Outdoor and indoor particle air pollution and its health consequences in African cities: New evidence and an exhortation

ABSTRACT

Particulate matter (PM) air pollution has been identified as the leading cause of disease burden in Africa. A greater understanding of particle air pollution and its negative health effects is critical for developing effective and long-term solutions to air pollution. The current research on outdoor and indoor particle pollution concentrations and their health effects in populated African cities was summarised in this study. In academic research databases, 71 articles published in peer-reviewed journals between 2010 and 2023 were located, with 45 reporting on PM concentrations and 27 examining the health impacts of exposure to airborne particles. A narrative synthesis technique was used in the systematic review to critically analyse and provide descriptive summaries of study findings in tabular form. According to the study, most of the research that assessed particle air pollution burdens focused on either PM_2.5 or both PM_2.5 and PM₁₀. PM_2.5 and PM₁₀ levels in ambient and home air surpassed WHO-recommended threshold values. Sub-Saharan Africa has greater PM concentrations than North Africa. Chronic exposure to outdoor and indoor PM_2.5 raised the risk of respiratory infections and pulmonary illnesses, with females, children, and the elderly being more vulnerable. The high levels of PM promote the spread of COVID-19 and cause human capital loss, poverty, low agricultural productivity, a decline in food supply, and a decrease in GDP. Reduced energy consumption, environmentally friendly mobility, increased renewable fuel and clean energy generation, and a shift to sustainable clean cooking are all required to reduce particle air pollution in populated African cities.

KEYWORDS:

• aerosol particulates
• clean fuel
• COVID-19
• household air
• sustainable environment
• respiratory infections

Public Interest Statement

Particulate air pollution in developing cities is on the rise because of increasing population growth, rapid urbanisation, increasing vehicular traffic, and the quest for fast economic growth. Citizens in Africa breathe much higher levels of particulate matter (PM) than those living in other parts of the world. Short-term exposure to PM induces coughing, wheezing, chest pain, and shortness of breath while long-term exposure increases the risk of respiratory and cardiovascular diseases. A better insight into outdoor and indoor particulate pollution and its adverse health implications is vital for developing effective sustainable interventions for abating air pollution. In this study, existing literature about PM pollution levels and their health implications in populated African cities was summarised and discussed. The summarised data will help inform the rationale for specific intervention studies and the design of targeted public health actions to deal with the PM pollution menace in Africa.

1. Introduction

Particulate air pollution has been a major challenge worldwide, particularly in developing countries (Abera et al., 2020). To date, both outdoor and indoor particulate air pollution is the biggest environmental risk to health (Olaniyan et al., 2015). As a result, airborne particulates have been identified as a global environmental health issue in the Sustainable Development Goals (Hua et al., 2014; Lepeule et al., 2012) and regulated to protect public health (World Health Organisation, 2014). Globally, 7 million people die every year because of ambient particulate air pollution with more than 90% of the deaths occurring in low- and middle-income countries, mostly in Asia and Africa (Landrigan et al., 2017).

Information on particle size distribution is a fundamental prerequisite for understanding the characteristics of particle pollution (Dominick et al., 2018). Particles of different sizes have different characteristics and are governed by different physical laws (Prabhu et al., 2022). The sizes of airborne particulates include particles with an aerodynamic diameter less or equal to 10 µm (PM₁₀), between 2.5 and 10 µm (PM_2.5–10), less than 2.5 µm (PM_2.5), and less or equal to 0.1 µm (PM_0.1) known as inhalable, coarse, fine, and ultrafine particulates, respectively.

The origin of aerosol particulate emissions in Africa differs from that in developed countries (Naidja et al., 2018). The world’s substantial concentrations of dust particulates emanate from North Africa (Huneeus et al., 2011). The immense use of old vehicles with weak engines is the major contributor to outdoor particulate air pollution while improper domestic waste management and biomass burning for residential heating, cooking and fish smoking account for indoor particulate air pollution in sub-Saharan Africa (Allaouat et al., 2021; Hitchcock et al., 2014; Kumar et al., 2021; Le et al., 2021; Yakubu, 2018). More than 80% of African households burn solid fuels, with about 70% depending on wood-based biomass as their primary cooking and fish-smoking fuel (Word Bank, 2011). The particulate pollution levels depend on the extent of biomass use and vehicle density on road highways (Zhou et al., 2014).

Airborne particulate concentrations vary substantially between and within regions of the world. It is established that more than 90% of the global population thrives in vicinities where the concentration of fine and inhalable particulates exceeds the WHO air quality guideline (WHO, 2016). Moreover, studies have shown that particulate matter pollution loads in cities of developing countries are high (Alli et al., 2021; Awokola et al., 2020; Hussein et al., 2019). A study in Constantine City in Algeria found that the daily mean PM₁₀ level exceeded the WHO permissible limit (Terrouche et al., 2015) while the outcome of research carried out by Cai et al. (2021) showed that the average PM_2.5 in sub-Saharan African countries spanned from 8.9 to 64.6 µg/m³. Other studies conducted in cities in Africa had similar findings (Dotse et al., 2012; Novela et al., 2020). The heterogeneity in research findings about particulate matter pollution loads makes planning and development of effective particulate air pollution abatement actions difficult, hence the need to summarise and discuss findings of literature on aerosol particulate pollution levels.

Airborne particulate matter (PM) pollution in developing cities is on the rise because of increasing population growth, rapid urbanisation, increasing vehicular traffic, and the quest for fast economic growth (Hag & Schwela, 2012; Katoto et al., 2019; Singh et al., 2021). High air pollution promotes pandemic emergence and spread of diseases (Coccia, 2023; Núñez-Delgado et al., 2021). Populations thriving in environments with high particulate air pollution experience increased mortality of Coronavirus Disease 2019 (COVID-19), because of the quick effusion of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in polluted and populated cities (Bontempi & Coccia, 2021; Coccia, 2020a).

The growth of PM air pollution in Africa has serious repercussions on human health. Acute exposure to airborne PM induces coughing, wheezing, shortness of breath, and chest pain while chronic exposure increases the risk of cardiovascular, respiratory, metabolic, and neurological diseases (Kwon, 2020; Sangkharat et al., 2019). The intensity of health effects of PM exposure is hinged on the dose, chemical constituents, duration of exposure, and individual characteristics such as age, sex, nutritional status, family traits, lifestyle, and state of health (Alli et al., 2021; Safo-Adu et al., 2023). Other factors that control particulate pollution health impacts in society include geographical structure, seasons, and meteorological conditions (Chen et al., 2023; Coccia, 2021). Lei et al. (2023) reported that the respiratory diseases associated with PM_2.5 and PM_2.5–10 exposure varied substantially among different age groups, with greater vulnerability among females, children, and older populations. Similar observations were made in other studies conducted in African cities (Fallahzadeh et al., 2022; Glenn et al., 2022; Woolley et al., 2023). Furthermore, epidemiological studies have shown that exposure of populations to PM_2.5 increased hospital admissions for respiratory diseases such as asthma, chronic obstructive pulmonary diseases (COPDs) and pulmonary fibrosis in the hot weather than the cold weather periods (Coccia, 2021; Moradi et al., 2022; Spencer-Hwag et al., 2023). It is imperative to understand particulate air pollution and respiratory infections to implement public health strategies to stop further mortality (Monoson et al., 2023).

