Abstract
Purpose
This study aimed to characterize the concentrations of indoor pollutants (such as carbon dioxide (CO2), ozone (O3), nitrogen dioxide (NO2) and sulfur dioxide (SO2), as well as particulate matter (PM) (PM1, PM2.5 and PM10) in Akure, Nigeria, as well as the relationship between the parameters’ concentrations.
Design/methodology/approach
The evaluation, which lasted four months, used a low-cost air sensor that was positioned two meters above the ground. All sensor procedures were correctly carried out.
Findings
CO2 (430.34 ppm), NO2 (93.31 ppb), O3 (19.94 ppb), SO2 (40.87 ppb), PM1 (29.31 µg/m3), PM2.5 (43.56 µg/m3), PM10 (50.70 µg/m3), temperature (32.4°C) and relative humidity (50.53%) were the average values obtained. The Pearson correlation depicted the relationships between the pollutants and weather factors. With the exception of April, which had significant SO2 (18%) and low PM10 (49%) contributions, NO2 and PM10 were the most common pollutants in all of the months. The mean air quality index (AQI) for NO2 indicated that the AQI was “moderate” (51–100). In contrast to SO2, whose AQI ranged from “moderate” to “very unhealthy,” O3's AQI ranged from “good” (50) to “unhealthy” (151–200). Since PM1, PM2.5 and PM10 made up the majority of PC1’s contribution, both PM2.5 and PM10 were deemed “hazardous.”
Practical implications
The practical implication of indoor air pollution is long-term health effects, including heart disease, lung cancer and respiratory diseases such as emphysema. Indoor air pollution can also cause long-term damage to people’s nerves, brain, kidneys, liver and other organs.
Originality/value
Lack of literature in terms of indoor air quality (IAQ) in Akure, Ondo State. With this work, the information obtained will assist all stakeholders in policy formulation and implementation. Again, the low-cost sensor used is new to this part of the world.
Keywords
Citation
Abulude, F.O., Suriano, D., Oluwagbayide, S.D., Akinnusotu, A., Abulude, I.A. and Awogbindin, E. (2023), "Study on indoor pollutants emission in Akure, Ondo State, Nigeria", Arab Gulf Journal of Scientific Research, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/AGJSR-05-2023-0201
Publisher
:Emerald Publishing Limited
Copyright © 2023, Francis Olawale Abulude, Domenico Suriano, Samuel Dare Oluwagbayide, Akinyinka Akinnusotu, Ifeoluwa Ayodeji Abulude and Emmanuel Awogbindin
License
Published in Arab Gulf Journal of Scientific Research. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
Introduction
Indoor air quality (IAQ) is the term used to describe the air quality both inside and outside of buildings and other structures, particularly with regard to how it affects the well-being and convenience of building inhabitants. Your likelihood of experiencing indoor health issues can be decreased by being aware of and in control of common indoor contaminants. IAQ issues are acknowledged as significant health risks in low-, middle- and high-income nations alike. Because people spend a large percentage of their time indoors, indoor air is equally significant. Indoor air pollution impacts demographic groups that are especially sensitive due to their health status or age in homes, daycare centers, retirement homes and other unique contexts. Following an individual’s contact with a pollutant or multiple exposures, some health impacts may become apparent quickly. Such symptoms include fatigue, headaches, nausea and throat, nose and eye discomfort. These immediate effects are typically transient and manageable. If the source of the pollution can be found, sometimes the only therapy required is to stop the patient from being exposed to it. Other consequences for health could manifest years after contact, only after prolonged or recurrent exposure or both. These side effects, which can include cancer, heart disease and certain respiratory illnesses, can be fatal or extremely disabling. Even if no symptoms are present, it is advisable to work on boosting the IAQ in your house (USEPA, Updated, 2022).
Along with climate change, air pollution is one of the largest environmental dangers to human health (WHO, 2021). Increasing air quality can support efforts to reduce emissions and combat climate change. Reducing emissions will also increase air quality. Countries that strive to meet these benchmark levels will be reducing global climate change while also protecting public health.
According to estimates, exposure to air pollution results in 7 million premature deaths per year and millions more healthy years of life lost. This may result in slowed lung development and function, respiratory infections and worsened asthma in children. The most frequent causes of early death in adults attributed to outdoor air pollution are ischemic heart disease and stroke, though proof is now mounting for other outcomes like diabetes and neurological diseases. This compares the disease burden caused by air pollution to other significant global health concerns, including poor diet and cigarette use. According to a fast scenario analysis conducted by the World Health Organization (WHO), the world could avert over 80% of PM2.5-related fatalities if current air pollution levels were lowered to those suggested in the new recommendation. The burden of sickness would also be reduced as a result of meeting intermediate goals, with large populations and nations with high PM2.5 concentrations seeing the greatest benefits.
In six pollutants, where research on the impacts of exposure on health is currently at its greatest, WHO’s revised guidelines prescribe air quality levels (WHO, 2021). Particulate matter (PM), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2) and carbon monoxide (CO) are examples of what are known as “classical pollutants,” but when these are addressed, other harmful pollutants are also impacted.
Particularly relevant to public health are the health concerns linked to PM with a diameter of 10 and 2.5 microns (m), respectively. Both PM2.5 and PM10 have the ability to penetrate deeply into the lungs, but only PM2.5 has the ability to enter the bloodstream, which predominantly affects the cardiovascular and respiratory systems while also having an impact on other organs. PM is largely produced by the combustion of fuel in a variety of industries, including transportation, energy, residential buildings, businesses and agriculture. The International Agency for Research on Cancer (IARC) of the WHO categorized outdoor air pollution and particle matter as carcinogenic in 2013.
Governments from all over the world concur on the significance of air quality monitoring in order to reduce the effects of air pollution on human health and the environment (Yi et al., 2015). Because of this, industrialized nations all over the world have categorically adopted methods that support promoting exposure monitoring of air contaminants. For instance, a network of air quality monitors is typically employed in developed nations to gather ongoing information on the most significant air contaminants in metropolitan areas (Solomon, Hopke, Froines, & Scheffe, 2008). This approach provides industrialized countries with information on air quality that aids in evaluating air pollution exposure and its health effects. A variety of assessment methods have been created to assess exposure to air pollution in order to achieve this (Fisher et al., 2021). Regulation pollutants, such as nitrogen oxides (NOx, NO and NO2) and PM (PM10 and PM2.5), among others, have been acknowledged worldwide as environmental priority air pollutants because they can endanger both human health and the environment (Mustafić et al., 2012; Castell et al., 2017). This method makes use of permanent monitoring stations that can gather these contaminants. These operations are regarded as expensive because they range in price from €5,000 to €30,000 (Castell et al., 2017; Madonsela, Maphanga, & Mahlakwana, 2023). According to Forbes and Rohwer (2008), a lack of funding is the main cause of the scant availability of data on air pollution exposure in Africa. The prohibitive costs associated with the installation and upkeep of stationary monitoring stations prevent developing nations from conducting effective air quality exposure (Castell et al., 2017). Therefore, before being used in the fields, the low-cost sensors were conceived, designed and tested. The placement of several sensors across a limited region could have the advantage of enhancing standard monitoring networks with more precise geographic and temporal measurements (Table 1). The inexpensive sensors are helpful in both indoor and outdoor settings.
