Preliminary study of black carbon content in airborne particulate matters from an open site in the city of Yaoundé, Cameroon

IoT-based monitoring system and air quality prediction using machine learning for a healthy environment in Cameroon

This paper is aimed at developing an air quality monitoring system using machine learning (ML), Internet of Things (IoT), and other elements to predict the level of particulate matter and gases in the air based on the air quality index (AQI). It is an air quality assessor and therefore a means of achieving the Sustainable Development Goals (SDGs), in particular, SDG 3.9 (substantial reduction of the health impacts of hazardous substances) and SDG 11.6 (reduction of negative impacts on cities and populations). AQI quantifies and informs the public about air pollutants and their adverse effects on public health. The proposed air quality monitoring device is low-cost and operates in real-time. It consists of a hardware unit that detects various pollutants to assess air quality as well as other airborne particles such as carbon dioxide (CO2), methane (CH4), volatile organic compounds (VOCs), nitrogen dioxide (NO2), carbon monoxide (CO), and particulate matter with an aerodynamic diameter of 2.5 microns or less (PM2.5). To predict air quality, the device was deployed from November 1, 2022, to February 4, 2023, in certain bauxite-rich areas of Adamawa and certain volcanic sites in western Cameroon. Therefore, machine learning algorithm models, namely, multiple linear regression (MLR), support vector regression (SVR), random forest regression (RFR), XGBoost (XGB), and K-nearest neighbors (KNN) were applied to analyze the collected concentrations and predict the future state of air quality. The performance of these models was evaluated using mean absolute error (MAE), coefficient of determination (R-square), and root mean square error (RMSE). The obtained data in this study show that these pollutants are present in selected localities albeit to different extents. Moreover, the AQI values obtained range from 10 to 530, with a mean of 132.380 ± 63.705, corresponding to moderate air quality state but may induce an adverse effect on sensitive members of the population. This study revealed that XGB regression performed better in air quality forecasting with the highest R-squared (test score of 0.9991 and train score of 0.9999) and lowest RMSE (test score of 1.5748 and train score of 0. 0073) and MAE (test score of 0.0872 and train score of 0.0020), while the KNN model had the worst prediction (lowest R-squared and highest RMSE and MAE). This embryonic work is a prototype for projects in Cameroon as measurements are underway for a national spread over a longer period of time.

Environmental impact assessment of the coal yard and ambient pollution

This study investigates the vertical distribution of pollutants emitted from coal yards using unmanned aerial vehicles (UAVs). Vertical concentration measurements of black carbon (BC) and particulate matter (PM) in a range of 1 m to 100 m above ground level (AGL) in the central coal yard showed clear spatial patterns and gradients of these pollutants. In addition, measurements were taken at specific heights (1 m, 30 m AGL, and 60 m AGL) at seven locations approximately 3 km from the yard. Thirteen measurements were carried out during the non-heating period under similar weather conditions. The measured BC concentrations decreased significantly with increasing altitude, with ground-level concentrations reaching 1.88 ± 0.61 µg/m3 and decreasing by over 46% at 80 m AGL. Similarly, PM10 concentrations at 60 m AGL decreased by 21.7%, with values of 25.99 ± 9.24 µg/m3 measured near the ground level and 16.52 ± 8.31 µg/m3 at 60 m AGL. The maximum coal particle pollution from the coal depot ranges from 500 to 1,000 m. The study showed a significant decrease in BC concentrations with height above the coal yard surface. Concentrations of PM10 and PM10-TSP showed a complex distribution influenced by local emissions and long-range particle transport. Meteorological factors, especially wind speed and direction, significantly influenced the pollutant dispersion. In addition, higher pollutant concentrations were measured during dry periods than after rainfall. The findings of this study contribute to a better understanding of the dispersion patterns and potential impacts of coal dust, enabling the implementation of targeted mitigation strategies and improved pollution control measures.

Spatial-temporal patterns of ambient fine particulate matter (PM2.5) and black carbon (BC) pollution in Accra

Sub-Saharan Africa (SSA) is rapidly urbanizing, and ambient air pollution has emerged as a major environmental health concern in growing cities. Yet, effective air quality management is hindered by limited data. We deployed robust, low-cost and low-power devices in a large-scale measurement campaign and characterized within-city variations in fine particulate matter (PM2.5) and black carbon (BC) pollution in Accra, Ghana. Between April 2019 and June 2020, we measured weekly gravimetric (filter-based) and minute-by-minute PM2.5 concentrations at 146 unique locations, comprising of 10 fixed (∼1 year) and 136 rotating (7 day) sites covering a range of land-use and source influences. Filters were weighed for mass, and light absorbance (10-5m-1) of the filters was used as proxy for BC concentration. Year-long data at four fixed sites that were monitored in a previous study (2006-2007) were compared to assess changes in PM2.5 concentrations. The mean annual PM2.5 across the fixed sites ranged from 26 μg m-3 at a peri-urban site to 43 μg m-3 at a commercial, business, and industrial (CBI) site. CBI areas had the highest PM2.5 levels (mean: 37 μg m-3), followed by high-density residential neighborhoods (mean: 36 μg m-3), while peri-urban areas recorded the lowest (mean: 26 μg m-3). Both PM2.5 and BC levels were highest during the dry dusty Harmattan period (mean PM2.5: 89 μg m-3) compared to non-Harmattan season (mean PM2.5: 23 μg m-3). PM2.5 at all sites peaked at dawn and dusk, coinciding with morning and evening heavy traffic. We found about a 50% reduction (71 vs 37 μg m-3) in mean annual PM2.5 concentrations when compared to measurements in 2006-2007 in Accra. Ambient PM2.5 concentrations in Accra may have plateaued at levels lower than those seen in large Asian megacities. However, levels are still 2- to 4-fold higher than the WHO guideline. Effective and equitable policies are needed to reduce pollution levels and protect public health.