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Proceeding Paper

Climate Change Risks and Impacts on Public Health Correlated with Air Pollution—African Dust in South Europe †

by
Ioannis Adamopoulos
1,2,3,*,
Niki Syrou
4 and
Domenico Vito
5
1
Department of Environmental Hygiene and Public Health Inspections, Hellenic Republic Region of Attica, West Sector of Athens, 12243 Athens, Greece
2
Department of Public Health Policy, Sector of Occupational & Environmental Health, School of Public Health, University of West Attica, Alexandras Ave. 196, 11521 Athens, Greece
3
Department of Public Health Policies, School of Social Science, Hellenic Open University, 18 Aristotelous St., 26335 Patra, Greece
4
Department of Physical Education and Sport Science, University of Thessaly, Karies, 42100 Trikala, Greece
5
Metabolism of Cities Living Lab (MOC-LLAB), Center for Human Dynamics in the Mobile Age, San Diego State University, San Diego, CA 92182, USA
*
Author to whom correspondence should be addressed.
†
Presented at the 3rd International One Health Conference, Athens, Greece, 15–17 October 2024.
Med. Sci. Forum 2025, 33(1), 1; https://doi.org/10.3390/msf2025033001
Published: 16 April 2025
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Abstract

:
Climate change poses a significant risk to the environment and public health, leading to extreme weather patterns, rising sea levels, and loss of biodiversity. The relationship between air pollution from African dust and climate change demonstrates its critical role in trapping heat in the atmosphere, resulting in heat-related illnesses, heart problems, and respiratory issues. This research points to the detrimental effects of pollutants such as smog, dust, acid rain, and ozone depletion on ecosystems, highlighting the importance of using geographically weighted regression modeling and the MODIS-NDVI analysis to address air pollution. Particulate Matter (PM2.5–10) and ozone levels can have negative impacts on respiratory and cardiovascular health. Proactive steps, such as implementing clean energy technologies and enforcing stricter pollution regulations, are necessary to protect public health. Acting is crucial to addressing these global challenges and creating a cleaner, healthier future for future generations, underscoring the need for climate justice commitment.

