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Sustainability
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Air Quality Modelling and Forecasting towards Sustainable Development
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Air Quality Modelling and Forecasting towards Sustainable Development

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Air, Climate Change and Sustainability".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 9742

Special Issue Editors

Dr. Norazian Mohamed Noor

E-Mail Website
Guest Editor
Faculty of Civil Engineering Technology, Universiti Malaysia Perlis, Jejawi, Arau 02600, Perlis, Malaysia
Interests: air quality; ozone; particulate matter; air pollution modelling
Dr. Ahmad Zia Ul-Saufie Mohamad Japeri

E-Mail Website
Guest Editor
Faculty of Computer and Mathematical Science, Universiti Teknologi Mara (UiTM), Shah Alam 40450, Selangor, Malaysia
Interests: air pollution modelling; machine learning; data analytics; statistical analysis; environmental modelling
Dr. Mohd Remy Rozainy Mohd Arif Zainol

E-Mail Website
Guest Editor
School of Civil Engineering, Universiti Sains Malaysia, Engineering Campus, Nibong Tebal 14300, Pulau Pinang, Malaysia
Interests: numerical simulation; hydraulic physical modelling; water resource engineering

Special Issue Information

Dear Colleagues,

Air pollution remains one of the most prominent threats to the worldwide population by affecting human health, ecosystems, and economies in both developed and developing countries. Rapid developments in technology, urbanization, and industrialization have been recognized as the main factors that contributed to the increase in air pollution, locally or regionally. According to the World Health Organization (WHO), nine out of ten people in the world breathe polluted air and it is estimated that almost 7 million people die yearly as a consequence of polluted air. Due to this emergence of air quality deterioration and its effect on humankind, air quality modeling and forecasting can be deliberated as one of the principal approaches to improve air quality management systems. Therefore, to ensure humanity can live sustainably while fulfilling our basic needs, further research on improving air quality modeling and forecasting, as well as interactions of atmospheric pollution, are urgently needed.

Accordingly, this Special Issue aims to reveal the most recent findings on new approaches or improved versions for estimating, modeling, and forecasting local or reginal air pollution. Additionally, urban areas with dense populations are more prone to serious effects of air pollution; hence, studies related to forecasting urban air quality are encouraged. Furthermore, predicting the spatiotemporal behavior/prediction of air quality is of interest in this Issue. Discussion relating to the association of atmospheric pollution and specific Sustainable Development Goals (SDGs) is highly sought, as it helps policymakers to develop pollution-reduction policies.

Topics to be covered include, but are not limited to, the following:                   

  • Air Pollution monitoring and modelling
  • Data Management and Statistics for Air Pollution Datasets
  • Machine learning algorithms and or modified methods for improved air quality model and forecasting
  • Univariate and multivariate models
  • Numerical methods
  • Spatio-temporal prediction
  • Modified Air Quality models
  • Environmental Remote Sensing Applications
  • GIS for Environmental Assessment
  • Air Quality Assessment
  • Ecosystem Assessment

The link between air quality/pollution and the 2030 Agenda SDGs and Targets

Dr. Norazian Mohamed Noor
Dr. Ahmad Zia Ul-Saufie Mohamad Japeri
Dr. Mohd Remy Rozainy Mohd Arif Zainol
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. Sustainability is an international peer-reviewed open access semimonthly 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.

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Published Papers (5 papers)