One of the primary research gaps in this study is the lack of comprehensive data specific to Akure and Ondo State regarding IAQ and pollutant emissions. Existing studies may not adequately cover this region, making it difficult to assess the extent of the problem accurately. Also, the research may need to identify specific pollutants that are prevalent in indoor environments in Akure, which may differ from those in other regions. Understanding the local sources of indoor pollution is crucial for effective mitigation strategies. Again, the study delves into the health effects of indoor pollutants on the local population in Akure. This could involve assessing the prevalence of respiratory diseases, allergies and other health conditions associated with indoor air pollution. Investigating the effectiveness of existing or potential mitigation strategies for reducing indoor pollutant emissions in Akure, Ondo State, is essential. Lastly, assessing the level of awareness and understanding of IAQ and pollutants among the local population is important. This study may identify gaps in knowledge and opportunities for behavior change interventions. By addressing these research gaps, a study on indoor pollutant emissions in Akure, Ondo State, Nigeria, can provide valuable insights into local air quality issues, contribute to public health knowledge and inform effective strategies for reducing indoor pollution in the region.
The study might have a limited sample size, which can restrict the representativeness of the findings. A small sample might not adequately capture the diversity of indoor pollutant sources and behaviors in Akure. Also, the study is focused on a specific geographic location (Akure, Ondo State), which may limit the generalizability of the results to other regions in Nigeria or globally. Again, indoor pollutant sources and behaviors can vary significantly across different regions. The study might not account for changing factors, such as evolving indoor pollutant sources due to technological advancements or changes in household behaviors. Acknowledging these limitations is essential for a comprehensive understanding of the study’s scope and applicability. Researchers and policymakers should consider these constraints when interpreting the findings and planning future research or interventions related to IAQ in Akure or similar settings.
In this region of the country, there is a lack of data on air quality, particularly IAQ. It would be appropriate if this gap were to be filled since the data produced has been a source of information for all stakeholders in mitigating air pollution worldwide. By addressing these research gaps, a study on indoor pollutant emissions in Akure, Ondo State, Nigeria, can provide valuable insights into local air quality issues, contribute to public health knowledge and inform effective strategies for reducing indoor pollution in the region. This study’s objective is to assess the temperature, CO2, NO2, SO2, O3, PM1, PM2.5 and PM10 concentrations in an indoor setting in Akure, Ondo State, Nigeria. It is believed that this study would advance understanding and aid in the formulation of policies in Ondo State and throughout Nigeria.
Materials and methods
Oba-Ile town (Figure 1), located in Akure, Ondo State, Nigeria (latitude/longitude: 7 16 04.4 N 5 14 29.1 E), is the monitoring station used in this study. The community is surrounded by towns like Araromi, Owode, Akure, Igoba and Osi. All of the residents of these towns engage in extensive farming, which results in numerous forest fires, particularly during the dry season when farming and animal hunting are at their highest. The main road in the town between Akure, the capital city of Ondo State and Owode, where the state’s airport is located, as well as the expressway connecting Ondo State to the west, east and northern parts of the country, are particularly busy with motorcycles, cars, minibuses and trailers. The study area is a residential building surrounded by unpaved roads (Figure 2). The building is made of a big parlor and three rooms with no extractors. At different times of the day, up to six people might be found in the flat (Figure 1), where typical everyday activities and events took place – utilizing a gas stove to cook meals (frying and baking), applying insecticides, utilizing a mosquito coil, lighting candles, etc. – all of which resulted in varied quantities of CO2, SO2, NO2, O3, PM1, PM2.5 and PM10. The time of the various events was not carefully noted because the goal of this study was not to link gas concentration levels to a specific type of source, but some clues are given in relation to the most important events. The flat is divided into daylight and nighttime portions by a door. The door dividing the living room from the nighttime section was kept closed during the trial, and the experimental apparatus was put in the daytime space.
A group of researchers from the Italian National Agency for New Technologies, Energy and the Environment (ENEA), Department of Sustainable Development, Brindisi Research Center, Italy, designed and tested SentinAir (Plate 1) the low-cost sensor used for this study (Suriano, 2020, 2021). The sensor was placed at the center of the building near the kitchen. The majority of the meals are prepared in the completely electric cooker, which includes a hob, oven and grill since there is no central gas heating in the house. The sensor protocol was followed to the letter. For 4 months, the sensor measured temperature, relative humidity, PM1, PM2.5, PM10, CO2, O3, NO2 and SO2. Four meters above the ground, the sensor box was installed on a rack. The distance between the residential building and the unpaved roads was around 6 meters.
At the conclusion of the evaluation, the collected sensor data was examined and evaluated. The basic description, the Pearson sample correlation coefficient (r), the matrix plot and the PCA were all computed using the Minitab version.
In accordance with World Health Organization recommendations, the mean PM2.5 and PM10 values were calculated (WHO, 2023). The 24-h mean for PM2.5 is 10 µg/m3, the yearly mean is 25 µg/m3 and the standards for PM10 are 20 µg/m3 and 50 µg/m3, respectively. These mean values were used to determine the air quality index (AQI). Equation (1) was used to generate the indices for each pollutant (the average of the total sum from each sampling location) (USEPA, 2018):
The health consequences of breathing dirty air for a few hours or days are what the AQI is worried about. The AQI ratings (AirNow, 2018) are shown in Table 6. The table also shows the PM2.5 and PM10-specific pollutant concentration ranges. Generally speaking, the better the air quality, the lower the AQI is (USEPA, 2014).
Results and discussion
Table 2 depicts the basic description of the parameters studied. The minimum and maximum values for the month of January are as follows: CO2 (306.90–561 ppm), NO2 (0.00–266.00 ppb), O3 (0.00–45.00 ppb), SO2 (0.00–95.00 ppb), PM1 (22.00–37.00 µg/m3), PM2.5 (33.00–53.00 µg/m3) and PM10 (37.00–67 µg/m3). The standard deviations of O3, PM1, PM2.5 and PM10 were less than 10, meaning low variations in the evaluations for the month. The coefficient of variation (%) also depicted low variations in CO2, O3, PM1, PM2.5 and PM10 (<12%). The mean results for the month of April showed as follows: CO2 (292.99 ppm), NO2 (95.71 ppb), O3 (26.50 ppb), SO2 (104.37 ppb), PM1 (16.43 µg/m3), PM2.5 (25.17 µg/m3) and PM10 (28.24 µg/m3). The Q1 and Q3 were CO2 (224.03 and 310.38), NO2 (64 and 111), O3 (22 and 31), SO2 (102 and 127), PM1 (10 and 19), PM2.5 (16 and 29) and PM10 (17 and 32). The overall results, which are the summary of the four months, depicted the minimum and maximum values of the months at a glance, while the skewness and kurtosis were CO2 (0.4 and 4.79), NO2 (1.24 and 1.40), O3 (0.38 and −0.25), SO2 (2.51 and 6.43), PM1 (3.80 and 24.30), PM2.5 (3.60 and 32.16) and PM10 (2.95 and 25.94). There were differences in the concentrations obtained in the different months. For example, the PM values obtained for the months of November, December and January were higher than that of April. The simple reason was that there was less rainfall, RH and high temperatures in these months (Zhang & Jiang, 2017).
