A spatial approach to assessing PM2.5 exposure level of a brickmaking community in South Africa.

Globally, particulate matter with an aerodynamic diameter of 2.5 µm or less poses a significant threat to human health. The first step in quantifying human health impacts caused by exposure to PM2.5 pollution is exposure assessment. Population-weighted exposure level (PWEL) estimation is one of the methods that provides a more precise exposure assessment since it incorporates the spatiotemporal distribution of population with the pollution concentration estimate. In this study, PM2.5 exposure levels in the local communities around brickmaking industries were investigated, using the population census data of the study area and 1-year data from nine PM2.5 monitoring stations installed in and around the brickmaking industries. The observed PM2.5 data was spatially interpolated using inverse distance weight (IDW). Data on PM2.5 levels across the study area were classified based on the World Health Organization interim target (IT) guidelines and the South African National ambient air quality standard (NAAQS). An annual PM2.5 population weighted exposure level of 27.6 µg/m3 was estimated for the study area. However, seasonal exposure levels of 28.9, 37.6, 26.5, and 20.7 µg/m3 were estimated for the autumn, winter, spring, and summer seasons, respectively. This implies that local communities around the brick kiln in the Vhembe District are exposed to high levels of PM2.5, especially in winter. The PM2.5 levels in the brickmaking industries as well as its other sources in the Vhembe District, therefore, need to be lowered. Findings from population exposure level to pollutants can provide valuable data for formulating policies and recommendations on exposure reduction and public health protection.Implications: PM2.5 concentration in any given environment has high spatial and temporal variability due to the presence of diffused sources in the environment. Using ambient air concentrations to directly estimate population exposure without taking into consideration the disproportionate spatial and temporal distribution of the pollutant and the population may not yield accurate results on human exposure levels. It is, therefore, important to assess the aggregated PM2.5 exposure of a populace within a given area. This study therefore examines the PM2.5 population-weighted-exposure level of the host communities of the brickmaking industry in Vhembe District, South Africa.

Quantification of ambient PM2.5 concentrations adjacent to informal brick kilns in the Vhembe District using low-cost sensors.

The widespread exposure to ambient PM2.5 poses a substantial health risk globally, with a more pronounced impact on low- to medium-income nations. This study investigates the spatiotemporal distribution of PM2.5 in the communities hosting informal brickmaking industries in Vhembe District. Utilizing Dylos DC1700, continuous monitoring of PM2.5 was conducted at nine stations adjacent to informal brick kilns from March 2021 to February 2022. The study determined the correction factor for PM2.5 measurements obtained from the Dylos DC1700 when it was collocated with the GRIMM Environmental Dust Monitor 180. Additionally, the diurnal and seasonal variations across monitoring stations were assessed, and potential PM2.5 sources were identified. The study also evaluated the compliance of ambient PM2.5 concentrations across the stations with the South African National Ambient Air Quality Standard (NAAQS) limits. Annual PM2.5 concentrations for the stations ranged from 22.6 to 36.2 µgm-3. Diurnal patterns exhibited peak concentrations in the morning and evening, while seasonal variations showed higher concentrations in winter and lower concentrations in summer and spring. All monitoring stations reported the highest daily exceedance with respect to the daily NAAQS limit in the winter. Major PM2.5 sources included domestic biomass combustion, vehicular emissions, industrial emissions, and construction sites. Well-calibrated low-cost sensors could be employed in suburb regions with scarce air quality data. Findings from the study could be used for developing mitigation strategies to reduce health risks associated with PM2.5 exposure in the area.

Determinants of Solid Fuel Use and Emission Risks among Households: Insights from Limpopo, South Africa.

Emissions from residential solid fuels reduce ambient air quality and cause indoor air pollution resulting in adverse human health. The traditional solid fuels used for cooking include coal, straws, dung, and wood, with the latter identified as the prevalent energy source in developing countries. Emissions from such fuel sources appear to be significant hazards and risk factors for asthma and other respiratory diseases. This study aimed at reporting factors influencing the choice of dominant solid fuel for cooking and determine the emission risk from such solid fuel in three villages of Phalaborwa, Limpopo province, South Africa. The study used descriptive analysis to show the relationship between the socio-economic variables and the choice of cooking fuel at the household level. Multiple correspondence analysis (MCA) was used further to detect and represent underlying structures in the choice of dominant fuels. MCA shows the diversity and existing relationship of how variables are related analytically and graphically. Generalised linear logistic weight estimation procedure (WLS) was also used to investigate the factors influencing choice of fuel used and the inherent emission risks. In the three villages, wood was the prevalent cooking fuel with 76.8% of participant households using it during the summer and winter seasons. Variables such as low monthly income, level of education, and system of burning are revealed as strong predictors of wood fuel usage. Moreover, income, water heating energy, types of wood, and number of cooking hours are significant (p ≤ 0.05) in influencing emission from wood fuel in the community. A notable conclusion is that variables such as income, education status and system of burning are determinants of wood fuel usage in the three villages, while income, water heating energy, types of wood and number of hours influence vulnerability to household emission and possible health risks in the use of solid energy sources.

Concentrations, distributions and critical level exceedance assessment of SO2, NO2 and O3 in South Africa.

South Africa has been identified as a source of industrial pollution that is significant at a global scale. This study was designed to provide quantitative information, by direct measurement, across northeastern South Africa, which includes the highly industrialised Mpumalanga Highveld. The specific aim of the study was to evaluate whether or not acidic atmospheric pollution poses a threat to soils, plants and water bodies of South Africa. To address this aim, a network of 37 passive sampling sites was established to measure monthly mean concentrations of near-surface SO(2), NO(2) and ozone. The area covered extended over the northern and eastern interior of South Africa while avoiding sources of local emissions such as towns, mines and highways. The field campaign was conducted between August 2005 and September 2007. Spatial distributions and temporal trends for these pollutant gases were assessed. Critical levels analysis comparisons were made against applicable air quality standards, guidelines and limits to evaluate the potential for adverse atmospheric pollution impacts on regional environments. The assessment indicates that only in the central source area of the South African industrial Highveld are some levels exceeded. In remote areas, including the sensitive forested regions of the Drakensberg escarpment, pollutant concentrations are below the critical thresholds for environmental damage.

Statistical evaluation of aerosol data from Ben Macdhui mountain, South Africa.

The aim of this study is re-evaluation of the data collected during Ben Macdhui High Altitude Trace Gas and Aerosols Transport Experiment (BHATTEX) to identify the dominant species of aerosols and their interactions in the atmosphere. Particularly, investigations of seasonal variations, the origin of sulphates and the formation of the ammonia sulphate were essential topics of this study. Such analyses were done by application of unique combination of supervised and unsupervised learning classification methods. Namely discriminant function analysis (DFA) for simple identification of parameters and principal component analysis (PCA) for the further investigations on hidden structure of data has been applied. The analysis shows that sulphate concentration and C,N isotopic ratios can classify winter and summer patterns of data. Differences between sulphate concentrations in summer and winter samples indicated by second PCA component were probably related to meteorological conditions over that region. The relations between anthropogenic compounds and ammonia or sulphate were much more stronger in summer samples than in winter ones, that is related with seasonal transport of that particles from industry to the investigated regions. The hypothesis of competitive reaction between sulphates, nitrates and ammonia ions has been also proven by application of simple regression analysis. Moreover the analysis of correlations coefficients shows that those relations are independent on seasons.