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 performance of the WRF model in simulating squall line processes over the South African highveld
(MDPI, 2025-09-06) Mbokodo , Innocent L.; Burger , Roelof P.; Fridlind, Ann; Ndarana, Thando; Maisha , Robert; Chikoore, Hector; Bopape, Mary-Jane M.
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
(International Mire Conservation Group and International Peatland Society, 2025-08) Ndlela, Thandeka; Beckedahl, Heinz; Glatzel, Stephan; Grundling, Piet-Louis
Please read abstract in the article.
Transforming air pollution and health research into action in low- and middle-income countries
(Lippincott, Williams and Wilkins, 2025-12) 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
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
(Wiley, 2025-08-21) Mapuru, Morena; Xulu, Sifiso; Gebreslasie, Michael; Sadiki, Maleho Mpho
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
(Elsevier, 2025-08) Tsele, Philemon; Ramoelo, Abel; Moleleki, Lucy Novungayo; Laurie, Sunette; Mphela, Whelma; Tshuma, Natasha; philemon.tsele@up.ac.za
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
(Sage, 2025-02) Otto, Arnoldus F.; Ferreira, Johannes Theodorus; Tomarchio, Salvatore Daniele; Bekker, Andriette, 1958-; Punzo, Antonio; arno.otto@up.ac.za
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.
Human health risk assessment of PM2.5, NO2, and SO2 and its impact on mortality in Nkangala and Gert Sibande, South Africa
(Ubiquity Press, 2025-08) Millar, Danielle Ann; Kapwata, Thandi; Howard, Marcus A.; Oosthuizen, Rietha; Naidoo, Natasha; Wright, Caradee Yael
Please read abstract in the article.
Monitoring coastal estuarine habitats for biodiversity along the temperate bioregion of South Africa
(Wiley, 2025-10) Campbell, Anthony; Adam, Elhadi; Adams, Janine B.; Barrenblitt, Abigail; Fatoyinbo, Temilola; Jensen, Daniel; Naidoo, Laven; Riddin, Taryn; Simard, Marc; Smith, Kyle; Thakali, Pati; Van Deventer, Heidi; Van Niekerk, Lara; Stovall, Atticus
Coastal wetlands provide critical ecosystem services, including the enhancement of biodiversity, carbon sequestration, and flood protection. Although these ecosystems have been mapped for country-level biodiversity typing, improved extent mapping is necessary to account for estuarine dynamics and improved reporting to the Kunming-Montreal Global Biodiversity Framework (GBF) by 2030. We achieved an overall coastal wetland accuracy of 90.7% (95% confidence interval: 90.2%–91.4%) utilizing a dense time series of very high spatial resolution (3 m) PlanetScope satellite imagery to map coastal wetlands with a combination of Random Forest to develop training data, U-Net convolutional neural networks, and a final decision tree to determine discrete ecosystem extents. Across the 84 mapped estuaries totaling 67,452 ha and 2,135 images, we mapped 9,131.1 ± 1,596.9 ha (13.5% of total estuarine functional zone extent) of salt marsh & reed beds and 1,718.6 ± 234.3 ha (2.5%) of Submerged Aquatic Vegetation (SAV). In addition to our earth observation analysis, we calculated tidal amplitudes and water level trends for 20 water level gauges across the region. We found tidal amplitude was a significant driver of salt marsh extent, explaining 33.6% of the variation (F (1,19) = 9.62, p = 0.005). We demonstrate a repeatable methodology for improved mapping of ecosystem zonation and utilize water level data to explore potential drivers of ecosystem distribution. Our method could be incorporated into a robust earth observation approach for reporting progress toward the goals of the/reporting to the GBF and Sustainable Development Goals (SDGs). PLAIN LANGUAGE SUMMARY Coastal