CAMS-Net: The Clean Air Monitoring and Solutions Network

2021 ◽  
Author(s):  
Daniel Westervelt ◽  
Celeste McFarlane ◽  
Faye McNeill ◽  
R (Subu) Subramanian ◽  
Mike Giordano ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Low Cost ◽  
Air Monitoring ◽  
Quality Data ◽  
Calibration Methods ◽  
The World ◽  
Clean Air

<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>

scholarly journals High-resolution mapping of urban air quality with heterogeneous observations: a new methodology and its application to Amsterdam

2019 ◽  
Author(s):  
Bas Mijling
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Low Cost ◽  
Urban Air Pollution ◽  
Heterogeneous Data ◽  
Urban Air Quality ◽  
Quality Data ◽  
Urban Air ◽  
Reference Measurements ◽  
Concentration Patterns

Abstract. In many cities around the world people are exposed to elevated levels of air pollution. Often local air quality is not well known due to the sparseness of official monitoring networks, or unrealistic assumptions being made in urban air quality models. Low-cost sensor technology, which has become available in recent years, has the potential to provide complementary information. Unfortunately, an integrated interpretation of urban air pollution based on different sources is not straightforward because of the localized nature of air pollution, and the large uncertainties associated with measurements of low-cost sensors. In this study, we present a practical approach to producing high spatio-temporal resolution maps of urban air pollution capable of assimilating air quality data from heterogeneous data streams. It offers a two-step solution: (1) building a versatile air quality model, driven by an open source atmospheric dispersion model and emission proxies from open data sources, and (2) a practical spatial interpolation scheme, capable of assimilating observations with different accuracies. The methodology, called Retina, has been applied and evaluated for nitrogen dioxide (NO2) in Amsterdam, the Netherlands, during the summer of 2016. The assimilation of reference measurements results in hourly maps with a typical accuracy of 39 % within 2 km of an observation location, and 53 % at larger distances. When low-cost measurements of the Urban AirQ campaign are included, the maps reveal more detailed concentration patterns in areas which are undersampled by the official network. During the summer holiday period, NO2 concentrations drop about 10 % due to reduced urban activity. The reduction is less in the historic city center, while strongest reductions are found around the access ways to the tunnel connecting the northern and the southern part of the city, which was closed for maintenance. The changing concentration patterns indicate how traffic flow is redirected to other main roads. Overall, we show that Retina can be applied for an enhanced understanding of reference measurements, and as a framework to integrate low-cost measurements next to reference measurements in order to get better localized information in urban areas.

scholarly journals Establishing a Community Air Monitoring Network in a Wildfire Smoke-Prone Rural Community: The Motivations, Experiences, Challenges, and Ideas of Clean Air Methow’s Clean Air Ambassadors

2020 ◽  
Vol 17 (22) ◽  
pp. 8393
Author(s):  
Amanda Durkin ◽  
Rico Gonzalez ◽  
Tania Busch Isaksen ◽  
Elizabeth Walker ◽  
Nicole A. Errett
Keyword(s):  
Air Quality ◽  
Environmental Science ◽  
Low Cost ◽  
Geographic Location ◽  
Monitoring Network ◽  
Air Monitoring ◽  
Wildfire Smoke ◽  
Clean Air ◽  
Quality Issues ◽  
Future Project

In response to wildfire-related air quality issues as well as those associated with winter wood stove use and prescribed and agricultural burning, Clean Air Methow’s Clean Air Ambassador program established a community air monitoring network (CAMN) to provide geospatially specific air quality information and supplement data generated by the two Washington State Department of Ecology nephelometers situated in the area. Clean Air Ambassadors (CAAs) were purposefully selected to host low-cost air sensors based on their geographic location and interest in air quality. All 18 CAAs were interviewed to understand their motivations for participation, experiences using the data, challenges encountered, and recommendations for future project directions. Interview transcripts were coded, and a qualitative analysis approach was used to identify the key themes in each domain. The reported motivations for participation as a CAA included reducing personal exposure, protecting sensitive populations, interest in air quality or environmental science, and providing community benefits. CAAs used CAMN data to understand air quality conditions, minimize personal or familial exposure, and engage other community members in air quality discussions. Opportunities for future project directions included use for monitoring other seasonal air quality issues, informing or reducing other pollution-generating activities, school and community educational activities, opportunities for use by and engagement of different stakeholder groups, and mobile-friendly access to CAMN information. Limited challenges associated with participation were reported. Additional research is necessary to understand the community-level impacts of the CAMN. The findings may be informative for other rural wildfire smoke-prone communities establishing similar CAMNs.

