Edokpayi, J. N.Piketh, S. J.Enitan, Ibironke Titilayo2025-09-122025-09-122025-09-05Enitan, I.T. 2025. Ambient air pollution monitoring, modelling and human health risks in Thulamela Municipality, Vhembe District of Limpopo Province, South Africa. . .https://univendspace.univen.ac.za/handle/11602/2927PhDENVDepartment of Geography and Environmental SciemcesAmbient air pollution is a pressing global environmental concern, with pollutants from natural and anthropogenic sources contributing to deteriorating air quality. This research investigates the impact of sulfur dioxide (SO₂), nitrogen oxides (NOₓ), carbon monoxide (CO), and particulate matter (PM2.5 and PM10) on human health and the environment. PM2.5 in particular is a significant public health risk linked to respiratory and cardiovascular diseases. The study focuses on Thulamela Local Municipality (LM) in Limpopo Province, South Africa, a region with diverse semi-urban and rural environments, where air quality data is limited despite ongoing urbanisation and economic growth. Given the lack of comprehensive baseline data, this study aims to quantify, monitor, and model air pollution while assessing its potential health impacts. This research integrates the development of an emission inventory for criteria pollutants (SO₂, NOₓ, CO, PM2.5, and PM₁₀), real-time PM2.5 monitoring using Dylos DC1700 low-cost sensors (LCSs), PM2.5 dispersion modelling with AERMOD, health risk assessment (HHRA), and machine learning-based predictive modelling. By evaluating pollution sources, exposure risks, and predictive trends, the study provides critical insights to inform air quality management strategies in semi-urban settings. This study developed the first-ever 10-year vehicle emission inventory (VEI) for criteria pollutants (SO₂, NOₓ, CO, PM2.5, and PM₁₀) in Thulamela Local Municipality (LM), South Africa. In addition, it produced the first comprehensive bottom-up emission inventory for the year 2021, quantifying total SO₂, NOₓ, CO, PM2.5, and PM₁₀ emissions from two major anthropogenic sources: vehicular emissions and residential wood combustion (RWC). The results indicated annual emissions of 66.3, 1821.7, 21002.6, 2348.7, and 2649.3 tonnes, for SO₂, NOₓ, CO, PM2.5, and PM₁₀, respectively. RWC contributed over 96% of PM2.5 and PM₁₀ emissions, while NOₓ (84%) and SO₂ (60%) originated primarily from vehicular sources. The spatial analysis revealed that emissions were highest in the southwest and northwest, where population density and human activities were most concentrated. A one-year PM2.5 monitoring campaign (March 2021 – February 2022) was conducted at 14 sites, covering urban, suburban, and rural areas. The strong correlation (R² = 0.75) between Dylos and reference monitors (GRIMM EDM180) confirmed the reliability of LCS performance for PM2.5 measurements. The study found that annual PM2.5 concentrations exceeded both WHO guidelines and SA NAAQS, except at one site. Monthly trends showed higher PM2.5 levels from May to September, with diurnal peaks from 6–8 am and 6–8 pm, primarily influenced by traffic emissions and residential wood combustion. The bivariate polar plot (BVP) analysis identified pollution hotspots near traffic intersections, residential areas, and commercial zones. The study identified significant spatial and temporal variations in PM2.5 levels across different monitoring sites, with higher concentrations in suburban areas than rural ones due to vehicular emissions and wood-burning. VTV2 recorded the highest annual concentration (32.3 μg/m³), while RL4 had the lowest (19.1 μg/m³). The average seasonal PM2.5 levels, despite differences, consistently exceeded air quality guidelines, except in summer for specific locations. The results also showed that evening PM2.5 concentrations were generally higher than morning peaks, highlighting significant differences in pollution levels across the area. Using AERMOD, PM2.5 dispersion was modelled to assess spatial patterns and regulatory compliance. Results showed that pollution hotspots exceeded SA NAAQS (40 μg/m³ daily, 20 μg/m³ annually) and WHO standards (15 μg/m³ daily, 5 μg/m³ annually), with the highest daily PM2.5 concentration (49.2 μg/m³) in densely populated areas. Model validation comparing modelled and monitored PM2.5 showed systematic underestimation (R² = 0.176, negative fractional bias), indicating unaccounted sources such as biomass burning, unpaved roads, industrial activities, and agricultural emissions. At Lunungwi village (RL receptor group), the modelled hourly PM2.5 concentration (47.12 μg/m³) exceeded the observed value (32.27 μg/m³, FAC2 = 0.25), indicating localised overestimation. Daily peaks aligned with morning and evening rush hours. At the same time, monthly trends from May to September reflected seasonal variations and increased residential wood combustion, while a notable decline in July 2021 was due to COVID-19 lockdown restrictions, highlighting the impact of reduced human activity on air quality. Although AERMOD has limitations in accounting for some emission sources, it effectively identified pollution hotspots, offering valuable insights for policy interventions and air quality management. The study further assessed long-term exposure and health risks using the HHRA model, focusing on risk characterisation. The USEPA health risk assessment revealed that PM2.5 exposure posed both non-carcinogenic and carcinogenic