From Table 3, it could be observed that other studies showed higher values of CO2 than our study. The following could explain the differences in the results: (1) Since CO2 is extensively absorbed by the atmosphere after being released by factories, automobiles and other sources, its concentration is rather constant across the world. Depending on how near sources or sinks you are, there may be some variations (Climate Portal, 2021); (2) plants absorb more CO2 through photosynthesis throughout the day or in the spring and summer than they emit through respiration (Copernicus Atmosphere Monitoring Service, 2019). As a result, air concentrations of CO2 decline. Due to the Industrial Revolution and the exponential growth of manufacturing activity worldwide, the concentration of CO2 has risen particularly (Blokhin, 2023). In comparison to other research, this study’s average NO2 level was higher (Table 4). Cooking, baking, mosquito coils, garbage burning, candle burning, outdoor vehicle emissions and other sources of pollutants could be the causes (Lee & Wang, 2006). In addition, CO, VOCs, SO2, NO2, PM2.5 and PM10 were discovered in the mosquito coil’s emissions, according to Hogarh et al. (2018). The O3 values in our study are higher than those found in earlier research. There are numerous causes to blame for the disparity, including (i) O3 is generated on warm, sunny days when the temperature is high and the relative humidity is low; (ii) the fact that O3 levels are frequently higher in rural than urban regions and (iii) that sunlight causes pollutants released by vehicles, power plants, industrial boilers, refineries, chemical plants and other sources to chemically react with one another is still a truth (USEPA, 2023). These facts are supported by the study’s findings because of the study location’s rural setting, low RH and high temperature. The study’s PM1 is higher than the levels recorded for Indonesia (28.25 µg/m3) and Cyprus (10 µg/m3) by Syafei et al. (2020) and Konstantinou et al. (2022), respectively. The PM2.5 levels reported in this study are significantly lower than those discovered in Bangladesh (159.0 µg/m3), Saudi Arabia (1100.00 µg/m3), China (222.0 µg/m3) and India (149.4 µg/m3) Zaman et al. (2021), Niu, Guinot, Cao, Xu, and Sun (2015) and Taushiba et al. (2023), respectively, but significantly higher than 69.28 µg/m3 and 11.5 µg/m3 by Abulude et al. (2021) and Ezeonyejiaku, Okoye, Ezeonyejiaku, and Obiakor (2022), respectively. The PM10 results from our investigation were lower than those of Salama and Berekaa (2016), Niu et al. (2015), Filonchyk and Yan (2018) and Shafi and Khelif (2021) but higher than the one (54.9 µg/m3) obtained by Oweisana, Gobo, Daka, and Ideriah (2021). It was found that PM2.5 exceeded the WHO standard by 30 times (24 hours) and 88 times (annually), while PM10 exceeded it by 10 times (24 hours) and 32 times (annually) when the pollutants' data from this study were compared with the WHO 2021 standard (Table 4). NO2 occurred 3.65 times annually and 14.6 times continuously. O3 was 1.58 times during peak season, although less than 100 was advised for exposure for 8 hours, while SO2 was 7.9 times for 24 hours. The consequences of the discrepancies may lead to health issues or even death. The issue with WHO scientific guidelines is that they have a global viewpoint and ignore the distinct economic circumstances of each country. The primary goal of the air quality standards that make up the legal framework for the repeated exposure threshold and limit is to develop a standardized method of defending against the detrimental effects of air pollution on both human health and the ecosystem as a whole (WHO, 2021).
The contributions of each pollutant for several months were shown in the donut-shaped visual diagrams (Figure 3). In January, April, November and December, respectively, NO2 (12%), PM10 (61%), SO2 (18%), PM10 (49%), NO2 (16%), PM10 (62%) and NO2 (10%), PM10 (63%) were the concentrations. The average overall result supported the findings. In this investigation, average pollutant concentrations were discovered together with low secondary pollutant concentrations. It is connected to RH and typical temperatures. The particle concentrations are highest at 10 nm in diameter, but they get progressively higher as the size goes up. Bousiotis et al. (2023) made this observation; however, their findings were related to higher-than-average temperatures, the planetary boundary layer (PBL) heights, wind speeds and lower-than-average RH.
Using Pearson’s correlation analysis, the study’s parameters’ correlation coefficient was determined. RH and CO2 (p = 0.46) and PM1 and CO2 (p = 0.33), PM2.5 and CO2 (p = 0.32) and PM10 and CO2 (p = 0.34) all showed only minor associations. RH and Temp, however, displayed marginally significant negative associations (Figure 4 and Table 5). Strong positive associations between PM1, PM2.5 and PM10 were all observed (p = 0.97). Significant correlations between pairs typically imply that they have a combined or common origin, whereas weak correlations show that they have separate origins (Baguma et al., 2022; Opolot et al., 2023). As a result, the couples’ positive correlation points to possible anthropogenic and related pathways into the terrestrial environment as a result of their shared origins, reciprocal dependences and identical transport patterns.
Consider the AQI as an indicator with a scale of 0 to 500. The quantity of air pollution and the resulting health risk increase with increasing AQI values (AirNow, updated 2018). As an illustration, an AQI score of 50 or less indicates healthy air quality, whereas one of over 300 indicates hazardous air quality (Table 6). The short-term global ambient air quality guidelines for the protection of public health are typically equivalent to an ambient air level of 100 for each pollutant. In general, AQI levels of 100 or less are considered to be good. Air quality is unhealthy when AQI values are over 100 – initially for some vulnerable groups of individuals, then for everybody as AQI values rise. There are six groupings that make up the AQI. A varying level of medical concern relates to each group. Additionally, each group has a unique color. Individuals can immediately detect if the air quality in their neighborhoods has reached harmful levels (Wambebe & Duan, 2020). The AQI of the five contaminants assessed in this investigation is shown in Table 6. They were shown the mean, maximum, minimum and range. The maximum AQI was 152 (unhealthy for sensitive groups), but the mean of the NO2 showed that the AQI was within the range of “moderate” (51–100). O3 AQI ranged from “good” (50) to “unhealthy” (151–200), whereas SO2 ranged from “moderate” to “very unhealthy.” Both PM2.5 and PM10 were classified as “hazardous,” but PM2.5 was more prevalent (>400) than PM10. Table 7 shows how these AQI incidences have an impact. A PM2.5 AQI of 121, for instance, indicates that some members of the general population and members of vulnerable groups may experience health impacts. The general public is likely to be impacted,” while “Health warning of emergency conditions: everyone is more likely to be affected” is displayed on >400.
Extraction of the eigenvalues and eigenvectors from the correlation matrix was used to replace principal component analysis (PCA) and determine the number of significant principal components and the proportion of total variance that each one accounted for. PM1, PM2.5 and PM10 accounted for the majority of PC1’s contribution of 38.20% of the variance. The major contributor to PC2’s explanation of 24.5% of the variance is temperature. The findings (Table 8) indicate that they might explain 77.30% of the entire variance based on eigenvalues greater than 1 at p > 0.05 (Zhang, Li, Zhang, Zhao, & Norback, 2014) RH, CO2 and O3. In PC3, the eigenvalue (1.32) explained approximately 14.50% of the overall variation. This principal component (PC) can be seen as a representation of effects from single points, such as frying, cooking and insecticide application.
