Research Articles (Geography, Geoinformatics and Meteorology)

Permanent URI for this collectionhttp://hdl.handle.net/2263/1936

A collection containing some of the full text peer-reviewed/ refereed articles published by researchers from the Department of Geography

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Assessing the economic viability of sustainable pasture and rangeland management practices : a review
(MDPI, 2025-04) Rapiya, Monde; Mndela, Mthunzi;; Truter, Wayne; Ramoelo, Abel
The livestock sector is crucial for global food security and economic development, particularly in developing nations, as it supports the livelihoods of approximately 1.3 billion people. However, with the global population expected to reach 9.2 billion by 2050, the sector must address increasing demand for livestock products while ensuring environmental sustainability. This study used the available literature to evaluate the economic viability of sustainable pasture and rangeland management practices to enhance livestock production. The key findings demonstrate that strategies such as rotational grazing and nitrogen fertilization can decrease winter feed costs by up to 40% while simultaneously improving pasture productivity and animal weight gains. Initial investments in these improved forage practices offer high internal rates of return, indicating their profitability. To guide sustainable pasture production and rangeland management, we propose a conceptual framework that balances cultivated pastures and natural rangelands. This framework assesses critical factors, including input costs, expected outputs (enhanced biodiversity and livestock production), and interventions to mitigate land degradation. For successful adoption of these practices, targeted policies are essential. Governments should develop financial support mechanisms for smallholder farmers, improve transportation infrastructure for efficient feed logistics, and provide technical assistance to educate producers on sustainable practices. Engaging stakeholders to align policies with local needs is also vital. By implementing these strategic interventions, the resilience of livestock systems can be strengthened, contributing to long-term sustainability and supporting food security and rural community well-being.
A power-cardioid candidate for wind direction modelling motivated by two South African case studies
(Springer, 2025-04) Van Wyk-de Ridder, Delene; Rad, Najmeh Nakhaei; Arashi, Mohammad; Ferreira, Johan; Bekker, Andriette, 1958-; johan.ferreira@up.ac.za
Wind energy claims a positive image globally; therefore, accurate modelling of wind direction at generation sites accurately can enhance the potential of this green energy source. The uncertain nature of wind direction can be modelled through probability distributions; in this paper, we propose a flexible yet simple distribution, namely the Power-Cardioid distribution, as an alternative and implementable candidate to model wind direction. After discussing some characteristics, the performance of the Power-Cardioid distribution is evaluated via a simulation study and applied to datasets of two wind farms in South Africa. The numerical results demonstrate that this distribution is a promising and exciting new candidate compared to well-known models within circular statistics.
Cross-realm biodiversity profile of the South African coastal zone
(Taylor and Francis, 2025) Harris, L.R.; Adams, J.B.; Dayaram, A.; Dunga, L.V.; Job, N.; Kirkman, S.P.; Lamberth, S.J.; Pfaff, M.C.; Raw, J.L.; Rishworth, G.M.; Robbins, A.; Sink, K.J.; Skowno, A.L.; Van Deventer, Heidi; Van Niekerk, L.
South Africa’s coast is 3 113 km long and includes microtidal shores that experience semi-diurnal tides and mostly high wave energy. From west to east, the cool Benguela Current and the warm Agulhas Current drive steep gradients in climate and environmental conditions, resulting in diverse coastal ecosystem types. Here, we review the biodiversity of South Africa’s coastal zone, focusing on the constituent ecosystem types from the terrestrial, freshwater, estuarine and marine realms, and provide a brief overview of cross-realm biodiversity patterns. We also give guidance on coastal boundaries to improve standardisation in this complex area to support assessment, planning and management. The ecologically determined coastal zone currently comprises 193 ecosystem types: 83 vegetation types (e.g. seashore vegetation, strandveld, duneveld, coastal forest); 22 estuary and 3 micro-estuary ecosystem types; and 85 marine ecosystem types (e.g. shores, islands, reefs, kelp forests, bays), with planned inclusion of freshwater types (e.g. coastal lakes, forested wetlands, dune slacks) in the future. Species richness is generally highest along the south and east coasts, with the highest levels of endemism mostly reported for the south coast. The South African coast is a national asset that warrants careful management for long-term sustainability to safeguard its unique biodiversity and many associated benefits for current and future generations.