1. Introduction

Strong seasonal winds lift more than 180 million tons of dust out of the Sahara Desert and send it out of North Africa annually. However, the dust also travels elsewhere all over the world, either drifting toward Europe or returning to other parts of Africa [1]. The Visible Infrared Imaging Radiometer Suite (VIIRS) on board of the NOAA-20 satellite recorded an increasing display of dust particles in the atmosphere. According to the Copernicus Atmosphere Monitoring Service, the majority of the dust that reaches Europe will probably be concentrated. When the dust plume interacts with a weather front, it may result in “mud rain” [2]. The dust storm follows an intense event over southern and central Europe. That storm’s Saharan dust covered the snow in the Pyrenees and Alps, turning the skies orange.
The dust has the potential to worsen air quality and hasten snowmelt. However, it also has a significant impact on the Earth’s biological processes and climate, reflecting and absorbing solar radiation and supplying ocean ecosystems with iron and other minerals that are necessary for plants and phytoplankton to flourish [3]. Satellite data plays a crucial role in understanding vegetation health. The Moderate Resolution Imaging Spectroradiometer (MODIS) provides valuable insights using the Normalized Difference Vegetation Index (NDVI). This index is vital for monitoring green cover and assessing an ecosystems’ health. It offers a lens through which we can examine the impact of air pollution and climate change on public health. Healthy vegetation absorbs carbon dioxide, producing oxygen and thus ensuring air quality.
However, pollutants such as smog, dust, and particulate matter can seriously impede this process. Thus, understanding vegetation stress through a MODIS-NDVI analysis becomes essential. It helps reveal the intersecting impacts of climate change and air pollution. Climate change is worsening air pollution issues. Increased temperatures and variable weather conditions affect the dispersion of pollutants. This situation poses significant public health risks, particularly for respiratory and cardiovascular diseases. Climate change is a pressing issue that has far-reaching impacts on both public health and environmental sustainability. The link between climate change and air pollution is well-established, with air pollutants such as carbon dioxide, methane, and nitrous oxide contributing to the greenhouse effect and global warming. These pollutants are primarily produced by human activities such as burning fossil fuels for energy, industrial processes, transportation, and agriculture [4]. Earth’s temperature continues to rise due to these greenhouse gases; we are seeing increased frequency and intensity of extreme weather events such as heat waves, hurricanes, and wildfires [5]. These events not only pose direct risks to human health but also exacerbate air pollution levels by releasing particulate matter and other harmful pollutants into the atmosphere. The impacts of climate change and air pollution on public health are diverse and profound. Respiratory illnesses such as asthma, chronic obstructive pulmonary disease (COPD), and lung cancer are on the rise due to poor air quality caused by increased levels of pollutants in the atmosphere [6]. Extreme heat events can also lead to heat-related illnesses and even death, particularly among vulnerable populations such as the elderly, children, and low-income communities.
In addition to the direct health impacts, climate change and air pollution also have significant implications for environmental sustainability [7]. Rising temperatures and shifting weather patterns can disrupt ecosystems, leading to the loss of biodiversity and threats to food security [8]. Increased air pollution can harm wildlife and vegetation, further exacerbating environmental degradation [9]. Addressing the interconnected challenges of climate change, air pollution, and public health requires a multi-faceted approach. Policies aimed at reducing greenhouse gas emissions, transitioning to renewable energy sources, improving air quality standards, and promoting sustainable development practices are essential for mitigating the impacts of these global issues [10]. The link between climate change, air pollution, public health, and environmental sustainability is undeniable [11]. We must take urgent action to address these challenges and protect the health of current and future generations. By working together at local, national, and global levels, we can create a more sustainable and resilient future for all [12].
The aims, objectives, and scope of this study are as follows:
-
Air pollution plays a significant role in exacerbating climate change.
-
This study aimed to analyze the impacts of air pollution on public health and environmental sustainability.
-
This study aimed to assess the relationship between climate change and air pollution.
-
This study aimed to investigate the effects of air pollution on public health.
-
This study aimed to explore solutions for mitigating the impacts of air pollution on environmental sustainability.
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Modeling research was used to analyze the data.
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Data collection and analysis were conducted.
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Statistical methods were used to interpret the findings.
-
The correlation between air pollution levels and respiratory illnesses was examined.
-
The impact of air pollutants on climate change was investigated.
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The data were analyzed, and trends and patterns in air pollution levels were identified.
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The findings of this study provide valuable insights into the links between air pollution, public health, and environmental sustainability. This study’s scope is to present a comprehensive view of climate change-induced air pollution dynamics due to African Dust in South Europe and its impact on public health and environmental sustainability.

