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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Analysis of rainfall and temperature trends in Eswatini from 1981 to 2020 : a perspective of climate change and variability
Ndlela, Thandeka; Sifundza, Lungile S.; Beckedahl, Heinz; Kapwata, Thandi; Laban, Tracey L.; Mahlangeni, Nomfundo; Wright, Caradee Yael (Academy of Science of South Africa, 2026-01)
Rainfall and temperature are key climatic indicators essential for monitoring climate variability and change. Understanding long-term trends in these parameters is crucial for evidence-based policy formulation, particularly in vulnerable regions. We examined rainfall and temperature trends in Eswatini over a 40-year period (1981–2020) using meteorological data from five physiographic regions. Trends in monthly, seasonal and annual rainfall, alongside minimum and maximum temperatures, were analysed using the Mann–Kendall test and Sen’s slope estimator. The results reveal high interannual variability and shifting seasonal precipitation patterns, with an overall decline in annual rainfall. Statistically significant declines were noted in June and October, especially in the Lowveld and Highveld regions, whereas certain summer months (December to February) recorded increasing rainfall trends at some stations. Temperature analysis indicated significant warming trends in maximum temperature at four stations (Big Bend, Mbabane, Malkerns and Nhlangano), with increases in minimum temperature most evident in Mbabane and Big Bend. A cooling trend was observed at Mhlume in the Western Lowveld, highlighting geographic temperature variability. These findings align with regional studies that have reported increased climate variability across southern Africa. The results emphasise the urgency of implementing adaptive strategies, including improved water resource management and the development of early warning systems. This research provides a foundation for informed climate policy interventions in Eswatini. SIGNIFICANCE : This study provides a detailed assessment of long-term rainfall and temperature trends in Eswatini based on meteorological station data from 1981 to 2020. The findings show a general decline in rainfall and rising temperatures, with important seasonal and geographical differences across the country’s physiographic regions. These changes have implications for water availability, ecological function and the vulnerability of climate-sensitive ecosystems. By linking observed trends to broader regional patterns and known climate drivers such as the El Niño-Southern Oscillation, the study offers a baseline for national climate planning and contributes to a better understanding of climate variability in southern Africa.
Growth and physiological responses of two sugarcane cultivars exposed to elevated surface ozone
Laban, Tracey L.; Van Zyl, Pieter G.; Liebenberg, Shawn C.; Beukes, Johan P.; Berner, Jacques M.; Van Heerden, Philippus Daniel Riekert; Wright, Caradee Yael (Academy of Science of South Africa, 2026-01)
Surface ozone (O3) pollution is known to have a detrimental effect on agriculture whilst rising carbon dioxide (CO2) concentrations are sometimes found to offer plants protection against O3 effects. Considering the important role of sugarcane (Saccharum spp. hybrids) as a major food crop in South Africa and its contribution to the national economy, the tolerance of this crop to O3 damage must be established. A pilot study using open-top chambers was conducted whereby two local commercial sugarcane cultivars (NCo376 and N31) were fumigated during the summer growth season to explore the effects of elevated O3 as well as the interacting effects of O3 and CO2 on various stress and crop quality indicators. Statistical significance of differences in treatment means was analysed by hierarchical linear modelling to account for variability between chamber and pots in explaining changes across individual plants. The results revealed a significant reduction in the number of dead leaves (senescing) for the N31 cultivar exposed to elevated O3 compared with the other treatments. There was also a statistically significant decrease in chlorophyll fluorescence (used to assess photosynthetic performance) in the O3-treated NCo376 plants. This pilot study shows limited effects of O3 fumigation on growth and physiology, with preliminary indications that sugarcane is less sensitive to O3 than other crops. An increase in O3 concentrations associated with future climate change is expected, which will have implications for cultivar selection as a possible adaptation strategy to reduce susceptibility of this crop to O3. SIGNIFICANCE : • This article adds to the existing literature on sugarcane and ozone (O3). We present a pilot study for two cultivars of sugarcane and explore interacting effects of O3 and carbon dioxide (CO2) on various stress and crop quality indicators. • We employed a mixed effects model to account for variability between chamber and pots, a challenge when working with plants. • This is the first time African sugarcane has been investigated and, although the findings show limited statistical effect of O3 and CO2, future studies can vary the conditions of this experiment to produce more data points for a dose-response function.
