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. 2021 Sep 4;5:100263. doi: 10.1016/j.envc.2021.100263

Research trends in the field of ambient air quality monitoring and management in South Africa: A bibliometric review

Newton R Matandirotya 1
PMCID: PMC9767470  PMID: 37519332

Abstract

Air pollution is a leading environmental-health challenge facing the world today. Besides, the emergency of the COVID-19 pandemic has also put some spotlight on issues related to air pollution as both attack the same human respiratory organs. The purpose of this study was to provide an overview of research performance, trends and evolution in the field of ambient air quality monitoring and management in South Africa over the last decade (2010-2021) through the application of a bibliometric approach and a data mining software VOSViewer. Findings were that there has been a steady increase in the number of ambient air quality monitoring and management publications per year. Over the period under review, 2014 contributed 14 % while 2020 contributed 27 % of the total publications. Also, the study established that throughout the period South African scientist collaborated extensively with scientists from Finland, the United States of America, France, and Switzerland. Besides raising awareness levels in the field, the increase in studies can also assist policy formulation and development. In the meantime, the South African National government has also put in place several mitigation strategies to reduce emissions for example the enactment of ambient air quality guidelines.

Keywords: Ambient air, Air quality, Bibliometric, Particulate matter, VOS viewer

1. Introduction

Air pollution is a leading environmental-health challenge facing the world today and is signified by the presence of substances beyond the natural occurrence limit in the atmospheric air (Sweileh et al., 2018, Tshehla and Wright, 2019). One of the key drivers of both indoor and ambient air pollution in the Sub-Saharan Africa region is a reliance on solid biomass fuel combustion to satisfy domestic energy requirements (Wassie and Adaramola, 2021). Globally, an estimated 3 billion still rely on biomass fuel to meet their domestic energy needs (Ezzati and Kammen, 2002) while in Sub-Saharan Africa an estimated 980 million still rely on solid biomass fuels (wood, coal, charcoal and crop residues) to satisfy their domestic energy needs (Wassie and Adaramola, 2021). Besides, other leading emission sources include vehicles, coal-based power generation, biomass burning, waste burning, and mining activities (Health Effects Institute 2020, Landrigan et al., 2018).

Pollutants are broadly classified into gaseous, fine particulate matter, heavy metals and coarse particulate matter (Dettori et al., 2021). Fine particulate matter 2.5 (PM2.5) are regarded as PM with an aerodynamic diameter of ≤ 2.5 um while PM10 are PM with an aerodynamic diameter of ≤10 µm and often regarded as coarse PM (Hua et al., 2021). Negative human health outcomes of these various pollutants depend on such factors as the duration of exposure, concentration levels of pollutants as well individual levels of immunity (Dettori et al., 2021). PM2.5 is highly respirable and have been attributed to disproportionate damage to human cardiovascular-respiratory organs that lead to cause-specific premature deaths (Sweileh et al., 2018, Antonel and Chowdhury, 2014, Brauer et al., 2011, Boys et al., 2014, Burnett et al., 2014).

Globally in 2019, 90 % of the world population were estimated to have been exposed to annual average PM2.5 that were over the WHO recommended annual mean levels of 10 µg/m3 (Health Effects Institute 2020). Furthermore, ambient and household air PM2.5 were estimated to have caused 6.4 million (5.7 million to 7.3 million deaths in 2015 while cardiovascular disease accounted for the highest number of deaths attributable to ambient air pollution in 2015 (Cohen et al., 2017). Also, an increase in particulate matter 2.5 (PM2.5) have been linked to lung cancer and chronic obstructive pulmonary disease (Lelieveld et al., 2015). A study by (Global Burden of Disease 2015) estimated that exposure to ambient PM2.5 to be the fifth-ranked mortality risk factor that contributed towards the death of 4.2 million to global deaths in 2015 while pollution-related premature deaths were estimated at 9 million worldwide during the same year (Landrigan et al., 2018). Furthermore, the same study on GBD (2015) estimated that deaths attributable to exposure to PM2.5 increased from 3.5 million to 4 million (Cohen et al., 2017).

