The proliferation of industrial activities globally has led to the increase in the concentration of hazardous pollutants in the atmosphere. We thus hypothesized that Port Harcourt, an industrialized city, and the host to a foremost refinery in Africa, would have a high concentration of pollutants in indoor and outdoor environments. We took air samples with a gas monitor (Aero qual series 500) from indoor and outdoor environments in 40 residential areas. The sampling sites were georeferenced with Garmin GPS and geospatially analyzed using ArcGIS. Predictive models were used to determine the concentration of Sulphur dioxide (SO 2), Nitrogen dioxide (NO 2), and Carbon monoxide (CO). Our results reveal that SO 2, NO 2, and CO concentrations were high in the high-density areas compared to the low-density regions. In most areas, the concentration is higher than the FMEnv and NAAQS permissible limits in both the dry and wet seasons. Diobu, a highly populated has the highest pollution level. For example, the concentration of CO in this location was >15 ppm during the wet season. The study revealed that a high influx of vehicular traffic, indoor and outdoor cooking with stoves and firewood, use of fossil fuel generators, and tobacco smoking are some factors that led to a high concentration of gases in the residential areas. We thus recommend that old vehicles should be banned; also the use of firewood should be discouraged to reduce pollution. There should also be regular monitoring of the indoor and outdoor air quality.
· The concentration of CO, SO2, and NO2 was high in indoor and outdoor areas.
· Proximity to the road influenced concentration of noxious gases in Port Harcourt City.
· High-density areas have more concentration of noxious gases than low-density areas.
· Seasons influenced the concentration of noxious gases in indoor and outdoor areas.
· Model showed high correlation between predicted and measured values of pollutants.
Life has significantly increased in abundance, complexity, and diversity over the earth’s history but has continuously altered the earth’s environment, posing a severe threat to earth’s inhabitants (Kleidon, 2010). According to Shikazono (2012), the atmosphere, hydrosphere, and lithosphere make up the earth’s system. While the atmosphere itself is composed of the following molecules: nitrogen (78%), oxygen (21%), argon (1%), and then trace amounts of carbon dioxide, neon, helium, methane, krypton, hydrogen, nitrous oxide, xenon, ozone, iodine, carbon monoxide, ammonia, and quantities of water vapour at lower altitudes (Hu et al., 2022; Saha, 2008).
According to Chernyaeva and Wang (2019), air pollution is generally referred to as the introduction of chemical, biological and physical substances into the air, thereby altering the air’s natural concentration. Harmful gases cause air pollution, and so impact the atmospheric equilibrium. Some activities that cause this situation are burning coal, oil, and natural gas.
The industrialization process involves converting raw materials into valuable products and waste (Babla et al., 2022), and when the waste is released, it affects the environmental quality (Bhat et al., 2022). Anthropogenic activities such as deforestation, movement of vehicles, building construction, agriculture, construction of roads, and road traffic congestion have also impacted the eco-environment. According to Breysse et al. (2010), air pollution inside homes consists of a complex mixture of agents penetrating from outdoor air and agents generated by indoor sources that have the potential of causing significant health implications.
The primary sources of indoor air pollution worldwide can be attributed to the combustion of fuels, tobacco, coal, ventilation systems, emissions from furnishings and construction materials (Pérez-Padilla et al., 2010; Wu et al., 2022). Indoor fires can produce black carbon particles, nitrogen oxides, sulphur oxides, and mercury compounds, among other emissions (Apte & Salvi, 2016).
