First Measurements of Ambient PM2.5 in Kinshasa, Democratic Republic of Congo and Brazzaville, Republic of Congo Using Field-calibrated Low-cost Sensors

McFarlane, Celeste; Isevulambire, Paulson Kasereka; Lumbuenamo, Raymond; Ndinga, Arnold Murphy Elouma; Dhammapala, Ranil; Jin, Xiaomeng; McNeill, V. Faye; Malings, Carl; Subramanian, R.; Westervelt, Daniel M.

doi:10.4209/aaqr.200619

Cited by 43 publications

(60 citation statements)

References 21 publications

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“…In residential areas with informal settlements, much of the contribution to PM 2.5 in the evening is due to increased cooking activities. Other studies have also observed similar trends [ 13 , 44 – 47 ]. There is a decrease in PM 2.5 concentration levels at mid-morning (10 a.m.) to late afternoon/early evening (4 p.m.) due to the increase in the atmospheric boundary layer and low road traffic flows [ 45 ].…”

Section: Resultssupporting

confidence: 77%

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

Kiai

Kanali

Sang

et al. 2021

Journal of Environmental and Public Health

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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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Section: Resultssupporting

confidence: 77%

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

Kiai

Kanali

Sang

et al. 2021

Journal of Environmental and Public Health

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“…Surface concentrations and diurnal trends of ARISense CO and PM in Malawi were comparable to studies in Kenya, Rwanda, Ethiopia, Uganda and South Africa (Scheel et al, 1998;Toihir et al, 2015;Laakso et al, 2008;DeWitt et al, 2019;Subramanian et al, 2020;Nthusi, 2017;McFarlane et al, 2021) 500 ppb, at their rural and urban sites (Subramanian et al, 2020). Both studies of Rwanda found mean ambient O3 concentrations of 30 to 40 ppb (DeWitt et al, 2019;Subramanian et al, 2020).…”

Section: Comparison To Other Ambient Measurements In Ssasupporting

confidence: 64%

Performance Characterization of Low-cost Air Quality Sensors for Off-grid Deployment in Rural Malawi

Bittner

Cross²,

Hagan³

et al. 2021

Preprint

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Abstract. Low-cost gas and particulate sensor packages offer a compact, lightweight, and easily transportable solution to address global gaps in air quality (AQ) observations. However, regions that would benefit most from widespread deployment of low-cost AQ monitors often lack the reference grade equipment required to reliably calibrate and validate them. In this study, we explore approaches to calibrating and validating three integrated sensor packages before a 1-year deployment to rural Malawi using collocation data collected at a regulatory site in North Carolina, USA. We compare the performance of five computational modelling approaches to calibrate the electrochemical gas sensors: k-Nearest Neighbor (kNN) hybrid, random forest (RF) hybrid, high-dimensional model representation (HDMR), multilinear regression (MLR), and quadratic regression (QR). For the CO, Ox, NO, and NO2 sensors, we found that kNN hybrid models returned the highest coefficients of determination and lowest error metrics when validated; they also appeared to be the most transferable approach when applied to field data collected in Malawi. We compared calibrated CO observations to remote sensing data in two regions in Malawi and found qualitative agreement in spatial and annual trends. However, the monthly mean surface observations were 2 to 4 times higher than the remote sensing data, possibly due to proximity to small-scale combustion activity not resolved by satellite imaging. We also compared the performance of the integrated Alphasense OPC-N2 optical particle counter to a filter-corrected nephelometer using collocation data collected at one of our deployment sites in Malawi. We found the performance of the OPC-N2 varied widely with environmental conditions, with the worst performance associated with high relative humidity (RH > 70 %) conditions and influence from emissions from nearby biomass cookstoves. We did not find obvious evidence of systematic sensor performance decay after the 1-year deployment to Malawi; however, overall data recovery was limited by insufficient power and access to technical resources at deployment sites. Future low-cost sensor deployments to rural Sub-Saharan Africa would benefit from adaptable power systems, standardized sensor calibration methodologies, and increased regulatory grade regional infrastructure.