The other class of pollutants is constituted by gaseous pollutants which are also anthropogenic driven. These harmful gases include ozone (O3), carbon dioxide (CO2) being the most dominant greenhouse gas (GHG), methyl (CH3), nitrogen oxides (NO2, NOx), sulphur dioxide (SO2) and related Volatile Organic Compounds (VOCs) (Kasim et al., 2018). VOCs, CO2, CH3 have been documented to contribute to climate change (Kasim et al., 2018). Carbon monoxide (CO) is emitted as a result of incomplete combustion of hydrocarbon fuels such as petroleum products, coal, natural gas and wood (Kasim et al., 2018, Musa and Evuti, 2012). The deterioration of air quality is causing considerable wide-ranging negative human health problems symbolised by increased hospital admissions, diseases as well as premature deaths (Landrigan et al., 2018, Brauer et al., 2011, Joss et al., 2017, Cao et al., 2011, Olaniyan et al., 2015, Awokola et al., 2020). There are close relationships between increased levels of ozone (O3) to diseases incidences of chronic obstructive pulmonary disease (COPD) acute lower respiratory disease (ALRI), cerebrovascular disease (CEV), ischaemic heart disease (IHD) (Landrigan et al., 2018, Lelieveld et al., 2015).

Additionally, The Lancet Commission on air pollution for 2018 attributed 92 % of air pollution-related premature deaths to low-middle-income countries (Lelieveld et al., 2015). (Cohen et al., 2017) further estimated that over the past 25 years ambient air pollution has contributed to the global burden of disease as well as mortality. In 2019, air pollution became the 4th leading cause of premature death globally (Health Effects Institute 2020). Cardiovascular and respiratory diseases rank among the top three killer diseases in South Africa (Statistics South Africa 2014, Altieri and Keen, 2019). Prolonged exposure associated with biomass burning, vehicle emissions and industrial emissions have also been linked to preterm deliveries and low birth weight (Kabera et al., 2020). In the context of the COVID-19 pandemic, clean air is key in the reduction of an increase in co-morbidities within the population as studies have shown evidence of a strong association between high ambient air, the existence of co-morbidities, pollution levels, COVID-19 infections and mortality (Dettori et al., 2021, Das et al., 2021, Zhu et al., 2020).

Scientific research and discoveries form part of a key cog in air quality policy formulation that is meant to manage and mitigate the challenge of air pollution around the world. The purpose of this study was to provide an overview of research developments, trends and evolution in the field of ambient air quality monitoring and management in South Africa. To the best of the authors’ knowledge, no study has been done to explore these developments. The study was also aimed at investigating the spatial gaps that exist regarding regions where air quality monitoring studies have not been conducted. Besides, the study can also be beneficial to other scientists in the developing world where the field is still evolving. This article is arranged as follows: Section 2 presents the materials and methods used in the study, Section 3 highlights the results, case studies and discussion while Section 4 presents the significant conclusions from the study.

2. Materials and methods

The study adopted a bibliometric approach in the identification, display, visualisation, and analysis of literature data. The bibliometric approach was developed by Allen Richard in 1969 (Liao et al., 2018) borrowing from earlier developments by Coles and Eales in 1917 (Osareh, 1996). Bibliometric approaches are used to identify trends and developments in each scientific field (Liao et al., 2018, Ahmad et al., 2021, Mattos et al., 2020, Palmas et al., 2021). Also, bibliometric analysis enjoys the strength in that it accords a researcher some deep understanding of advancements into each field which in this case is ambient air quality monitoring and management. This is achieved through the analysis of citations, co-citations, geographical distribution, word frequency and co-authorship to conclude developments taking place in a field of study (Liao et al., 2018). Data were gathered from the SCOPUS database (https://www.scopus.com) one of the largest scientific databases (Sweileh et al., 2016). A three-step procedure was followed that included (i) data compilation (ii) data arrangement and cleaning (iii) analysis, interpretation, and visualisation (Briones-Bitar et al., 2020). To select relevant articles the study developed a search string that included the following keywords (TS= ((“ambient* OR *air*’’ OR “pollution OR “south’’ OR “Africa”. The asterisk was used to search query articles with keywords that included ambient air pollution. Furthermore, Quotation marks were used to increase the accuracy of the search (Sweileh et al., 2016). Fig. 1 outlines the steps used in the identification, screening and analysis of articles.

Fig. 1.

Fig 1:

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Flow chart diagram showing the steps followed during the identification, screening and analysis of articles

