scholarly journals Airborne particulate matter monitoring in Kenya using calibrated low cost sensors

Author(s):  
Francis D. Pope ◽  
Michael Gatari ◽  
David Ng’ang’a ◽  
Alexander Poynter ◽  
Rhiannon Blake

Abstract. East African countries face an increasing threat from poor air quality, stemming from rapid urbanisation, population growth and a steep rise in fuel use and motorization rates. With few air quality monitoring systems available, this study provides the much needed high temporal resolution data to investigate the concentrations of particulate matter (PM) air pollution in Kenya. Calibrated low cost optical particle counters (OPCs) were deployed in Kenya in three locations: two in the capital of Nairobi and one in a rural location in the outskirts of Nanyuki, which is upwind of Nairobi. The two Nairobi sites consist of an urban background site and a roadside site. The instruments were composed of an Alphasense OPC-N2 optical particle counter (OPC) ran with a raspberry pi low cost microcomputer, packaged in a weather proof box. Measurements were conducted over a two-month period (February–March 2017) with an intensive study period when all measurements were active at all sites lasting two weeks. When collocated, the three OPC-N2 instruments demonstrated good inter-instrument precision with a coefficient of variance of 8.8 ± 2.0 % in the PM2.5 fraction. The low cost sensors had an absolute PM mass concentration calibration using a collocated gravimetric measurement at the urban background site in Nairobi. The mean daily PM1 mass concentration measured at the urban roadside, urban background and rural background sites were 23.9, 16.1, 8.8 µg m−3. The mean daily PM2.5 mass concentration measured at the urban roadside, urban background and rural background sites were 36.6, 24.8, 13.0 µg m−3. The mean daily PM10 mass concentration measured at the urban roadside, urban background and rural background sites were 93.7, 53.0, 19.5 µg m−3. The urban measurements in Nairobi showed that particulate matter concentrations regularly exceed WHO guidelines in both the PM10 and PM2.5 size ranges. Following a Lenschow type approach we can estimate the urban and roadside increments that are applicable to Nairobi. Median urban and roadside increments are 33.1 and 43.3 µg m−3 for PM10, respectively, the median urban and roadside increments are 7.1 and 18.3 µg m−3 for PM2.5, respectively, and the median urban and roadside increments are 4.7 and 12.6 µg m−3 for PM1, respectively. These increments highlight the importance of both the urban and roadside increments to urban air pollution in Nairobi. A clear diurnal behaviour in PM mass concentration was observed at both urban sites, which peaks during the morning and evening Nairobi rush hours; this was consistent with the high measured roadside increment indicating vehicular traffic being a dominant source of particulate matter in the city, accounting for approximately 48.1, 47.5, and 57.2 % of the total particulate matter loading in the PM10, PM2.5 and PM1 size ranges, respectively. Collocated meteorological measurements at the urban sites were collected, allowing for an understanding of the location of major sources of particulate matter at the two sites. The potential problems of using low cost sensors for PM measurement without gravimetric calibration available at all sites are discussed. This study shows that calibrated low cost sensors can be used successfully to measure air pollution in cities like Nairobi. It demonstrates that low cost sensors could be used to create an affordable and reliable network to monitor air quality in cities.

2018 ◽  
Vol 18 (20) ◽  
pp. 15403-15418 ◽  
Author(s):  
Francis D. Pope ◽  
Michael Gatari ◽  
David Ng'ang'a ◽  
Alexander Poynter ◽  
Rhiannon Blake

