Growing evidence demonstrated that exposure to ambient fine particulate matter (PM_2.5) increases mental health risk via neuroinflammation and oxidative stress. However, less is known about the relative contribution of PM_2.5 originating from different emission sources on mental health, such as depression and anxiety, particularly in low- and middle-income countries like India. Therefore, we examined the associations of short- and long-term exposure to total and source-specific PM_2.5 with depression and anxiety in Indian adults.

A cross-sectional analysis has been conducted in 12 Indian states using data from the National Mental Health Survey (NMHS), 2015-16, a nationally representative and population-based study in India. This study includes a total of 34,357 participants, 18 years and older. The 1-month and 12-month mean exposure to PM_2.5 and its source originating from 8 emission sources were assessed using a 1 km x 1 km high-resolution satellite-derived database and the WRF-CMAQ model, respectively, at participants' residential addresses before the NMHS interview date. The Mini International Neuropsychiatric Interview (MINI) version 6.0.0 was used to evaluate depression and anxiety disorders in adults. Adjusted odds ratios (ORs) were estimated for depression and anxiety per IQR increase in PM_2.5 using a logistic mixed-effects regression model after adjusting for the individual and household level covariates.

In this study, the weighted prevalence of the current depressive and anxiety disorders among adults was 2.69% (95% CI-2.66-2.72) and 2.96% (95% CI-2.93-2.99), respectively. The estimated mean PM_2.5 exposure for 1-month and 12-months was 55.8±19.6 and 44.3±13.5 µg/m³ respectively. Each IQR increase in PM_2.5 exposure was significantly and strongly associated with depressive disorder (OR = 1.13; 95% CI: 1.05–1.21) for a 1-month exposure window and anxiety disorder (OR = 1.16; 95% CI: 1.07–1.26) for a 12-month exposure after adjusting for potential confounders. PM_2.5 originating from different emission sectors was associated with mental health outcomes, with the strongest associations for power, transport, international transboundary, and domestic sources for at least one health endpoint, whereas agricultural sources showed protective associations with both outcomes. Subgroup analyses showed stronger associations among individuals with lower household incomes and lower education.

Our study suggests that interventions to reduce PM_2.5 from key emitting sources may reduce the burden of mental health in India, although cohort studies are recommended to determine the causal relationship.

How to cite: Kundu, P., Dey, S., Krishnan, A., Ghosh, S., N Rao, G., Benegal, V., Varghese, M., and Gururaj, G.: Association between exposure to fine particulate matter from different emission sources and mental health outcomes in India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-687, https://doi.org/10.5194/egusphere-egu25-687, 2025.

Recent research has reported an increase in Ground Level Ozone (GLO) concentrations in South Asia, with ongoing climate change being one of the contributing factors to this rise. In the lower-middle income economies such as India, studies related to cognitive impacts of GLO are insignificant and scarce. This time-series study aims to quantify the risk of cognitive disorders associated with ozone exposure for Delhi. The high-resolution gridded (5km*5km) daily maximum 8-hour mean ozone concentration data (MDA8) retrieved from WRF-Chem simulation were linked to geocoded-anonymized daily hospital admissions data of several cognitive disorders (e.g., depression, anxiety, Parkinson's disease, etc.). The WRF-Chem model was simulated for the Delhi domain over a four-year (2016-2019), for the same period hospital admissions data were collected. The generalized additive model (GAM) with Poisson distribution was utilized for examine an association of O₃ exposure with cognitive disorders. The delayed effect of exposure was assessed employing 20-days lag. The results of relative risk (RR) against lag days showed inverted-U shape curve with highest RR of 1.0092 (95% CI: 1.0051-1.0134) on 10^th lag day. The age-gender-stratified analysis revealed that females (RR: 1.0085, lag-day: 17) exhibited slightly higher risk compared to males (RR: 1.0071, lag-day: 9), while the younger demographic (age≤60 years) were at marginally elevated risks than elderly (age>60 years). In India, the mitigation measures and policies are predominantly aimed at reducing particulate matter pollution. The findings of this study are pertinent to present and future contexts, whereby evidences of intensifying effects of climate change on ozone are more pronounced than particulate matter. The research offers significant insights into the relationship between ‘public health’ and ‘air pollution’, contributing to the existing literature on highly polluted urban environment like Delhi.

Keywords: Ground level ozone (GLO); Cognitive impacts; Generalized Additive Model (GAM); WRF-Chem

How to cite: Parmar, P. D., Chandra, M., Sharma, S., and Kota, S. H.: Cognitive impacts of Ground Level Ozone (GLO) exposure in Delhi: Estimating a risk in the highly polluted urban environment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4878, https://doi.org/10.5194/egusphere-egu25-4878, 2025.

