Urban Air Quality Modelling
A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality".
Deadline for manuscript submissions: 30 April 2024 | Viewed by 9552
Special Issue Editor
Special Issue Information
Dear Colleagues,
Urban air pollution has become the leading-order environmental risk for human health. As estimated by the World Health Organization (WHO), there are about 4.2 million annual premature deaths attributed to ambient air pollution. The WHO has updated its Air Quality Guidelines in September 2021, reflecting the fact that even exposure to lower levels of air pollutant can affect human health. It is important to better understand sources and processes of air pollutants and to develop effective clean air policies to reduce air pollution levels in the atmosphere.
High-resolution air quality modeling can simulate combined effects of emission sources, chemical and physical processes. As air quality modeling has predictive capability, it can be used to develop effective policies for clean air in urban environments.
We call for papers on the modeling of physicochemical processes, improved understanding of air quality dispersion, source apportionment, quantification of the impacts of air pollution control policies (or co-benefits of Net Zero policies) on air pollution levels, at a variety of scales ranging from street canyon to neighborhood and city scales.
Dr. Jian Zhong
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 modeling
- emission sources
- physicochemical processes
- dispersion
- clean air policies
- net zero policies
- street canyon
- urban environments
Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Effects of diesel emissions on black carbon and number concentrations in the Eastern U.S.
Laura N. Posner a and Spyros N. Pandis b
a Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
b Department of Chemical Engineering, University of Patras, Patras, Greece
Abstracts: The effects of emissions of diesel engines on black carbon and particle number concentrations as well as climate-relevant aerosol properties are explored for a summertime period in the Eastern U.S. using the chemical transport model PMCAMx-UF. The effects are explored both for urban areas but also regionally. A 50% reduction of diesel particulate emissions results as expected in lower (23%) black carbon mass concentrations and similar changes both in magnitude (27-30%) and spatial pattern for the absorption coefficient. However, an average 2% increase of the total particle number concentrations is predicted due to a decrease in the coagulation and condensation sinks and, at the same time, a 2% decrease in N100 (particles larger than 100 nm) concentrations. The diesel reduction results suggest that mitigation of large diesel particles and/or particle mass emissions will reduce absorption climate-relevant properties related to black carbon and have health benefits; however the changes could also have the unintended effect of increased ultrafine particle number concentrations and therefore changes in cloud condensation nuclei (CCN) are predicted to be significantly less than expected assuming a proportional reduction during this photochemically active period. Doubling of the diesel emissions results in a domain-averaged 3% decrease in total particle number concentrations and a 3% increase in N100 concentrations. PM2.5 BC concentrations increase on average by 46%, and similar changes (52-60%) are predicted for the absorption coefficient. Extinction coefficients for both perturbation simulations changed by only a few percent due to the dominance of scattering aerosols in the Eastern U.S. during this time period.
Forecasting the exceedances of air pollution in an urban area
Stavros-Andreas Logothetis1, Georgios Kosmopoulos1, Orestis Panagopoulos1, Vasileios Salamalikis2, Andreas Kazantzidis1*
1Laboratory of Atmospheric Physics, Physics Department, University of Patras
2NILU – Norwegian Institute for Air Research, P. O. Box 100, Kjeller 2027, Norway
*Corresponding Author e-mail: akaza@upatras.gr
Abstracts:Particular matter (PM) is one of the major air pollutants. Human exposure to fine PM (PM with a median diameter less than or equal to 2.5μm, PM2.5) has many negative and diverse outcomes for human health, such as respiratory mortality, lung cancer, etc. Accurate air quality forecasting on a regional scale enables local agencies to design and apply appropriate policies (e.g., meet specific emissions limitations) to tackle the problem of air pollution. Under this framework, a new generation of low-cost sensors have emerged lately, facilitating the spatiotemporal monitoring of air pollution on a local scale. The aim of this study is to present a deep learning approach to forecasting the intra-day air pollution exceedances in an urban area. The PM2.5 data used in this study were collected from almost 20 well-calibrated low-cost sensors (Purple Air) located in the Municipality of Thermi in Thessaloniki, Greece. In addition to PM2.5 data, auxiliary forecast data on atmospheric composition and meteorology were used from the Copernicus Atmosphere Monitoring Service (CAMS), which is operated by ECMWF. A long short-term memory (LSTM) neural network is implemented to inherently learn to forecast air pollution exceedances from historical PM2.5 data and auxiliary data. The proposed methodology can be used operationally to inform the local community of forthcoming heavy air pollution episodes.