scholarly journals GeoAir—A Novel Portable, GPS-Enabled, Low-Cost Air-Pollution Sensor: Design Strategies to Facilitate Citizen Science Research and Geospatial Assessments of Personal Exposure

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3761
Author(s):  
Yoo Min Park ◽  
Sinan Sousan ◽  
Dillon Streuber ◽  
Kai Zhao
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Low Cost ◽  
Rapid Evolution ◽  
Personal Exposure ◽  
Quality Data ◽  
Portable Devices ◽  
Design Strategies ◽  
Laboratory Test Results ◽  
Voc Sensor

The rapid evolution of air sensor technologies has offered enormous opportunities for community-engaged research by enabling citizens to monitor the air quality at any time and location. However, many low-cost portable sensors do not provide sufficient accuracy or are designed only for technically capable individuals by requiring pairing with smartphone applications or other devices to view/store air quality data and collect location data. This paper describes important design considerations for portable devices to ensure effective citizen engagement and reliable data collection for the geospatial analysis of personal exposure. It proposes a new, standalone, portable air monitor, GeoAir, which integrates a particulate matter (PM) sensor, volatile organic compound (VOC) sensor, humidity and temperature sensor, LTE-M and GPS module, Wi-Fi, long-lasting battery, and display screen. The preliminary laboratory test results demonstrate that the PM sensor shows strong performance when compared to a reference instrument. The VOC sensor presents reasonable accuracy, while further assessments with other types of VOC are needed. The field deployment and geo-visualization of the field data illustrate that GeoAir collects fine-grained, georeferenced air pollution data. GeoAir can be used by all citizens regardless of their technical proficiency and is widely applicable in many fields, including environmental justice and health disparity research.

MOPGA/Make Air Quality Great Again: Filling in the air quality data gap in Africa using lower-cost RAMP monitors

2020 ◽  
Author(s):  
Ramachandran Subramanian ◽  
Matthias Beekmann ◽  
Carl Malings ◽  
Anais Feron ◽  
Paola Formenti ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Electrochemical Sensors ◽  
Low Cost ◽  
Fine Particulate Matter ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Quality Data ◽  
Rural Site ◽  
Data Gap

<p>Ambient air pollution is a leading cause of premature mortality across the world, with an estimated 258,000 deaths in Africa (UNICEF/GBD 2017). These estimated impacts have large uncertainties as many major cities in Africa do not have any ground-based air quality monitoring. The lack of data is due in part to the high cost of traditional monitoring equipment and the lack of trained personnel. As part of the “Make Air Quality Great Again” project under the “Make Our Planet Great Again” framework (MOPGA), we propose filling this data gap with low-cost sensors carefully calibrated against reference monitors.</p><p>Fifteen real-time affordable multi-pollutant (RAMP) monitors have been deployed in Abidjan, Côte d'Ivoire; Accra, Ghana; Kigali, Rwanda; Nairobi, Kenya; Niamey, Niger; and Zamdela, South Africa (near Johannesburg). The RAMPs use Plantower optical nephelometers to measure fine particulate matter mass (PM<sub>2.5</sub>) and four Alphasense electrochemical sensors to detect pollutant gases including nitrogen dioxide (NO<sub>2</sub>) and ozone (O<sub>3</sub>).</p><p>Using a calibration developed in Créteil, France, the deployments thus far reveal morning and evening spikes in combustion-related air pollution. The median hourly NO<sub>2</sub> in Accra and Nairobi for September-October 2019 was about 11 ppb; a similar value was observed across November-December 2019 in Zamdela. However, a previous long-term deployment of the RAMPs in Rwanda showed that, for robust data quality, low-cost sensors must be collocated with traditional reference monitors to develop localized calibration models. Hence, we acquired regulatory-grade PM<sub>2.5</sub>, NO<sub>2</sub>, and O<sub>3</sub> monitors for Abidjan and Accra. We also collocated RAMPs with existing reference monitors in Zamdela, Kigali, Abidjan, and Lamto (a rural site in Côte d'Ivoire). In this talk, we will present results on spatio-temporal variability of collocation-based sensor calibrations across these different cities, source identification, and challenges and plans for future expansion.</p>

scholarly journals Field Calibration of Low-Cost Air Pollution Sensors

2019 ◽  
Author(s):  
Andres Gonzalez ◽  
Adam Boies ◽  
Jacob Swason ◽  
David Kittelson
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Relative Humidity ◽  
Low Cost ◽  
Quality Data ◽  
Ambient Conditions ◽  
Field Calibration ◽  
Broadcasting System ◽  
The Cost ◽  
Environmental Agencies