WHO (2006) reported that over 1.6 million people died from cooking stove fumes globally (Patha et al., 2017). About 396,000 of the 1.6 million deaths occurred in sub-Sahara Africa, with Nigeria having the highest incidents (Margulis et al., 2006). Health complications emanating from indoor air pollution (IAP) include pneumonia in children, asthma, tuberculosis, upper airway cancer, and cataract (Omole & Ndambuki, 2014). According to Margulis et al. (2006), other familiar sources of IAP include mosquito repellent fumes, electricity generator fumes, and smoke from cigarettes. There has been a rapid increase in generators over the past decade as an alternative source of power for homes and commercial activities in Nigeria (Onwuka et al., 2017). The use of generators has led to high concentrations of carbon monoxide (CO), nitrogen dioxide (NO2), and sulphur dioxide (SO2) in the atmosphere (Sulaiman et al., 2017). Carbon monoxide (CO) is a very hazardous, colorless, and odourless gas emitted from incomplete combustion of fuel in power generator sets, automobiles, and firewood. Global data shows that indoor air pollution (IAP) is far more lethal than outdoor air pollution (OAP) (Omole & Ndambuki, 2014). The objective of the current study is to assess and compare the indoor air quality (IAQ) over different residential categories in Port Harcourt Metropolis, and (2) to forecast dry and wet season indoor and outdoor air quality in both high- and low-density areas.
Research StructureTo achieve the above objectives, the research structure was outlined by studying forty residential areas, which were georeferenced and the density of population delineated into high and low and placed in geospatial maps with GPS. The gaseous samples (SO2, NO2 and CO), the dependent variables, were taken with gas monitors at indoor and outdoor environment, in low and high density aareas during the dry and wet seasons (independent variables) (
Port Harcourt is the capital city of River Sate (
The city has a flat topography with an inadequate drainage facility. Its elevation varies between 3 m and 15 m above mean sea level. The stream is a south-flowing stream, turbid during the wet season due to the discharge of clay and silt into the drainage channels. However, the water discharge and turbidity are reduced during the dry season.
Port Harcourt has been under the sub-equatorial climate and experiences a more extended rainy season, characteristic of a tropical wet climate (Numbere, 2022). This climate often experiences lengthy and heavy rainy seasons of about 182 days with a temporary cessation of rain within the rainy season, commonly referred to as “August break” and short dry seasons (Numbere & Camilo, 2018).
The conceptual model below shows a hypothetical relationship between the main ideas of the study (
We used a Garmin GPS (Model 76Cx) to take the coordinates of the sampling points. We also used a gas monitor (Aero qual series 500) to assess the gaseous pollutants in forty (40) residences in the study area (TableA1). The gas monitor is a portable meter with highly sensitive replaceable sensors of different gaseous air pollutants. We then used a portable meter to measure the three gases, namely: Sulphur Dioxide (SO2), Carbon Monoxide (CO), and Nitrogen Dioxide (NO2), by the principle of light absorption and emission. Nitrogen Dioxide (NO2), has 0.001 ppm detection limit, while Sulphur Dioxide (SO2) and Carbon Monoxide (CO) have 0.01 ppm detection limit. The infra-red wavelength of the parameters is not the same.
Data were analyzed using geospatial and geostatistical techniques with the mean values of the air pollutant concentrations estimated for measurement collected. Statistical test of significance was estimated as the null hypothesis for significance testing. The mean, standard deviations, and coefficient of variations were also calculated. The P-value represents the probability associated with the outcome of a test of a null hypothesis (Bowling, 2014). A normality test was carried out to determine whether the data followed a normal distribution. An analysis of variance (ANOVA) was done to determine the significant difference between multiple locations and sampling units (Logan, 2010). Mann-Whitney test of significance was used to compare the air quality between the high-density area and the low-density area. All analyses were done in R Development Core Team (2013).
The indoor air quality index (IAQI) was determined using the existing air quality index (IAQI) (USEPA, 2003) as shown in Equation (1).
I p = ( C p − B P L o ) × I H i − I H i B P H i − B P L o + I L o (1)
where:
Ip = Index value for pollutant p,
Cp = Rounded concentration of pollutant p,
BPHi = Higher Breakpoint value of Cp,
BPLo = Lower Breakpoint value of Cp,
IHi = Index Breakpoint value of BPHi,
ILo = Index Breakpoint value of BPLo.
An ArcGIS 10.2 software was used to map the indoor air quality contours. This software is a Geographic Information System program that integrates spatial data and attributes (indoor air quality values), stores them, and analyses input variables for graphic presentation.