The variables with the greatest impact on each component are shown on the loading plot in Figure 5. The figure shows loadings from −1 to 1. Loadings that are near −1 or 1 show that the variable has a significant impact on the component. Loadings that are not far from 0 show that the variable has little effect on the component. The variable factor map shown in Figure 5 shows which PCA quadrant each individual pollutant belonged in. The first group (top right) consists of climatic variables (RH) and pollutants (CO2). PM1, PM2.5 and PM10 are in the second group in quadrant 2, which is in the center (bottom right). The third group includes the quadrant three elements of temperature: SO2, NO2 and O3. The first and third groups have a significant impact on the components, as seen above.
The dendrogram diagram and the eight amalgamation steps are shown in Table 9 and Figure 6, respectively. The similarities are listed in Table 9, ranging from 15.97 to 99.46. The similarities displayed wide variances, and there were 2, 3 and 2 clusters. To assess whether there were any existing parallels among the study’s obtained parameters, cluster analysis was also conducted. Figure 6 shows the dendrogram of several contaminants and climatic factors derived using Ward’s method of linking and Euclidean distance as a comparison metric. The findings indicated that the study’s parameters (also known as variables) were divided into two main clusters. CO2, RH, PM1, PM2.5 and PM10 combined to create cluster 1, whereas temperature, SO2, NO2 and O3 made up cluster 2. The findings suggested that a certain cluster of contaminants may have come from anthropogenic sources (Kabir et al., 2022; Abulude, Oluwafemi, Arifalo, Elisha, & Kenni, 2023).
Conclusion
The study used a cheap sensor to assess the IAQ in Akure, Ondo State, Nigeria. Temperature, RH, PM1, PM2.5, PM10, CO2, SO2, NO2, O3 and other parameters were resolute. The findings revealed that (i) the concentrations of the pollutants were above the WHO 2021 guidelines, (ii) there were associations between the pollutants and meteorological parameters and (iii) the majority of the pollutants in PC1 were PM1, PM2.5 and PM10. According to the pollutants' AQI, certain people in general and those in sensitive groups might have health effects.
Figures
Literature review of studies on indoor assessment
| City, country | Study | Results | Reference |
|---|---|---|---|
| Dhaka (Bangladesh) | Indicators of hospital IAQ and toxicity potentials | IAQ indicators had an overall average concentration of 104.1 ± 67.6 (PM1), 137.4 ± 89.2 (PM2.5), and 159.0 ± 103.3 (PM10µg/m3; 0.11 ± 0.02 (NO2), 1047.1 ± 234.2 (CO2), and 176.5 ± 117.7 (TVOC) ppm | Zaman et al. (2021) |
| Sulaymaniyah (Iraq) | Evaluation of CO2 levels, interior temperature, and relative humidity | Buildings with non-centralized heating and unventilated systems have higher CO2 levels than those with centralized systems Thermal comfort is influenced by weather and building placement | Alshrefy, Yousif, AL-Rifaee, and Mohammed (2020) |
| Cairo (Egypt) | IAP in houses varies seasonally | In the summer, when ventilation rates are higher, IAP is heavily influenced by atmospheric levels, but interior concentrations are more influenced by indoor sources in the winter, when indoor activities are more prevalent and ventilation is lower In both seasons, the volume of rooms and the number of occupants had an impact on indoor PM, CO, and CO2 levels | Abdel-Salam (2021) |
| Akure (Nigeria) | Evaluation of Akure, South West, Nigeria’s indoor air quality | Indoor PM1, PM2.5, and PM10 averages were 11.81, 10.03, and 7.242 µg/m3, respectively | Abulude, Fagbayide, Akinnusotu, Makinde, and Elisha (2019) |
| Addis Ababa (Ethiopia) | Cook-stove indoor air pollution in Ethiopia | Clean, upgraded, and conventional stoves’ geometric means of PM varied from 10.8 to 235, 23.6–462, and 36.4–591 µg/m3, respectively | Embiale, Chandravanahi, Zewge, and Sahle-Demennie (2020) |
| Desa, Malaysia | An evaluation of air pollution at a construction site in Malaysia | The outcome reveals that the mean PM10 levels were 62.71 µg/m3, the average PM2.5 levels was 18.32 µg/m3, and the average PM1 concentration is 14.04 µg/m3. When compared to PM2.5 and PM1, PM10 has the highest reading. The particulate matter content and climatic parameters do not significantly correlate. There is a strong correlation between some variables and the concentrations of CO, CO2, SO2, and NO2 | Saudi, Nurulshyha, Mahmud, and Rizman (2017) |
| Nsukka, Nigeria | NO2, SO2, and O3 indoor concentration levels in homes in Nsukka, Nigeria | In the kitchens, the indoor concentrations of NO2, SO2, and ozone range from 15 to 722 µg/m3 (median: 174, IQR: 74–336 µg/m3), 3–101 µg/m3 (median: 5, IQR: 4–9 µg/m3), and 2–46 µg/m3(median: 10, IQR: 5–15 µg/m3). Contrary to SO2 (3–4 µg/m3) and O3 (14–20 µg/m3) median levels, which were respectively similar in all rooms within the urban home, NO2 (94, IQR: 64–175 µg/m3) concentrations in kitchens were at least two times higher than in other rooms | Agbo, Walgraeve, Eze, Ugwuoke, and Ukoha (2021) |
| Taiyuan, China | Among Students in Chinese Schools: A Longitudinal Investigation of Sick Building Syndrome (SBS) in Relation to SO2, NO2, O3, and PM10 | There were favorable correlations between the contaminants and the newly developing symptoms of the skin, mucosa, and overall health | Zhang et al. (2014) |
