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Atmosphere
Special Issues
Modeling and Monitoring of Air Quality: From Data to Predictions
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Modeling and Monitoring of Air Quality: From Data to Predictions

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 1349

Special Issue Editor

Dr. Agnieszka Rorat

E-Mail Website
Guest Editor
ATMO Hauts-de-France, 199 Rue Colbert, Lille, France
Interests: air quality modelling; analysis and interpretation of data comming from air quality modelling and monitoring; statistics; evaluation of models

Special Issue Information

Dear Colleagues,

Spatial modeling of air quality relies on diverse environmental and meteorological datasets to analyze and predict pollution levels across different regions. By integrating data from meteorological stations, remote sensing technologies, and sensor networks, these models evaluate the transport, transformation, and dispersion of pollutants such as nitrogen dioxide, sulfur dioxide, ozone, and particulate matter. Computational simulations provide insights into pollutant distribution and trends, facilitating early warning systems and policy interventions. Despite advancements in predictive modeling, challenges remain, including the need for more comprehensive data integration, the inclusion of emerging pollutants, and the expansion of models into underrepresented regions. Strengthening interdisciplinary approaches and leveraging artificial intelligence can further enhance the accuracy and applicability of air quality assessments, contributing to improved urban air quality management and public health outcomes.

Dr. Agnieszka Rorat
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
  • dispersion modeling
  • air quality index
  • advanced statistic
  • urban air pollution
  • emission inventory
  • air quality standards
  • source apportionment

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Published Papers (2 papers)

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Research

23 pages, 6569 KiB  
Article
Comparative Analysis of the Impact of Built Environment and Land Use on Monthly and Annual Mean PM2.5 Levels
by Anjian Song, Zhenbao Wang, Shihao Li and Xinyi Chen
Atmosphere 2025, 16(6), 682; https://doi.org/10.3390/atmos16060682 - 5 Jun 2025
Viewed by 480
Abstract
Urban planners are progressively recognizing the significant effects of the built environment and land use on PM2.5 levels. However, in analyzing the drivers of PM2.5 levels, researchers’ reliance on annual mean and seasonal means may overlook the monthly variations in PM [...] Read more.
Urban planners are progressively recognizing the significant effects of the built environment and land use on PM2.5 levels. However, in analyzing the drivers of PM2.5 levels, researchers’ reliance on annual mean and seasonal means may overlook the monthly variations in PM2.5 levels, potentially impeding accurate predictions during periods of high pollution. This study focuses on the area within the Sixth Ring Road of Beijing, China. It utilizes gridded monthly and annual mean PM2.5 data from 2019 as the dependent variable. The research selects 33 independent variables from the perspectives of the built environment and land use. The Extreme Gradient Boosting (XGBoost) method is employed to reveal the driving impacts of the built environment and land use on PM2.5 levels. To enhance the model accuracy and address the randomness in the division of training and testing sets, we conducted twenty comparisons for each month. We employed Shapley Additive Explanations (SHAP) and Partial Dependence Plots (PDP) to interpret the models’ results and analyze the interactions between the explanatory variables. The results indicate that models incorporating both the built environment and land use outperformed those that considered only a single aspect. Notably, in the test set for April, the R2 value reached up to 0.78. Specifically, the fitting accuracy for high pollution months in February, April, and November is higher than the annual mean, while July shows the opposite trend. The coefficient of variation for the importance rankings of the seven key explanatory variables exceeds 30% for both monthly and annual means. Among these variables, building density exhibited the highest coefficient of variation, at 123%. Building density and parking lots density demonstrate strong explanatory power for most months and exhibit significant interactions with other variables. Land use factors such as wetlands fraction, croplands fraction, park and greenspace fraction, and forests fraction have significant driving effects during the summer and autumn seasons months. The research on time scales aims to more effectively reduce PM2.5 levels, which is essential for developing refined urban planning strategies that foster healthier urban environments. Full article
(This article belongs to the Special Issue Modeling and Monitoring of Air Quality: From Data to Predictions)
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24 pages, 3083 KiB  
Article
Modelling of Nanoparticle Number Emissions from Road Transport—An Urban Scale Emission Inventory
by Said Munir, Haibo Chen and Richard Crowther
Atmosphere 2025, 16(4), 417; https://doi.org/10.3390/atmos16040417 - 3 Apr 2025
Viewed by 597
Abstract
Atmospheric nanoparticles, due to their tiny size up to 100 nanometres in diameter, have negligible mass and are better characterised by their particle number concentration. Atmospheric nanoparticle numbers are not regulated due to insufficient data availability, which emphasises the importance of this research. [...] Read more.
Atmospheric nanoparticles, due to their tiny size up to 100 nanometres in diameter, have negligible mass and are better characterised by their particle number concentration. Atmospheric nanoparticle numbers are not regulated due to insufficient data availability, which emphasises the importance of this research. In this paper, nanoparticle number emissions are estimated using nanoparticle number emission factors (NPNEF) and road traffic characteristics. Traffic flow and fleet composition were estimated using the Leeds Transport Model, which showed that the road traffic in Leeds consisted of 41% petrol cars, 43% diesel cars, 9% LGV, 2% HGV, and 4.5% buses and coaches. Two approaches were used for emission estimation: (a) a detailed model, which required detailed information on traffic flow and fleet composition and NPNEFs of various vehicle types; and (b) a simple model, which used total traffic flow and a single NPNEF of mixed fleet. The estimations of both models demonstrated a strong correlation with each other using the values of R, RMSE, FAC2, and MB, which were 1, 2.77 × 1017, 0.95, and −1.92 × 1017, respectively. Eastern and southern parts of the city experienced higher levels of emissions. Future work will include fine-tuning the road traffic emission inventory and quantifying other emission sources. Full article
(This article belongs to the Special Issue Modeling and Monitoring of Air Quality: From Data to Predictions)
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