scholarly journals Joint Statement on new opportunities for air quality sensing - lower-cost sensors for public authorities and citizen science initiatives

2019 ◽  
Vol 5 ◽  
Author(s):  
Sven Schade ◽  
Wiebke Herding ◽  
Arne Fellermann ◽  
Alexander Kotsev
Keyword(s):  
Air Quality ◽  
Data Quality ◽  
Citizen Science ◽  
Lower Cost ◽  
Sensor Data ◽  
Quality Data ◽  
Monitoring Networks ◽  
Lower Accuracy ◽  
Reference Quality ◽  
The Right

Low-cost air quality sensors continue to spread. While their measurement quality does not compete with high-end instrumentation deployed in official air quality monitoring stations, they have a great potential to complement existing air quality assessments. However, we still see challenges related to data quality, data interoperability, and for collaborating on data assimilation and calibration. In order to move ahead we gathered as a group of 38 organisations from 14 different countries, including governmental authorities, network operators, citizen science initiatives, environmental Non-Governmental Organisations (NGOs), and academic researchers to explore how we can collaborate and better leverage each other’s work. This statement captures our joint findings and recommendations. Our key observations include: Co-operation between official monitoring networks (reference quality data) and lower-cost sensor operators is a key to make air quality data more usable. To be able to combine forces and benefit from each other’s expertise, the different perspectives of all stakeholders should be taken into account. There is a need to ensure that all users understand the possibilities and the limitations of making sense out of observations from different sensors. It is not realistic to expect that in the near future the data quality of lower-cost sensors will be as good as that of the official data. A way to make use of data that is of lower accuracy is by employing them in air quality modelling. Transparency about data quality is important to build more trust in the data, and to avoid unrealistic expectations. The need for interoperability should be clearly articulated and promoted by potential data users. There a need (and an opportunity) to provide guidance and standard operating procedures for the deployment and calibration of lower-cost sensors in order to increase the data quality delivered by participants of citizen science projects. Presently, we prefer to consider fixed-stationary sensors in a network instead of mobile sensor data. Furthermore, stationary data should not be aggregated with data from mobile sensors. Publishing and sharing this statement is only small step in the right direction and further actions have to be taken, inlcuding more in-depth discussions of the recommendations in smaller groups and follow-up meetings on dedicated topics. Co-operation between official monitoring networks (reference quality data) and lower-cost sensor operators is a key to make air quality data more usable. To be able to combine forces and benefit from each other’s expertise, the different perspectives of all stakeholders should be taken into account. There is a need to ensure that all users understand the possibilities and the limitations of making sense out of observations from different sensors. It is not realistic to expect that in the near future the data quality of lower-cost sensors will be as good as that of the official data. A way to make use of data that is of lower accuracy is by employing them in air quality modelling. Transparency about data quality is important to build more trust in the data, and to avoid unrealistic expectations. The need for interoperability should be clearly articulated and promoted by potential data users. There a need (and an opportunity) to provide guidance and standard operating procedures for the deployment and calibration of lower-cost sensors in order to increase the data quality delivered by participants of citizen science projects. Presently, we prefer to consider fixed-stationary sensors in a network instead of mobile sensor data. Furthermore, stationary data should not be aggregated with data from mobile sensors. Publishing and sharing this statement is only small step in the right direction and further actions have to be taken, inlcuding more in-depth discussions of the recommendations in smaller groups and follow-up meetings on dedicated topics.

scholarly journals Joint Statement on new opportunities for air quality sensing - lower-cost sensors for public authorities and citizen science initiatives

2019 ◽  
Vol 5 ◽  
Author(s):  
Sven Schade ◽  
Wiebke Herding ◽  
Arne Fellermann ◽  
Alexander Kotsev ◽  
Michel Gerboles ◽  
...  
Keyword(s):  
Air Quality ◽  
Data Quality ◽  
Citizen Science ◽  
Lower Cost ◽  
Sensor Data ◽  
Quality Data ◽  
Monitoring Networks ◽  
Lower Accuracy ◽  
Reference Quality ◽  
The Right