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16 pages, 541 KiB  
Article
A Multi-Country Statistical Analysis Covering Turkey, Slovakia, and Romania in an Educational Framework
by Tugce Pekdogan, Mihaela Tinca Udriștioiu, Silvia Puiu, Hasan Yildizhan and Martin Hruška
Sustainability 2023, 15(24), 16735; https://doi.org/10.3390/su152416735 - 11 Dec 2023
Cited by 2 | Viewed by 989
Abstract
This paper uses hierarchical regression analysis, a statistically robust method, to explore the correlations between two meteorological parameters and three particulate matter concentrations. The dataset is provided by six sensors located in three cities from three countries, and the measurements were taken simultaneously [...] Read more.
This paper uses hierarchical regression analysis, a statistically robust method, to explore the correlations between two meteorological parameters and three particulate matter concentrations. The dataset is provided by six sensors located in three cities from three countries, and the measurements were taken simultaneously for three months at each minute. Analyses and calculations were performed with the Statistical Package for the Social Sciences (SPSS). The results underscore that the complexity of air pollution dynamics is affected by the location even when the same type of sensors is used, and emphasize that a one-size-fits-all approach cannot effectively address air pollution. The findings are helpful from three perspectives: for education, to show how to handle and communicate a solution for local communities’ issues about air pollution; for research, to understand how easy a university can generate and analyze open-source data; and for policymakers, to design targeted interventions addressing each country’s challenges. Full article
(This article belongs to the Special Issue Air Quality Modelling and Forecasting towards Sustainable Development)
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17 pages, 3245 KiB  
Article
Ground-Level Particulate Matter (PM2.5) Concentration Mapping in the Central and South Zones of Peninsular Malaysia Using a Geostatistical Approach
by Siti Hasliza Ahmad Rusmili, Firdaus Mohamad Hamzah, Lam Kuok Choy, R. Azizah, Lilis Sulistyorini, Ririh Yudhastuti, Khuliyah Chandraning Diyanah, Retno Adriyani and Mohd Talib Latif
Sustainability 2023, 15(23), 16169; https://doi.org/10.3390/su152316169 - 21 Nov 2023
Cited by 3 | Viewed by 1496
Abstract
Fine particulate matter is one of the atmospheric contaminants that exist in the atmosphere. The purpose of this study is to evaluate spatial–temporal changes in PM2.5 concentrations in the central and south zones of Peninsular Malaysia from 2019 to 2020. The study [...] Read more.
Fine particulate matter is one of the atmospheric contaminants that exist in the atmosphere. The purpose of this study is to evaluate spatial–temporal changes in PM2.5 concentrations in the central and south zones of Peninsular Malaysia from 2019 to 2020. The study area involves twenty-one monitoring stations in the central and south zones of Peninsular Malaysia, using monthly and annual means of PM2.5 concentrations. The spatial autocorrelation of PM2.5 is calculated using Moran’s I, while three semi-variogram models are used to measure the spatial variability of PM2.5. Three kriging methods, ordinary kriging (OK), simple kriging (SK), and universal kriging (UK), were used for interpolation and comparison. The results showed that the Gaussian model was more appropriate for the central zone (MSE = 14.76) in 2019, while the stable model was more suitable in 2020 (MSE = 19.83). In addition, the stable model is more appropriate for both 2019 (MSE = 12.68) and 2020 (8.87) for the south zone. Based on the performance indicator, universal kriging was chosen as the best interpolation method in 2019 and 2020 for both the central and south zone. In conclusion, the findings provide a complete map of the variations in PM2.5 for two different zones, and show that interpolation methods such as universal kriging are beneficial and could be extended to the investigation of air pollution distributions in other areas of Peninsular Malaysia. Full article
(This article belongs to the Special Issue Air Quality Modelling and Forecasting towards Sustainable Development)
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24 pages, 12639 KiB  
Article
A Prediction Hybrid Framework for Air Quality Integrated with W-BiLSTM(PSO)-GRU and XGBoost Methods
by Wenbing Chang, Xu Chen, Zhao He and Shenghan Zhou
Sustainability 2023, 15(22), 16064; https://doi.org/10.3390/su152216064 - 17 Nov 2023
Cited by 7 | Viewed by 1262
Abstract
Air quality issues are critical to daily life and public health. However, air quality data are characterized by complexity and nonlinearity due to multiple factors. Coupled with the exponentially growing data volume, this provides both opportunities and challenges for utilizing deep learning techniques [...] Read more.
Air quality issues are critical to daily life and public health. However, air quality data are characterized by complexity and nonlinearity due to multiple factors. Coupled with the exponentially growing data volume, this provides both opportunities and challenges for utilizing deep learning techniques to reveal complex relationships in massive knowledge from multiple sources for correct air quality prediction. This paper proposes a prediction hybrid framework for air quality integrated with W-BiLSTM(PSO)-GRU and XGBoost methods. Exploiting the potential of wavelet decomposition and PSO parameter optimization, the prediction accuracy, stability and robustness was improved. The results indicate that the R2 values of PM2.5, PM10, SO2, CO, NO2, and O3 predictions exceeded 0.94, and the MAE and RMSE values were lower than 0.02 and 0.03, respectively. By integrating the state-of-the-art XGBoost algorithm, meteorological data from neighboring monitoring stations were taken into account to predict air quality trends, resulting in a wider range of forecasts. This strategic merger not only enhanced the prediction accuracy, but also effectively solved the problem of sudden interruption of monitoring. Rigorous analysis and careful experiments showed that the proposed method is effective and has high application value in air quality prediction, building a solid framework for informed decision-making and sustainable development policy formulation. Full article
(This article belongs to the Special Issue Air Quality Modelling and Forecasting towards Sustainable Development)
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20 pages, 6916 KiB  
Article
Applying Machine Learning Techniques in Air Quality Prediction—A Bucharest City Case Study
by Grigore Cican, Adrian-Nicolae Buturache and Radu Mirea
Sustainability 2023, 15(11), 8445; https://doi.org/10.3390/su15118445 - 23 May 2023
Cited by 3 | Viewed by 1997
Abstract
Air quality forecasting is very difficult to achieve in metropolitan areas due to: pollutants emission dynamics, high population density and uncertainty in defining meteorological conditions. The use of data, which contain insufficient information within the model training, and the poor selection of the [...] Read more.
Air quality forecasting is very difficult to achieve in metropolitan areas due to: pollutants emission dynamics, high population density and uncertainty in defining meteorological conditions. The use of data, which contain insufficient information within the model training, and the poor selection of the model to be used limits the air quality prediction accuracy. In this study, the prediction of NO2 concentration is made for the year 2022 using a long short-term memory network (LSTM) and a gated recurrent unit (GRU). this is an improvement in terms of performance compared to traditional methods. Data used for predictive modeling are obtained from the National Air Quality Monitoring Network. The KPIs(key performance indicator) are computed based on the testing data subset when the NO2 predicted values are compared to the real known values. Further, two additional predictions were performed for two days outside the modeling dataset. The quality of the data is not as expected, and so, before building the models, the missing data had to be imputed. LSTM and GRU performance in predicting NO2 levels is similar and reasonable with respect to the case study. In terms of pure generalization capabilities, both LSTM and GRU have the maximum R2 value below 0.8. LSTM and GRU represent powerful architectures for time-series prediction. Both are highly configurable, so the probability of identifying the best suited solution for the studied problem is consequently high. Full article
(This article belongs to the Special Issue Air Quality Modelling and Forecasting towards Sustainable Development)
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Other