African contributions are missing from cryosphere research in Africa and worldwide
Asante, Christian K.; Hansen, Christel D.; Hardy, Douglas R.; Hotaling, Scott (Frontiers Media, 2026-01-08)
No abstract available.
Land use/land cover (LULC) change and irrigated area monitoring in Eritrea : insights into horticultural production and sustainability
Haile, Bereket T.; Ramoelo, Abel; Dougill, Andrew J.; Qabaqaba, Mcebisi (Springer, 2025-12)
In arid and semi-arid regions, where water is scarce and climatic variability is high, monitoring changes in irrigated land is essential for ensuring food security and building resilience. However, few studies have assessed irrigation dynamics in the Horn of Africa using remote sensing, and empirical data from Eritrea remain limited. This study investigates the spatio-temporal dynamics of irrigated agriculture in two contrasting regions of Eritrea, Dighe and Gala Nefhi, using multi-temporal Sentinel-2 imagery and Supporting climatic and agricultural datasets from 2015 to 2024. It aims to map the spatial distribution of irrigated fields, assess their changes over time, and examine relationships with rainfall variability, horticultural crop production, and market fluctuations by comparing trends throughout the study period. A supervised Random Forest classification approach was implemented in Google Earth Engine, incorporating spectral indices and post-classification comparison to quantify the Land Use/Land Cover (LULC) transitions. The classification was based on dry-season imagery to distinguish irrigated from rainfed areas, with seven LULC classes identified. Overall classification accuracy ranged from 0.82–0.86 in Dighe and 0.87–0.89 in Gala Nefhi, with Kappa coefficients of 0.70–0.81 and 0.85–0.86, respectively. Results show a 115.5% increase in irrigated area in Dighe and 65.6% in Gala Nefhi. While Gala Nefhi showed synchronized growth in irrigation and horticultural crop production, Dighe exhibited inconsistent yields despite expanded irrigation. The study shows that expanding irrigation alone cannot increase production without reliable water sources, favorable climate conditions, and institutional support.
Drought, grazing, and nitrogen input influence nutrient supply and soil faunal activity in a semi-arid savannah grassland
Munjonji, Lawrence; Behn, Kai; Mokoka, Malesela Vincent; Ayisi, Kingsley Kwabena; Nielsen, Uffe; Linstädter, Anja (Nature Research, 2025-10-01)
Dryland grasslands cover approximately 16% of Earth’s land surface and support the livelihoods of people worldwide. However, the mechanisms driving their nutrient dynamics under changing environmental conditions remain poorly understood. This study, conducted in a dry savanna ecosystem in South Africa, investigated how grassland management interacted with drought and nitrogen addition in their effects on soil faunal activity and plant-available macro- and micronutrients. Extreme drought did not significantly affect soil invertebrates’ feeding activity in the top 8 cm, likely due to consistently dry conditions during the experimental period. In contrast, moderate grazing stimulated soil fauna feeding activity in the topsoil. Both nitrogen addition and grazing increased faunal activity, particularly at 7–8 cm depth. Drought conditions were associated with higher concentrations of manganese, zinc, and sulphur, while ambient rainfall conditions resulted in higher total nitrogen, magnesium, iron, and copper. Nitrogen addition enhanced mineral nitrogen availability and led to a fivefold increase in iron, and manganese, and doubling of copper. These findings suggest that moderate grazing management improves soil health in savanna grasslands, even under challenging climatic conditions.