Citizens in Africa, Asia, and the Middle East breathe much higher levels of particulate matter than those living in other parts of the world (WHO, 2016). Val et al. (2014) reported that the African population is more highly exposed to toxic particulate air pollution than populations on other continents. The disease burden from indoor particulate air pollution is higher in Africa than in other parts of the world, and numerous cities of the continent witness death rates linked with indoor particulate air pollution that is remarkably higher than the global average of 30 deaths per 100,000 people (Health Effects Institute, 2022; Zhu & Shi, 2023). Reports that summarised and discussed current research findings on particulate air pollution levels and their health implications were few in Africa (Glenn et al., 2022; Woolley et al., 2022). This has served as an obstacle to understanding airborne PM pollution and its health outcomes, which are vital for designing effective and sustainable actions for combating particulate air pollution in Africa. The limited databases with extensive geographical coverage and long temporal periodicity hamper most of the efforts to efficiently assess issues concerning particulate matter pollution. This paper seeks to fill this literature void. This paper summarised the existing literature about outdoor and indoor air particulate pollution levels and their health implications in populated African cities.

The study answered the following questions:

1. What are the reported levels of outdoor and indoor air particulate matter?

2. What are the reported health impacts associated with exposure to outdoor and indoor particulate air pollution?

3. What sustainable interventions are needed to combat particulate air pollution?

The paper provides policymakers with comprehensive information to formulate policies and sustainable strategies for abating indoor and outdoor particulate air pollution and their associated health implications in populated African cities. The summarised data will help inform the rationale for specific intervention studies and the development of targeted public health actions to deal with the particulate matter pollution menace in the African sub-regions.

2. Methodology

2.1. Search strategy and selection criteria

A literature search was carried out using Scopus, PubMed, Web of Science and Google Scholar databases for papers published in peer-reviewed journals from 2010 to 2023. Studies that investigated outdoor and indoor particulate air pollution levels and their associated health implications in populated African cities were searched. Figure 1 is a map showing the populated cities in Africa, where particulate pollution research has been carried out and published. Other sources of literature that were searched included databases of Africa Index Medicus, national agencies and WHO. To ensure a wide scope of papers, databases were comprehensively searched laterally using broad search terms or keywords combined by Boolean operators such as the ‘AND/OR’ command. The keywords used during the search included: ‘indoor particulate matter’ OR ‘household particulates pollution’, ‘ambient particulate air pollution’ OR ‘outdoor particulate air pollution’, health effects of outdoor PM_2.5’, OR ’link between outdoor PM_2.5 and health’, ‘health impacts of outdoor PM₁₀’OR ‘link between outdoor PM₁₀ and health, health effects of PM_2.5–10 ’ OR ‘association between PM_2.5–10 and health, health effects of indoor PM_2.5’ OR ‘link between indoor PM_2.5 and health’, health effects of indoor PM₁’ OR ‘link between indoor PM₁ and health. The search was limited to papers written in English.

Figure 1. Map showing the populated cities in Africa, where particulate pollution research has been done and published.

2.2. Inclusion and exclusion criteria

Studies that focused on household and ambient particulate air pollution loads in populated cities in Africa were considered in this review. Epidemiological studies that measured the exposure levels of particulate matter, examined the self-reported health impacts of exposure to particulate air pollution or identified particulate matter as a risk factor for any measured health effects among inhabitants in Africa were included. Again, studies that looked at the health issues regarding the exposure of susceptible groups such as pregnant women, infants, children, and the elderly to particulate matter in Africa were also included in this review. Four study designs that formed part of the criteria for selecting the epidemiological studies included a cross-sectional study, longitudinal study, experimental study, and cohort study. Studies that gave reports from countries other than those in Africa were exempted. Again, studies that were conducted in several countries including those in Africa and other continents were also excluded. Again, air pollution studies that reported on pollutants such as carbon monoxide, nitrogen oxides, sulphur oxides, volatile organic compounds, and methane in indoor and outdoor environments were precluded. Epidemiological reports that do not capture the health impacts of particulate matter in Africa were excluded. Previous studies that investigated the chemical composition and diagnosed the sources of particulate matter pollution without reporting the exposure levels of particulate matter were also exempted from this review. A summary of the criteria for inclusion and exclusion of studies is shown in Table 1.

Table 1. Criteria for inclusion and exclusion of studies

Criteria	Inclusion	Exclusion
Period	2010–2023	Before 2010
Language	English	Other languages
Study location	Cities in Africa	Cities on other continents
Type of studies	Primary studies	Reviews, commentaries, letters
Study domain	Reports on outdoor and indoor particulate air pollution levels	Reports on CO, NOx, SOx, CH4, VOC
	Reports on self-reported health outcomes associated with exposure to particulates. Reports on relationships between particulate matter exposure and health	Epidemiological studies that dealt with species other than human beings.

PM = Particulate matter, CO = Carbon monoxide, NOx = Nitrogen oxides, SOx = Sulphur oxides, VOC = Volatile organic compounds.

2.3. Data extraction

The titles, abstracts and full texts of papers were screened following the inclusion and exclusion criteria outlined above. The eligibility of the paper was appraised by two independent reviewers and any disagreement was resolved through consensus. The data extracted about indoor and outdoor particulate air pollution included information on the author (s), year of publication, study location (city and country), particulate matter sources in the study location, particulate matter (PM₁, PM_2.5, PM_2.5–10, and PM₁₀) concentration levels, and summary of main conclusions. The data extracted on the health outcomes associated with exposition to particulate air pollution included information on the author (s), study location, study design, study participants, the pollutant type and summary of main conclusions. Two independent reviewers appraised the quality of the papers, and a third expert resolved the discords.

2.4. Data analysis

An interpretive narrative synthesis method was used to synthesise the findings of the studies included in the review. The narrative method of synthesis was utilised because the studies included in the systematic review were of different characteristics. The conceptual framework of narrative synthesis proposed by Snilstveit et al. (2012) was adopted in this work. First, studies included in the review were categorised into thematic groups. Descriptive summaries of findings were then produced in a tabular form considering the priori identified themes. Afterwards, the findings were discussed.

3. Results and discussion

A narrative synthesis approach was used to systematically review the literature about outdoor and indoor air particulate levels and their associated health impacts in densely populated African cities. The search and selection processes for papers included in this review are outlined in Figure 2.

Figure 2. A flow diagram explaining the study search and selection process.

In all, 2580 peer-reviewed papers were identified through the four databases during the search process while 850 papers were obtained through other sources. The search provided 1501 non-duplicate studies. These papers were thoroughly screened and excluded 1279 papers from the review. The remaining 222 full-text articles were appraised for eligibility. One hundred and fifty full-text papers did not meet the pre-instituted inclusive criteria; hence, they were excluded while 72 full-text articles were retained for qualitative synthesis. Forty-five articles subjected to narrative synthesis reported on outdoor and indoor air PM concentration levels while 27 reported on the health implications connected to particulate air pollution exposure in an outdoor and indoor environment.