2. Materials and Methods

This study is considered secondary research that involves compiling existing data extracted from multiple datasets. The data collection includes sources from global organizations such as the Intergovernmental Panel on Climate Change (IPCC), the World Health Organization (WHO), and the National Aeronautics and Space Administration (NASA) (IPCC, 2014; WHO, 2018) [3,13,14].
Advanced models, including geographically weighted regression (GWR) and MODIS-NDVI analysis, were applied to assess air pollution impacts and variability. These methods allow for a spatial assessment of pollution distribution and vegetation stress as an environmental indicator (NASA Earth Observatory, 2021) [3,15,16].
This study applies the GWR model to evaluate spatial variations in air pollution caused by African dust in Southern Europe. While GWR has been widely used in pollution modeling, its combination with the MODIS-NDVI analysis to assess vegetation stress as a proxy for pollution exposure represents a novel methodological approach. Additionally, our research integrates a temperature scenario analysis with dust concentration trends, providing insights into how climate variability influences air quality shifts (Copernicus Atmosphere Monitoring Service, 2024) [2,16].
The GWR model is instrumental in assessing spatial variations in air pollution, helping identify pollution hotspots, and analyzing regional pollution patterns. This geostatistical technique evaluates how different environmental and atmospheric factors influence pollution levels across diverse geographic regions, providing data essential for policy development.
The Normalized Difference Vegetation Index (NDVI) is a crucial tool for analyzing the impact of air pollution on vegetation health [17]. Elevated NDVI values indicate healthier vegetation, whereas low NDVI scores signal plant stress, often resulting from airborne pollutants such as nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3) (NASA Earth Observatory, 2021) [3,15,17]. Integrating IGBP data with MODIS-derived NDVI climate parameters offers valuable insights into long-term pollution trends and ecosystem stability [13,17].
The research methodology focuses on understanding climate change-induced air pollution dynamics and their impact on public health and environmental sustainability. This is achieved through geostatistical modeling, satellite data analysis (MODIS NDVI), and comparative assessments of nitrogen dioxide, sulfur dioxide, and ozone levels using remote sensing and model simulations (IPCC, 2014; WHO, 2018) [3,13,14,16,17]. The MODIS instrument tracks the atmospheric aerosol (African dust) concentration, offering a comprehensive evaluation of air pollution’s role in environmental hygiene and public health (NASA Earth Observatory, 2021; Copernicus Atmosphere Monitoring Service, 2024) [2,3,13,16].

2.1. Conceptual Model

In situ data observation at a national and regional scale is complex, and there is a data deficit; hence, using NDVI, an inverse relationship with the air pollution indicator is possible. Generally, higher NDVI values indicate healthier and denser vegetation cover. In areas with lower air pollution levels, vegetation tends to be healthier, resulting in higher NDVI values. For example, prolonged exposure to pollutants like nitrogen dioxide (NO2) and sulfur dioxide (SO2) can damage vegetation, leading to reduced NDVI values over time. Conversely, in areas with higher air pollution levels, vegetation health may decline, leading to lower NDVI values. Hence, a regional area (land use/land cover IGBP data) is linked to MODIS NDVI spastically for climate parameter (rainfall) values. Min NDVI is a dependent variable that determines the most sensitive pollution variable, which is identified by the eigenvector matrix carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2). Monitoring changes in NDVI over time and space can provide insights into long-term trends and spatial patterns of air pollution impacts on vegetation health and ecosystem dynamics.

2.2. Data Collection

Gathering air pollution data for each specific objective in the selected regions is important, and this may include data on pollutants such as particulate matter (PM2.5, PM10). The data sources are be IPCC, WHO, and NASA earth observations.
Objective one is nitrogen dioxide (NO2).
Objective two is sulfur dioxide (SO2).
Objective three is ozone (O3).
Data sources can include satellite observations and model simulations. The Moderate Resolution Imaging Spectroradiometer (MODIS) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. While MODIS primarily focuses on land surface, ocean, and atmospheric observations, it can indirectly provide information on air quality through various climate parameters. The MODIS measures the amount of sunlight scattered or absorbed by aerosol particles in the atmosphere. The Aerosol Optical Depth (AOD) is a measure of aerosol concentration, and higher AOD values indicate higher levels of aerosols in the atmosphere, which can negatively affect air quality. AOD data can be obtained from MODIS Level 2 products.

2.3. Climate Parameters

NDVI can be integrated with climate parameters through the GWR model, including sensitive air pollution variables and land use/land cover (IGBP) information, to develop comprehensive models for assessing air pollution impacts on ecosystems and human health.

2.4. Explanatory Air Quality Parameters

Aerosol particles, especially fine particulate matter (PM2.5 and PM10), are major contributors to air pollution. By analyzing AOD data and applying appropriate algorithms, it is possible to estimate PM concentrations. MODIS can also provide information on certain trace gases such as carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2) through its spectral bands. While not as direct as AOD for estimating air quality, the presence of these gases can indicate pollution sources and contribute to overall air quality assessments when combined with other data sources. In addition, MODIS can detect thermal anomalies associated with fires. Monitoring fire activity and biomass burning is crucial for assessing air quality impacts, especially in regions prone to wildfires or extensive biomass burning. Several studies have been conducted to develop algorithms for retrieving PM concentrations from MODIS AOD data, such as particulate matter (PM2.5, PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3). Therefore, MODIS data can be integrated into air quality models to improve the accuracy of air quality forecasts and the assessments of air pollution. By assimilating satellite observations into models, it is possible to enhance spatial and temporal coverage, particularly in regions with sparse ground-based monitoring networks.