Low-cost air quality sensors continue to spread. While their measurement quality does not compete with high-end instrumentation deployed in official air quality monitoring stations, they have a great potential to complement existing air quality assessments. However, we still see challenges related to data quality, data interoperability, and for collaborating on data assimilation and calibration. In order to move ahead we gathered as a group of 38 organisations from 14 different countries, including governmental authorities, network operators, citizen science initiatives, environmental Non-Governmental Organisations (NGOs), and academic researchers to explore how we can collaborate and better leverage each other’s work. This statement captures our joint findings and recommendations. Our key observations include: Co-operation between official monitoring networks (reference quality data) and lower-cost sensor operators is a key to make air quality data more usable. To be able to combine forces and benefit from each other’s expertise, the different perspectives of all stakeholders should be taken into account. There is a need to ensure that all users understand the possibilities and the limitations of making sense out of observations from different sensors. It is not realistic to expect that in the near future the data quality of lower-cost sensors will be as good as that of the official data. A way to make use of data that is of lower accuracy is by employing them in air quality modelling. Transparency about data quality is important to build more trust in the data, and to avoid unrealistic expectations. The need for interoperability should be clearly articulated and promoted by potential data users. There a need (and an opportunity) to provide guidance and standard operating procedures for the deployment and calibration of lower-cost sensors in order to increase the data quality delivered by participants of citizen science projects. Presently, we prefer to consider fixed-stationary sensors in a network instead of mobile sensor data. Furthermore, stationary data should not be aggregated with data from mobile sensors. Publishing and sharing this statement is only small step in the right direction and further actions have to be taken, inlcuding more in-depth discussions of the recommendations in smaller groups and follow-up meetings on dedicated topics. Co-operation between official monitoring networks (reference quality data) and lower-cost sensor operators is a key to make air quality data more usable. To be able to combine forces and benefit from each other’s expertise, the different perspectives of all stakeholders should be taken into account. There is a need to ensure that all users understand the possibilities and the limitations of making sense out of observations from different sensors. It is not realistic to expect that in the near future the data quality of lower-cost sensors will be as good as that of the official data. A way to make use of data that is of lower accuracy is by employing them in air quality modelling. Transparency about data quality is important to build more trust in the data, and to avoid unrealistic expectations. The need for interoperability should be clearly articulated and promoted by potential data users. There a need (and an opportunity) to provide guidance and standard operating procedures for the deployment and calibration of lower-cost sensors in order to increase the data quality delivered by participants of citizen science projects. Presently, we prefer to consider fixed-stationary sensors in a network instead of mobile sensor data. Furthermore, stationary data should not be aggregated with data from mobile sensors. Publishing and sharing this statement is only small step in the right direction and further actions have to be taken, inlcuding more in-depth discussions of the recommendations in smaller groups and follow-up meetings on dedicated topics.

scholarly journals Vayu: An Open-Source Toolbox for Visualization and Analysis of Crowd-Sourced Sensor Data

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7726
Author(s):  
Sachit Mahajan
Keyword(s):  
Air Quality ◽  
Open Source ◽  
Time Series Data ◽  
Sensor Data ◽  
Quality Data ◽  
Sensor Technology ◽  
Data Conversion ◽  
Series Data ◽  
Monitoring Networks ◽  
Systematic Analysis

Recent advances in sensor technology and the availability of low-cost and low-power sensors have changed the air quality monitoring paradigm. These sensors are being widely used by scientists and citizens for monitoring air quality at finer spatial-temporal resolution. Such practices are opening up opportunities to enhance the traditional monitoring networks, but at the same time, these sensors are producing large data sets that can become overwhelming and challenging when it comes to the scientific tools and skills required to analyze the data. To address this challenge, an open-source, robust, and cross-platform sensor data analysis toolbox called Vayu is developed that allows researchers and citizens to do detailed and reproducible analyses of air quality data. Vayu combines the power of visualization and statistical analysis using a simple and intuitive graphical user interface. Additionally, it offers a comprehensive set of tools for systematic analysis such as data conversion, interpolation, aggregation, and prediction. Even though Vayu was developed with air quality research in mind, it can be used to analyze different kinds of time-series data.