Advancing action on the UN sustainable development goals
(Public Library of Science, 2024-01-04) Archer, Emma Rosa Mary; Males, Jamie
The UN Sustainable Development Goals (SDGs) are a set of objectives that were agreed by the global community in 2015 as a “shared blueprint for peace and prosperity for people and the planet, now and into the future” [1]. Climate action is directly embodied in one of the seventeen goals (SDG 13), in recognition of the extreme risks to humanity posed by the impacts of climate change. However, as highlighted in a recent report by the World Meteorological Organization [2], the intensifying effects of climate change are rapidly undermining progress on almost all the SDGs. These interactions look set to become even more important as it appears increasingly likely that we will exceed 1.5 C of warming.
Influences of climate variability on land use and land cover change in rural South Africa
(MDPI, 2024-04) Mogonong, Buster Percy; Twine, Wayne; Feig, Gregor Timothy; Van der Merwe, Helga; Fisher, J.T. (Jolene)
Changes in land use and land cover over space and time are an indication of biophysical, socio-economic, and political dynamics. In rural communities, land-based livelihood strategies such as agriculture are crucial for sustaining livelihoods in terms of food provision and as a source of local employment and income. In recent years, African studies have documented an overall decline in the extent of small-scale crop farming, with many crop fields left abandoned. This study uses rural areas in three former apartheid homelands in South Africa as a case study to quantify patterns and trends in the overall land cover change and small-scale agricultural lands related to changes in climate over a 38-year period. Random forest classification was applied on the Landsat imagery to detect land use and land cover change, achieving an overall accuracy of above 80%. Rainfall and temperature anomalies, as well as the Standardized Precipitation Evapotranspiration Index (SPEI) were used as climate proxies to assess the influence of climate variability on crop farming, as the systems investigated rely completely on rainfall. Agricultural land declined from 107.5 km2 to 49.5 km2 in Umhlabuyalingana; 54 km2 to 1.6 km2 in Joe Morolong; and 254.6 km2 to 7.4 km2 in Mangaung between 1984 and 2022. Declines in cropland cover, precipitation, and the SPEI were highly correlated. We argue that climatic variability influences crop farming activities; however, this could be one factor in a suite of drivers that interact together to influence the cropping practices in rural areas.
Evaluation of selected Sentinel-2 remotely sensed vegetation indices and MODIS GPP in representing productivity in semi-arid South African ecosystems
(Wiley, 2024-04) Maluleke, Amukelani; Feig, Gregor Timothy; Brummer, Christian; Rybchak, Oksana; Midgley, Guy
The ability to validate satellite observations with ground‐based data sets is vital for the spatiotemporal assessment of productivity trends in semi‐arid ecosystems. Modeling ecosystem scale parameters such as gross primary production (GPP) with the combination of satellite and ground‐based data however requires a comprehensive understanding of the associated drivers of how the carbon balance of these ecosystems is impacted under climate change. We used GPP estimates from the partitioning of net ecosystem measurements (net ecosystem exchange) from three Eddy Covariance (EC) flux tower sites and applied linear regressions to evaluate the ability of Sentinel‐2 vegetation indices (VIs) retrieved from Google Earth Engine to estimate GPP in semi‐arid ecosystems. The Sentinel‐2 normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and the land surface water index (LSWI) were each assessed separately, and also in combination with selected meteorological variables (incoming radiation, soil water content, air temperature, vapor pressure deficit) using a bi‐directional stepwise linear regression to test whether this can improve GPP estimates. The performance of the MOD17AH2 8‐day GPP was also tested across the sites. NDVI, EVI and LSWI were able to track the phase and amplitude patterns of EC estimated gross primary production (GPPEC) across all sites, albeit with phase delays observed especially at the Benfontein Savanna site (Ben_Sav). In all cases, the VI estimates improved with the addition of meteorological variables except for LSWI at Middleburg Karoo (Mid_Kar). The least improvement in R2 was observed in all EVI‐based estimates —indicating the suitability of EVI as a single VI to estimate GPP. Our results suggest that while productivity assessments using a single VI may be more favorable, the inclusion of meteorological variables can be applied to improve single VIs estimates to accurately detect and characterize changes in GPP. In addition, we found that standard MODIS products better represent the phase than amplitude of productivity in semi‐arid ecosystems, explaining between 68% and 83% of GPP variability.