wetlands provide many benefits to humans, including as habitat for a variety of species, accumulation of carbon in their soils, and protection from flooding and storm events. Global and regional maps of these ecosystems exist, but they lack precision in their identification of ecosystem zones. Improved maps could be used for improved reporting to international agreements and inform coastal management. We mapped three coastal wetland habitats to a high degree of accuracy (90.7%) utilizing a time series of commercial satellite data and machine learning algorithms. Across the 84 mapped estuaries totaling 67,452 ha, we mapped 9131.1 ± 1596.9 ha (13.5% of total estuarine functional zone extent) of salt marsh & reed beds and 1718.6 ± 234.3 ha (2.5%) of Submerged Aquatic Vegetation (SAV). We conducted additional analysis on how tidal amplitude, water level, and impervious surface influence the distribution of habitats in the region, finding that higher tidal amplitudes correlated with more salt marsh extent. Our methodology is repeatable and could improve the monitoring of these ecosystems in South Africa. KEY POINTS • We mapped coastal wetland habitats (salt marsh, reeds and sedges, and submerged aquatic vegetation) with a U-Net approach at >90% accuracy • We identified tidal amplitude as a major driver of salt marsh habitat, explaining 33.6% of the variation (F (1,19) = 9.62, p = 0.005). • Salt marsh was only 19.4% of coastal wetland extent, 40% of this extent was found within Knysna Estuary and Langebaan Lagoon.
Globale biodiversiteitsraamwerk vir varswatervleilande van Suid-Afrika : voorlopige berekening van die vordering om die restourasiemikpunt van doelwit 2 te bereik
(Suid-Afrikaanse Akademie vir Wetenskap en Kuns, 2025-07) Van Deventer, Heidi
AFRIKAANS : Die Nasionale Biodiversiteitsanalise van 2018 het bevind dat vleilande (riviermondings en varswatervleilande) die mees bedreigde van al die ekostelsels in Suid-Afrika is. Teen 2030 moet Suid-Afrika aan die Verenigde Nasies se Globale Biodiversiteitsraamwerk (GBR) rapporteer of Doelwit 2 bereik is, naamlik om 30% van gedegradeerde stelsels in die proses van herstel te hê. Hierdie studie het beoog om die voorlopige omvang as persentasie van varswatervleilande wat aan ekologiese ingrypings onderworpe was in verhouding tot die totale omvang van gedegradeerde varswatervleilande in Suid-Afrika te bereken. Ons het ook die persentasieomvang in verhouding tot eienaarskap van die gedegradeerde varswatervleilande en dié wat onder ekologiese restourasie is, bepaal. Die Werk vir Vleilandeprogram en die Werk vir Waterprogram se beskikbare data is ingesamel en met die Nasionale Vleilandkaart weergawe 6, gekombineer om die persentasies te bereken. Die meerderheid van Suid-Afrika se varswatervleilande (51%) is gemodelleer om degradasie te bepaal, met >2.0 miljoen hektaar van die 4 miljoen hektaar Suid-Afrikaanse vleilande wat impakte toon ten opsigte van veranderinge aan die hidrologiese siklus, waterkwaliteitsimpakte, fragmentasie en verlies van habitatte, voorkoms van indringerspesies en klimaatverandering, of ‘n kombinasie van hierdie impakte. Die 30% van Doelwit 2 beteken dus dat amper 613 136 ha van varswatervleilande teen 2030 onder ekologiese herstel moet wees. Ekologiese herstelprogramme het tot dusver slegs ongeveer 203 283 ha (10%) van Doelwit 2 bereik. Die meerderheid (82,8%) van vleilande is op privaat grond geleë, waarvan meer as die helfte gedegradeer is. Baie van die impakte op vleilande, asook die restourasieinisiatiewe wat deur die privaat sektor of individue uitgevoer word, word nie in hierdie berekening weerspieël nie. Monitering en kwantifisering van alle varswaterhabitatte