| Coimbra, Portugal | Primary schools air quality exploratory research | Particularly in the fall and winter, the highest concentration levels discovered inside the rooms were crucial (5,320 ppm). In a few schools, the highest concentrations of VOC and PM10 average concentrations surpassed the legal standard. CO, formaldehyde, NO2, SO2, and O3 levels (risk) were not significant | Ferreira and Cardoso (2013) |
Basic description of the results
| CO2 (ppm) | NO2 (ppb) | O3 (ppb) | SO2 (ppb) | PM1 (µg/m3) | PM2.5 (µg/m3) | PM10 (µg/m3) | Temp (oC) | RH (%) | |
|---|---|---|---|---|---|---|---|---|---|
| January | |||||||||
| Mean | 399.85 | 79.40 | 19.08 | 26.19 | 28.17 | 42.14 | 51.16 | 32.38 | 54.82 |
| Std Dev | 47.17 | 47.48 | 6.59 | 21.66 | 2.70 | 4.08 | 5.81 | 1.76 | 4.88 |
| CoeffVar | 11.80 | 59.80 | 34.56 | 82.72 | 9.58 | 9.69 | 11.36 | 5.44 | 8.91 |
| Min | 306.90 | 0.00 | 0.00 | 0.00 | 22.00 | 33.00 | 37.00 | 26.50 | 46.40 |
| Q1 | 363.00 | 54.00 | 15.00 | 0.00 | 26.00 | 39.00 | 47.00 | 26.50 | 46.40 |
| Q3 | 423.40 | 79.50 | 22.00 | 44.00 | 30.00 | 45.00 | 55.00 | 33.50 | 55.95 |
| Max | 561.00 | 266.00 | 45.00 | 95.00 | 37.00 | 53.00 | 67.00 | 35.20 | 69.80 |
| Skewness | 0.97 | 2.42 | 1.50 | 0.43 | 0.51 | 0.34 | 0.31 | −1.52 | 1.50 |
| Kurtosis | 0.41 | 5.55 | 2.94 | −0.05 | 0.03 | −0.28 | −0.30 | 1.71 | 1.66 |
| April | |||||||||
| Mean | 282.99 | 95.71 | 26.50 | 104.37 | 16.43 | 25.17 | 28.24 | 39.26 | 33.02 |
| Std Dev | 93.74 | 52.32 | 7.90 | 41.69 | 12.34 | 19.37 | 21.64 | 3.06 | 9.34 |
| CoeffVar | 33.13 | 54.66 | 2.81 | 39.94 | 75.13 | 76.96 | 76.63 | 7.81 | 28.28 |
| Min | 274.60 | 22.00 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | 26.50 | 22.80 |
| Q1 | 224.03 | 64.00 | 22.00 | 102.00 | 10.00 | 16.00 | 17.00 | 37.60 | 26.10 |
| Q3 | 310.38 | 111.00 | 31.00 | 127.00 | 19.00 | 29.00 | 32.00 | 41.40 | 37.18 |
| Max | 882.70 | 356.00 | 58.00 | 203.00 | 178.00 | 288.00 | 306.00 | 43.60 | 71.50 |
| Skewness | 1.69 | 1.75 | 0.41 | −1.66 | 6.68 | 7.09 | 6.20 | −1.23 | 1.57 |
| Kurtosis | 4.18 | 2.86 | 0.61 | 1.74 | 72.93 | 78.93 | 64.37 | 2.09 | 2.55 |
| November | |||||||||
| Mean | 451.26 | 112.37 | 21.85 | 39.31 | 24.06 | 35.98 | 41.23 | 32.48 | 55.77 |
| Std Dev | 92.06 | 76.28 | 15.21 | 93.08 | 13.70 | 21.44 | 24.24 | 3.23 | 11.30 |
| CoeffVar | 20.40 | 67.88 | 69.59 | 236.76 | 56.94 | 59.59 | 58.78 | 9.93 | 20.27 |
| Min | 217.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 4.00 | 23.40 | 31.70 |
| Q1 | 391.08 | 58.00 | 9.00 | 0.00 | 17.00 | 25.00 | 26.00 | 30.00 | 47.98 |
| Q3 | 495.70 | 135.00 | 35.00 | 8.00 | 27.00 | 41.00 | 50.00 | 35.10 | 64.00 |
| Max | 1307.60 | 296.00 | 90.00 | 316.00 | 267.00 | 443.00 | 481.00 | 40.40 | 105.40 |
| Skewness | 1.74 | 1.17 | 0.38 | 2.27 | 5.15 | 5.58 | 4.60 | 0.30 | 0.14 |
| Kurtosis | 10.13 | 0.17 | −0.91 | 3.41 | 63.96 | 70.34 | 54.13 | −0.72 | 0.40 |
| December | |||||||||
| Mean | 453.35 | 70.66 | 16.54 | 25.87 | 37.45 | 55.25 | 65.05 | 30.62 | 50.43 |
| Std Dev | 122.70 | 58.73 | 13.00 | 22.92 | 15.00 | 22.18 | 22.61 | 4.88 | 14.18 |
| CoeffVar | 27.06 | 76.61 | 78.43 | 88.58 | 39.95 | 40.15 | 34.76 | 15.93 | 28.11 |
| Min | 32.90 | 0.00 | 0.00 | 0.00 | 20.00 | 22.00 | 29.00 | 17.50 | 17.50 |
| Q1 | 377.35 | 26.25 | 4.00 | 1.00 | 29.00 | 44.00 | 53.00 | 27.80 | 41.30 |
| Q3 | 509.87 | 105.00 | 26.00 | 41.00 | 42.00 | 62.00 | 72.00 | 33.60 | 58.90 |
| Max | 1464.90 | 339.00 | 95.00 | 92.00 | 241.00 | 403.00 | 430.00 | 53.90 | 276.00 |
| Skewness | 0.90 | 0.90 | 0.51 | 0.62 | 3.53 | 4.26 | 4.50 | 0.56 | 1.95 |
| Kurtosis | 6.14 | 1.25 | 0.49 | −0.33 | 22.76 | 33.54 | 37.99 | 2.53 | 18.91 |
| Overall | |||||||||
| Mean | 430.34 | 93.31 | 19.94 | 40.87 | 29.31 | 43.56 | 50.70 | 32.46 | 50.53 |
| Std Dev | 120.23 | 67.48 | 13.776 | 67.24 | 16.03 | 24.08 | 26.6 | 4.84 | 14.24 |
| CoeffVar | 27.94 | 72.32 | 69.02 | 164.51 | 54.69 | 55.29 | 52.76 | 14.90 | 28.17 |
| Min | 332.90 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | 17.50 | 17.50 |
| Q1 | 357.40 | 52.00 | 9.00 | 0.00 | 20.00 | 31.00 | 34.00 | 29.60 | 40.00 |
| Q3 | 494.90 | 114.00 | 30.00 | 47.00 | 34.00 | 50.00 | 62.00 | 35.40 | 60.50 |
| Max | 1464.90 | 365.00 | 95.00 | 316.00 | 267.00 | 443.00 | 481.00 | 53.90 | 76.00 |
| Skewness | 0.74 | 1.24 | 0.38 | 2.51 | 3.80 | 3.60 | 2.95 | 0.25 | 0.83 |
| Kurtosis | 4.79 | 1.40 | −0.25 | 6.43 | 24.30 | 32.16 | 25.94 | 0.74 | 8.19 |
Comparisons of results of this study with others
| City, country | Results (mean) | References | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| CO2 | NO2 | O3 | SO2 | PM1 | PM2.5 | PM10 | Temp | RH | ||
| Akure, Nigeria | 430.34 | 93.31 | 19.94 | 40.87 | 29.31 | 43.56 | 50.70 | 32.46 | 50.53 | This Study |
| Lucknow City, India | – | – | – | – | – | 149.4 | – | – | – | Taushiba et al. (2023) |
| Dhaka, Bangladesh | 1047.1 | 0.11 | – | – | 104.1 | 137.4 | 159.0 | – | – | Zaman et al. (2021) |
| Twelve Global Cities | 567 | – | – | – | – | – | – | 27.00 | 66.00 | Kumar et al. (2022a, 2022b) |
| Twelve Global Cities | – | – | – | – | 45.00 | 65.00 | – | – | – | Kumar et al. (2022a, 2022b) |
| Abha, Saudi Arabia | 650 | – | – | – | 4000 | 1100.00 | 90.00 | – | – | Algarni, Khan, Khan, and Mubarak (2021) |
| Dammam, Saudi Arabia | 963 | 0.19 | 0.02 | 1.46 | 240.00 | 192.00 | 442.00 | 40.4 | 42.3 | Salama and Berekaa (2016) |
| Xi’an, China | 622.5 | – | – | – | 148.0 | 222.0 | 333.0 | 17.1 | 74.4 | Niu et al. (2015) |
