Search Results (80)

Aerosol optical depth (AOD) is essential for air quality monitoring and climate research. However, satellite-based retrievals suffer from cloud-related data gaps, and reanalysis products are limited by coarse spatial resolution and substantial production latency. This study develops a real-time, gap-free, high-resolution (1.5 km) AOD retrieval system for South Korea. The system integrates Copernicus Atmosphere Monitoring Service (CAMS) forecasts, high-resolution meteorological fields, and ground-based air quality observations within a machine learning framework. Three models with varying training periods were systematically evaluated using cross-validation and independent validation with 2024 Aerosol Robotic Network (AERONET) data. The optimal model, trained on 2015–2023 data, achieved a mean absolute error (MAE) of 0.075 and a correlation coefficient (R) of 0.841 during the 2024 independent validation, significantly outperforming the original CAMS forecast. The system demonstrated robust and consistent performance across varying land cover types, seasons, and AOD conditions, from clean to highly polluted. Empirical orthogonal function (EOF) analysis confirmed that the product successfully captures physically meaningful spatiotemporal patterns, including transboundary pollution transport, regional emission gradients, and topographic effects. Providing real-time, gap-free, 3-hourly daytime AOD, the proposed model overcomes the limitations of cloud-induced gaps in satellite data and the latency and coarseness of reanalysis products. This enables robust operational monitoring and aerosol research across the Korean Peninsula. Full article

Air quality monitoring and forecasting has been a challenging problem for years. In addition to traditional ground-based observational stations, in recent years there have been more geostationary and polar orbiting satellite observations on air quality. However, evaluation of performance of these observations is lacking, especially for the region of southern China, which is rarely affected by severe sand/dust weather. In the spring of 2025, two events of sand/dust weather, one case of sand/dust spreading to southern China in April and another case of sand/dust confining to northern China in May, provide a good opportunity for detailed case study and examination of the performance of the tools. The surface particulate matter (PM) concentration retrieved from a geostationary satellite, Geostationary Korea Multi-Purpose Satellite—2B (GEO-KOMPSAT-2B, or GK2B), is studied by checking consistency with the analysis of two numerical models: the Copernicus Atmosphere Monitoring Service model of the European Centre of Medium Range Weather Forecast (ECMWF-CAMS) and Chinese Unified Atmospheric Chemistry Environment model of the China Meteorological Administration (CMA-CUACE). The former shows comparable PM concentration with satellite observations, while overestimation is found with the latter. It is also found that there may be latitude dependence of the quality of the satellite-based data. To further validate the satellite observation data, it is directly compared with the ground-based station measurements in Hong Kong for the event in mid-April 2025, the performance of satellite data points near Hong Kong is generally satisfactory. For polar orbiting satellite, there is information about the aerosol classification in addition to aerosol optical depth, and the classification result is found to be reasonable by comparison with ground-based observation, though some refinements appear to be necessary. The geostationary satellite images provide high spatial coverage and frequently updated air quality data, which are confirmed to be useful in monitoring the southward spread of sand/dust weather to southern China which is a very rare event. The monitoring can be both qualitative and quantitative. The performance of various monitoring and forecasting tools is examined in details based on the cases. It also forms a reference for the use in operation, and opens up a new era for air quality study for southern China. Full article