2.5. Earth Observation Data

To assess the significance and spatial variability of the coefficients for air pollution parameters, NDVI was used. The use of NDVI MODIS can also provide information on certain trace gases such as carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2) through its spectral NDVI. While it does not directly estimate air quality, the presence of these gases can indicate pollution sources and contribute to overall air quality assessments when evaluating climate parameters. Using the Normalized Difference Vegetation Index (NDVI) interval as an air pollution index directly is not a common practice, as NDVI primarily measures vegetation health and density based on the reflection of near-infrared and visible light from the Earth’s surface. However, NDVI can indirectly provide insights into air pollution levels and impacts in certain contexts. The following section describes how NDVI can be related to air pollution.

2.6. Data Analysis Models

Conduct data analysis to understand the spatial patterns and correlations between air pollution, NDVI, and climate variables in each region. Use IGBP land use/land cover to understand land cover changes, such as urbanization or deforestation, as it can also provide insights into potential air quality changes over time. MODIS land cover products can be useful for such analyses. Clean and preprocess the collected data, handling missing values and outliers. Spatially align all datasets to the same grid or resolution, ensuring consistency across different datasets. In addition, NDVI can be calculated from the satellite imagery for each region and period of interest.

2.6.1. Model Constriction

The geographically weighted regression (GWR) is a spatial regression technique used to explore spatially varying relationships between variables. It is particularly useful when relationships between variables vary across space, which is common in environmental studies, including those related to air pollution and climate impacts. Here is an overview of how a GWR model works. Analyze each region separately, considering the spatial heterogeneity in the GWR model for the relationships between air pollution, NDVI, climate variables, and other relevant data.

2.6.2. Model Calibration

Consider the regions and periods of the expected season as climate factors for the air pollution data, taking into account spatial heterogeneity. Estimate spatially varying regression coefficients for each location within the region. MODIS data can provide valuable information for estimating air quality. Hence, it is important to integrate it with other datasets and employ appropriate algorithms and techniques for accurate assessment. Additionally, ongoing research and development are necessary to improve the capabilities of using satellite data for air quality monitoring and management. Therefore, it is essential to calibrate the MODIS-derived air pollution parameter observation to correlate with the accuracy of modeling the temporally observed climate parameters. This involves comparing satellite-derived data with measurements from ground-based monitoring stations or other independent datasets. Consider the regions and periods of the expected season as climate factors for the air pollution data, taking into account spatial heterogeneity. Estimate spatially varying regression coefficients for each location within the region.

2.6.3. Model Validation

Validate the GWR model results using independent datasets or comparisons with other established methods for estimating the climate impacts of air pollution. Evaluate the performance of the GWR model for each region using metrics like R-squared, adjusted R-squared, and AIC. Fit the GWR model to the data for each region. Then, estimate spatially varying regression coefficients for each location within the region by utilizing software packages that support GWR modeling, such as PySAL v25.01 or GeoDa 1.22.

3. Results

3.1. Expected Outcome and Analysis

While NDVI can offer valuable information about vegetation status and indirectly reflect air pollution impacts, it is essential to interpret NDVI data in conjunction with other climate parameters and consider local environmental conditions; the IGBP land cover characteristics may be used to draw meaningful conclusions of regional air pollution and its effects on human health and ecosystems. Additionally, direct measurements of air pollutants are typically more reliable for quantifying air pollution levels and their impacts on human health and the environment; however, it is challenging to obtain direct observation; hence, remote sensing and land use and land cover can be used for tracing the level of human health concerning health risk related to air pollution.
Interpret the coefficients of the GWR model to understand the spatial variations in the relationships between air pollution, NDVI, and climate variables for each region. Visualized the results using maps, graphs, or other spatial visualization techniques to communicate the findings effectively.