The search was conducted on the 4th of April 2021 covering the period 2010-2021 with the initial search yielding a total of 354. Relevant titles and abstracts were screened which resulted in 74 articles being selected for final analysis. Additionally, a thesaurus file was developed to merge synonymous terms. From SCOPUS literature data (CSV format) was exported into VOSViewer version 1.6.14 (www.vosviewer.com) for bibliometric analysis, display and visualisation. The VOS program is a mapping technique where the VOS means the visualisation of similarities (Van Eck and Waltman, 2010). This tool is useful in tracking the evolution of scholarship in a variety of fields (Gobster, 2014, Heersmink et al., 2011). VOSViewer offers several options on its dashboard to come up with co-occurrence maps that highlight the associations and relationships of highly used terms in a field. To illustrate the network of frequent keywords used in the field of ambient air pollution the study used the co-occurrence option to create a hot spot of words used in the field (Yeung et al., 2017). Out of the 74 articles, 69 meet the 5-word keyword threshold hence analysis for visualisation was done on this basis. For interpretation purposes, the size of the circle corresponds to the overall frequency in the dataset (Palmas et al., 2021, Van Eck and Waltman, 2014) therefore the larger the size the higher the frequency therefore frequently mentioned terms emerge with larger sized circles while strongly linked terms cluster closer to each other (Mattos et al., 2020). Furthermore, strongly related tend to be located close to each other while terms that do not have a strong relationship being located away from each other (Waltman et al., 2014). A similar bibliometric approach was applied in (Ahmad et al., 2021, Mattos et al., 2020, Palmas et al., 2021, Donthu et al., 2020, Bartolacci et al., 2020, Zurita et al., 2020, Laengle et al., 2021).

3. Results and discussion

This section presents the results, case studies and discussion of the study.Fig. 2 illustrates the location of some of the studies identified in the study. From the spatial distribution, it emerged that most of the studies were conducted in the province of Gauteng, Mpumalanga, Western Cape and North-West while few were identified for Free State and none for the Northern Cape and Eastern Cape. Research gaps exist in the provinces of the Northern Cape and Eastern Cape as well as the Free State.

Fig. 2.

Fig 2:

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Spatial distribution of studies focusing on air quality monitoring studies

Fig. 3 illustrates three distinct clusters emerging from studies which were conducted between 2010-2021 in South Africa. The size of a dot represents the frequency a keyword appears in the articles therefore the bigger the size the higher the frequency of mention. Under the green cluster, the most dominant term was South Africa which was closely related to such terms as seasonal variation, atmospheric pollution as well as air monitoring. On the other hand, the red cluster had the term human as the most dominant term which was closely linked to such terms as air pollutants and nitrogen oxide. Meanwhile, the blue cluster's most dominant term was air quality and was closely associated with such terms as sulphur dioxide and ozone. The majority of the studies were done in three provinces namely Gauteng, North-West and Mpumalanga. These are the provinces that make the main economic hub of the country. The same provinces are also where the majority of coal power plants are located. Besides power generation the Highveld block has several mines that are a source of dust particulates.

Fig. 3.

Fig 3:

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Co-occurrence network of keywords in published articles during 2010-2021: Cluster analysis

Fig. 4 shows that 27 % of the published articles were published in 2020 while the lowest percentage number of studies were published in 2010 contributing 1 % to the total publications. Fig. 4 can be read together with Fig. 5 which is a representation of publications per year over the period under review.

Fig. 4.

Fig 4:

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Overlay visualisation of the co-occurrence network of keywords for articles during the period 2010-2021

Fig. 5.

Fig 5:

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Number of publications per year on ambient air quality monitoring and management during 2010-2021

The highest number of publications were in the year 2020 contributing a total of 20 publications. This was followed by 2014 which contributed 10 publications and the least number of publications were in 2010 wherein there was only 1 publication in the field. There was a mild positive linear association of r2 =0.37 between years and the number of publications per year while the exponential relationship showed a much stronger positive association at r2 =0.53. This highlights an exponential rise of publications within the field especially from 2014-2021.

Collaborations form part of knowledge development, skills sharing and transfer of discoveries. Fig. 6 shows the collaborative network between South African scientist and scientists from other countries as it has become the norm (Wang et al., 2021). Significantly a stronger network of collaborative work was observed between South Africa and the United States. This was closely followed by some relatively strong network between South African scientist and those in Finland. On the other hand, the least network was between South Africa and France. Table 1 shows a summarised representation of case studies of the most recent studies with a focus on the year 2020 and 2021 and these showcases the latest developments in the field as well as giving an insight into the state of air quality across South Africa.

Fig. 6.

Fig 6:

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Collaborative network between South African authors and authors from other countries

Table 1.

