Lower Kuiseb River sediments and their control on dust emission

Includes bibliographical references.Previous studies, using remote sensing, have identified the Kuiseb River in Namibia as the dustiest river in Southern Africa. Dust plumes detected from this basin are mostly associated with the Lower Kuiseb River, between the end of the bedrock canyon at Natab and the Kuiseb Delta towards the Atlantic Ocean. The purpose of this study was to examine the surface materials of the Lower Kuiseb River and establish their potential towards dust production, leading to such plumes. This investigation focused predominantly on the size characteristics of 153 surface sediment samples collected from the Kuiseb main channel, its terraces, delta, gravel plain surfaces and tributaries, dunes and interdune, all of which were analysed using a Malvern Mastersizer 2000 laser diffractometer. In addition, other sediment characteristics such as mineralogy, organic matter content, soluble salts; and selected surface roughness elements were also considered. Furthermore MODIS satellite imagery was used to assess the dust emission activity from each of the geomorphological units sampled in the field for the period from 2005 to 2013. This study has demonstrated surface sediments suitable for dust production to increase towards the coast with particular "dusty" floodplain surfaces between Swartbank and Rooibank, as well as the Kuiseb Delta. It appears that silt crusts formed as the flood water dissipate, provide a main source of appropriately sized material for deflation. The crusts consist entirely of silt and clay sized material, with a maximum of 97% <63&#956;m, 39% <10&#956;m and 6% <&#956;m. Dust producing surfaces of the gravel plain include the gravel plain drainage, which has the largest quantity of clay sized material (maximum of 11% <&#956;m). Anthropogenic disturbances of the surface are likely playing a role in the production of dust, with livestock farming causing a fragmentation of crusts in the river valley, and mining and off-road driving disturbing the gravel plain

Characterising the potential health risks associated with coal dust

Coal dust is inextricably linked to the development of dust diseases. To date, the role of mineral matter in coal has been investigated for its links to pulmonary damage; however, no consensus has been reached on which characteristics are relevant to pulmonary toxicity. This study hypothesises that the toxic potential of inhalable coal dust can be attributed to reactive mineralogy and the specific surface area for interaction between the particles and primary phagocytes such as macrophages. To test this hypothesis, the study developed an advanced understanding of the relationship between the physicochemical and mineralogical characteristics of coal particles and pulmonary toxicity. Three objectives were constructed to achieve this aim. Objective 1 developed a detailed particle characterisation dataset on coal particle samples utilising both routine (X-ray diffraction and X-ray fluorescence) and advanced methods of coal analysis (automated scanning electron microscopy systems). Objective 2 elucidated multivariant relationships between the particle characteristics and the immunological responses from exposed macrophage cells in vitro using advanced statistical methods. Lastly, objective 3 developed a protocol to empirically characterise the relative risk of coal dust-related damage on a cellular level. In developing a detailed characterisation dataset on the coal samples, both routine and automated analysis tools were used to define general, chemical, mineralogical, and mineral specific characteristics. An auto-SEM-EDS-XRD (Automated scanning electron microscope coupled with Energy Dispersive X-ray Spectroscopy and analyses generated by X-ray Diffraction) protocol was developed to obtain a broad spectrum of particle data by mineralogically mapping each particle. This protocol involved the rigorous analysis of uncertainty in the data using comparative datasets generated from XRD and XRF (X-ray Fluorescence) analyses. In summary, the study demonstrated that the combined use of both routine and advanced particle analysis tools allowed for the classification of chemical and mineralogical distributions as well as a discrimination between general and mineral specific particle characteristics. Generally, these results suggested that features relating to general particle characteristics (size, shape, roughness, and surface area) are more strongly a function of mechanical breakage and deformation than compositional variation. To assess the multivariant relationships between the numerous characteristics defined and response measures of cellular toxicity, a PLSR (partial least squares regression) was applied in a novel approach to attempt a single model comparison of such relationships. This model was chosen for its ability to relate response and explanatory variables based on a new set of variables which have undergone dimensionality reduction whilst maximising the covariance. The results from the relationship analysis showed that physical characteristics (particle shape in particular) displayed a greater influence on cytotoxicity and lipid peroxidation over mineral and chemical-based characteristics. Relating this observation to previous research it was suggested that the influence of shape and roughness on phagocytosis may have strong implications for magnitude of direct and indirect cellular harm and the predominance of either intracellular or extracellular damage. The results also showed that, apart from the influence of particle shape, radical-induced stress and cytotoxicity displayed a strong dependency on (1) the chemical and mineralogical reactivity Ca hosted in gypsum, (2) the release/inhibition of Fe from pyrite and Fe-sulfates, and (3) the surface activity of quartz based on its crystallite size. However, the relationships defined in the context of cytotoxicity displayed a more nuanced dependency with the silicate mineral content and their associated properties compared to lipid peroxidation. From this it was suggested that non-radical related pathways to cytotoxicity could also occur from coal dust exposure. Ultimately, the study demonstrates the first analysis which assesses relative impact and magnitude of multiple particle characteristics on cytotoxicity and cellular stress. Finally, to provide a more easily interpretable format for the analysis of the PLSR relationships, a protocol was developed to screen variables based on: (1) their level of importance to the defined relationship and (2) the rank of importance for each influential variable represented on a unified scale. Elements which explained the variability within the sample characteristics and the responses were clustered using the k-means algorithm to determine classes of samples which display similar characteristics or levels of toxicity. The comparison of the classes grouping samples with similar properties versus samples groups with similar toxicity levels showed that even though samples may share similar properties, their reported level of toxicity may differ. This confirms the observations from previous studies which have shown that the relative toxicity of coal dust cannot be explained on the basis of isolated properties. Rather the set of ‘influential variables' showed that a combination of general, chemical, mineralogical and mineral specific data are needed to determine the differences between levels of toxicity. Ultimately, the application of this protocol on 17 different dust-sized coal samples demonstrated the key differences between samples and their influence on levels of cytotoxicity and lipid peroxidation, which until this study have not been demonstrated by a single regression. As an outcome of such results, this study provides a robust analysis strategy for elucidating particle cell relations which can further advance the understanding of coal dust induced disease pathology. Additionally, the protocol has demonstrated the usefulness of disseminating the complex data structures to more easily interpretable data formats such that a generalisable analysis of risk factors related to coal dust-based cellular damage can be utilised by stakeholders in data-based decision making. Ultimately, the results of this study propose that the toxic potential of coal dust is primarily a function of the reactive mineralogical and chemical components within the particles, however, the magnitude of this intrinsic reactivity is subject to the mitigative factors which can either neutralise of supress the anticipated reactivity