This indoor modelling uses a mass balance approach to estimate indoor air pollutant concentrations in the study area. It is based on indoor modelling techniques used by Davis and Cornwell (2008). In this modelling approach, a house was considered a simple box, as shown in
Rate of pollutant increase in box = Rate of pollutant entering box from outdoors + Rate on pollutant entering box from indoor emissions − Rate of pollutant leaving box by leakage to outdoor − Rate of pollutant leaving box by decay
The governing mass balance model for indoor air pollution as contained in Davis and Cornwell (2008) is expressed as given in Equation (2)
V d C d t = Q C a + E − Q C − k C V (2)
where, C = concentrations (μg/m3);
Ca = ambient concentrations (μg/m3);
Q = rate of infiltration of air into and out of box (m3/s);
V = volume of box (m3);
E = emission rate of pollutant into box from indoor source (g/s);
k = pollutant decay constant or rate of reaction coefficient (/s).
Volume of Box (V)The average dimensions of rooms measured in the high-density area are 3.5 m × 3.5 m × 6 m = 73.5 m3. Therefore, V for the high-density area was assumed to be 75 m3. The average dimensions of rooms measured in the low-density area are 9 m × 4.6 m × 6 m = 248.4 m3, whereV for the low-density area was assumed to be 250 m3. The reaction rate of coefficient, k, is given as 0.0/s for CO, 4.17 × 10−5 for NO2, and 6.39 × 10−5 for SO2. For conservative purposes, the rate of air infiltration into and out of the box, Q, was assumed to be 0.025 m3/s at the same time, the taken modelling time is t = 1 hour (3600 s).
The indoor concentrations of air pollutants in both the high-density and low-density areas and during the dry and wet seasons were spatially interpolated to estimate indoor air pollutants. The spatial interpolation was carried out on three criteria pollutants of SO2, NO2, and CO. The global pollutants limits were used as reference points for this study (See Tables A1-A4). The interpolation maps of spatial distribution of the gases are shown in Figures 4(A)-(F). The maps in
while in contrast it is the opposite for CO where there was a graual reduction of cocnetrion from low to high density areas for both the wet and the dry seasons (
The interpolation map (
The dry season interpolation map (
Interpolation result for dry season (
Modelling the indoor air quality in the study area using Equation (1) aims to forecast the dry and wet season indoor air quality in both the high density and low-density areas. The indoor modelling result for the high-density area is shown in Figures 5(A)-5(F), while the indoor modelling result for the low-density area is shown in Figures 5(G)-5(L).
The result of the indoor air quality for SO2 is shown in
The result of the indoor air quality is shown in
The result of the indoor air quality is shown in
The result of the indoor air quality is shown in
The result of the indoor air quality is shown in
The result of the indoor air quality is shown in
The result of the indoor air quality is shown in
The result of the indoor air quality is shown in
The result of the indoor air quality is shown in
The result of the indoor air quality is shown in
The result of the indoor air quality is shown in
The result of the indoor air quality is shown in
The result reveals a high concentration of pollutants (SO2, NO2, and CO) in the high-density areas (Wang et al., 2022) compared to the low-density areas (see TableA2) and Diobu, a highly polluted zone and less developed part of the city, has the highest pollution level. A higher population means higher anthropogenic activity, such as commercial, vehicular, and domestic, leading to more pollutants (e.g., Nazar & Niedoszytko, 2022). For instance, in the Diobu, numerous commercial houses utilize generators to produce light energy. These fossil fuel generators are often old and emit a lot of smoke into the atmosphere (Ubong & Osaghae, 2018). Many persons in this part of the city use firewood or kerosene stoves for cooking their meals, which also generate a lot of pollutants (Xie et al., 2022). Small-scale industrial activities in the high-density areas (e.g., roadside food sellers who use firewood for cooking) also contribute to the production of smoke and fumes. Diobu, one of the most polluted residential areas of the study in the high-density areas, has an IAQI range of (201 - 300), which is unhealthy for the citizens. Indoor air pollution could be very harmful and pose a more significant health hazard because many people spend more hours indoors (Rahman & Sarkar, 2006). Subsequently, indoor and outdoor SO2 concentrations were high in the HDA with a maximum of 4.07 ppm in the dry season. This value is high compared to the international limit of 0.02 (TableA4).
