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Air Pollution Monitoring and Environmental Sustainability
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Air Pollution Monitoring and Environmental Sustainability

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".

Deadline for manuscript submissions: closed (15 December 2020) | Viewed by 29769

Special Issue Editor

Dr. Kabindra M. Shakya

E-Mail Website
Guest Editor
Department of Geography & the Environment, Villanova University, Villanova, PA 19085, USA
Interests: indoor and outdoor air quality; particulate matter; environmental health; environmental sustainability; environmental education

Special Issue Information

Dear Colleagues,

Air pollution is currently considered as one of the main risk factors for global deaths, and it is a major threat to environmental sustainability. Its adverse effects on human health have impacts on sustainable development. Several urban cities are currently experiencing poor air quality and ignoring the fact that environmental sustainability can exacerbate the air quality issue. How can we continue to develop cities and nations without significantly threatening the life of future generations? It is only possible when environmental sustainability is considered, and air pollution is an integral component of environmental sustainability. Regular air-pollution-monitoring programs are a basic step to mitigating any air-pollution-related future disasters, to guide possible strategies to achieve a sustainable future, and to warn the citizens about potential adverse health effects. With the rise of inexpensive air pollution sensors and engagement with citizen scientists, it is possible to monitor air pollution over finer spatial scales. Air pollution monitoring can create general awareness about the effects of air pollution and how it impacts human well-being. To achieve a sustainable future, we must demonstrate that the survival of future generations is not compromised. This Special Issue is intended to provide a platform for studies that examine the impacts of air pollution on environmental sustainability. For this Special Issue, we invite submissions that closely interlink air pollution with sustainability indicators, and how air pollution monitoring can help to achieve the sustainability goals.

Dr. Kabindra M. Shakya
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. 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.

Keywords

  • Urban air pollution
  • Indoor air quality
  • Particulate matter
  • Well-being
  • Sustainable solutions
  • Sustainable practices
  • Sustainable development goals
  • Sustainability indicators
  • Sustainable air pollution management
  • Sustainable transportation
  • Citizen science

Published Papers (7 papers)

