Special Issue "Air Quality Characterisation and Modelling"

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

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Dr. José Carlos Magalhães Pires
E-Mail Website
Guest Editor
LEPABE - Laboratory for Process Engineering, Environment, Biotechnology and Energy, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
Interests: CO2 capture; wastewater treatment; microalgal biofuels; process modelling
Special Issues and Collections in MDPI journals
Prof. Dr. Álvaro Gómez-Losada
E-Mail Website
Guest Editor
Departamento de Estadística e Investigación Operativa, Facultad de Matemáticas, Universidad de Sevilla, 41012 Sevilla, Spain
Interests: air pollution; environmental data science; knowledge discovery from databases; spatial and temporal forecasting; statistics data mining methods; machine learning

Special Issue Information

Dear Colleagues,

Air pollution is a mixture of particles and gases, which can reach unsafe concentrations for human health, the environment, vegetation and materials. It has become one of the main sustainability issues and a concerning topic in atmospheric science. According to the World Health Organization (WHO), 90% of the world’s population lives in highly polluted environments, and about 7 million premature deaths are caused every year by outdoor and indoor air pollution. The combination of fast-growing populations, transport, fossil fuels, and biomass burning is leading to pollution levels being especially high in some urban areas. Agriculture and natural phenomena are also an important source of pollution, underscoring the multi-faceted and transboundary nature of air pollution. The monitoring and understanding of the temporal and spatial behaviours of air pollutant concentrations are essential for both the implementation of air quality policies and the definition of effective measures to mitigate air pollution and its effects. Quantifying and monitoring exposure to air pollution in terms of public health is also a critical component in policy discussion.

This Special Issue will present recent research activities concerning the characterization of air pollution and the applied modelling approaches.

Dr. José Carlos Magalhães Pires
Prof. Dr. Álvaro Gómez-Losada
Guest Editors

Manuscript Submission Information

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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 1900 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

  • particulate matter
  • African dust
  • nitrogen oxides
  • ground-level ozone
  • development, evaluation and application of models
  • statistical models
  • data mining and machine-learning-based models
  • integrated modelling and assessment

Published Papers (4 papers)