Unlike natural dust (NDust), which primarily affects sparsely populated areas, mitigating health disparities from anthropogenic dust (ADust) fine particulate matter (PM_2.5) is crucial. ADust PM_2.5 has significant effects on public health and socio-economic conditions. With internal economic inequality widening within countries globally, urbanization, and aging populations exacerbating social vulnerability, assessing the health burden of ADust PM_2.5 pollution is crucial for achieving Sustainable Development Goal 3.9. This study integrates annual population and economic data with dust (include ADust and NDust) PM_2.5 concentrations to evaluate mortality due to this exposure and its relationship with income inequality. Our findings reveal a significant association between income inequality and mortality due to dust PM_2.5 exposure, considering variables such as the Gini index, GDP per capita, and exposed population structure. Greater income inequality and significant demographic change amplify the public health impacts of dust PM_2.5 pollution. Addressing wealth distribution inequalities is essential in pollution risk research and policy-making. Optimizing wealth distribution and enhancing control of ADust can effectively reduce health risks, fostering sustainable social development.

How to cite: Lian, L., Chen, S., and Huang, J.: Wealth inequality amplified the anthropogenic dust mortality, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8028, https://doi.org/10.5194/egusphere-egu25-8028, 2025.

In the framework of the Next Generation EU program, the Vitality project was founded to 
develop different research activities related to the sustainability and environmental protection. 
One of them, called One health: Telemedicine and Environment coordinated by the University 
‘G. d’Annunzio’, Italy, is focused on the impacts of climate changes and pollutant changes, on 
the evolution of some human health diseases. Here we report the infrastructure development to 
study how temperature and other meteorological parameters, air pollutant, such us ozone, 
nitrogen oxides, PM10, impact the evolution of diabetes. One of the main activities is putting 
together the last five years of meteorological and composition data and those of hospital 
admissions and clinical analyses of more that 13,000 patients. Another activity is to study the 
real life of diabetes patients monitoring continuously their physiological parameters, 
atmospheric parameters of the region where they live and indoor air quality of their houses. 
First results of the project, strengths and weakness will be discussed.

How to cite: Di Carlo, P., Aruffo, E., Mascitelli, A., and Chiacchiaretta, P.: Impact of the atmospheric composition and climate changes on the evolution of diabetes in Central Italy: the Vitality project, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7076, https://doi.org/10.5194/egusphere-egu25-7076, 2025.

Exposure to atmospheric compounds increases the risk of type 2 diabetes. In our study, we will show a derived exposure-response curve from the relative risk to develop type 2 diabetes because of exposure to different pollutants, i.e. particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), and ozone (O3). The curve is used to estimate a worldwide map of relative risk and the percentage of the attributable burden for each pollutant, using high resolution dataset of atmospheric pollutants from satellite observations. Finally, we will show the validation of the model comparing the modeled percentage of the numbers of patients that are affected by type 2 diabates also because of pollutants exposure with a regional analysis of the attributable patients affected by type 2 diabetes.

How to cite: Aruffo, E., Mascitelli, A., Chiacchiaretta, P., Carrieri, F., Baldassarre, M. P. A., Formoso, G., Consoli, A., and Di Carlo, P.: A worldwide study to estimate the relative risk to develop type 2 Diabetes Mellitus because of atmospheric pollutants exposure, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12304, https://doi.org/10.5194/egusphere-egu25-12304, 2025.

While increasing heat is a direct impact of climate change on health, epidemiological studies are quite limited in India. Here we examined the impact of heat on neonatal (children less than 28 days) and very early (children died on the first day) neonatal mortality using the fifth round of the National Family Health Survey (NHFS-5) dataset for 2019-2021. For heat exposure, we used a global daily temperature dataset at 1-km by 1-km saptial scale. First, we evaulated the global temperature dataset with station-based measurements for India and found reasonable accuracy for further application. In the NHFS-5, health and demographic information was collected from 30456 clusters spanning across urban and rural India covering every district. The estimated very early neonatal mortality and neonatal mortality values were 17.1 and 23.4 per 1000 live births, respectively. We then assinged exposure to daily maximum and minimum temperature at household level, and using a generalized logistic regression model, estimated the effect of heat after adjusting for the covariates. For every 1 degree increase in maximum and minimum temperature, very early neonatal mortality increase by 2.7% (95% CI: 1.6-3.8) and 2.0% (1.0-2.9), respectively. We found larger effect of heat on neonates born in the 'poorest' households (3.3% and 3.0% highesr risk for every 1 degree increase in maximum and minum temperature) with the effect declining (but still significant) with an increase in wealth index. We also found larger effect on male child than on female child, and on neonates in rural region than in urban region, and the effect fizzles out with a few days lag. As temperature is expected to rise further due to climate change, adequate adaptation startegy is required to protect the most vulnerable group; without which India cannot meet the sustainable development goal of reducing very early neonatal mortality and neonatal below 7 and 10 per 1000 live births, respectively, by 2030.          

How to cite: Dey, S., Gaur, G., and Philip, S.: Impact of heat on neonatal mortality in India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2681, https://doi.org/10.5194/egusphere-egu25-2681, 2025.

How to cite: Jiang, S., Li, C., and Wu, X.: Impact of global warming on the anemia among women of reproductive age in the global south, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4778, https://doi.org/10.5194/egusphere-egu25-4778, 2025.

