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Atmosphere
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Advances in Air Quality Data Analysis and Modeling
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Advances in Air Quality Data Analysis and Modeling

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

Deadline for manuscript submissions: closed (15 October 2021) | Viewed by 21365

Special Issue Editor

Dr. Ashok Kumar

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, The University of Toledo, Toledo, OH 43606, USA
Interests: air quality modeling; indoor air quality; environmental information technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of air quality data analysis and modelling has grown exponentially after the passage of the Clean Air Act in 1970. This growth is largely driven by the need to protect public health and to solve environmental problems as a result of the release of emissions in the atmosphere around the globe. Those efforts led to field studies which collected air pollution data and the development of computational resources and analytical techniques. This issue aims to provide readers state-of-the art solutions on model development using advanced predictive and cognitive analytical techniques and the analysis of air quality and associated health data using the concepts of big data, machine learning, artificial intelligence (AI), geographical information systems, and statistics. This issue invites authors to submit papers that exploit the use of advanced analytics in solving air pollution related problems. It is strongly recommended that the authors provide a detailed description of the relevant models/mathematical algorithms and procedures adopted in their respective studies. The papers may range from database development to emerging air quality models incorporating AI.

This Special Issue on innovative data analysis and modelling invites you to submit papers across the broader spectrum of air pollution science and engineering (e.g., air quality modelling, climate change, risk, exposure assessment, remote sensing, air monitoring, greenhouse gases, and online learning).  The submission of research work by interdisciplinary teams and multi-country groups are of significant interest.

Prof. Dr. Ashok Kumar
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 quality modelling and health risk
  • Atmospheric chemistry
  • Climate change
  • Exposure Assessment and Health Effects
  • Regulatory modelling
  • Air pollution measurements and monitoring
  • Fence line monitoring
  • Monitoring networks
  • Satellite data analysis
  • Greenhouse gas inventories
  • Landfill
  • Waste-to-Energy
  • Virus analysis and its impact
  • Advanced Analytics
  • Machine Learning
  • Big Data
  • Statistics
  • Artificial Intelligence
  • Geographic Information Systems
  • Pollution Information Technology
  • Environmental Management Systems
  • Artificial Intelligence
  • Emission Rate Modeling

Published Papers (7 papers)