Aeolian dust emission dynamics across spatial scales: landforms, controls and characteristics

Variable erodibility (surface characteristics) and erosivity factors (meteorological conditions) result in dust emission dynamics being complex in both space and time. Accounting for localscale surface variability is critical to our understanding of dust emitting processes. This study identifies mineral dust using remote sensing, establishes emission thresholds through field measurements and identifies particle chemistry for major dust sources in the Central Namib Desert. Examining over 2000 Landsat images over a period from 1972 to 2016, identified 40 days of visually detectable dust, originating from sub-km scale point sources. The observations suggest that dust sources can be identified at the landform scales which particularly include ephemeral river valleys and saline pan surfaces. These persist throughout the 25-year record; however, a gradual shift in source point clusters is noted through time, which can be tentatively attributed to anthropogenic modification of the hydrological systems. A PI-SWERL (Portable In-Situ Wind ERosion Lab) wind tunnel was used to measure the emission potential of the Landsat derived targets. The most emissive sources were paleostockpiles of alluvial silt deposits and associated degraded nebkhas within the Kuiseb River Delta. These had a geometric mean emission flux of 0.076 mg m-2 s -1. In comparison, the active channel had a geometric mean emission flux of 0.008 mg m-2 s -1, undisturbed desert pavement 0.007 mg m-2 s -1, pan surfaces 0.001 mg m-2 s -1 and wadis within the gravel plains 0.030 mg m-2 s -1. The emission thresholds were augmented with site-specific field measurements such gravel cover (%), moisture content (%), particle size (µm), elemental composition (%) and shear and compressive strength (kg cm-2). A Boosted Regression Tree (BRT) machine-learning algorithm identified the most important surface and sediment characteristics determining dust emission from the measured surfaces. The model explained 70.8% of the deviance in the measured dust flux with the top predictor variables and their relative importance (%) as follows: gravel cover, 16%; moisture content, 14%; kurtosis, 13%; very coarse silt, 13%; very fine sand, 11%; fine sand, 8%; compressive strength, 7%, calcium, 7% and magnesium, 6%. Such an analysis can be used to identify critical thresholds for dust emission and standardise testing protocols. Linking landforms with such emission measurements allow for the assessment of two existing dust emission schemes: the Preferential Dust Scheme (PDS; Bullard et al. 2011) and the Sediment Supply Map (SSM; Parajuli et al. 2017). Although these schemes represent a major advance in our representation of dust emission source areas and erodibility, this study shows that these schemes still need to be improved to accurately depict dust emission potential. For the PDS this would include producing a global rasterised output with quantified dust emission potential and for the SSM, a more accurate classification of the highly emissive geomorphic units. Landsat source point sediments were subjected to physical and geochemical analyses and compared to samples obtained from passive collectors such as the Big Spring Number Eight (BSNE) and active PI-SWERL exhaust emissions, using an auto-SEM (QEMSCAN). This provided individual particle mineralogy (>2 µm resolution) for a total of approximately 10000 to 60000 particles per sample which enabled a comparison of particle size, shape and mineralogy. The samples consist of a mixture of minerals reflecting the varied metamorphic geology and consists predominantly of feldspar, quartz, mica, other aluminosilicates such as the alteration products epidote and chlorite and low to medium grade metamorphics such as amphibole and pyroxene, iron oxihydroxides, titanium minerals, carbonates and clay minerals

