Scielo RSS <![CDATA[Clean Air Journal]]> vol. 30 num. 1 lang. en <![CDATA[SciELO Logo]]> <![CDATA[<b>Air Pollution in Africa in the time of COVID-19: the air we breathe indoors and outdoors</b>]]> <![CDATA[<b>Characterisation of semi-volatile hydrocarbon emissions from diesel engines</b>]]> Exhaust emissions from diesel vehicles have recently been receiving global attention, due to potential human health effects associated with exposure to emitted pollutants. In addition, a link has recently been established between unburnt hydrocarbon (HC) emissions from diesel engines and photochemical smog. Despite being present at very low concentrations in the exhaust, these HCs may act as precursors in the formation of photochemical smog pollution. While short-chain HCs are easier to characterise and have been successfully reduced in many developed cities, longer chain HCs, most likely arising from diesel exhaust emissions, have been poorly quantified to date, and a limited range of HCs from this source has been studied. In this study, transient cycle tests were conducted to collect exhaust emissions from a Euro 3 compliant, 1.6 L test engine fuelled with three diesel fuels; a highly paraffinic fuel, a South African market fuel and a European reference fuel. Portable denuder samplers were used to collect the emissions and analysis was done by thermal desorption-comprehensive 2D gas chromatography-time of flight mass spectrometry (TD-GC x GC-TofMS). The South African market diesel had the greatest n-alkane emissions, with greater emissions observed in the earlier phases (low and medium phase) of the WLTC test cycle. The total n-alkane emissions for this fuel ranged from 34.80 mg/km - 282.67 mg/km from the low to the extra-high phase. The paraffinic diesel had the second highest n-alkane emissions with the total emissions ranging from 35.43 mg/km - 164.99 mg/km. The European reference diesel had the lowest n-alkane emissions amongst the three fuels, with the total emissions ranging from 22.46 mg/km - 82.56 mg/km. Substituted alkyl-benzenes were also detected in the gas phase emissions from each fuel, however only semi-quantitative analysis of these compounds was conducted. The results showed that long-chain HCs were present at easily detectable concentrations in diesel engine exhaust emissions, which is critical in understanding their contribution to photochemical ozone and informing appropriate mitigation and management strategies. <![CDATA[<b>Evaluating the potential of remote sensing imagery in mapping ground-level fine particulate matter (PM<sub>25</sub>) for the Vaal Triangle Priority Area</b>]]> The quality of air breathed in South Africa is of great concern, especially in industrialised regions where PM25 concentrations are high. Long term exposure to PM25 is associated with serious adverse health impacts. Traditionally, PM25 is monitored by a network of ground-based instruments. However, the coverage of monitoring networks in South Africa is not dense enough to fully capture the spatial variability of PM25 concentrations. This study explored whether satellite remote sensing could offer a viable alternative to ground-based monitoring. Using an eight-year record (2009 to 2016) of satellite retrievals (MODIS, MISR and SeaWIFS) for PM25 concentrations, spatial variations and temporal trends for PM2.5 were evaluated for the Vaal Triangle Airshed Priority Area (VTAPA). Results were compared to corresponding measurements from the VTAPA surface monitoring stations. High PM25 concentrations were clustered around the centre and towards the south-west of the VTAPA over the highly industrialised cities of Vanderbijlpark and Sasol-burg. Satellite retrievals tended to overestimate PM25 concentrations. Overall, there was a poor agreement between satellite-retrieved PM25 estimates and ground-level PM25 measurements. Root mean square error values ranged from 6 to 11 μg/m³ and from -0.89 to 0.32 for the correlation coefficient. For satellite remote sensing to be effectively exploited for air quality assessments in the VTAPA and elsewhere, further research to improve the precision and accuracy of satellite-retrieved PM25 is required. <![CDATA[<b>Changes in health risk associated with air pollution and policy response effectiveness, Richards Bay, South Africa</b>]]> Research shows that more than 5.5 million people die prematurely every year due to household and outdoor air pollution placing it as the fourth highest-ranking risk factor for mortality globally. In South Africa, air pollution is a key concern in urban areas with high population density, but also in rurl areas where electricity is not the main source of energy. Approximately 10% of total mortalities in 2015 were attributed to respiratory diseases. With this, pollution policy intervention, both national and international, has become not only a necessary but a vital tool for the protection of air quality. The National Ambient Air Quality Standards and Minimum Emission Standards were introduced in 2009 and 2012, respectively. To ascertain the effectiveness of these interventions, this study used the case of Richards Bay, a highly industrialised town, to determine changes in health risk associated with air quality pollution exposure. Twenty years' data of air pollution-related mortality causes between 1997 and 2016 were analysed to determine the changes in trends, ranking and the Years of Life Lost as a result of pollution exposure. Results indicate a slight improvement in air quality and related health benefits. There was a 24% decrease in the Years of Life Lost due to air quality-related diseases post 2009. Cases of cerebrovascular diseases, which is the main cause of pollution-related mortality, remains an issue that requires continuous attention. The study concludes that air quality