| Minneapolis, USA | 564.0 | 15.40 | 25.5 | – | – | 35.5 | – | – | – | Gonzalez, Boies, Swanson, and Kittelson (2022) |
| Cyprus | 518.0 | – | – | – | 10.60 | 14.60 | 16.10 | 29.30 | 46.40 | Konstantinou et al. (2022) |
| Surabaya City, Indonesia | – | 0.10 | – | – | 28.35 | 43.60 | 70.96 | – | Syafei et al. (2020) | |
| Saudi Arabia | – | 0.09 | 0.09 | 0.61 | – | – | – | 37.29 | 29.82 | Radaideh (2017) |
| Obrikom and Omoku, Nigeria | 686.0 | 0.02 | – | 0.05 | – | 25.20 | 55.9 | 27.50 | 74.30 | Oweisana et al. (2021) |
| Dammam, Saudi Arabia | 406.30 | 0.00 | 0.07 | 0.60 | – | 118.30 | 195.20 | 23.70 | 24.00 | Shafi and Khelif (2021) |
| Lanzhou, China | – | – | – | – | – | 53.2 | 124.54 | – | – | Filonchyk and Yan (2018) |
| Ogbomoso, Nigeria | 17.9–32.3 | 26.5–60.5 | 154.5–230.8 | – | – | Jelili, Gbadegesin, and Alabi (2020) | ||||
| Abuja and Benin, Nigeria | – | – | 30.79–139.06 | 29.31–165.39 | – | – | Lala, Onwunzo, Adesina, and Sonibare (2023) | |||
| Lagos, Nigeria | 32.52–38.7 | – | 69.28 | 107.38 | – | – | Abulude et al. (2021) | |||
| Awka, Nigeria | 10925 | ND | ND | ND | – | 11.5 | 26.5 | – | – | Ezeonyejiaku et al. (2022) |
WHO 2021 pollutants air quality guideline (AQG)
| Pollutant | Averaging time | Interim target | AQG level | |||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | |||
| PM2.5 µg/m3 | Annual | 35 | 25 | 15 | 10 | 5 |
| 24 – hour* | 75 | 50 | 37.5 | 25 | 15 | |
| PM10 µg/m3 | Annual | 70 | 50 | 30 | 20 | 15 |
| 24 – hour* | 150 | 100 | 75 | 50 | 45 | |
| O3 µg/m3 | Peak Season | 100 | 70 | – | – | 60 |
| 8 – houra | 160 | 120 | – | – | 100 | |
| NO2 µg/m3 | Annual | 40 | 30 | 20 | – | 10 |
| 24 – houra | 120 | 50 | – | – | 25 | |
| SO2 µg/m3 | 24 – houra | 125 | 50 | – | – | 40 |
| CO mg/m3 | 24 – houra | 7 | – | – | – | 4 |
Note(s): a99th percentile (i.e. 3–4 exceedance days per year)
bAverage of daily maximum 8-h mean O3 concentration in the six consecutive in six months with the highest six-month running-average O3 concentration
Source(s): WHO (2021)
Pearson correlation of the pollutants and the meteorological parameters
| CO2 | 1 | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Temp | −0.68 | 1 | |||||||
| RH | 0.46 | −0.47 | 1 | ||||||
| NO2 | 0.02 | 0.20 | 0.04 | 1 | |||||
| O3 | −0.23 | 0.46 | −0.15 | 0.47 | 1 | ||||
| SO2 | −0.32 | 0.42 | −0.26 | 0.12 | 0.08 | 1 | |||
| PM1 | 0.33 | −0.24 | 0.04 | 0.05 | −0.01 | −0.10 | 1 | ||
| PM2.5 | 0.32 | −0.24 | 0.02 | 0.05 | 0.01 | −0.09 | 0.99 | 1 | |
| PM10 | 0.34 | −0.27 | 0.00 | 0.04 | −0.01 | −0.11 | 0.97 | 0.99 | 1 |
| CO2 | Temp | RH | NO2 | O3 | SO2 | PM1 | PM2.5 | PM10 |
The air quality index (AQI) of the pollutants (overall)
 |
AQI color codes, levels of concern and description
|
Principal component analysis of the parameters
| Variable | PC1 | PC2 | PC3 |
|---|---|---|---|
| Eigenvalue | 3.44 | 2.21 | 1.32 |
| Variability (%) | 38.20 | 24.50 | 14.50 |
| Cumulative (%) | 38.20 | 62.70 | 77.30 |
| CO2 | 0.36 | 0.29 | −0.28 |
| Temperature | −0.34 | −0.42 | 0.06 |
| Relative humidity | 0.18 | 0.37 | −0.44 |
| NO2 | −0.33 | −0.29 | −0.67 |
| O3 | −0.13 | −0.39 | −0.49 |
| SO2 | −0.19 | −0.30 | 0.11 |
| PM1 | 0.47 | −0.30 | 0.09 |
| PM2.5 | 0.47 | −0.31 | 0.09 |
| PM10 | 0.48 | −0.30 | 0.10 |
The cluster analysis table of the pollutants and meteorological parameters: amalgamation steps
| Step | Number of clusters | Similarity level | Distance level | Clusters joined | New cluster | Numberof obs in new Cluster | |
|---|---|---|---|---|---|---|---|
| 1 | 8 | 99.4572 | 0.01086 | 8 | 9 | 8 | 2 |
| 2 | 7 | 98.5271 | 0.02946 | 7 | 8 | 7 | 3 |
| 3 | 6 | 73.7224 | 0.52555 | 4 | 5 | 4 | 2 |
| 4 | 5 | 73.5661 | 0.52868 | 1 | 3 | 1 | 2 |
| 5 | 4 | 70.5915 | 0.58817 | 2 | 6 | 2 | 2 |
| 6 | 3 | 54.1684 | 0.91663 | 2 | 4 | 2 | 4 |
| 7 | 2 | 50.1004 | 0.99799 | 1 | 7 | 1 | 5 |
| 8 | 1 | 15.9682 | 1.68064 | 1 | 2 | 1 | 9 |
Funding: This research received no external funding.
Supplementary materials: Not applicable
Author contributions: Conceptualization, F.O.A., S.D.O. and A.A.; methodology, F.O.A. and D.S.; software, I.A.A.; validation, K.M.A., F.O.A. and A.A.; formal analysis, E.A.; investigation, F.O.A.; data curation, I.A.A. and S.D.O.; writing—original draft preparation, F.O.A. and A.A.; writing—review and editing, D.S., F.O.A., S.D.O. and A.A.; visualization, T.O. All authors have read and agreed to the published version of the manuscript.
Data availability statement: Data supporting the conclusions of this study are available on request from the authors.
Conflicts of interest: The authors declare no conflict of interest
References
Abdel-Salam, M. M. (2021). Seasonal variation in indoor concentrations of air pollutants in residential buildings. Journal of the Air and Waste Management Association, 71, 761–777.
Abulude, F. O., Damodharan, U., & Acha, S., et al. (2021). Preliminary assessment of air pollution quality levels of Lagos, Nigeria. Aerosol Science and Engineering, 5, 275–284. doi:10.1007/s41810-021-00099-1.
Abulude, F. O., Fagbayide, S. D., Akinnusotu, A., Makinde, O. E., & Elisha, J. J. (2019). Assessment of the indoor air quality of Akure, South-West, Nigeria. Quality of Life, 10, 15–27.
Abulude, F. O., Oluwafemi, M. O., Arifalo, K. M., Elisha, J. J., & Kenni, A. M. (2023). Assessment of indoor household air quality using sentin air's cost-effective sensor. Tropical Aquatic and Soil Pollution, 3(1), 15–23. doi: 10.53623/tasp.v3i1.131.
