Spray dry scrubbing is widely used for SO₂ abatement, but high removal efficiencies are required for economical operation. Whereas SO₂ removal dependence on the drying rate has been investigated, available modelling work has not addressed the impact of selected drying models on the removal efficiency; instead, a single drying model is often assumed. In the present work, computational fluid dynamics is used to numerically model the SO₂ removal in a laboratory-scale spray dry scrubber. The Euler–Lagrangian framework is used to simulate the multiphase interaction and two drying models are used: the widely used classical D2-law model and the mechanistic model. In addressing shortcomings from previous works, this study also provides a comprehensive model development and robust model validation with quantifiable metrics for goodness-of-fit, including R². Also presented are key parameters associated with SO₂-removal efficiency, including the exit product moisture content and droplet dynamics. The mechanistic model gave a better representation of the SO₂-removal efficiency. The latter was found to be dependent on the inlet temperature, the calcium-to-sulphur and liquid-to-gas (L/G) ratios, with a high L/G ratio having the most significant impact on the removal efficiency, although resulting in a higher product outlet moisture content.

Residential biomass combustion in rural communities is recognised as a major contributor to air pollution, representing the second-largest source of gaseous emissions and the primary source of atmospheric particulate matter. In many African countries, including South Africa, residential emission inventories and air quality assessments rely largely on international emission factors (EFs), introducing considerable uncertainty due to regional differences in fuel characteristics, combustion technologies, and household energy-use practices. Generating locally derived EFs is therefore essential for improving the accuracy of emission inventories and supporting effective air quality management and climate policy development. This study quantified the EFs of CO₂, CO, NO, SO₂, and PM₁₀ from the combustion of the 12 predominantly preferred fuelwood species used for household energy in rural Limpopo Province, South Africa. Controlled combustion experiments were conducted using a traditional three-legged cookstove in a simulated village kitchen under laboratory conditions. The mean EFs determined for CO₂, CO, NO, SO₂, and PM₁₀ were 1389 ± 149, 131.38 ± 91.4, 1.22 ± 0.63, 5.16 ± 4.9, and 64.7 ± 76.3 g kg⁻¹, respectively. Modified combustion efficiency (MCE) exhibited a statistically significant negative association with the EFs of CO and SO₂, indicating that lower combustion efficiency was associated with higher emissions of these pollutants. In contrast, fuel moisture content was not significantly associated with the EFs of CO₂, NO, or PM₁₀, while no statistically significant relationship was observed between fuel nitrogen (N) content and NO EFs. Furthermore, no statistically significant differences were detected among the investigated fuelwood species in the individual EFs of CO₂, CO, NO, SO₂, and PM₁₀. This study provides the first laboratory-derived, species-specific EF dataset for the predominantly preferred fuelwood species used in rural Limpopo Province. The findings reduce uncertainties associated with the application of international default EFs and provide a robust basis for improving residential biomass emission inventories, air quality modelling, and the development of evidence-based air pollution mitigation and climate management strategies in South Africa and other regions with similar household energy-use patterns.