The impact of rolling blackouts on environmental health in South Africa
(EHP Publishing, 2024-12-10) Wright, Caradee Yael; Mathee, Angela; Kapwata, Thandi; Laban, Tracey; Mahlangeni, Nomfundo; Shezi, Busisiwe; Nkambule, Sizwe; Webster, Candice; Naidoo, Natasha; Street, Renee
BACKGROUND : Rolling blackouts (planned power outages) are common in low- and middle-income countries, including South Africa. Recently, South Africa has experienced longer and more frequent rolling blackouts owing to its reliance on an aging electricity grid, among other challenges. During rolling blackouts, parts of the electricity grid are shut down, and the loss of power in homes, businesses, and industries across vast areas leads to a breakdown of key amenities required for environmental health. OBJECTIVES : This commentary contextualizes the existing consequences and potential implications of rolling blackouts for environmental health in South Africa. DISCUSSION : We examined key areas where rolling blackouts affect environmental health, including water and sanitation, air quality, food safety, and socioeconomic challenges. Power outages have led to contamination of freshwater bodies with raw sewage due to resultant interruptions of wastewater treatment works. The use of generators and burning of dirty fuels during blackouts have added to outdoor and household air pollution. Rolling blackouts also expose people to unsafe food. Finally, we discuss some ways forward and the benefits of using renewable energy sources. A critical evaluation of these impacts underscores the urgent need for more sustainable energy solutions that safeguard environmental health in South Africa.
Leveraging historic streamflow and weather data with deep learning for enhanced streamflow predictions
(IWA Publishing, 2024-04-01) Schutte, Christiaan E.; Van der Laan, Michael; Van der Merwe, Barend Jacobus
Streamflow information is crucial for effectively managing water resources. The declining number of active gauging stations in many rivers is a global concern, necessitating the need for reliable streamflow estimates. Deep learning techniques offer potential solutions, but their application in southern Africa remains largely underexplored. To fill this gap, this study evaluated the predictive performance of gated recurrent unit (GRU) and long short-term memory (LSTM) networks using two headwater catchments of the Steelpoort River, South Africa, as case studies. The model inputs included rainfall, maximum, and minimum temperature, as well as past streamflow, which was utilized in an autoregressive sense. The inclusion of streamflow in this way allowed for the incorporation of simulated streamflow values into the look-back window for predicting the streamflow of the testing set. Two modifications were required to the GRU and LSTM architectures to ensure physically consistent predictions, including a change in the activation function of the GRU/LSTM cells in the final hidden layer, and a non-negative constraint that was used in the dense layer. Models trained using commercial weather station data produced reliable streamflow estimates, while moderately accurate predictions were obtained using freely available gridded weather data.