Agbo, K., Walgraeve, C., Eze, J. K., Ugwuoke, P. U., & Ukoha, P. O. (2021). Household indoor concentration levels of NO2, SO2 and O3 in Nsukka, Nigeria. Atmospheric Environment, 244, 11778. doi: 10.1016/j.atmosenv.2020.117978.
AirNow (2018). Air quality index (AQI) basics. Available from: https://www.airnow.gov/aqi/aqi-basics/ (accessed 12 March 2023).
Algarni, S., Khan, R. A., Khan, N. A., & Mubarak, N. M. (2021). Particulate matter concentration and health risk assessment for a residential building during COVID-19 pandemic in Abha, Saudi Arabiaa. Environmental Science and Pollution Research, 28, 65822–65831. doi: 10.1007/s11356-021-15534-6.
Alshrefy, Z. A., Yousif, S. S., AL-Rifaee, S. H., & Mohammed, O. H. (2020). Assessment of relative humidity, indoor temperature and (CO2) amount with different air conditioning systems and ventilation in Northern Technical University buildings in Mosul, Iraq. Solid State Technol, 63, 1894–1912.
Baguma, G., Musasizi, A., Twinomuhwezi, H., Gonzaga, A., Nakiguli, C. K., Onen, P., … et al. (2022). Heavy metal contamination of sediments from an exoreic african great lakes’ shores (Port Bell, Lake Victoria), Uganda. Pollutants, 2, 407–421.
Blokhin, A. (2023). The 5 countries that produce the most carbon dioxide (CO2), Available from: https://www.investopedia.com/articles/investing/092915/5-countries-produce-most-carbon-dioxide-co2.asp (accessed 12 March 2023).
Bousiotis, D., Alconel, L. S., Beddows, D. C. S., Harrison, R. M., Pope, F. D. (2023). Monitoring and apportioning sources of indoor air quality using low-costparticulate matter sensors. Environment International, 107907. doi: 10.1016/j.envint.2023.107907.
Castell, N., Dauge, F. R., Schneider, P., Vogt, M., Lerner, U., Fishbain, B., … Bartonova, A. (2017). Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates? Environment International, 99: 293-302. doi: 10.1016/j.envint.2016.
Climate Portal (2021). Is there a place in the atmosphere where carbon dioxide is concentrated, and if so, can we remove it?, March 22, 2021. Available from: https://climate.mit.edu/ask-mit/there-place-atmosphere-where-carbon-dioxide-concentrated-and-if-so-can-we-remove-it (accessed 12 March 2023).
Copernicus Atmosphere Monitoring Service (2019). Carbon dioxide levels are rising: Is it really that simple? 28th may 2019. Available from: https://atmosphere.copernicus.eu/carbon-dioxide-levels-are-rising-it-really-simple (accessed 12 March 2023).
Embiale, A., Chandravanahi, B. S., Zewge, F., & Sahle-Demennie, E. (2020). Indoor pollution from cook-stoves during injera baking in Ethiopia, exposure, and health risk assessment. Archives of Environmental and Occupational Health, 76, 103–115.
Ezeonyejiaku, C. D., Okoye, C. O., Ezeonyejiaku, N. J., & Obiakor, M. O. (2022). Air quality in Nigerian urban environments: A comprehensive assessment of gaseous pollutants and particle concentrations. Current Applied Science and Technology, 22(5), 1–15. doi: 10.55003/cast.2022.05.22.011.
Ferreira, A.M.C., & Cardoso, S.M. (2013). Exploratory study of air quality in elementary schools, Coimbra, Portugal. Rev Saúde Pública, 47(6), 1–9. doi: 10.1590/S0034-8910.2013047004810.
Filonchyk, M., & Yan, H. (2018). The characteristics of air pollutants during different seasons in the urban area of Lanzhou, Northwest China. Environmental Earth Sciences, 77(2018), 763. doi: 10.1007/s12665-018-7925-1.
Fisher, S., Bellinger, D. C., Cropper, M. L., Kumar, P., Binagwaho, A., Koudenoukpo, J. B., … Landrigan, P. J. (2021). Air pollution and development in africa: Impacts on health, the economy, and human capital. The Lancet Planetary Health, 5(10), e681–e688. doi: 10.1016/S2542-5196(21)00201-1.
Forbes, P. B. C., & Rohwer, E. R. (2008). Monitoring of trace organic air pollutants-a developing country perspective. WIT Transactions on Ecology and the Environment, 116(11), 345–355. doi: 10.2495/AIR08.
Gonzalez, A., Boies, A., Swanson, J., & Kittelson, D. (2022). Measuring the air quality using low-cost air sensors in a parking garage at University of Minnesota, USA. International Journal of Environmental Research and Public Health, 19(22), 15223. doi: 10.3390/ijerph192215223.
Hogarh, J. N., Agykum, T. P., Bemah, C. K., Owusu-Ansah, E. D. J., Avicor, S. W., Awandare, G. A., … Obiri-Danso, K. (2018). Environmental health risks and benefits of the use of mosquito coils as malaria prevention and control strategy. Malaria Journal, 17, 265, doi: 10.1186/s12936-018-2412-4.
Jelili, M.O., Gbadegesin, A.S., & Alabi, A.T. (2020). Comparative analysis of indoor and outdoor particulate matter concentrations and air quality in Ogbomoso, Nigeria. Journal of Health and Pollution, 10(28), 201205. doi: 10.5696/2156-9614-10.28.201205.
Kabir, M. H., Kormoker, T., Shammi, R. S., Tusher, T. R., Islam, M. S., Khan, R., … Idris, A. M.A. (2022). Comprehensive assessment of heavy metal contamination in road dusts along a hectic national highway of Bangladesh: Spatial distribution, sources of contamination, ecological and human health risks. Toxin Rev, 41, 860–879.
Konstantinou, C., Constantinou, A., Kleovoulou, E. G., Kyriacou, A., Kakoulli, C., Milis, G., & Makris, K. C. (2022). Assessment of indoor and outdoor air quality in primary schools of Cyprus during the COVID–19 pandemic measures in May–July 2021. Heliyon, 8, e09354. doi: 10.1016/j.heliyon.2022.e09354.
Kumar, P., Hama, S., Abbass, R. A., Nogueira, T., Brand, V.S., Wu, H.W., Abulude, F.O., Adelodun, A.A., Anand, P., de Fatima Andrade, M. and Apondo, W. (2022a). In-kitchen aerosol exposure in twelve cities across the globe. Environment International, 162, 107155. doi: 10.1016/j.envint.2022.107155.
Kumar, P., Hama, S., Abbass, R. A., Nogueira, T., Brand, V.S., Wu, H.W., Abulude, F.O., Adelodun, A.A., de Fatima Andrade, M., Asfaw, A. and Aziz, K.H. (2022b). CO2 exposure, ventilation, thermal comfort and health risks in low-income home kitchens of twelve global cities. Journal of Building Engineering, 61, 105254. doi: 10.1016/j.jobe.2022.105254.
Lala, M. A., Onwunzo, C. S., Adesina, O. A., & Sonibare, J. A. (2023). Particulate matters pollution in selected areas of Nigeria: Spatial analysis and risk assessment. Case Studies in Chemical and Environmental Engineering, 7(2023), 100288. doi: 10.1016/j.cscee.2022.100288.