Case studies of most recent studies on ambient air pollution done in South Africa

Title of article Scope References
The sensitivity of simulated surface-level pollution concentration to WRF-ARW- model PBL parameterisation schemes over the Highveld of South Africa The study compared the meteorological outputs from the Advanced Research Weather Research and Forecasting (WRF-ARW) model with four PBL schemes used as inputs for an air quality model. Simulations were compared from different scheme combinations using observed air pollution concentration data gathered in 2016. Findings were that no PBL scheme was able to perform consistently for the simulated five pollutants in the two seasons. (De Lange et al., 2021)
A hybrid air pollution/land-use regression model for predicting air pollution concentrations in Durban, South Africa. The study incorporated source meteorological information into atmospheric dispersion models for the prediction of NO2, SO2 and PM10. The findings were that higher levels of NO2 and SO2 were predicted in South Durban than North Durban. The authors attributed the high levels of these gases to the existence of industrial sources. On the other hand, PM10 concentrations levels were higher in Durban North than South and the authors attributed this to vehicular emissions, bush fires and domestic solid fuel burning. (Tuluram et al., 2021)
Comparing Methods to Impute Missing Daily Ground-Level PM10 Concentrations between 2010-2017 in South Africa The study developed innovative modelling approaches to impute missing air quality data through the use of spatiotemporal predictor variables supported by random forest machine learning method to explore the relationship between average daily PM10 concentrations and spatiotemporal predictors for example meteorological elements, land use and source-related variables. The findings were that this approach is a feasible approach to compensate for missing data. (Arowosegbe et al., 2021)
Health Risk Assessment of PM2.5 and PM2.5-Bound Trace Elements in Thohoyandou, South Africa The study investigated the health risks of fine particulate matter grade PM2.5 in ambient air and its trace elemental components in a rural community of South Africa. The study established that health risks exposure was high particularly for children and infants. The annual average was estimated to be 11 µg/m3 which is above WHO standards but below South African Ambient Air quality standards. (Edlund et al., 2021)
Solar Ultraviolet Radiation in Pretoria and Its Relations to Aerosols and Tropospheric Ozone during the Biomass Burning Season The study explored the impact of aerosols and tropospheric ozone on solar ultraviolet radiation through ground-based surface measurements of aerosols, tropospheric ozone and modelled solar ultraviolet. Findings were that the difference between modelled and observed solar ultraviolet index at solar noon was 7 % while on the other hand excluding aerosols resulted in 10 % between modelled and observed UVI. (Du Preez et al., 2021)
Ambient Gaseous Pollutants in an Urban Area in South Africa: Levels and Potential Human Health Risk Annual levels of different criteria pollutants were as follows; NO2 (39.442 µg/m3), SO2 (22.464 µg/m3), CO (722.003 µg/m3). while the 8-hour concentration was CO (649.902 µg/m3) and O3 (33.556 µg/m3) and did not exceed the South African ambient National Air Quality Standards. The annual mean concentration of all gaseous pollutants under investigation did not exceed South African recommended standards. (Morakinyo et al., 2020)
Impacts of population growth and land use on air quality. A case study of Tshwane, Rustenburg and Emalahleni, South Africa The study looked into the relationship between population growth and ambient air pollution. A positive association was established between population growth and black carbon, carbon monoxide and sulphur dioxide. (Shikwambana and Tsoeleng, 2020)
Contrasting indoor and ambient particulate matter concentrations and thermal comfort in coal and non-coal burning households at South Africa Highveld Ambient PM4 concentrations were below PM2.5 24-hour average of 40 ug/m3. Diurnal outdoor PM4 concentrations were similar to indoor concentrations. During burning episodes, the PM4 was higher at a coal-burning dwelling than at a non-coal burning dwelling. PM4 high concentrations were recorded between 4.00 hours and 8.00 hours as well as 15.00 hours and 20.00 hours. (Adesina et al., 2020)
The impacts of commissioning coal-fired power stations in South Africa: insights from ambient monitoring stations The study established that there was no statistically significant association between emissions from power plants and ambient concentrations of air for nearby areas. (Morosele and Langerman, 2020)
Atmospheric Toluene and Benzene Mole Fractions at Cape Town and Cape Point and an estimation of the Hydroxyl Radical Concentrations in the Air above the Cape Peninsula South Africa The study established that polluted air that migrates from Cape Town is the major source of benzene and toluene mole fractions which were recorded at Cape Point. (Kuyper et al., 2020)
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Table 1 provides a summary of recent ambient pollution studies done in South Africa. These studies showcase the recent advancements in the field of ambient air quality monitoring and management. The study established that even in 2020-2021 most publications remain concentrated within the province of Gauteng followed by the North-West and Western Cape. No studies were identified for the provinces of Eastern Cape and Northern Cape thus presenting an opportunity for future studies in these regions.