The impact of past and current district-level climatic shifts on maize production and the implications for South African farmers
(Springer, 2025-02) Mangani, Robert; Mazarura, Jocelyn; Matlou, Solly; Marquart, Arnim; Archer, Emma Rosa Mary; Creux, Nicky; nicole.creux@fabi.up.ac.za
South Africa’s climate studies generally focus on coarser provincial levels, which aid policy recommendations, but have limited application at the farm level. District level climate studies are essential for farmer participation in climate change mitigation strategies and management. Our study aimed to investigate historical climate data for trends and their influence on maize yields at the magisterial level. Six sites were selected from three major maize-producing provinces in South Africa: Mpumalanga, Northwest, and Free State. Magisterial districts in each province were selected from different Köppen-Geiger climate zones. The climate variables assessed by the Mann–Kendall trend test included maximum or minimum temperature, rainfall, number of extreme high-temperature days, rainfall onset and cessation from 1986 to 2016. The average maximum temperatures were observed to have significant upward trends in most locations, except for Schweizer-Reneke and Bethlehem. The fastest rate of change was observed at Klerksdorp (0.1 °C per 30 years of study), while the Schweizer-Reneke district was the slowest (0.05 °C per 30 years of study). No significant changes were observed in rainfall onset, cessation, or total rainfall in Schweizer-Reneke, Standerton, and Bethlehem, which are scattered across the different provinces. The other districts in each province showed significant changes in these parameters. Rainfall accounted for the significant variation in maize yields over the study period, explaining between 18 and 40% of the variation in the North West, and between 1 and 17% in the Free State. These findings highlight the importance of understanding location-specific changes at a finer scale, which can help farming communities adjust agronomic practices and adapt to local climate shifts.
An analysis of the effects of clouds in high-resolution forecasting of surface shortwave radiation in South Africa
(American Meteorological Society, 2024-02) Mendes, Joana; Zwane, Nosipho; Mabasa, Brighton; Tazvinga, Henerica; Walter, Karen; Morcrette, Cyril J.; Botai, Joel Ongego
We assess site-specific surface shortwave radiation forecasts from two high-resolution configurations of the South African Weather Service numerical weather prediction model, at 4 and 1.5 km. The models exhibit good skill overall in forecasting surface shortwave radiation, with zero median error for all radiation components. This information is relevant to support a growing renewable energy sector in South Africa, particularly for photovoltaics. Further model performance analysis has shown an imbalance between cloud and solar radiation forecasting errors. In addition, cloud overprediction does not necessarily equate to underestimating solar radiation. Overcast cloud regimes are predicted too often with an associated positive mean radiation bias, whereas the relative abundance of partly cloudy regimes is underpredicted by the models with mixed radiation biases. Challenges highlighted by the misrepresentation of partly cloudy regimes in solar radiation error attribution may be used to inform improvements to the numerical core, namely, the cloud and radiation schemes. SIGNIFICANCE STATEMENT : This paper provides the first comprehensive assessment of high-resolution site-specific NWP forecasts of surface shortwave radiation in South Africa, exploring clouds as the main drivers of prediction biases. Error attribution analyses of this kind are close to none for this part of the world. Our study contributes to understanding how cloud and radiation schemes perform over South Africa, representing a step forward in the state of the art. In addition to the scientific interest, the capabilities developed through this work may benefit the second largest economy of the continent. In a country where energy security is of critical relevance, the availability of useful and usable weather information is paramount to support its industry and socioeconomic growth.
Alternative skew Laplace scale mixtures for modeling data exhibiting high-peaked and heavy-tailed traits
(Springer, 2024-11) Otto, A.F.; Bekker, Andriette, 1958-; Ferreira, Johannes Theodorus; Arslan, O.; johan.ferreira@up.ac.za
The search and construction of appropriate and flexible models for describing and modelling empirical data sets incongruent with normality retains a sustained interest. This paper focuses on proposing flexible skew Laplace scale mixture distributions to model these types of data sets. Each member of the collection of distributions is obtained by dividing the scale parameter of a conditional skew Laplace distribution by a purposefully chosen mixing random variable. Highly-peaked, heavy-tailed skew models with relevance and impact in different fields are obtained and investigated, and elegant sampling schemes to simulate from this collection of developed models are proposed. Finite mixtures consisting of the members of the skew Laplace scale mixture models are illustrated, further extending the flexibility of the distributions by being able to account for multimodality. The maximum likelihood estimates of the parameters for all the members of the developed models are described via a developed EM algorithm. Real-data examples highlight select models’ performance and emphasize their viability compared to other commonly considered candidates, and various goodness-of-fit measures are used to endorse the performance of the proposed models as reasonable and viable candidates for the practitioner. Finally, an outline is discussed for future work in the multivariate realm for these models.