risks, with infants and toddlers being the most vulnerable. Hazard Quotient (HQ) values exceeded safe limits (1.10 in winter) due to high PM2.5 levels in colder months. Excess Lifetime Cancer Risk (ELCR) values surpassed the acceptable threshold (10⁻⁴) for all age groups, with adults facing the highest long-term cancer risks (2.35 × 10⁻¹). These findings emphasise the urgent need for air pollution control measures, particularly in areas with high residential fuel combustion and vehicular emissions. To enhance PM2.5 predictions, machine learning models were applied using meteorological variables. Random Forest (RF) outperformed Multiple Linear Regression (MLR) and Polynomial Ridge Regression (PRR), with the highest R² (0.80) at VP4 for site-specific predictions and 0.604 for area-wide estimates. Seasonal performance showed better RF accuracy in winter (R² = 0.58, RMSE = 2.10 μg/m³) than in summer (R² = 0.45, RMSE = 4.72 μg/m³), reflecting the impact of seasonal meteorology on PM2.5 dispersion. In conclusion, this research provides a holistic approach to quantifying air pollution sources, exposure risks, and predictive modelling in Thulamela Municipality. The study highlights urgent public health risks and the need for stronger air quality regulations, improved monitoring networks, and targeted mitigation strategies by establishing a clear link between air quality and human health. The findings underscore the need to expand emission inventories, refine meteorological inputs, and integrate multi-pollutant assessments to develop a comprehensive air quality management strategy. To address identified challenges, the study proposes mitigation strategies, including eco-friendly phytoremediation techniques, promotion of cleaner cooking and transport technologies, and urban planning measures to minimise exposure in densely populated areas. By combining scientific research with practical applications, this work aims to support sustainable air quality management, improve public health, and contribute to a healthier environment.1 online resource ()enUniversity of VendaAmbient air pollutionUCTDPm25Emission inventoryCriteria air pollutantsMonitoringDispersion ModellingHealth Risk AssessmentMachine LearningThulamela MunicipalitySouth Africa363.73920968257Air -- PollutionAir quality -- South Africa -- LimpopoEnvironmental quality -- South Africa -- LimpopoAir -- Standards -- South Africa -- LimpopoEnvironmental monitoring -- South Africa -- LimpopoThesisEnitan IT. Ambient air pollution monitoring, modelling and human health risks in Thulamela Municipality, Vhembe District of Limpopo Province, South Africa. []. , 2025 [cited yyyy month dd]. Available from:Enitan, I. T. (2025). <i>Ambient air pollution monitoring, modelling and human health risks in Thulamela Municipality, Vhembe District of Limpopo Province, South Africa</i>. (). . Retrieved fromEnitan, Ibironke Titilayo. <i>"Ambient air pollution monitoring, modelling and human health risks in Thulamela Municipality, Vhembe District of Limpopo Province, South Africa."</i> ., , 2025.TY - Thesis AU - Enitan, Ibironke Titilayo AB - Ambient air pollution is a pressing global environmental concern, with pollutants from natural and anthropogenic sources contributing to deteriorating air quality. This research investigates the impact of sulfur dioxide (SO₂), nitrogen oxides (NOₓ), carbon monoxide (CO), and particulate matter (PM2.5 and PM10) on human health and the environment. PM2.5 in particular is a significant public health risk linked to respiratory and cardiovascular diseases. The study focuses on Thulamela Local Municipality (LM) in Limpopo Province, South Africa, a region with diverse semi-urban and rural environments, where air quality data is limited despite ongoing urbanisation and economic growth. Given the lack of comprehensive baseline data, this study aims to quantify, monitor, and model air pollution while assessing its potential health impacts. This research integrates the development of an emission inventory for criteria pollutants (SO₂, NOₓ, CO, PM2.5, and PM₁₀), real-time PM2.5 monitoring using Dylos DC1700 low-cost sensors (LCSs), PM2.5 dispersion modelling with AERMOD, health risk assessment (HHRA), and machine learning-based predictive modelling. By evaluating pollution sources, exposure risks, and predictive trends, the study provides critical insights to inform air quality management strategies in semi-urban settings. This study developed the first-ever 10-year vehicle emission inventory (VEI) for criteria pollutants (SO₂, NOₓ, CO, PM2.5, and PM₁₀) in Thulamela Local Municipality (LM), South Africa. In addition, it produced the first comprehensive bottom-up emission inventory for the year 2021, quantifying total SO₂, NOₓ, CO, PM2.5, and PM₁₀ emissions from two major anthropogenic sources: vehicular emissions and residential wood combustion (RWC). The results indicated annual emissions of 66.3, 1821.7, 21002.6, 2348.7, and 2649.3 tonnes, for SO₂, NOₓ, CO, PM2.5, and PM₁₀, respectively. RWC contributed over 96% of PM2.5 and PM₁₀ emissions, while NOₓ (84%) and SO₂ (60%) originated primarily from vehicular sources. The spatial analysis revealed that emissions were highest in the southwest and northwest, where population density and human activities were most concentrated. A one-year PM2.5 monitoring campaign (March 2021 – February 2022) was conducted at 14 sites, covering urban, suburban, and rural