Seasonal AOD analysis based on AERONET observations in North and West Africa over 2010–2019

This study analyzes AOD and AE data (2010–2019) from AERONET to classify aerosols over Eurafrican stations. Results show Saharan dust dominates in the east, while finer aerosols appear in the west. Seasonal cycles highlight the monsoon as the main dust period.

Published in Earth & Environment

Like

Share this post

Choose a social network to share with, or copy the URL to share elsewhere

This is a representation of how your post may appear on social media. The actual post will vary between social networks

Explore the Research

SpringerLink
SpringerLink SpringerLink

Seasonal AOD analysis based on AERONET observations in North and West Africa over 2010–2019 - Discover Environment

The use of aerosol optical depth (AOD) properties Version 3 (level 2) of the surface-based AERONET was used to characterize aerosols at Eurafrican stations during the last decade of 2010–2019. The quality-assured AOD and Angstrom exponent (AE) data from Cairo_EMA_2 (30.081 N, 31.290E) and Tamanrasset_INM (22.790 N, 5.530E) are used to classify aerosols. Two additional stations from the west IER Cinzana (13.3 N, 5.9 W) and Cape Verde (16.7 N, 22.9 W) were compared as control to see the regional aerosols typing. The analyzed AOD data were first detrend to remove seasonal trend from the data and may provide difficulty in comparing relative AOD changes. Therefore, validated AOD and AE were employed to characterize the AOD type and determine the seasonal predominance. This method of analysis was derived by the deviation of the monthwise mean from the AOD data. The dominant aerosol types are coarsely absorbed due to dust from the Sahara. Saharan dust was observed in Tamanrasset_INM with AOD < 1 and AE < 1 and in Cairo_EMA_2 with AOD < 1 and AE < 1 over the spectral decadal trend. The west stations showed both AOD and AE > 1 for IER Cinzana and Cape Verde. The winter mean and standard deviation are − 0.18 ± 0.14 with AOD (− 0.009 ± 0.06) for the east. This indicates that the AOD dominance varies with the site and is heavily dependent on meteorological cycles. In the premonsoon season, the AE had AOD characteristics of 0.13 ± 0.15 (− 0.003 ± 0.03). The seasonal cycle indicates pure AOD characteristics, and the results have good confidence that the monsoon season is the major dust-driven season. The results of the study present aerosol characterization over Eurafrican stations and provide better insight into regional climate and local air pollution.

This study presents a comprehensive characterization of aerosols across selected Eurafrican stations over a decade (2010–2019), utilizing Aerosol Optical Depth (AOD) and Angstrom Exponent (AE) data from the surface-based AERONET Version 3, Level 2 dataset. The main objective is to understand the spatial and seasonal variability of aerosols and their relationship with regional meteorological patterns, with particular focus on how different aerosol types dominate across varied geographic and climatic regions.

Four stations were selected for analysis, representing both eastern and western parts of the Eurafrican domain: Cairo_EMA_2 (Egypt) and Tamanrasset_INM (Algeria) in the east, and IER Cinzana (Mali) and Cape Verde in the west. To ensure accurate and reliable comparisons across sites, the AOD data were first detrended to remove inherent seasonal trends. Validated AOD and AE values were then employed to classify aerosol types and assess their seasonal behavior, providing insight into the dominant aerosol sources and their temporal dynamics.

Aerosol Type Classification and Key Findings

The results confirm that Saharan dust is the dominant aerosol type at the eastern stations of Cairo and Tamanrasset. This is evidenced by AOD values less than 1 and AE values less than 1, which indicate the prevalence of large, coarse particles typical of desert dust. In contrast, the western stations of IER Cinzana and Cape Verde display both AOD and AE values greater than 1, signifying finer aerosol particles. These finer particles are likely influenced by sources such as biomass burning and anthropogenic pollution, which are common in these regions.

Seasonal analysis further revealed that the monsoon season is the period of highest dust activity. Conversely, the winter season exhibited lower AOD values at the eastern stations, highlighting the strong seasonal influence of regional meteorological cycles on aerosol loading and type. The seasonal cycles show that the monsoon not only drives dust transport but also affects aerosol optical properties through changes in humidity, wind patterns, and atmospheric mixing.

Linear regression analysis between AOD and AE was conducted for the various sites. At Tamanrasset_INM, an inverse relationship was observed, with AE mean and standard deviation of approximately 0.01 ± 0.28, supporting the dominance of dust aerosols driven by strong surface winds. This inverse coupling between AE and AOD is significant because fine-mode aerosols generally have AE > 1, while coarse-mode aerosols show AE < 1. The root mean square error (RMSE) and mean absolute error (MAE) for regression fits were low, indicating good agreement and reliable characterization of aerosol optical properties.

Detrending of AOD time series was necessary to separate seasonal variations from longer-term trends. Seasonal patterns can otherwise mask relative changes in aerosol loading. This approach enables a more precise understanding of aerosol behavior over time and across regions.

Seasonal cycles were examined in detail, classifying data into winter, premonsoon, monsoon, and postmonsoon periods, which collectively represent about 25% of the dataset. The study found that AOD spectral dominance begins in the premonsoon and extends through the monsoon season, with characteristic dust loading. AE values during this period ranged from 0.22 to 0.33, all below 1, further confirming the coarse dust particle presence.

Winter was characterized by drier local conditions, with AE values peaking at 0.33 and showing typical dust aerosol peaks in AOD. These results emphasize the key role that local meteorology and atmospheric chemistry play in influencing aerosol concentrations. For instance, low-pressure regions tend to have higher AOD loading due to enhanced aerosol uplift and transport.