Results of the season reveal that seasons influence toxic gas concentrations (Guo et al., 2022; Mor et al., 2022). In terms of seasonal difference, our result indicates that in the high-density area during the dry season, there was a relatively low concentration of SO2 and NO2, while the concentration of CO was high (Figures 5(A)-5(L)). The higher rate of burning activities during the dry season in that region, such as burning the bush to pave the way for farming activities, caused high CO (Chukwu et al. 2022; Sahak et al. 2022). And the burning of waste from homes and from farms after harvest. Other factors that caused the high indoor concentration of CO in the residential area include vehicular exhaust emissions because of the nearness of these areas to major road junctions, use of kerosene stove that emits CO due to incomplete combustion of the flames, and indoor smoking by residents. Lower atmospheric humidity facilitates these activities during the dry season, especially during the harmattan season in the Niger Delta region that occurs from November to February each year (Ogaji et al., 2021). A similar situation was observed in the low-density area as seen in the high-density area (Figures 5(G)-(L)). For example, indoor and outdoor concentrations of CO showed a maximum value of 15.7 ppm in the wet season. The problem here is that the indoor and outdoor mean concentrations of SO2 and NO2 in the high-density area far exceeded the FMEnv and NAAQS permissible limits (TableA4) in the dry and wet seasons is detrimental to human health. In contrast, the HDA mean values of indoor and outdoor concentrations for CO are within both FMEnv and NAAQS permissible limits (see Appendix TableA3 and TableA4) in both the dry and wet seasons. Furthermore, the high indoor NO2 pollution in high-density areas in the dry season may be due to the high volume of vehicle activities observed in the area.
There were more fluctuations in SO2 and NO2 in the dry seasons than in the wet season, while there were more fluctuations of CO in the wet season than in the dry season (See Figures 5(A)-(L)). Both the dry and wet seasons indoor air quality indices computed for high-density areas indicate hazardous indoor air pollution above the EPA standards, i.e., 300 (IAQI > 300) (See TableA2) (USEPA, 2003).
We used a box modelling approach (Davis & Cornwell, 2008) to forecast the concentrations of indoor air quality in the high-density and low-density areas in both the dry and wet seasons based on the outdoor concentrations. All the models have >50% (i.e., R2 = 50% - 90%) with a similarity between the predicted and measured results at a significant level of P = 0.0001. High significance means the predictive model is good with a high level of confidence, which means the high level of pollutants circulating in the city’s indoor and outdoor environment is confirmed and a severe threat to health. Therefore, our model can be used to forecast indoor and outdoor SO2, NO2, and CO concentrations in the high and low-density areas in the dry and wet seasons. Our values were higher than those obtained by Palanivelraja and Manirathinem (2009). They used a linear regression modelling approach to derive a value of 47.0% (R2 = 0.470) and 56.0% (R2 = 0.560) for indoor and outdoor CO respectively. Song et al. (2014) used the same box model approach we used for indoor air quality and obtained R2 between 0.750 and 96, while Mengoli et al. (2022) used the land surface models to predict the dynamics of photosynthesis on land.
The concentrations of indoor air pollutants predicted in this study agree with measured values in natural settings. Thus, the model offers a practical, easy-to-apply methodology with acceptable accuracy for forecasting the concentrations of indoor air pollutants. The model can also serve as a vital tool for indoor air quality risk assessment to evaluate the levels of human exposure in a locality. There should, therefore, be constant monitoring of pollution-generating activities such as the use of firewood, burning of waste, roadside cooking, and the use of old cars that emit smoke in the city to reduce the concentration of pollutants to prevent a public health disaster. Lastly, the government should establish air quality monitoring stations (AQMS) in different residential areas in Port Harcourt.