areas. The strong correlation (R² = 0.75) between Dylos and reference monitors (GRIMM EDM180) confirmed the reliability of LCS performance for PM2.5 measurements. The study found that annual PM2.5 concentrations exceeded both WHO guidelines and SA NAAQS, except at one site. Monthly trends showed higher PM2.5 levels from May to September, with diurnal peaks from 6–8 am and 6–8 pm, primarily influenced by traffic emissions and residential wood combustion. The bivariate polar plot (BVP) analysis identified pollution hotspots near traffic intersections, residential areas, and commercial zones. The study identified significant spatial and temporal variations in PM2.5 levels across different monitoring sites, with higher concentrations in suburban areas than rural ones due to vehicular emissions and wood-burning. VTV2 recorded the highest annual concentration (32.3 μg/m³), while RL4 had the lowest (19.1 μg/m³). The average seasonal PM2.5 levels, despite differences, consistently exceeded air quality guidelines, except in summer for specific locations. The results also showed that evening PM2.5 concentrations were generally higher than morning peaks, highlighting significant differences in pollution levels across the area. Using AERMOD, PM2.5 dispersion was modelled to assess spatial patterns and regulatory compliance. Results showed that pollution hotspots exceeded SA NAAQS (40 μg/m³ daily, 20 μg/m³ annually) and WHO standards (15 μg/m³ daily, 5 μg/m³ annually), with the highest daily PM2.5 concentration (49.2 μg/m³) in densely populated areas. Model validation comparing modelled and monitored PM2.5 showed systematic underestimation (R² = 0.176, negative fractional bias), indicating unaccounted sources such as biomass burning, unpaved roads, industrial activities, and agricultural emissions. At Lunungwi village (RL receptor group), the modelled hourly PM2.5 concentration (47.12 μg/m³) exceeded the observed value (32.27 μg/m³, FAC2 = 0.25), indicating localised overestimation. Daily peaks aligned with morning and evening rush hours. At the same time, monthly trends from May to September reflected seasonal variations and increased residential wood combustion, while a notable decline in July 2021 was due to COVID-19 lockdown restrictions, highlighting the impact of reduced human activity on air quality. Although AERMOD has limitations in accounting for some emission sources, it effectively identified pollution hotspots, offering valuable insights for policy interventions and air quality management. The study further assessed long-term exposure and health risks using the HHRA model, focusing on risk characterisation. The USEPA health risk assessment revealed that PM2.5 exposure posed both non-carcinogenic and carcinogenic risks, with infants and toddlers being the most vulnerable. Hazard Quotient (HQ) values exceeded safe limits (1.10 in winter) due to high PM2.5 levels in colder months. Excess Lifetime Cancer Risk (ELCR) values surpassed the acceptable threshold (10⁻⁴) for all age groups, with adults facing the highest long-term cancer risks (2.35 × 10⁻¹). These findings emphasise the urgent need for air pollution control measures, particularly in areas with high residential fuel combustion and vehicular emissions. To enhance PM2.5 predictions, machine learning models were applied using meteorological variables. Random Forest (RF) outperformed Multiple Linear Regression (MLR) and Polynomial Ridge Regression (PRR), with the highest R² (0.80) at VP4 for site-specific predictions and 0.604 for area-wide estimates. Seasonal performance showed better RF accuracy in winter (R² = 0.58, RMSE = 2.10 μg/m³) than in summer (R² = 0.45, RMSE = 4.72 μg/m³), reflecting the impact of seasonal meteorology on PM2.5 dispersion. In conclusion, this research provides a holistic approach to quantifying air pollution sources, exposure risks, and predictive modelling in Thulamela Municipality. The study highlights urgent public health risks and the need for stronger air quality regulations, improved monitoring networks, and targeted mitigation strategies by establishing a clear link between air quality and human health. The findings underscore the need to expand emission inventories, refine meteorological inputs, and integrate multi-pollutant assessments to develop a comprehensive air quality management strategy. To address identified challenges, the study proposes mitigation strategies, including eco-friendly phytoremediation techniques, promotion of cleaner cooking and transport technologies, and urban planning measures to minimise exposure in densely populated areas. By combining scientific research with practical applications, this work aims to support sustainable air quality management, improve public health, and contribute to a healthier environment. DA - 2025-09-05 DB - ResearchSpace DP - Univen KW - Ambient air pollution KW - Pm25 KW - Emission inventory KW - Criteria air pollutants KW - Monitoring KW - Dispersion Modelling KW - Health Risk Assessment KW - Machine Learning KW - Thulamela Municipality KW - South Africa LK - https://univendspace.univen.ac.za PY - 2025 T1 - Ambient air pollution monitoring, modelling and human health risks in Thulamela Municipality, Vhembe District of Limpopo Province, South Africa TI - Ambient air pollution monitoring, modelling and human health risks in Thulamela Municipality, Vhembe District of Limpopo Province, South Africa UR - ER -