scholarly journals Tackling Data Quality When Using Low-Cost Air Quality Sensors in Citizen Science Projects

2021 ◽  
Vol 9 ◽  
Author(s):  
Ågot K. Watne ◽  
Jenny Linden ◽  
Jens Willhelmsson ◽  
Håkan Fridén ◽  
Malin Gustafsson ◽  
...  
Keyword(s):  
Air Quality ◽  
Data Quality ◽  
Citizen Science ◽  
Low Cost ◽  
Quality Data ◽  
High Data ◽  
Science Projects ◽  
Air Quality Measurements ◽  
Air Quality Data ◽  
Intended Use

Using low-cost air quality sensors (LCS) in citizen science projects opens many possibilities. LCS can provide an opportunity for the citizens to collect and contribute with their own air quality data. However, low data quality is often an issue when using LCS and with it a risk of unrealistic expectations of a higher degree of empowerment than what is possible. If the data quality and intended use of the data is not harmonized, conclusions may be drawn on the wrong basis and data can be rendered unusable. Ensuring high data quality is demanding in terms of labor and resources. The expertise, sensor performance assessment, post-processing, as well as the general workload required will depend strongly on the purpose and intended use of the air quality data. It is therefore a balancing act to ensure that the data quality is high enough for the specific purpose, while minimizing the validation effort. The aim of this perspective paper is to increase awareness of data quality issues and provide strategies to minimizing labor intensity and expenses while maintaining adequate QA/QC for robust applications of LCS in citizen science projects. We believe that air quality measurements performed by citizens can be better utilized with increased awareness about data quality and measurement requirements, in combination with improved metadata collection. Well-documented metadata can not only increase the value and usefulness for the actors collecting the data, but it also the foundation for assessment of potential integration of the data collected by citizens in a broader perspective.

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.

A Cluster Analysis of PM2.5 Using CMAQ Model Results for Representativeness of Air Quality Monitoring Networks in Busan, Korea

2020 ◽  
Author(s):  
Woo-Sik Jung ◽  
Woo-Gon Do
Keyword(s):  
Air Pollution ◽  
Cluster Analysis ◽  
Air Quality ◽  
Monitoring Network ◽  
Hierarchical Cluster ◽  
Quality Monitoring ◽  
Quality Data ◽  
Air Quality Monitoring ◽  
Monitoring Networks ◽  
Monitoring Stations

<p><strong>With increasing interest in air pollution, the installation of air quality monitoring networks for regular measurement is considered a very important task in many countries. However, operation of air quality monitoring networks requires much time and money. Therefore, the representativeness of the locations of air quality monitoring networks is an important issue that has been studied by many groups worldwide. Most such studies are based on statistical analysis or the use of geographic information systems (GIS) in existing air quality monitoring network data. These methods are useful for identifying the representativeness of existing measuring networks, but they cannot verify the need to add new monitoring stations. With the development of computer technology, numerical air quality models such as CMAQ have become increasingly important in analyzing and diagnosing air pollution. In this study, PM2.5 distributions in Busan were reproduced with 1-km grid spacing by the CMAQ model. The model results reflected actual PM2.5 changes relatively well. A cluster analysis, which is a statistical method that groups similar objects together, was then applied to the hourly PM2.5 concentration for all grids in the model domain. Similarities and differences between objects can be measured in several ways. K-means clustering uses a non-hierarchical cluster analysis method featuring an advantageously low calculation time for the fast processing of large amounts of data. K-means clustering was highly prevalent in existing studies that grouped air quality data according to the same characteristics. As a result of the cluster analysis, PM2.5 pollution in Busan was successfully divided into groups with the same concentration change characteristics. Finally, the redundancy of the monitoring stations and the need for additional sites were analyzed by comparing the clusters of PM2.5 with the locations of the air quality monitoring networks currently in operation.</strong></p><p><strong>This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(2017R1D1A3B03036152).</strong></p>

scholarly journals Benefits and Drawbacks of Citizen Science to Complement Traditional Data Gathering Approaches for Medically Important Hard Ticks (Acari: Ixodidae) in the United States