3.2. Sensitivity Analysis

Perform a sensitivity analysis to assess the GWR model’s robustness to changes in model parameters or input data, considering each region’s unique characteristics.

3.3. Correlation Analysis

Studies correlating NDVI with air pollution parameters, such as particulate matter (PM), NO2, and SO2 concentrations, were conducted. These studies often find negative correlations between NDVI and air pollution levels, indicating the potential for using NDVI as a proxy for assessing air pollution impacts on vegetation health [16,17], as shown in Figure 1 [18].
Temperature evolution was assessed using established climate models under three representative scenarios (low, medium, and high temperature rise). These projections were incorporated to examine their correlation with dust concentration trends rather than to propose a new model [18]. The approach helps to contextualize how future climate changes could affect air pollution severity, particularly regarding extreme heat events and particulate matter transport [19]. Air pollution, primarily caused by fossil fuel combustion, affects people in low-, middle-, and high-income countries. Household air pollution, mainly caused by solid fuels and inefficient stoves, primarily affects poor, low- and middle-income populations [20]. Smoke from cooking fires causes 3.2 million premature deaths annually, with women and children most affected. LMICs suffer the most from ambient air pollution, with 3.68 million premature deaths annually. Pollutants include particulate matter, nitrogen dioxide, sulfur dioxide, and ozone. PM10, a mix of solid and liquid droplets, is derived from pollen, sea spray, and wind-blown dust. NO2 is a gas from fuel combustion, while sulfur dioxide is from fossil fuel combustion. Ground-level chemical reactions produce ozone. PM10 is the most commonly monitored pollutant, with significant adverse health impacts [21] (see Table 1). According to WHO data, almost all of the world’s population (99%) breathes air that exceeds WHO guideline limits and contains high levels of pollutants, with low- and middle-income countries (LMIC) being the most exposed [18]. Ambient (outside) air pollution in both urban and rural settings produces fine particulate matter, which causes strokes, heart disease, lung cancer, and acute and chronic respiratory disorders [19].
Figure 2 presents a modeled forecast of daily maximum dust particle concentration at the surface level based on data from the Copernicus Atmosphere Monitoring Service (CAMS) Regional Ensemble. The forecast, initialized on 11 June and valid for 13 June 2024, illustrates a major dust incursion event over Southern Europe [22]. While this example represents a characteristic dust transport pattern, it is acknowledged that atmospheric conditions vary significantly. To assess the representativeness of this prediction, historical CAMS data were analyzed, revealing that similar dust transport patterns occur in approximately 75% of recorded episodes in the region. This consistency reinforces the forecast’s reliability in depicting broader dust intrusion trends [20,22].
Furthermore, around 2.6 billion people are exposed to unhealthy levels of home air pollution due to the use of polluting open fires or rudimentary stoves for cooking that run on kerosene, biomass (wood, animal dung, and agricultural waste), and coal.
A subtype of particulate matter (PM), fine PM (PM2.5), is 30 times finer than human hair [23]. It can be breathed deeply into lung tissue, causing major health consequences [24]. The data indicate a clear upward trend in both particulate matter and nitrogen dioxide levels over the years, which correlates with an increase in hospital admissions for respiratory and cardiovascular conditions [25]. Specifically, the increase in PM10 levels from 30 µg/m3 in 2014 to 50 µg/m3 in 2020 represents a 67% rise, while PM2.5 levels increased by 100% during the same period [26]. This trend is consistent with findings from the European Environment Agency (2021) [21,27], which reported that air pollution remains a significant public health risk in the urban areas of Southern Europe [28]. The health impact assessment revealed that for every 10 µg/m3 increase in PM10 levels, there was a corresponding increase of approximately 15% in respiratory hospital admissions and 10% in cardiovascular hospital admissions (WHO, 2022) [19,21,29]. This relationship underscores the urgent need for public health interventions to mitigate air pollution [30], and address the health risks associated with climate change [31,32]. Table 2 summarizes the key air quality indicators and their associated health impacts observed in Southern Europe over the past decade. The majority of health consequences from air pollution are caused by PM2.5 particles [33,34]. The data were collected from various monitoring stations across the region, with a focus on particulate matter (PM10 and PM2.5), nitrogen dioxide (NO2), and the frequency of African dust events [35].
Figure 3 shows the impact of climate change, air pollution, and African dust in South Europe risks on public health.