is dus noodsaaklik om Doelwit 2 van die GBR teen 2030 te bereik. ENGLISH : The National Biodiversity Assessment of 2018 listed wetlands (estuaries and freshwater ecosystems) as the most threatened ecosystem of South Africa. By 2030, South Africa must report to the United Nations’ Global Biodiversity Framework (GBF) to which degree we have reached Target 2 that aims to have 30% of the extent of degraded ecosystems under restoration. This study aimed to calculate the preliminary extent as a percentage of wetlands that have been under ecological restoration interventions, relative to the total extent of degraded freshwater wetlands of South Africa. We also assessed the percentage of extent relative to ownership of the degraded wetlands and those that are under ecological restoration. Data released by the Working for Wetlands and Working for Water programmes were combined with the National Wetland Map version 6 as well as information on land ownership and protection level status of the country. The majority of the freshwater wetlands (51%) were modelled as degraded, with > 2 million ha of the 4 million ha of wetlands showed impacts resulting from various pressures, including changes to the hydrological cycle, water quality, fragmentation and degradation of habitats, climate change, or a combination of these pressures. The 30% GBF Target 2 requires that almost 613 136 ha of freshwater wetlands should be under restoration by 2030. The government’s two restoration programmes have reached only 203 283 ha (10%) of the desired target. The majority (82,8%) of freshwater wetlands is located on private land, of which the majority is degraded. Many of the impacts and none of the restoration interventions undertaken by the private sector or individuals are reflected. Monitoring and quantification of all freshwater habitats are therefore needed to attain the 30% extent target of the GBF.
Remote sensing monitoring of soil moisture for South African wetlands
(Water Research Commission, 2025-04) Van Deventer, Heidi; Naidoo, Laven; Le Roux, Jason; Blaauw, Ciara; Tema, Hebert
Surface soil moisture is an essential climate variable (ECV; https://gcos.wmo.int/en/essential-climate-variables/soilmoisture) which is monitored to inform our understanding of changes in the atmosphere and earth. Soil moisture is also an important indicator, in addition to vegetation and soil types, of the presence of a wetland.
African political ecologies
(Annual Reviews, 2025-10) Ramutsindela, Maano F.; Mba, Chika C.; Mushonga, Tafadzwa; Aremu, Adeyemi Oladapo; Mutune, Jane Mutheu; Matose, Frank; Dzingirai, Vupenyu; Muthama Muasya, A.; Dorvlo, Selorm Y.; Odhiambo, E.G.
This review locates African political ecologies at the intersection of the broader fields of political ecology and African studies. It focuses on African ecological thought and practices in relation to environmental challenges in Africa. Methodologically, it eschews the sectoral approach to the study of African environments by drawing together three interrelated themes of African epistemologies, African agency, and socioecological debt. It treats these themes as the bedrock of African political ecologies that are crucial for engaging and resolving socioecological challenges on the continent and for governing the African commons. These foundational themes offer avenues for appreciating African epistemologies, experiences, and actions as well as the possibilities for context-specific environmental justice within the broader frame of reparation. The review concludes by delineating the scope and agenda for the African political ecologies crucial for broadening political ecological research and African studies.