The descriptive summary of outdoor and indoor air PM concentration levels is presented in Tables 2, 3, and 4, respectively. Eleven studies representing 24.5% investigated PM levels in both outdoor and indoor air while 15 (33.3%) and 19 (42.2%) studies focused on particulate levels in only indoor and outdoor PM, respectively. Out of 30 studies that investigated the levels of PM in outdoor air, 3 (10.0%), 13 (43.3%), and 9 (30.0%) studies focused on PM₁₀, PM_2.5, and both PM_2.5 and PM₁₀, respectively. Only four studies representing 13.3% determined simultaneously the levels of the four size fractions of PM (PM₁, PM_2.5, PM_2.5–10 and PM₁₀) while 1 study quantified concurrently the concentrations of PM_2.5–10 and PM₁₀ in ambient air. From Table 4, most studies (88.5%) either measured the levels of PM_2.5 or PM_2.5 and PM₁₀ in indoor air. However, only one study quantified the concentration of PM_2.5–10 while none of the studies determined the level of PM₁ in indoor air. Analysis of findings shows that the concentration of PM₁ and PM_2.5–10 has been understudied in populated cities in Africa.

Table 2. Descriptive summary of outdoor PM₁ and PM_2.5 levels in populated African cities

PM	Country	City	Emission sources	Tools/method used to measure PM	24 hours mean level (µg/m^3)	Summary of the main conclusion	Author (s)
PM1	Kenya	Nairobi	Vehicle traffic	Calibrated low-cost sensors	23.9	Vehicle traffic accounted for PM1 loads	Pope et al. (2018)
	Algeria	Algiers	Vehicle traffic	High-volume sampler with cascade impactor	18.2	PM1 level in the air was high	Talbi et al. (2017)
	Nigeria	Oyo State	Biomass burning	Air PM monitor	27.0	PM1 level in the air was high	Jelili et al. (2020)
	Burkina Faso	OG	Traffic emissions	Aerocet − 531S	20.0	PM1 level in the air was high	Ouarma et al. (2020)
PM2.5	Nigeria	Lagos	Biomass burning	Mini-volume air particulate samplers	60.0	Biomass burning contributed to PM2.5	Sulaymon et al. (2022)
		Yenogua	Vehicle emissions	PM wireless sensor, IQ air satellite technology	16.3	The mean PM2.5 level exceeded the WHO guideline	Abulude et al. (2022)
		Yenogua	Residential emissions	Air PM monitor	41.60	The mean PM2.5 level exceeded the WHO guideline	Jelili et al. (2020)
		Abuja	Vehicle traffic	CW-HAT200 aerosol PM monitor	40.0	Ambient air was not quality	Otse et al. (2020)
		Abuja	Vehicle emissions	CW-HAT200 aerosol PM monitor	Dry (55.0) & and wet (42.0) periods	PM2,5 levels were higher in the dry than in the wet seasons	Kanee et al. (2020)
	Ghana	Accra	Industrial & Residential	Low-cost sensors	Harmattan (89.0) and non-Harmattan (23.0) seasons	CBI sites had higher PM2.5 levels than residential areas.	Alli et al. (2021)
		Ashaiman	Biomass burning	Gent air sampler with stacked filter unit	21.6	The mean PM2.5 level exceeded the WHO guideline	Ofosu et al. (2016)
	Cote d’Ivoire	Abidjan, Korhogo	Vehicle emissions	Low-cost sensors	68.6	Study areas recorded high levels of PM 2.5	Gnamien et al. (2020)
		Andokoi, Lubafrique	Biomass burning	Active device 3M ENVM-7	Andokoi (117.0) and Lubafrique (41.0) for PM2.5	Mean PM2.5 exceeded the WHO standard limit	Kouao et al. (2019)
	Burkina Faso	OG	Roadside emissions	Aerocet − 531S	87.0	High levels of PM2.5 were recorded in the study area	Ouarma et al. (2020)
	Senegal	Dakar	Vehicle and industrial	Air PM monitors	46.5	The mean PM2.5 level exceeded the WHO guideline	Sow et al. (2021)
OG= Ouagadougou, PM= Particulate matter, WHO = World Health Organisation
PM2.5	Botswana	Palapye	Vehicle emissions	Low-cost surface-based optical depth sensor	14.0	The mean PM2.5 level was below the WHO guideline	Lassman et al. (2020)
	South Africa	Thohoyandou	Biomass burning	Air PM2.5 sampler	26.6	Mean PM2.5 exceeded the WHO standard limit	Edlund et al. (2021)
		Bloemfontein	Industrial emissions	Gil Air 5 Sampler (Sensidyne, USA)	11.0	The mean PM2.5 level was below the WHO guideline	Westhuizen et al. (2022)
	Rwanda	Kigali	Traffic and biomass	Low-cost real-time time multi-purpose monitor	52.0	PM2.5 in the dry season was 4 times that of the wet season	Subramanian et al. (2020)
	Uganda	Fort Portal	Charcoal burning	Multi-purpose laser particle detector	175.9	High levels of PM2.5 were recorded in ambient air	Kansiime et al. (2022)
	Kenya	Nairobi	Vehicle emissions	Calibrated low-cost sensors	36.6	Vehicle traffic accounted for PM2.5 loads	Pope et al. (2018)
	DR Congo	Kinshasa	Wildfire emissions	Low-cost sensors	43.5	The mean PM2.5 level exceeded the WHO guideline	Mcfarlane et al. (2020)
	Tanzania	Dar es Salaam	Vehicle emissions	Integrated air particulate sampler	30.1	The PM2.5 level was higher at traffic sites than in the urban background	Njee et al. (2016)
	East African countries	Three East African cities	Roadside emissions	Calibrated low-cost sensors	48.5, 73.1 and 90.1 for PM2.5 in three cities	The mean PM2.5 level exceeded the WHO guideline	Singh et al. (2021)
	Ethiopia	Addis Ababa	Biomass burning	Betta attenuator monitor (BAM-1022)	42.4	PM2.5 daily levels varied across days and seasons	Kumie et al. (2021)
	Countries in SSA	Cities in SSA	Vehicle emissions	Low-cost sensor (Purple Air-II-SD)	38.0 across all sites	The highest PM2.5 level was recorded at Kampala	Awokola et al. (2020)
	Egypt	Cairo	Industrial emissions	Thermo scientific particulate monitor	75.0	Outdoor air in Cairo had a high level of PM2.5	Wheida et al. (2018)
		Alexandria, city	Liquid fuel-burning	Marple PM samplers	53.5	The mean PM2.5 level exceeded the WHO threshold limit	Abdel-Salam (2013)
	Morocco	Urban city	Residential emissions	Dichotomous sampler (M 241, Fulton, USA)	17.2	PM2.5 pollution was higher in autumn than in winter	Bounakhla et al. (2023)
	Algeria	Algiers	Vehicle emissions	High-volume sampler with cascade impactor	32.2	The mean PM2.5 level exceeded the WHO standard	Talbi et al. (2017)

Table 3. Descriptive summary of outdoor PM_2.5–10 and PM₁₀ levels in populated African cities

