Air pollution-related health impacts from domestic waste burning and associated interventions: the merits of a traditional versus machine learning scoping review methodology - Thandi Kapwata; Candice Webster; Wen Guan Gavin Chen; Muhammad Ibrahim Suder; Devon Jarvis; Alex Vogt; Nomfundo Mahlangeni; Tracey Laban; Natasha Naidoo; Pierpaolo Mudu; Kerolyn Shairsingh; Caradee Y. Wright (Environmental Monitoring and Assessment, 2026)
Regularly updating repositories of air pollution impacts on health is essential for evidence-based policies, interventions, and progress monitoring. However, this is often time-consuming and labour-intensive. Automation can ensure up-to-date evidence; reduce retrieval, screening time, and costs; and make information accessible to non-academic stakeholders. This study investigated whether a machine learning approach can perform any stages of a traditional scoping review on the health impacts, policies, and interventions related to air pollution due to domestic waste burning. A traditional approach was conducted in parallel with machine learning methods enabling a comparison of the efficiency and quality of the partially automated approach against the manual review. Findings from the two approaches were compared and the final set of included articles were considered for (1) reported impacts of waste burning on health outcomes and (2) recommendations on solutions and interventions to prevent/reduce adverse effects on health from waste burning. We found a range of health impacts associated with waste burning, including low birth weight, hypertensive disorders of pregnancy, adverse respiratory outcomes like asthma and wheeze, cancer risk, and mortality. Few studies proposed solutions or evaluated the effectiveness of interventions. The ML approach showed a tendency towards false positives, which are preferable to false negatives (where relevant papers were excluded). Results showed that the model can conduct initial searches and decisions for the review. However, the articles included in the model should be screened manually for final acceptance. Therefore, we propose a hybrid approach be used until the automated model can be further refined.

Heat in the transport sector: measured heat exposure and interventions to address heat-related health impacts in the minibus taxi industry in South Africa - Caradee Y. Wright; Thandi Kapwata; Siyathemba Kunene; Ngwako Kwatala; Nomfundo Mahlangeni; Tracey Laban; Candice Webster (International Journal of Biometeorology, 2025)
High temperatures, heat and heatwaves are being experienced more frequently and with greater intensity in many parts of the world, including South Africa, and record-breaking maximum temperatures are becoming more common. Exposure to heat has adverse impacts on human health and wellbeing. The transport sector and its users are vulnerable to heat both inside vehicles as well as in places where people wait for public transport. We sought to assess the temperatures experienced in minibus taxis, a common mode of transport in South Africa and in minibus taxi ranks as well as the heat-related perceptions of minibus taxi drivers working in the Chesterville Taxi Association in Durban. We also observed heat-related elements in minibus taxi ranks. Data from temperature loggers showed that temperatures inside minibus taxis reached up to 39 °C and were between 3–4 °C warmer than outdoors. For around 11 h every day, temperatures inside minibus taxis were warmer than 27 °C – the temperature that is linked to heat-health symptoms. Taxi drivers ( N = 16) all agreed they feel hot in the minibus taxi and more than 90% said they drink water to try to cool down. Taxi ranks were lacking in supply of drinking water and shade for minibus taxis and seating. With the projected increase in temperatures caused by climate change, it is imperative to co-develop mitigation and adaptation strategies to minimise heat-related human health impacts in minibus taxis and taxi ranks especially in low- and middle-income countries.

Household PM2.5 in a South African urban and rural setting: A comparative analysis using low-cost sensors - Matthew Benyon; Ngwako Kwatala; Tracey Laban; Thandi Kapwata; Chiara Batini; Samuel Cai; Lisa K. Micklesfield; Rikesh Panchal; Siyathemba Kunene; Sizwe B. Zondo; Brigitte Language; Bianca Wernecke; et al. (Atmospheric Pollution Research, 2025)

Reviewing the methodologies used to assess household PM2.5 air pollution in sub-Saharan Africa - Rikesh Panchal; Matthew Benyon; Tracey Laban; Ngwako Kwatala; Bianca Wernecke; Caradee Y. Wright; Joshua Vande Hey (Building and Environment, 2024)