Browsing by Author "Adong, Priscilla"

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    Applying machine learning for large scale field calibration of low-cost PM2.5 and PM10 air pollution sensors
    (Applied AI Letters, 2022) Adong, Priscilla; Bainomugisha, Engineer; Okure, Deo; Sserunjogi, Richard
    Low-cost air quality monitoring networks can potentially increase the availability of high-resolution monitoring to inform analytic and evidence-informed approaches to better manage air quality. This is particularly relevant in low and middle-income settings where access to traditional reference-grade monitoring networks remains a challenge. However, low-cost air quality sensors are impacted by ambient conditions which could lead to over- or underestimation of pollution concentrations and thus require field calibration to improve their accuracy and reliability. In this paper, we demonstrate the feasibility of using machine learning methods for large-scale calibration of AirQo sensors, lowcost PM sensors custom-designed for and deployed in Sub-Saharan urban settings. The performance of various machine learning methods is assessed by comparing model corrected PM using k-nearest neighbours, support vector regression, multivariate linear regression, ridge regression, lasso regression, elastic net regression, XGBoost, multilayer perceptron, random forest and gradient boosting with collocated reference PM concentrations from a Beta Attenuation Monitor (BAM). To this end, random forest and lasso regression models were superior for PM2.5 and PM10 calibration, respectively. Employing the random forest model decreased RMSE of raw data from 18.6 μg/m3 to 7.2 μg/m3 with an average BAM PM2.5 concentration of 37.8 μg/m3 while the lasso regression model decreased RMSE from 13.4 μg/m3 to 7.9 μg/m3 with an average BAM PM10 concentration of 51.1 μg/m3. We validate our models through cross-unit and cross-site validation, allowing analysis of AirQo devices' consistency. The resulting calibration models were deployed to the entire large-scale air quality monitoring network consisting of over 120 AirQo devices, which demonstrates the use of machine learning systems to address practical challenges in a developing world setting.
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    Exploring PM2.5 variations from calibrated low-cost sensor network in Greater Kampala, during COVID-19 imposed lockdown restrictions: Lessons for Policy
    (Clean Air Journal, 2022) Green, Paul; Okure, Deo; Adong, Priscilla; Sserunjogi, Richard; Bainomugisha, Engineer
    Air pollution is considered a major public health risk globally, and the global South including sub-Saharan Africa face particular health risks, but there is limited data to quantify the level of pollution for different air quality contexts. The COVID-19 lockdown measures led to reduced human activities, and provided a unique opportunity to explore the impacts of reduced activities on urban air quality. This paper utilises calibrated data from a low-cost sensor network to explore insights from the diverse ambient air quality profile for four urban locations in Greater Kampala, Uganda before and during lockdown from March 31 to May 5 2020, highlighting the uniqueness of air pollution profiles in a sub-Saran Africa context. All locations saw year to year improvements in 24-hour mean PM2.5 between 9 and 25μg/m3 (i.e. 17-50% reduction from the previous year) and correlated well with reduction in traffic (up to approx. 80%) and commercial activities. The greatest improvement was observed in locations close to major transport routes in densely populated residential areas between 8 pm and 5 am. This suggests that the reduction in localised pollution sources such as nocturnal polluting activities including traffic and outdoor combustion including street cooking characteristic of fast-growing cities in developing countries, coupled with meteorological effects led to amplified reductions that continued well into the night, although meteorological effects are more generalised. Blanket policy initiatives targeting peak pollution hours could be adopted across all locations, while transport sector regulation could be very effective for pollution management. Likewise, because of the clustered and diffuse nature of pollution, community driven initiatives could be feasible for long-term mitigation.
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    A Multi-Model Fusion-Based Indoor Positioning System Using Smartphone Inertial Measurement Unit Sensor Data
    (IEEE, 2020) Adong, Priscilla; Eyobu, Odongo Steven; Oyana, Tonny J.; Han, Dong Seog
    We propose novel multi-model fusion-based step detection and step length estimation approaches that use the Kalman filter. The proposed step detection approach combines results from three conventional step detection algorithms, namely, findpeaks, localmax, and advanced zero crossing to obtain a single and more accurate step count estimate. The proposed step length estimation approach combines results from two popular step length estimation algorithms namely Weinberg’s and Kim’s methods. In our experiment, we consider five different smartphone placements, that is, when the smartphone is handheld, handheld with an arm swing, placed in the backpack, placed in a trousers’ back pocket and placed in a handbag. The system relies on inertia measurement unit sensors embedded in smartphones to generate accelerometer, gyroscope and magnetometer values from the human subject’s motion. Results from our experiments show that our proposed fusion based step detection and step length estimation approaches outperform the convectional step detection and step length estimation algorithms, respectively. Our Kalman fusion approach achieves a better step detection, step length estimation for all the five smart phone placements hence providing a better positioning accuracy. The performance of the proposed multimodel fusion-based positioning system was measured using the root mean square error (RMSE) of the displacement errors and step count errors exhibited by all the the step length and step count algorithms. The results show that the proposed Kalman fusion approach for step count estimation and step length estimation provides the least RMSE for all the smartphone placements. The proposed approach provides an average RMSE of 0.26 m in terms of the final position estimate for all the smartphone placements.
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    Seeing the air in detail: Hyperlocal air quality dataset collected from spatially distributed AirQo network
    (Data in Brief, 2022) Sserunjogi, Richard; Ssematimba, Joel; Okure, Deo; Ogenrwot, Daniel; Adong, Priscilla; Muyama, Lillian; Nsimbe, Noah; Bbaale, Martin; Bainomugisha, Engineer
    Air pollution is a major global challenge associated with an increasing number of morbidity and mortality from lung can- cer, cardiovascular and respiratory diseases, among others. However, there is scarcity of ground monitoring air quality data from Sub-Saharan Africa that can be used to quantify the level of pollution. This has resulted in limited targeted air pollution research and interventions e.g. health impacts, key drivers and sources, economic impacts, among others; ultimately hindering the establishment of effective manage- ment strategies. This paper presents a dataset of air quality observations collected from 68 spatially distributed monitor- ing stations across Uganda. The dataset includes hourly PM 2 . 5 and PM 10 data collected from low-cost air quality monitoring devices and one reference grade monitoring device over a pe- riod ranging from 2019 to 2020. This dataset contributes to- wards filling some of the data gaps witnessed over the years in ground level monitored ambient air quality in Sub-Saharan Africa and it can be useful to various policy makers and re- searchers.