Spatial Extent and Distribution of Ambient Airborne Particulate Matter (PM2.5) in Selected Land Use Sites in Nairobi, Kenya

Air pollution is one of the most important environmental and public health concerns worldwide. Urban air pollution has been increasing since the industrial revolution due to rapid industrialization, mushrooming of cities, and greater dependence on fossil fuels in urban centers. Particulate matter (PM) is considered to be one of the main aerosol pollutants that causes a significant adverse impact on human health. Low-cost air quality sensors have attracted attention recently to curb the lack of air quality data which is essential in assessing the health impacts of air pollutants and evaluating land use policies. This is mainly due to their lower cost in comparison to the conventional methods. The aim of this study was to assess the spatial extent and distribution of ambient airborne particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) in Nairobi City County. Seven sites were selected for monitoring based on the land use type: high- and low-density residential, industrial, agricultural, commercial, road transport, and forest reserve areas. Calibrated low-cost sensors and cyclone samplers were used to monitor PM2.5 concentration levels and gravimetric measurements for elemental composition of PM2.5, respectively. The sensor percentage accuracy for calibration ranged from 81.47% to 98.60%. The highest 24-hour average concentration of PM2.5 was observed in Viwandani, an industrial area (111.87 μg/m³), and the lowest concentration at Karura (21.25 μg/m³), a forested area. The results showed a daily variation in PM2.5 concentration levels with the peaks occurring in the morning and the evening due to variation in anthropogenic activities and the depth of the atmospheric boundary layer. Therefore, the study suggests that residents in different selected land use sites are exposed to varying levels of PM2.5 pollution on a regular basis, hence increasing the potential of causing long-term health effects.

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Using a Network of Locally Developed Low Cost Particulate Matter Sensors for Land Use Regression Modeling of PM2.5 in Urban Uganda

10.20944/preprints202006.0158.v1 ◽

2020 ◽

Author(s):

Eric S. Coker ◽

Ssematimba Joel ◽

Engineer Bainomugisha

Keyword(s):

Land Use ◽

Air Pollution ◽

Particulate Matter ◽

Air Quality ◽

Low Cost ◽

Additive Models ◽

Pollution Monitoring ◽

Sub Saharan Africa ◽

Land Use Regression ◽

Sub Saharan

Background: There are major air pollution monitoring gaps in sub-Saharan Africa. Developing capacity in the region to conduct air monitoring in the region can help estimate exposure to air pollution for epidemiology research. The purpose of our study is to develop a land use regression (LUR) model using low-cost air quality sensors developed by a research group in Uganda (AirQo). Methods: Using these low-cost sensors, we collected continuous measurements of fine particulate matter (PM2.5) between May 1, 2019 and February 29, 2020 at 22 monitoring sites across urban municipalities of Uganda. We compared average monthly PM2.5 concentrations from the AirQo sensors with measurements from a BAM-1020 reference monitor operated at the US Embassy in Kampala. Monthly PM2.5 concentrations were used for LUR modeling. We used eight Machine Learning (ML) algorithms and ensemble modeling; using 10-fold cross validation and root mean squared error (RMSE) to evaluate model performance. Results: Monthly PM2.5 concentration was 60.2 µg/m3 (IQR: 45.4-73.0 µg/m3; median= 57.5 µg/m3). For the ML LUR models, RMSE values ranged between 5.43 µg/m3 - 15.43 µg/m3 and explained between 28% and 92% of monthly PM2.5 variability. Generalized additive models explained the largest amount of PM2.5 variability (R2=0.92) and produced the lowest RMSE (5.43 µg/m3) in the held-out test set. The most important predictors of monthly PM2.5 concentrations included monthly precipitation, major roadway density, population density, latitude, greenness, and percentage of households using solid fuels. Conclusion: To our knowledge, ours is the first study to model the spatial distribution of urban air pollution in sub-Saharan Africa using air monitors developed from the region itself. Non-parametric ML for LUR modeling performed with high accuracy for prediction of monthly PM2.5 levels. Our analysis suggests that locally produced low-cost air quality sensors can help build capacity to conduct air pollution epidemiology research in the region.

