Human and animal vector-borne diseases, particularly mosquito-borne diseases, are emerging or re-emerging worldwide. Six Aedes invasive mosquito (AIM) species were introduced to Europe since the 1970s: Aedes aegypti, Ae. albopictus, Ae. japonicus, Ae. koreicus, Ae. atropalpus and Ae. triseriatus. Here, we report the results of AIMSurv2020, the first pan-European surveillance effort for AIMs. Implemented by 42 volunteer teams from 24 countries. And presented in the form of a dataset named “AIMSurv Aedes Invasive Mosquito species harmonized surveillance in Europe. AIM-COST Action. Project ID: CA17108”. AIMSurv2020 harmonizes field surveillance methodologies for sampling different AIMs life stages, frequency and minimum length of sampling period, and data reporting. Data include minimum requirements for sample types and recommended requirements for those teams with more resources. Data are published as a Darwin Core archive in the Global Biodiversity Information Facility- Spain, comprising a core file with 19,130 records (EventID) and an occurrences file with 19,743 records (OccurrenceID). AIM species recorded in AIMSurv2020 were Ae. albopictus, Ae. japonicus and Ae. koreicus, as well as native mosquito species.

Precipitation holds significant importance as a climate parameter in various applications, including studies on the impacts of climate change. However, its simulation or projection accuracy is low, primarily due to its high stochasticity. Specifically, climate models often overestimate the frequency of light rainy days while simultaneously underestimating the total amounts of extreme observed precipitation. This phenomenon, known as “drizzle bias”, specifically refers to the model's tendency to overestimate the occurrence of light precipitation events. Consequently, even though the overall precipitation totals are generally well represented, there is often a significant bias in the number of rainy days. The present study aims to minimize the drizzle bias in model output by developing and applying two statistical approaches. In the first approach, the number of rainy days is adjusted based on the assumption that the relationship between observed and simulated rainy days remains the same in time (thresholding). In the second, a machine learning method (random forest or RF) is used for the development of a statistical model that describes the relationship between several climate (modelled) variables and the observed number of wet days. The results demonstrate that employing a multivariate approach yields results that are comparable to the conventional thresholding approach when correcting sub-periods with similar climate characteristics. However, the importance of utilizing RF becomes evident when addressing periods exhibiting extreme events, marked by a significantly distinct frequency of rainy days. These disparities are particularly pronounced when considering higher temporal resolutions. Both methods are illustrated on data from three EURO-CORDEX climate models. The two approaches are trained during a calibration period, and they are applied for the selected evaluation period.

The Asian tiger mosquito, Aedes albopictus, is an important vector of arboviruses that cause diseases such as dengue, chikungunya, and zika. The vector is highly invasive and adapted to survive in temperate northern territories outside its native tropical and sub-tropical range. Climate and socio-economic change are expected to facilitate its range expansion and exacerbate the global vector-borne disease burden. To project shifts in the global habitat suitability of the vector, we developed an ensemble machine learning model, incorporating a combination of a Random Forest and XGBoost binary classifiers, trained with a global collection of vector surveillance data and an extensive set of climate and environmental constraints. We demonstrate the reliable performance and wide applicability of the ensemble model in comparison to the known global presence of the vector, and project that suitable habitats will expand globally, most significantly in the northern hemisphere, putting at least an additional billion people at risk of vector-borne diseases by the middle of the 21st century. We project several highly populated areas of the world will be suitable for Ae. albopictus populations, such as the northern parts of the USA, Europe, and India by the end of the century, which highlights the need for coordinated preventive surveillance efforts of potential entry points by local authorities and stakeholders.

Climate change poses a significant threat to agriculture, with potential impacts on food security, economic stability, and human livelihoods. Dairy cattle, a crucial component of the livestock sector, are particularly vulnerable to heat stress, which can adversely affect milk production, immune function, and feed intake and, in extreme cases, lead to mortality. The Temperature Humidity Index (THI) is a widely used metric to quantify the combined effects of temperature and humidity on cattle. However, the THI was previously estimated using daily-level data, which do not capture the daily thermal load and cumulative heat stress, especially during nights when cooling is inadequate. To address this limitation, we developed a machine learning approach to temporally downscale daily climate data to hourly THI values. Utilizing historical ERA5 reanalysis data, we trained an XGBoost model and generated hourly THI datasets for 12 NEX-GDDP-CMIP6 climate models under two emission scenarios (SSP2-4.5 and SSP5-8.5) extending to the end of the century. These high-resolution THI data provide an accurate quantification of heat stress in dairy cattle, enabling improved predictions and management strategies to mitigate the impacts of climate change on this vital agricultural sector. The dataset created in this study is publicly available at https://doi.org/10.26050/WDCC/THI (Georgiades, 2024b).

Atmospheric pollution causes millions of excess deaths annually, with particulate matter (PM) being a major concern. While research has traditionally focused on PM ₁₀ and PM _2.5 , ultrafine particles (UFPs, diameter < 100 nm) have emerged as a critical human health risk due to their ability to penetrate deeply into the respiratory system, transmigrate into the bloodstream and induce systemic health impacts. The total particle number concentration (PNC) serves as a proxy measure for UFP prevalence, as UFPs dominate particle number counts despite contributing minimally to total particle mass. This study presents the first global datasets of PNCs and UFPs at 1 km resolution over land by combining ground station measurements with machine learning. We developed an XGBoost model to predict annual PNC levels from 2010–2019, integrating diverse environmental and anthropogenic variables available at the global scale. Our model achieves an R ² of ≥0.9 and a mean relative error of about 30% for polluted urban areas, based on comparison with test datasets, and its performance was evaluated by including spatial and temporal cross-validation schemes. We find that global annual mean PNCs near the Earth’s surface vary between a few thousand per cm ³ in pristine environments up to more than 40,000 per cm ³ in some urban centres and that UFPs contribute about 91% to PNCs. The model incorporates a conformal prediction framework to provide reliable coverage intervals, making local-to-global PNC and UFP data available and supporting exposure assessments and health impact studies.

Understanding the intricate relationship between air quality and human health necessitates a comprehensive assessment of the spatial distribution of pollutants at high resolutions. Here, we employ deep learning methodologies for downscaling atmospheric concentrations of pollutants demonstrated to adversely affect human health (NO ₂ and PM _2.5 ). We train a model using data fusing multi-source information from the Copernicus Atmosphere Monitoring Service (CAMS) and in-situ observations from ground-based stations in North Italy. Our model demonstrates robust generalisation capabilities by effectively improving bias compared to ground-truth station observations when applied to the state of California, at an order of magnitude different downscaling resolution, despite not previously being trained on or exposed to this region. In addition to the demonstrated transferability, we developed and applied an occlusion-based method over the fused data sources for interpretability. Our results suggest that our model adeptly leverages auxiliary informed data encompassing past, present and future insights for inference. Thus, we are able to quantify the influence of the input variables on both predictability and on the uncertainty associated with the physical deterministic model.