Impact of Heat Exposure during Pregnancy in Ethiopian Cities

Desalew Meseret Moges^1*, Per-Ola Olsson², Ebba Malmqvist³, Masresha Tessema¹, Eleni Papadopoulou⁴, Kristoffer Mattisson³

¹ Nutrition, Environmental Health and Non-communicable Disease Research Directorate, Ethiopian Public Health Institute, Addis Ababa, Ethiopia.

² Department of Physical Geography and Ecosystem Science, Lund University, Sweden.

³ Division of Occupational and Environmental Medicine, Lund University, Sweden.

⁴ Global Health Cluster, Norwegian Institute of Public Health, Oslo, Norway.

Abstract
Climate change poses a significant public health threat, particularly for vulnerable groups such as pregnant women and children. Heat stress, when the body struggles to regulate its internal temperature due to high temperatures, presents increased health risks during pregnancy. Exposure to heat stress during pregnancy can result in adverse health outcomes for both the mother and fetus, including preterm birth, low birth weight, stillbirth, and pregnancy complications. However, research on the effects of heat exposure in epidemiological studies remains limited and inconsistent in low-resource countries like Ethiopia. This is mainly due to a lack of comprehensive data and resources. These regions often face limited infrastructure, scarce ground monitors, unreliable data collection systems, and insufficient technological support.

To address these gaps, this heat exposure study, which is part of the EU-funded ENABLE (Enabling Environments for Non-communicable Disease (NCD) risk reduction in Ethiopia) project, with the overarching aim to investigate the impact of urban heat exposure on maternal health outcomes in four Ethiopian cities: Addis Ababa, Jimma, Adama, and Harar. The present study's primary objective is to utilize remote sensing data to evaluate heat exposure.

Land Surface Temperature (LST), which measures the Earth's surface temperature, and the Discomfort Index (DI), which combines air temperature and humidity, will be used to assess heat stress. Data will be collected from satellite sensors (Landsat, MODIS; Moderate Resolution Imaging Spectroradiometer), climate data (ERA5; the fifth generation of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis of the global climate), and ground measurement from PurpleAir monitors. Heat stress will be assessed using hot days or heat waves when LST and DI exceed the 95th, 97th, or 99th percentiles for two consecutive days. This will involve creating high spatial resolution maps of heat exposure hotspots in Ethiopian cities.

The results from the present study will later be used in the ENABLE project to assess individual exposure to heat stress and effects on pregnancy outcomes. The planned epidemiological studies will include pregnant women recruited within the ENABLE project, with a target enrollment of 5000 participants, following their pregnancies from initiation till birth. Pregnancy outcomes collected from hospitals and public health records will be linked to heat metrics using GPS data from maternal residential addresses. This research provides critical insights into the intersection of climate change and urban heat stress in Ethiopia. The results can potentially inform Ethiopia’s climate-resilient urban planning and maternal health policies.

How to cite: Moges, D. M., Olsson, P.-O., Malmqvist, E., Tessema, M., Papadopoulou, E., and Mattisson, K.: Impact of Heat Exposure during Pregnancy in Ethiopian Cities, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12913, https://doi.org/10.5194/egusphere-egu25-12913, 2025.

As part of the AdaptNet project, which aims to adapt and network general practitioner and specialist medical care to the health impacts of climate change, interactive maps are being produced for Germany that estimate current and future health risks. For heat, flooding, air quality, allergens, vectors and forest fires, it will be possible to obtain corresponding hazard levels at the level of districts and independent cities (corresponding to the NUTS3 regions in Germany). Estimating the health risks associated with climate change helps to avoid over- and under-adaptation of ambulant care to the consequences of climate change.

The methodology developed is based on the assessment of the most important factors for each hazard. The high spatial resolution requires a correspondingly high-resolution data base to be able to represent regional characteristics in the risk assessment. For the assessment of the current situation, data from recent years was used to include the already advanced climate change of the early 21st century. The future estimates refer to data around the year 2050.

The methodology was evaluated using two test regions (urban and rural). Very complex and data-intensive risk assessments were carried out for the two test regions and compared with a simpler approach, which was then applied to the whole of Germany.

When developing risk assessments relevant to emergency and disaster risk management in the health sector, WHO recommends that three factors be considered: hazard, exposure and vulnerability. We ensured that hazard and exposure were covered by factors in the risk assessment itself. Vulnerable groups were deliberately not included in the risk assessment, because they are individually targeted in an adaptation toolbox developed in the AdaptNet project.

How to cite: Kaspar-Ott, I., Álvarez, F., and Hertig, E.: Assessment of health risks due to climate change in Germany, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8023, https://doi.org/10.5194/egusphere-egu25-8023, 2025.

Sultriness is formed by the interaction of several weather factors. It is the state of the atmosphere when the water vapor pressure exceeds 18.7 hPa. This condition has adverse physiological effects on plants, animals and especially on the human body. For this reason, in this research, emphasis was placed on the time evolution of sultriness at the meteorological station Hurbanovo in the Slovak Republic. The paper will examine the 40-year period (1981 – 2020). The study is a continuation of the work of Štefan Kveták, who examined the previous 30-year period (1951 – 1980). We hypothesized that the number of sultry days is also increasing due to climate change. The basis of the whole assumption was hourly data from meteorological stations in the database of the Slovak Hydrometeorological Institute. As the scientific goals of the project, we preferred the categorization of sultriness according to various criteria, the evaluation of their frequency and time trends of occurrence, and we compared their development with the previous period.