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Research

18 pages, 4849 KiB  
Article
Indoor and Outdoor Air Quality for Sustainable Life: A Case Study of Rural and Urban Settlements in Poor Neighbourhoods in Kenya
by Anne Wambui Mutahi, Laura Borgese, Claudio Marchesi, Michael J. Gatari and Laura E. Depero
Sustainability 2021, 13(4), 2417; https://doi.org/10.3390/su13042417 - 23 Feb 2021
Cited by 13 | Viewed by 3535
Abstract
This paper reports on the indoor and outdoor air quality in informal urban and rural settlements in Kenya. The study is motivated by the need to improve consciousness and to understand the harmful health effects of air quality to vulnerable people, especially in [...] Read more.
This paper reports on the indoor and outdoor air quality in informal urban and rural settlements in Kenya. The study is motivated by the need to improve consciousness and to understand the harmful health effects of air quality to vulnerable people, especially in poor communities. Ng’ando urban informal settlement and Leshau Pondo rural village in Kenya are selected as representative poor neighborhoods where unclean energy sources are used indoor for cooking, lighting and heating. Filter based sampling for gravimetrical, elemental composition and black carbon (BC) analysis of particulate matter with an aerodynamic diameter less than 2.5 µm (PM2.5) is performed. findings from Ng’ando and Leshau Pondo showed levels exceeding the limit suggested by the world health organization (WHO), with rare exceptions. Significantly higher levels of PM2.5 and black carbon are observed in indoors than outdoor samples, with a differences in the orders of magnitudes and up to 1000 µg/m3 for PM2.5 in rural settlements. The elemental composition reveals the presence of potentially toxic elements, in addition to characterization, emission sources were also identified. Levels of Pb exceeding the WHO limit are found in the majority of samples collected in the urban locations near major roads with heavy traffic. Our results demonstrate that most of the households live in deplorable air quality conditions for more than 12 h a day and women and children are more affected. Air quality condition is much worse in rural settlements where wood and kerosene are the only available fuels for their energy needs. Full article
(This article belongs to the Special Issue Air Pollution Monitoring and Environmental Sustainability)
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17 pages, 2272 KiB  
Article
Air Pollution Prediction Using an Ensemble of Dynamic Transfer Models for Multivariate Time Series
by Taewoon Kong, Dongguen Choi, Geonseok Lee and Kichun Lee
Sustainability 2021, 13(3), 1367; https://doi.org/10.3390/su13031367 - 28 Jan 2021
Cited by 9 | Viewed by 1822
Abstract
Entering a new era of big data, analysis of large amounts of real-time data is important, and air quality data as streaming time series are measured by several different sensors. To this end, numerous methods for time-series forecasting and deep-learning approaches based on [...] Read more.
Entering a new era of big data, analysis of large amounts of real-time data is important, and air quality data as streaming time series are measured by several different sensors. To this end, numerous methods for time-series forecasting and deep-learning approaches based on neural networks have been used. However, they usually rely on a certain model with a stationary condition, and there are few studies of real-time prediction of dynamic massive multivariate data. Use of a variety of independent variables included in the data is important to improve forecasting performance. In this paper, we proposed a real-time prediction approach based on an ensemble method for multivariate time-series data. The suggested method can select multivariate time-series variables and incorporate real-time updatable autoregressive models in terms of performance. We verified the proposed model using simulated data and applied it to predict air quality measured by five sensors and failures based on real-time performance log data in server systems. We found that the proposed method for air pollution prediction showed effective and stable performance for both short- and long-term prediction tasks. In addition, traditional methods for abnormality detection have focused on present status of objects as either normal or abnormal based on provided data, we protectively predict expected statuses of objects with provided real-time data and implement effective system management in cloud environments through the proposed method. Full article
(This article belongs to the Special Issue Air Pollution Monitoring and Environmental Sustainability)
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28 pages, 7571 KiB  
Article
Analysis of the Effectiveness of Air Pollution Control Policies Based on Historical Evaluation and Deep Learning Forecast: A Case Study of Chengdu-Chongqing Region in China
by Hao Gao, Weixin Yang, Jiawei Wang and Xiaoyun Zheng
Sustainability 2021, 13(1), 206; https://doi.org/10.3390/su13010206 - 28 Dec 2020
Cited by 32 | Viewed by 4938
Abstract
Air pollution is a common problem for many countries around the world in the process of industrialization as well as a challenge to sustainable development. This paper has selected Chengdu-Chongqing region of China as the research object, which suffers from severe air pollution [...] Read more.
Air pollution is a common problem for many countries around the world in the process of industrialization as well as a challenge to sustainable development. This paper has selected Chengdu-Chongqing region of China as the research object, which suffers from severe air pollution and has been actively involved in air pollution control in recent years to achieve sustainable development. Based on the historical data of 16 cities in this region from January 2015 to November 2019 on six major air pollutants, this paper has first conducted evaluation on the monthly air quality of these cities within the research period by using Principal Component Analysis and the Technique for Order Preference by Similarity to an Ideal Solution. Based on that, this paper has adopted the Long Short-Term Memory neural network model in deep learning to forecast the monthly air quality of various cities from December 2019 to November 2020. The aims of this paper are to enrich existing literature on air pollution control, and provide a novel scientific tool for design and formulation of air pollution control policies by innovatively integrating commonly used evaluation models and deep learning forecast methods. According to the research results, in terms of historical evaluation, the air quality of cities in the Chengdu-Chongqing region was generally moving in the same trend in the research period, with distinct characteristics of cyclicity and convergence. Year- on-year speaking, the effectiveness of air pollution control in various cities has shown a visible improvement trend. For example, Ya’an’s lowest air quality evaluation score has improved from 0.3494 in 2015 to 0.4504 in 2019; Zigong’s lowest air quality score has also risen from 0.4160 in 2015 to 0.6429 in 2019. Based on the above historical evaluation and deep learning forecast results, this paper has proposed relevant policy recommendations for air pollution control in the Chengdu-Chongqing region. Full article
(This article belongs to the Special Issue Air Pollution Monitoring and Environmental Sustainability)
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12 pages, 248 KiB  
Article
Does Green Energy Complement Economic Growth for Achieving Environmental Sustainability? Evidence from Saudi Arabia
by Montassar Kahia, Anis Omri and Bilel Jarraya