Utility of UAS-LIDAR for estimating forest structural attributes of the Miombo woodlands in Zambia
Shamaoma, Hastings; Chirwa, Paxie W.; Zekeng, Jules C.; Ramoelo, Abel; Hudak, Andrew T.; Handavu, F.; Syampungani, Stephen (Public Library of Science, 2025-03-11)
The ability to collect precise three-dimensional (3D) forest structural information at a fraction of the cost of airborne light detection and ranging (lidar) makes uncrewed aerial systems-lidar (UAS-lidar) a remote sensing tool with high potential for estimating forest structural attributes for enhanced forest management. The estimation of forest structural data in area-based forest inventories relies on the relationship between field-based estimates of forest structural attributes (FSA) and lidar-derived metrics at plot level, which can be modeled using either parametric or non-parametric regression techniques. In this study, the performance of UAS-lidar metrics was assessed and applied to estimate four FSA (above ground biomass (AGB), basal area (BA), diameter at breast height (DBH), and volume (Vol)) using multiple linear regression (MLR), a parametric technique, at two wet Miombo woodland sites in the Copperbelt province of Zambia. FSA were estimated using site-specific MLR models at the Mwekera and Miengwe sites and compared with FSA estimates from generic MLR models that employed combined data from the two sites. The results revealed that the model fit of site-specific MLR models was marginally better (Adj-R2: AGB = 0.87–0.93; BA = 0.88–0.89; DBH = 0.86–0.96; and Vol = 0.87–0.98 than when using a generic combined data model (AGB = 0.80; BA = 0.81; DBH = 0.85; and Vol = 0.85). However, the rRMSE (2.01 – 20.89%) and rBias (0.01-1.03%) of site specific MLR models and combined data model rRMSE (3.40-16.71%) and rBias (0.55-1.16%) were within the same range, suggesting agreement between the site specific and combined data models. Furthermore, we assessed the applicability of a site-specific model to a different site without using local training data. The results obtained were inferior to both site-specific and combined data models (rRMSE: AGB = 36.29%–37.25%; BA = 52.98–54.52%; DBH = 55.57%–64.59%; and Vol = 26.10%–30.17%). The results obtained from this indicate potential for application in estimating FSA using UAS-lidar data in the Miombo woodlands and are a stepping stone towards sustainable local forest management and attaining international carbon reporting requirements. Further research into the performance of UAS-lidar data in the estimation of FSA under different Miombo vegetation characteristics, such as different age groups, hilly terrain, and dry Miombo, is recommended.
Multiple fuel use in low-income communities: socio-economic determinants and impacts on household air pollution and respiratory health in South Africa
Wernecke, Bianca; Wright, Caradee Yael; Langerman, Kristy; Mathee, Angela; Abdelatif, Nada; Howard, Marcus A.; Jafta, Nkosana; Pauw, Christiaan; Phaswana, Shumani; Asharam, Kareshma; Seocharan, Ishen; Smith, Hendrik; Naidoo, Rajen N. (Elsevier, 2025)
Domestic fuel use contributes significantly to household air pollution levels and to the disease burden in low-income households in South Africa. The link between residential fuel stacking and switching, and respiratory health, mediated by household air pollution, remains underexplored, posing challenges to transition to cleaner fuels. This study identified socio-economic determinants of fuel use patterns in two low-income communities of KwaZamokuhle and eMzinoni in South Africa. It also examined the impacts of these patterns on household air pollution levels and respiratory health outcomes. Over half of households relied on dirty fuels across all needs. Average household PM2.5 levels exceeded national daily standards (40 μg/m3). Education level and employment status were significant factors in determining fuel choice, with employed participants less likely to rely on dirty fuels. Town-specific characteristics also influenced household fuel use patterns. In terms of health, 9.5 % of participants had obstructive airways disease and 26.9 % tested positive for inhalant allergens. Heating fuels were strongest predictor of obstructive airways disease (>75 %) whereas cooking fuels were the main predictor of allergen sensitivity (∼75 %). The stepwise introduction of cleaner fuels predicted better respiratory health outcomes. The findings of this study suggest that even the partial adoption of cleaner fuels has health benefits and supports the formulation of context-specific mitigation efforts aiming to address negative health effects associated with household air pollution.