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Research

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14 pages, 1540 KiB  
Article
Dominant Contributions of Secondary Aerosols and Vehicle Emissions to Water-Soluble Inorganic Ions of PM2.5 in an Urban Site in the Metropolitan Hangzhou, China
by Chun Xiong, Shaocai Yu, Xue Chen, Zhen Li, Yibo Zhang, Mengying Li, Weiping Liu, Pengfei Li and John H. Seinfeld
Atmosphere 2021, 12(11), 1529; https://doi.org/10.3390/atmos12111529 - 19 Nov 2021
Cited by 7 | Viewed by 1707
Abstract
Water soluble inorganic ions (WSIIs) are important components in PM2.5 and could strongly affect the acidity and hygroscopicity of PM2.5. In order to achieve the seasonal characteristics and determine the potential sources of WSIIs in PM2.5 in Hangzhou, online [...] Read more.
Water soluble inorganic ions (WSIIs) are important components in PM2.5 and could strongly affect the acidity and hygroscopicity of PM2.5. In order to achieve the seasonal characteristics and determine the potential sources of WSIIs in PM2.5 in Hangzhou, online systems were used to measure hourly mass concentrations of WSIIs (SO42−, NO3−, NH4+, Cl−, Na+, K+, Ca2+ and Mg2+) as well as PM2.5, NO2 and SO2 at an urban site for one month each season (May, August, October, December) in 2017. Results showed that the hourly mass concentrations of PM2.5 during the whole campaign varied from 1 to 292 μg·m−3 with the mean of 56.03 μg·m−3. The mean mass concentration of WSIIs was 26.49 ± 20.78 μg·m−3, which contributed 48.28% to averaged PM2.5 mass. SNA (SO42−, NO3− and NH4+) were the most abundant ions in PM2.5 and on average, they comprised 41.57% of PM2.5 mass. PM2.5, NO2, SO2 and WSIIs showed higher mass concentrations in December, possibly due to higher energy consumption emissions, unfavorable meteorological factors (e.g., lower wind speed and temperature) and regional transport. Results from PCA models showed that secondary aerosols and vehicle emissions were the dominant sources of WSIIs in the observations. Our findings highlight the importance of stronger controls on precursor (e.g., SO2 and NO2) emissions in Hangzhou, and show that industrial areas should be controlled at local and regional scales in the future. Full article
(This article belongs to the Special Issue Advances in Air Quality Data Analysis and Modeling)
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34 pages, 13940 KiB  
Article
The UrbEm Hybrid Method to Derive High-Resolution Emissions for City-Scale Air Quality Modeling
by Martin Otto Paul Ramacher, Anastasia Kakouri, Orestis Speyer, Josefine Feldner, Matthias Karl, Renske Timmermans, Hugo Denier van der Gon, Jeroen Kuenen, Evangelos Gerasopoulos and Eleni Athanasopoulou
Atmosphere 2021, 12(11), 1404; https://doi.org/10.3390/atmos12111404 - 26 Oct 2021
Cited by 14 | Viewed by 3363
Abstract
As cities are growing in size and complexity, the estimation of air pollution exposure requires a detailed spatial representation of air pollution levels, rather than homogenous fields, provided by global- or regional-scale models. A critical input for city-scale modeling is a timely and [...] Read more.
As cities are growing in size and complexity, the estimation of air pollution exposure requires a detailed spatial representation of air pollution levels, rather than homogenous fields, provided by global- or regional-scale models. A critical input for city-scale modeling is a timely and spatially resolved emission inventory. Bottom–up approaches to create urban-scale emission inventories can be a demanding and time-consuming task, whereas local emission rates derived from a top–down approach may lack accuracy. In the frame of this study, the UrbEm approach of downscaling gridded emission inventories is developed, investing upon existing, open access, and credible emission data sources. As a proof-of-concept, the regional anthropogenic emissions by Copernicus Atmospheric Monitoring Service (CAMS) are handled with a top–down approach, creating an added-value product of anthropogenic emissions of trace gases and particulate matter for any city (or area) of Europe, at the desired spatial resolution down to 1 km. The disaggregation is based on contemporary proxies for the European area (e.g., Global Human Settlement population data, Urban Atlas 2012, Corine, OpenStreetMap data). The UrbEm approach is realized as a fully automated software tool to produce a detailed mapping of industrial (point), (road-) transport (line), and residential/agricultural/other (area) emission sources. Line sources are of particular value for air quality studies at the urban scale, as they enable explicit treatment of line sources by models capturing among others the street canyon effect and offer an overall better representation of the critical road transport sector. The UrbEm approach is an efficient solution for such studies and constitutes a fully credible option in case high-resolution emission inventories do not exist for a city (or area) of interest. The validity of UrbEm is examined through the evaluation of high-resolution air pollution predictions over Athens and Hamburg against in situ measurements. In addition to a better spatial representation of emission sources and especially hotspots, the air quality modeling results show that UrbEm outputs, when compared to a uniform spatial disaggregation, have an impact on NO2 predictions up to 70% for urban regions with complex topographies, which corresponds to a big improvement of model accuracy (FAC2 > 0.5), especially at the source-impacted sites. Full article
