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.

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 NanoSen-AQM: From Sensors to Users

2020 ◽  
Vol 16 (04) ◽  
pp. 51
Author(s):  
Pedro Lucas ◽  
Jorge Silva ◽  
Filipe Araujo ◽  
Catarina Silva ◽  
Paulo Gil ◽  
...  
Keyword(s):  
Air Quality ◽  
Low Cost ◽  
Sensor Data ◽  
Quality Data ◽  
Easy Access ◽  
The Public ◽  
Nano Sensors ◽  
The Cost ◽  
Air Quality Data ◽  
Monitoring Platform

With the raising of environmental concerns regarding pollution, interest in monitoring air quality is increasing. However, air pollution data is mostly originated from a limited number of government-owned sensors, which can only capture a small fraction of reality. Improving air quality coverage in-volves reducing the cost of sensors and making data widely available to the public. To this end, the NanoSen-AQM project proposes the usage of low-cost nano-sensors as the basis for an air quality monitoring platform, capa-ble of collecting, aggregating, processing, storing, and displaying air quality data. Being an end-to-end system, the platform allows sensor owners to manage their sensors, as well as define calibration functions, that can im-prove data reliability. The public can visualize sensor data in a map, define specific clusters (groups of sensors) as favorites and set alerts in the event of bad air quality in certain sensors. The NanoSen-AQM platform provides easy access to air quality data, with the aim of improving public health.

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 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 The influence of meteorology and air quality on parkrun athletic performance

2020 ◽  
Author(s):  
James Robert Hodgson ◽  
Stephanie Elliott ◽  
Lee Chapman ◽  
Chris Hudson ◽  
Francis D Pope
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Relative Humidity ◽  
Athletic Performance ◽  
General Public ◽  
Urban Air Pollution ◽  
Quality Data ◽  
Urban Air ◽  
Wind Speeds ◽  
Healthy Activity

Abstract Background Despite increased awareness of climate change and urban air pollution, little research has been performed to examine the influence of meteorology and air quality on athletic performance of the general public and recreational exercisers. Anecdotal evidence of increased temperatures and wind speeds as well as higher relative humidity conditions resulting in reduced athletic performance has been presented in the past, whilst urban air pollution can have negative short- and long-term impacts on health. Furthermore, pollutants such as Ozone, Nitrogen Dioxide and Particulate Matter can cause respiratory and cardiovascular distress, which can be heightened during physical activity. Previous research has examined these impacts on marathon runners, or have been performed in laboratory settings. Instead, this paper focuses on the potential impacts on the general public. With the rise of parkrun events (timed 5 km runs) across the UK and worldwide concerns regarding public health in relation to both air quality and activity levels, the potential influence of air quality and meteorology on what is viewed as a ‘healthy’ activity has been investigated. A weekly dataset of parkrun participants at fifteen events, located in London UK, from 2011–2016 alongside local meteorological and air quality data has been analysed. Results The biggest influencer on athletic performance is meteorology, particularly temperature and wind speed. Regression results between parkrun finishing times and temperature predominantly show positive relationships, supporting previous laboratory tests. Increased relative humidity also causes slower finishing times but in several cases is not statistically significant. Higher wind speeds also result in slower times and in contrast to temperature and relative humidity, male participants are more influenced than female by this variable. Although air quality does influence athletic performance to an extent, the heterogeneity of pollutants within London and between parkrun events and monitoring sites makes this difficult to prove decisively. Conclusions It has been determined that temperature and relative humidity can have the largest detrimental impact on parkrun performance, with Ozone also having an impact. The influence of other variables cannot be discounted however and it is recommended that modelling is performed to further determine the extent to which ‘at event’ meteorology and air quality has on performance. In the future, there results can be used to determine safe operating and exercise conditions for parkrun and other public athletics events.

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 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 Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3338
Author(s):  
Ivan Vajs ◽  
Dejan Drajic ◽  
Nenad Gligoric ◽  
Ilija Radovanovic ◽  
Ivan Popovic
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Relative Humidity ◽  
Low Cost ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Lower Accuracy ◽  
Wide Range ◽  
The Impact ◽  
Monitoring Stations

Existing government air quality monitoring networks consist of static measurement stations, which are highly reliable and accurately measure a wide range of air pollutants, but they are very large, expensive and require significant amounts of maintenance. As a promising solution, low-cost sensors are being introduced as complementary, air quality monitoring stations. These sensors are, however, not reliable due to the lower accuracy, short life cycle and corresponding calibration issues. Recent studies have shown that low-cost sensors are affected by relative humidity and temperature. In this paper, we explore methods to additionally improve the calibration algorithms with the aim to increase the measurement accuracy considering the impact of temperature and humidity on the readings, by using machine learning. A detailed comparative analysis of linear regression, artificial neural network and random forest algorithms are presented, analyzing their performance on the measurements of CO, NO2 and PM10 particles, with promising results and an achieved R2 of 0.93–0.97, 0.82–0.94 and 0.73–0.89 dependent on the observed period of the year, respectively, for each pollutant. A comprehensive analysis and recommendations on how low-cost sensors could be used as complementary monitoring stations to the reference ones, to increase spatial and temporal measurement resolution, is provided.

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