How to cite: Szabóová, K.: Number of Sultry Days in the Territory of Slovakia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9655, https://doi.org/10.5194/egusphere-egu25-9655, 2025.

Tropical deforestation causes local climate warming and is a potential risk to human health. Previous studies have shown tropical deforestation causes increased heat stress and reduces safe outdoor working hours, but the excess mortality due to warming from deforestation has not been quantified. Here we use remote sensing Earth observations to make the first pan-tropical assessment of the population-weighted warming due to tropical deforestation and the associated heat-related mortality burden. We focus our analysis on tropical deforestation that has occurred during 2001 to 2020. We use spatially explicit satellite datasets of annual forest cover change and land surface temperature to identify areas of surface warming that are co-located with forest loss and use data on population distribution to map population-weighted exposure to this warming. We use data on non-accidental mortality combined with relationships between heat exposure and excess mortality from the literature, to estimate the heat-attributable excess mortality due to nearby tropical deforestation. We examine how population exposure to deforestation-induced warming varies by region and by the degree of tropical forest loss. Overall, our analysis shows tropical deforestation during 2001 to 2020 exposed over 350 million people to local climate warming with population-weighted daytime land surface warming of 0.27°C. We estimate this warming results in around 28,000 additional deaths per year, accounting for 39% of the total heat-related mortality burden caused by global climate change and deforestation combined. The impacted populations (those living near deforested areas) are predominantly from lower-income groups, often traditional and indigenous communities, with limited access to adaptive measures to protect against the impacts of climate warming. Our analysis provides important evidence of the negative human health impacts of tropical deforestation at local, regional and national scales.

How to cite: Reddington, C., Smith, C., Butt, E., Baker, J., Oliveira, B., Yamba, E., and Spracklen, D.: Impacts of tropical deforestation on local climate and human health, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18273, https://doi.org/10.5194/egusphere-egu25-18273, 2025.

Building systems resilient to the societal and health impacts of future climate extremes requires actionable, context-based scenarios. Historically, public health and epidemiology have relied on retrospective analyses, which can be inadequate for preparing for unprecedented events.

To overcome this, we propose a methodology to develop context-based scenarios of health impacts (e.g. cardiovascular mortality) from future climate extremes also considering adaptation mechanisms (e.g. early warning system, health care improvements). This builds upon a methodology introduced by Shepherd et al. in 2018, which has been further developed for use on societal impacts including population health. The approach uses qualitative integration of various components to develop context-based scenarios. Here are some examples of these components:

• Historical and Future Climate Data: Using historical climate data and numerical projections to create geographically situated scenarios of extreme weather events.
• Analysis of Past Extremes: Considering health impacts from past extreme events of different magnitudes within the same geographic area.
• Cross-contextual Analysis: Considering health impacts from past extreme events in different settings and conceptually applying those scenarios, while considering contextual differences.
• Awareness: Considering the level of awareness within the population regarding climate extremes and their potential health impacts captured using semi-structure interviews, which can influence community preparedness and adaptation.

This approach is designed to leverage the insights from natural and critical social sciences while making room for methodological and epistemological differences. The integration of quantitative and qualitative data will occur through an iterative process, where both types of data complement each other in developing context-based scenarios. Quantitative data will provide the statistical foundation (e.g., projected cardiovascular mortality), while qualitative data will add depth by capturing social dynamics and adaptation strategies. The two will be synthesized in the final scenarios to ensure a comprehensive understanding of the impacts of climate extremes on different population groups.

How to cite: Raffetti, E., Messori, G., and Rusca, M.: Storyline approaches to characterize population health impacts of future climate extremes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19538, https://doi.org/10.5194/egusphere-egu25-19538, 2025.

How to cite: Di Sabatino, S. and the TRIGGER Consortium: Building a pathway to improve climate and health research: the case of the TRIGGER project , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19612, https://doi.org/10.5194/egusphere-egu25-19612, 2025.

The China-Pakistan Economic Corridor (CPEC), a cornerstone of China’s Belt and Road Initiative (BRI), has significantly enhanced economic connectivity, infrastructure, and mobility between China and Pakistan, with investments exceeding $60 billion. However, this rapid transformation raises critical questions about the intersection of climate, trade mobility, and public health. Increased connectivity under CPEC may amplify the risk of Japanese Encephalitis (JE), a zoonotic, mosquito-borne disease endemic to several Asian countries, including China. JE transmission is influenced by complex ecological and climatic factors, including temperature, precipitation, and land-use changes, which impact mosquito vectors (Culex tritaeniorhynchus) and their habitats.

This study evaluates the risk of JE outbreaks in Pakistan through a One Health framework, highlighting the interplay of climate, mobility, and health. Specifically, it focuses on cross-sectoral collaboration across public health, veterinary, and environmental agencies to mitigate emerging threats. Objectives include assessing JE transmission risks along CPEC and proposing climate-sensitive, One Health interventions for prevention and control.