(This article belongs to the Special Issue Advances in Air Quality Data Analysis and Modeling)
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23 pages, 15239 KiB  
Article
The Neural Network Assisted Land Use Regression
by Jan Bitta, Vladislav Svozilík and Aneta Svozilíková Krakovská
Atmosphere 2021, 12(4), 452; https://doi.org/10.3390/atmos12040452 - 01 Apr 2021
Viewed by 2214
Abstract
Land Use Regression (LUR) is one of the air quality assessment modelling techniques. Its advantages lie mainly in a much simpler mathematical apparatus, quicker and simpler calculations, and a possibility to incorporate more factors affecting pollutant concentration than standard dispersion models. The goal [...] Read more.
Land Use Regression (LUR) is one of the air quality assessment modelling techniques. Its advantages lie mainly in a much simpler mathematical apparatus, quicker and simpler calculations, and a possibility to incorporate more factors affecting pollutant concentration than standard dispersion models. The goal of the study was to perform the LUR model in the Polish-Czech-Slovakian Tritia region, to test two sets of pollution data input factors, i.e., factors based on emission data and pollution dispersion model results, to test regression via neural networks and compare it with standard linear regression. Both input datasets, emission data and pollution dispersion model results, provided a similar quality of results in the case when standard linear regression was used, the R2 of the models was 0.639 and 0.652. Neural network regression provided a significantly higher quality of the models, their R2 was 0.937 and 0.938 for the factors based on emission data and pollution dispersion model results respectively. Full article
(This article belongs to the Special Issue Advances in Air Quality Data Analysis and Modeling)
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16 pages, 2079 KiB  
Article
Monitoring Air Pollution Variability during Disasters
by Earthea Nance
Atmosphere 2021, 12(4), 420; https://doi.org/10.3390/atmos12040420 - 25 Mar 2021
Cited by 1 | Viewed by 2219
Abstract
National environmental regulations lack short-term standards for variability in fine particulate matter (PM2.5); they depend solely on concentration-based standards. Twenty-five years of research has linked short-term PM2.5, that is, increases of at least 10 μg/m3 that can occur [...] Read more.
National environmental regulations lack short-term standards for variability in fine particulate matter (PM2.5); they depend solely on concentration-based standards. Twenty-five years of research has linked short-term PM2.5, that is, increases of at least 10 μg/m3 that can occur in-between regulatory readings, to increased mortality. Even as new technologies have emerged that could readily monitor short-term PM2.5, such as real-time monitoring and mobile monitoring, their primary application has been for research, not for air quality management. The Gulf oil spill offers a strategic setting in which regulatory monitoring, computer modeling, and stationary monitoring could be directly compared to mobile monitoring. Mobile monitoring was found to best capture the variability of PM2.5 during the disaster. The research also found that each short-term increase (≥10 μg/m3) in fine particulate matter was associated with a statistically significant increase of 0.105 deaths (p < 0.001) in people aged 65 and over, which represents a 0.32% increase. This research contributes to understanding the effects of PM2.5 on mortality during a disaster and provides justification for environmental managers to monitor PM2.5 variability, not only hourly averages of PM2.5 concentration. Full article
(This article belongs to the Special Issue Advances in Air Quality Data Analysis and Modeling)
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15 pages, 5555 KiB  
Article
Origin and Transport Pathway of Dust Storm and Its Contribution to Particulate Air Pollution in Northeast Edge of Taklimakan Desert, China
by Aishajiang Aili, Hailiang Xu, Tursun Kasim and Abudumijiti Abulikemu
Atmosphere 2021, 12(1), 113; https://doi.org/10.3390/atmos12010113 - 14 Jan 2021
Cited by 19 | Viewed by 3069
Abstract
The Taklimakan Desert in Northwest China is the major source of dust storms in China. The northeast edge of this desert is a typical arid area which houses a fragile oasis eco-environment. Frequent dust storms cause harmful effects on the oasis ecosystem and [...] Read more.
The Taklimakan Desert in Northwest China is the major source of dust storms in China. The northeast edge of this desert is a typical arid area which houses a fragile oasis eco-environment. Frequent dust storms cause harmful effects on the oasis ecosystem and negative impacts on agriculture, transportation, and human health. In this study, the major source region, transport pathway, and the potential contribution of dust storms to particulate air pollution were identified by using both trajectory analysis and monitoring data. To assess the source regions of dust storms, 48 h backward trajectories of air masses arriving at the Bugur (Luntai) County, which is located at the northeast edge of Taklimakan Desert, China on the dusty season (spring) and non-dusty month (August, representing non-dusty season) in the period of 1999–2013, were determined using Hybrid Single Particle Lagrangian Integrated