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.

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 City Wide Participatory Sensing of Air Quality

2021 ◽  
Vol 9 ◽  
Author(s):  
Andrew Rebeiro-Hargrave ◽  
Pak Lun Fung ◽  
Samu Varjonen ◽  
Andres Huertas ◽  
Salla Sillanpää ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Low Cost ◽  
Age Groups ◽  
Personal Exposure ◽  
Urban Environments ◽  
Sensor Data ◽  
Automated System ◽  
Participatory Sensing ◽  
Good Correspondence

Air pollution is a contributor to approximately one in every nine deaths annually. Air quality monitoring is being carried out extensively in urban environments. Currently, however, city air quality stations are expensive to maintain resulting in sparse coverage and data is not readily available to citizens. This can be resolved by city-wide participatory sensing of air quality fluctuations using low-cost sensors. We introduce new concepts for participatory sensing: a voluntary community-based monitoring data forum for stakeholders to manage air pollution interventions; an automated system (cyber-physical system) for monitoring outdoor air quality and indoor air quality; programmable platform for calibration and generating virtual sensors using data from low-cost sensors and city monitoring stations. To test our concepts, we developed a low-cost sensor to measure particulate matter (PM2.5), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) with GPS. We validated our approach in Helsinki, Finland, with participants carrying the sensor for 3 months during six data campaigns between 2019 and 2021. We demonstrate good correspondence between the calibrated low-cost sensor data and city’s monitoring station measurements. Data analysis of their personal exposure was made available to the participants and stored as historical data for later use. Combining the location of low cost sensor data with participants public profile, we generate proxy concentrations for black carbon and lung deposition of particles between districts, by age groups and by the weekday.

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 Establishing A Sustainable Low-Cost Air Quality Monitoring Setup: A Survey of the State-of-the-Art

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 394
Author(s):  
Mannam Veera Narayana ◽  
Devendra Jalihal ◽  
S.M. Shiva Nagendra
Keyword(s):  
Air Quality ◽  
State Of The Art ◽  
Low Cost ◽  
Personal Exposure ◽  
Reliable Data ◽  
The State ◽  
Quality Monitoring ◽  
Quality Data ◽  
Air Quality Monitoring ◽  
Step Procedure

Low-cost sensors (LCS) are becoming popular for air quality monitoring (AQM). They promise high spatial and temporal resolutions at low-cost. In addition, citizen science applications such as personal exposure monitoring can be implemented effortlessly. However, the reliability of the data is questionable due to various error sources involved in the LCS measurement. Furthermore, sensor performance drift over time is another issue. Hence, the adoption of LCS by regulatory agencies is still evolving. Several studies have been conducted to improve the performance of low-cost sensors. This article summarizes the existing studies on the state-of-the-art of LCS for AQM. We conceptualize a step by step procedure to establish a sustainable AQM setup with LCS that can produce reliable data. The selection of sensors, calibration and evaluation, hardware setup, evaluation metrics and inferences, and end user-specific applications are various stages in the LCS-based AQM setup we propose. We present a critical analysis at every step of the AQM setup to obtain reliable data from the low-cost measurement. Finally, we conclude this study with future scope to improve the availability of air quality data.

Improving the Protection of Children Against Air Pollution Threats in Romania � The RokidAIR Project Approach and Future Perspectives

2017 ◽  
Vol 68 (4) ◽  
pp. 841-846
Author(s):  
Hai-Ying Liu ◽  
Daniel Dunea ◽  
Mihaela Oprea ◽  
Tom Savu ◽  
Stefania Iordache
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Health Effects ◽  
Urban Areas ◽  
Behavioural Change ◽  
Health Assessment ◽  
Warning System ◽  
Personal Exposure ◽  
Mobile Technologies ◽  
Quality Maps

This paper presents the approach used to develop the information chain required to reach the objectives of the EEA Grants� RokidAIR project in two Romanian cities i.e., Targoviste and Ploiesti. It describes the PM2.5 monitoring infrastructure and architecture to the web-based GIS platform, the early warning system and the decision support system, and finally, the linking of air pollution to health effects in children. In addition, it shows the analysis performance of the designed system to process the collected time series from various data sources using the benzene concentrations monitored in Ploiesti. Moreover, this paper suggests that biomarkers, mobile technologies, and Citizens� Observatories are potential perspectives to improve data coverage by the provision of near-real-time air quality maps, and provide personal exposure and health assessment results, enabling the citizens� engagement and behavioural change. This paper also addresses new fields in nature-based solutions to improve air quality, and studies on air pollution and its mental health effects in the urban areas of Romania.

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.

scholarly journals User-Centred Design of a Final Results Report for Participants in Multi-Sensor Personal Air Pollution Exposure Monitoring Campaigns

2021 ◽  
Vol 18 (23) ◽  
pp. 12544
Author(s):  
Johanna Amalia Robinson ◽  
Rok Novak ◽  
Tjaša Kanduč ◽  
Thomas Maggos ◽  
Demetra Pardali ◽  
...  
Keyword(s):  
Air Pollution ◽  
Design Thinking ◽  
Low Cost ◽  
Personal Exposure ◽  
Final Report ◽  
Draft Report ◽  
Horizon 2020 ◽  
User Centred Design ◽  
Exposure Monitoring ◽  
Spatio Temporal

Using low-cost portable air quality (AQ) monitoring devices is a growing trend in personal exposure studies, enabling a higher spatio-temporal resolution and identifying acute exposure to high concentrations. Comprehension of the results by participants is not guaranteed in exposure studies. However, information on personal exposure is multiplex, which calls for participant involvement in information design to maximise communication output and comprehension. This study describes and proposes a model of a user-centred design (UCD) approach for preparing a final report for participants involved in a multi-sensor personal exposure monitoring study performed in seven cities within the EU Horizon 2020 ICARUS project. Using a combination of human-centred design (HCD), human–information interaction (HII) and design thinking approaches, we iteratively included participants in the framing and design of the final report. User needs were mapped using a survey (n = 82), and feedback on the draft report was obtained from a focus group (n = 5). User requirements were assessed and validated using a post-campaign survey (n = 31). The UCD research was conducted amongst participants in Ljubljana, Slovenia, and the results report was distributed among the participating cities across Europe. The feedback made it clear that the final report was well-received and helped participants better understand the influence of individual behaviours on personal exposure to air pollution.

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