The risk assessment integrates data from human health, veterinary, and environmental sectors using interdisciplinary methodologies:

• Climate and environmental mapping of Culex breeding sites along CPEC using satellite imagery and meteorological data, identifying that over 50% of CPEC-associated regions, particularly in Sindh and Punjab, have optimal conditions for mosquito breeding due to rice paddies, irrigation systems, and seasonal climatic variability.
• Analysis of trade and mobility data, showing a 240% increase in human and animal movement along CPEC, intensifying vector and amplifying host exposure.
• Stakeholder interviews, revealing critical gaps in JE surveillance, real-time communication, and coordinated climate-informed response strategies.

Findings highlight that approximately 60% of identified high-risk areas are vulnerable to JE outbreaks, driven by favorable climatic and environmental conditions for vector proliferation. This underscores the urgent need for integrated strategies that account for climate variability and its impacts on vector dynamics.

Key recommendations include:

• Developing GIS-based vector surveillance systems to monitor climate-driven changes in mosquito breeding habitats.
• Establishing real-time data-sharing platforms for JE surveillance between China and Pakistan, incorporating climate and environmental data.
• Promoting JE vaccination programs for vulnerable populations in high-risk, climate-sensitive areas.
• Enhancing diagnostic and response capacity across public health and veterinary laboratories through climate-informed training initiatives.
• Raising community awareness through public health campaigns on vector control, emphasizing climate adaptation strategies.
• Formulating a joint JE outbreak preparedness and response framework, integrating climate projections, vector control, and rapid response teams.

This study demonstrates the critical need for interdisciplinary approaches to address health risks at the nexus of climate, trade mobility, and emerging infectious diseases. By integrating climate science into One Health strategies, the research underscores how transdisciplinary collaboration can build resilience against the multifaceted challenges of climate change and global connectivity.

How to cite: Ahmad, S. and Idrees, F.: From Trade Routes to Transmission Routes: Climate, Mobility, and Risk Assessment of Japanese Encephalitis Outbreak in Pakistan under the China-Pakistan Economic Corridor: A One Health Approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-529, https://doi.org/10.5194/egusphere-egu25-529, 2025.

The spread of malaria is a major health burden, affects many people in Africa, depends on climate but also socio-economic conditions. Thus, it is important to gauge the impact of anthropogenic global warming on malaria and attribute anthropogenic causes. Here we compute the Time Of Emergence (TOE) of vector density and of the Entomological Inoculation Rate (EIR) in the SSP3-7.0 scenario using 50 bias-corrected members of Community Earth System Model version 2 (CESM2) Large Ensemble simulations. This reveals that vector density, which depends on climate conditions, and EIR, which depends on both climate and population density, will rise significantly and permanently above the pre-industrial background variability due to anthropogenic causes in Africa. Both the vector density and EIR have areas, mainly in central Africa, where anthropogenic causes have already significantly changed, and many more areas will experience anthropogenic caused changes in the 2030 and 2040s and towards the end of this century. Our simulations also show clear evidence that extremes of vector density and EIR increase in the future by almost 100%, suggesting that major malaria epidemic outbreaks will become much more likely. We also perform simulations with constant population and with no climate change which partly reveal underlying malaria dynamics. Our results highlight the need to prepare for an expansion and intensification of the malaria burden if no health interventions are being taken.

How to cite: Franzke, C. and Parihar, R. S.: Time of Emergence and Future Projections ofExtremes of Malaria Infections in Africa, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2188, https://doi.org/10.5194/egusphere-egu25-2188, 2025.

Climate change is a key determinant of public health, influencing disease patterns, and public and environmental well-being. Mosquito population dynamics are largely determined by climatic factors and water availability. Therefore, understanding the linkage between local water resources and mosquito dynamics is crucial for better predicting current and future health risks, and informing effective disease control and health risk reduction. Here, we investigate how temporal and spatial distribution of water availability affects mosquito populations in a natural wetland area under current and future climate r scenarios. The study was conducted in the natural park of the  Aiguamolls de l’Empordà,  connected to La Muga and El-Fluvia river basins in Catalonia, Northeast Spain. Empirical data on river runoff and local water levels were collected from several discharge stations, while abundance estimates of mosquito populations were obtained from mosquito traps spread across the study area. The hydrological assessment is carried out with the Soil Water Assessment Tool (SWAT). This model uses observed rainfall and air temperature from the gridded earth observation dataset over Europe (E-OBS) to simulate streamflow and hydrological responses of the study area. Based on the in situ data and hydrological simulation outputs, we derive a relationship between water availability and mosquito population abundance that can be used to predict future disease risk in the study area. Our results will be integrated within the “Infectious Disease decision-support tools and Alert systems to build climate Resilience to emerging health Threats (IDAlert)” project funded by the European Union. This decision-support tool plays a critical role in targeted interventions in water management and the health sector, directly contributing to reducing health risks due to mosquito-borne diseases.