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Research

Article
Mitigation of Suspendable Road Dust in a Subpolar, Oceanic Climate
Sustainability 2021, 13(17), 9607; https://doi.org/10.3390/su13179607 (registering DOI) - 26 Aug 2021
Viewed by 228
Abstract
Tire and road wear particles (TRWP) are a significant source of atmospheric particulate matter and microplastic loading to waterways. Road wear is exacerbated in cold climate by the widespread use of studded tires. The goal of this research was to assess the anthropogenic [...] Read more.
Tire and road wear particles (TRWP) are a significant source of atmospheric particulate matter and microplastic loading to waterways. Road wear is exacerbated in cold climate by the widespread use of studded tires. The goal of this research was to assess the anthropogenic levers for suspendable road dust generation and climatic conditions governing the environmental fate of non-exhaust particles in a wet maritime winter climate. Sensitivity analyses were performed using the NORTRIP model for the Capital region of Reykjavík, Iceland (64.1° N). Precipitation frequency (secondarily atmospheric relative humidity) governed the partitioning between atmospheric and waterborne PM10 particles (55% and 45%, respectively). Precipitation intensity, however, increased proportionally most the drainage to waterways via stormwater collection systems, albeit it only represented 5% of the total mass of dust generated in winter. A drastic reduction in the use of studded tires, from 46% to 15% during peak season, would be required to alleviate the number of ambient air quality exceedances. In order to achieve multifaceted goals of a climate resilient, resource efficient city, the most important mitigation action is to reduce overall traffic volume. Reducing traffic speed may help speed environmental outcomes. Full article
(This article belongs to the Special Issue Air Quality Characterisation and Modelling)
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Article
Considering Condensable Particulate Matter Emissions Improves the Accuracy of Air Quality Modeling for Environmental Impact Assessment
Sustainability 2021, 13(8), 4470; https://doi.org/10.3390/su13084470 - 16 Apr 2021
Viewed by 394
Abstract
This study examines environmental impact assessment considering filterable particulate matter (FPM) and condensable particulate matter (CPM) to improve the accuracy of the air quality model. Air pollutants and meteorological data were acquired from Korea’s national monitoring station near a residential development area in [...] Read more.
This study examines environmental impact assessment considering filterable particulate matter (FPM) and condensable particulate matter (CPM) to improve the accuracy of the air quality model. Air pollutants and meteorological data were acquired from Korea’s national monitoring station near a residential development area in the target district and background site. Seasonal emissions of PM2.5, including CPM, were estimated using the California puff (CALPUFF) model, based on Korea’s national emissions inventory. These results were compared with the traditional environmental impact assessment results. For the residential development area, the seasonal PM2.5 concentration was predicted by considering FPM and CPM emissions in the target area as well as the surrounding areas. In winter and spring, air quality standards were not breached because only FPM was considered. However, when CPM was included in the analysis, the results exceeded the air quality standards. Furthermore, it was predicted that air quality standards would not be breached in summer and autumn, even when CPM is included. In other words, conducting an environmental impact assessment on air pollution including CPM affects the final environmental decision. Therefore, it is concluded that PM2.5 should include CPM for greater accuracy of the CALPUFF model for environmental impact assessment. Full article
(This article belongs to the Special Issue Air Quality Characterisation and Modelling)
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Article
Use of Simulated and Observed Meteorology for Air Quality Modeling and Source Ranking for an Industrial Region
Sustainability 2021, 13(8), 4276; https://doi.org/10.3390/su13084276 - 12 Apr 2021
Viewed by 494
Abstract
The Gaussian-based dispersion model American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) is being used to predict concentration for air quality management in several countries. A study was conducted for an industrial area, Chembur of Mumbai city in India, to assess the agreement [...] Read more.
The Gaussian-based dispersion model American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) is being used to predict concentration for air quality management in several countries. A study was conducted for an industrial area, Chembur of Mumbai city in India, to assess the agreement of observed surface meteorology and weather research and forecasting (WRF) output through AERMOD with ground-level NOx and PM10 concentrations. The model was run with both meteorology and emission inventory. When results were compared, it was observed that the air quality predictions were better with the use of WRF output data for a model run than with the observed meteorological data. This study showed that the onsite meteorological data can be generated by WRF which saves resources and time, and it could be a good option in low-middle income countries (LIMC) where meteorological stations are not available. Also, this study quantifies the source contribution in the ambient air quality for the region. NOx and PM10 emission loads were always observed to be high from the industries but NOx concentration was high from vehicular sources and PM10 concentration was high from industrial sources in ambient concentration. This methodology can help the regulatory authorities to develop control strategies for air quality management in LIMC. Full article
(This article belongs to the Special Issue Air Quality Characterisation and Modelling)
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Article
Can Carbon Trading Policies Promote Regional Green Innovation Efficiency? Empirical Data from Pilot Regions in China
Sustainability 2021, 13(5), 2891; https://doi.org/10.3390/su13052891 - 07 Mar 2021
Cited by 1 | Viewed by 720
Abstract
Although the emission reduction and innovation effects of carbon emissions trading have attracted considerable interest among academics and policy makers, there is a lack of empirical research on how carbon trading pilots in China promote regional green innovation. Therefore, we measured the green [...] Read more.
Although the emission reduction and innovation effects of carbon emissions trading have attracted considerable interest among academics and policy makers, there is a lack of empirical research on how carbon trading pilots in China promote regional green innovation. Therefore, we measured the green innovation efficiency of 30 provinces and cities in mainland China from 2005 to 2018, using selected panel data within a super-efficient SBM model that incorporated undesirable outputs. We used a double differential model to evaluate the impacts of carbon trading policies on the green innovation efficiency of seven carbon trading pilot regions. These impacts were confirmed using the double differential propensity score matching method. Our findings were as follows. (1) The implementation of carbon trading policies has a significant and continuous effect of promoting and improving green innovation efficiency in the pilot areas. (2) Carbon trading induces technological innovation effects, enabling green innovation potential to be realized. Regional green innovation efficiency is further improved through energy substitution and structural upgrading effects and subsequently through all three of the above effects. (3) The synergy between the three major effects of carbon trading policies amplifies regional green innovation efficiency. Therefore, the Chinese government should accelerate its efforts to develop and improve carbon markets, promote carbon trading policies, and actively foster synergy among the three effects to achieve green and sustainable regional development. Full article
(This article belongs to the Special Issue Air Quality Characterisation and Modelling)
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