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Ground- and Satellite-Based Remote Sensing for Air Quality Monitoring

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 690

Special Issue Editors


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Guest Editor
CNR-IIA, Institute of Atmospheric Pollution Research, Italian National Research Council, Montelibretti, 00010 Rome, Italy
Interests: aerosol; remote sensing

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Guest Editor
Institute of Atmospheric Pollution Research of the Italian National Research Council (CNR-IIA), Research Area of Rome 1, Provincial Road 35d, Montelibretti, 00010 Rome, Italy
Interests: remote sensing; earth observation; air pollution; aerosol and trace gases; atmospheric radiative modelling
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Special Issue Information

Dear Colleagues,

The accurate monitoring of air quality is essential for understanding atmospheric composition, assessing public health impacts, and supporting environmental policy. While satellite-based remote sensing offers broad spatial coverage, ground-based techniques provide a critical high-resolution, and localized data that complement and validate satellite observations. Among these, differential optical absorption spectroscopy (DOAS), MAX-DOAS, and other ground-based systems have emerged as vital tools for detecting trace gases such as NO2, SO2, O3, and formaldehyde. These instruments enable continuous, long-term observations with high temporal resolution, which are essential for capturing local emission sources, atmospheric dynamics, and short-term pollution episodes. Recent advances in retrieval algorithms, instrumentation, and data assimilation are enhancing the accuracy and applicability of ground-based measurements. This Special Issue brings together innovative research and case studies that highlight the evolving role of ground-based remote sensing in air quality monitoring, especially in synergy with satellite data and atmospheric models. By focusing on developments in measurement techniques, calibration strategies, and integrated observational networks, this issue aims to foster interdisciplinary collaboration and support the development of more robust, multi-scale air quality monitoring frameworks.

Dr. Patrizio Tratzi
Dr. Cristiana Bassani
Guest Editors

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Keywords

  • ground-based remote sensing
  • DOAS
  • air quality monitoring
  • tropospheric pollution
  • long-term observations

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Published Papers (1 paper)

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Research

23 pages, 4198 KB  
Article
Surface Ozone Estimation over the Beijing–Tianjin–Hebei Region: A Case Study Using EMI-II Total Ozone Observations and Machine Learning Integration
by Hua Cheng, Jian Chen, Zhiyi Zhang, Yihui Huang and Keke Zhu
Remote Sens. 2026, 18(8), 1187; https://doi.org/10.3390/rs18081187 - 15 Apr 2026
Viewed by 173
Abstract
Surface ozone monitoring remains challenging due to sparse ground networks and limited satellite boundary-layer sensitivity. This study evaluates, for the first time, China’s Environmental Trace Gases Monitoring Instrument II (EMI-II) for estimating surface ozone over the Beijing–Tianjin–Hebei (BTH) region. EMI-II total ozone columns [...] Read more.
Surface ozone monitoring remains challenging due to sparse ground networks and limited satellite boundary-layer sensitivity. This study evaluates, for the first time, China’s Environmental Trace Gases Monitoring Instrument II (EMI-II) for estimating surface ozone over the Beijing–Tianjin–Hebei (BTH) region. EMI-II total ozone columns (TOCs) are retrieved using the differential optical absorption spectroscopy (DOAS) algorithm and validated against the TROPOspheric Monitoring Instrument (TROPOMI) (R = 0.96), Geostationary Environment Monitoring Spectrometer (GEMS) (R = 0.97), and the World Ozone and Ultraviolet Radiation Data Centre (WOUDC) ground measurements (R > 0.92, bias < 4%). TOCs are then combined with ERA5 meteorology, satellite NO2/HCHO, and surface observations within machine learning models, achieving cross-validated R2 of 0.94 and RMSE of 12.05 μg/m3 for surface ozone estimation. EMI-II estimates show strong agreement with independent observations (R = 0.91, RMSE = 10.83 μg/m3) and reproduce seasonal gradients, with summer concentrations (131 μg/m3) more than double winter levels (61 μg/m3). Estimation skill is regime-dependent: performance comparable to TROPOMI occurs under strong photochemical activity, while reduced sensitivity occurs under weak radiation and stable boundary layers—consistent with averaging kernel diagnostics. This first comprehensive validation demonstrates that EMI-II, despite vertical sensitivity limitations, provides meaningful surface ozone constraints under favorable atmospheric conditions. The framework is potentially applicable to other regions and sensors under similar conditions, providing a case study for integrating national satellite products into multi-source surface ozone estimation. Full article
(This article belongs to the Special Issue Ground- and Satellite-Based Remote Sensing for Air Quality Monitoring)
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