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“…The low cost of outdoor PurpleAir sensors (USD 230-260) has enabled them to be widely used with thousands of sensors publicly reporting across the US. Previous work has explored the performance and accuracy of PurpleAir sensors under outdoor ambient conditions in a variety of locations across the United States including in Colorado (Ardon-Dryer et al, 2020;Tryner et al, 2020a); Utah (Ardon-Dryer et al, 2020;Kelly et al, 2017;Sayahi et al, 2019); Pennsylvania (Malings et al, 2020); North Carolina (Magi et al, 2019); and in California, where the most work has occurred to date (Ardon-Dryer et al, 2020;Bi et al, 2020;Feenstra et al, 2019;Mehadi et al, 2020;Schulte et al, 2020;Lu et al, 2021). Their performance has been explored in a number of other parts of the world as well including in Korea (Kim et al, 2019), Greece (Stavroulas et al, 2020), Uganda (McFarlane et al, 2021), and Australia (Robinson, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Additional work has been done to evaluate their performance under wildland-firesmoke-impacted conditions (Bi et al, 2020;Delp and Singer, 2020;Holder et al, 2020), indoors (Z. Wang et al, 2020), and during laboratory evaluations (Kelly et al, 2017;Kim et al, 2019;Li et al, 2020;Mehadi et al, 2020;Tryner et al, 2020a;Zou et al, 2020a, b). The performance of their dual Plantower PMS5003 laser scattering particle sensors has also been explored in a variety of other commercial and custombuilt sensor packages (He et al, 2020;Tryner et al, 2019;Kuula et al, 2020a;Ford et al, 2019;Si et al, 2020;Zou et al, 2020b;Tryner et al, 2020b).…”

Section: Introductionmentioning

confidence: 99%

Development and application of a United States-wide correction for PM<sub>2.5</sub> data collected with the PurpleAir sensor

2021

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Abstract. PurpleAir sensors, which measure particulate matter (PM), are widely used by individuals, community groups, and other organizations including state and local air monitoring agencies. PurpleAir sensors comprise a massive global network of more than 10 000 sensors. Previous performance evaluations have typically studied a limited number of PurpleAir sensors in small geographic areas or laboratory environments. While useful for determining sensor behavior and data normalization for these geographic areas, little work has been done to understand the broad applicability of these results outside these regions and conditions. Here, PurpleAir sensors operated by air quality monitoring agencies are evaluated in comparison to collocated ambient air quality regulatory instruments. In total, almost 12 000 24 h averaged PM2.5 measurements from collocated PurpleAir sensors and Federal Reference Method (FRM) or Federal Equivalent Method (FEM) PM2.5 measurements were collected across diverse regions of the United States (US), including 16 states. Consistent with previous evaluations, under typical ambient and smoke-impacted conditions, the raw data from PurpleAir sensors overestimate PM2.5 concentrations by about 40 % in most parts of the US. A simple linear regression reduces much of this bias across most US regions, but adding a relative humidity term further reduces the bias and improves consistency in the biases between different regions. More complex multiplicative models did not substantially improve results when tested on an independent dataset. The final PurpleAir correction reduces the root mean square error (RMSE) of the raw data from 8 to 3 µg m−3, with an average FRM or FEM concentration of 9 µg m−3. This correction equation, along with proposed data cleaning criteria, has been applied to PurpleAir PM2.5 measurements across the US on the AirNow Fire and Smoke Map (https://fire.airnow.gov/, last access: 14 May 2021) and has the potential to be successfully used in other air quality and public health applications.

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First Measurements of Ambient PM2.5 in Kinshasa, Democratic Republic of Congo and Brazzaville, Republic of Congo Using Field-calibrated Low-cost Sensors

Cited by 43 publications

References 21 publications

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

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

Performance Characterization of Low-cost Air Quality Sensors for Off-grid Deployment in Rural Malawi

Development and application of a United States-wide correction for PM<sub>2.5</sub> data collected with the PurpleAir sensor

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First Measurements of Ambient PM2.5 in Kinshasa, Democratic Republic of Congo and Brazzaville, Republic of Congo Using Field-calibrated Low-cost Sensors

Cited by 43 publications

References 21 publications

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

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

Performance Characterization of Low-cost Air Quality Sensors for Off-grid Deployment in Rural Malawi

Development and application of a United States-wide correction for PM&lt;sub&gt;2.5&lt;/sub&gt; data collected with the PurpleAir sensor

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Product

Resources

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Development and application of a United States-wide correction for PM<sub>2.5</sub> data collected with the PurpleAir sensor