Abstract. To implement effective policies and strategies to control air pollution, it is crucial to obtain accurate air quality data. Stationary air monitoring stations (AMSs) help local authorities and environmental agencies in achieving these goals; however, these measurements have limitations. AMSs provide detailed temporal data on air quality, but only at discrete locations at relatively high cost. An alternative method, low-cost mobile air quality monitoring (LCMAQM) sensors, complement AMSs. LCMAQM sensors can cover larger areas and the cost of typical sensors for LCMAQM are $150–200 each. We have developed a wireless Mobile Autonomous Air Quality Sensor box (MAAQSbox) to measure air pollution. The MAAQSbox contains LCMAQM sensors (gas and particle) and a wireless broadcasting system, which enables autonomous field operation for varied mobile applications. Nitrogen dioxide (NO2), nitric oxide (NO), carbon monoxide (CO), and ozone (O3) gases are measured by B4 sensors. Particulate matter (PM2.5) is measured by OPC-N2. A field calibration has been performed by making side by side measurements with the MAAQSbox and Minnesota Pollution Agency AMS. The calibrations of LCMAQM sensors were determined by multivariate linear regressions (MLR). MLR results for all sensors were improved by including the temperature and relative humidity as independent variables. The R2 of CO, NO, NO2, and O3 gas sensors are 0.96, 0.97, 0.81, and 0.95 respectively, while the R2 of PM2.5 particle sensor is 0.6. B4 sensors are sensitive to ambient conditions such as temperature and relative humidity. The results with OPC-N2 differs from the AMS indicating further developments are needed to enable more accurate PM2.5 measurements.

scholarly journals High-resolution mapping of urban air quality with heterogeneous observations: a new methodology and its application to Amsterdam

2020 ◽  
Vol 13 (8) ◽  
pp. 4601-4617
Author(s):  
Bas Mijling
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Low Cost ◽  
Urban Air Pollution ◽  
Urban Air Quality ◽  
Quality Data ◽  
City Centre ◽  
Urban Air ◽  
Reference Measurements ◽  
Concentration Patterns

Abstract. In many cities around the world people are exposed to elevated levels of air pollution. Often local air quality is not well known due to the sparseness of official monitoring networks or unrealistic assumptions being made in urban-air-quality models. Low-cost sensor technology, which has become available in recent years, has the potential to provide complementary information. Unfortunately, an integrated interpretation of urban air pollution based on different sources is not straightforward because of the localized nature of air pollution and the large uncertainties associated with measurements of low-cost sensors. This study presents a practical approach to producing high-spatiotemporal-resolution maps of urban air pollution capable of assimilating air quality data from heterogeneous data streams. It offers a two-step solution: (1) building a versatile air quality model, driven by an open-source atmospheric-dispersion model and emission proxies from open-data sources, and (2) a practical spatial-interpolation scheme, capable of assimilating observations with different accuracies. The methodology, called Retina, has been applied and evaluated for nitrogen dioxide (NO2) in Amsterdam, the Netherlands, during the summer of 2016. The assimilation of reference measurements results in hourly maps with a typical accuracy (defined as the ratio between the root mean square error and the mean of the observations) of 39 % within 2 km of an observation location and 53 % at larger distances. When low-cost measurements of the Urban AirQ campaign are included, the maps reveal more detailed concentration patterns in areas which are undersampled by the official network. It is shown that during the summer holiday period, NO2 concentrations drop about 10 %. The reduction is less in the historic city centre, while strongest reductions are found around the access ways to the tunnel connecting the northern and the southern part of the city, which was closed for maintenance. The changing concentration patterns indicate how traffic flow is redirected to other main roads. Overall, it is shown that Retina can be applied for an enhanced understanding of reference measurements and as a framework to integrate low-cost measurements next to reference measurements in order to get better localized information in urban areas.

scholarly journals Highlighting the compound risk of COVID-19 and environmental pollutants using geospatial technology

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ram Kumar Singh ◽  
Martin Drews ◽  
Manuel De la Sen ◽  
Prashant Kumar Srivastava ◽  
Bambang H. Trisasongko ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Global Economy ◽  
Respiratory Diseases ◽  
Environmental Pollutants ◽  
Atmospheric Pollutants ◽  
Geospatial Technology ◽  
The World ◽  
Community Transmission ◽  
Global Threat