Sustainability 2021, 13(1), 180; https://doi.org/10.3390/su13010180 - 27 Dec 2020
Cited by 23 | Viewed by 2984
Abstract
This study extends previous environmental sustainability literature by investigating the joint impact of economic growth and renewable energy on reducing CO2 emissions in Saudi Arabia over the period 1990–2016. Using the fully modified ordinary least-square (FMOLS) and dynamic ordinary least-square DOLS estimators, [...] Read more.
This study extends previous environmental sustainability literature by investigating the joint impact of economic growth and renewable energy on reducing CO2 emissions in Saudi Arabia over the period 1990–2016. Using the fully modified ordinary least-square (FMOLS) and dynamic ordinary least-square DOLS estimators, we find that economic growth increases CO2 emissions in all estimated models. Moreover, the validity of the environmental Kuznets curve (EKC) hypothesis is only supported for CO2 emissions from liquid fuel consumption. The invalidity of the EKC hypothesis in the most commonly used models implies that economic growth alone is not sufficient to enhance environmental quality. Renewable energy is found to have a weak influence on reducing the indicators of environmental degradation. We also find that the joint impact of renewable energy consumption and economic growth on the indicators of CO2 emissions is negative and insignificant for all the estimated models, meaning that the level of renewable energy consumption in Saudi Arabia is not sufficient to moderate the negative effect of economic growth on environmental quality. Implications for policy are also discussed. Full article
(This article belongs to the Special Issue Air Pollution Monitoring and Environmental Sustainability)
22 pages, 690 KiB  
Article
A Novel One-Dimensional CNN with Exponential Adaptive Gradients for Air Pollution Index Prediction
by Mohammed G. Ragab, Said J. Abdulkadir, Norshakirah Aziz, Qasem Al-Tashi, Yousif Alyousifi, Hitham Alhussian and Alawi Alqushaibi
Sustainability 2020, 12(23), 10090; https://doi.org/10.3390/su122310090 - 03 Dec 2020
Cited by 54 | Viewed by 3514
Abstract
Air pollution is one of the world’s most significant challenges. Predicting air pollution is critical for air quality research, as it affects public health. The Air Pollution Index (API) is a convenient tool to describe air quality. Air pollution predictions can provide accurate [...] Read more.
Air pollution is one of the world’s most significant challenges. Predicting air pollution is critical for air quality research, as it affects public health. The Air Pollution Index (API) is a convenient tool to describe air quality. Air pollution predictions can provide accurate information on the future pollution situation, effectively controlling air pollution. Governments have expressed growing concern about air pollution due to its global effect on human health and sustainable growth. This paper proposes a novel forecasting model using One-Dimensional Deep Convolutional Neural Network (1D-CNN) and Exponential Adaptive Gradients (EAG) optimization to predict API for a selected location, Klang, a city in Malaysia. The proposed 1D-CNN–EAG exponentially accumulates past model gradients to adaptively tune the learning rate and converge in both convex and non-convex areas. We use hourly air pollution data over three years (January 2012 to December 2014) for training. Parameter optimization and model evaluation was accomplished by a grid-search with k-folds cross-validation. Results have confirmed that the proposed approach achieves better prediction accuracy than the benchmark models in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and the Correlation Coefficient (R-Squared) with values of 2.036, 2.354, 4.214 and 0.966, respectively, and time complexity. Full article
(This article belongs to the Special Issue Air Pollution Monitoring and Environmental Sustainability)
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5 pages, 561 KiB  
Communication
Real-Time Measurement of Indoor PM Concentrations on Daily Change of Endocrine Disruptors in Urine Samples of New Mothers
by Dohyeong Kim, Ju Hee Kim and SungChul Seo
Sustainability 2020, 12(15), 6166; https://doi.org/10.3390/su12156166 - 31 Jul 2020
Cited by 6 | Viewed by 2390
Abstract
The recent innovation of IoT-based sensor technologies facilitates real-time monitoring of indoor air pollutants, such as particulate matter (PM), but its dynamic impacts on the level of endocrine disruptors in human body remain understudied. This feasibility study analyzed if the constant measurements of [...] Read more.
The recent innovation of IoT-based sensor technologies facilitates real-time monitoring of indoor air pollutants, such as particulate matter (PM), but its dynamic impacts on the level of endocrine disruptors in human body remain understudied. This feasibility study analyzed if the constant measurements of indoor PM concentrations collected at every five minutes are meaningfully associated with the levels of 15 types of endocrine disruptors in urine samples collected three times a day from nine new breastfeeding mothers in Seoul, Korea. Some promising results are observed in terms of detecting cumulative effects of PM10 and PM2.5 on some phthalate metabolites (MnBP, MiBP, MiNP, MCOP, MEOHP and MEHHP), BPA and TCS, at least for some participants. The findings from this study are expected to provide valuable directions for guiding future studies that discover potential associations between indoor PM concentrations and exposure to endocrine disruptors, which is still far from the consensus in the literature. Such efforts should offer empirical and scientific evidences for designing technology-based early warning/alarm services and evidence-based interventions to mitigate the level of exposure to PM and endocrine disruptors in their living environments. Full article
(This article belongs to the Special Issue Air Pollution Monitoring and Environmental Sustainability)
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17 pages, 6271 KiB  
Article
Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models
by Thanongsak Xayasouk, HwaMin Lee and Giyeol Lee
Sustainability 2020, 12(6), 2570; https://doi.org/10.3390/su12062570 - 24 Mar 2020
Cited by 116 | Viewed by 8770
Abstract
Many countries worldwide have poor air quality due to the emission of particulate matter (i.e., PM10 and PM2.5), which has led to concerns about human health impacts in urban areas. In this study, we developed models to predict fine PM [...] Read more.
Many countries worldwide have poor air quality due to the emission of particulate matter (i.e., PM10 and PM2.5), which has led to concerns about human health impacts in urban areas. In this study, we developed models to predict fine PM concentrations using long short-term memory (LSTM) and deep autoencoder (DAE) methods, and compared the model results in terms of root mean square error (RMSE). We applied the models to hourly air quality data from 25 stations in Seoul, South Korea, for the period from 1 January 2015, to 31 December 2018. Fine PM concentrations were predicted for the 10 days following this period, at an optimal learning rate of 0.01 for 100 epochs with batch sizes of 32 for LSTM model, and DAEs model performed best with batch size 64. The proposed models effectively predicted fine PM concentrations, with the LSTM model showing slightly better performance. With our forecasting model, it is possible to give reliable fine dust prediction information for the area where the user is located. Full article
(This article belongs to the Special Issue Air Pollution Monitoring and Environmental Sustainability)
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