Soft computing for the posterior of a matrix t graphical network
(Elsevier, 2025-05) Pillay, Jason; Bekker, Andriette, 1958-; Ferreira, Johannes Theodorus; Arashi, Mohammad; andriette.bekker@up.ac.za
Modeling noisy data in a network context remains an unavoidable obstacle; fortunately, random matrix theory may comprehensively describe network environments. Noisy data necessitates the probabilistic characterization of these networks using matrix variate models. Denoising network data using a Bayesian approach is not common in surveyed literature. Therefore, this paper adopts the Bayesian viewpoint and introduces a new version of the matrix variate t graphical network. This model's prior beliefs rely on the matrix variate gamma distribution to handle the noise process flexibly; from a statistical learning viewpoint, such a theoretical consideration benefits the comprehension of structures and processes that cause network-based noise in data as part of machine learning and offers real-world interpretation. A proposed Gibbs algorithm is provided for computing and approximating the resulting posterior probability distribution of interest to assess the considered model's network centrality measures. Experiments with synthetic and real-world stock price data are performed to validate the proposed algorithm's capabilities and show that this model has wider flexibility than the model proposed by [13]. HIGHLIGHTS • Expanding the framework for denoising financial data inside the realm of graphical network theory, where the assumption of normality in the model is inadequate to account for the variation. • Introduction of the matrix variate gamma and inverse matrix variate gamma as priors for the covariance matrices; the univariate scale parameter β may be fixed or subject to a prior. • Following Bayesian inference with more flexible priors, there is an improvement based on relevant accuracy measures. • Experimental results indicate that our proposed framework and results outperform those of [13].
The potential of Sentinel-1 for monitoring forage productivity in rangeland ecosystems : a review
(Elsevier, 2026-02) Rapiya, Monde; Ngcoliso, Nasiphi; Qabaqaba, Mcebisi; Truter, Wayne Frederick; Ramoelo, Abel; u16400829@tuks.co.za
Rangelands are vital ecosystems that support forage production essential for livestock and biodiversity conservation, yet they face increasing degradation driven by anthropogenic pressures and climate variability. Remote sensing technologies offer scalable and non-destructive means to monitor forage productivity, with optical sensors limited by cloud cover and dense vegetation saturation. Synthetic Aperture Radar (SAR), particularly from the Sentinel-1 constellation, provides all-weather, high-resolution data capable of capturing structural and moisture-related vegetation attributes. This review evaluates the potential of Sentinel-1 SAR data for assessing and monitoring forage productivity in rangeland ecosystems. It highlights recent applications demonstrating Sentinel-1's effectiveness in forage productivity estimation and its integration with optical sensors like Sentinel-2 to enhance monitoring accuracy. Despite its advantages, challenges such as spatial resolution constraints, ecological sensitivity, and complex data processing impede full operational deployment. Future directions emphasize advanced data fusion techniques, machine learning approaches, and enhanced preprocessing algorithms to optimize Sentinel-1's utility. Integrating SAR with optical datasets promises to facilitate scalable, cost-effective, and reliable rangeland management strategies, supporting sustainable forage utilization and ecosystem resilience. Therefore, governments, the private sector, and NGOs should invest in Earth Observation infrastructure and capacity-building to translate remote sensing into actionable policies that promote sustainable rangeland management, climate change adaptation, and food security. HIGHLIGHTS • Sentinel-1 SAR offers all-weather, high-resolution rangeland monitoring. • Combines with optical sensors to estimate vegetation structure and biomass. • Data fusion and advanced algorithms boost accuracy and usability. • Enables large-scale, cost-effective forage and ecosystem health assessments.
