AirKit: A Citizen-Sensing Toolkit for Monitoring Air Quality

Mahajan, Sachit; Gabrys, Jennifer; Armitage, Joanne

doi:10.3390/s21124044

Cited by 13 publications

(13 citation statements)

References 37 publications

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“…The data correlation function allows the user to perform data correlation. Vayu allows a user to calculate different correlation coefficients that are widely used in air quality research [35][36][37][38]: Pearson's correlation, Kendall Tau's Correlation, and Spearman correlation. Figure 4 shows the output of the data correlation function.…”

Section: Discussionmentioning

confidence: 99%

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

Mahajan

2021

Sensors

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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.

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Section: Discussionmentioning

confidence: 99%

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

Mahajan

2021

Sensors

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“…Motivation While the use of low-cost sensors has improved the air quality data availability and access, several challenges still need to be addressed. Data quality and accuracy of low-cost sensors remains one of the key challenges 38 – 40 . It has been widely discussed how an IoT application could be considered useless due to poor sensor data quality 41 .…”

Section: Introductionmentioning

confidence: 99%

Design and development of an open-source framework for citizen-centric environmental monitoring and data analysis

Mahajan

2022

Sci Rep

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Cities around the world are struggling with environmental pollution. The conventional monitoring approaches are not effective for undertaking large-scale environmental monitoring due to logistical and cost-related issues. The availability of low-cost and low-power Internet of Things (IoT) devices has proved to be an effective alternative to monitoring the environment. Such systems have opened up environment monitoring opportunities to citizens while simultaneously confronting them with challenges related to sensor accuracy and the accumulation of large data sets. Analyzing and interpreting sensor data itself is a formidable task that requires extensive computational resources and expertise. To address this challenge, a social, open-source, and citizen-centric IoT (Soc-IoT) framework is presented, which combines a real-time environmental sensing device with an intuitive data analysis and visualization application. Soc-IoT has two main components: (1) CoSense Unit—a resource-efficient, portable and modular device designed and evaluated for indoor and outdoor environmental monitoring, and (2) exploreR—an intuitive cross-platform data analysis and visualization application that offers a comprehensive set of tools for systematic analysis of sensor data without the need for coding. Developed as a proof-of-concept framework to monitor the environment at scale, Soc-IoT aims to promote environmental resilience and open innovation by lowering technological barriers.

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“…The use of remote sensing instrumentation is another growing approach. In the past several years, remote sensing technology—particularly through Internet of Things (IoT) technology—has become increasingly accessible [ 6 , 7 , 8 ]. While it is highly effective at observing the spatial distribution of air pollutants on global and national scales, these instruments are currently limited in their capacity to obtain more localized, near-surface readings.…”

Section: Introductionmentioning

confidence: 99%

“…With air quality monitoring tools directly in the hands of the general public, the implications of air quality data should resultantly become a more personal matter. By bringing these observations to a more localized scale, concerned individuals or communities are better equipped to bring matters of environmental health into an actionable and political context [ 7 , 12 , 13 , 14 ]. The Luftdaten project found that IoT/PM monitoring has increased visibility and utility of PM data amongst more media coverage on local air quality issues in Stuttgart, Germany [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Particulate Matter with Wearable Sensors and the Influence on Student Environmental Attitudes

Kane

Abbate

Landahl

et al. 2022

Sensors

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The mobile monitoring of air pollution is a growing field, prospectively filling in spatial gaps while personalizing air-quality-based risk assessment. We developed wearable sensors to record particulate matter (PM), and through a community science approach, students of partnering Chicago high schools monitored PM concentrations during their commutes over a five- and thirteen-day period. Our main objective was to investigate how mobile monitoring influenced students’ environmental attitudes and we did this by having the students explore the relationship between PM concentrations and urban vegetation. Urban vegetation was approximated with a normalized difference vegetation index (NDVI) using Landsat 8 satellite imagery. While the linear regression for one partner school indicated a negative correlation between PM and vegetation, the other indicated a positive correlation, contrary to our expectations. Survey responses were scored on the basis of their environmental affinity and knowledge. There were no significant differences between cumulative pre- and post-experiment survey responses at Josephinum Academy, and only one weakly significant difference in survey results at DePaul Prep in the Knowledge category. However, changes within certain attitudinal subscales may possibly suggest that students were inclined to practice more sustainable behaviors, but perhaps lacked the resources to do so.

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AirKit: A Citizen-Sensing Toolkit for Monitoring Air Quality

Cited by 13 publications

References 37 publications

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

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

Design and development of an open-source framework for citizen-centric environmental monitoring and data analysis

Monitoring Particulate Matter with Wearable Sensors and the Influence on Student Environmental Attitudes

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