Global trend assessment of land use and land cover changes : a systematic approach to future research development and planning
(Elsevier, 2024-08) Afuye, Gbenga Abayomi; Nduku, Lwandile; Kalumba, Ahmed M.; Santos, Celso A.G.; Orimoloye, Israel R.; Ojeh, Vincent N.; Thamaga, Kgabo H.; Sibandze, Phila
The diverse landscape of global land use and land cover (LULC) change studies were evaluated to uncover the current advances in data and future research potential through bibliometrics and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach. A total of 2710 published articles with the search phrase “Land use and Land cover change” OR “Land-use and Land cover change” OR “Land use/Land cover change” OR “Land use and Land cover changes” were retrieved using Scopus, Web of Science (WOS), and ScienceDirect databases from 1993 to 2022. The findings indicated a 24.37% annual growth rate in LULC change studies, reflecting a rapid overall increase in published articles. China and the USA emerged as the most influential countries regarding article numbers, total citations, and single-country publications. Ethiopia, Ghana, and South Africa, among the top 20 global rankings of the most influential countries in LULC change studies, underscore the global importance of this research. However, the disparity in research output between multiple-country publications and the dominant trend of single-country publications highlights a geographical bias in LULC change studies, particularly in the Global South. This finding underscores the need for a more balanced research approach and can stimulate further investigation. The results also revealed that remote sensing, a rapidly growing field utilising advanced computing techniques, is the most prevalent keyword and has significant applications in reducing land degradation. These findings can significantly enhance research, climate policy programs, land management, and forest ecology planning, which are crucial in the face of the growing demand for agriculture and habitable land.
Application of a dual-locus metabarcoding approach for a more comprehensive account of cattle dietary items in a semi-arid African savanna with special reference to forbs
(Pensoft Publishers, 2024) Botha, Danielle; Barnard, Sandra; Du Plessis, Morne; Allam, Mushal; Behn, Kai; Ismail, Arshad; Linstädter, Anja; Mokoka, Malesela V.; Mnisi, Zamantungwa T.H.; Siebert, Frances
Increasing livestock densities and more severe drought events challenge sustainable management in South Africa’s semi-arid savannas. Effective mitigation strategies require accurate assessments of livestock foraging behaviour. By utilising high-throughput sequencing technology, this study evaluated the use of a dual-locus metabarcoding approach (trnL and rbcL) together with study-area-specific reference libraries, to analyse cattle diets in two bioregions of the eastern semi-arid South African savanna. Both markers demonstrated the ability to identify various plant families, but trnL exhibited a higher diversity in terms of family and genus identification at both sampling sites. Forbs, although comprising a diverse component of savanna plant communities, have relatively small above-ground biomass, but can still serve as crucial forage items, especially during dry periods. Our study underscores the significant role of forbs in cattle diets, demonstrating a shift in cattle foraging preferences from grass-based diets to higher inclusions of forbs and woody taxa during the drier season. Although grasses, such as Setaria, were still prevalent, forbs, belonging to the genera Malvastrum, Asparagus, Pollichia and Ipomoea were also important food items for cattle as well as woody taxa belonging to Fabaceae, Combretaceae, Ebenaceae, and Malvaceae with a selection of food items from trees and shrubs from genera Albizia, Combretum, Euclea and Vachellia. Furthermore, our study highlights the value of a dual-locus metabarcoding approach for understanding herbivorous diets. Using trnL and rbcL markers, with study-area-specific reference libraries, improves taxonomic resolution for accurately reconstructing cattle diets in semiarid savannas. This study may improve biodiversity estimates and inform sustainable rangeland management strategies in semi-arid African savanna ecosystems.
Projected changes in daily temperature extremes for selected locations over South Africa
(Elsevier, 2025-03) McBride, Charlotte M.; Kruger, Andries C.; Johnston, Charmaine; Dyson, Liesl L.
