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Special Issue "Machine Learning in Air Pollution"

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A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: 31 March 2023 | Viewed by 2153

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Special Issue Editors

Dr. HwaMin Lee

E-Mail Website
Guest Editor

College of Medicine, Korea University, Seoul 02841, Korea
Interests: machine learning; deep learning; air pollution; particulate matter; cloud computing; big data
Special Issues, Collections and Topics in MDPI journals

Dr. Sungroul Kim
E-Mail Website
Guest Editor

Department of Environmental Health Sciences, Soonchunhyang University, Asan 31538, Korea
Interests: air pollutants measurement; exposure assessment; environmental health

Special Issue Information

Dear Colleagues,

Air pollution has emerged as a global problem beyond those of major cities. The OECD warns that urban air pollution will account for the largest proportion of deaths in the future, rather than water scarcity or poor sanitation. In particular, the results of a WHO survey found that 7 million people worldwide die early due to fine dust, illustrating that the health of citizens is threatened by air pollution. Therefore, we need to make efforts to solve the air pollution problem together across national and urban boundaries. Machine learning is a field of artificial intelligence (AI) that automates model creation for data analysis so that software learns and finds patterns based on data. With the advent of new computing technologies, machine learning today is different from machine learning in the past. With the development of new technologies, including deep learning, various machine learning algorithms are being developed that can be applied to big data analysis faster and faster by repeating complex calculations. In the field of air pollution, artificial intelligence has the potential for expansion to a variety of research areas, such as various monitoring, analysis, and prediction tasks using machine learning. This Special Issue intends to publish papers describing methods and studies using a variety of machine learning techniques, including deep learning, in air pollution. For this Special Issue, we invite submissions that closely interlink air pollution with machine learning, and which illustrate how machine learning can help to achieve air pollution research goals.

Dr. HwaMin Lee
Dr. Sungroul Kim
Guest Editors

Manuscript Submission Information

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Keywords

intelligent application for air pollution
air pollution
machine learning
deep learning
artificial intelligence
air quality
particulate matter

Published Papers (3 papers)

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Research

Open AccessArticle

Short-Term Air Pollution Forecasting Using Embeddings in Neural Networks

and

Atmosphere 2023, 14(2), 298; https://doi.org/10.3390/atmos14020298 - 02 Feb 2023

Viewed by 562

Abstract

Air quality is a highly relevant issue for any developed economy. The high incidence of pollution levels and their impact on human health has attracted the attention of the machine-learning scientific community. We present a study using several machine-learning methods to forecast NO₂ concentration using historical pollution data and meteorological variables and apply them to the city of Erfurt, Germany. We propose modelling the time dependency using embedding variables, which enable the model to learn the implicit behaviour of traffic and offers the possibility to elaborate on local events. In addition, the model uses seven meteorological features to forecast the NO₂ concentration for the next hours. The forecasting model also uses the seasonality of the pollution levels. Our experimental study shows that promising forecasts can be achieved, especially for holidays and similar occasions which lead to shifts in usual seasonality patterns. While the MAE values of the compared models range from

4.3

to 15, our model achieves values of

4.4

7.4

and thus outperforms the others in almost every instance. Those forecasts again can for example be used to regulate sources of pollutants such as, e.g., traffic. Full article

(This article belongs to the Special Issue Machine Learning in Air Pollution)

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Open AccessArticle

Development of a CNN+LSTM Hybrid Neural Network for Daily PM_2.5 Prediction

and

Atmosphere 2022, 13(12), 2124; https://doi.org/10.3390/atmos13122124 - 17 Dec 2022

Cited by 2 | Viewed by 616

Abstract

A CNN+LSTM (Convolutional Neural Network + Long Short-Term Memory) based deep hybrid neural network was established for the citywide daily PM_2.5 prediction in South Korea. The structural hyperparameters of the CNN+LSTM model were determined through comprehensive sensitivity tests. The input features were obtained from the ground observations and GFS forecast. The performance of CNN+LSTM was evaluated by comparison with PM_2.5 observations and with the 3-D CTM (three-dimensional chemistry transport model)-predicted PM_2.5. The newly developed hybrid model estimated more accurate ambient levels of PM_2.5 compared to the 3-D CTM. For example, the error and bias of the CNN+LSTM prediction were 1.51 and 6.46 times smaller than those by 3D-CTM simulation. In addition, based on IOA (Index of Agreement), the accuracy of CNN+LSTM prediction was 1.10–1.18 times higher than the 3-D CTM-based prediction. The importance of input features was indirectly investigated by sequential perturbing input variables. The most important meteorological and atmospheric environmental features were geopotential height and previous day PM_2.5. The obstacles of the current CNN+LSTM-based PM_2.5 prediction were also discussed. The promising result of this study indicates that DNN-based models can be utilized as an effective tool for air quality prediction. Full article

(This article belongs to the Special Issue Machine Learning in Air Pollution)

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Open AccessFeature PaperArticle

A Novel Komodo Mlipir Algorithm and Its Application in PM2.5 Detection

Linxuan Li

and

Ming Zhao

Atmosphere 2022, 13(12), 2051; https://doi.org/10.3390/atmos13122051 - 07 Dec 2022

Viewed by 480

Abstract

The paper presents an improved Komodo Mlipir Algorithm (KMA) with variable inertia weight and chaos mapping (VWCKMA). In contrast to the original Komodo Mlipir Algorithm (KMA), the chaotic sequence initialization population generated by Tent mapping and Tent Chaos disturbance used in VWCKMA can effectively prevent the algorithm from falling into a local optimal solution and enhance population diversity. Individuals of different social classes can be controlled by the variable inertia weight, and the convergence speed and accuracy can be increased. For the purpose of evaluating the performance of the VWCKMA, function optimization and actual predictive optimization experiments are conducted. As a result of the simulation results, the convergence accuracy and convergence speed of the VWCKMA have been considerably enhanced for single-peak, multi-peak, and fixed-dimensional complex functions in different dimensions and even thousands of dimensions. To address the nonlinearity of PM2.5 prediction in practical problems, the weights and thresholds of the BP neural network were iteratively optimized using VWCKMA, and the BP neural network was then used to predict PM2.5 using the optimal parameters. Experimental results indicate that the accuracy of the VWCKMA-optimized BP neural network model is 85.085%, which is 19.85% higher than that of the BP neural network, indicating that the VWCKMA has a certain practical application. Full article

(This article belongs to the Special Issue Machine Learning in Air Pollution)

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