PM	Country	City	Emission sources	Tools/methods used to measure PM	24 hours mean level (µg/m^3)	Summary of the main conclusion	Author (s)
PM2.5–10	Ghana	Ashaiman	Biomass burning	Gent air sampler with stacked filter unit	89.2	PM2.5–10 was emitted from anthropogenic origin	Ofosu et al. (2016)
PM10		Accra	Vehicle emissions	Mini-volume air particulate samplers	222.6, 211.0, 186.2 and 324.0 for four areas	Mean PM10 levels were higher than the WHO guideline	Sulemana et al. (2019)
	Nigeria	Abuja,	Vehicle traffic	CW-HAT200 air sampler	150.0	Ambient air quality was poor	Otse et al. (2020)
		Oyo State	Residential emissions	Particle counter	175.5	Ambient air quality was poor	Jelili et al. (2020)
		Abuja	Vehicle emissions	CW-HAT200 aerosol PM monitor	Dry (46.0) and wet (36.0) seasons	PM10 levels were higher in the dry season than in the wet season	Kanee et al. (2020)
	Senegal	Dakar	Vehicle emissions		150.0	Dakar cities experience poor air quality	Sow et al. (2021)
	Burkina Faso	OG	Roadside emissions	Aerocet − 531S	951.0	The mean PM10 level in air pollution was high	Ouarma et al. (2020)
	Cote d’Ivoire	Abidjan, Korhogo	Vehicle emissions	Low-cost sensors	99.1	Study areas recorded high levels of PM10	Gnamien et al. (2020)
	Tanzania	Dar es Salaam	Traffic emissions	Integrated air particulate sampler	86.0	PM10 level was higher at traffic sites than in urban background	Njee et al. (2016)
	Kenya	Nairobi	Vehicle emissions	Calibrated low-cost sensors	93.7	Vehicle traffic accounted for PM10 loads	Pope et al. (2018)
	Uganda	Three urban cities	Biomass and vehicle	Environmental monitor (EM, Model 200, USA)	116, 167 and 130 for PM10 in three cities	Long-term measurements showed unhealthy outdoor air	Onyango et al. (2019)
	Countries in East Africa	East African cities	Roadside emissions	Calibrated low-cost sensors	103.5, 157.2 and 155.5 for PM10	Mean PM10 levels exceeded the WHO threshold limits	Singh et al. (2021)
	Morocco	Urban city	Residential emissions	Dichotomous sampler (M 241, Fulton, USA)	51.1	PM10 pollution was higher in autumn than in winter	Bounakhla et al. (2023)
		Casablanca	Industrial emissions		75.1	PM10 level exceeded the WHO guideline value	Nejjari et al. (2021)
	Egypt	Alexandria City	Liquid fuel burning	Marple PM samplers	77.2	PM2.5 accounted for 53.7 % of the PM10 loads	Abdel-Salam (2013)
	Algeria	Algiers	Vehicle emissions	High-volume sampler with cascade impactor	60.1	PM10 levels exceeded WHO standards	Talbi et al. (2017)

Table 4. Descriptive summary of indoor air particulate levels in populated African cities

PM	Country	City	Emission sources	Tool/Method used	24 hours mean level (µg/m^3)	Summary of the main conclusion	Author (s)
PM2.5	Nigeria	Akure	Biomass burning	PM wireless sensor, IQ air satellite technology	68.5	The mean PM2.5 level exceeded WHO standard	Abulude et al. (2022)
		Oyo State	Biomass burning	Particle counter	41.60	Indoor air quality was poor	Jelili et al. (2020)
	Ghana	Accra	Solid fuel burning	Portable PM monitor	25.0	Households recorded high levels of PM2.5	Zhou et al. (2011)
		Tema	Dust particulates	Casella 712 micro dust pro kit equipment	38.0	The mean PM2.5 level exceeded WHO standard	Sarpong et al. (2021)
	Cote d’Ivoire	Andokoi, Lubafrique	Biomass burning	Active device 3M ENVM-7	Andokoi (121.0) and Lubafrique (32.0) for PM2.5	Biomass smoke accounted for the high PM2.5	Kouao et al. (2018)
	Mali	Bamako	Wood burning	Optical PM sensors	Homes (163.0) and offices (16.0) for PM2.5	The maximum PM2.5 was obtained in homes	Sidibe et al. (2022)
	Kenya	Nairobi	Industrial emissions	Air PM monitor	35.0	The mean PM2.5 level exceeded the WHO guideline	Were et al. (2020)
		Cities in Kenya	Wood burning	Portable 3016 IAQ PM counter	268.8	Households were exposed to high levels of PM2.5	Musyoka (2022)
		Three cities in Kenya	Charcoal burning	DustTrak II model 8532 monitors	124.0 and 82.2 for PM2.5 in two locations	Residents were exposed to high levels of PM2.5	Muindi et al. (2016)
	Uganda	Mbarara	Charcoal burning	UCB particle and temperature sensor	487.5	High levels of PM2.5 were recorded in kitchens	Nakora et al. (2020)
		Fort Portal	Biomass burning	Multi-purpose laser particle detector	124.2	Homes had high levels of PM2.5	Kansiime et al. (2022)
	Rwanda	Cities in Rwanda	Wood biomass	Portable air quality sensors, Air Visual Node	550.0	PM10 levels in homes reached 1000 µg/m^3	Irankunda and Gasore (2021)
		Kigali	Biomass burning	IQ Air Visual Pro sensor	93.0	PM2.5 exceeded the WHO permissible limit	Kabera et al. (2020)
PM = Particulate matter, CCB = University of California Berkeley.
PM	Country	City	Emission sources	Tool/methods used to measure PM	24 hours mean level (µg/m^3)	Summary of the main conclusion	Author (s)
PM2.5	Zambia	Cities in Zambia	Biomass burning	Foobot (Model FBT0002100, China)	444.5	Mean PM2.5 level varied greatly among households	Mulenga et al. (2018)
	Countries in SSA	Cities in SSA	Biomass burning	Satellite measurement and CTM	36.7	High levels of PM2.5 were observed	Cai et al. (2021)
	Egypt	Alexandria City	Liquid fuel burning	Marple PM samplers	53.5	PM10 levels correlated with PM2.5 loadings	Abdel-Salam (2013)
		Cairo	Vehicle emissions	Microcontrollers and electronic sensors	15.3	The mean PM2.5 level was above the WHO standard	Marzouk and Atef (2022)
PM2.5–10	Ghana	Accra	Solid fuel burning	Portable PM monitor	28.0	Households recorded high levels of PM2.5–10	Zhou et al. (2011)
PM10	Nigeria	Akure	Biomass burning	PM wireless sensor, IQ air satellite technology	73.2	Households had high levels of PM10	Abulude et al. (2022)
		Oyo State	Biomass burning	Particle counter	175.5	Air quality is relatively poor	Jelili et al. (2020)
	Kenya	Nairobi	Industrial emissions	Air PM monitor	164.0	The mean PM10 level exceeded the WHO guideline	Were et al. (2020)
		Kenya	Wood burning	Portable 3016 IAQ PM counter	529.3	Households were exposed to high levels of PM10	Musyoka (2022)
	Rwanda	Cities in Rwanda	Wood burning	Portable air quality sensors, Air Visual Node	750.0	PM10 levels in homes reached 1200 µg/m^3	Irankunda and Gasore (2021)
	Cameroon	Yaoundé	Dust particulates	Digital Aeroqual dust sentry	91.3, 97.6, 82.9 for three sites	PM10 levels exceeded the WHO guideline value	Feuyit et al. (2019)
	Sierra Leon	Waterloo	Wood burning	Air particulate samplers	1686.0	PM10 level exceeded the WHO guideline	Taylor and Nakai (2012)
	Egypt	Alexandria City	Gas stoves	Marple PM samplers	77.2	Indoor air quality was poor	Abdel-Salam (2013)

PM = Particulate matter, CTM= Chemical transport model.

