Air Quality and the Implementation of Sustainable Development Goals

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

Deadline for manuscript submissions: closed (18 January 2024) | Viewed by 3890

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Guest Editor
Division of Hydraulics and Environmental Engineering, Department of Civil Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: water resource management; sustainable development; renewable energy sources; environment; modeling; climate change
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Guest Editor
Department of Marine and Energy Engineering, Ovidius University of Constanta, 900527 Constanta, Romania
Interests: computational fluid dynamics; air pollution; sustainable development; energy systems
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Guest Editor
Environmental Informatics Research Group, School of Mechanical Engineering, Faculty of Engineering, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece
Interests: environmental informatics; computational intelligence oriented data analytics and modelling; urban air quality management and information systems; computational calibration and performance improvement of low-cost environmental sensors; quality of life information services; citizen science; mechanical engineering
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Guest Editor
Department of Environmental Science, Aarhus University, 4000 Roskilde, Denmark
Interests: modelling and measurements of air pollution (regional- to street-scale); human exposure to air pollution and health consequences; smart and sustainable cities; people and well-being; artificial intelligence and machine learning; aerosol dynamics; informatics; air quality management; low- and medium-cost sensors for air quality monitoring; Internet of Things (IoT); big data analytics; new and improved methods to predict air pollution in cities

Special Issue Information

Dear Colleagues,                                                                                                    

In 2015, the UN General Assembly adopted the 2030 Agenda for Sustainable Development. In this document, the heads of UN member states have committed their countries to the following: “Until [..] 2030, to end poverty and hunger everywhere; to combat inequalities within and among countries; to build peaceful, just and inclusive societies; to protect human rights and promote gender equality and the empowerment of women and girls, and to ensure the lasting protection of the planet and its natural resources. [..] also to create conditions for sustainable, inclusive and sustained economic growth, shared prosperity and decent work for all, considering different levels of national development and capacities.”

This outlined the 17 Sustainable Development Goals—SDGs—and besides SDG 11 on Sustainable Cities and Communities, almost all the other SDGs are connected to the air quality or quality of the habitat and the inner-related topics.

This Special Issue of Atmosphere aims to promote the scientific and technical communications reporting results and propose new innovative approaches for ensuring air quality by the implementation of the SDGs. Original results about modelling, field and controlled investigations, and review papers related to ensuring air quality through the implementation of SDGs are all welcome contributions.

The topics of interest for this Special Issue include but are not limited to:

  • Emission inventory from anthropogenic sources;
  • Monitoring techniques of air quality;
  • Impacts of SDGs on air quality;
  • Interactions of air quality with other SDGs;
  • Comprehensive impacts of policies for implementing SDGs on emission control and greenhouse gas reductions;
  • Impacts of air quality on human health, marine and terrestrial ecosystem and climate change;
  • Citizen science, air quality and SDGs;
  • Urban air quality interactions with SDGs;
  • New air quality monitoring and modelling methods towards SDG qualitative and quantitative assessment.

Prof. Dr. Nikolaos P. Theodossiou
Prof. Dr. Eden Mamut
Prof. Dr. Kostas Karatzas
Dr. Jibran Khan
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
  • aerosols
  • trace elements
  • emission inventory
  • human health
  • climate change
  • energy efficiency
  • sustainable development goals
  • air quality monitoring and modelling
  • citizen science

Published Papers (3 papers)