Extreme events, particularly very high temperatures, are expected to increase because of climate change. It is thus essential that localised studies be done to quantify the magnitude of potential changes so that proper planning, especially effective adaptation measures, can be affected. This study analysed annual extreme daily maximum temperatures for future climate change scenarios at 22 locations in South Africa, through analysis of a subset of the Coordinated Regional Downscaling Experiment (CORDEX) model ensemble datasets. The multi-model simulations were validated against observational data obtained from the South African Weather Service for the period 1976–2005. Two study periods of mid- (2036–2065) and far-future (2066–2095) were analysed for two Representative Concentration Pathways, i.e., RCP4.5 and RCP8.5. Bias correction was done on the model data to correct simulated historical climate data, to be more characteristic of observed measurements. While the method included adjustment for variance, systematic underestimations of extremes were still evident. The Generalized Extreme Value distributions were fitted to the bias-corrected projections, and 10-, 50- to 100-year return periods quantile values were estimated. The return period quantile values are likely to increase under both Representative Concentration Pathways in the mid- and far-future periods, with the largest increase in return period quantile values set to occur towards the end of the century under the highest emission scenario. All stations showed an increase in the frequency of days with maximum temperatures above specific critical thresholds, with some stations under the RCP8.5 scenario projected to experience temperatures of greater than 32°C (35°C) for more than 200 (100) days per year by the end of the century, an increase from a baseline of approximately 70 to 150 (14 to 83). For the same scenario, Return periods for 38°C for most stations are projected to be shorter than a year. From the above and considering the likely underestimation in the severity of the projected changes, i.e. too low return period quantile values, the general implication is a strong likelihood that most places in South Africa is likely to experience a strong increase in the intensity, duration, and frequency of very hot extremes in future, with potentially dire consequences to relevant socio-economic sectors. We suggest that future research, comprised of the full set of CORDEX data be conducted to optimise the results of this study.
Seasonal monitoring of forage C:N:ADF ratio in natural rangeland using remote sensing data
(Springer, 2025-01) Rapiya, Monde; Ramoelo, Abel; Truter, Wayne Frederick; u16400829@tuks.co.za
In recent decades, natural rangelands have emerged as vital sources of livelihood and ecological services, particularly in Southern Africa, supporting communities in developing regions. However, the escalating global demand for food, driven by a growing human population, has led to the extensive expansion of cultivated areas, resulting in continuous nutrient leaching in rangelands. To ensure the long-term viability of these ecosystems, there is a need to develop effective approaches for managing and monitoring the seasonality of forage quality. This study aims to achieve this by utilizing multispectral Sentinel-1 (S1) and Sentinel-2 (S2) data to monitor the seasonal distribution and occurrence of carbon (C), nitrogen (N), acid detergent fiber (ADF), and the (C:N:ADF) ratio in mesic rangelands. Six sites were randomly selected from Welgevonden and Hoogland private game reserves in Limpopo, South Africa, representing varying vegetation cover and standing biomass. Transects, each with ten fixed sample sites (30 × 30 m) characterized by homogeneous vegetation, were established. The grass samples and aboveground biomass were collected during each season and analyzed for biochemical parameters using a near-infrared spectroscopy (NIRS) machine. S1 and S2 data from Google Earth Engine (GEE) were employed, and the random forest (RF) modelling algorithm revealed significant seasonality impacts on the distribution of forage C:N:ADF ratios. The study demonstrates that integrating S1 and S2 data enhances the estimation of forage nutrients. This study offers valuable insights for a diverse range of stakeholders, including ecologists, resource managers, farmers, and park managers. By giving an understanding of nutrient limitations and facilitating a deeper understanding of resource availability and animal distribution in rangelands, this research serves as a crucial tool for informed decision-making and sustainable management practices.