Notable findings were that over Durban (Tuluram et al., 2021) observed higher ambient concentrations for NO2 and SO2 were predicted around Durban South more than Durban North and this was attributed to high industrial activities in the region while PM10 concentrations were higher than Durban South which was linked to biomass burning as well domestic solid fuel utilisation. In Thohoyandou (Edlund et al., 2021) recorded that ambient PM2.5 annual mean concentrations was 11 ug/m3 which is above the WHO guidelines currently pegged at 10 ug/m3. The high PM2.5 was linked to seasonal meteorological variations, domestic solid fuels moreso as these were observed in cold winter season compared to summer season. Meanwhile in Pretoria, (Morakinyo et al., 2020) established that NO2 and SO2 ambient concentrations were at 11.50 ug/m3 and 18.68 ug/m3 while PM10 was observed at 48.3 ug/m3. Meanwhile (Adesina et al., 2020) observed that during winter PM4 ambient concentrations were recorded at 60.9 ug/m to 207 ug/m while at a non-coal burning structure the concentrations ranged between 15.3 ug/m3 to 84.2 ug/m3. On the other hand, in summer the concentrations ranged at a coal burning structure the concentrations ranged between 17.4 ug/m3 to 36.6 ug/m3 and 14.2 ug/m3 to 39.9 ug/m3 at the non-coal burning structure. This was attributed to the use of coal within the different structures sampled besides power plants emissions. Over the city of Cape Town (Kuyper et al., 2020) estimated that toluene and benzene concentrations were between 7.2 and 3.5 × 10 molecules cm while those of Hydroxyl were high in Cape Town and this was attributed to various anthropogenic activities around the city. Besides, the strides which have been made with regards air quality monitoring activities there have also been some progress regarding alternative prediction mechanisms as performed by (De Lange et al., 2021, Du Preez et al., 2021) experimented the use of Advanced Research Weather Research and Forecasting (WRF-ARW) model with four PBL schemes and ground-based measurements integrated with modelled solar ultraviolet respectively. Both approaches showed a lot of potential within the South African context as instruments of air quality monitoring.

On a legislative level, the South African government introduced National Ambient Air Quality Standards in 2009 and 2012 respectively that prescribe the level of emissions that industry and other big emitters are supposed to be within however as of today there has been some exemption towards adherence up to 2030 for industries (Tshehla and Wright, 2019) while independent power producers have been contracted for the generation of solar and wind power. Other steps which the National government has taken towards net zero emissions include the introduction of provincial-based Air Quality Management Plans which are further cascaded to local municipalities for enforcement (Tshehla and Wright, 2019). On the domestic front, there have also been steps to move away from reliance on solid fuels towards the use of LPG, Solar and Wind with efforts have being made to improve the thermal performance of residential dwellings through retrofits (Matandirotya et al., 2019). Retrofit interventions are meant to reduce the reliance by households on solid fuels for space heating and other domestic purposes thus ultimately reducing both household and ambient air pollution. The next section highlights the study limitations.

3.1. Study limitations

The study had some limitations which included the reliance on one database (SCOPUS). This meant that any potential good works which were not published on SCOPUS were left out of the study. The study did not delve into other online databases such as WEB of Science, Sabinet Online, African Journals, Science Direct and JSTOR. This provided only a limited number of articles that were analysed. The benefit however of obtaining data from SCOPUS was that it makes available articles from other databases for example Science Direct. Section 4 presents the conclusion of the study.

4. Conclusion

Our current study provided an overview of research performance and trends within the field of air quality monitoring and management in South Africa over the period 2010-2021. Overally, the study established that there has been some steady increase in the number of scientific publications over the period under review with the most publications being produced in 2020 at 20 publications contributing a 27 % to the total number of publications. The practical implication of this study is that it ascertained the existing gaps regarding the spatial distribution of where most studies have been concentrated for example the majority of ambient air quality studies were conducted on the Highveld, North-West, Gauteng and Western Cape provinces however not much has been done in the provinces of the Northern Cape, Kwa-Zulu Natal and the Eastern Cape. This can be attributed to the fact that these are the dominant economic hubs of the country as well as the main sources of anthropogenic air pollution. Additionally, the study shows that there were no collaborative studies among authors residing within countries on the African continent thus this can be an opportunity for further collaborations. This was shown by the non-existence of co-authorships between South African scientist and fellow scientist on the African continent. The steady increase in scientific research outputs over the years therefore, offers an opportunity for policymakers to transform scientific research into robust air quality management policy formulation.

Declaration of Competing Interest

The author declares no personal or financial interest in the research. The views expressed in this article are the personal views of the author.

Acknowledgements

The author would like to acknowledge the Unit for Environmental Sciences and Management of the North-West University for availing resources to conduct this research. The study did not receive any financial grant

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