Cyprus is located between Europe, Asia and Africa, and its location is vulnerable to dust transport from the Sahara Desert, wildfire smoke particles from surrounding regions, and other anthropogenic emissions caused by several factors, mostly due to business activities on harbor areas. Moreover, the country suffers from heavy traffic conditions caused by the limited public transportation system in Cyprus. Therefore, taking into consideration the country’s geographic location, heavy commercial activities, and lack of good public transportation system, Cyprus is exposed to dust episodes and high anthropogenic emissions associated with multiple health and environmental issues. Therefore, continuous and qualitative air quality monitoring is essential. The Department of Labor Inspection of Cyprus (DLI) has established an air quality monitoring network that consists of 11 stations at strategic geographic locations covering rural, residential, traffic and industrial zones. This network measures the following pollutants: nitrogen oxide, nitrogen dioxide, sulfur dioxide, ozone, carbon monoxide, particulate matter 2.5, and particulate matter 10. This case study compares and evaluates the agreement between Copernicus Atmosphere Monitoring Service (CAMS) air quality products and ground-truth data from the DLI air quality network. The study period spans from January to December 2024. This study focuses on the following three pollutants: particulate matter 2.5, particulate matter 10, and ozone, using Ensemble Median, EMEP, and CHIMERE near-real-time model data provided by CAMS. A data analysis was performed to identify the agreement and the error rate between those two datasets (i.e., ground-truth air quality data and CAMS air quality data). In addition, this study assesses the reliability of assimilated datasets from CAMS across rural, residential, traffic and industrial zones. The results showcase how CAMS near-real-time analysis data can supplement air quality monitoring in locations without the availability of ground-truth data. Full article

The Copernicus Atmosphere Monitoring Service (CAMS) offers a broad portfolio of global and regional atmospheric products that support environmental monitoring, air quality assessment, health applications and climate policy. Under the CAMS National Collaboration Programme (NCP), the ERATOSTHENES Centre of Excellence (ECoE) serves as the national coordinator for Cyprus, working to bridge the gap between CAMS outputs and local end-user needs. This paper presents the strategy and implementation framework adopted by ECoE to facilitate CAMS uptake in Cyprus. Efforts focus on integrating CAMS data into national systems, developing tailored applications (e.g., UV forecasting, dust event alerts), building stakeholder capacity, and supporting regulatory reporting. Outcomes also include the deployment of the AirData Hub platform and initial steps toward institutionalizing CAMS-derived workflows in public health and environmental planning. The work highlights both the opportunities and technical challenges of customizing CAMS products for small-island contexts. Full article

Methane is a short-lived climate forcer (SLCF) that has a pivotal influence on the Earth’s climate. This work focuses on mean methane concentrations and their year-to-year variability for the period 2003–2014 between four CMIP6 (Coupled Model Intercomparison Project Phase 6) Earth System Model simulations and CAMS (Copernicus Atmosphere Monitoring Service) reanalysis fields. The selected CMIP6 models are CNRM-ESM2-1, GFDL-ESM4.1, UKESM1, and EC-Earth3-AerChem, while monthly averaged fields from the CAMS global greenhouse gas reanalysis (EGG4) were employed. It is shown that the EC-Earth3-AerChem model closely aligns with CAMS methane concentration pattern, whereas other models display notable differences. Full article

In this study, Copernicus Atmosphere Monitoring Service (CAMS) reanalysis data (EAC4 & EGG4) are used. To capture short-term variations and analyze long-term changes in CO₂ and CH₄, this study focuses on two specific regions of interest in each of three European countries: Greece, Italy, and France. Both CO₂ and CH₄ exhibit a positive trend with seasonally averaged increases of over 6% and 2%, respectively, compared to the reference period 2003–2013. Enhanced CH₄ concentrations in Greece are observed during winter, primarily linked to anthropogenic sources such as fossil fuel combustion, heating, industrial activities, and gas distribution. Additionally, positive CH₄ residuals exceeding 0.6% were detected in autumn, likely due to regional agricultural activities in N. Greece and/or wildfires in Athens. Winter, spring, and autumn are the seasons during which CH₄ concentrations are typically highest in the Basilicata and Po Valley regions of Italy, primarily due to agricultural activities, waste management processes, and natural gas extraction, particularly in the Val d’Agri region. Higher CH₄ variability was found during winter in France. Regarding CO₂, all countries show a large diurnal variability (approximately ± 2 ppm), that of a typical mid-northern-hemisphere site, largely associated with the biospheric cycle of photosynthesis and enhanced by anthropogenic emissions and wildfire episodes. Full article