Keywords: Health risk, SWAT, Spatial and temporal distribution of water, Mosquito populations, IDAlert

How to cite: Sirisena, J., Stiles, P., Rodriguez, J., Berenguer, S. B., Bartumeus, F., Costa, M. M., and Bouwer, L. M.: Developing a prediction model for the relationship between climate, water resources, and mosquito dynamics: application to a case study in  a human-impacted Mediterranean wetland, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5618, https://doi.org/10.5194/egusphere-egu25-5618, 2025.

EpiOutlook is an epidemiological indicator platform currently under development as part of the IDAlert project, a research consortium taking a OneHealth approach to understanding the impacts of climate change on the emergence and spread of infectious diseases in Europe and Bangladesh. The aim of the platform is to provide short-term early warning indicators of epidemiological risks, including those related to extreme weather and climate-sensitive infectious diseases (CSIDs). Currently, one climatic extreme indicator is operational for use in EpiOutlook which relates to drought, and makes use of the Standardised Precipitation-Evapotranspiration Index. Here, we propose two new climate extreme indicators currently under development, one related to heat and a second related to flood risk. We make use of fine-scale climate data (0.25x0.25) to categorise grid cells by the two proposed indicators (heat and flooding), which can then be extrapolated to the scale of interest. The impacts of extreme heat on health are well documented (e.g., extreme low and high temperatures and humidity leading to more adverse health outcomes), particularly for vulnerable groups such as pregnant women and children. Less well established are specific temperature ranges which puts people at risk to the highest number of climate-related health risks including CSIDs. We propose making use of our current CSID indicators (malaria, tick-borne diseases, leishmaniasis, Vibrio spp., West Nile Virus and Aedes-borne diseases), all of which consider the impacts of temperature and humidity. We aim to categorise temperature and humidity ranges which create ideal conditions for the highest number of CSIDs, weighed against the non-communicable disease impacts such as heat stress/stroke and adverse pregnancy outcomes, to provide a comprehensive spatial and temporal outlook for the effects of heat on health. Flooding is a major climatic risk in Europe, leading to destruction of property and livelihoods and infectious disease risk. We aim to develop a simple categorisation of flood risk via fluvial and pluvial flooding over Europe, incorporating several elements of the traditional water balance model, but producing an output which will be more interpretable by a wider range of end users. Risk will be assessed based on precipitation, elevation, land cover, potential evapotranspiration/soil moisture, groundwater recharge rate, proximity to a river, and river runoff/flow. Coastal flooding will not be considered at this stage, due to different flooding mechanisms, and instead proximity to a coastline will be seen as preventative as a source of drainage. Flood risk will be validated using flood data, and if the categorisation is proved accurate in representing flood risk, the occurrence of a flood in the preceding years will be considered in the categorisation. We aim to use the results from the flood indicator to provide valuable input to a leptospirosis indicator, a water-borne disease which is closely related to flooding. We believe that these indicators will provide easy to interpret quantification of climate extremes which relate to health in Europe, useful for public health decision-makers to make necessary adjustments to the current and near future risks posed by climate change.

How to cite: Charnley, G. E. C., Ball, E., Llabrés-Brustenga, A., San José Plana, A., Colgate, A., and Lowe, R.: Developing climate extreme indicators for EpiOutlook, a climate-informed subseasonal-to-seasonal forecast platform for epidemiological risks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6073, https://doi.org/10.5194/egusphere-egu25-6073, 2025.

Vector-borne diseases are responsible for over 700,000 deaths annually and are expected to spread to new regions and become more frequent due to climate change. This is primarily because vectors (such as insects and ticks) are ectothermic ("cold-blooded") and highly influenced by environmental conditions.

We are broadly interested in better understanding how climate change, in combination with land use changes, will affect the spread and frequency of vector-borne diseases. This will be achieved by using machine learning models, such as random forest and deep learning algorithms, to predict disease spread and frequency. By adopting a One Health approach, where we consider human health as interconnected with animal and environmental health, we will integrate multiple data sources (e.g., climate, land use, socio-economic factors, human health, and animal health) to improve our predictions.

As an example, in the EU project Planet4Health, we will employ various machine learning models to predict outbreaks of vector-borne diseases (leishmania and mosquito-borne diseases) in the Iberian Peninsula. The project aims to identify the model that gives the most accurate and meaningful predictions, and later incorporate it as part of an early warning systems for predicting such outbreaks.  

How to cite: Fossen, E.: Using machine learning to predict vector-borne diseases in a changing climate, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6441, https://doi.org/10.5194/egusphere-egu25-6441, 2025.

We used wavelet transform cross-correlation analysis to inform the model of the number of sand flies as a function of meteorological and environmental variables. To that end we used historical sand fly monitoring datasets from several past and ongoing collaborations in Europe, Turkey and Israel (projects EDENext, VectorNet, CLIMOS and PLANET4HEALTH), and correlated those with the corresponding temperature, precipitation, and soil moisture data.

We were looking into how the number of these disease vectors depends on all these variables and were interested to define the time lags between the changes of the meteorological and environmental drivers and change (particularly rise) in numbers of sand flies. We were additionally interested in how the change in climatic suitability for sand fly development will influence their spread in Europe. Finally, we researched if the modelled behavior can be universal across the sand fly species, or should be developed separately by species, and climatic regions.