Lee, S. C., & Wang, B. (2006). Characteristics of emissions of air pollutants from mosquito coils and candles burning in a large environmental chamber. Atmospheric EnvironmentVolume, 40(12), 2128–2138, April. doi: 10.1016/j.atmosenv.2005.11.047.
Madonsela, B. S., Maphanga, T., & Mahlakwana, G. (2023). Advancement in the monitoring techniques of particulate matter and nitrogen oxides in african states: A systematic review with meta-analysis. International Journal of Environmental Impacts, 6(1), 37–47. doi: 10.18280/ijei.060105.
Mustafić, H., Jabre, P., Caussin, C., Murad, M. H., Escolano, S., Tafflet, M., … Jouven, X. (2012). Main airpollutants and myocardial infarction: A systematic review and meta-analysis. Jama, 307(7), 713-721. doi: 10.1001/jama.2012.126.
Niu, X., Guinot, B., Cao, J., Xu, H., & Sun, J. (2015). Particle size distribution and air pollution patterns in three urban environments in Xi’an, China. Environ Geochem Health, 37, 801–812. doi: 10.1007/s10653-014-9661-0.
Opolot, M., Omara, T., Adaku, C., & Ntambi, E. (2023). Pollution status, source apportionment, ecological and human health risks of potentially (Eco)toxic element-laden dusts from urban roads, highways and pedestrian bridges in Uganda. Pollutants, 3, 74–88. doi: 10.3390/pollutants3010007.
Oweisana, I., Gobo, A. E., Daka, E. R., & Ideriah, T. J. K. (2021). Evaluation of ambient air quality in Obrikom and Omoku communities in Rivers state Nigeria. Journal of Research in Environmental and Earth Sciences, 7(4), 18–39.
Radaideh, J.A. (2017). Effect of meteorological variables on air pollutants variation in arid climates. Journal of Environmental and Analytical Toxicology, 7, 478. doi: 10.4172/2161-0525.1000478.
Salama, K. F., & Berekaa, M. M. (2016). Assessment of air quality in Dammam slaughter houses, Saudi Arabia. International Journal of Medical Science and Public Health, 5(2), 287–291.
Saudi, A. S. M., Nurulshyha, M. Y., Mahmud, M., & Rizman, Z. I. (2017). A study on air pollution concentration at Desa Park city construction site. Journal of Fundamental and Applied Sciences, 9(6S), 587–599. doi: 10.4314/jfas.v9i6s.44.
Shafi, S., & Khelif, B.Y. (2021). Environmental monitoring of ambient outdoor, indoor air quality pollutants, PM10 and PM2.5 conducted to evaluate its impact analysis and quantification in industrial area of Dammam. KSA. Journal of Geoscience and Environment Protection, 9, 100–114. doi: 10.4236/gep.2021.97007.
Solomon, P. A., Hopke, P. K., Froines, J., & Scheffe, R. (2008). Key scientific findings and policy-and health-relevant insights from the US environmental protectionagency’s particulate matter supersites program and related studies: An integration and synthesis of results. Journal of the Air and Waste Management Association, 58(12), S3-92.
Suriano, D. (2020). Users Guide version (v. 1.3) refers to the SentinAir system version 1.3 available at the Github SentinAir repository. Available from: file:///C:/Users/Wilolud/Desktop/Folders/Domenico/sentinair-system-userguide.pdf
Suriano, D. (2021). A portable air quality monitoring unit and a modular, flexible tool for on-field evaluation and calibration of low-cost gas sensors. HardwareX, 9, e00198. doi: 10.17632/j.ohx.2021.e001981.
Syafei, A. D., Surahman, U., Sembiring, A. C., Pradana, A. W., Ciptaningayu, T. N., Ahmad, I. S., … Hermana, J. (2020). Factors affecting the indoor air quality of middle-class apartments in major cities in Indonesia: A case study in surabaya city. In AIP Conference Proceedings (Vol. 2296), 020008. doi: 10.1063/5.0030403.
Taushiba, A., Dwivedi, S., Zehra, F., Shukla, P. N., & Lawrence, A. J. (2023). Assessment of indoor air quality and their inter-association in hospitals of northern India—a cross-sectional study. Air Quality, Atmosphere, and Health, 16(5), 1023–1036. doi:10.1007/s11869-023-01321-4.
USEPA (2014). National emissions inventory report. Available from: https://gispub.epa.gov/neireport/2014/ (accessed 13 March 2023).
USEPA (2018). Ground-level ozone basics. Available from: https://www.epa.gov/ground-level-ozone-pollution/ground-level-ozone-basics (accessed 12 March 2023).
USEPA (2022). Introduction to indoor air quality, Updated 2022. Available from: https://www.epa.gov/indoor-air-quality-iaq/introduction-indoor-air-quality (accessed 20 March 2023).
USEPA (2023). EPA region 1. Eight-hour average ozone concentrations. Available from: https://www3.epa.gov/region1/airquality/avg8hr.html (accessed 13 March 2023).
Wambebe, N. M., & Duan, X. (2020). Air quality levels and health risk assessment of particulate matters in abuja Municipal area, Nigeria. Atmosphere, 11, 817. doi: 10.3390/atmos11080817.2020.
World Health Organization (2021). WHO global air quality guidelines: Particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. World Health Organization. 2021. Available from: https://apps.who.int/iris/handle/10665/345329 (accessed 13 March 2023).
WHO (2023). “Air pollution: Ambient air pollution: Health impacts”, WHO: Geneva, 2018, Available from: https://www.who.int/airpollution/ambient/health-impacts/en/ (accessed 12 March 2023).
Yi, W. Y., Lo, K. M., Mak, T., Leung, K. S., Leung, Y., Meng, M. L. (2015). A survey of wireless sensor network based air pollution monitoring systems. Sensors, 15(12), 31392-31427. doi: 10.3390/s15122.
Zaman, S. U., Yesmin, M., Pavel, M. R. S., Jeba, F., & Salam, A. (2021). Indoor air quality indicators and toxicity potential at the hospitals’ environment in Dhaka, Bangladesh. Environmental Science and Pollution Research, 28(28), 37727–37740, 202128. doi:10.1007/s11356-021-13162-8.
Zhang, Y., & Jiang, W. (2017). Pollution characteristics and influencing factors of atmospheric particulate matter (PM2.5) in Chang-Zhu-Tan area. IOP Confernce Series: Earth and Environmental Science, 108, 042047. doi: 10.1088/1755-1315/108/4/042047.
Zhang, X., Li, F., Zhang, L., Zhao, Z., & Norback, D. (2014). A longitudinal study of Sick building Syndrome (SBS) among pupils in relation to SO2, NO2, O3 and PM10 in schools in China. PLoS ONE, 9(11), e112933. doi: 10.1371/journal.pone.0112933.
Further reading
CPCB (2014). National air quality index report. New Delhi: Central Pollution Control Board.
Salau, O. R., Momoh, M., Olaleye, O. A., & Owoeye, R. S. (2016). Effects of changes in temperature, rainfall and relative humidity on banana production in Ondo state, Nigeria. World Scientific News, 44, 143–154.
Acknowledgements
The authors are grateful to all the family members who provided one helping hand or another, especially the security of the sensor.