The high concentration of atmospheric pollutants (SO2, NO2, and CO) in residential areas in both indoor and outdoor environments is detrimental to health because of its ability to cause disease among residents. There should be regulation and control of industrial, commercial, and domestic activities that increase atmospheric gases. Excessive production of smoke in homes should be monitored and stopped. The model developed by the study can be used to predict the concentration of poisonous gases in other parts of the Niger Delta region to ensure accurate results of the concentration of atmospheric pollutants.
We wish to thank the Director of Institute of Natural Resources, Environment and Sustainable Development (INRES), Prof. A.I. Hart, for providing a supporting letter for the lead researcher, M.A. Ubom for facilitating the approval letter from the University and Mr. E. Oti of the Ministry of Urban Development for facilitating access to residential settlements.
The authors declare no conflicts of interest regarding the publication of this paper.
Ogaji, F. M., Obafemi, A., Numbere, A. O., & Ogaji, D. S. (2022). Geospatial Analysis and Modeling of Indoor Air Quality in Some Residential Areas in the Niger Delta, Nigeria. Low Carbon Economy, 13, 1-24. https://doi.org/10.4236/lce.2022.131001
| Sampling point code | Description of sampling point location | Coordinates | Source | Season | Frequency of collection | |
|---|---|---|---|---|---|---|
| SP01 | 5 Owo Street, Diobu | N4˚47'42" | E6˚59'9" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP02 | 4 Owo Street, Diobu | N4˚47'43" | E6˚59'08" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP03 | 11 Owo Street, Diobu | N4˚47'44" | E6˚59'08" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP04 | 7 Owo Street, Diobu | N4˚47'43" | E6˚59'8" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP05 | 8 Owo Street, Diobu | N4˚47'45" | E6˚59'08" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP06 | 2 Ekwulebia Street | N4˚47'46" | E6˚59'9" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP07 | 11 Ekwulobia Street, Diobu | N4˚47'47" | E6˚59'09" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP08 | 4 Ekwulobia Street, Diobu | N4˚47'45" | E6˚59'09" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP09 | 8 Ekwulobia Street, Diobu | N4˚47'4" | E6˚59'8" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP10 | 15 Ekwulobia Street, Diobu | N4˚47'4" | E6˚59'08" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP11 | Ndoki Estate, Town | N4˚45'20" | E7˚02'30" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP12 | Ndoki Estate, Town | N4˚45'25" | E7˚2'14" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP13 | Ndoki Estate, Town | N4˚45'20" | E7˚2'26" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP14 | Ndoki Estate, Town | N4˚45'22" | E7˚2'27" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP15 | Ndoki Estate, Town | N4˚45'22" | E7˚2'27" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP16 | Ndoki Estate, Town | N4˚45'21" | E7˚2'27" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP17 | Ndoki Estate, Town | N4˚45'22" | E7˚2'26" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP18 | Ndoki Estate, Town | N4˚45'21" | E7˚02'27" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP19 | Ndoki Estate, Town | N4˚45'20" | E7˚2'27" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP20 | Ndoki Estate, Town | N4˚45'13" | E7˚02'30" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP21 | 2 Incha Street, Delta Park | N4˚51'12" | E6˚54'20" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP22 | 6 Incha Street, Delta Park | N4˚54'11" | E6˚54'14" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP23 | 8 Incha Street, Delta Park | N4˚54'14" | E6˚54'19" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP24 | Nchia Street, Delta Park | N4˚54'06' | E6˚54'10" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP25 | 9 Degema Street, Delta Park | N4˚53'57" | E6˚54'9" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP26 | Ghana Ama Street, Delta Park | N4˚54'15" | E6˚54'30" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP27 | Ghana Ama Street, Delta Park | N4˚55'01" | E6˚54'17" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP28 | Ghana Ama Street, Delta Park | N4˚54'16" | E6˚54'29" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP29 | Ghana Ama Street, Delta Park | N4˚54'16" | E6˚54'28" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP30 | Ghana Ama Street, Delta Park | N4˚54'15" | E6˚54'28" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP31 | Degema Street, University Park | N4˚51'18" | E6˚54'50" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP32 | Ali Carpe Verde, University Park | N4˚54'23" | E6˚54'37" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP33 | Ali Carpe Verde, University Park | N4˚54'22" | E6˚54'4" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP34 | Preyi Crescent, University Park | N4˚54'26" | E6˚54'42" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP35 | Gambia Ama, University Park | N4˚54'29' | E6˚54'45" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP36 | 2 Nchia Street, Delta Park | N4˚54'3" | E6˚54'25" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP37 | Nchia Street, Delta Park | N4˚54'3" | E6˚54'24" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP38 | Nchia Street, Delta Park | N4˚54'2" | E6˚54'28" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP39 | Nchia Street, Delta Park | N4˚54'1" | E6˚54'28" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| SP40 | Ali Cape Verde, Delta Park | N4˚53'55" | E6˚54'26" | Indoor, Outdoor | Wet, Dry | Morning, Afternoon, Evening |
| Stations | Dry season AQI | Wet season AQI | ||
|---|---|---|---|---|
| HDSP 01 | 429 | Hazardous | 332 | Hazardous |
| HDSP 02 | 291 | Very Unhealthy | 355 | Hazardous |
| HDSP 03 | 316 | Hazardous | 252 | Very Unhealthy |
| HDSP 04 | 333 | Hazardous | 350 | Hazardous |
| HDSP 05 | 293 | Very Unhealthy | 256 | Very Unhealthy |
| HDSp 06 | 213 | Very Unhealthy | 207 | Very Unhealthy |
| HDSp 07 | 240 | Very Unhealthy | 302 | Hazardous |
| HDSp 08 | 466 | Hazardous | 189 | Unhealthy for Sensitive Groups |
| HDSp 09 | 212 | Very Unhealthy | 214 | Very Unhealthy |
| HDSp 10 | 173 | Very Unhealthy | 224 | Very Unhealthy |
| HDSp 11 | 86 | Moderate | 192 | Unhealthy |
| HDSp 12 | 113 | Unhealthy for Sensitive Groups | 347 | Hazardous |
| HDSp 13 | 124 | Unhealthy for Sensitive Groups | 251 | Very Unhealthy |
| HDSp 14 | 199 | Unhealthy | 184 | Unhealthy |
| HDSp 15 | 186 | Unhealthy | 243 | Very Unhealthy |
| HDSp 16 | 94 | Moderate | 235 | Very Unhealthy |
| HDSp 17 | 74 | Moderate | 158 | Unhealthy |
| HDSp 18 | 202 | Very Unhealthy | 39 | Good |
| HDSp 19 | 68 | Moderate | 513 | Hazardous |
| HDSp 20 | 77 | Moderate | 42 | Good |
| HDSp 21 | 55 | Moderate | 36 | Good |
| Pollutant | Acceptable limit | Unit |
|---|---|---|
| CO (ppm) | 10 9 | mg/m3 ppm |
| Ozone (O3) | 120 0.06 | µg/m3 ppm |
| SO2 (24 hours) | 120 | µg/m3 |
| NO2 (24 hours) | 120 | µg/m3 |
(Source: FGN, 2014).
| Pollutant | 24-hour ppm (ug/m3) | Annual ppm (mg/m3) | ||
|---|---|---|---|---|
| WHO | NAAQS | WHO | NAAQS | |
| CO | 9 (FMEnv) | 9 (50 - OSHA), (35 - NIOSH), 25 - ACGIH | 9 (FMEnv) | 9 |
| SO2 | 0.02 | 0.075 | 0.02 | 0.075 |
| NO2 | 0.2 | 0.1 | 0.2 | 0.053 |
(Source: WHO, 2006). EEA-ETC/AQ: European Environmental Agency/Air Quality; OSHA: Occupational Safety and Health Administration; NIOSH: The National Institute for Occupational Safety and Health; ACGIH: The American Conference of Government Industrial Hygienists; 1 part per million (PPM) = 1000 microgram per meter cubed (ug/m3); 1 part per million (PPM) = 1 milligrams/cubic meter (mg/m3).