The inverse correlation between AE and AOD was again confirmed for Cairo_EMA_2, with seasonal variations clearly indicating that different aerosol types dominate at different times of the year. Winter spectral means showed negative deviations in AOD and AE, consistent with dust dominance, whereas premonsoon periods exhibited more varied aerosol properties.

The spatial trends highlight the asymmetric AOD variations, particularly pronounced in the eastern region after detrending. This asymmetry may result from additional aerosol types such as black carbon or other absorbing aerosols contributing to the optical depth.

The comprehensive decadal analysis across the Eurafrican stations revealed distinct regimes of aerosol sources and types, including dust, biomass burning, and urban pollution, each with unique optical and physical-chemical properties. These sources are affected by long-range transport and seasonality, demonstrating the complex nature of aerosol dynamics in the region.

The findings provide a clearer understanding of the spatial heterogeneity and seasonal shifts in aerosol properties across Eurafrican regions. Saharan dust remains the principal aerosol type in eastern stations, while western stations show more influence from finer aerosols related to biomass burning and human activities. The study confirms the monsoon season as a major driver for dust transport and highlights how meteorological cycles shape aerosol characteristics.

The study link is here: Recent study in aerosol distribution

Caption

Please sign in or register for FREE

If you are a registered user on Research Communities by Springer Nature, please sign in

Follow the Topic

Earth Sciences
Physical Sciences > Earth and Environmental Sciences > Earth Sciences

Related Collections

With Collections, you can get published faster and increase your visibility.

Environmental Pollutants: Origins, Pathways, Impacts, and Sustainable Solutions

Pollution is a critical threat to ecosystems, human health, and the planet’s future. From industrial waste in China to microplastics in the Mediterranean and PFAS contamination in the U.S., pollutants spread across air, water, and soil, harming wildlife and communities worldwide. Understanding how these contaminants move, transform, and impact the environment is key to designing effective solutions.

This collection brings together cutting-edge research on pollution sources, environmental behavior, risks, and innovative cleanup strategies—covering everything from heavy metals in mining regions to pharmaceutical waste in urban waterways. We highlight advances in environmental science, green technology, and policy to tackle both long-standing and emerging threats like e-waste and AI-driven monitoring. The topics include, but are not limited to, the following:

• Pollution Origins: Industrial, agricultural, and urban sources, including legacy and emerging contaminants.

• Environmental Pathways: How pollutants travel through air, water, soil, and food chains.

• Risks and Impacts: Effects on biodiversity and human health, from local hotspots to global crises.

• Cleanup and Prevention: Nature-based solutions (like wetland restoration) and high-tech innovations (such as catalytic oxidation).

• Policy and Tools: Smart regulations, predictive modeling, and new detection methods.

This Collection supports and amplifies research related to: SDG 11

Keywords:PFAS, Heavy metals, Microplastics, Emerging Contaminants, Emission sources, Environmental forensics, Ecological indicators, Pollution sources, Contaminant transport, Ecological risk assessment, Pollutant fate and transformation, Bioaccumulation, Ecological restoration, Remediation technologies, Sustainable pollution management

Publishing Model: Open Access

Deadline: May 01, 2026

Mapping Sustainability: Geospatial Tools for Environmental Challenges

The escalating complexity of global ecological challenges demands innovative approaches to understanding, monitoring, and managing the environment. Geospatial technologies, including Geographic Information Systems (GIS), remote sensing, and spatial analytics, have become indispensable tools in addressing these issues. These technologies have demonstrated remarkable impact across various domains. For instance, in disaster management, the NASA-Disaster Response Coordination System utilizes satellite imagery and GIS analytics to assess damages from natural calamities, while the Copernicus Emergency Management Service (EMS) provides real-time mapping and early warning systems. In biodiversity conservation, initiatives like Global Forest Watch (GFW) and the Integrated Biodiversity Assessment Tool (IBAT) employ satellite data and spatial datasets to monitor deforestation and support conservation planning. Additionally, in urban sustainability, projects such as the Landsat Urban Heat Mapping Initiative help urban planners mitigate rising temperatures through targeted green infrastructure solutions.

This collection, "Mapping Sustainability: Geospatial Tools for Environmental Challenges," addresses critical gaps in the current literature by showcasing pioneering research that leverages geospatial technologies to confront urgent environmental issues. While existing research extensively explores geospatial methods, there is a significant need for more integrated, interdisciplinary approaches that translate data-driven insights into actionable solutions for sustainability. This collection advances the field by bridging science and policy, enhancing urban sustainability, advancing climate resilience, promoting data-driven conservation, and innovating spatial decision support systems (SDSS). Contributions that emphasize interdisciplinary research, innovative case studies, global perspectives, and policy insights are highly encouraged.

Keywords:Geospatial Technologies; Environmental Sustainability; Climate Change Analysis; Biodiversity Conservation; Pollution Monitoring; Water Resource Management; Spatial Data Analytics; Sustainable Development; Ecological Resilience; Geospatial Modeling; Spatial Decision Support Systems (SDSS); Remote Sensing Applications; Sustainable Urban Planning; Land Use and Land Cover Change (LULC); Disaster Risk Reduction (DRR); Ecosystem Monitoring; GIS-based Policy Analysis; Smart Cities and Resilient Infrastructure

Publishing Model: Open Access

Deadline: Jan 11, 2026