2020 ◽  
Author(s):  
Lars Eisen ◽  
Rebecca J Eisen
Keyword(s):  
Data Quality ◽  
Citizen Science ◽  
Tick Species ◽  
Life Stage ◽  
Data Gathering ◽  
The United States ◽  
Medical Community ◽  
Quality Data ◽  
Human Pathogens ◽  
Record Data

Abstract Tick-borne diseases are increasing in North America. Knowledge of which tick species and associated human pathogens are present locally can inform the public and medical community about the acarological risk for tick bites and tick-borne infections. Citizen science (also called community-based monitoring, volunteer monitoring, or participatory science) is emerging as a potential approach to complement traditional tick record data gathering where all aspects of the work is done by researchers or public health professionals. One key question is how citizen science can best be used to generate high-quality data to fill knowledge gaps that are difficult to address using traditional data gathering approaches. Citizen science is particularly useful to generate information on human–tick encounters and may also contribute to geographical tick records to help define species distributions across large areas. Previous citizen science projects have utilized three distinct tick record data gathering methods including submission of: 1) physical tick specimens for identification by professional entomologists, 2) digital images of ticks for identification by professional entomologists, and 3) data where the tick species and life stage were identified by the citizen scientist. We explore the benefits and drawbacks of citizen science, relative to the traditional scientific approach, to generate data on tick records, with special emphasis on data quality for species identification and tick encounter locations. We recognize the value of citizen science to tick research but caution that the generated information must be interpreted cautiously with data quality limitations firmly in mind to avoid misleading conclusions.

scholarly journals Measure particulate matter by yourself: data-quality monitoring in a citizen science project

2019 ◽  
Vol 8 (2) ◽  
pp. 317-328 ◽  
Author(s):  
Aboubakr Benabbas ◽  
Martin Geißelbrecht ◽  
Gabriel Martin Nikol ◽  
Lukas Mahr ◽  
Daniel Nähr ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Data Quality ◽  
Urban Areas ◽  
Low Cost ◽  
Quality Monitoring ◽  
Sensor Data ◽  
And Control ◽  
Future Work ◽  
The Impact

Abstract. The concern about air quality in urban areas and the impact of particulate matter (PM) on public health is turning into a big debate. A good solution to sensitize people to this issue is to involve them in the process of air quality monitoring. This paper presents contributions in the field of PM measurements using low-cost sensors. We show how a low-cost PM sensor can be extended to transfer data not only over Wi-Fi but also over the LoRa protocol. Then, we identify some of the correlations existing in the data through data analysis. Afterwards, we show how semantic technologies can help model and control sensor data quality in an increasing PM sensor network. We finally wrap up with a conclusion and plans for future work.

Sensor network for traffic-related pollutant monitoring in urban public transport interchanges

2020 ◽  
Author(s):  
Li Sun ◽  
Peng Wei ◽  
Jieqing He ◽  
Dane Westerdahl ◽  
Zhi Ning
Keyword(s):  
Hong Kong ◽  
Air Quality ◽  
Public Transport ◽  
Natural Ventilation ◽  
Ambient Air ◽  
Sensor Data ◽  
Quality Data ◽  
Concentration Limit ◽  
Traffic Emission ◽  
Concentration Levels