Temporal variations in the short-term effects of ambient air pollution on cardiovascular and respiratory mortality : a pooled analysis of 380 urban areas over a 22-year period
(Elsevier, 2024-09) Schwarz, Maximilian; Peters, Annette; Stafoggia, Massimo; De’Donato, Francesca; Sera, Francesco; Bell, Michelle L; Guo, Yuming; Honda, Yasushi; Huber, Veronika; Jaakkola, Jouni J.K.; Urban, Aleš; Vicedo-Cabrera, Ana Maria; Masselot, Pierre; Lavigne, Eric; Achilleos, Souzana; Kyselý, Jan; Samoli, Evangelia; Hashizume, Masahiro; Sheng Ng, Chris Fook; Das Neves Pereira da Silva, Susana; Madureira, Joana; Garland, Rebecca M.; Tobias, Aurelio; Armstrong, Ben; Schwartz, Joel; Gasparrini, Antonio; Schneider, Alexandra; Breitner, Susanne; Kan, Haidong; Osorio, Samuel; Orru, Hans; Indermitte, Ene; Maasikmets, Marek; Ryti, Niilo; Pascal, Mathilde; Katsouyanni, Klea; Analitis, Antonis; Entezari, Alireza; Mayvaneh, Fatemeh; Kim, Yoonhee; Alahmad, Barrak; Zanobetti, Antonella; Diaz, Magali Hurtado; Arellano, Eunice Elizabeth Félix; Rao, Shilpa; Palomares, Alfonso Diz-Lois; Scovronick, Noah; Acquaotta, Fiorella; Kim, Ho; Lee, Whanhee; Íñiguez, Carmen; Forsberg, Bertil; Ragettli, Martina S.; Guo, Yue Leon; Pan, Shih-Chun; Li, Shanshan
BACKGROUND : Ambient air pollution, including particulate matter (such as PM10 and PM2·5) and nitrogen dioxide (NO2), has been linked to increases in mortality. Whether populations’ vulnerability to these pollutants has changed over time is unclear, and studies on this topic do not include multicountry analysis. We evaluated whether changes in exposure to air pollutants were associated with changes in mortality effect estimates over time. METHODS : We extracted cause-specific mortality and air pollution data collected between 1995 and 2016 from the Multi-Country Multi-City (MCC) Collaborative Research Network database. We applied a two-stage approach to analyse the short-term effects of NO2, PM10, and PM2·5 on cause-specific mortality using city-specific time series regression analyses and multilevel random-effects meta-analysis. We assessed changes over time using a longitudinal meta-regression with time as a linear fixed term and explored potential sources of heterogeneity and two-pollutant models. FINDINGS : Over 21·6 million cardiovascular and 7·7 million respiratory deaths in 380 cities across 24 countries over the study period were included in the analysis. All three air pollutants showed decreasing concentrations over time. The pooled results suggested no significant temporal change in the effect estimates per unit exposure of PM10, PM2·5, or NO2 and mortality. However, the risk of cardiovascular mortality increased from 0·37% (95% CI –0·05 to 0·80) in 1998 to 0·85% (0·55 to 1·16) in 2012 with a 10 μg/m3 increase in PM2·5. Two-pollutant models generally showed similar results to single-pollutant models for PM fractions and indicated temporal differences for NO2. INTERPRETATION : Although air pollution levels decreased during the study period, the effect sizes per unit increase in air pollution concentration have not changed. This observation might be due to the composition, toxicity, and sources of air pollution, as well as other factors, such as socioeconomic determinants or changes in population distribution and susceptibility.
Simulation of the African ITCZ during austral summer seasons and ENSO phases : application of an RCM derived from stretched grid ESM
(Frontiers Media, 2025-07) Ramotubei, Teke Solomon; Landman, Willem Adolf; Mateyisi, Mohau J.; Nangombe, Shingirai S.; Beraki, Asmerom Fissehatsion
INTRODUCTION : Climate predictability across timescales in a changing climate presents a unique opportunity and challenges for state-of-the-art climate models. The use of regional climate models (RCMs) forced with interactively coupled Earth System Models (ESMs) for the sub-seasonal, seasonal, and decadal predictions is an actively growing research area. METHODS : The study explores a stretched-grid RCM constrained with an ESM which integrates a climate change signature. Spectral relaxation paradigm is applied to limit the climate drift within the range of the multi-model sea-surface temperature (SST) and sea-ice concentration (SIC) variability. The model retroactive ensemble simulations for November initialization are evaluated on the seasonal migration of the ITCZ during El-Niño and La-Niña phases, exploring both the spatial and zonal positions. The model is also evaluated on the ITCZ process’ characteristics that include the Hadley cell (HC), stream function and the subtropical jet stream (STJ) using quantitative methods. RESULTS : The RCM and the driving ESM demonstrate skillful performance in identifying the seasonal trajectory of both the spatial and zonal migration of the ITCZ during El-Niño and La-Niña. Moreover, the RCM also demonstrates a good skill in determining both the descending edge of the HC and the STJ with the highest mean percentage error of 16.3 and 7.5% for the HC and STJ latitudes, respectively. CONCLUSIONS : The November initialization of the RCM skillfully simulates the seasonal migration of the ITCZ (and related characteristics) aligned to the observations and reanalysis datasets. Notwithstanding, the RCM manifests a tendency of more dynamic error growth relative to its driving ESM as the lead time increases. Furthermore, the RCM is also out of phase with a southerly shift of the stream function compared to the 500 hPa reanalysis stream function. The modeling framework offers process oriented and teleconnection studies. It also provides great potential for climate applications with suitable bias corrections techniques, albeit the source and mechanism of its dynamic error growth deserve further investigation.