Our results should assist development of the early warning systems for the spread of sand fly borne diseases that can be used by public health authorities for efficient and effective preparedness.

Funding: The CLIMOS consortium is co-funded by the European Commission grant 101057690 and UKRI grants 10038150 and 10039289. The six Horizon Europe projects, BlueAdapt, CATALYSE, CLIMOS, HIGH Horizons, IDAlert, and TRIGGER, form the Climate Change and Health Cluster. The PLANET4HEALTH consortium is co-funded by the European Commission grant 101136652. The five Horizon Europe projects, GO GREEN NEXT, MOSAIC, PLANET4HEALTH, SPRINGS, and TULIP, form the Planetary Health Cluster.

How to cite: Blesic, S. M., Tosic, M., Matic, V., Waitz, Y., Kirstein, O., Antoniou, M., and Maia, C.: Modeling climate drivers of the current and future spread of sand flies in Europe and neighboring countries with the use of wavelet transform analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8258, https://doi.org/10.5194/egusphere-egu25-8258, 2025.

How to cite: Bonfiglio, D., Bianco, S., Maragliano, M., Corcione, V., Rodi, G. C., Marangoni, S., Roberto, P., and Mosca, A.: Predicting Vector-Borne Disease Risk using Earth Observation and Machine Learning: A Case Study in northern Italy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16611, https://doi.org/10.5194/egusphere-egu25-16611, 2025.

Mosquito-borne arboviruses pose a grave threat to millions of people worldwide each year, with climate change rapidly expanding hotspots of deadly Aedes-related diseases. Aware of potential compound effects regarding other important diseases, it has become imperative for health authorities to maintain a detailed surveillance of key variables that can trigger Aedes-borne epidemic episodes. Disease transmission is generally conditioned by multiple socio-economic factors, and among them, the environmental suitability for vectors and viruses to proliferate is a necessary –although not sufficient– condition that needs to be closely monitored. As such, a comprehensive service that allows stakeholders to detect and predict environmental suitability on affected hotspots is crucial for communities to better prepare in the case of present and future outbreaks.

To this end, AeDES2 is a next generation climate-and-health operational service that reproduces and improves computation of Aedes-borne Diseases Environmental Suitability over its previous version (Muñoz et al., 2020), expanding its temporal and spatial scope while simultaneously enhancing observational and forecasting quality of Aedes-related disease transmissibility. Users can consult the historical evolution of the environmental suitability values on any grid point of interest, as well as the expected future evolution up to three seasons in advance. Aside from environmental suitability values, health authorities can additionally utilize AeDES2 to analyse the estimated percentage of population at risk –crucial for governing bodies to implement control measures in order to reduce the spread of the disease.

AeDES2 incorporates four different environmental suitability models, translating temperature and precipitation values into environmental suitability outputs while considering epidemiological factors for transmission probability. Its monitoring system generates an up-to-date 12-member ensemble reference, providing a continuously updated historical sequence of environmental suitability values. On the forecasting side, AeDES2 builds on its predecessor’s pattern-based multi-model calibration, by assimilating state-of-the-art calibration methods such as causality-based calibration or multi-calibration techniques, aiming to reliably reproduce key non-linear patterns that are used as predictors in the cross-validated forecast system.

How to cite: Corvillo Guerra, J., Torralba, V., González Romero, C., Pérez-Zanón, N., Llabrés-Brustenga, A., Riviére-Cinnamond, A., and Garikoitz Muñoz, Á.: From climate variables to health information - Predicting and monitoring mosquito-borne disease outbreaks with AeDES2, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15204, https://doi.org/10.5194/egusphere-egu25-15204, 2025.

The perception of green and blue spaces has been widely recognized for its positive impact on health and can be assessed through surveys that capture individuals’ experiences of their surrounding environment. While such surveys provide data that can be seen as ground truth, their implementation is often constrained by privacy concerns, time limitations, and inefficiencies. To address these challenges, quantitative datasets—such as the Normalized Difference Vegetation Index (NDVI) and land use data—can serve as inputs for spatial measurement methods, including buffer models, street view analyses, and viewshed analyses, to estimate green and blue space exposure. However, existing spatial measurement methods often fail to align with how people perceive green and blue spaces in their environment.

This study aims to address the question: How can green and blue space perception be modeled using spatial exposure measurement methods? To explore this, three spatial measurement approaches are applied: Euclidean buffer models, Streetview analyses, and viewshed analyses. These results are converted into Spearman correlation coefficients. Additionally, survey data collected in the Netherlands, where participants assessed green and blue spaces within their residential surroundings, are also analyzed using Spearman correlations. The correlations derived from spatial measurement methods are compared with those from the survey data to evaluate how well these methods capture perceived green and blue space exposure.

The findings aim to identify which spatial measurement methods best model individuals’ perceptions and offer insights into improving urban planning and policy. By enhancing the alignment between spatial models and human perception, this research contributes to more effective evaluations of green and blue space distribution in the Netherlands and highlights areas that may benefit from additional green and blue infrastructure.