Abstract. East African countries face an increasing threat from poor air quality stemming from rapid urbanization, population growth, and a steep rise in fuel use and motorization rates. With few air quality monitoring systems available, this study provides much needed high temporal resolution data to investigate the concentrations of particulate matter (PM) air pollution in Kenya. Calibrated low-cost optical particle counters (OPCs) were deployed in Kenya in three locations: two in the capital Nairobi and one in a rural location in the outskirts of Nanyuki, which is upwind of Nairobi. The two Nairobi sites consist of an urban background site and a roadside site. The instruments were composed of an AlphaSense OPC-N2 ran with a Raspberry Pi low-cost microcomputer, packaged in a weather-proof box. Measurements were conducted over a 2-month period (February–March 2017) with an intensive study period when all measurements were active at all sites lasting 2 weeks. When collocated, the three OPC-N2 instruments demonstrated good inter-instrument precision with a coefficient of variance of 8.8±2.0 % in the fine particle fraction (PM2.5). The low-cost sensors had an absolute PM mass concentration calibration using a collocated gravimetric measurement at the urban background site in Nairobi.The mean daily PM1 mass concentration measured at the urban roadside, urban background and rural background sites were 23.9, 16.1 and 8.8 µg m−3, respectively. The mean daily PM2.5 mass concentration measured at the urban roadside, urban background and rural background sites were 36.6, 24.8 and 13.0 µg m−3, respectively. The mean daily PM10 mass concentration measured at the urban roadside, urban background and rural background sites were 93.7, 53.0 and 19.5 µg m−3, respectively. The urban measurements in Nairobi showed that PM concentrations regularly exceed WHO guidelines in both the PM10 and PM2.5 size ranges. Following a Lenschow-type approach we can estimate the urban and roadside increments that are applicable to Nairobi (Lenschow et al., 2001). The median urban increment is 33.1 µg m−3 and the median roadside increment is 43.3 µg m−3 for PM2.5. For PM1, the median urban increment is 4.7 µg m−3 and the median roadside increment is 12.6 µg m−3. These increments highlight the importance of both the urban and roadside increments to urban air pollution in Nairobi.A clear diurnal behaviour in PM mass concentration was observed at both urban sites, which peaks during the morning and evening Nairobi rush hours; this was consistent with the high roadside increment indicating that vehicular traffic is a dominant source of PM in the city, accounting for approximately 48.1 %, 47.5 % and 57.2 % of the total PM loading in the PM10, PM2.5 and PM1 size ranges, respectively. Collocated meteorological measurements at the urban sites were collected, allowing for an understanding of the location of major sources of particulate matter at the two sites. The potential problems of using low-cost sensors for PM measurement without gravimetric calibration available at all sites are discussed.This study shows that calibrated low-cost sensors can be successfully used to measure air pollution in cities like Nairobi. It demonstrates that low-cost sensors could be used to create an affordable and reliable network to monitor air quality in cities.


Author(s):  
Eric S. Coker ◽  
Ssematimba Joel ◽  
Engineer Bainomugisha

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.


2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Caroline Kiai ◽  
Christopher Kanali ◽  
Joseph Sang ◽  
Michael Gatari

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.


Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 251
Author(s):  
Evangelos Bagkis ◽  
Theodosios Kassandros ◽  
Marinos Karteris ◽  
Apostolos Karteris ◽  
Kostas Karatzas

Air quality (AQ) in urban areas is deteriorating, thus having negative effects on people’s everyday lives. Official air quality monitoring stations provide the most reliable information, but do not always depict air pollution levels at scales reflecting human activities. They also have a high cost and therefore are limited in number. This issue can be addressed by deploying low cost AQ monitoring devices (LCAQMD), though their measurements are of far lower quality. In this paper we study the correlation of air pollution levels reported by such a device and by a reference station for particulate matter, ozone and nitrogen dioxide in Thessaloniki, Greece. On this basis, a corrective factor is modeled via seven machine learning algorithms in order to improve the quality of measurements for the LCAQMD against reference stations, thus leading to its on-field computational improvement. We show that our computational intelligence approach can improve the performance of such a device for PM10 under operational conditions.


2021 ◽  
Vol 11 (3) ◽  
pp. 1212
Author(s):  
Miloš Davidović ◽  
Sonja Dmitrašinović ◽  
Maja Jovanović ◽  
Jelena Radonić ◽  
Milena Jovašević-Stojanović

Changes in air pollution in the region of the city of Novi Sad due to the COVID-19 induced state of emergency were evaluated while using data from permanently operating air quality monitoring stations belonging to the national, regional, and local networks, as well as ad hoc deployed low-cost particulate matter (PM) sensors. The low-cost sensors were collocated with reference gravimetric pumps. The starting idea for this research was to determine if and to what extent a massive change of anthropogenic activities introduced by lockdown could be observed in main air pollutants levels. An analysis of the data showed that fine and coarse particulate matter, as well as SO2 levels, did not change noticeably, compared to the pre-lockdown period. Isolated larger peaks in PM pollution were traced back to the Aralkum Desert episode. The reduced movement of vehicles and reduced industrial and construction activities during the lockdown in Novi Sad led to a reduction and a more uniform profile of the PM2.5 levels during the period between morning and afternoon air pollution peak, approximately during typical working hours. Daily profiles of NO2, NO, and NOX during the state of emergency proved lower levels during most hours of the day, due to restrictions on vehicular movement. CO during the state of the emergency mainly exhibited a lower level during night. Pollutants having transportation-dominated source profiles exhibited a decrease in level, while pollutants with domestic heating source profiles mostly exhibited a constant level. Considering local sources in Novi Sad, slight to moderate air quality improvement was observed after the lockdown as compared with days before. Furthermore, PM low-cost sensors’ usefulness in air quality assessment was confirmed, as they increase spatial resolution, but it is necessary to calibrate them at the deployment location.


2021 ◽  
Author(s):  
Daniel Westervelt ◽  
Celeste McFarlane ◽  
Faye McNeill ◽  
R (Subu) Subramanian ◽  
Mike Giordano ◽  
...  