Impact of solid fuel use on household air pollution and respiratory health in two-low-income communities in Mpumalanga, South Africa
Wernecke, Bianca; Langerman, Kristy; Mathee, Angela; Abdelatif, Nada; Howard, Marcus A.; Jafta, Nkosana; Pauw, Christiaan; Phaswana, Shumani; Asharam, Kareshma; Seocharan, Ishen; Smith, Hendrik; Naidoo, Rajen N.; Naidoo , Natasha; Wright, Caradee Yael (Ubiquity Press, 2025-10-08)
INTRODUCTION : Household air pollution from domestic solid fuel use remains a global public health concern, particularly in low‑income communities. This study assessed associations between household fuel use, indoor air pollution, and respiratory health outcomes in two Mpumalanga communities in South Africa. METHODS : A cross‑sectional study was conducted in KwaZamokuhle and eMzinoni between July 2019 and February 2020. Indoor PM2‧5 concentrations were measured using Airmetrics MiniVol samplers and TSI DustTrak II monitors. We carried out household surveys, lung function tests and allergen sensitivity testing and performed multivariable logistic regression to assess associations between indoor pollutant exposure and respiratory health outcomes. RESULTS : Indoor and ambient PM2‧5 concentrations in KwaZamokuhle were more than twice as high as those in eMzinoni, exceeding both national standards and WHO Air Quality Guidelines. Coal use for heating was more prevalent in KwaZamokuhle and appeared directly related to elevated PM2‧5 levels. Approximately 9% of participants exhibited signs of obstructive airway disease, and 25% had positive results for allergen sensitisation. Although the associations between PM2‧5 levels, solid fuel use and measured respiratory outcomes did not reach statistical significance, consistent trends in the expected direction were observed, suggesting a potential relationship that warrants longitudinal studies with larger sample sizes. CONCLUSION : These findings suggest complex, possibly nonlinear relationships between indoor air pollution and respiratory health effects. The study underscores the urgent need for a greater use of clean energy alternatives and increased public awareness about the risks of household air pollution in low‑income South African communities.
Bush encroachment and invasive alien plant species' linkage to outmigration
Newete, Solomon W.; Chirima, Johannes George; Tswai, Richard (Springer, 2025-06)
The most prominent drivers of international migration across borders and internal migration-rural to urban areas are explained by the ‘push and pull’ migration model. However, this model falls short in addressing migrations driven by the impacts of climate change, such as the movement from rural to urban area as a coping strategy for environmental degradation. Factors like deforestation, desertification, droughts, and floods are key drivers of such migration. Additionally, bush encroachment and the spread of invasive alien plant species disrupt landscapes and negatively affect ecosystem goods and services in many arid and semi-arid regions around the world. This phenomenon directly affects the livelihoods of rural communities by depriving them of their croplands, rangelands, and ranches. Despite this, there is a lack of sufficient information on how these factors are linked to migratory movements, whether from rural to urban areas or between rural regions. To explore this connection, this study reviewed scientific publications, including journal articles and books using key phrases such as, ‘push and pull migration factors’, ‘impact of bush encroachment on migration’ and ‘impact of invasive alien plants on migration factors’, among others. A total of 155 documents were downloaded via Google Scholar, of which 99 were thoroughly reviewed and included in the study. The remaining 53 documents were skimmed and excluded due to their irrelevance, or limited contribution to the research. The study found that the bush encroachment and invasive plant species in rangelands are significant push factors for driving migration, both between rural areas and rural to urban areas. It is, therefore, recommended that these two factors be given a greater attention when addressing outmigration from rural regions.