Strategies for addressing conflicts arising from blue growth initiatives : insights from three case studies in South Africa

South Africa has vigorously embraced the concept of the ‘blue economy’ and is aggressively pursuing a blue growth strategy to expand the ocean economy, create jobs, and alleviate poverty. However, many of these ‘blue initiatives’ are leading to conflicts amongst various stakeholders with different histories, relationships with resources and areas, worldviews, and values. Investment in the ocean economy is being prioritized by government and planning, environmental assessment, and decision-making processes are being fast-tracked. Consequently, historical inequities as well as environmental and social justice considerations are not being given due consideration. Communities are not being effectively consulted. This has resulted in tensions and conflicts amongst proponents of these projects and local communities living in areas affected by these initiatives. We examine the drivers of conflict and then explore the strategies that local communities and their social partners have employed in these case studies to challenge contentious developments, defend coastal and marine areas, and make their voices heard. The cases involve conflicts over air quality in an expanding marine industrial zone at Saldanha Bay, prospecting and mining applications in the vicinity of the Olifants Estuary in the Western Cape, and the expansion of the Richard’s Bay Port, mining activities, and conservation initiatives in KwaZulu-Natal. The barriers and potential opportunities to opening up deliberative spaces, shifting values and views, and co-producing knowledge, in contexts that are characterised by structural inequality, poverty, and power asymmetries, are discusse

A new framework for evaluating dust emission model development using dichotomous satellite observations of dust emission

Dust models are essential for understanding the impact of mineral dust on Earth's systems, human health, and global economies, but dust emission modelling has large uncertainties. Satellite observations of dust emission point sources (DPS) provide a valuable dichotomous inventory of regional dust emissions. We develop a framework for evaluating dust emission model performance using existing DPS data before routine calibration of dust models. To illustrate this framework's utility and arising insights, we evaluated the albedo-based dust emission model (AEM) with its areal (MODIS 500 m) estimates of soil surface wind friction velocity (u(s*)) and common, poorly constrained grain-scale entrainment threshold (u(*ts)) adjusted by a function of soil moisture (H). The AEM simulations are reduced to its frequency of occurrence, P(u(s*) > u(*ts)H). The spatio-temporal variability in observed dust emission frequency is described by the collation of nine existing DPS datasets. Observed dust emission occurs rarely, even in North Africa and the Middle East, where DPS frequency averages 1.8 %, (similar to 7 days y(-1)), indicating extreme, large wind speed events. The AEM coincided with observed dust emission similar to 71.4 %, but simulated dust emission similar to 27.4 % when no dust emission was observed, while dust emission occurrence was over-estimated by up to 2 orders of magnitude. For estimates to match observations, results showed that grain- scale u(*ts) needed restricted sediment supply and compatibility with areal u(s*). Failure to predict dust emission during observed events, was due to u(s*) being too small because reanalysis winds (ERA5-Land) were averaged across 11 km pixels, and inconsistent with u(s*)across 0.5 km pixels representing local maxima. Assumed infinite sediment supply caused the AEM to simulate dust emission whenever P(u(s*)>u(*ts)H), producing false positives when wind speeds were large. The dust emission model scales of existing parameterisations need harmonising and a new parameterisation for u(*ts) is required to restrict sediment supply over space and time