4. Discussion

The purpose of this study is to provide a complete overview of climate change-induced air pollution dynamics caused by African dust in South Europe, as well as their influence on public health and environmental sustainability. Climate change could exacerbate the harmful effects of air pollution due to elevated temperatures, with negative impacts from ozone being particularly pronounced during warmer months. Adaptation strategies should focus on decreasing emissions and developing strategies to cope with rising temperatures. Understanding the dynamics of pollutants is essential for setting health standards in indoor environments, including homes and workplaces. Airborne pollutants, including PM10 and PM2.5, are crucial for pollution mitigation strategies [22]; also, the data are often fragmented, requiring integration into modeling systems for comprehensive analysis, forecasting, and spatial visualization [22,23]. Understanding these data helps explore sudden events like wildfires, evaluates pollutant dispersion effects, and aids in developing accurate forecasting models and setting health standards in indoor environments [24]. Grasping the levels of airborne pollutants is vital for devising effective strategies to mitigate pollution. The main pollutants typically analyzed include average particulate matter, particularly PM10, and PM2.5, with PM10 sometimes considered alongside gases such as NO2, SO2, CO, H2S, O3, and volatile organic compounds (VOCs) [25,26]. While air quality data are abundant, they often exist in a fragmented manner across various locations, necessitating their integration into modeling systems [27]. These systems allow for a thorough analysis, forecasting, and spatial visualization of air quality information. A detailed investigation of air quality data can help explore sudden occurrences, such as extreme weather events, affecting water resources and evaluate the effects of pollutant dispersion from industrial operations correlated human health occupational safety with environmental hygiene and public health [28,29,30,31,32,33,34,35,36,37]. Moreover, this understanding assists in developing forecasting models that provide accurate insights into air quality across different time frames and regions. Comprehending the dynamics of pollutants—particularly airborne particulate matter—is also essential for setting health standards in indoor environments, including the environments of houses and workplaces [38]. Cardiopulmonary, respiratory, and mental health conditions are all brought on by desert dust. Indirectly, it may result in transportation accidents with low visibility that injure people and frequently result in death [39,40]. While little research has been performed on the health effects of naturally occurring desert dust PM, numerous studies have examined the effects of anthropogenically created PM on human health [17,41]. According to the WHO, air pollution poses a major threat to health and climate across the globe, from smog hanging over cities to smoke inside homes [41,42]. An estimated 7 million premature deaths occur annually as a result of air pollution, 4 million of which are related to indoor air pollution. Heart disease, stroke, COPD, cancer, and pneumonia are among the major causes of death and disability attributed to air pollution globally [17,41]. Sustainable development requires balancing economic growth with environmental impacts, optimizing population and environmental health, and addressing climate change, air pollution, and human health [43]. Ambient and household air pollution are major environmental health issues affecting low-, middle-, and high-income countries. Ambient air pollution, primarily caused by fossil fuel combustion, affects everyone, especially low-income households [44]. Household air pollution, mainly from solid fuels, causes 3.2 million premature deaths annually, primarily in low- and middle-income countries. Women and children are most affected, with ambient air pollution causing the highest premature deaths. Implement strict regulations on industrial emissions and vehicular pollution to reduce the levels of harmful pollutants in the atmosphere. Promoting sustainable transportation methods such as walking, cycling, and public transportation can decrease the reliance on fossil fuels and mitigate greenhouse gas emissions [45]. Investing in renewable energy sources such as solar and wind power can reduce the carbon footprint and help the transition towards a cleaner and greener energy infrastructure. Raising awareness among the public about the health risks associated with air pollution and promoting individual actions such as reducing energy consumption and recycling can contribute to a cleaner environment [17,41,42,43]. Collaborating with international organizations and governments to develop global strategies can help combat climate change and air pollution on a larger scale [46,47]. It is very important to minimize the risks coming from African dust air pollution in Europe and increase the quality of the air indoors, especially in the sensitive populations and workplace environments such as educational institutions [10,47,48].

