Wireless Sensor Network Dependable Monitoring for Urban Air Quality

Nguyen, Huynh Mai; Ha, Q.P.

doi:10.1109/access.2022.3166904

Cited by 11 publications

(8 citation statements)

References 32 publications

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“…The combined CCAM-CTM has been implemented in NSW since 2017 to flexibly scale the predictions at different resolutions (80 km × 80 km, 27 km × 27 km, 9 km × 9 km, and 3 km × 3 km) respectively in accordance with four grid domains, namely Australia, NSW, GMR and Sydney basin for modeling accurately the transportation of air pollutants across a wide region [4]. In our study, we use the GMR domain (60 × 60 grid cells at 9 km × 9 km) for CCAM-CTM values based on the average distance between the air-quality monitoring stations [3].…”

Section: Ccam-ctmmentioning

confidence: 99%

“…3. Similarly for particle concentrations, the correlation changes were observed owing to the impact of local meteorologies (e.g., wind, rainfall, air humidity and others) [3]. The above rationale has motivated us to develop a technique to remove or scale down the influence of lowcorrelated stations during the imputation for incoming model inputs.…”

Section: A Correlation Of Spatio-temporal Profiles Of Air Pollutantsmentioning

confidence: 99%

“…Air pollution, regardless of emissions sources, has become an important topic in environmental research and management [1], [2], particularly in urban areas [3]. To effectively control air quality, it requires an accurate and reliable solution for urban air pollution forecasting.…”

Section: Introductionmentioning

confidence: 99%

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Long Short-Term Memory Bayesian Neural Network for Air Pollution Forecast

Nguyen

et al. 2023

IEEE Access

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This paper presents a data fusion framework to enhance the accuracy of air-pollutant forecast in the state of New South Wales (NSW), Australia using deep learning (DL) as a core model. Here, we propose a long short-term memory Bayesian neural network (LSTM-BNN) to improve performance of the predictive profiles via quantifying uncertainties and adjusting model parameters. For this, we develop a new inferring technique for kernel density estimation with subdivision tuning to ensure both forecast accuracy and computational efficiency with a limited number of samples from the prediction distributions. Moreover, a novel algorithm called spatially-adjusted multivariate imputation by chained equation is also developed to take into account spatial correlations between nearby air-quality stations for correctly imputing the incoming data, and hence, to enable forecasting at a local scale. The LSTM-BNN framework is evaluated with observed datasets collected from stations and modeling outputs generated by the Conformal Cubic Atmospheric Model -Chemical Transport Model (CCAM-CTM) currently used in NSW. The airborne pollutants under investigation are P M 2.5 and ozone, which frequently exceed the standards. The results obtained from data fusion with our framework demonstrated high performance of the proposed LSTM-BNN model in air-pollutant prediction with reductions of over 30% in root mean square error compared to CCAM-CTM and over 50 % in inferring time compared to a DL model with Gaussian-based inference. Accuracy and reliability of the proposed model were also achieved with air pollution forecast in various seasons and suburbs.

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Section: Ccam-ctmmentioning

confidence: 99%

Section: A Correlation Of Spatio-temporal Profiles Of Air Pollutantsmentioning

confidence: 99%

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Long Short-Term Memory Bayesian Neural Network for Air Pollution Forecast

Nguyen

et al. 2023

IEEE Access

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“…This limitation constrains the precise monitoring and analysis of microclimatic conditions and local-scale air pollution emissions. The advent of Internet-of-Thing (IoT) technology, dominated by low-cost wireless sensor networks (LWSNs), has recently offered a promising solution for localized observations and contributed to enhancing our ability to assess the potential risks associated with air pollution [4]. For instance, the AirU pollution monitor network was designed and deployed in street-level locations in Salt Lake City, Utah, USA to evaluate the trapping pollution on the valley floor [5].…”

Section: Introductionmentioning

confidence: 99%

“…For instance, the AirU pollution monitor network was designed and deployed in street-level locations in Salt Lake City, Utah, USA to evaluate the trapping pollution on the valley floor [5]. A dependable wireless sensing framework was proposed to enhance the reliability of an LWSN for local monitoring of dust emission from construction sites at Melrose Park, in the state of New South Wales (NSW), Australia [4].…”

Section: Introductionmentioning

confidence: 99%

Particulate Matter Monitoring and Forecast with Integrated Low-cost Sensor Networks and Air-quality Monitoring Stations

Nguyen,

Le,

et al. 2024

E3S Web Conf.

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The fusion of low-cost sensor networks with air quality stations has become prominent, offering a cost-effective approach to gathering fine-scaled spatial data. However, effective integration of diverse data sources while maintaining reliable information remains challenging. This paper presents an extended clustering method based on the Girvan-Newman algorithm to identify spatially correlated clusters of sensors and nearby observatories. The proposed approach enables localized monitoring within each cluster by partitioning the network into communities, optimizing resource allocation and reducing redundancy. Through our simulations with real-world data collected from the state-run air quality monitoring stations and the low-cost sensor network in Sydney’s suburbs, we demonstrate the effectiveness of this approach in enhancing localized monitoring compared to other clustering methods, namely K-Means Clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Agglomerative Clustering. Experimental results illustrate the potential for this method to facilitate comprehensive and high-resolution air quality monitoring systems, advocating the advantages of integrating low-cost sensor networks with conventional monitoring infrastructure.

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Low‐cost air quality monitoring networks for long‐term field campaigns: A review

Carotenuto,

Bisignano,

Brilli

et al. 2023

Meteorological Applications

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The application of low‐cost air quality monitoring networks has substantially grown over the last few years, following the technological advances in the production of cheap and portable air pollution sensors, thus potentially greatly increasing the limited spatial information on air quality conditions provided by traditional stations. However, the use of low‐cost air quality sensors still presents many limitations, mostly related to the reliability of their measurements. Despite the increasing number of papers focusing on these issues, some of the challenges connected to the use of low‐cost air quality sensors are still poorly investigated and understood, considering in particular those related to long‐term applications of low‐cost air quality networks and their integration within the reference air quality monitoring system. The present review aims at filling this gap, by analysing the characteristics of low‐cost air quality monitoring networks that were run across long‐term field campaigns, including their geographical location, the pollutants monitored, the type and number of stations employed, and the length of the field campaign, with a particular attention on assessing the aims for their deployment and on the evaluation of their integration within official air quality monitoring networks. Moreover, a critical analysis of the most insightful suggestions and recommendations delivered in the literature, as well as of the relevant critical issues, is presented, highlighting still open research areas and outlining future challenges.

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Wireless Sensor Network Dependable Monitoring for Urban Air Quality

Cited by 11 publications

References 32 publications

Long Short-Term Memory Bayesian Neural Network for Air Pollution Forecast

Long Short-Term Memory Bayesian Neural Network for Air Pollution Forecast

Particulate Matter Monitoring and Forecast with Integrated Low-cost Sensor Networks and Air-quality Monitoring Stations

Low‐cost air quality monitoring networks for long‐term field campaigns: A review

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