| Parameter | SSE | MSE | F-stat. | P-value | R2 | MAPE |
|---|---|---|---|---|---|---|
| Model | 8.492 | 8.492 | 23.386 | <0.0001 | 0.5517 | 39.506 |
| Residual sum of squares | 6.899 | 0.363 | ||||
| Total sum of squares | 15.391 |
| Parameter | SSE | MSE | F-stat. | P-value | R2 | MAPE |
|---|---|---|---|---|---|---|
| Model | 5.429 | 5.429 | 318.96 | <0.0001 | 0.9438 | 17.105 |
| Residual sum of squares | 0.323 | 0.017 | ||||
| Total sum of squares | 5.752 |
| Parameter | SSE | MSE | F-stat. | P-value | R2 | MAPE |
|---|---|---|---|---|---|---|
| Model | 19.15 | 19.15 | 241.45 | <0.0001 | 0.927 | 4.661 |
| Residual sum of squares | 1.507 | 0.079 | ||||
| Total sum of squares | 20.657 |
| Parameter | SSE | MSE | F-stat. | P-value | R2 | MAPE |
|---|---|---|---|---|---|---|
| Model | 6.756 | 6.756 | 42.243 | <0.0001 | 0.8105 | 33.483 |
| Residual sum of squares | 1.580 | 0.083 | ||||
| Total sum of squares | 8.336 |
| Parameter | SSE | MSE | F-stat. | P-value | R2 | MAPE |
|---|---|---|---|---|---|---|
| Model | 5.314 | 5.314 | 147.23 | <0.0001 | 0.8857 | 22.905 |
| Residual sum of squares | 0.686 | 0.036 | ||||
| Total sum of squares | 6.000 |
| Parameter | SSE | MSE | F-stat. | P-value | R2 | MAPE |
|---|---|---|---|---|---|---|
| Model | 152.160 | 152.160 | 661.76 | <0.0001 | 0.972 | 13.181 |
| Residual sum of squares | 4.369 | 0.036 | ||||
| Total sum of squares | 156.528 |
| Parameter | SSE | MSE | F-stat. | P-value | R2 | MAPE |
|---|---|---|---|---|---|---|
| Model | 1.613 | 1.613 | 193.78 | <0.0001 | 0.8939 | 72.821 |
| Residual sum of squares | 0.191 | 0.008 | ||||
| Total sum of squares | 1.805 |
| Parameter | SSE | MSE | F-stat. | P-value | R2 | MAPE |
|---|---|---|---|---|---|---|
| Model | 0.122 | 0.122 | 139.00 | <0.0001 | 0.8518 | 62.018 |
| Residual sum of squares | 0.020 | 0.001 | ||||
| Total sum of squares | 0.143 |
| Parameter | SSE | MSE | F-stat. | P-value | R2 | MAPE |
|---|---|---|---|---|---|---|
| Model | 11.114 | 11.114 | 26.592 | <0.0001 | 0.7665 | 12.885 |
| Residual sum of squares | 9.613 | 0.418 | ||||
| Total sum of squares | 20.726 |
| Parameter | SSE | MSE | F-stat. | P-value | R2 | MAPE |
|---|---|---|---|---|---|---|
| Model | 0.517 | 0.517 | 3566.2 | <0.0001 | 0.9936 | 3.387 |
| Residual sum of squares | 0.003 | 0.000 | ||||
| Total sum of squares | 0.521 |
| Parameter | SSE | MSE | F-stat. | P-value | R2 | MAPE |
|---|---|---|---|---|---|---|
| Model | 0.059 | 0.059 | 205.73 | <0.0001 | 0.8995 | 17.541 |
| Residual sum of squares | 0.007 | 0.000 | ||||
| Total sum of squares | 0.066 |
| Parameter | SSE | MSE | F-stat. | P-value | R2 | MAPE |
|---|---|---|---|---|---|---|
| Model | 46.414 | 46.414 | 273.55 | <0.0001 | 0.923 | 9.469 |
| Residual sum of squares | 3.903 | 0.170 | ||||
| Total sum of squares | 50.316 |