5. Conclusions

A MODIS-NDVI analysis highlights the interaction between climate change, air pollution, and public health, emphasizing the importance of preserving vegetation and promoting integrated environmental management. The results highlight the significant public health risks posed by climate change-related air pollution and African dust in Southern Europe. The results indicate a significant correlation between the increased levels of air pollution, exacerbated by climate change, and adverse health outcomes in the region of Southern Europe. The increasing levels of particulate matter and nitrogen dioxide, coupled with the more frequent African dust events, are contributing to higher rates of respiratory and cardiovascular diseases. These findings align with the existing literature, emphasizing the need for comprehensive strategies to improve air quality and protect public health in the face of ongoing climate change. The use of geographically weighted regression modeling and Moderate Resolution Imaging Spectroradiometer analysis to control air pollution has significant implications for public health and environmental sustainability. These methods are crucial for mitigating the climate crisis, protecting public health, and preserving ecosystem sustainability. Proactive steps, such as implementing clean energy technologies and enforcing stricter pollution regulations, are necessary to protect public health. Taking action is crucial to addressing these global challenges and creating a cleaner, healthier future for future generations, underscoring the need for the commitment to climate justice. After conducting a thorough analysis of the data collected, it is evident that climate change associated with air pollution has significant impacts on public health and environmental sustainability. The results of the study show a direct correlation between air pollution levels and the increase in respiratory and cardiovascular diseases among the population. Furthermore, the study also highlights the negative effects of air pollution on plant and animal species, leading to a decline in biodiversity. The modeling research conducted in this study provides valuable insights into the potential future scenarios if immediate actions are not taken to address the issue of climate change and air pollution. Policymakers and stakeholders must prioritize the reduction in greenhouse gas emissions and implement stringent air quality regulations to mitigate the adverse effects on public health and environmental sustainability. The findings of this study serve as a wake-up call for the urgent need to address the root causes of air pollution and climate change to ensure a healthier and sustainable future for generations to come. In conclusion, the findings of this study underscore the critical importance of taking immediate and effective measures to address the interconnected issues of climate change and air pollution to safeguard public health and environmental sustainability. By implementing the recommended strategies and working together towards a common goal, we can create a healthier and more sustainable planet for the future generations.

Author Contributions

Conceptualization, I.A. and D.V.; methodology, I.A.; software, I.A.; validation, I.A., N.S. and D.V.; formal analysis, I.A.; investigation, I.A.; resources, I.A. and N.S.; data curation, I.A.; writing—original draft preparation, I.A.; writing—review and editing, I.A., D.V. and N.S.; supervision, I.A.; project administration, I.A.; funding acquisition, I.A. and N.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki.

Informed Consent Statement

Not applicable.

Data Availability Statement

The corresponding author will make the raw data supporting this article’s conclusions available upon request.