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Analysis of the variability of airborne particulate matter with prevailing meteorological conditions across a semi-urban environment using a network of low-cost air quality sensors

Heliyon ◽

10.1016/j.heliyon.2020.e04207 ◽

2020 ◽

Vol 6 (6) ◽

pp. e04207 ◽

Cited By ~ 2

Author(s):

Opeyemi R. Omokungbe ◽

Olusegun G. Fawole ◽

Oyediran K. Owoade ◽

Olalekan A.M. Popoola ◽

Roderic L. Jones ◽

...

Keyword(s):

Particulate Matter ◽

Air Quality ◽

Urban Environment ◽

Low Cost ◽

Airborne Particulate Matter ◽

Meteorological Conditions ◽

Airborne Particulate

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Improving accuracy of air pollution exposure measurements: Statistical correction of a municipal low-cost airborne particulate matter sensor network

Environmental Pollution ◽

10.1016/j.envpol.2020.115833 ◽

2021 ◽

Vol 268 ◽

pp. 115833

Author(s):

Ellen M. Considine ◽

Colleen E. Reid ◽

Michael R. Ogletree ◽

Timothy Dye

Keyword(s):

Air Pollution ◽

Particulate Matter ◽

Low Cost ◽

Airborne Particulate Matter ◽

Airborne Particulate ◽

Statistical Correction ◽

Air Pollution Exposure ◽

Improving Accuracy ◽

Pollution Exposure ◽

Exposure Measurements

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Abstract P391: Inhalation Of Airborne Particulate Matter Alters Cardiovascular Function Of Alzheimer-prone Mice

Circulation Research ◽

10.1161/res.129.suppl_1.p391 ◽

2021 ◽

Vol 129 (Suppl_1) ◽

Author(s):

Devin R O'Piela ◽

Ty A Saldana ◽

David M Aslaner ◽

Matthew W Gorr ◽

Amy R Mackos ◽

...

Keyword(s):

Heart Failure ◽

Air Pollution ◽

Risk Factor ◽

Particulate Matter ◽

Mouse Model ◽

Transgenic Mouse ◽

Airborne Particulate Matter ◽

Transgenic Mouse Model ◽

Cardiovascular Dysfunction ◽

Airborne Particulate

Air pollution has detrimental effects on cardiovascular and lung function, and the extent of its pathological consequences continues to be uncovered. Recently, air pollution has been implicated in the development of Alzheimer Disease (AD) progression. AD and heart failure are common co-morbidities, giving reason to believe that cardiovascular dysfunction may contribute to AD. A known contributor to cardiovascular dysfunction-particulate matter (PM 2.5 , < 2.5 μm diameter)—is a critical component of air pollution and is considered a risk factor for heart failure and AD development. This co-morbidity pattern and shared environmental risk factor prompted the hypothesis that PM 2.5 contributes to cardiovascular dysfunction in a transgenic mouse model of AD. We tested our hypothesis by subjecting 6-month-old transgenic (APP) and non-carrier wildtype (WT) male mice to filtered air (FA) or PM 2.5 for 5 days/week, 6 hours/day for 3 months (n = 34). Following exposure, echocardiography, pressure-volume (PV) loops, and respiratory mechanics were performed to detect cardiac and pulmonary changes associated with genotype and exposure conditions among the 3-month group. Echocardiography revealed left ventricular anterior wall thickness in systole was significantly elevated among PM-exposed APP mice compared to FA-exposed APP controls. PV data demonstrated significant reduced end-systolic elastance in PM-exposed mice compared to FA-exposed mice in both WT and APP mouse models, demonstrating impaired contractility. PV loops also showed that the time constant of isovolumetric relaxation was increased in PM-exposed compared to FA-exposed WT mice. APP mice experienced higher lung resistance and central airway resistance with an increasing dose of methacholine. Taken together, these findings indicate airborne particulate matter exposure causes cardiac and pulmonary dysfunction in a transgenic mouse model of AD.

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