From Tables 2, 3 and 4, outdoor and indoor particulate air pollution concentration loads varied in geographical locations in Africa. The highest mean PM₁₀ mass concentration was reported to be 324.0 µg/m³ for outdoor air and 1686.0 µg/m³ for indoor air in the city of Accra, Ghana (Sulemana et a., 2018) and Waterloo, Sierra Leon (Taylor & Nakai, 2012), respectively. The lowest mean PM₁₀ mass concentration was quantified to be 51.1 µg/m³ for outdoor air and 73.2 µg/m³ for indoor air at an urban city in Morocco (Bounakhla et al., 2023) and Akure, Nigeria (Abulude et al., 2022), respectively. Furthermore, the highest mean concentration of PM_2.5 was reported to be 175.9 µg/m³ for outdoor air and 550.0 µg/m³ for indoor air at Fort Portal city (Kansiime et al., 2022) and cities in Rwanda (Irankunda & Gasore, 2021), respectively. The lowest mean PM_2.5 concentration was reported to be 11.0 µg/m³ for outdoor air and 15.3 µg/m³ for indoor air at Bloemfontein, South Africa (Westhuizen et al., 2022) and Cairo, Egypt (Marzouk & Atef, 2022), respectively. The findings of all studies that investigated indoor air particulate levels (Table 4) reported that the mean indoor PM_2.5 and PM₁₀ mass concentrations exceeded the WHO threshold limits of 15 and 45 µg/m³, respectively for assessing indoor air quality. This suggests that indoor air in populated African cities was poor and unsafe. Domestic burning of biomass primarily wood and charcoal for cooking, heating and fish smoking activities was a major source of household particulate air pollution in populated African cities (Mulenga et al., 2018; Taylor & Nakai, 2012). More than 80% of African households burn solid fuels, with about 70% depending on wood-based biomass as their primary cooking and fish-smoking fuel (World Bank, 2011). The impact of household wood burning on PM was studied (Irankunda & Gasore, 2021). The results showed that homes using wood for cooking experienced high PM_2.5 and PM₁₀ mass concentrations of 1000 and 1200 µg/m³, respectively.

About 92.0% of studies which measured PM_2.5 levels in outdoor air showed that the mean PM_2.5 mass concentrations exceeded the World Health Organization (2021) acceptable limit of 15.0 µg/m³ for evaluating ambient air quality, indicating that outdoor air quality in populated African cities was poor. Notwithstanding, all studies which focused on PM₁₀ level reported that the average PM₁₀ mass concentration level exceeded the WHO permissible value of 45.0 µg/m³ for appraising ambient air quality. This suggests that ambient air quality in populated cities in Africa was poor and unsafe. This finding is in line with the finding of the research conducted by the Health Effects Institute (2022), which revealed that countries in Africa experience some of the highest outdoor PM_2.5 exposures in the world. Analysis of global data on air pollution indicates an increasing trend of outdoor PM air pollution levels in urban cities of developing countries with the highest increase observed for the African region (WHO, 2014). Vehicular emissions including the resuspension of dust on road highways account for the high levels of aerosol particulate in ambient air (Allaouat et al., 2021; Kumar et al., 2021) in populated African cities.

Comparatively, studies conducted in sub-Saharan African cities reported higher levels of ambient PM_2.5 than cities in Northern Africa. The finding is in line with the finding of the review carried out in sub-Saharan Africa (SSA) by Katoto et al. (2019) which revealed that ambient air pollution in cities in SSA is high compared to international standards. Similarly, the outcome of the research that evaluated the potential and practicability of long-term measurements of outdoor PM_2.5 in 13 cities in Africa revealed that the mean 24-hour concentration level (38.0 µg/m³) of PM_2.5 exceeded the WHO PM_2.5 recommended threshold of 15.0 µg/m³ (Awokola et al., 2020). Also, Singh et al. (2021) found that three East African cities had high levels of PM₁₀ and PM_2.5 in the outdoor air environment. Vehicular and residential emissions were reported to account for the concentration levels of outdoor air particulates (Singh et al., 2021). Ayetor et al. (2021) investigated the state of road vehicle emissions in Africa and reported that new vehicles imported to some SSA cities failed the emission tests. It was further revealed that barely five African countries had standards to control emissions most of which were not being enforced.

Research that concurrently analysed PM loads in outdoor and indoor environments revealed that outdoor air had higher concentrations of PM than indoor air. A comparative analysis of ambient and household air PM loadings was conducted in Ogbomoso, Nigeria (Jelili et al., 2020). The study found that on average, outdoor PM₁, PM_2.5 and PM₁₀ levels, were 46.3, 175.5 and 188.8 µg/m³, respectively. These were higher than the indoor PM₁, PM_2.5 and PM₁₀ levels, which were 23.6, 27.7 and 41.6 µg/m³, respectively. Furthermore, the indoor PM samples were taken in parallel with outdoor PM in an urban area in Alexandra City, Egypt (Abdel-Salam, 2013). The study concluded that indoor PM_2.5 levels contributed, on average, to 68.8% of the total PM₁₀ levels, whereas outdoor PM_2.5 accounted for 53.7% of the total PM₁₀ levels. Besides, on average, outdoor PM_2.5 and PM₁₀ levels of 66.2 and 123.8 µg/m³, respectively were higher than indoor PM_2.5 and PM₁₀ concentrations of 53.5 and 77.2 µg/m³, respectively. Similar observation was reported by a study conducted in Lubafrique, Cote d’Ivoire (Kouao et al., 2018). Vehicle-related emissions coupled with biomass burning have been reported to account for the higher levels of PM in outdoor air than in indoor air in African cities (Gaita et al., 2014).

Studies have demonstrated that PM levels vary across seasons and are affected by meteorological conditions (Bounakhla et al., 2023; Emekwuru & Ejohwomu, 2023). Subramanian et al. (2020) investigated the seasonal trend of outdoor PM loadings in Kigali, Rwanda and found that PM_2.5 concentrations in the dry season were two times that recorded in the wet season. Research carried out in other populated African cities had similar findings (Ibeneme et al., 2022; Ofosu et al., 2016; Were et al., 2020). The significantly lower outdoor PM levels in the wet season than dry season have been attributed to the increase in relative humidity due to high rainfalls that enhance the adsorption of water vapour onto particles resulting in rapid settling and deposition of PM (Emekwuru & Ejohwomu, 2023). In contrast, the dry season is characterised by a rise in ambient temperature which increases the reactivity of atmospheric gases enhancing the rate of PM production through photochemical reactions (Enotoriuwa et al., 2016). Furthermore, PM levels have been reported to be higher in Harmattan than in non-Harmattan seasons in populated West African cities (Alli et al., 2021). According to Ofosu et al. (2016), windblown dust from the Sahara Desert, which occurs every year from December to March across most of West Africa contributes to the huge levels of PM during the Hamattan season. Previous studies carried out in populated cities in North Africa demonstrated that outdoor PM concentrations in winter were higher than those measured in summer (Bounakhla et al., 2023; Khadidja et al., 2019; Merabet et al., 2019). The low height of the atmospheric boundary layer and wind speed increase the levels of PM in winter. The correlation between PM_2.5 and meteorological factors was investigated in Niger Delta, Nigeria (Shaibu & Weli, 2017). It was found that there was a positive relationship between PM_2.5 and temperature. However, a weak association was observed between PM_2.5 and wind speed. Low wind speed impedes the dispersion of airborne particles while a high wind speed support supports the dilution and removal of atmospheric particulates (Coccia, 2021).