Perspectives on aquatic emerging pollutant monitoring in sub-Saharan Africa with a focus on disinfection byproducts
Van der Merwe, Petra; Booysen, Amogelang; Forbes, Patricia B.C. (Oxford University Press, 2025)
The provision of clean water is of key importance in sub-Saharan Africa (SSA) where there is a rapid rate of urbanization. Aside from socio-economic factors, aspects such as a changing climate and polluted water sources create additional challenges towards achieving Sustainable Development Goal (SDG) 6: to "Ensure availability and sustainable management of water and sanitation for all". In order to allow for sustainable development and improved quality of life, water safety must be prioritized, which requires the effective monitoring of a range of potential water contaminants, including emerging chemical pollutants (ECPs), which pose risks to both human health and the environment. Here we provide perspectives on the current monitoring status of ECPs in the SSA region, with a focus on disinfection byproducts. Regulatory frameworks and reported monitoring practices are discussed in the context of suitability and accessibility for SSA. It was found that in recent years efforts to increase monitoring of ECPs has grown in some countries, although the majority of the countries in the region do not demonstrate these efforts.
Extreme event attribution using km-scale simulations reveals the pronounced role of climate change in the Durban floods
Engelbrecht , Francois A.; Steinkopf, Jessica; Chang, Nicolette; Biskop, Sophie; Malherbe, Johan; Engelbrecht, Christina Johanna; Grab, Stefan; Le Roux, Alize; Vogel, Coleen; Padavatan, Jonathan; Thatcher, Marcus; McGregor, John L. (Nature Research, 2025-07-01)
The Durban floods of 11–12 April 2022 is the worst flood disaster in South Africa’s history and raised questions about the role of climate change in the event. Meso-scale dynamics, involving processes that cannot be resolved at the spatial resolutions of current global climate models, played an important role in the heavy falls of rain. Here we report on the development of a convection-permitting conditional extreme event attribution modelling system, well-suited to explore the role of climate change in meso- and convective-scale extreme weather events. The African-based attribution system makes use of a computationally-efficient variable-resolution atmospheric model and runs on a local high-performance computer in South Africa. Similar systems can potentially be rolled out across the Global South (and North). The system relies on a km-scale perturbed-physics ensemble to describe the simulation/structural uncertainty associated with an extreme weather event in an anthropogenically-warmed world, compared to counterfactual cooler worlds where the effects of anthropogenic forcing are (partially) removed. Simulations reveal a pronounced role of climate change in the Durban floods. Average rainfall in the Durban region is simulated to have been at least 40% higher during the two days of the flood, relative to rainfall in a counterfactual cooler world.
Participatory governance for people and nature in multifunctional landscapes — insights from Biosphere Reserves
Sinare, Hanna; Coetzer, Kaera L.; Schultz, Lisen (Elsevier, 2025-12)
Participatory approaches are put forward to ensure that governance for the well-being of humans and nature is legitimate and effective, particularly responding to global challenges of ecosystem degradation and climate change. As model areas for sustainable development with explicit goals of participation, the UNESCO World Network of Biosphere Reserves can provide insights on participatory governance arrangements, outcomes of participation, and obstacles for participation. Through a literature review, we found that transparent communication and fair distribution of benefits and trade-offs enhance participation. Early involvement, skilled facilitation, and the capacity to develop shared values among diverse interests improve outcomes. Project-driven participation, deficient capacity to handle conflicting interests, and mechanisms of exclusion hinder participation. Biosphere Reserves (BRs) can leverage already existing actor initiatives, local knowledge, and practices. We identified a need for studies of causal links between participation and BR outcomes, including unpacking the meaning of different modes of participation. HIGHLIGHTS • Experts and BR actors consider participation to be key to success in BRs. • Awareness of the BR and its purpose is a necessary first step, but not sufficient. • Fair distribution of benefits and trade-offs enhances participation. • Early involvement of actors and capacity to develop shared values are key. • Studies of causal links between participation and BR outcomes are needed.
