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Using Remote Sensing and Earth System Models to Improve Air Quality and Public Health in Megacities

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 3208

Special Issue Editor

Dr. Federico Karagulian

E-Mail Website
Guest Editor
Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Via Anguillarese 301, 00123 S. Maria di Galeria, Rome, Italy
Interests: air quality; transportation; sustainable mobility; satellite remote sensing; computer vision; health impacts
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Satellite data represent the most advanced disruptive technology to target research on adaptation strategies. Air quality, climate change, health impacts and environmental impacts are hot topics that can be studied using earth observations from satellites. The large availability of past and newly released satellite observations from ESA and NASA allow the scientific community to perform interesting analysis using top-down information about the concentrations of pollutants, such as trace gases and fine particulate matter.

The aim of this Special Issue is to use all possible open source Earth observations and extract pollutant concentrations over the largest megacities in the world. This information should be accompanied by the collection of health data possibly obtained from public authorities. The correlation between health data and Earth observation will help us to understand the impact of pollution on human health, quantify the risk and tackle the major pollutant sources responsible of highest impacts.

Topics could cover the estimation of time-series concentrations of surface particle matters, as well as tropospheric columns of dinitrogen dioxide or ozone. Articles in the field of satellite remote sensing for environmental studies are welcome, but also articles in the epidemiological field where the time series of pollutants is fundamental to estimate the impact on human health.

Dr. Federico Karagulian
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. Remote Sensing 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 2700 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

  • satellite remote sensing
  • air quality
  • health impact
  • public health
  • particulate matter
  • Earth observations

Published Papers (2 papers)

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Research

16 pages, 3064 KiB  
Article
Assessment of Spatio-Temporal Variations in PM2.5 and Associated Long-Range Air Mass Transport and Mortality in South Asia
by Md Sariful Islam, Shimul Roy, Tanmoy Roy Tusher, Mizanur Rahman and Ryley C. Harris
Remote Sens. 2023, 15(20), 4975; https://doi.org/10.3390/rs15204975 - 16 Oct 2023
Cited by 2 | Viewed by 1269
Abstract
Fine particulate matter (PM2.5) is associated with adverse impacts on ambient air quality and human mortality; the situation is especially dire in developing countries experiencing rapid industrialization and urban development. This study assessed the spatio-temporal variations of PM2.5 and its [...] Read more.
Fine particulate matter (PM2.5) is associated with adverse impacts on ambient air quality and human mortality; the situation is especially dire in developing countries experiencing rapid industrialization and urban development. This study assessed the spatio-temporal variations of PM2.5 and its health impacts in the South Asian region. Both satellite and station-based data were used to monitor the variations in PM2.5 over time. Additionally, mortality data associated with ambient particulate matter were used to depict the overall impacts of air pollution in this region. We applied the Mann–Kendall and Sen’s slope trend analysis tool to investigate the trend of PM2.5. At the same time, clustering of backward trajectories was used for identifying the long-range air mass transport. The results revealed that the mean annual PM2.5 mass concentration was the highest (46.72 µg/m3) in Bangladesh among the South Asian countries during 1998–2019, exceeding the national ambient air quality standards of Bangladesh (i.e., 15 µg/m3) and WHO (10 µg/m3), while lower PM2.5 was observed in the Maldives and Sri Lanka (5.35 µg/m3 and 8.69 µg/m3, respectively) compared with the WHO standard. The trend analysis during 1998–2019 suggested that all South Asian countries except the Maldives experienced an increasing trend (p < 0.05) of PM2.5. The study showed that among the major cities, the mean annual PM2.5 value was the highest in New Delhi (110 µg/m3), followed by Dhaka (85 µg/m3). Regarding seasonal variation, the highest PM2.5 was found during the pre-monsoon season in all cities. The findings of this research would help the concerned governments of South Asian countries to take steps toward improving air quality through policy interventions or reforms. Moreover, the results would provide future research directions for studying the trend and transport of atmospheric PM2.5 in other regions. Full article
(This article belongs to the Special Issue Using Remote Sensing and Earth System Models to Improve Air Quality and Public Health in Megacities)
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16 pages, 12935 KiB  
Article
Machine Learning-Based Modeling of Air Temperature in the Complex Environment of Yerevan City, Armenia
by Garegin Tepanosyan, Shushanik Asmaryan, Vahagn Muradyan, Rima Avetisyan, Azatuhi Hovsepyan, Anahit Khlghatyan, Grigor Ayvazyan and Fabio Dell’Acqua
Remote Sens. 2023, 15(11), 2795; https://doi.org/10.3390/rs15112795 - 27 May 2023
Cited by 1 | Viewed by 1402
Abstract
Machine learning (ML) was used to assess and predict urban air temperature (Tair) considering the complexity of the terrain features in Yerevan (Armenia). The estimation was performed based on the Partial Least-Squares Regression (PLSR) model with a high number (30) of [...] Read more.
Machine learning (ML) was used to assess and predict urban air temperature (Tair) considering the complexity of the terrain features in Yerevan (Armenia). The estimation was performed based on the Partial Least-Squares Regression (PLSR) model with a high number (30) of input variables. The relevant parameters include a newly purposed modification of spectral index IBI-SAVI, which turned out to strongly impact Tair prediction together with land surface temperature (LST). Cross-validation analysis on temperature predictions across a station-centered 1000 m circular area revealed quite a high correlation (R2Val = 0.77, RMSEVal = 1.58) between the predicted and measured Tair from the test set. It was concluded the remote sensing is an effective tool to estimate Tair distribution where a dense network of weather stations is not available. However, further developments will include incorporation of additional weather parameters from the weather stations, such as precipitation and wind speed, as well as the use of non-parametric ML techniques. Full article
(This article belongs to the Special Issue Using Remote Sensing and Earth System Models to Improve Air Quality and Public Health in Megacities)
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