Short- and long-term exposure to PM has negative effects on human health. The adverse effects of PM in the human body are illustrated in Figure 3.

Figure 3. A diagram illustrating the effects of PM on human health (Choi & Kim, 2021).

The health implications associated with outdoor and indoor PM have been studied in populated African cities. A descriptive summary of the health impacts due to particulate air pollution exposure in populated African cities is shown in Table A1 (appendix). Findings of 21 out of the 27 reports summarised in Table A1 carried out the research in discrete locations rather than countrywide coverage. However, 6 studies reported on research findings from more than one city and intercity studies within different sub-regions in Africa. Most studies (17, 63.0%) reported on the health effects associated with air particulate exposure using cross-sectional study design which involved questionnaires while few (10, 37.0%) made use of longitudinal, experimental and cohort designs. The use of cross-sectional study design in most studies impeded the detailed exploration of the temporal correlation between air particulates and health outcomes. Findings of studies showed that outdoor and indoor air particulates such as smoke derived from biomass combustion served as a risk factor for anaemia, asthma, coughing, and catarrh (Amegah et al., 2022; Dida et al., 2022; Nkosi et al., 2017; Nti et al., 2020). An outcome of research carried out in Windhoek, a Southern African city (Hamatui & Beynon, 2017) found that PM exposure among residents had an increased odds ratio (OR) for phlegm and cough (OR: 2.5, 95% CI) while women who smoked fish using biomass fuel in Cape Coast, a West African city had 80% risk of being anaemic (Armo-Annor et al., 2021). A similar outcome was observed in research in an East African city where the use of kerosene and charcoal fuel types was ascertained to be a major risk factor associated with anaemia status in pregnant women (Andarge et al., 2021).

Analysis of research findings indicated that human exposure to ambient and household air PM₂₅ and PM₁₀ increased the risk of acute respiratory infections and chronic obstructive pulmonary disease (Nti et al., 2020; Opara et al., 2021; Thabethe et al., 2021) in Africa. A report issued by Larson et al. (2022) about health impacts due to exposition to particulate air pollution in Kenya indicated that chronic exposure to PM_2.5 increased the risk for respiratory infections. Similar findings were observed in other East African countries (Adarge et al., 2021; Dida et al., 2022). The aerosol particulate level and its link with pulmonary diseases were studied in a West African city (Ibeneme et al., 2022). It was observed that cement workers and auto-mobile spray painters showed high risks of obstructive pulmonary diseases while woodworkers had restrictive lung diseases. Again, Millar et al. (2022) investigated the respiratory health among adolescents living in air pollution priority areas in South Africa and reported that half of the teenagers who had respiratory illness were exposed to tobacco smoke in dwellings. Other studies conducted in North and East African cities reported similar findings (Ahamad et al., 2021; Nejjari et al., 2021; Wheida et al., 2018).

The health impacts due to outdoor and indoor PM exposure in populated African cities varied considerably among different age groups, with greater vulnerability among females, children, and older populations (Alli et al., 2021). Research carried out in Casablanca, a Northern African city revealed that outdoor air PM₁₀ increased 4% respiratory infections in children (Nejjari et al., 2021) while Wheida et al. (2018) reported that 11% of mortality in the population older than 30 years can be attributed to outdoor PM_2.5 in the city of Cairo in North African. Acute respiratory infections (ARIs) related to indoor particulate pollution were studied in Bamenda City, Central Africa (Nsoh et al., 2019). The study found that exposure to biomass-burning smoke exacerbated the risk of respiratory infections among infants, children, and elders. Furthermore, Kanee et al. (2020) investigated the health impact of indoor PM_2.5 in Abuja, Nigeria and concluded that infants below five years and pregnant women were noted to have high surface areas and absorptive capacity for PM_2.5 while Armo-Annor et al. (2021) showed that women engaged in biomass-based fish smoking had 80% risk of being anaemic. Similar observations were recorded in other African cities (Daffe et al., 2022; Larson et al., 2022).

The link between outdoor and indoor PM exposure and its related health outcomes has been established. The results of research carried out in Benin City, Nigeria (Eghomwanre et al., 2022) showed that indoor PM concentrations had a significant positive relationship with reported cases of asthma (R = 0.498–0.542, p = 0.001). Notwithstanding, Bagula et al. (2021) found that ambient PM_2.5 in Western Cape, a Southern African city substantially correlated with the prevalence of self-reported chest pain [Odds ratio: 1.38 (95% CI: 1.06–1.80]. Studies that reported similar findings include research work conducted in cities in East Africa (Dida et al., 2022; Were et al., 2020) and West Africa (Amegah et al., 2022; Daffe et al., 2022).

Environmental factors such as season and meteorological conditions have been reported to affect the health consequences linked with exposure to PM. The report of a study carried out by Nsoh et al. (2019) concluded that dry and dusty weather increased acute respiratory infections among the exposed population in Bamenda City, Cameroon. Besides, the Odd Ratio (OR) showed that being exposed to dust particulates increased the risk of ARIs by 3.2 times compared to the non-exposed population.

Particulate air pollution is one of the risk factors for the diffusion of Coronavirus Disease (COVID-19). The significant levels of PM can mix with new pathogens, generating mutations and resistance of these new infectious agents that exacerbate their transmissibility and infectivity with a negative effect on human health (Coccia, 2023; Núñez-Delgado et al., 2021; Bontempi & Coccia, 2021). The relationship between air pollution and the spread of COVID-19 was studied (Bontempi & Coccia, 2021; Coccia, 2020b). It was demonstrated that PM_2.5 pollution supports the spread of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in populated cities. Furthermore, seasonal weather conditions and concentrations of air PM influence the spread of COVID-19. A high wind speed supports the dilution and removal of particles, decreasing the concentration of viral agents in the air and transmission dynamics of viral infectivity among people. On the contrary, low wind speed in cities hinders dispersion of air pollutants thereby increasing the concentration of PM (Coccia, 2021).

Africa is undergoing both environmental and epidemiological transition. Outdoor and indoor particle air pollution remains the dominant form of air pollution. The relative contribution of each type varies from one populated city to the other. The reported particulate air pollution loadings are high and are anticipated to rise as African cities develop economically, industrialise, and become connected to the global supply chain (Mlambo et al., 2023). Africa’s PM pollution challenges threaten public health and have adverse social and economic implications. Air pollution was responsible for 1.1 million deaths across Africa in 2019. Ambient and household air pollution accounted for 394,000 and 697, 000 deaths in Africa. Two-thirds of outdoor air pollution-related mortality is attributed to non-communicable respiratory diseases (Samantha et al., 2021). The huge PM air pollution loadings and their derived health consequences lead to the spread of COVID-19, loss of human capital, low agricultural productivity, decreased food supply, food insecurity, poverty, and reduction in Gross Domestic Product (GDP) in African cities. The economic output lost to air pollution-related health diseases was $ 3.0 billion in Ethiopia (1.16% of GDP), $1.6 billion in Ghana (0.95% of GDP) and $ 394 million in Rwanda (1.19% of GDP) (Samantha et al., 2021). PM_2.5 was responsible for 1.96 billion lost intelligence quotient (IQ) points among children in Africa. This significant intelligence loss underpins Africa’s human capital progress and economic development (Mlambo et al., 2023).