Multisystemic resilience and its impact on youth mental health : reflections on co-designing a multi-disciplinary, participatory study
(Frontiers Media, 2025-03) Theron, Linda C.; Bergamini, Matteo; Chambers, Cassey; Choi, Karmel; Fawole, Olufunmilayo I.; Fyneface, Fyneface Dumnamene; Höltge, Jan; Kapwata, Thandi; Levine, Diane T.; Mai Bornu, Zainab; Makape, Makananelo; Matross, Celeste; McGrath, Brian; Olaniyan, Olanrewaju; Stekel, Dov J.; Hey, Josh Vande; Wright, Caradee Yael; Zion, Ameh Abba; Ungar, Michael; linda.theron@up.ac.za
Youth depression is a global emergency. Redressing this emergency requires a sophisticated understanding of the multisystemic risks and biopsychosocial, economic, and environmental resources associated with young people's experiences of no/limited versus severe depression. Too often, however, personal risks and a focus on individual-level protective resources dominate accounts of young people's trajectories towards depression. Further, studies of depression in high-income countries (i.e., “western”) typically inform these accounts. This article corrects these oversights. It reports on the methodology of the Wellcome-funded R-NEET study: a multidisciplinary, multisystemic, mixed method longitudinal study of resilience among African youth whose status as “not in education, employment or training” (NEET) makes them disproportionately vulnerable to depression. Co-designed by academics, community-based service providers and youth in South Africa and Nigeria, with partnerships in the United Kingdom, Canada and the United States, the R-NEET study is identifying the physiological, psychological, social, economic, institutional, and environmental risks and resources associated with distinct trajectories of depression. Using the methodology of the R-NEET study as exemplar, this article advances an argument for understanding resilience as a contextually and culturally rooted capacity that draws on the multiple, co-occurring systems that young people depend upon to support their wellbeing. Acknowledging and harnessing the multiple systems implicated in resilience is critical to researchers and mental health providers who seek to support young people to thrive, and to young people themselves when protecting or promoting their mental wellbeing.