<p>There is a severe lack of air pollution data around the world. This includes large portions of low- and middle-income countries (LMICs), as well as rural areas of wealthier nations as monitors tend to be located in large metropolises. Low cost sensors (LCS) for measuring air pollution and identifying sources offer a possible path forward to remedy the lack of data, though significant knowledge gaps and caveats remain regarding the accurate application and interpretation of such devices.</p><p>The Clean Air Monitoring and Solutions Network (CAMS-Net) establishes an international network of networks that unites scientists, decision-makers, city administrators, citizen groups, the private sector, and other local stakeholders in co-developing new methods and best practices for real-time air quality data collection, data sharing, and solutions for air quality improvements. CAMS-Net brings together at least 32 multidisciplinary member networks from North America, Europe, Africa, and India. The project establishes a mechanism for international collaboration, builds technical capacity, shares knowledge, and trains the next generation of air quality practitioners and advocates, including domestic and international graduate students and postdoctoral researchers. </p><p>Here we present some preliminary research accelerated through the CAMS-Net project. Specifically, we present LCS calibration methodology for several co-locations in LMICs (Accra, Ghana; Kampala, Uganda; Nairobi, Kenya; Addis Ababa, Ethiopia; and Kolkata, India), in which reference BAM-1020 PM2.5 monitors were placed side-by-side with LCS. We demonstrate that both simple multiple linear regression calibration methods for bias-correcting LCS and more complex machine learning methods can reduce bias in LCS to close to zero, while increasing correlation. For example, in Kampala, Raw PurpleAir PM2.5 data are strongly correlated with the BAM-1020 PM2.5 (r<sup>2</sup> = 0.88), but have a mean bias of approximately 12 μg m<sup>-3</sup>. Two calibration models, multiple linear regression and a random forest approach, decrease mean bias from 12 μg m<sup>-3 </sup>to -1.84 µg m<sup>-3</sup> or less and improve the the r<sup>2</sup> from 0.88 to 0.96. We find similar performance in several other regions of the world. Location-specific calibration of low-cost sensors is necessary in order to obtain useful data, since sensor performance is closely tied to environmental conditions such as relative humidity. This work is a first step towards developing a database of region-specific correction factors for low cost sensors, which are exploding in popularity globally and have the potential to close the air pollution data gap especially in resource-limited countries. </p><p> </p><p> </p>


2018 ◽  
Author(s):  
Suzane S. de Sá ◽  
Brett B. Palm ◽  
Pedro Campuzano-Jost ◽  
Douglas A. Day ◽  
Weiwei Hu ◽  
...  

Abstract. Fundamental to quantifying the influence of human activities on climate and air quality is an understanding of how anthropogenic emissions affect the concentrations and composition of airborne particulate matter (PM). The central Amazon basin, especially around the city of Manaus, Brazil, has experienced rapid changes in the past decades due to ongoing urbanization. Herein, changes in the concentration and composition of submicron PM due to pollution downwind of the Manaus metropolitan region are reported as part of the GoAmazon2014/5 experiment. A high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) and a suite of other gas- and particle-phase instruments were deployed at the T3 research site, 70 km downwind of Manaus, during the wet season. At this site, organic components represented on average 79 ± 7 % of the non-refractory PM1 mass concentration, which was in the same range as several upwind sites. The organic PM1 was, however, considerably more oxidized at T3 compared to upwind measurements. Positive-matrix factorization (PMF) was applied to the time series of organic mass spectra collected at the T3 site, yielding three factors representing secondary processes (73 ± 15 % of total organic mass concentration) and three factors representing primary anthropogenic emissions (27 ± 15 %). Fuzzy c-means clustering (FCM) was applied to the afternoon time series of concentrations of NOy, ozone, total particle number, black carbon, and sulfate. Four clusters were identified and characterized by distinct airmass origins and particle compositions. Two clusters, Bkgd-1 and Bkgd-2, were associated with background conditions. Bkgd-1 appeared to represent near-field atmospheric PM production and oxidation of a day or less. Bkgd-2 appeared to represent material transported and oxidized for two or more days, often with out-of-basin contributions. Two other clusters, Pol-1 and Pol-2, represented the Manaus influence, one apparently associated with the northern region of Manaus and the other with the southern region of the city. A composite of the PMF and FCM analyses provided insights into the anthropogenic effects on PM concentration and composition. The increase in mass concentration of submicron PM ranged from 25 % to 200 % under polluted compared to background conditions, including contributions from both primary and secondary PM. Furthermore, a comparison of PMF factor loadings for different clusters suggested a shift in the pathways of PM production under polluted conditions. Nitrogen oxides may have played a critical role in these shifts. Increased concentrations of nitrogen oxides can shift pathways of PM production from HO2-dominant to NO-dominant as well as increase the concentrations of oxidants in the atmosphere. Consequently, the oxidation of biogenic and anthropogenic precursor gases as well as the oxidative processing of pre-existing atmospheric PM can be accelerated. The combined set of results demonstrates the susceptibility of atmospheric chemistry, air quality, and associated climate forcing to anthropogenic perturbations over tropical forests.