How to cite: Vamos, C., Huss, A., Scheider, S., and Vermeulen, R.: Modeling the perception of green and blue space using spatial exposure measurement methods, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9016, https://doi.org/10.5194/egusphere-egu25-9016, 2025.

Extremes in the Earth System are significant drivers of adverse population health outcomes. To fully understand potential future impacts on our health, Earth System Models (ESMs) and their output are increasingly integrated with and connected to Planetary Health applications.

To emphasize the role of ESM in understanding interactions between natural systems and their implications for human health, we conduct a systematic literature review focusing on the linkage between Earth System Modeling and Planetary Health applications.  By analyzing the use of ESM data in health applications, we identify variables across different Earth System spheres, evaluate their reliability, and highlight gaps in translating ESM outputs into health applications. Variables such as temperature, precipitation, and air quality are explored for their direct and indirect effects on health outcomes, including increased risks of infectious diseases, heat stress, and malnutrition.

The reviewed studies employ diverse Earth System Models (ESMs) and dynamic downscaling techniques to project future health scenarios, mainly relying on simple linkage rather than fully coupling Planetary Health applications. Key findings reveal substantial increases in mortality and morbidity rates linked to cardiovascular and respiratory diseases, exacerbated by prolonged exposure to extreme heat and degraded air quality. For instance, regional analyses indicate significant health risks in densely populated urban areas and low-income regions, emphasizing the need for tailored mitigation strategies. Notably, applications such as simulating the impacts of heatwaves on mortality in Europe and assessing adaptation measures like green space-based cooling systems exemplify the need for integration of ESM and Planetary Health.

Our synthesis highlights the critical interplay of socioeconomic, demographic, and Earth System factors in shaping health vulnerabilities, underscoring the importance of intersectionality in climate health research. Advancing the integration of ESM and Planetary Health is crucial for promoting climate resilience and equity in health outcomes.

How to cite: Thiele-Eich, I., Rahmen, M., and Falkenberg, T.: Linking Earth System Modeling and Planetary Health: A Systematic Literature Analysis of Interactions and Impacts on Human Health, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19710, https://doi.org/10.5194/egusphere-egu25-19710, 2025.

Jordan is one of the most water-scarce regions in the world, facing climate change impacts on water, energy, and food—the core components of the WEF Nexus. Health, as an additional dimension of the nexus, is being investigated through the NIH-funded Global Center on Climate Change, Water, Energy, Food, and Health Systems (GC3WEFH). A key component of the Center is its Data Hub, which focuses on providing analytical access to datasets that reflect the WEFH nexus components and assembling an open-source software ecosystem to support integrative research while adhering to FAIR (Findable, Accessible, Interoperable, Reusable) principles.

This presentation demonstrates how large language models (LLMs) are transforming our ability to explore the complex interdependencies within the WEFH Nexus. By extracting insights from interdisciplinary sources—such as scientific articles, policy documents, and environmental and health datasets—LLMs provide powerful tools for integrated data analysis and decision-making across these critical domains. Built on the SuAVE (Survey Analysis via Visual Exploration, suave.sdsc.edu) platform, the Data Hub catalog enables intuitive browsing, querying, and faceted searches of Jordan-specific datasets. To enhance accessibility, LLM-based applications are integrated into the hub, allowing natural language queries to generate tables, maps, and visualizations, revealing interrelationships among nexus indicators such as the effects of climate change on water quality and health outcomes. Additional tools evaluate the AI-readiness of datasets and implement strategies to improve their usability for machine learning applications. These innovations enable deeper insights into the WEFH Nexus, supporting simulations of system sustainability and assessing the health impacts of water, food, and energy-focused strategies in environmentally stressed regions.

To further understand the global research landscape of the nexus, we constructed and analyzed a global co-authorship network of research articles referencing all four nexus components in their titles or abstracts. Using OpenAlex, an open-access bibliographic database, and the network analysis extension of the SuAVE platform, we visualized and examined the evolution of research collaborations, emerging topics, and knowledge gaps. Our analysis revealed that over 60% of WEFH-related publications have been produced in the last four years, reflecting a rapidly expanding but still fragmented field. The co-authorship network exhibits higher clustering and fragmentation compared to more established research areas, such as the Water-Energy-Food Nexus, which is characteristic of emerging disciplines. Key topics identified within the WEFH Nexus emphasize sanitation, water quality, and water treatment (water); wellness, safety, and public health systems (health); crop yields, food security, and nutrition (food); and renewable energy and emissions reduction (energy).

While the United States leads global contributions, accounting for nearly 30% of publications in the field, significant opportunities remain to foster stronger global collaborations and reduce fragmentation in the network. The GC3WEFH is leading this effort through a multi-institutional, international collaboration focused on modeling the climate impacts on vulnerable communities in water-scarce areas of Jordan.

This work is supported by the US National Institutes of Health, Fogarty International Center, under award # 1P20TW012709-01.

How to cite: Zaslavsky, I., Al-Delaimy, W., Mohtar, R., and Kirkpatrick, C.: Leveraging AI, Large Language Models, and Co-Authorship Network Visualization to Globally Understand the Water-Energy-Food-Health Nexus, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12362, https://doi.org/10.5194/egusphere-egu25-12362, 2025.