Cities are centres of business, productivity, and socio-economic development. Nonetheless, enormous challenges exist to maintain clean air in many African cities. The challenges in cities include but are not limited to a lack of funds to provide basic services, uncontrolled urban growth, and increasing motorisation and congestion. Most African countries lack emission standards on vehicles, and emission inventories and have no air quality monitoring networks. Besides, poor regulatory frameworks on vehicle and biomass-burning emissions are common among countries in SSA (Hag & Schwela, 2012). Few African countries have national PM standards and comprehensive air quality policies as compared to developed countries. The health and socio-economic impacts of quick escalation of PM pollution could undermine efforts to advance economic development, build human capital and attain the Sustainable Development Goals (SDGs). There is the need for prompt and sustainable actions for abating particulate air pollution in populated cities in Africa. Suggested interventions for controlling PM pollution and its related adverse health outcomes are summarised in Table A2 (Appendix).

Studies that summarised current research findings on particulate air pollution levels and their health implications were few in Africa (Glenn et al., 2022; Woolley et al., 2022). Moreover, these few studies focused either on outdoor or indoor PM pollution and its health outcomes and not both indoor and outdoor PM pollution. This work summarised and discussed current findings on outdoor and indoor particle air pollution and its health consequences in populated African cities. Besides, this study proposed sustainable interventions that can be employed to abate PM pollution in Africa.

In summary, few African countries had steady and real-time air quality monitoring networks. Ambient and household air in populated cities in Africa was poor as exposure concentration levels of PM_2.5 and PM₁₀ exceeded WHO recommended threshold limits. This finding confirms the assertion that the African population is more exposed to toxic particulate pollution than the developed world (Val et al., 2013). Research that concurrently analysed PM loads in outdoor and indoor environments revealed that outdoor air had higher concentrations of PM than indoor air. PM levels vary across geographical locations and seasons and are affected by meteorological conditions. The dry season had higher levels of PM than the wet season. Outdoor and indoor air particulates were reported to be a risk factor for anaemia, asthma, coughing, catarrh, and Phlegm production. Chronic exposure to PM_2.5 increases the risk for respiratory infections and chronic obstructive pulmonary diseases of infants, children, the elderly, and pregnant women in populated African cities. These findings are in line with the findings of review reports that focused on Africa (Glenn et al., 2022; Woolley et al., 2022). This study provides information that can be exploited for policy formation and mitigation actions to control particulate air pollution in African countries. Sustainable environment plays a vital role in reducing particle pollution and the spread of COVID-19. The adoption of a sustainable lifestyle and the use of cleaner fuels for domestic cooking and heating will help promote a sustainable environment. Besides, the development of emission control and urban air quality monitoring plans and national air quality standards coupled with effective implementation of environmental regulations and existing policies will be needed in African countries. Banning of importation of old vehicles with weak engines and the unwholesome burning of garbage need to be discussed and enforced in populated cities in Africa.

4. Conclusions

The data gathered from the available literature on outdoor and indoor PM concentrations, as well as their health implications, vary by geographical location and may not reflect the levels of a given nation or the whole African continent. Most research used a cross-sectional study design, which hampered extensive investigation of the temporal association between air particles and health outcomes. This review also considered various air sampling methods, measurement methodologies, and temporal coverage. This might have resulted in differences in the data obtained. Despite these limitations, the study used a systematic review with a narrative synthesis to summarise research findings in the literature on outdoor and indoor particles and their consequences on health in densely populated African cities.

The following are the study’s key findings:

• Approximately, 80–88% of research concentrated on either PM_2.5 or both PM_2.5 and PM₁₀. Few studies have been conducted to assess the concentrations of outdoor and indoor PM₁ and PM_2.5-10.

• More than 90% of studies found that daily mean exposure concentrations of PM_2.5 and PM₁₀ in outdoor and indoor air exceeded the WHO recommended limits of 15 and 45 g/m³, respectively. The elevated levels of outdoor and interior PM were mostly caused by vehicular emissions and residential biomass-burning activities.

• Studies in sub-Saharan African cities found greater levels of ambient PM_2.5 than in Northern African cities.

• Outdoor PM_2.5 and PM₁₀ concentration levels were double those observed in inside air, according to studies that measured both outdoor and indoor PM loadings. Furthermore, ambient air PM levels in the dry season were double those in the rainy season.

• Acute exposure to ambient and home air PM_2.5 and PM₁₀ caused coughing, chest discomfort, and phlegm formation, whereas chronic exposure raised the risk of acute respiratory infections and chronic obstructive pulmonary disease (COPD) in the African population.

• Indoor PM was shown to have a significant correlation with self-reported asthma (R = 0.498-0.542, p = 0.001) and chest symptoms (OR: 1.38, 95% CI: 1.06-1.80). The health consequences of PM exposure differed by age group, with females, children, and the elderly being more vulnerable.

• Outdoor PM₁₀ increased 4% respiratory illnesses in children, whereas PM_2.5 exposure increased 11% mortality in the elderly group. Women who smoked biomass-based fish had an 80% chance of being anaemic.

The high levels of PM cause the spread of COVID-19, the loss of human capital, poverty, low agricultural productivity, a decline in food supply, and a decrease in Gross Domestic Product. To combat the onset of pandemics and particle pollution-related health concerns, African nations must adopt environmental, health, and crisis management strategies, as well as social policies such as pandemic emergency management policies. In addition, to prevention of pandemics, regular vaccination and the use of non-pharmaceutical control measures are essential. Reduced energy consumption, environmentally friendly mobility, increased renewable fuel and clean energy generation, and a shift to sustainable clean cooking are all required to reduce particle air pollution in densely populated African cities.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the manuscript.

Additional information

Notes on contributors

Godfred Safo-Adu

Godfred Safo-Adu is a Lecturer in the Department of Environmental Science at the Faculty of Science Education of the University of Education, Winneba in Ghana. Currently, he is pursuing Ph.D. in Environmental Engineering and Management at the University of Energy and Natural Resources, Sunyani in Ghana. With his background in Chemistry and Environmental Science, he worked as a Process Plant Supervisor in an Integrated Oily Waste Treatment Facility in the Western Region of Ghana for over eight years. He has published in reputable refereed journals locally and at the international level in Environmental Science. His research interest includes groundwater quality modelling, aerosol science, design of indigenous techniques for wastewater recycling, environmental epidemiology, waste-to-energy, soil chemistry, and toxicology.

Appendix

Table A1. Descriptive summary of the health implications associated with particulate matter pollution exposure in populated African cities.