Integrating air quality and climate change : a policy imperative for human well-being and equity for the G20
Feig, Gregor Timothy, Tumelo; Garland, Rebecca M.; Langerman, Kristy E.; Perumal, Sarisha (National Association for Clean Air, 2025-06)
South Africa has assumed the Presidency of the Group of 20 (G20) of the world’s most significant economies in 2025 with a theme of “Fostering Solidarity, Equity and Sustainable Development.” During this period, the Academy of Science of South Africa (ASSAf) has led discussions among the academies of the G20 countries including the African Union in a process known as the Science 20 (S20). The theme for the S20 is “Climate Change and Well-being.” In February 2025, ASSAf hosted a series of discussions with the aim of producing a Communiqué for submission to the G20 Summit in September 2025.
Identification of maize leaf diseases using red, green, blue-based images with convolutional neural network (CNN) and residual network (ResNet50) models
Nkuna, Basani Lammy; Abutaleb, Khaled; Chirima, Johannes George; Newete, Solomon W.; Van der Walt, Adriaan Johannes; Nyamugama, Adolph (Elsevier, 2025-12)
Maize (Zea mays) is a crucial global staple crop that serves as a primary source of food and income, especially for smallholder farmers. However, it is susceptible to diseases that drastically reduce yields if not controlled. Traditional methods of disease detection of visual inspections are often inaccurate and uncertain. Recent advances in computer vision and deep learning techniques have shown promise in improving image recognition for crop disease detection. This study aims to develop models for detecting maize leaf diseases at the subfieldlevel using red, green, and blue (RGB)-based images using convolutional neural network (CNN) and residual network (ResNet50) models. A dataset of 1500 maize leaf images representing seven categories of maize disease symptoms was collected from the maize fields in Mopani District, Limpopo, South Africa. The data were processed to train and compare two deep learning models, CNNs and ResNet50. Both models demonstrated good classification accuracy with ResNet50 outperforming CNN, achieving an accuracy of 78.76% compared to 71.01% for CNN. The findings underscore ResNet50 enhanced capability to classify maize leaf diseases more accurately than CNN, attributed to its deeper architecture. This study illustrates the potential for deploying deep learning model in detecting maize leaf diseases. This study supports the transformative potential of deep learning in advancing agricultural practices, serving as a vital tool for early disease detection and contributing to food security in maize-producing regions, particularly smallholder farming systems. Therefore, this study trains the models that can be included in the mobile applications to be used to detected diseases in a sub-field level of the smallholder farms. HIGHLIGHTS • Maize disease detection with CNN and ResNet50 models using RGB images. • Dataset included both nutrient deficiencies and disease symptoms. • Image preprocessing and data augmentation to increase training data and reduce overfitting. • Demonstrated the potential of deep learning for multi-disease detection.
Assessing the performance of the WRF model in simulating squall line processes over the South African highveld
Mbokodo , Innocent L.; Burger , Roelof P.; Fridlind, Ann; Ndarana, Thando; Maisha , Robert; Chikoore, Hector; Bopape, Mary-Jane M. (MDPI, 2025-09-06)
Squall lines are some of the most common types of mesoscale cloud systems in tropical and subtropical regions. Thunderstorms associated with these systems are among the major causes of weather-related disasters and socio-economic losses in many regions across the world. This study investigates the capability of the Weather Research and Forecasting (WRF) model in simulating squall line features over the South African Highveld region. Two squall line cases were selected based on the availability of South African Weather Service (SAWS) weather radar data: 21 October 2017 (early austral summer) and 31 January–1 February 2018 (late austral summer). The European Centre for Medium-Range Weather Forecasts ERA5 datasets were used as observational proxies to analyze squall line features and compare them with WRF simulations. Mid-tropospheric perturbations were observed along westerly waves in both cases. These perturbations were coupled with surface troughs over central interior together with the high-pressure systems to the south and southeast of the country creating strong pressure gradients over the plateau, which also transports relative humidity onshore and extending to the Highveld region. The 2018 case also had a zonal structured ridging High, which was responsible for driving moisture from the southwest Indian Ocean towards the eastern parts of South Africa. Both ERA5 and WRF captured onshore near surface (800 hPa) winds and high-moisture contents over the eastern parts of the Highveld. A well-defined dryline was observed and well simulated for the 2017 event, while both ERA5 and WRF did not show any dryline for the 2018 case that was triggered by orography. While WRF successfully reproduced the synoptic-scale processes of these extreme weather events, the simulated rainfall over the area of interest exhibited a broader spatial distribution, with large-scale precipitation overestimated and convective rainfall underestimated. Our study shows that models are able to capture these systems but with some shortcomings, highlighting the need for further improvement in forecasts.
