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Big Data and Artificial Intelligence for Air Quality Assessment and...
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Big Data and Artificial Intelligence for Air Quality Assessment and Forecasting

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 12170

Special Issue Editors

Dr. Esther Hontañón

E-Mail Website
Guest Editor
Department for Sensors and Ultrasonic Systems, Institute for Physical and Information Technologies, Spanish National Research Council (CSIC), 28006 Madrid, Spain
Interests: air quality monitoring; wireless sensor networks; low-cost sensors; nanostructured gas sensors
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Bernardete Ribeiro

E-Mail Website
Guest Editor
Centre for Informatics and Systems of the University of Coimbra (CISUC) , University of Coimbra, 3030-290 Coimbra, Portugal
Interests: machine learning; pattern recognition; financial engineering; text classification; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Clean air is a key issue for guaranteeing human rights such as the right to life, health, and well-being and to a safe, clean, healthy, and sustainable environment. For this reason, the evaluation and prediction of air quality (AQ) are essential for decision-making and the development and regulation of AQ policy. As such, future strategies for AQ assessment and forecasting face challenges such as building a multiscale approach from the global to the sub-urban level and predicting AQ on a  subseasonal basis. Therefore, the monitoring, analysis, and modelling of AQ have become strategic research areas, which are currently facing a major revolution that is driven by the availability of wireless sensor networks (WSN) coupled with the internet of things (IoT) (which provide massive real-time measurements of pollutant concentrations in the air) and the rapid advancement of high-performance computational and analytical capabilities such as supercomputing, cloud-computing, and analytical big data (BD).  

This Special Issue (SI) aims to discuss the role and applicability of top BD technologies in the evaluation and prediction of AQ based on massive air pollution measurement data provided by sensor networks. Scientists and researchers are invited to contribute to this SI by submitting manuscripts (research papers, communications, review articles) describing the fundamentals, underlying models and algorithms, and practical cases of analytical BD and AI technologies for the assessment and forecasting of AQ in real scenarios.

This SI is targeted mainly towards low-cost gas and particulate matter (PM) sensors, data mining (DM), fusion (DF), machine learning (ML), and deep learning (DL) techniques, but other BD- and AI-related technologies may also be considered. Additionally, the SI covers both ambient (outdoor) and indoor air scenarios.

Dr. Esther Hontañón
Prof. Bernardete Ribeiro
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • air quality assessment and forecast
  • low-cost sensors
  • analytical big data
  • artificial intelligence
  • machine learning
  • deep learning

Published Papers (5 papers)

