GeoHealth

The impact of weather on the health of Americans and the American health system is substantial. Using available health and economic data, we developed a data-driven scenario that describes a compounded heat emergency in an archetypal community in the United States. We then characterize the potential human and economic costs of such a heat emergency to demonstrate the widespread impact on health outcomes, health systems, and society.

Dengue fever risk and burden has increased globally in the past decade, with record-breaking outbreaks driving high case numbers, outbreaks increasing in existing transmission suitable regions, and occurring in new locations. A combination of global change processes, including climate change, have provided the environmental backdrop for introductions and resurgences of mosquito-transmitted dengue virus. Understanding shifts in exposure risk is integral to public health preparedness. This study provides global mapping of the thermal suitability of dengue transmission for CMIP6 climate scenarios, across a range of general circulation models (GCMs), and we created spatially explicit demographic projections of transmission risk using year-matched RCP-SSP frameworks for demographic and emissions scenarios. Globally, poleward shifts in projected distributions of suitability for transmission for both Ae. aegypti and Ae. albopictus suitability are shown in both the near term (2030s) and longer term (2050). Under a 'middle of the road' climate scenario (CMIP6 SSP2-4.5), regions in Africa and Asia are the major areas driving increases in year-round (12 months) population at risk (PAR) through 2050, with an anticipated net gain in 932 million people at risk for Ae. aegypti transmission and 24 million for Ae. albopictus, which includes multiple regions losing areas of year-round suitability as temperatures exceed the higher thermal boundary for transmission. In contrast, the estimated net increase in PAR for one or more months of transmission suitability at a global scale by 2050 is 3.29 billion people for Ae. aegypti transmission and 3.30 billion for Ae. albopictus transmission. This snapshot of a 'middle-of-the-road' combination of climate and demographic driven increases in potential dengue transmission exposure emphasizes the importance of both expanding suitability in new areas, and growing populations in areas approaching and becoming exposed year-round. Globalization, urbanization, and shipping will continue to provide the potential for introductions into newly suitable areas as season lengths increase, sparking outbreaks in unexposed populations. This is compounded and becomes ever more probable as the number of people and places at year-round risk also increases. This project provides all global gridded outputs for onward mapping and reuse, to add to the toolkit to anticipate and prepare for prevention and response to dengue in a changing world.

Climate change detection and attribution (D&A) methods have become vital for quantifying the influence of anthropogenic forcing on the Earth's systems, including human health. Health impact attribution (HIA) studies seek to disentangle climate-driven health effects from natural variability yet are often constrained by the availability of accessible counterfactual climate scenarios. This tutorial paper presents a flexible, reproducible framework for developing counterfactual climates without reliance on computationally intensive global circulation models. We provide practical, R-based methodologies for constructing both trend-based (temperature and non-temperature) and event-based counterfactual, using a variety of techniques including model residual detrending, data-driven decomposition (e.g., Singular Spectrum Analysis and Empirical Mode Decomposition) and stochastic weather generators. The tutorial also explores the incorporation of greenhouse gas concentrations as forcing variables, rather than global mean temperature anomalies. By operationalising these methods through worked examples and an open code repository, this paper aims to build capacity within the HIA community, enhance methodological transparency, and foster interdisciplinary collaboration between climate and health researchers.

The 2026 Bundibugyo Ebola outbreak in eastern Democratic Republic of the Congo (DRC) has already generated international spread to Uganda, raising concerns about further regional and international dissemination. Using International Air Transport Association origin-destination passenger flows, we assessed relative exposure to Ebola virus disease importation into Europe under six outbreak expansion scenarios reflecting plausible pathways of geographical spread, including cross-border transmission and amplification in highly connected regional capitals. Relative exposure patterns remained largely unchanged under localized transmission in eastern DRC and border-spillover scenarios. Expansion into South Sudan generated a first structural increase in importation pressure to Europe through the connectivity associated with Juba, while hypothetical amplification in Kampala, Kigali, and Kinshasa substantially increased importation pressure and reshaped exposure patterns across Europe. Across all scenarios, France, Italy, and the United Kingdom remained among the most exposed countries. Mobility-informed scenario analyses support preparedness as the geography of the outbreak evolves.