Study design	Country	City	Target group	Exposure	Summary of main conclusions	Author (s)
Longitudinal study	Nigeria	Benin City	Adults	Indoor PM1.0 and PM2.5	Indoor particulate concentrations correlated positively with reported cases of asthma and contributed to 40.6 % of asthma	Eghomwanre et al. (2022)
		Owerri	Children and adults, Infants	Outdoor PM10, PM2.5	PM10 and PM2.5 concentration levels have the potential to cause adverse health effects in infants, children, and adults	Opara et al. (2021)
		Abuja	Infants and adults	Outdoor PM2.5, PM10	Infants below five years and pregnant women were noted to have high surface areas and absorptive capacity for PM2.5	Kanee et al. (2020)
	Kenya	Urban city	Children	Indoor & outdoor PM	Chronic exposure to PM2.5 increases the risk of respiratory infections among children under five years	Larson et al. (2022)
	Ethiopia	Addis Ababa	Adults and children	Outdoor PM2.5	PM2.5 results in a huge burden of attributable deaths	Kumie et al. (2021)
	Countries in SSA	Cities in SSA	Children	Indoor PM	HAP exposure was associated with under-five mortality, adjusted odds ratio (OR) was 1.33 (95% CI [1.03–1.71])	Bickton et al. (2020)
	Egypt	Cairo	Adults	PM2.5	PM2.5 posed major health risks. 11 % of mortality of the population older than 30 years can be attributed to PM2.5	Wheida et al. (2018)
Cross-sectional study	Ghana	Accra	Street traders	Outdoor PM2.5	High PM2.5 exposure of street traders correlated with increased odds of coughing, rapid heartbeat, and catarrh	Amegah et al. (2022)
	Ghana	Cape Coast	Women	Indoor PM	Fish smokers had an 80 % risk of being anaemic than livelihoods not involved in fish smoking	Armo-Annor et al. (2021)
	Nigeria	Enugu	Adults	Indoor PM	Cement workers and auto-mobile spray painters showed high risks of obstructive pulmonary diseases while woodworkers had restrictive lung diseases	Ibeneme et al. (2022)
	Cameroon	Bamenda	Adults and children	Outdoor PM	Dry and dusty weather increased the ARIs	Nsoh et al. (2019)
	Senegal and Mali	Bamako and Dakar	Adult	Outdoor PM	The African population is more highly exposed to toxic particulate pollution than the developed world.	Val et al. (2013)
	Ethiopia	Urban city	Pregnant women	Indoor PM	Application of either kerosene or charcoal was associated with anaemia in pregnant women	Andarge et al. (2021)
ARIs= Acute respiratory infections; CI = Confident interval, HAP= Household air pollution
Cross-sectional study	Kenya	Western Kenya	Women and Children	Indoor PM	Exposure to wood smoke caused increased coughing, phlegm, wheezing and eye problems. An association exists between indoor cooking and indoor air pollution health effects	Dida et al. (2022)
	Kenya	Nairobi	Children	Indoor PM2.5 and PM10	The association existed between PM2.5 and PM10 and lung functioning deficits	Were et al. (2020)
	Malawi	Blantyre	Children and adults	Indoor PM	Biomass fuel use is likely to be the main driver of existing communicable respiratory disease and cardiovascular diseases	Piddock et al. (2016)
	Tanzania	Three cities	Adults	Indoor PM	Exposure to biomass fuel for cooking poses a high risk to health	Ahamad et al. (2021)
	South Africa	Cities in South Africa	Adults	Outdoor PM	Half of the teenagers who had a respiratory illness were exposed to tobacco smoke in their households	Millar et al. (2022)
	South Africa	Western Cape	Adult	Outdoor PM2.5	Ambient PM2.5 was highly correlated with the prevalence of chest pain [Odds ratio: 1.38(95 % CI: 1.06–1.80]	Bagula et al. (2021)
	South Africa	Johannesburg	Elderly	Outdoor PM10	There were independent associations between ambient PM10, environmental conditions and susceptible groups	Thabethe et al. (2021)
	Namibia	Windhoek	Adult and children	Indoor PM	PM exposure was ascertained to be a risk factor for coughing and phlegm.	Hamatui and Beynon (2017)
	Countries in SSA	Cities in SSA	Children and adults	Indoor PM2.5	Household air pollution was associated with ARI symptoms	Daffe et al. (2022)
	Countries in SSA	Cities in SSA	Children	Outdoor PM	In urban areas, cough, shortness of breath, fever and ARIs were associated with outdoor cooking	Woolley et al. (2023)
	Morocco	Casablanca	Children	Outdoor PM10	PM10 increased 4 % respiratory infections in children	Nejjari et al. (2021)
Cohort study	Ghana	Accra	Adults	Outdoor PM2.5	Exposure of e-waste workers to PM caused a decline in lung functioning and risk for asthma and COPD	Nti et al. (2020)
Experimental study	Senegal	Dakar	Children	Indoor PM	Bronchial cells exposed to urban PM had an inflammatory response and oxidative damage	Ba et al. (2018)
Experimental study	South Africa	Gauteng	Children	Indoor PM10	Indoor respirable dust levels could have an impact on children with asthma	Nkosi et al. (2017)

COPD= Chronic obstructive pulmonary diseases; PM = Particulate matter; SSA= Sub-Sahara Africa.

Table A2. Sustainable actions for controlling outdoor and indoor PM pollution in African cities.

S/N	Sustainable interventions for abating ambient and household particulate pollution
1.	The adoption of cleaner fuels such as liquified petroleum gas (LPG) should be substituted for wood and charcoal during household heating and cooking to help reduce particulate emissions and their related health effects. Besides the adoption of sustainable technology should be employed for fish smoking to avert indoor particle air pollution.
2.	There is a need for policymakers’ and citizens’ engagement to deliberate on issues about the enforcement of legislation to promote cleaner air. Banning of unwholesome burning of garbage and utilisation of dirty fuel for domestic cooking activities need to be discussed and enforced in populated cities in Africa.
3.	Banning the importation of vehicles with old and weak engines, proper maintenance of vehicles and fixing of emission control devices on vehicles are needed to control vehicular traffic emissions and abate PM pollution in African countries.
4.	The establishment of air quality monitoring networks and longitudinal measurements of PM is needed to help produce quality data for predicting future trends and effective management of ambient air quality in most countries in Sub-Sahara Africa.
5.	The acquisition of cost-effective particulate monitors is needed to help gather adequate data on indoor PM pollution levels. This will increase the reproducibility, reliability, and validity of household air particulates measurements, to better estimate the health risk of exposure to particulate matter.
6.	National emission standards and comprehensive air quality policy formulation are needed in countries with no such policies. Again, strict enforcement of regulations and laws needs to be encouraged in African cities.
7.	Future research should adopt a longitudinal design for continuous exposure measurements of PM to account for environmental factors such as seasonality that vary between the African sub-regions. This would permit the appraisal of dose-response associations and estimate disease burden due to acute and chronic exposures.
8.	Massive educational campaigns should be done to sensitise the population about the need to use sustainable means of transportation such as bicycles and encourage the use of public transport and non-motorised modes.
9.	More epidemiological studies should be carried out to examine the impacts of PM on health. Epidemiological studies should focus on the socio-economic impacts of indoor and outdoor PM exposures on respiratory health. Again, particulate pollution risk factors should be examined in populated African cities.
10.	Reduction of sulphur content of petrol and diesel and the use of catalytic converters in vehicles are needed to help avert the release of sulphur oxides and nitrogen oxides that may undergo atmospheric reactions during the dry seasons to increase PM concentrations. Besides the adoption of more efficient industrial technology is encouraged in populated African cities.
11.	Periodic vaccination and the adoption of non-pharmaceutical measures of control are required to combat pandemics in Africa.
12.	The development of environmental, health and crisis management plans as well as social policies such as pandemic emergency management policies are needed in African countries to help fight the emergence of pandemics and particulate pollution health problems.

PM = Particulate matter, LPG = Liquified petroleum gas,