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Special Issue "Advances in Integrated Air Quality Management: Emissions, Monitoring, Modelling (2nd Volume)"

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

Deadline for manuscript submissions: 10 July 2023 | Viewed by 3796

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Special Issue Editors

Dr. Adrianos Retalis

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Guest Editor

Institute for Environmental Research & Sustainable Development, National Observatory of Athens, I. Metaxa & Vas. Pavlou, Lofos Koufou, Penteli, GR 15236 Athens, Greece
Interests: environmental applications of remote sensing; atmospheric correction; air quality assessment/monitoring; aerosols; natural hazards; land cover/use change; GIS; spatial data analysis; climate change; natural disasters and extremes; desertification; precision farming; soil erosion
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Dr. Vasiliki Assimakopoulos

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Institute for Environmental Research & Sustainable Development, National Observatory of Athens, GR-15236 Athens, Greece
Interests: emission inventory development; chemical transport modeling; urban air quality; air pollution mitigation strategies
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Dr. Kyriaki-Maria Fameli

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Guest Editor

Institute for Environmental Research & Sustainable Development, National Observatory of Athens, GR-15236 Athens, Greece
Interests: emission inventory development (classical pollutants and GHGs); air and particulate pollution over urban areas; GIS; air quality modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is a follow-up of the first Special Issue entitled “Advances in Integrated Air Quality Management: Emissions, Monitoring, Modelling” (https://www.mdpi.com/journal/atmosphere/special_issues/advances_air_management) published in Atmosphere and will cover all aspects of air quality management issues.

Air pollution has become an increasingly important environmental issue on a global scale, since the sources that cause poor air quality and are responsible for climate change are common. Both natural and anthropogenic components of air pollution have been long recognized and are continuously being investigated to identify links with local and regional air quality, impact on climate, health and ecosystems, new sources and pollutants, as well as links between emissions and air pollution management.

Air quality is monitored at the surface through ground-based monitors, official networks, low-cost sensors and, recently, cheap and easy-to-use sensors by citizens. Monitoring aims to identify pollution sources, air quality, compliance with ambient air quality standards, exposure and impact from other parameters (meteorology, topography, accidental release, etc.). Current research focuses on the study of intra-urban, local, regional and intercontinental transport of air pollutants, such as particulate matter (PM₁₀, PM_2.5), O₃, NOx and so on. However, there is need for additional data on air pollution, both spatially and temporally, by including satellite data (in terms of aerosol optical thickness), synoptic information, visualization to ground-based air quality data modeling, advanced statistical relations to forecast air quality, new emissions sources and new pollutants.

This proposal aims to gather research papers focused on methodologies based on emission inventories, classical and novel methodologies, remote and in situ experimental observations, meteorological and climate parameters that affect air pollution, application of chemical transport and/or development of statistical models for forecasting air pollution levels and assisting the monitoring and mapping of air pollution close to major sources or in greater areas.

Dr. Adrianos Retalis
Dr. Vasiliki Assimakopoulos
Dr. Kyriaki-Maria Fameli
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 2000 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

emission inventory
air pollution monitoring
air pollution assessment
exposure
climate change and air pollution
PM2.5, PM10
ozone
aerosols
statistical forecasting models
chemical transport models
urban air pollution
remote sensing

Published Papers (6 papers)

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Research

Open AccessArticle

The Source Apportionment of Heavy Metals in Surface Dust in the Main District Bus Stops of Tianshui City Based on the Positive Matrix Factorization Model and Geo-Statistics

and

Atmosphere 2023, 14(3), 591; https://doi.org/10.3390/atmos14030591 - 20 Mar 2023

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Abstract

For the pollution assessment and quantitative source apportionment of heavy metals in surface dust, a total of 52 surface dust samples were collected from bus stops in Tianshui City. The geoaccumulation index (I_geo) and potential ecological risk index (RI) were used to analyze the pollution levels caused by heavy metals. The Positive Matrix Factorization (PMF) of the receptor modeling and geo-statistics were employed to analyze the source of the heavy metals. The results were as follows. ① Except for Mn, Co and V, the mean concentrations of other heavy metals have exceeded the local background value of Gansu. The percentage of excessive concentrations of Cu, Zn, Sr, Ba and Pb in the samples was 100%, and that of Cr, Ni and As were 96.15%, 94.23%, and 96.15%, respectively. ② Semivariogram model fitting showed that the block-based coefficients of Cu, Zn, Sr, Ba, Pb, Cr, Ni, and As were between 0.25 and 0.75, indicating that they were mainly affected by human factors. The high values of Pb, Zn, Ni and As were mainly distributed in the eastern part of the study area, and the high values of Cu, Sr, Ba and Cr were distributed in a spot-like pattern in the study area. ③ The I_geo results showed that As, Cu, Zn, and Pb were the main contamination factors, and the optimized RI showed that the heavy metals were the overall ecological risk of intensity, among which Pb, As and Cu were the main ecological factors and should be taken as the priority control objects. ④ Based on the PMF, there are four main sources of eleven heavy metals. V, Mn, and Co were attributed to natural sources, accounting for 18.33%; Cu, Sr, and Ba were from mixed sources of pollution from transportation and industrial alloy manufacturing, accounting for 26.99%; Cr and Ni were from sources of construction waste pollution, accounting for 17.17%, As, Zn and Pb were mainly produced by coal-traffic mixed pollution emissions, accounting for 37.52%. Overall, the study area was dominated by coal-traffic emissions. Full article