This study investigates the impact of a thermal power plant site on air quality in Bucharest, Romania. It emphasizes the importance of accurate air pollutant inmission measurements in urban areas by utilizing mobile measurements of low-cost sensors, Copernicus’ Copernicus Atmosphere Monitoring Service (CAMS) and Copernicus Land Monitoring Service (CLMS), and satellite retrieval to better understand climate change drivers and their potential impact on near- surface concentrations and column densities of ${\mathrm{NO}}_2$, CO, and PM (particulate matter). It focuses the attention on the need of considering the placement of power plants in relation to metropolitan areas while making this assessment. The research highlights the limits of typical mesoscale air quality models in effectively capturing pollution dispersion and distribution using LUR (Land Use Regressions) retrievals. The authors investigate a variety of ways to better understand air pollution in metropolitan areas, including satellite observations, mobile measurements, and land use regression models. The study focuses largely on Bucharest, the capital of Romania, which has air pollution issues caused by vehicle traffic, industrial activity, heating systems, and power plants. The results indicate how the placement of a power plant may affects air quality in the nearby residential areas. Full article

In recent years, air pollution has become a significant issue for megacities. This study analyzed the air pollution levels in Tehran and the relationship between pollutant concentrations and atmospheric quantities during 2023. The correlation coefficients between wind speed, temperature, mean sea level pressure (MSLP), and relative humidity (RH) were calculated against the concentrations of NO₂, NO_x, PM₁₀, and PM_2.5. Additionally, one case study was conducted for each pollutant. Approximately 72% of haze phenomena in Tehran were recorded in November, December, and January. The monthly pattern of PM₁₀ concentration indicated higher levels in the southern and western parts of Tehran. For PM_2.5, in addition to these areas, significant concentrations were also observed in the central and eastern parts. NO₂ concentrations were found to be higher in the northeast and northern areas. An inverse relationship was found between wind speed and temperature with pollutant concentrations. Positive correlations between MSLP and pollutant concentrations suggested that the pollutant levels also increased as air pressure rose. RH showed a significant direct relationship with PM_2.5 and NO_x. Synoptic analysis revealed that PM₁₀ case studies often occurred during the warm season, with a thermal low pressure situated over the Iranian plateau. During PM_2.5 and NO₂ pollution events, Tehran was influenced by high pressure, and 10 m wind speeds were weak. Finally, verification of the 24 h forecast of the CAMS model showed that, while the model accurately predicted the spatial distribution of pollutants in most cases, it consistently underestimated the concentration levels. Full article

Aerosols significantly impact the brightness temperature (BT) in thermal infrared (IR) channels, and ignoring their effects can lead to relatively large observation-minus-background (OMB) bias in radiance calculations. The accuracy of aerosol datasets is essential for BT simulations and bias reduction. This study incorporated aerosol reanalysis datasets from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Copernicus Atmosphere Monitoring Service (CAMS) into the Advanced Radiative Transfer Modeling System (ARMS) to compare their impacts on BT simulations from the Geostationary Interferometric Infrared Sounder (GIIRS) and their effectiveness in reducing OMB biases. The results showed that, for a sandstorm event on 10 April 2023, incorporating total aerosol data from the MERRA-2 improved the BT simulations by 0.56 K on average, surpassing CAMS’s 0.11 K improvement. Dust aerosols notably impacted the BT, with the MERRA-2 showing a 0.17 K improvement versus CAMS’s 0.06 K due to variations in the peak aerosol level, thickness, and column mass density. Improvements for sea salt and carbonaceous aerosols were concentrated in the South China Sea and Bay of Bengal, where the MERRA-2 outperformed CAMS. For sulfate aerosols, the MERRA-2 excelled in the Bohai Sea and southern Bay of Bengal, while CAMS was better in the northern Bay of Bengal. These findings provide guidance for aerosol assimilation and retrieval, emphasizing the importance of quality control and bias correction in data assimilation systems. Full article

