Air Quality Index, Air Pollution, Carbon dioxide, Carbon monoxide, World Health Organisation","lastPublishedDoi":"10.21203/rs.3.rs-3668485/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3668485/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe air quality within academic institutions in Nigeria with highly vulnerable student populations has not received adequate attention. The University of Lagos is located within the highly populated and industrialised state of Lagos, Nigeria. To assess the university\u0026rsquo;s air quality, the concentrations of Carbon Dioxide (CO\u003csub\u003e2\u003c/sub\u003e) and Carbon Monoxide (CO) were mapped and evaluated. Data was collected through direct field measurements using handheld gas sensors. The analysis of ambient air quality was done by applying the Exceedance Factor (EF) method where the presence of CO and CO\u003csub\u003e2\u003c/sub\u003e average concentrations are classified into different categories. In addition, the USEPA Air Quality Index rating scale was used to evaluate the ambient air quality with respect to ASHRAE standards, and the pollutant concentration levels in different land use types were assessed. Regarding CO\u003csub\u003e2\u003c/sub\u003e, five air quality monitoring stations were found to be in the \u0026ldquo;high\u0026rdquo; category while others were in the \u0026ldquo;moderate\u0026rdquo; emission class. For CO, two stations were categorized as \u0026ldquo;moderate\u0026rdquo;, and others as \u0026ldquo;low\u0026rdquo;. The results show that CO\u003csub\u003e2\u003c/sub\u003e emission is substantial along road corridors in the campus. These findings are valuable to inform researchers, policy makers and other stakeholders on mitigative measures for air quality management in academic institutions.\u003c/p\u003e","manuscriptTitle":"Geo-spatial mapping of Carbon Dioxide and Carbon Monoxide within the University of Lagos, Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-11-28 19:29:43","doi":"10.21203/rs.3.rs-3668485/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":"75735c71-5068-49d9-a6b8-3506a7d50441","owner":[],"postedDate":"November 28th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":26741392,"name":"Environmental Chemistry"}],"tags":[],"updatedAt":"2023-11-28T19:29:43+00:00","versionOfRecord":[],"versionCreatedAt":"2023-11-28 19:29:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3668485","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3668485","identity":"rs-3668485","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}