SensEURCity: A multi-city air quality dataset collected for 2020/2021 using open low-cost sensor systems

Poppel, Martine Van; Schneider, Philipp; Peters, Jan; Yatkin, Sinan; Gerboles, Michel; Matheeussen, C.; Bartoňová, Alena; Davila, Silvije; Signorini, Marco; Vogt, Matthias; Dauge, Franck René; Skaar, Jøran Solnes; Haugen, Rolf

doi:10.1038/s41597-023-02135-w

Cited by 7 publications

(2 citation statements)

References 15 publications

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“…However, edge computing also brings new challenges for data collection, integration, and management due to the heterogeneity of sensing devices used to measure the various types of pollutants and the related social, meteorological, and ambient conditions. Depending on the ultimate objective and budget, data can be sampled at different locations and in different time periods with a sensing system deployed with high density [60]. Figure 5 depicts the main steps in harnessing various data sources and applying advanced artificial intelligence techniques to monitor and predict air quality.…”

Section: Edge Computing and Intelligence For Air Quality Monitoring A...mentioning

confidence: 99%

Towards Federated Learning and Multi-Access Edge Computing for Air Quality Monitoring: Literature Review and Assessment

Abimannan,

El-Alfy,

Hussain

et al. 2023

Sustainability

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Systems for monitoring air quality are essential for reducing the negative consequences of air pollution, but creating real-time systems encounters several challenges. The accuracy and effectiveness of these systems can be greatly improved by integrating federated learning and multi-access edge computing (MEC) technology. This paper critically reviews the state-of-the-art methodologies for federated learning and MEC-enabled air quality monitoring systems. It discusses the immense benefits of federated learning, including privacy-preserving model training, and MEC, such as reduced latency and improved response times, for air quality monitoring applications. Additionally, it highlights the challenges and requirements for developing and implementing real-time air quality monitoring systems, such as data quality, security, and privacy, as well as the need for interpretable and explainable AI-powered models. By leveraging such advanced techniques and technologies, air monitoring systems can overcome various challenges and deliver accurate, reliable, and timely air quality predictions. Moreover, this article provides an in-depth analysis and assessment of the state-of-the-art techniques and emphasizes the need for further research to develop more practical and affordable AI-powered decentralized systems with improved performance and data quality and security while ensuring the ethical and responsible use of the data to support informed decision making and promote sustainability.

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Section: Edge Computing and Intelligence For Air Quality Monitoring A...mentioning

confidence: 99%

Towards Federated Learning and Multi-Access Edge Computing for Air Quality Monitoring: Literature Review and Assessment

Abimannan,

El-Alfy,

Hussain

et al. 2023

Sustainability

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“…Providing high-resolution data, both in terms of spatial and temporal resolution, regarding the monitoring of airborne pollutants is fundamental for an efficient implementation of air quality guidelines and policies. High-resolution monitoring could supplement data from air quality monitoring stations that are used to assess the ambient air quality as defined in Europe in the Directive 2008/50/EC [ 5 ]. The development and availability of new monitoring technologies in the past few decades have opened several new possibilities and applications in air quality monitoring, mainly thanks to their characteristics of good portability, user-friendliness, low power consumption, high data storage capability, and relatively low cost.…”

Section: Introductionmentioning

confidence: 99%

An In-Field Assessment of the P.ALP Device in Four Different Real Working Conditions: A Performance Evaluation in Particulate Matter Monitoring

Fanti,

Borghi,

Campagnolo

et al. 2024

Toxics

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This study aimed to assess the performance, in terms of precision and accuracy, of a prototype (called “P.ALP”—Ph.D. Air Quality Low-cost Project) developed for monitoring PM2.5 concentration levels. Four prototypes were co-located with reference instrumentation in four different microenvironments simulating real-world and working conditions, namely (i) office, (ii) home, (iii) outdoor, and (iv) occupational environments. The devices were evaluated for a total of 20 monitoring days (approximately 168 h) under a wide range of PM2.5 concentrations. The performances of the prototypes (based on the light-scattering working principle) were tested through different statistical methods. After the data acquisition and data cleaning processes, a linear regression analysis was performed to assess the precision (by comparing all possible pairs of devices) and the accuracy (by comparing the prototypes against the reference instrumentation) of the P.ALP. Moreover, the United States Environmental Protection Agency (US EPA) criteria were applied to assess the possible usage of this instrumentation, and to evaluate the eventual error trends of the P.ALP in the data storage process, Bland–Altman plots were also adopted. The outcomes of this study underlined that the P.ALP performed differently depending on the microenvironment in which it was tested and, consequently, on the PM2.5 concentrations. The device can monitor PM2.5 variations with acceptable results, but the performance cannot be considered satisfactory at extremely low and remarkably high PM2.5 concentrations. Thanks to modular components and open-source software, the tested device has the potential to be customized and adapted to better fit specific study design needs, but it must be implemented with ad hoc calibration factors depending on the application before being used in field.

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Data fusion of sparse, heterogeneous, and mobile sensor devices using adaptive distance attention

Lepioufle,

Schneider,

Hamer

et al. 2024

Environ. Data Science

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In environmental science, where information from sensor devices are sparse, data fusion for mapping purposes is often based on geostatistical approaches. We propose a methodology called adaptive distance attention that enables us to fuse sparse, heterogeneous, and mobile sensor devices and predict values at locations with no previous measurement. The approach allows for automatically weighting the measurements according to a priori quality information about the sensor device without using complex and resource-demanding data assimilation techniques. Both ordinary kriging and the general regression neural network (GRNN) are integrated into this attention with their learnable parameters based on deep learning architectures. We evaluate this method using three static phenomena with different complexities: a case related to a simplistic phenomenon, topography over an area of 196 $ {km}^2 $ and to the annual hourly $ {NO}_2 $ concentration in 2019 over the Oslo metropolitan region (1026 $ {km}^2 $ ). We simulate networks of 100 synthetic sensor devices with six characteristics related to measurement quality and measurement spatial resolution. Generally, outcomes are promising: we significantly improve the metrics from baseline geostatistical models. Besides, distance attention using the Nadaraya–Watson kernel provides as good metrics as the attention based on the kriging system enabling the possibility to alleviate the processing cost for fusion of sparse data. The encouraging results motivate us in keeping adapting distance attention to space-time phenomena evolving in complex and isolated areas.

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SensEURCity: A multi-city air quality dataset collected for 2020/2021 using open low-cost sensor systems

Cited by 7 publications

References 15 publications

Towards Federated Learning and Multi-Access Edge Computing for Air Quality Monitoring: Literature Review and Assessment

Towards Federated Learning and Multi-Access Edge Computing for Air Quality Monitoring: Literature Review and Assessment

An In-Field Assessment of the P.ALP Device in Four Different Real Working Conditions: A Performance Evaluation in Particulate Matter Monitoring

Data fusion of sparse, heterogeneous, and mobile sensor devices using adaptive distance attention

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