Land use and environmental drivers of methane and nitrous oxide emissions in Eswatini peatlands
Ndlela, Thandeka; Beckedahl, Heinz; Glatzel, Stephan; Grundling, Piet-Louis (International Mire Conservation Group and International Peatland Society, 2025-08)
Please read abstract in the article.
Transforming air pollution and health research into action in low- and middle-income countries
Samet, Jonathan; Shairsingh, Kerolyn; Ye, Wenlu; Gumy, Sophie; Mudu, Pierpaolo; Andersen, Zorana; Huang, Wei; Krzyzanowski, Michal; Mehta, Sumi; Petach, Helen; Peters, Annette; Pillarisetti, Ajay; West, Jason; Wright, Caradee Yael; Clasen, Thomas (Lippincott, Williams and Wilkins, 2025-12)
This commentary highlights the need for actionable and context-appropriate research on air pollution and health that will continue to drive policies to reduce exposures and disease burden. Research on air pollution and health has been substantial in high-income countries (HIC), leading to causal conclusions on the adverse effects of air pollution. Despite bearing the greatest disease burden from air pollution, low- and middle-income countries (LMICs) have had scant research funding, a trend that may well be aggravated due to changing political priorities in some HICs. High-quality data from LMICs is urgently needed to help motivate local, subnational, and national policies to raise awareness and identify priority actions to improve health. The new evidence will also provide a more complete understanding of air pollution and health globally. We highlight a framework for moving from research to action and address how this framework differs in HIC and LMIC contexts. We propose a hierarchy of research needs that begins with having the necessary air pollution monitoring and health data, and the capacity to use the data for informative analytics, risk assessment, valuation, and policy formulation. Building technical capacity may be needed for this purpose, as will development of a functioning regulatory system in parallel. We call for greater emphasis on surveillance studies to demonstrate the benefits of action and address barriers to action. The global community would benefit from a broad research agenda with priorities and adequate funding dedicated to building evidence that leads to positive policy change. We urge priority for advancing actionable research and improving research capacity in LMICs, including investments in routine collection of relevant data, emphasizing the foundation of risk monitoring and health data systems, and building a cadre of researchers and informed policy-makers.
Detecting and mapping invasive Populus alba species in mountainous ecosystems using Sentinel-2 imagery and random forest classification
Mapuru, Morena; Xulu, Sifiso; Gebreslasie, Michael; Sadiki, Maleho Mpho (Wiley, 2025-08-21)
Mapping invasive alien plants (IAPs) has become essential for land and biodiversity conservation authorities, as these species can transform the areas they invade. Fortunately, advances in remote sensing using publicly available products such as Sentinel-2 have improved this process, especially in hard-to-access mountainous regions. In South Africa, poplar (Populus alba) is among the IAPs of concern and is found in the eastern Free State and elsewhere in the country, but remote sensing has not yet been used to map this species. Using Sentinel-2 imagery and the random forest (RF) algorithm, this study allowed us to: (a) map and distinguish poplar trees from other land covers throughout the year in the eastern Free State’s mountainous region, (b) evaluate influential bands and their combinations in classification, and (c) assess the accuracy of the classification for the first and second halves of the year. The results showed that images from the first half of the year (January–June) had higher classification accuracy (overall accuracy [OA] = 91% and kappa = 0.89) than those from the second half (Jul–Dec) (OA = 87% and kappa = 0.84). Poplar and other classes were separable, with poplar mostly found in riparian areas. The study identified variables such as short-wave infrared-1 (SWIR-1), normalized difference vegetation index (NDVI), blue, poplar detection index-1 (PI-1), modified normalized difference water index (MNDWI), near-infrared (NIR), and PI-3 as key parameters for classifying poplar trees in mountainous regions. Overall, our findings demonstrate that Sentinel-2 bands and indices combined with an RF classifier provide an effective method for mapping poplar invasive trees in mountainous ecosystems.