AbstractThe new COVID-19 coronavirus disease has emerged as a global threat and not just to human health but also the global economy. Due to the pandemic, most countries affected have therefore imposed periods of full or partial lockdowns to restrict community transmission. This has had the welcome but unexpected side effect that existing levels of atmospheric pollutants, particularly in cities, have temporarily declined. As found by several authors, air quality can inherently exacerbate the risks linked to respiratory diseases, including COVID-19. In this study, we explore patterns of air pollution for ten of the most affected countries in the world, in the context of the 2020 development of the COVID-19 pandemic. We find that the concentrations of some of the principal atmospheric pollutants were temporarily reduced during the extensive lockdowns in the spring. Secondly, we show that the seasonality of the atmospheric pollutants is not significantly affected by these temporary changes, indicating that observed variations in COVID-19 conditions are likely to be linked to air quality. On this background, we confirm that air pollution may be a good predictor for the local and national severity of COVID-19 infections.

scholarly journals Pathways of China's PM2.5 air quality 2015–2060 in the context of carbon neutrality

2021 ◽  
Author(s):  
Jing Cheng ◽  
Dan Tong ◽  
Qiang Zhang ◽  
Yang Liu ◽  
Yu Lei ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Critical Role ◽  
Climate Mitigation ◽  
Air Pollution Control ◽  
Carbon Neutrality ◽  
Climate Action ◽  
Clean Air ◽  
Near Term ◽  
Who Guideline

ABSTRACT Clean air policies in China have substantially reduced PM2.5 air pollution in recent years, primarily by curbing end-of-pipe emissions. However, further reaching the WHO guideline may instead depend upon the air quality co-benefits of ambitious climate action. Here, we assess pathways of Chinese PM2.5 air quality from 2015 to 2060 under a combination of scenarios which link Global and China's climate mitigation pathways (i.e. global 2°C- and 1.5°C-pathways, NDC pledges, and carbon neutrality goals) to local clean air policies. We find that China can achieve both its near-term climate goals (peak emissions) and PM2.5 air quality annual standard (35 μg/m3) by 2030 by fulfilling its NDC pledges and continuing air pollution control policies. However, the benefits of end-of-pipe control reductions are mostly exhausted by 2030, and reducing PM2.5 exposure of the majority of the Chinese population to below 10 μg/m3 by 2060 will likely require more ambitious climate mitigation efforts such as China's carbon neutrality goals and global 1.5°C-pathways. Our results thus highlight that China's carbon neutrality goals will play a critical role in reducing air pollution exposure to the WHO guideline and protecting public health.

scholarly journals Scalable kernel-based SVM classification algorithm on imbalance air quality data for proficient healthcare

2021 ◽  
Author(s):  
Shwet Ketu ◽  
Pramod Kumar Mishra
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Classification Algorithm ◽  
Quality Data ◽  
Pollution Level ◽  
Classification Problems ◽  
Chi Square ◽  
The Impact

AbstractIn the last decade, we have seen drastic changes in the air pollution level, which has become a critical environmental issue. It should be handled carefully towards making the solutions for proficient healthcare. Reducing the impact of air pollution on human health is possible only if the data is correctly classified. In numerous classification problems, we are facing the class imbalance issue. Learning from imbalanced data is always a challenging task for researchers, and from time to time, possible solutions have been developed by researchers. In this paper, we are focused on dealing with the imbalanced class distribution in a way that the classification algorithm will not compromise its performance. The proposed algorithm is based on the concept of the adjusting kernel scaling (AKS) method to deal with the multi-class imbalanced dataset. The kernel function's selection has been evaluated with the help of weighting criteria and the chi-square test. All the experimental evaluation has been performed on sensor-based Indian Central Pollution Control Board (CPCB) dataset. The proposed algorithm with the highest accuracy of 99.66% wins the race among all the classification algorithms i.e. Adaboost (59.72%), Multi-Layer Perceptron (95.71%), GaussianNB (80.87%), and SVM (96.92). The results of the proposed algorithm are also better than the existing literature methods. It is also clear from these results that our proposed algorithm is efficient for dealing with class imbalance problems along with enhanced performance. Thus, accurate classification of air quality through our proposed algorithm will be useful for improving the existing preventive policies and will also help in enhancing the capabilities of effective emergency response in the worst pollution situation.

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