(This article belongs to the Special Issue Advances in Integrated Air Quality Management: Emissions, Monitoring, Modelling (2nd Volume))

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Open AccessArticle

Study of the Dynamical Relationships between PM2.5 and PM10 in the Caribbean Area Using a Multiscale Framework

Thomas Plocoste

Adarsh Sankaran

and

Lovely Euphrasie-Clotilde

Atmosphere 2023, 14(3), 468; https://doi.org/10.3390/atmos14030468 - 27 Feb 2023

Viewed by 632

Abstract

The Caribbean basin is a geographical area with a high prevalence of asthma due to mineral dust. As such, it is crucial to analyze the dynamic behavior of particulate pollutants in this region. The aim of this study was to investigate the relationships between particulate matter with aerodynamic diameters less than or equal to 2.5 and 10 μm (PM2.5 and PM10) using Hilbert–Huang transform (HHT)-based approaches, including the time-dependent intrinsic correlation (TDIC) and time-dependent intrinsic cross-correlation (TDICC) frames. The study utilized datasets from Puerto Rico from between 2007 and 2010 to demonstrate the relationships between two primary particulate matter concentration datasets of air pollution across multiple time scales. The method first decomposes both time series using improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) to obtain the periodic scales. The Hilbert spectral analysis identified two dominant peaks at a weekly scale for both PM types. High amplitude contributions were sustained for long and continuous time periods at seasonal to intra-seasonal scales, with similar trends in spectral amplitude observed for both types of PM except for monthly and intra-seasonal scales of six months. The TDIC method was used to analyze the resulting modes with similar periodic scales, revealing the strongest and most stable correlation pattern at quarterly and annual cycles. Subsequently, lagged correlations at each time scale were analyzed using the TDICC method. For high-frequency PM10 intrinsic mode functions (IMFs) less than a seasonal scale, the value of the IMF at a given time scale was found to be dependent on multiple antecedent values of PM2.5. However, from the quarterly scale onward, the correlation pattern of the PM2.5-PM10 relationship was stable, and IMFs of PM10 at these scales could be modeled by the lag 1 IMF of PM2.5. These results demonstrate that PM2.5 and PM10 concentrations are dynamically linked during the passage of African dust storms. Full article

(This article belongs to the Special Issue Advances in Integrated Air Quality Management: Emissions, Monitoring, Modelling (2nd Volume))

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Open AccessArticle

A Hybrid Deep Learning Model for Air Quality Prediction Based on the Time–Frequency Domain Relationship

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Atmosphere 2023, 14(2), 405; https://doi.org/10.3390/atmos14020405 - 20 Feb 2023

Viewed by 874

Abstract

Deep learning models have been widely used in time-series numerical prediction of atmospheric environmental quality. The fundamental feature of this application is to discover the correlation between influencing factors and target parameters through a deep network structure. These relationships in original data are affected by several different frequency factors. If the deep network is adopted without guidance, these correlations may be masked by entangled multifrequency data, which will cause the problem of insufficient correlation feature extraction and difficult model interpretation. Because the wavelet transform has the ability to separate these entangled multifrequency data, and these correlations can be extracted by deep learning methods, a hybrid model combining wavelet transform and transformer-like (WTformer) was designed to extract time–frequency domain features and prediction of air quality. The 2018–2021 hourly data in Guilin was used as the benchmark training dataset. Pollutants and meteorological variables in the local dataset are decomposed into five frequency bands by wavelet. The analysis of the WTformer model showed that particulate matter (PM_2.5 and PM₁₀) had an obvious correlation in the low-frequency band and a low correlation in the high-frequency band. PM_2.5 and temperature had a negative correlation in the high-frequency band and an obvious positive correlation in the low-frequency band. PM_2.5 and wind speed had a low correlation in the high-frequency band and an obvious negative correlation in the low-frequency band. These results showed that the laws of variables in the time–frequency domain could be found by the model, which made it possible to explain the model. The experimental results show that the prediction performance of the established model was better than that of multilayer perceptron (MLP), one-dimensional convolutional neural network (1D-CNN), gate recurrent unit (GRU), long short-term memory (LSTM) and Transformer, in all time steps (1, 4, 8, 24 and 48 h). Full article

(This article belongs to the Special Issue Advances in Integrated Air Quality Management: Emissions, Monitoring, Modelling (2nd Volume))

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Open AccessArticle

Adverse Health Effects (Bronchitis Cases) Due to Particulate Matter Exposure: A Twenty-Year Scenario Analysis for the Greater Athens Area (Greece) Using the AirQ+ Model