Leaf area index-based phenotypic assessment of sweet potato varieties using UAV multispectral imagery and a hybrid retrieval approach
Tsele, Philemon; Ramoelo, Abel; Moleleki, Lucy Novungayo; Laurie, Sunette; Mphela, Whelma; Tshuma, Natasha (Elsevier, 2025-08)
Phenotyping based on the estimation of plant traits such as the leaf area index (LAI) could aid the identification and monitoring of the sweet potato health, growth status and gross primary productivity. Integrating radiative transfer models (RTMs), active learning algorithms and non-parametric regression methods using unmanned aerial vehicle (UAV) multispectral imagery have the potential for accurately estimating LAI across multiple crop varieties at varying growth stages. This study tested the boosted regression trees (BRT) and kernel ridge regression (KRR) for inversion of the PROSAIL RTM to retrieve LAI across 20 sweet potato varieties during peak growth stage. Furthermore, the study attempted to constrain the inversion process by using active learning (AL) techniques which ensured the selection of informative samples from a pool of RTM simulations. Results show that the most accurate LAI retrieval over the heterogeneous sweet potato canopy was achieved by integrating smaller PROSAIL simulations with the random sampling AL and KRR methods. The LAI retrieval accuracy had a coefficient of determination (R2) of 0.52, root mean squared error (RMSE) of 0.88 m2.m-2 and relative RMSE of 12.23 %. However, the BRT performance in-comparison to KRR, captured more spatial variability of observed LAI with a better prediction accuracy across the 20 sweet potato varieties. The hybrid approach developed in this study, show potential for accurate phenotyping of LAI dynamics across multiple sweet potato varieties during a matured growth stage. These findings have significant implications for sweet potato breeding programmes that are critical for developing new cultivars in South Africa.
A contaminated regression model for count health data
Otto, Arnoldus F.; Ferreira, Johannes Theodorus; Tomarchio, Salvatore Daniele; Bekker, Andriette, 1958-; Punzo, Antonio (Sage, 2025-02)
In medical and health research, investigators are often interested in countable quantities such as hospital length of stay (e.g., in days) or the number of doctor visits. Poisson regression is commonly used to model such count data, but this approach can’t accommodate overdispersion—when the variance exceeds the mean. To address this issue, the negative binomial (NB) distribution (NB-D) and, by extension, NB regression provide a well-documented alternative. However, real-data applications present additional challenges that must be considered. Two such challenges are (i) the presence of (mild) outliers that can influence the performance of the NB-D and (ii) the availability of covariates that can enhance inference about the mean of the count variable of interest. To jointly address these issues, we propose the contaminated NB (cNB) distribution that exhibits the necessary flexibility to accommodate mild outliers. This model is shown to be simple and intuitive in interpretation. In addition to the parameters of the NB-D, our proposed model has a parameter describing the proportion of mild outliers and one specifying the degree of contamination. To allow available covariates to improve the estimation of the mean of the cNB distribution, we propose the cNB regression model. An expectation-maximization algorithm is outlined for parameter estimation, and its performance is evaluated through a parameter recovery study. The effectiveness of our model is demonstrated via a sensitivity analysis and on two health datasets, where it outperforms well-known count models. The methodology proposed is implemented in an R package which is available at https://github.com/arnootto/cNB.

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