C.J. de W. Rautenbach

Assessment of chemical and mineralogical characteristics of airborne dust in the Sistan region, Iran

Windblown transport and deposition of dust is widely recognized as an important physical and chemical concern to climate, human health and ecosystems. Sistan is a region located in southeast Iran with extensive wind erosion, severe desertification and intense dust storms, which cause adverse effects in regional air quality and human health. To mitigate the impact of these phenomena, it is vital to ascertain the physical and chemical characteristics of airborne and soil dust. This paper examines for the first time, the mineralogical and chemical properties of dust over Sistan by collecting aerosol samples at two stations established close to a dry-bed lake dust source region, from August 2009 to August 2010. Furthermore, soil samples were collected from topsoil (0-5 cm depth) at several locations in the dry-bed Hamoun lakes and downwind areas. These data were analyzed to investigate the chemical and mineralogical characteristics of dust, relevance of inferred sources and contributions to air pollution. X-ray Diffraction (XRD) analysis of airborne and soil dust samples shows that the dust mineralogy is dominated mainly by quartz (30-40%), calcite (18-23%), muscovite (10-17%), plagioclase (9-12%), chlorite (~6%) and enstatite (~3%), with minor components of dolomite, microcline, halite and gypsum. X-ray Fluorescence (XRF) analyses of all the samples indicate that the most important oxide compositions of the airborne and soil dust are SiO(2), CaO, Al(2)O(3), Na(2)O, MgO and Fe(2)O(3), exhibiting similar percentages for both stations and soil samples. Estimates of Enrichment Factors (EFs) for all studied elements show that all of them have very low EF values, suggesting natural origin from local materials. The results suggest that a common dust source region can be inferred, which is the eroded sedimentary environment in the extensive Hamoun dry lakes lying to the north of Sistan.

The implementation of fog water collection systems in South Africa

Two fog water collection systems (FWCS) have been implemented in South Africa. Both are located in areas where communities experience acute water shortages but which are prone to frequent fog episodes. The first was located at a high elevation site at the Tshanowa Junior Primary (JP) School in the Soutpansberg located in the Northern Province and the other near a small rural community at Lepelfontein along the West Coast. The former represents a mountainous site, while the latter is located on a low level coastal plain. The principal aim of the projects was to implement operational FWCSs to supply the communities with water. During the period 1999 to 2001 the total recorded cloud water yields at the Tshanowa JP School and Lepelfontein water collection sites were in the region of 72 422 and 148 691 1, respectively. This is equivalent to just over 2 1m -2 day -1 at the Tshanowa JP School and 4.6 1 m -2 day -1 at the Lepelfontein site. Despite the relatively low average daily yields recorded, the total water volume collected on a particular day may be considerable. In fact, at both sites the maximum daily yield exceeded 3800 1. Fog deposition accounted for around 25% and 88% of the total water yield measured at the Tshanowa JP School and Lepelfontein sites, respectively. Both experiments indicated that fog water collection holds considerable potential as an alternative water source in the mountainous regions and along the West Coast of South Africa.

Teleconnections between global sea-surface temperatures and the interannual variability of observed and model simulated rainfall over southern Africa

Abstract Sea-surface temperatures (SSTs) for the thirty-year period 1961–1990 are prescribed as boundary forcing for a series of atmospheric general circulation model (AGCM) experiments. The results of five separate multidecadal AGCM runs, each forced with the same SSTs, but different initial conditions, have been used to form an ensemble. The interannual variability of AGCM simulated rainfall over South Africa and Namibia (southern Africa) are compared with observations. Empirical orthogonal function (EOF) analyses revealed a strong correlation between the dominant observed austral summer season (October–March) and mid-summer season (December and January) rainfall patterns over southern Africa and the corresponding model results. It is shown that the AGCM simulated austral summer season rainfall variability in the dominant amplitude time series (first principle component or PC1 time series) compare well with SST fluctuations over the equatorial Pacific and tropical western Indian Oceans. Although the associated dominant observed rainfall time series accounts for a larger fraction of the total variability (relative to the AGCM), it also compares well with equatorial Pacific and tropical western Indian Ocean SST fluctuations. To a certain degree these results accentuates the models ability to capture the major summer seasonal rainfall variability as a response of global SST forcing, which might make the AGCM suitable for future use in seasonal rainfall forecasting research. Similar associations could not be found for the austral winter season (May–August).

Dynamical Downscaling of Austral Summer Climate Forecasts over Southern Africa Using a Regional Coupled Model

AbstractThe prediction skill of dynamical downscaling is evaluated for climate forecasts over southern Africa using the Advanced Research Weather Research and Forecasting (WRF) model. As a case study, forecasts for the December–February (DJF) season of 2011/12 are evaluated. Initial and boundary conditions for the WRF model were taken from the seasonal forecasts of the Scale Interaction Experiment-Frontier Research Center for Global Change (SINTEX-F) coupled general circulation model. In addition to sea surface temperature (SST) forecasts generated by nine-member ensemble forecasts of SINTEX-F, the WRF was also configured to use SST generated by a simple mixed layer Price–Weller–Pinkel ocean model coupled to the WRF model. Analysis of the ensemble mean shows that the uncoupled WRF model significantly increases the biases (errors) in precipitation forecasted by SINTEX-F. When coupled to a simple mixed layer ocean model, the WRF model improves the spatial distribution of precipitation over southern Africa t...

Landsat satellite derived environmental metric for mapping mosquitoes breeding habitats in the Nkomazi municipality, Mpumalanga Province, South Africa

Abstract The advancement, availability and high level of accuracy of satellite data provide a unique opportunity to conduct environmental and epidemiological studies using remotely sensed measurements. In this study, information derived from remote sensing data is used to determine breeding habitats for Anopheles arabiensis which is the prevalent mosquito species over Nkomazi municipality. In particular, we have utilized the normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) coupled with land surface temperature (LST) derived from Landsat 5 TM satellite data. NDVI, NDWI and LST are considered as key environmental factors that influence the mosquito habitation. The breeding habitat was derived using multi-criteria evaluation (MCE) within ArcGIS using the derived environmental metric with appropriate weight assigned to them. Additionally, notified malaria cases were analysed and spatial data layers of water bodies, including rivers and dams, were buffered to further illustrate areas at risk of malaria. The output map from the MCE was then classified into three classes which are low, medium and high areas. The resulting malaria risk map depicts that areas of Komatieport, Malelane, Madadeni and Tonga of the district are subjected to high malaria incidence. The time series analysis of environmental metrics and malaria cases can help to provide an adequate mechanism for monitoring, control and early warning for malaria incidence.

Environmental factors and population at risk of malaria in Nkomazi municipality, South Africa

Nkomazi local municipality of South Africa is a high‐risk malaria region with an incidence rate of about 500 cases per 100 000. We examined the influence of environmental factors on population (age group) at risk of malaria.