A new meteorological station (DMS) was established at the Morning Glory summit in Zhejiang Province to provide regional background information on atmospheric composition in the Yangtze River Delta (YRD) region, China. This study investigated the first carbon monoxide (CO) records at DMS from September 2020 to January 2022. The annual average concentration of CO was 233.4 ± 3.8 ppb, which exceeded the measurements recorded at the other Asian background sites. The winter CO concentration remained elevated but peaked in March in the early spring due to the combined effect of regional emissions within the YRD and transportation impacts of North China and Southeast Asia sources. The diurnal cycle had a nocturnal peak and a morning valley but with a distinct afternoon climb, as the metropolis in the YRD contributed to a local concentration enhancement. The back trajectory analysis and the Weighted Potential Sources Contribution Function (WPSCF) maps highlighted emissions from Anhui, Jiangxi, Zhejiang, and Jiangsu provinces as significant sources. Due to well-mixed air conditions and fewer anthropogenic influences, DMS records closely aligned with the CO averages derived from the Copernicus Atmospheric Monitoring Service (CAMS) covering the YRD, confirming its representativeness for regional CO levels. This study underscored DMS as a valuable station for monitoring and understanding CO spatiotemporal characteristics in the YRD region. Full article

High dust loading significantly impacts air quality, climate, and public health. Early warning is crucial for mitigating short-term effects, and accurate dust field estimates are needed for forecasting. The Copernicus Atmosphere Monitoring Service (CAMS) offers global reanalysis datasets and forecasts of particulate matter with a diameter of under 10 μm (PM₁₀), which approximate dust, but recent studies highlight discrepancies between CAMS data and ground in-situ measurements. Since CAMS is often used for forecasting, errors in PM₁₀ fields can hinder accurate dust event forecasts, which is particularly challenging for models that use artificial intelligence (AI) due to the scarcity of dust events and limited training data. This study proposes a machine-learning approach to correct CAMS PM₁₀ fields using in-situ data to enhance AI-based dust event forecasting. A correction model that links pixel-wise errors with atmospheric and meteorological variables was taught using gradient-boosting algorithms. This model is then utilized to predict CAMS error in previously unobserved pixels across the Eastern Mediterranean, generating CAMS error fields. Our bias-corrected PM₁₀ fields are, on average, 12 μg m⁻³ more accurate, often reducing CAMS errors by significant percentages. To evaluate the contribution, we train a deep neural network to predict city-scale dust events (0–72 h) over the Balkans using PM₁₀ fields. Comparing the network’s performance when trained on both original and bias-corrected CAMS PM₁₀ fields, we show that the correction improves AI-based forecasting performance across all metrics. Full article

This study assesses the performance of the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis dataset in estimating ground-level concentrations (GLCs) of aerosols and criteria pollutants across the Himawari-8 observational area, covering China, Indonesia, and Australia, from 2016 to 2023. Ground-based monitoring networks in these regions are limited in scope, making it necessary to rely on satellite-derived aerosol optical depth (AOD) as a proxy for GLCs. While AOD offers broad coverage, it presents challenges, particularly in capturing surface-level pollution accurately during episodic events. CAMS, which integrates satellite data with atmospheric models, is evaluated here to determine its effectiveness in addressing these issues. The study employs square root transformation to normalize pollutant concentration data and calculates monthly–hourly long-term averages to isolate pollution anomalies. Geographically weighted regression (GWR) and Jacobian matrix (dY/dX) methods are applied to assess the spatial variability of pollutant concentrations and their relationship with meteorological factors. Results show that while CAMS captures large-scale pollution episodes, such as the 2019/2020 Australian wildfires, discrepancies in representing GLCs are apparent, especially when vertical aerosol stratification occurs during short-term pollution events. The study emphasizes the need for integrating CAMS data with higher-resolution satellite observations, like Himawari-8, to improve the accuracy of real-time air quality monitoring. The findings highlight important implications for public health interventions and environmental policy-making, particularly in regions with insufficient ground-based data. Full article