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Research

13 pages, 1093 KiB  
Article
Mixture Regression for Clustering Atmospheric-Sounding Data: A Study of the Relationship between Temperature Inversions and PM10 Concentrations
by Peter Mlakar and Jana Faganeli Pucer
Atmosphere 2023, 14(3), 481; https://doi.org/10.3390/atmos14030481 - 28 Feb 2023
Viewed by 949
Abstract
Temperature inversions prevent the mixing of air near the surface with the air higher in the atmosphere, contributing to high concentrations of air pollutants. Inversions can be identified by sampling temperature data at different heights, usually done with radiosondes. In our study, we [...] Read more.
Temperature inversions prevent the mixing of air near the surface with the air higher in the atmosphere, contributing to high concentrations of air pollutants. Inversions can be identified by sampling temperature data at different heights, usually done with radiosondes. In our study, we propose using the SMIXS clustering algorithm to cluster radiosonde temperature data as longitudinal data into clusters with distinct temperature profile shapes. We clustered 8 years of early morning radiosonde data from Ljubljana, Slovenia, into 15 clusters and investigated their relationship to PM10 pollution. The results show that high PM10 concentrations (above 50 g/m3, which is the daily limit value) are associated with early morning temperature inversions. The highest concentrations are typical for winter days with the strongest temperature inversions (temperature difference of 5 ∘C or more in the inversion layer) while the lowest concentrations (about 10 g/m3) are typical for days with no early morning temperature inversion. Days with very strong temperature inversions are quite rare. We show that clustering temperature profiles into a distinct number of clusters adds to the interpretability of radiosonde data. It simplifies the characterization of temperature inversions, their frequency, occurrence, and their impact on PM10 concentrations. Full article
(This article belongs to the Special Issue Big Data and Artificial Intelligence for Air Quality Assessment and Forecasting)
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15 pages, 3310 KiB  
Article
Towards On-Site Implementation of Multi-Step Air Pollutant Index Prediction in Malaysia Industrial Area: Comparing the NARX Neural Network and Support Vector Regression
by Rosminah Mustakim, Mazlina Mamat and Hoe Tung Yew
Atmosphere 2022, 13(11), 1787; https://doi.org/10.3390/atmos13111787 - 29 Oct 2022
Cited by 2 | Viewed by 1133
Abstract
Malaysia has experienced public health issues and economic losses due to air pollution problems. As the air pollution problem keeps increasing over time, studies on air quality prediction are also advancing. The air quality prediction can help reduce air pollution’s damaging impact on [...] Read more.
Malaysia has experienced public health issues and economic losses due to air pollution problems. As the air pollution problem keeps increasing over time, studies on air quality prediction are also advancing. The air quality prediction can help reduce air pollution’s damaging impact on public health and economic activities. This study develops and evaluates the Nonlinear Autoregressive Exogenous (NARX) Neural Network and Support Vector Regression (SVR) for multi-step Malaysia’s Air Pollutant Index (API) prediction, focusing on the industrial areas. The performance of NARX and SVR was evaluated on four crucial aspects of on-site implementation: Input pre-processing, parameter selection, practical predictability limit, and robustness. Results show that both predictors exhibit almost comparable performance, in which the SVR slightly outperforms the NARX. The RMSE and R2 values for the SVR are 0.71 and 0.99 in one-step-ahead prediction, gradually changing to 6.43 and 0.68 in 24-step-ahead prediction. Both predictors can also perform multi-step prediction by using the actual (non-normalized) data, hence are simpler to be implemented on-site. Removing several insignificant parameters did not affect the prediction performance, indicating that a uniform model can be used at all air quality monitoring stations in Malaysia’s industrial areas. Nevertheless, SVR shows more resilience towards outliers and is also stable. Based on the trends exhibited by the Malaysia API data, a yearly update is sufficient for SVR due to its strength and stability. In conclusion, this study proposes that the SVR predictor could be implemented at air quality monitoring stations to provide API prediction information at least nine steps in advance. Full article
(This article belongs to the Special Issue Big Data and Artificial Intelligence for Air Quality Assessment and Forecasting)
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16 pages, 1897 KiB  
Article
Prediction of Monthly PM2.5 Concentration in Liaocheng in China Employing Artificial Neural Network
by Zhenfang He, Qingchun Guo, Zhaosheng Wang and Xinzhou Li
Atmosphere 2022, 13(8), 1221; https://doi.org/10.3390/atmos13081221 - 02 Aug 2022
Cited by 27 | Viewed by 2644
Abstract
Fine particulate matter (PM2.5) affects climate change and human health. Therefore, the prediction of PM2.5 level is particularly important for regulatory planning. The main objective of the study is to predict PM2.5 concentration employing an artificial neural network (ANN). [...] Read more.