policy and its implementation is working to a reasonable extent. However, the increase in mortality due to certain disease cases such as bronchus and lung cancer could signify that the pollution control efforts need to continue and be enhanced. The increase in acute lower respiratory infections, which adversely affects children, is also of concern. <![CDATA[<b>Perceptions of external costs of dust fallout from gold mine tailings: West Wits Basin</b>]]> Mining is essential for the South African economy, just like in many developing African nations. In 2017, mining was reported to contribute 6.8 % to the South African GDP and provided more than 460 000 jobs. Though the sector adds an enormous amount of value to the country, its activities have significant impacts on the environment and the socio-economic factors of society. The environmental impact of mining operations includes air pollution from dust and the well-documented impact on water resources in the form of Acid Mine Drainage (AMD), creation of sinkholes and pollution of agricultural soils. Dust remains a persistent problem in South African urban areas due to the climatic conditions, extensive surface quarrying, unrehabilitated tailings storage facilities and mineral processing. However, very little is reported on the social and economic costs that accrue due to poor ecological management. Some scholars assert that despite the Mine Health and Safety Act, deposition monitoring guidelines and national dust regulations, South Africa still experiences persistent dust problems, especially in coal and gold mining districts. This paper investigates the perceptions of society on the effect of gold production dust pollution in and around a gold mining village (hereinafter referred to as the "gold mining village") in South Africa. A mixed method was used, where a questionnaire and interviews were conducted to examine the gold mining village perceptions on dust pollution and their socio-economic environment. This paper further examines perceptions on how poor and pre-mature mine closure through liquidation results in unrehabilitated mine tailings and how this has significant impacts on the quality of life of individuals and surrounding businesses. The community being investigated in this study perceives the dust fallout impact to be a threat on their living conditions. The paper finds that the community believes it incurs medical and financial expenses due to treating respiratory-related diseases triggered by dust fallout. <![CDATA[<b>Public perceptions of air quality status and suggestions for improvement: The case of Richards Bay and its surroundings, uMhlathuze Local Municipality, South Africa</b>]]> Whereas industrial growth is instrumental in unlocking poverty and advancing development, often, the effect of pollution on the environment, particularly air quality, is seldom accurately predicted. The effects, which include mortality, morbidity, and loss of productive time, are demonstrated later after the damage is done. The views of the pollution-exposed public in industrialised centres is important to ascertain if policy intervention is enhancing environmental protection for all and justice by extension. Through an online survey, 215 residents of the rapidly industrialising Richards Bay and surrounding areas in South Africa responded to the questions about their perceptions of air quality and recommendations to improve air quality management. Results indicate a concern over air quality with most residents perceiving the air quality as fair or poor. Industrial emission was cited as the leading cause of pollution followed by sugar cane and agrarian burning. Irritation of the ear, nose and throat, as well as sneezing and coughing, were the health effects experienced by residents for which air pollution can be partly attributed. The public recommends an improvement in air quality monitoring, consequence management, technology and public transport system. In addition, they recommended the introduction of air quality offsets, incentives schemes, more public involvement, coordinated planning and better collaboration as a recipe for success in air quality management. <![CDATA[<b>Using Microsoft© Power BI© to visualise Rustenburg Local Municipality's Air Quality Data</b>]]> Microsoft© Power BI© is a business analytics tool that visualises data in an accessible manner. It creates visual data reports quickly in a series of panels to give an overview of data while still offering access to more sophisticated visualisation methods too. While statistical tools, like R and MatLab, remain the 'gold standard' for analysing air quality data, simple methods to visualise data are also helpful. Here, we explored the use of Power BI Desktop© to visualise and interpret air quality data for the Rustenburg Local Municipality. Rustenburg is in the Waterberg-Bojanala Priority Area - the third air pollution priority area for air quality management. Ambient PM10 data for three towns, namely, Thlabane, Marikana and Boiketlong, were obtained for six years (2013-2018) in South Africa. Data underwent quality control before being imported into Power BI©. A four-panel dashboard was generated to show (in) compliance with the daily and annual average South African National Ambient Air Quality Standard for PM, annual and average concentrations, frequency of exceedances and a summary of data availability by site. Generally, PM10 data quantity and quality were poor and where data were available, concentrations were high. This type of data visualisation tool can be applied with relative ease by Air Quality Officers and Environmental Health Practitioners for a snapshot overview of the air quality in their area of jurisdiction. The interactive dashboard is also useful for making graphics for policy documents and reports.