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Atmosphere
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Application of Machine Learning in Air Pollution
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Application of Machine Learning in Air Pollution

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

Deadline for manuscript submissions: closed (15 November 2020) | Viewed by 11057

Special Issue Editor

Prof. HwaMin Lee

E-Mail Website
Guest Editor
Department of Computer Software & Engineering, Soonchunhyang University, Asan, Korea
Interests: deep learning; air quality; particulate matter; machine learning; cloud computing; big data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The air pollution problem has emerged as a global problem beyond the problems of major cities. The OECD warns that urban air pollution will account for the largest proportion of deaths in the future, not water scarcity or poor sanitation. In particular, the results of the WHO survey that 7 million people die early in the world due to fine dust show 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, this shows the possibility of research expansion, such as monitoring, analysis, and prediction using machine learning. This Special Issue is intended to investigate applications and studies using a variety of machine learning approaches, including deep learning in air pollution. For this Special Issue, we invite submissions that closely interlink air pollution with machine learning, and how machine learning can help to achieve air pollution research goals.

Prof. HwaMin Lee
Guest Editor

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 pollution
  • Machine learning
  • Deep learning
  • Artificial intelligence
  • Air quality
  • Particulate matter
  • Air pollution management application

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Published Papers (3 papers)

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Research

18 pages, 7808 KiB  
Article
MultiStep Ahead Forecasting for Hourly PM10 and PM2.5 Based on Two-Stage Decomposition Embedded Sample Entropy and Group Teacher Optimization Algorithm
by Feng Jiang, Yaqian Qiao, Xuchu Jiang and Tianhai Tian
Atmosphere 2021, 12(1), 64; https://doi.org/10.3390/atmos12010064 - 3 Jan 2021
Cited by 10 | Viewed by 3480
Abstract
The randomness, nonstationarity and irregularity of air pollutant data bring difficulties to forecasting. To improve the forecast accuracy, we propose a novel hybrid approach based on two-stage decomposition embedded sample entropy, group teaching optimization algorithm (GTOA), and extreme learning machine (ELM) to forecast [...] Read more.
The randomness, nonstationarity and irregularity of air pollutant data bring difficulties to forecasting. To improve the forecast accuracy, we propose a novel hybrid approach based on two-stage decomposition embedded sample entropy, group teaching optimization algorithm (GTOA), and extreme learning machine (ELM) to forecast the concentration of particulate matter (PM10 and PM2.5). First, the improvement complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is employed to decompose the concentration data of PM10 and PM2.5 into a set of intrinsic mode functions (IMFs) with different frequencies. In addition, wavelet transform (WT) is utilized to decompose the IMFs with high frequency based on sample entropy values. Then the GTOA algorithm is used to optimize ELM. Furthermore, the GTOA-ELM is utilized to predict all the subseries. The final forecast result is obtained by ensemble of the forecast results of all subseries. To further prove the predictable performance of the hybrid approach on air pollutants, the hourly concentration data of PM2.5 and PM10 are used to make one-step-, two-step- and three-step-ahead predictions. The empirical results demonstrate that the hybrid ICEEMDAN-WT-GTOA-ELM approach has superior forecasting performance and stability over other methods. This novel method also provides an effective and efficient approach to make predictions for nonlinear, nonstationary and irregular data. Full article
(This article belongs to the Special Issue Application of Machine Learning in Air Pollution)
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12 pages, 2453 KiB  
Article
Regional Delimitation of PM2.5 Pollution Using Spatial Cluster for Monitoring Sites: A Case Study of Xianyang, China
by Bo Zhang, Fang Zhou and Guojun Song
Atmosphere 2020, 11(9), 972; https://doi.org/10.3390/atmos11090972 - 11 Sep 2020
Cited by 2 | Viewed by 2672
Abstract
Fine particulate matter (PM2.5) pollution has been a major concerning issue in China, and many cities have developed emergency plans for heavy air pollution. The aim of this study is to delimitate PM2.5 pollution regions of Xianyang, which is very [...] Read more.
Fine particulate matter (PM2.5) pollution has been a major concerning issue in China, and many cities have developed emergency plans for heavy air pollution. The aim of this study is to delimitate PM2.5 pollution regions of Xianyang, which is very important to the regional environmental prevention and control. The result showed that PM2.5 air pollution had significant cross-administrative characteristics in Xianyang. Using spatial clustering algorithm under adjacent matrix constrain, this study classified the air quality monitoring sites into two clusters. For each monitoring site, we generated Voronoi polygons and ultimately Xianyang was delimitated into two regions, south and north. The air pollution of the southern region was more serious with 64 days of heavy and severe pollution since 2018, while the northern region had only 10 days. The southern region consisted of four complete administrative districts and parts of three administrative districts. While the northern region consisted of six complete administrative districts and parts of three administrative districts. Visualization of the spatiotemporal characteristics of the PM2.5 air pollution in the two regions further illustrated the significant difference. We suggest when heavy pollution happens, the two regions should be considered separately. If the southern region is heavily polluted while the northern region not, only the southern region needs to implement the emergency plan to minimize the damage to society and economy. Seventy-five percent of the city area, 2.3 million people, 59% of schools, and 43% of GDP would not be impacted if air pollution was controlled by region separately. The sensitive analysis shows that clustering result is robust against different pollution degree and missing values. Full article
(This article belongs to the Special Issue Application of Machine Learning in Air Pollution)
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9 pages, 1276 KiB  
Article
Prediction of Odor Concentration Emitted from Wastewater Treatment Plant Using an Artificial Neural Network (ANN)
by Jeong-Hee Kang, JiHyeon Song, Sung Soo Yoo, Bong-Jae Lee and Hyon Wook Ji
Atmosphere 2020, 11(8), 784; https://doi.org/10.3390/atmos11080784 - 24 Jul 2020
Cited by 21 | Viewed by 3089
Abstract
The odor emitted from a wastewater treatment plant (WWTP) is an important environmental problem. An estimation of odor emission rate is difficult to detect and quantify. To address this, various approaches including the development of emission factors and measurement using a closed chamber [...] Read more.
The odor emitted from a wastewater treatment plant (WWTP) is an important environmental problem. An estimation of odor emission rate is difficult to detect and quantify. To address this, various approaches including the development of emission factors and measurement using a closed chamber have been employed. However, the evaluation of odor emission involves huge manpower, time, and cost. An artificial neural network (ANN) is recognized as an efficient method to find correlations between nonlinear data and prediction of future data based on these correlations. Due to its usefulness, ANN is used to solve complicated problems in various disciplines of sciences and engineering. In this study, a method to predict the odor concentration in a WWTP using ANN was developed. The odor concentration emitted from a WWTP was predicted by the ANN based on water quality data such as biological oxygen demand, dissolved oxygen, and pH. The water quality and odor concentration data from the WWTP were measured seasonally in spring, summer, and autumn and these were used as input variations to the ANN model. The odor predicted by the ANN model was compared with the measured data and the prediction accuracy was estimated. Suggestions for improving prediction accuracy are presented. Full article
(This article belongs to the Special Issue Application of Machine Learning in Air Pollution)
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