Trajectory model version 4 (HYSPLIT 4). The trajectories were categorized by k-means clustering into 5 clusters (1a–5a) in the dusty season and 2 clusters (1b and 2b) in the non-dusty season, which show distinct features in terms of the trajectory origins and the entry direction to the site. Daily levels of three air pollutants measured at a station located in Bugur County were analyzed by using Potential Source Contribution Function (PSCF) for each air mass cluster in dusty season. The results showed that TSP is the major pollutant, with an average concentration of 612 µg/m3, as compared to SO2 (23 µg/m3) and NO2 (32 µg/m3) in the dusty season. All pollutants were increased with the dust weather intensity, i.e., from suspended dust to dust storms. High levels of SO2 and NO2 were mostly associated with cluster 1a and cluster 5a which had trajectories passing over the anthropogenic source regions, while high TSP was mainly observed in cluster 4a, which has a longer pathway over the shifting sand desert area. Thus, on strong dust storm days, not only higher TSP but also higher SO2 and NO2 levels were observed as compared to normal days. The results of this study could be useful to forecast the potential occurrence of dust storms based on meteorological data. Research focusing on this dust-storm-prone region will help to understand the possible causes for the changes in the dust storm frequency and intensity, which can provide the basis for mitigation of the negative effects on human health and the environment. Full article
(This article belongs to the Special Issue Advances in Air Quality Data Analysis and Modeling)
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18 pages, 5862 KiB  
Article
Mathematical Modeling of the Biogas Production in MSW Landfills. Impact of the Implementation of Organic Matter and Food Waste Selective Collection Systems
by Javier Rodrigo-Ilarri and María-Elena Rodrigo-Clavero
Atmosphere 2020, 11(12), 1306; https://doi.org/10.3390/atmos11121306 - 01 Dec 2020
Cited by 10 | Viewed by 4523
Abstract
Municipal solid waste (MSW) landfills are one of the main sources of greenhouse gas emissions. Biogas is formed under anaerobic conditions by decomposition of the organic matter present in waste. The estimation of biogas production, which depends fundamentally on the type of waste [...] Read more.
Municipal solid waste (MSW) landfills are one of the main sources of greenhouse gas emissions. Biogas is formed under anaerobic conditions by decomposition of the organic matter present in waste. The estimation of biogas production, which depends fundamentally on the type of waste deposited in the landfill, is essential when designing the gas capture system and the possible generation of energy. BIOLEACH, a mathematical model for the real-time management of MSW landfills, enables the estimation of biogas generation based on the waste mix characteristics and the local meteorological conditions. This work studies the impact of installing selective organic matter collection systems on landfill biogas production. These systems reduce the content of food waste that will eventually be deposited in the landfill. Results obtained using BIOLEACH on a set of scenarios under real climate conditions in a real landfill located in the Region of Murcia (Spain) are shown. Results demonstrate that actual CH4 and CO2 production depends fundamentally on the monthly amount of waste stored in the landfill, its chemical composition and the availability and distribution of water inside the landfill mass. Full article
(This article belongs to the Special Issue Advances in Air Quality Data Analysis and Modeling)
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21 pages, 672 KiB  
Review
Features Exploration from Datasets Vision in Air Quality Prediction Domain
by Ditsuhi Iskandaryan, Francisco Ramos and Sergio Trilles
Atmosphere 2021, 12(3), 312; https://doi.org/10.3390/atmos12030312 - 28 Feb 2021
Cited by 3 | Viewed by 2758
Abstract
Air pollution and its consequences are negatively impacting on the world population and the environment, which converts the monitoring and forecasting air quality techniques as essential tools to combat this problem. To predict air quality with maximum accuracy, along with the implemented models [...] Read more.
Air pollution and its consequences are negatively impacting on the world population and the environment, which converts the monitoring and forecasting air quality techniques as essential tools to combat this problem. To predict air quality with maximum accuracy, along with the implemented models and the quantity of the data, it is crucial also to consider the dataset types. This study selected a set of research works in the field of air quality prediction and is concentrated on the exploration of the datasets utilised in them. The most significant findings of this research work are: (1) meteorological datasets were used in 94.6% of the papers leaving behind the rest of the datasets with a big difference, which is complemented with others, such as temporal data, spatial data, and so on; (2) the usage of various datasets combinations has been commenced since 2009; and (3) the utilisation of open data have been started since 2012, 32.3% of the studies used open data, and 63.4% of the studies did not provide the data. Full article
(This article belongs to the Special Issue Advances in Air Quality Data Analysis and Modeling)
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