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Atmosphere
Special Issues
Advances in Air Quality Spatio-Temporal Mapping
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Advances in Air Quality Spatio-Temporal Mapping

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

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 2058

Special Issue Editor

Dr. Nima Afshar-Mohajer

E-Mail Website
Guest Editor
Air Quality Scientist, Gradient Corp, Boston, MA 02108, USA
Interests: air dispersion modeling; aerosol mechanics; computational fluid dynamics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of real-time and time-averaged air pollutants concentration maps is a crucial step in identifying hotspot exposure areas and designing control and mitigation plans on sources to reduce the public health risk. However, all concentration maps are subject to uncertainties. Recently, advances in computer techniques to obtain larger dataset, such as those offered by machine learning and artificial intelligence, application of drone-based sensors to facilitate sampling from any terrain, and rapid growth in development of low-cost sensors, to establish denser air-quality monitoring networks have set the stage to visualize reliable spatio-temporal concentration maps. These new tools have reduced both the uncertainty and cost of data acquisition. Therefore, this Special Issue seeks advances in the above-mentioned approaches leading to improved resolution in air-quality concentration mapping or reduction of the computational cost, as well as more accurate estimation of the health end-points, health hazards, and exposure risks.

Dr. Nima Afshar-Mohajer
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

  • low-cost sensors
  • numerical modeling
  • hazard maps
  • remote sensing
  • machine learning
  • deep learning
  • land use regression modeling

Published Papers (2 papers)

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Research

13 pages, 1554 KiB  
Article
Land-Use Regression Analysis of Summer Tropospheric Ozone Concentrations in Ireland
by Keelan McHugh, Thomas Cummins and Julian Aherne
Atmosphere 2023, 14(12), 1711; https://doi.org/10.3390/atmos14121711 - 21 Nov 2023
Viewed by 841
Abstract
Tropospheric ozone is a powerful oxidant that can damage living organisms; it is widely monitored, as air concentrations have more than doubled since the Industrial Revolution. However, in general air quality monitoring stations are limited spatially to large urban centres; accordingly, accurate prediction [...] Read more.
Tropospheric ozone is a powerful oxidant that can damage living organisms; it is widely monitored, as air concentrations have more than doubled since the Industrial Revolution. However, in general air quality monitoring stations are limited spatially to large urban centres; accordingly, accurate prediction of concentrations outside of cities is important for protecting human and plant health. Land-use regression has been successfully used for modelling air pollutant concentrations by establishing a relationship between observed concentrations and landscape features representing sources and sinks. In this study, we developed a land-use regression model that explained 68% of the variance of summer average ozone concentrations in the Republic of Ireland. Ozone was measured at 14 active and 20 passive monitoring sites; air concentrations varied spatially, with the highest ozone measured in rural upland (64.5 µg/m3) and Atlantic coastal (50.2–60.5 µg/m3) sites and the lowest generally in urban centres (38.9–45.7 µg/m3). A total of 74 land-use predictor variables were tested, and their inclusion in the model was based on their impact on the coefficient of determination (R2). The final model included variables linked primarily to deposition processes and included “forest woodland and scrub area” and “distance to coast”. The meteorological variable “rain” and an indicator for NOx emissions “distance to EPA Integrated Pollution Control facilities” were also included in the final model. Our results demonstrate the potential effectiveness of land-use regression modelling in predicting ozone concentrations, at a scale relevant for ecosystem protection. Full article
(This article belongs to the Special Issue Advances in Air Quality Spatio-Temporal Mapping)
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15 pages, 5206 KiB  
Article
Characteristics of Temporal and Spatial Changes in Ozone and PM2.5 and Correlation Analysis in Heilongjiang Province
by Lichun Xuan, Lei Li, Pengjie Wang, Yanfeng Xing, Chengcheng Feng and Rui Zhang
Atmosphere 2023, 14(10), 1526; https://doi.org/10.3390/atmos14101526 - 2 Oct 2023
Cited by 1 | Viewed by 826
Abstract
The escalating ambient ozone (O3) pollution in China has garnered significant attention, necessitating an intensified focus on O3 pollution control and the coordinated management of PM2.5 and O3. This study reviews and analyzes the spatiotemporal characteristics of [...] Read more.
The escalating ambient ozone (O3) pollution in China has garnered significant attention, necessitating an intensified focus on O3 pollution control and the coordinated management of PM2.5 and O3. This study reviews and analyzes the spatiotemporal characteristics of O3 and PM2.5 concentrations in 13 cities within Heilongjiang Province from 2019 to 2021. The analysis is based on data sourced from the ecological environment monitoring network. In addition to this, correlation analyses were executed to explore the interaction between the two pollutants. The findings reveal a declining trajectory in PM2.5 concentration over the past three years, while O3 concentration has exhibited an upward trend. Temporally, both O3 and PM2.5 concentrations display pronounced seasonal variations, with peaks evident during the spring and summer (May to July), as well as in the winter (January, February, and December). From a spatial standpoint, elevated O3 concentrations were identified in the southwestern cities of Harbin, Daqing, and Suihua, while the northwestern cities of Daxinganling and Heihe exhibited comparatively lower O3 concentrations, but the difference was not significant. Conversely, PM2.5 concentrations demonstrated substantial variation among the 13 cities (districts). Regarding their correlation, a noteworthy positive correlation between the two pollutants was observed in April and May, contrasted by a negative correlation in November and December. Weather categories such as excellent, good, lightly polluted, moderately polluted, and other weather showed a lower correlation, whereas heavily polluted and severely polluted categories demonstrated a stronger correlation. Furthermore, the correlation with severe pollution is greater than that with heavily polluted, further indicating that heavier air pollution is more conducive to the coexistence of O3 and PM2.5 to form composite pollution. On a provincial scale, the correlation between the two pollutants is progressively increasing annually. This signifies a closely intertwined and intricate interaction and transformation relationship between O3 and PM2.5, accentuating the urgency for synergistic control measures. Full article
(This article belongs to the Special Issue Advances in Air Quality Spatio-Temporal Mapping)
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