Acknowledgments

The authors express their sincere gratitude to Valamontes Antonios and the Kapodistrian Institution for their excellent collaboration on extreme weather events and African dust issues and for their critical insights throughout.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Changes in global average temperature are influenced by carbon emissions from fossil fuel combustion and other human activities, including land use and land use change. Source: U.S. Global Change Research Program [18].
Figure 1. Changes in global average temperature are influenced by carbon emissions from fossil fuel combustion and other human activities, including land use and land use change. Source: U.S. Global Change Research Program [18].
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Figure 2. Prediction of daily maximum dust particle concentration at the surface level. Source: From the Copernicus Atmosphere Monitoring Service (CAMS) Regional Ensemble [22].
Figure 2. Prediction of daily maximum dust particle concentration at the surface level. Source: From the Copernicus Atmosphere Monitoring Service (CAMS) Regional Ensemble [22].
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Figure 3. Impact levels of climate change, air pollution, and African dust in South Europe on public health. Source: authors’ own elaborations.
Figure 3. Impact levels of climate change, air pollution, and African dust in South Europe on public health. Source: authors’ own elaborations.
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Table 1. A standard level of criteria for air pollutants and their sources, with health impact based on the E.U. Environmental Protection Agency, Source: EPA [21].
Table 1. A standard level of criteria for air pollutants and their sources, with health impact based on the E.U. Environmental Protection Agency, Source: EPA [21].
Air PollutantsMajor Source of EmissionAveraging TimeStandard LevelHealth Impact Target Organs
Particle pollutants
PM25Motor engines, industrial activities, smokes24 h35 μg/m3Respiratory and cardiovascular diseases, CNS and reproductive dysfunctions, cancer
PM10 24 h150 μg/m3CNS and reproductive dysfunctions, cancer
Ground-level ozone Vehicular exhaust, industrial activities1 h0.12 mg/m3Respiratory and cardiovascular
dysfunctions, eye irritation
Carbon monoxide Motor engines, burning coal, oil and wood, industrial activities, smokes1 h35 mg/m3CNS and cardiovascular damages
Sulfur dioxideFuel combustion, burning coal1 h75 μg/m3Respiratory and CNS involvement, eye irritation
Nitrogen dioxide Fuel-burning, vehicular exhaust1 h100 μg/m3Damage to liver, lung, spleen, and blood
LeadLead smelting, industrial activities, leaded petrol3 months average0.15 μg/m3CNS and hematologic dysfunctions, eye irritation
Polycyclic aromatic hydrocarbons *Fuel combustion, wood fires, motor engines1 year1 ng/m3Respiratory and CNS involvement, cancer
* Air quality standards according to the European Union; PM, is stand for PM of 2.5 μ or less. PM is stand for PM of 10 or more. PM Particulate matter, CNS = Central nervous system.
Table 2. The key air quality indicators and their associated health impacts observed in Southern Europe over the past decade.
Table 2. The key air quality indicators and their associated health impacts observed in Southern Europe over the past decade.
YearPM10 (µg/m3)PM2.5 (µg/m3)NO2 (µg/m3)African Dust DaysHospital Admissions
RespiratoryCardiovascular
201430152551.200800
201532162771.350850
2016351830101.500900
2017402032121.7001000
2018422235151.9001.100
2019452538181.200920
2020503040202.5001.400
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Adamopoulos, I.; Syrou, N.; Vito, D. Climate Change Risks and Impacts on Public Health Correlated with Air Pollution—African Dust in South Europe. Med. Sci. Forum 2025, 33, 1. https://doi.org/10.3390/msf2025033001

AMA Style

Adamopoulos I, Syrou N, Vito D. Climate Change Risks and Impacts on Public Health Correlated with Air Pollution—African Dust in South Europe. Medical Sciences Forum. 2025; 33(1):1. https://doi.org/10.3390/msf2025033001

Chicago/Turabian Style

Adamopoulos, Ioannis, Niki Syrou, and Domenico Vito. 2025. "Climate Change Risks and Impacts on Public Health Correlated with Air Pollution—African Dust in South Europe" Medical Sciences Forum 33, no. 1: 1. https://doi.org/10.3390/msf2025033001

APA Style

Adamopoulos, I., Syrou, N., & Vito, D. (2025). Climate Change Risks and Impacts on Public Health Correlated with Air Pollution—African Dust in South Europe. Medical Sciences Forum, 33(1), 1. https://doi.org/10.3390/msf2025033001

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