<p>Public transport interchanges (PTI) are special transportation-impacted micro-environments in Hong Kong where public transport such as buses, taxis and mini-buses pass through or terminate, and where passengers queue for the transport. Hong Kong has 65 PTIs in total and most of these are located under residential or commercial buildings. Mechanical ventilation is included in all PTIs due to very limited natural ventilation and it is intended to limit the accumulation of air pollution from the various vehicles. However, numerous complaints were reported concerning PTIs’ air quality, and data are lacking to characterize pollution in these places. The purpose of this study was to determine the overall nature of pollutants in a sample of PTIs, to identify whether hot spots were present and how these might be related to both ventilation practices and bus activities in PTIs.</p><p> </p><p>8 PTIs were selected for simultaneous measurement of NO, NO<sub>2</sub> and PM<sub>2.5</sub> in 4 days of sampling. A monitoring network was formed by a group of sensor-based air monitoring facilities which were deployed at multiple points in the passenger waiting areas of each selected PTI and also at the ventilation intakes. Specific data calibration and validation protocols were well designed for sensor data control and assurance in such near-source monitoring application. </p><p> </p><p>NO, NO<sub>2</sub> and PM<sub>2.5­ </sub>measured inside the PTIs were compared with the ambient air quality data reported by nearby routine air quality monitoring stations. The average concentration levels of NO<sub>x</sub> were about 4-16 times higher than the ambient levels. NO, NO<sub>2 </sub>and NO<sub>x </sub>in the PTIs themselves showed similar daily repeatable variation patterns and NO concentration levels were always higher than NO<sub>2</sub>’s during the daytime, while the ambient showed opposite patterns. This indicates NO<sub>x</sub> pollutants inside the PTIs were mainly produced by the local bus activities. NO and NO<sub>2</sub> measured at some ventilation intakes had even higher concentration levels than those of PTIs, which means the existing ventilation systems were generally not adequate to control the pollution concentration and sometimes could even make the problem worse. Exceedances of NO<sub>2</sub>’s 1-hour concentration limit (0.30 mg/m<sup>3</sup>) were observed at several monitoring sites and were found mainly located in the middle of the PTIs where ventilation is poorer or close to the bus stops occupied by older buses. PM<sub>2.5</sub> measured inside the PTIs followed the patterns of ambient PM<sub>2.5</sub> and showed comparable concentration levels, which implies traffic emission, especially the exhaust from buses in the PTIs may not be the main source for particle pollution.</p><p> </p><p>Concern was raised on the implementation of pollution mitigation plans inside PTIs to satisfy the urgent health protection need for the commuters and also for the staff of bus companies who work there. To effectively control the PTI pollution and limit exposures, it is necessary to consider the bus volume, bus emission type and ventilation design.  </p>

scholarly journals Novel Air Pollution Measurement System Based on Ethereum Blockchain

2020 ◽  
Vol 9 (4) ◽  
pp. 49
Author(s):  
Daniele Sofia ◽  
Nicoletta Lotrecchiano ◽  
Paolo Trucillo ◽  
Aristide Giuliano ◽  
Luigi Terrone
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Query Language ◽  
Environmental Data ◽  
Quality Monitoring ◽  
Traffic Information ◽  
Quality Data ◽  
Air Quality Monitoring ◽  
Monitoring Networks

The need to protect sensitive data is growing, and environmental data are now considered sensitive. The application of last-generation procedures such as blockchains coupled with the implementation of new air quality monitoring technology allows the data protection and validation. In this work, the use of a blockchain applied to air pollution data is proposed. A blockchain procedure has been designed and tested. An Internet of Things (IoT)-based sensor network provides air quality data in terms of particulate matter of two different diameters, particulate matter (PM)10 and PM2.5, volatile organic compounds (VOC), and nitrogen dioxide (NO2) concentrations. The dataset also includes meteorological parameters and vehicular traffic information. This work foresees that the data, recovered from traditional Not Structured Query Language (NoSQL) database, and organized according to some specifications, are sent to the Ethereum blockchain daily automatically and with the possibility to choose the period of interest manually. There was also the development of a transaction management and recovery system aimed at retrieving data, formatting it according to the specifications and organizing it into files of various formats. The blockchain procedure has therefore been used to track data provided by air quality monitoring networks unequivocally.

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