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Real-Time Air Quality Monitoring Systems

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Dear Colleagues,

The World Health Organization (WHO) estimates that air pollution kills seven million people worldwide every year and that 9 out of 10 people breathe air with a concentration of pollutants exceeding its guideline limits. Providing timely, detailed, and accurate information to citizens and government officials about the air they breathe can, therefore, improve health quality. This is an interdisciplinary challenge requiring the joint effort of researchers from computer science, engineering, environment science, health, and other scientific areas.

Real-time air quality monitoring systems are a booming research and investment area, and we have witnessed an increasing interest in topics like low-cost sensor networks, development of wireless communication, crowdsourcing, intelligent prognosis, distributed storage systems, together with a growing public awareness about air quality.

We are organizing this Special Issue to advance the field of real-time air quality monitoring systems. We invite researchers to submit their work on theoretical and practical aspects of such systems, addressing open problems in sensors, data collection, fusion, and storage systems, privacy, intelligent forecasting and alarming as well as web and mobile applications directed at individuals and organizations for decision support.

Prof. Dr. Catarina Silva
Prof. Dr. Filipe Araujo
Guest Editors

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Keywords

air quality and air quality assessment
air quality sensors
sensor data collection and fusion
mobile computing technologies
innovative real-time monitoring approaches
intelligent methods for air quality monitoring
calibration and prognostic systems in air quality monitoring
cloud-based approaches to air quality systems
high availability of real-time air quality systems
security in air quality systems
applications of air quality monitoring data
Air quality in smart cities
quality issues with air monitoring data
data storage and privacy
IoT

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Research

34 pages, 11878 KiB

Open AccessArticle

Attention-Based Distributed Deep Learning Model for Air Quality Forecasting

by Axel Gedeon Mengara Mengara, Eunyoung Park, Jinho Jang and Younghwan Yoo

Sustainability 2022, 14(6), 3269; https://doi.org/10.3390/su14063269 - 10 Mar 2022

Cited by 15 | Viewed by 3226

Abstract

Air quality forecasting has become an essential factor in facilitating sustainable development worldwide. Several countries have implemented monitoring stations to collect air pollution particle data and meteorological information using parameters such as hourly timespans. This research focuses on unravelling a new framework for air quality prediction worldwide and features Busan, South Korea as its model city. The paper proposes the application of an attention-based convolutional BiLSTM autoencoder model. The proposed deep learning model has been trained on a distributed framework, referred to data parallelism, to forecast the intensity of particle pollution (

P M_{2.5}

and

P M_{10}

). The algorithm automatically learns the intrinsic correlation among the particle pollution in different locations. Each location’s meteorological and traffic data is extensively exploited to improve the model’s performance. The model has been trained using air quality particle data and car traffic information. The traffic information is obtained by a device which counts cars passing a specific area through the YOLO algorithm, and then sends the data to a stacked deep autoencoder to be encoded alongside the meteorological data before the final prediction. In addition, multiple one-dimensional CNN layers are used to obtain the local spatial features jointly with a stacked attention-based BiLSTM layer to figure out how air quality particles are correlated in space and time. The evaluation of the new attention-based convolutional BiLSTM autoencoder model was derived from data collected and retrieved from comprehensive experiments conducted in South Korea. The results not only show that the framework outperforms the previous models both on short- and long-term predictions but also indicate that traffic information can improve the accuracy of air quality forecasting. For instance, during

P M_{2.5}

prediction, the proposed attention-based model obtained the lowest MAE (5.02 and 22.59, respectively, for short-term and long-term prediction), RMSE (7.48 and 28.02) and SMAPE (17.98 and 39.81) among all the models, which indicates strong accuracy between observed and predicted values. It was also found that the newly proposed model had the lowest average training time compared to the baseline algorithms. Furthermore, the proposed framework was successfully deployed in a cloud server in order to provide future air quality information in real time and when needed. Full article

(This article belongs to the Special Issue Real-Time Air Quality Monitoring Systems)

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16 pages, 2380 KiB

Open AccessArticle

Comparison of H₂S Gas Sensors: A Sensor Management Procedure for Sewer Monitoring

by Micaela Pacheco Fernández, Daneish Despot and Matthias Barjenbruch

Sustainability 2021, 13(19), 10779; https://doi.org/10.3390/su131910779 - 28 Sep 2021

Cited by 6 | Viewed by 1876

Abstract

Hydrogen sulphide (H₂S) emissions are one of the major problems associated with sewer networks. This gas, with its characteristic smell of rotten eggs is highly toxic and leads to the corrosion of sewer infrastructures. To protect cities and ensure the safety of sewer workers, sewers are commonly monitored using H₂S gas sensors. In this work, three commercial H₂S gas sensors for air quality monitoring were compared at two different sites in Berlin, Germany. Two of the sensors provide online access to data, while the other one is a data logger. Moreover, based on statistical measures (RMSE, MAE, MB, and a graphical analysis), we evaluated whether a rotation/exchange between data logger (reference) and online sensors is possible without significant differences in the gas measurements. Experimental evaluation revealed that measurement differences are dependent on the H₂S concentration range. The deviation between sensors increases as the H₂S concentration rises. Therefore, the interchange between reference and online sensors depends on the application site and the H₂S levels. At lower ranges (0–10 ppm) there were no observed problems. Finally, to support practitioners on-site, a management procedure in the form of a decision-making tool is proposed for assessing whether gas sensors should be exchanged/rotated. Full article

(This article belongs to the Special Issue Real-Time Air Quality Monitoring Systems)

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