The population of Senegal faces health challenges related to desert dust and heat waves (HWs). This study aims to (a) update the documentation of HWs in Senegal, expanding on the work of Sambou et al. (2019); (b) investigate the modulation of dust indicators during HWs; and (c) assess the distinct impacts of dust content on night-time and daytime HWs. We use [i] the daily maximum air temperature (Tx), minimum air temperature (Tn), and apparent temperature (Ta) from 12 stations in the Global Surface Summary of the Day (GSOD) database and [ii] the Dust Aerosol Optical Depth (Dust AOD), particulate matter (PM) concentrations, 925 hPa wind, and Mean Sea Level Pressure (MSLP) from the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis. HWs are defined for each station in spring as periods when Tx, Tn, or Ta exceeds the 95th percentile for at least three consecutive days. Three homogeneous zones from the Atlantic coast to inland Senegal are identified using hierarchical cluster analysis: Zone 1 (Saint-Louis, Dakar-Yoff, Ziguinchor, and Cap Skirring), Zone 2 (Podor, Linguère, Diourbel, and Kaolack), and Zone 3 (Matam, Tambacounda, Kédougou, and Kolda). Our results show that Zone 1 records the highest number of HWs for Tx, Tn, and Ta, while Zone 3 experiences more HWs in terms of Tn and Ta than Zone 2. The influence of dust is notably stronger for HWs linked to Tn and Ta than for those related to Tx. Analysis of the mechanisms shows that the presence of dust in Senegal and its surrounding regions is detected up to four days before the onset of HWs. These findings suggest that dust conditions associated with spring HWs in Senegal may be better distinguished and predicted. Full article

Air pollution monitoring in industrial regions like Moravia-Silesia faces challenges due to complex environmental conditions. Low-cost sensors offer a promising, cost-effective alternative for supplementing data from regulatory-grade air quality monitoring stations. This study evaluates the accuracy and reliability of a prototype node containing low-cost sensors for carbon monoxide (CO) and particulate matter (PM), specifically tailored for the local conditions of the Moravian-Silesian Region during winter and spring periods. An analysis of the reference data observed during the winter evaluation period showed a strong positive correlation between PM, CO, and ${\mathrm{NO}}_2$ concentrations, attributable to common pollution sources under low ambient temperature conditions and increased local heating activity. The Sensirion SPS30 sensor exhibited high linearity during the winter period but showed a systematic positive bias in ${\mathrm{PM}}_{10}$ readings during Polish smog episodes, likely due to fine particles from domestic heating. Conversely, during Saharan dust storm episodes, the sensor showed a negative bias, underestimating ${\mathrm{PM}}_{10}$ levels due to the prevalence of coarse particles. Calibration adjustments, based on the ${\mathrm{PM}}_1{\mathrm{/PM}}_{10}$ ratio derived from Alphasense OPC-N3 data, were initially explored to reduce these biases. For the first time, this study quantifies the influence of particle size distribution on the SPS30 sensor’s response during smog episodes of varying origin, under the given local and seasonal conditions. In addition to sensor evaluation, we analyzed the potential use of data from the Copernicus Atmospheric Monitoring Service (CAMS) as an alternative to increasing sensor complexity. Our findings suggest that, with appropriate calibration, selected low-cost sensors can provide reliable data for monitoring air pollution episodes in the Moravian-Silesian Region and may also be used for future adjustments of CAMS model predictions. Full article

This study presents the first column-averaged dry-air mole fractions of carbon dioxide (XCO₂), methane (XCH₄) and carbon monoxide (XCO) in the coal mine aggregation area in Shanxi, China, using two portable Fourier transform infrared spectrometers (EM27/SUNs), in the framework of the Collaborative Carbon Column Observing Network (COCCON). The measurements, collected over two months, were analyzed. Significant daily variations were observed, particularly in XCH₄, which highlight the impact of coal mining emissions as a major CH₄ source in the region. This study also compares COCCON XCO with measurements from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5P satellite, revealing good agreement, with a mean bias of 7.15 ± 9.49 ppb. Additionally, comparisons were made between COCCON XCO₂ and XCH₄ data and analytical data from the Copernicus Atmosphere Monitoring Service (CAMS). The mean biases between COCCON and CAMS were −6.43 ± 1.75 ppm for XCO₂ and 15.40 ± 31.60 ppb for XCH₄. The findings affirm the stability and accuracy of the COCCON instruments for validating satellite observations and detecting local greenhouse gas sources. Operating COCCON spectrometers in coal mining areas offers valuable insights into emissions from these high-impact sources. Full article