Fine particulate matter (PM2.5) affects climate change and human health. Therefore, the prediction of PM2.5 level is particularly important for regulatory planning. The main objective of the study is to predict PM2.5 concentration employing an artificial neural network (ANN). The annual change in PM2.5 in Liaocheng from 2014 to 2021 shows a gradual decreasing trend. The air quality in Liaocheng during lockdown and after lockdown periods in 2020 was obviously improved compared with the same periods of 2019. The ANN employed in the study contains a hidden layer with 6 neurons, an input layer with 11 parameters, and an output layer. First, the ANN is used with 80% of data for training, then with 10% of data for verification. The value of correlation coefficient (R) for the training and validation data is 0.9472 and 0.9834, respectively. In the forecast period, it is demonstrated that the ANN model with Bayesian regularization (BR) algorithm (trainbr) obtained the best forecasting performance in terms of R (0.9570), mean absolute error (4.6 μg/m3), and root mean square error (6.6 μg/m3), respectively. The ANN model has produced accurate results. These results prove that the ANN is effective in monthly PM2.5 concentration predicting due to the fact that it can identify nonlinear relationships between the input and output variables. Full article
(This article belongs to the Special Issue Big Data and Artificial Intelligence for Air Quality Assessment and Forecasting)
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20 pages, 3061 KiB  
Article
PM2.5 Air Pollution Prediction through Deep Learning Using Multisource Meteorological, Wildfire, and Heat Data
by Pratyush Muthukumar, Kabir Nagrecha, Dawn Comer, Chisato Fukuda Calvert, Navid Amini, Jeanne Holm and Mohammad Pourhomayoun
Atmosphere 2022, 13(5), 822; https://doi.org/10.3390/atmos13050822 - 18 May 2022
Cited by 12 | Viewed by 3728
Abstract
Air pollution is a lethal global threat. To mitigate the effects of air pollution, we must first understand it, find its patterns and correlations, and predict it in advance. Air pollution is highly dependent on spatial and temporal correlations of prior meteorological, wildfire, [...] Read more.
Air pollution is a lethal global threat. To mitigate the effects of air pollution, we must first understand it, find its patterns and correlations, and predict it in advance. Air pollution is highly dependent on spatial and temporal correlations of prior meteorological, wildfire, and pollution structures. We use the advanced deep predictive Convolutional LSTM (ConvLSTM) model paired with the cutting-edge Graph Convolutional Network (GCN) architecture to predict spatiotemporal hourly PM2.5 across the Los Angeles area over time. Our deep-learning model does not use atmospheric physics or chemical mechanism data, but rather multisource imagery and sensor data. We use high-resolution remote-sensing satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument onboard the NASA Terra+Aqua satellites and remote-sensing data from the Tropospheric Monitoring Instrument (TROPOMI), a multispectral imaging spectrometer onboard the Sentinel-5P satellite. We use the highly correlated Fire Radiative Power data product from the MODIS instrument which provides valuable information about the radiant heat output and effects of wildfires on atmospheric air pollutants. The input data we use in our deep-learning model is representative of the major sources of ground-level PM2.5 and thus we can predict hourly PM2.5 at unparalleled accuracies. Our RMSE and NRMSE scores over various site locations and predictive time frames show significant improvement over existing research in predicting PM2.5 using spatiotemporal deep predictive algorithms. Full article
(This article belongs to the Special Issue Big Data and Artificial Intelligence for Air Quality Assessment and Forecasting)
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15 pages, 5199 KiB  
Article
Air Pollutant Concentration Prediction Based on a CEEMDAN-FE-BiLSTM Model
by Xuchu Jiang, Peiyao Wei, Yiwen Luo and Ying Li
Atmosphere 2021, 12(11), 1452; https://doi.org/10.3390/atmos12111452 - 03 Nov 2021
Cited by 15 | Viewed by 2097
Abstract
The concentration series of PM2.5 (particulate matter ≤ 2.5 μm) is nonlinear, nonstationary, and noisy, making it difficult to predict accurately. This paper presents a new PM2.5 concentration prediction method based on a hybrid model of complete ensemble empirical mode decomposition [...] Read more.
The concentration series of PM2.5 (particulate matter ≤ 2.5 μm) is nonlinear, nonstationary, and noisy, making it difficult to predict accurately. This paper presents a new PM2.5 concentration prediction method based on a hybrid model of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and bi-directional long short-term memory (BiLSTM). The new method was applied to predict the same kind of particulate pollutant PM10 and heterogeneous gas pollutant O3, proving that the prediction method has strong generalization ability. First, CEEMDAN was used to decompose PM2.5 concentrations at different frequencies. Then, the fuzzy entropy (FE) value of each decomposed wave was calculated, and the near waves were combined by K-means clustering to generate the input sequence. Finally, the combined sequences were put into the BiLSTM model with multiple hidden layers for training. We predicted the PM2.5 concentrations of Seoul Station 116 by the hour, with values of the root mean square error (RMSE), the mean absolute error (MAE), and the symmetric mean absolute percentage error (SMAPE) as low as 2.74, 1.90, and 13.59%, respectively, and an R2 value as high as 96.34%. The “CEEMDAN-FE” decomposition-merging technology proposed in this paper can effectively reduce the instability and high volatility of the original data, overcome data noise, and significantly improve the model’s performance in predicting the real-time concentrations of PM2.5. Full article
(This article belongs to the Special Issue Big Data and Artificial Intelligence for Air Quality Assessment and Forecasting)
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