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26 pages, 2529 KiB  
Systematic Review
Supervised Machine Learning Approaches for Predicting Key Pollutants and for the Sustainable Enhancement of Urban Air Quality: A Systematic Review
by Ismail Essamlali, Hasna Nhaila and Mohamed El Khaili
Sustainability 2024, 16(3), 976; https://doi.org/10.3390/su16030976 - 23 Jan 2024
Cited by 2 | Viewed by 2954
Abstract
Urban air pollution is a pressing global issue driven by factors such as swift urbanization, population expansion, and heightened industrial activities. To address this challenge, the integration of Machine Learning (ML) into smart cities presents a promising avenue. Our article offers comprehensive insights [...] Read more.
Urban air pollution is a pressing global issue driven by factors such as swift urbanization, population expansion, and heightened industrial activities. To address this challenge, the integration of Machine Learning (ML) into smart cities presents a promising avenue. Our article offers comprehensive insights into recent advancements in air quality research, employing the PRISMA method as a cornerstone for the reviewing process, while simultaneously exploring the application of frequently employed ML methodologies. Focusing on supervised learning algorithms, the study meticulously analyzes air quality data, elucidating their unique benefits and challenges. These frequently employed ML techniques, including LSTM (Long Short-Term Memory), RF (Random Forest), ANN (Artificial Neural Networks), and SVR (Support Vector Regression), are instrumental in our quest for cleaner, healthier urban environments. By accurately predicting key pollutants such as particulate matter (PM), nitrogen oxides (NOx), carbon monoxide (CO), and ozone (O3), these methods offer tangible solutions for society. They enable informed decision-making for urban planners and policymakers, leading to proactive, sustainable strategies to combat urban air pollution. As a result, the well-being and health of urban populations are significantly improved. In this revised abstract, the importance of frequently employed ML methods in the context of air quality is explicitly emphasized, underlining their role in improving urban environments and enhancing the well-being of urban populations. Full article
(This article belongs to the Special Issue Air Quality Modelling and Forecasting towards Sustainable Development)
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