Prevalence of aeroallergen sensitization in a polluted and industrialized area : a pilot study in South Africa's Vaal Triangle
(Springer, 2025-02) Gharbi, Dorra; Neumann, Frank Harald; Staats, Jurgens; Mcdonald, Marinda; Linde, Jo-hanné; Mmatladi, Tshiamo; Podile, Keneilwe; Piketh, Stuart; Burger, Roelof; Garland, Rebecca M.; Bester, Petra; Lebre, Pedro H.; Ricci, Cristian; rebecca.garland@up.ac.za
This pioneering study evaluates the prevalence of aeroallergens reactivity among atopic populations living in the Vaal Triangle Airshed Priority Area (VTAPA), South Africa. A total of 138 volunteers (51 males and 87 females), of African, colored, white, and Asian ethnicity, and with a mean (range) age of 22 (18–56) years were participating in the study. The study was conducted on the North-West University (NWU) campus in Vanderbijlpark/VTAPA. The International Study of Asthma and Allergies in Childhood questionnaire was utilized for pre-screening to identify individuals with probable allergic dispositions. Subsequently, skin prick testing was conducted using commercial aeroallergen extracts for all confirmed participants with allergy symptoms. One hundred six participants were clinically diagnosed with pollen and fungal spore allergies. The highest allergy prevalence was attributed to Cynodon dactylon ((L.) Pers) (Bermuda grass) (41.5%), followed by Lolium perenne (L.) (ryegrass), grass mix, and Zea mays (L.) (maize) (31.1%), respectively. Moreover, among the tree allergens, Olea (L.) (olive tree) was the most prevalent allergen (20; 18.8%), followed by Platanus (L.) (plane tree) (18; 16.9%). Among the weeds, 16 (15.1%) participants were allergic to the weed mix (Artemisia (L.) (wormwood), Chenopodium (Link) (goosefoot), Salsola (L.) (saltwort), Plantago (L.) (plantain), and 11 (10.3%) to Ambrosia (L.) (ragweed)). Regarding the fungal spores, Alternaria (Fr.) (9; 8.5%) followed by Cladosporium (Link) (5; 4.7%) had the highest skin sensitivity. In this pilot study, our findings provide insights into the prevalence of allergic responses in the study population—underlining the strong impact of allergens of exotic plants—and contribute to the existing aerobiological data in South Africa.
Assessment of explainable tree-based ensemble algorithms for the enhancement of Copernicus digital elevation model in agricultural lands
(Taylor and Francis, 2024-04-12) Okolie, Chukwuma; Adeleke, Adedayo; Mills, Jon; Smit, Julian; Maduako, Ikechukwu; Bagheri, Hossein; Komar, Tom; Wang, Shidong
There has been a rapid evolution of tree-based ensemble algorithms which have outperformed deep learning in several studies, thus emerging as a competitive solution for many applications. In this study, ten tree-based ensemble algorithms (random forest, bagging meta-estimator, adaptive boosting (AdaBoost), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), light gradient boosting (LightGBM), histogram-based GBM, categorical boosting (CatBoost), natural gradient boosting (NGBoost), and the regularised greedy forest (RGF)) were comparatively evaluated for the enhancement of Copernicus digital elevation model (DEM) in an agricultural landscape. The enhancement methodology combines elevation and terrain parameters alignment, with featurelevel fusion into a DEM enhancement workflow. The training dataset is comprised of eight DEM-derived predictor variables, and the target variable (elevation error). In terms of root mean square error (RMSE) reduction, the best enhancements were achieved by GBM, random forest and the regularised greedy forest at the first, second and third implementation sites respectively. The computational time for training LightGBM was nearly five-hundred times faster than NGBoost, and the speed of LightGBM was closely matched by the histogram-based GBM. Our results provide a knowledge base for other researchers to focus their optimisation strategies on the most promising algorithms.
Quantifying the impacts of marine aerosols over the southeast Atlantic Ocean using a chemical transport model : implications for aerosol-cloud interactions
(Copernicus Publications, 2024-12) Hossain, Mashiat; Garland, Rebecca M.; Horowitz, Hannah M.