Kleopatra Ntourou

Konstantinos Moustris

Georgios Spyropoulos

Kyriaki-Maria Fameli

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Nikolaos Manousakis

Atmosphere 2023, 14(2), 389; https://doi.org/10.3390/atmos14020389 - 16 Feb 2023

Viewed by 474

Abstract

It is well known that air pollution has a negative impact on human health. Research has shown an increasing trend in hospital admissions due to respiratory and heart diseases during and after consecutive days of high or even medium air pollution levels. The objective of this paper is to provide quantitative and qualitative data concerning the impact of long-term air pollution on the health of residents living in the Greater Athens Area (GAA). More accurately, the prevalence of bronchitis in children and the incidence of chronic bronchitis cases in adults due to particulate matter exposure are estimated utilizing the AirQ+ model. For this purpose, daily average concentrations of particulate matter with an aerodynamic diameter less than or equal to 10 μm (PM₁₀) from five different locations within the GAA, covering the period 2001–2020, are used. The results show a significant correlation between PM₁₀ concentrations and adverse health effects (R² = 0.9). Interestingly, there were more cases of children suffering from bronchitis disease than cases of adults. In addition, it was observed that the unhealthiest areas in the GAA are the center of Athens city (mean annual PM₁₀ concentration in 2019: 36 μgr/m³), as well as suburban areas (Lykovrissi and Marousi: mean annual PM₁₀ concentrations in 2019 were 27 μgr/m³ and 28 μgr/m³, respectively). Finally, a decreasing trend for both PM₁₀ concentrations and the prevalence of chronic bronchitis across the GAA was observed through the examined 20 years, which was significantly higher over the period 2010–2020. Full article

(This article belongs to the Special Issue Advances in Integrated Air Quality Management: Emissions, Monitoring, Modelling (2nd Volume))

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Open AccessArticle

Outdoor Microplastic Analysis Using Inlet Filters from an NOx Regulatory Air Quality Monitoring Device

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Atmosphere 2022, 13(12), 2017; https://doi.org/10.3390/atmos13122017 - 30 Nov 2022

Viewed by 515

Abstract

Atmospheric microplastics (MPs) are a ubiquitous environmental contaminant of emerging concern. Sampling methods provide information relating to surface area concentration and MP characteristics, without direct comparison with routinely measured standard air quality parameters. This study analysed 6 active air samples generated by a local authority as part of their routine air quality monitoring activities. Continuous sampling totalled 10 months, within the city centre of Kingston-upon-Hull. By using μFTIR analysis, levels of total particles detected using the NOx inlet filters ranged from 5139 ± 2843 particles m⁻² day⁻¹, comprising 1029 ± 594 MPs m⁻² day⁻¹. The controls displayed a mean level of 2.00 ± 3.49 MPs. The polymers nylon (32%) and polypropylene, PP (22%) were the most abundant. Small fragments of 47.42 ± 48.57 μm (length) and 21.75 ± 13.62 μm (width) were most common. An increase in MP levels during April 2020 coincided with an increase in PM₁₀ levels. This study used robust procedures to measure MPs in the air by exploiting existing air quality monitoring equipment. Knowing the levels, types, and characteristics of MPs can inform toxicity studies to provide more environmentally relevant exposures, which is urgent now that MPs have been reported in human lung tissue. Full article

(This article belongs to the Special Issue Advances in Integrated Air Quality Management: Emissions, Monitoring, Modelling (2nd Volume))

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Open AccessArticle

Measuring and Regression Modeling of Gas–Particle Partitioning of Atmospheric Oxidized Mercury at a Coastal Site in Shanghai

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Atmosphere 2022, 13(12), 2014; https://doi.org/10.3390/atmos13122014 - 30 Nov 2022

Viewed by 461

Abstract

Gas–particle partitioning between reactive gaseous mercury (RGM) and particle bound mercury (PBM) controls the fates of atmospheric oxidized mercury (namely reactive mercury, RM). We conducted a long-term observations of gaseous elemental mercury (GEM), RGM, PBM, and auxiliary parameters in Chongming Island, Shanghai, China, to understand the characteristics of speciated mercury and their gas–particle partitioning behaviors. The entire average abundances of GEM, RGM and PBM were 2.12 ± 0.94 ng/m³, 14.75 ± 9.94 pg/m³ and 21.81 ± 30.46 pg/m³, respectively. An observation data dependent empirical gas–particle partitioning relationship of partitioning coefficient and temperature log(1/K_P) = −2692.20/T + 10.57 was obtained, and it varied in different season being by the temperature. To further evaluate the influences of temperature, particulate matter (PM), relative humidity on RGM and PBM partitioning process, the particulate fraction (φ = PBM/(PBM + RGM)) was used in this study. High φ values (φ > 0.8) mainly occurred at low temperature domain (<281 K), and high PM concentration enhanced this influence. In addition, high relative humidity shifts RGM from atmosphere partitioning to PBM in response to the diurnal valley φ values at 13:00–16:00 in the summer. Photochemical reactions were proposed to play important roles on partitioning processes between RGM and PBM. This study will benefit for the understanding of oxidized mercury fate and influencing factors in the complex atmospheric pollutants. Full article

(This article belongs to the Special Issue Advances in Integrated Air Quality Management: Emissions, Monitoring, Modelling (2nd Volume))

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