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

Figures & data

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

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.

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

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.

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

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,

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Data availability statement

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