The southeast Atlantic region, characterized by persistent stratocumulus clouds, has one of the highest uncertainties in aerosol radiative forcing and significant variability across climate models. In this study, we analyze the seasonally varying role of marine aerosol sources and identify key uncertainties in aerosol composition at cloud-relevant altitudes over the southeast Atlantic using the GEOS-Chem chemical transport model. We evaluate simulated aerosol optical depth (AOD) and speciated aerosol concentrations against those collected from ground observations and aircraft campaigns such as LASIC, ORACLES, and CLARIFY, conducted during 2017. The model consistently underestimates AOD relative to AERONET, particularly at remote locations like Ascension Island. However, when compared with aerosol mass concentrations from aircraft campaigns during the biomass burning period, it performs adequately at cloud-relevant altitudes, with a normalized mean bias (NMB) between −3.5 % (CLARIFY) and −7.5 % (ORACLES). At these altitudes, in the model, organic aerosols (63 %) dominate during the biomass burning period, while sulfate (41 %) prevails during austral summer, when dimethylsulfide (DMS) emissions peak in the model. Our findings indicate that marine sulfate can account for up to 69 % of total sulfate during the high-DMS period. Sensitivity analyses indicate that refining DMS emissions and oxidation chemistry may increase sulfate aerosol produced from marine sources, highlighting that there remains large uncertainty as to the role of DMS emissions in the marine boundary layer. Additionally, we find marine primary organic aerosol emissions may substantially increase total organic aerosol concentrations, particularly during austral summer. This study underscores the imperative need to refine marine emissions and their chemical transformations, as aerosols from marine sources are a major component of total aerosols at cloud-relevant altitudes and may impact uncertainties in aerosol radiative forcing over the southeast Atlantic.
Adoption of artificial intelligence tools and resources policy
(National Association of Clean Air, 2024-12) Mpanza, Mbalenhle; Perumal, Sarisha; Feig, Gregor Timothy; Garland, Rebecca M.; Langerman, Kristy E.
The Clean Air Journal has adopted a new policy on the use of Artificial Intelligence (AI) tools and resources for all submissions to Clean Air Journal. This policy was written with the view that transparency about the use of AI is necessary to ensure trust between authors, reviewers, editors and readers. In addition, we believe that appropriate use of AI can support authors and research and be a great resource.
Identification of potential sites for rainwater harvesting structures as an adaptation to drought emergencies in Eswatini
(Water Research Commission, 2025-01) Sifundza, Lungile Senteni; Beckedahl, Heinz
Water scarcity is a global problem exacerbated by the ever-increasing population and climate change, especially in arid and semi-arid regions. Diferent water resource management strategies, such as rainwater harvesting, have been proposed and implemented worldwide to combat water shortage. Mapping of the optimum sites where these rainwater harvesting structures can be constructed is very important. The main objective of this study was to map and identify, using GIS, optimum sites for the construction of rainwater harvesting structures (farm ponds, check dams and percolation ponds) for agricultural and peri-urban purposes in Eswatini. The optimum sites were identified by overlaying various thematic layers including land use and cover, slope, runof potential, soil texture and depth and drainage density using ArcGIS 10.8. A general rainwater harvesting suitability map was produced for Eswatini, then potential sites for diferent rainwater harvesting structures were identified. The results of the study indicated that all three rainwater harvesting structures have suitable sites where they can be constructed. Check dams have potential sites which cover 22.7% of the suitable area in Eswatini, while farm pond and percolation pond sites covers 19.7% and 65%, respectively. Information on existing structures such as dams and earth dams for water storage may need to be gathered to verify the proposed sites of the rainwater harvesting structures. This study was able to identify new sites where structures can be constructed for rainwater harvesting which can improve water availability during dry seasons. Further evaluation may need to be done before implementation of these structures. Moreover, implementing this is subject to a number of other factors, such as the economy, feasibility studies as well as social implications. However, the results of this study will assist policy and decision makers in planning for potential sites for water storage as an adaptation to drought and climate change.

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