All-cause, cardiovascular, and respiratory mortality and wildfire-related ozone : a multicountry two-stage time series analysis
(Elsevier, 2024-07) Chen, Gongbo; Guo, Yuming; Yue, Xu; Xu, Rongbin; Yu, Wenhua; Ye, Tingting; Tong, Shilu; Gasparrini, Antonio; Bell, Michelle L.; Armstrong, Ben; Schwartz, Joel; Jaakkola, Jouni J.K.; Lavigne, Eric; Saldiva, Paulo Hilario Nascimento; Kan, Haidong; Royé, Dominic; Urban, Aleš; Vicedo-Cabrera, Ana Maria; Tobias, Aurelio; Forsberg, Bertil; Sera, Francesco; Lei, Yadong; Abramson, Michael J; Li, Shanshan; Abrutzky, Rosana; Coêlho, Micheline de Sousa Zanotti Stagliorio; Garcia, Samuel David Osorio; Correa, Patricia Matus; Ortega, Nicolás Valdés; Kyselý, Jan; Orru, Hans; Maasikmets, Marek; Ryti, Niilo R.I.; Pascal, Mathilde; Schneider, Alexandra; Breitner, Susanne; Katsouyanni, Klea; Samoli, Evangelia; Mayvaneh, Fatemeh; Entezari, Alireza; Goodman, Patrick; De’Donato, Francesca; Stafoggia, Massimo; Seposo, Xerxes; Hashizume, Masahiro; Honda, Yasushi; Hurtado-Díaz, Magali; Valencia, César De la Cruz; Overcenco, Ala; Ameling, Caroline; Houthuijs, Danny; Rao, Shilpa; Carrasco-Escobar, Gabriel; Madureira, Joana; Nunes, Baltazar; Holobaca, Iulian-Horia; Garland, Rebecca M.; Kim, Ho; Lee, Whanhee; Íñiguez, Carmen; Åström, Christofer; Ragettli, Martina S; Guo, Yue Leon; Pan, Shih-Chun; Zeka, Ariana; Alahmad, Barrak; Zanobetti, Antonella; Scovronick, Noah; Colistro, Valentina; Dang, Tran Ngoc; Dung, Do Van
BACKGROUND : Wildfire activity is an important source of tropospheric ozone (O3) pollution. However, no study to date has systematically examined the associations of wildfire-related O3 exposure with mortality globally. METHODS : We did a multi-country two-stage time series analysis. From the Multi-City Multi-Country (MCC) Collaborative Research Network, data on daily all-cause, cardiovascular, and respiratory deaths were obtained from 749 locations in 43 countries or areas, representing overlapping periods from Jan 1, 2000, to Dec 31, 2016. We estimated the daily concentration of wildfire-related O3 in study locations using a chemical transport model, and then calibrated and downscaled O3 estimates to a resolution of 0·25° × 0·25° (approximately 28 km² at the equator). Using a random-effects meta-analysis, we examined the associations of short-term wildfire-related O3 exposure (lag period of 0–2 days) with daily mortality, first at the location level and then pooled at the country, regional, and global levels. Annual excess mortality fraction in each location attributable to wildfire-related O3 was calculated with pooled effect estimates and used to obtain excess mortality fractions at country, regional, and global levels. FINDINGS : Between 2000 and 2016, the highest maximum daily wildfire-related O3 concentrations (≥30 μg/m³) were observed in locations in South America, central America, and southeastern Asia, and the country of South Africa. Across all locations, an increase of 1 μg/m³ in the mean daily concentration of wildfire-related O3 during lag 0–2 days was associated with increases of 0·55% (95% CI 0·29 to 0·80) in daily all-cause mortality, 0·44% (–0·10 to 0·99) in daily cardiovascular mortality, and 0·82% (0·18 to 1·47) in daily respiratory mortality. The associations of daily mortality rates with wildfire-related O3 exposure showed substantial geographical heterogeneity at the country and regional levels. Across all locations, estimated annual excess mortality fractions of 0·58% (95% CI 0·31 to 0·85; 31 606 deaths [95% CI 17 038 to 46 027]) for all-cause mortality, 0·41% (–0·10 to 0·91; 5249 [–1244 to 11 620]) for cardiovascular mortality, and 0·86% (0·18 to 1·51; 4657 [999 to 8206]) for respiratory mortality were attributable to short-term exposure to wildfire-related O3. INTERPRETATION : In this study, we observed an increase in all-cause and respiratory mortality associated with short-term wildfire-related O3 exposure. Effective risk and smoke management strategies should be implemented to protect the public from the impacts of wildfires.