Satellites reveal Earth's seasonally shifting dust emission sources

Establishing mineral dust impacts on Earth's systems requires numerical models of the dust cycle. Differences between dust optical depth (DOD) measurements and modelling the cycle of dust emission, atmospheric transport, and deposition of dust indicate large model uncertainty due partially to unrealistic model assumptions about dust emission frequency. Calibrating dust cycle models to DOD measurements typically in North Africa, are routinely used to reduce dust model magnitude. This calibration forces modelled dust emissions to match atmospheric DOD but may hide the correct magnitude and frequency of dust emission events at source, compensating biases in other modelled processes of the dust cycle. Therefore, it is essential to improve physically based dust emission modules. Here we use a global collation of satellite observations from previous studies of dust emission point source (DPS) dichotomous frequency data. We show that these DPS data have little-to-no relation with MODIS DOD frequency. We calibrate the albedo-based dust emission model using the frequency distribution of those DPS data. The global dust emission uncertainty constrained by DPS data (±3.8 kg m−2 y−1) provides a benchmark for dust emission model development. Our calibrated model results reveal much less global dust emission (29.1 ± 14.9 Tg y−1) than previous estimates, and show seasonally shifting dust emission predominance within and between hemispheres, as opposed to a persistent North African dust emission primacy widely interpreted from DOD measurements. Earth's largest dust emissions, proceed seasonally from East Asian deserts in boreal spring, to Middle Eastern and North African deserts in boreal summer and then Australian shrublands in boreal autumn-winter. This new analysis of dust emissions, from global sources of varying geochemical properties, have far-reaching implications for current and future dust-climate effects. For more reliable coupled representation of dust-climate projections, our findings suggest the need to re-evaluate dust cycle modelling and benefit from the albedo-based parameterisation

Development of a SEM-EDS-XRD Protocol for the Physicochemical and Automated Mineralogical Characterisation of Coal Dust Particles

Exposure to coal dust from mining-related activities has historically been linked to several preventable but incurable respiratory diseases. Although the findings of numerous biological studies have determined that the physicochemical and mineralogical aspects of dust particles greatly influence both cytotoxic and proinflammatory pathways, robust datasets which quantitatively define these characteristics of coal dust remain limited. This study aims to develop a robust characterisation routine applicable for real-world coal dust, using an auto-SEM-EDS system. In doing so, the study addresses both the validation of the particle mineralogical scans and the quantification of a range of coal particle characteristics relevant to respiratory harm. The findings presented demonstrate the application of auto-SEM-EDS-XRD systems to analyse and report on the physicochemical and mineralogical characteristics of thousands of dust-sized particles. Furthermore, by mineralogically mapping the particles, parameters such as liberation, mineral association and elemental distribution can be computed to understand the relationships between elements and minerals in the particles, which have yet to be quantified by other studies

Development of a SEM-EDS-XRD Protocol for the Physicochemical and Automated Mineralogical Characterisation of Coal Dust Particles

Exposure to coal dust from mining-related activities has historically been linked to several preventable but incurable respiratory diseases. Although the findings of numerous biological studies have determined that the physicochemical and mineralogical aspects of dust particles greatly influence both cytotoxic and proinflammatory pathways, robust datasets which quantitatively define these characteristics of coal dust remain limited. This study aims to develop a robust characterisation routine applicable for real-world coal dust, using an auto-SEM-EDS system. In doing so, the study addresses both the validation of the particle mineralogical scans and the quantification of a range of coal particle characteristics relevant to respiratory harm. The findings presented demonstrate the application of auto-SEM-EDS-XRD systems to analyse and report on the physicochemical and mineralogical characteristics of thousands of dust-sized particles. Furthermore, by mineralogically mapping the particles, parameters such as liberation, mineral association and elemental distribution can be computed to understand the relationships between elements and minerals in the particles, which have yet to be quantified by other studies