2017 ◽  
Author(s):  
Luke D. Schiferl ◽  
Colette L. Heald

Abstract. Ensuring global food security requires a comprehensive understanding of environmental pressures on food production, including the impacts of air quality. Surface ozone damages plants and decreases crop production; this effect has been extensively studied. In contrast, the presence of particulate matter (PM) in the atmosphere can be beneficial to crops given that enhanced light scattering leads to a more even and efficient distribution of photons which can outweigh total incoming radiation loss. This study quantifies the impacts of ozone and PM on the global production of maize, rice, and wheat in 2010 and 2050. We show that accounting for the growing season of these crops is an important factor in determining their air pollution exposure. We find that the effect of PM can offset much, if not all, of the reduction in yield associated with ozone damage. Assuming maximum sensitivity to PM, the current (2010) global net impact of air quality on crop production is positive (+6.0 %, +0.5 %, and +4.9 % for maize, wheat, and rice, respectively). Future emissions scenarios indicate that attempts to improve air quality can result in a net negative effect on crop production in areas dominated by the PM effect. However, we caution that the uncertainty in this assessment is large due to the uncertainty associated with crop response to changes in diffuse radiation; this highlights that more detailed physiological study of this response for common cultivars is crucial.


2019 ◽  
Vol 7 (1) ◽  
pp. 47-53
Author(s):  
Lalven tluanga ◽  
◽  
H. Lalramnghinglova

In recent years, Mizoram has made immense progress in various sectors and initiated developmental programmes for its economic growth. An important contributing factor to this development and growth is the transport connectivity projects. Although connectivity projects can boost economic growth, its negative effects cannot be neglected. In view of this, the present research studies the impacts of Champhai – Zokhawthar road construction on the air quality and noise quality of the region. This comparative study of the impact on the quality of air and noise pollution in the pre-construction phase and construction phase provide a clear-cut example of the negative impacts caused by road construction and provide a fresh outlook for formulation of improved management plans. Air quality assessment was carried out using High Volume Air Sampler and the following parameters were monitored – suspended particulate matter (SPM), respirable suspended particulate matter (RSPM), nitrogen dioxide (NO2) and sulphur dioxide (SO2). The results indicate that mean SPM concentration was increased by 22.82 µg/m3; RSPM concentration by 14.67 µg/m3; NO2 concentration by 4.08 µg/m3; and SO2 concentration was increased by 0.06 µg/m3 from the pre-construction phase to construction phase. Noise quality assessment was carried out at three sites – Zotlang, Melbuk and Zokhawthar by using Lutron SL-4001 Sound Level Meter and Leq, Lmax and Lmin were recorded and calculated. The mean noise level at Zotlang wasincreased by 8.72 dB (A) and at Melbuk the mean noise level was increased by 9.35 dB (A). However, at Zokhawthar, there was a decrease in mean noise level by 0.13 dB (A). From the present study, it is evident that road construction poses a threat to the air quality and noise quality of the study area and improved measuresneed to betaken to curb its negative impacts


2021 ◽  
Author(s):  
Sonu Kumar Jha ◽  
Mohit Kumar ◽  
Vipul Arora ◽  
Sachchida Nand Tripathi ◽  
Vidyanand Motiram Motghare ◽  
...  

<div>Air pollution is a severe problem growing over time. A dense air-quality monitoring network is needed to update the people regarding the air pollution status in cities. A low-cost sensor device (LCSD) based dense air-quality monitoring network is more viable than continuous ambient air quality monitoring stations (CAAQMS). An in-field calibration approach is needed to improve agreements of the LCSDs to CAAQMS. The present work aims to propose a calibration method for PM2.5 using domain adaptation technique to reduce the collocation duration of LCSDs and CAAQMS. A novel calibration approach is proposed in this work for the measured PM2.5 levels of LCSDs. The dataset used for the experimentation consists of PM2.5 values and other parameters (PM10, temperature, and humidity) at hourly duration over a period of three months data. We propose new features, by combining PM2.5, PM10, temperature, and humidity, that significantly improved the performance of calibration. Further, the calibration model is adapted to the target location for a new LCSD with a collocation time of two days. The proposed model shows high correlation coefficient values (R2) and significantly low mean absolute percentage error (MAPE) than that of other baseline models. Thus, the proposed model helps in reducing the collocation time while maintaining high calibration performance.</div>


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