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Research

18 pages, 3098 KiB  
Article
Application of Trigonometric Polynomial Fitting Method in Simulating the Spatial Distribution of PM2.5 Concentration in South-Central China
by Yang Chen, Ning Li, Minjie Xu, Wenqi Shi and Xianqing Lv
Atmosphere 2024, 15(1), 28; https://doi.org/10.3390/atmos15010028 - 25 Dec 2023
Viewed by 739
Abstract
Near-surface PM2.5 estimates remain a global scientific research challenge due to their effect on human fitness and atmospheric environmental quality. However, practical near-surface PM2.5 estimates are impeded by the incomplete monitoring data. In this study, we propose the trigonometric polynomial fitting [...] Read more.
Near-surface PM2.5 estimates remain a global scientific research challenge due to their effect on human fitness and atmospheric environmental quality. However, practical near-surface PM2.5 estimates are impeded by the incomplete monitoring data. In this study, we propose the trigonometric polynomial fitting (TPF) method to estimate near-surface PM2.5 concentrations in south-central China during 2015. We employ 10-fold cross-validation (CV) to assess the reliability of TPF in estimating practical PM2.5 values. When compared to alternative methods such as the orthogonal polynomial fitting (OBF) method based on Chebyshev basis functions, Kriging interpolation, and radial basis function (RBF) interpolation, our results show that utilizing TPF31, with a maximum order of 3 in the x direction and a maximum order of 1 in the y direction, leads to superior efficiency through error minimization. TPF31 reduces MAE and RMSE by 1.93%, 24%, 6.96% and 3.6%, 23.07%, 10.43%, respectively, compared to the other three methods. In addition, the TPF31 method effectively reconstructs the spatial distribution of PM2.5 concentrations in the unevenly distributed observation stations of Inner Mongolia and the marginal regions of the study area. The reconstructed spatial distribution is remarkably smooth. Despite the non-uniform distribution of observation stations and the presence of missing data, the TPF31 method demonstrates exceptional effectiveness in accurately capturing the inherent physical attributes of spatial distribution. The theoretical and experimental results emphasize that the TPF method holds significant potential for accurately reconstructing the spatial distribution of PM2.5 in China. Full article
(This article belongs to the Special Issue Air Quality and the Implementation of Sustainable Development Goals)
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16 pages, 2539 KiB  
Article
Soil-Derived Dust PM10 and PM2.5 Fractions in Southern Xinjiang, China, Using an Artificial Neural Network Model
by Shuang Gao, Yaxin Liu, Jieqiong Zhang, Jie Yu, Li Chen, Yanling Sun, Jian Mao, Hui Zhang, Zhenxing Ma, Wen Yang, Ningning Hong, Merched Azzi, Hong Zhao, Hui Wang and Zhipeng Bai
Atmosphere 2023, 14(11), 1644; https://doi.org/10.3390/atmos14111644 - 31 Oct 2023
Viewed by 1048
Abstract
Soil-derived dust emissions have been widely associated with health and environmental problems and should therefore be accurately and reliably estimated and assessed. Of these emissions, the inhalable PM10 and PM2.5 are difficult to estimate. Consequently, to calculate PM10 and PM [...] Read more.
Soil-derived dust emissions have been widely associated with health and environmental problems and should therefore be accurately and reliably estimated and assessed. Of these emissions, the inhalable PM10 and PM2.5 are difficult to estimate. Consequently, to calculate PM10 and PM2.5 emissions from soil erosion, an approach based on an artificial neural network (ANN) model which provides a multilayered, fully connected framework that relates input parameters and outcomes was proposed in this study. Owing to the difficulty in obtaining the actual emissions of soil-derived PM10 and PM2.5 over a broad area, the PM10 and PM2.5 simulated results of the ANN model were compared with the published results simulated by the widely used wind erosion prediction system (WEPS) model. The PM10 and PM2.5 emission results, based on the WEPS, agreed well with the field data, with R2 values of 0.93 and 0.97, respectively, indicating the potential for using the WEPS results as a reference for training the ANN model. The calculated r, RMSE and MAE for the results simulated by the WEPS and ANN were 0.78, 3.37 and 2.31 for PM10 and 0.79, 1.40 and 0.91 for PM2.5, respectively, throughout Southern Xinjiang. The uncertainty of the soil-derived PM10 and PM2.5 emissions at a 95% CI was (−66–106%) and (−75–108%), respectively, in 2016. The results indicated that by using parameters that affect soil erodibility, including the soil pH, soil cation exchange capacity, soil organic content, soil calcium carbonate, wind speed, precipitation and elevation as input factors, the ANN model could simulate soil-derived particle emissions in Southern Xinjiang. The results showed that when the study domain was reduced from the entire Southern Xinjiang region to its five administrative divisions, the performance of the ANN improved, producing average correlation coefficients of 0.88 and 0.87, respectively, for PM10 and PM2.5. The performances of the ANN differed by study period, with the best result obtained during the sand period (March to May) followed by the nonheating (June to October) and heating periods (November to February). Wind speed, precipitation and soil calcium carbonate were the predominant input factors affecting particle emissions from wind erosion sources. The results of this study can be used as a reference for the wind erosion prevention and soil conservation plans in Southern Xinjiang. Full article
(This article belongs to the Special Issue Air Quality and the Implementation of Sustainable Development Goals)
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16 pages, 3725 KiB  
Article
Spatiotemporal Variation and Driving Factors for NO2 in Mid-Eastern China
by Mingjian Yi, Yongqing Jiang, Qiang Zhao, Junxia Qiu and Yi Li
Atmosphere 2023, 14(9), 1369; https://doi.org/10.3390/atmos14091369 - 30 Aug 2023
Viewed by 894
Abstract
Nitrogen dioxide (NO2) is one of the major air pollutants in cities across mid-eastern China. Comprehending the spatial and temporal dynamics of NO2 drivers in various urban areas is imperative for tailoring effective air control strategies. Using data from ground-based [...] Read more.
Nitrogen dioxide (NO2) is one of the major air pollutants in cities across mid-eastern China. Comprehending the spatial and temporal dynamics of NO2 drivers in various urban areas is imperative for tailoring effective air control strategies. Using data from ground-based monitoring stations, we investigated the impact of socioeconomic and meteorological factors on NO2 concentrations in cities in mid-eastern China from 2015 to 2021 using the Geographically and Temporally Weighted Regression (GTWR) model. The findings reveal a notable reduction of over 10% in NO2 concentrations since 2015 in most cities, notably a 50.5% decrease in Bozhou. However, certain areas within Anhui and Jiangsu have experienced an increase in NO2 concentrations. Significant spatial heterogeneity is observed in the relationship between NO2 concentrations and influencing factors. The permanent population density (POP) and the electricity consumption (EC) of the entire society exhibited the strongest correlations with NO2 concentrations, with average coefficients of 0.431 and 0.520, respectively. Furthermore, other economic factors such as urbanization rate (UR), the share of secondary sector output in total GDP (IS), and the coverage rate of urban green areas (CG) were predominantly positively correlated, while GDP per capita (PGDP) and civil car vehicles (CV) demonstrated primarily negative correlations. Furthermore, we examined the correlations between four meteorological factors (temperature, relative humidity, wind speed, and precipitation) and NO2 concentrations. All these factors exhibited negative correlations with NO2 concentrations. Among them, temperature exhibited the strongest negative correlation, with a coefficient of −0.411. This research may contribute valuable insights and guidance for developing air emission reduction policies in various cities in mid-eastern China. Full article
(This article belongs to the Special Issue Air Quality and the Implementation of Sustainable Development Goals)
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