Road-associated variation in insect abundance differs between three common orders
(Springer, 2025-04) Sempe, Nhlanhla Pheletso Suzan; Sole, Catherine L.; Haussmann, Natalie S.; natalie.haussmann@up.ac.za
The ecological impacts of roads are well-researched for many vertebrates, but studies are relatively lacking with regards to invertebrates. Here, changes in the abundance of ground-dwelling species of the three most common insect orders, Hymenoptera (specifically ants), Hemiptera (true bugs) and Coleoptera (beetles), with distance from a gravel road in a grassland system in South Africa, are documented. Insects were collected by means of pitfall traps (n = 164) installed at 2, 5, 10 and 20 m perpendicular to a gravel road, and abundances of these three orders were compared statistically between the four distances. Whereas no significant differences in the numbers of Hymenoptera and Coleoptera were observed with distance from road, the abundance of Hemiptera was greater closer to the road. Our results show that quieter, low-traffic roads can affect the distribution of insect species at finer spatial scales.
Rossby wave breaking morphologies on the Southern Hemisphere dynamical tropopause
(American Meteorological Society, 2025-09) Barnes, Michael A.; Reeder, Michael J.; Ndarana, Thando
Rossby waves are fundamental meteorological structures in the extratropics of both hemispheres. Several extremes and weather regimes have been linked to the amplification and breaking of Rossby waves propagating along the extratropical waveguide. The morphology and evolution of Rossby wave breaking (RWB) on the Southern Hemisphere dynamical tropopause is studied here through an objective classification algorithm. Although the well-known classifications of RWB morphologies (LC1, LC2, P1, and P2) work well, the objective algorithm identifies important distinctions between RWB events with higher and lower amplitude structures and more meridional and zonal orientations. The different morphologies reflect the differences in the structure of the Rossby wave packets, the degree of phase locking with the low levels, the strength of the diabatic processes, and the amplitude of the nonlinearities in the flow. Anticyclonic RWB morphologies are associated with the decay of a Rossby wave packet, often producing a cyclonic–anticyclonic potential vorticity (PV) dipole in its wake. The cyclonic PV cutoff in this PV debris field can be stirred back into the extratropical waveguide resulting in a cyclonically overturned PV contour. Unlike anticyclonic RWB morphologies, cyclonic RWB morphologies in the Southern Hemisphere upper troposphere are associated with Rossby wave packet generation and downstream development. SIGNIFICANCE STATEMENT : This work aims to determine how well the well-known and much-used two- and four-type classifications of Rossby wave breaking (RWB) represent the full range of morphologies and analyze the differences in their evolution. An objective clustering technique identifies important distinctions between high- and low-amplitude patterns and more meridional and zonal orientations that the two- (anticyclonic, cyclonic) and four- (P1, P2, LC1, and LC2) type classifications do not. Anticyclonic RWB leads to the decay of a Rossby wave packet, whereas cyclonic RWB generates another Rossby wave packet and downstream development. This fundamental difference in the evolution, together with differences in the physical processes shaping these morphologies, deepens our understanding of RWB in the Southern Hemisphere upper troposphere.
A new look at the dirichlet distribution : robustness, clustering, and both together
(Springer, 2025-03) Tomarchio, Salvatore D.; Punzo, Antonio; Ferreira, Johannes Theodorus; Bekker, Andriette, 1958-
Compositional data have peculiar characteristics that pose significant challenges to traditional statistical methods and models. Within this framework, we use a convenient mode parametrized Dirichlet distribution across multiple fields of statistics. In particular, we propose finite mixtures of unimodal Dirichlet (UD) distributions for model-based clustering and classification. Then, we introduce the contaminated UD (CUD) distribution, a heavy-tailed generalization of the UD distribution that allows for a more flexible tail behavior in the presence of atypical observations. Thirdly, we propose finite mixtures of CUD distributions to jointly account for the presence of clusters and atypical points in the data. Parameter estimation is carried out by directly maximizing the maximum likelihood or by using an expectation-maximization (EM) algorithm. Two analyses are conducted on simulated data to illustrate the effects of atypical observations on parameter estimation and data classification, and how our proposals address both aspects. Furthermore, two real datasets are investigated and the results obtained via our models are discussed.

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