Residential greenness has been associated with reduced heat-related illness, yet the specific role of greenspace morphology at the neighborhood scale remains insufficiently understood. This study quantified the relationship between heat-related illness and multiple dimensions of greenspace morphology using an eight year (2016-2023) unbalanced panel dataset comprising 19,021 block group year observations across 2,427 census block groups in Arizona, USA. One meter high resolution National Agricultural Imagery Program aerial imagery was classified to calculate greenspace percentage, number of greenspaces, average size, shape complexity, connectedness, and distantness, at the block group level. We applied conditional spatial autoregressive models with a negative binomial distribution to estimate associations between each morphology metric and yearly heat-related illness counts, adjusting for sociodemographic and geographic covariates. We found higher greenspace percentage, aggregation, shape complexity, connectedness, and density were consistently associated with lower heat-related illness risk. A one standard deviation increases in shape complexity corresponded to a 12.4% decrease in expected heat-related illness counts (IRR=0.876, 95% CI: 0.834-0.921). Similarly, increases in greenspace percentage (14.6% decrease; IRR=0.855, 95% CI: 0.827-0.885), number of greenspace patches (3.7% decrease; IRR=0.963, 95% CI: 0.937-0.990), average size (4.5% decrease; IRR=0.955, 95% CI: 0.923-0.989), and connectedness (5.5% decrease; IRR=0.945, 95% CI: 0.918-0.972) were all protective. In contrast, larger inter greenspace distances were associated with increased heat-related illness risk (6.1% increase; IRR=1.061, 95% CI: 1.033-1.091). Our findings highlight the critical importance of multiple dimensions of greenspace morphology in mitigating heat-related health risks. These results suggest that heat reduction planning with greening initiatives should consider not only the amount of greenspace but also its spatial configuration to maximize cooling and result in health benefits.

Objectives To explore the prevalence, spatial aggregation, and demographic correlates of climate change awareness among adults in Nigeria, as well as impacts on humanitarian health preparedness. Design Nationally representative cross-sectional survey with multivariate logistic regression and Global Moran's I and LISA techniques of spatial autocorrelation analyses was applied. Setting All 36 states and the Federal Capital Territory, Nigeria. Participants 1,600 adults drawn from the Afrobarometer Round 9 nationally representative survey. Interventions None. Main Outcome Measures Prevalence, spatial aggregation, and demographic correlates of climate change awareness among adults in Nigeria, and impacts on humanitarian health preparedness. Results Less than one in three Nigerians (30.1%) was aware of climate change, significantly lower than the 65% found in the continent, and education is the most predictive factor, with tertiary-educated Nigerians more than ten times more likely to be aware of climate change than those with no formal education. Most critically, the poor performance in government climate policies is not found in low-awareness states, but in two geographically distinct risk corridors based on a different mechanism and requiring a different policy response. Conclusions The finding shows that the gap in climate awareness is not a communication problem, it is a structural problem - one that requires a national intervention to reduce and close, but that might not be enough because of educational inequality, gender disparity and geographic marginalization. To prepare the country for humanitarian needs, targeted state-level, gender-responsive programming based on Nigeria's Climate Change Act 2021 is required, and effective intervention to make adaptation to the health impacts of climate change happen will need to start with triggering awareness into adaptive health action before climate hazards surpass the country's humanitarian response capacity. Registration Not applicable. Keywords: Climate change awareness; spatial autocorrelation; humanitarian health preparedness; educational inequality; Nigeria

Background: Climate mitigation policies can lower air pollutant concentrations and deliver substantial health co-benefits. The French Ecological Transition Agency (ADEME) proposed four contrasting Transitions 2050 net-zero scenarios. We quantified mortality, morbidity, and health-economic co-benefits from projected PM2.5 and NO2 reductions across all four scenarios in continental France. Methods: Emission projections were input to the CHIMERE chemistry-transport model to estimate PM2.5 and NO2 concentrations for 2030 and 2050. Health impacts were assessed using disease-specific cessation-lag assumptions relative to 2019, covering premature mortality, morbidity, DALYs, and economic benefits across nine outcomes (hypertension, lung cancer, ischaemic heart disease, stroke, COPD, type-2 diabetes, acute lower respiratory infections, and asthma in children and adults). Findings: Population exposure is projected to decline by about 40% for PM2.5 and 70% for NO2 by 2050, with health gains remaining substantial and broadly equivalent across all four scenarios and modest differences between sufficiency-oriented and technology-driven pathways. Under delayed-impact assumptions, avoided premature deaths ranged from 21,300 to 22,100 for PM2.5 and 24,500 to 26,200 for NO2. Morbidity and disability-adjusted life year (DALY) reductions, as well as economic savings, spanned similarly; total avoided morbidity cases were 84,000-88,000, direct medical cost reductions were e1.0-1.1 billion/year, and intangible cost savings of e41-43 billion and e36-39 billion, respectively. Interpretation: Health co-benefits are substantial, consistent across contrasting scenarios, and increase markedly from 2030 to 2050. Explicitly incorporating these co-benefits into climate policy appraisals may strengthen the case for ambitious mitigation and improve decision-maker acceptability.

During the 2026 Bundibugyo virus disease outbreak in the Democratic Republic of the Congo and Uganda, we projected potential airline-mediated importation risk using contemporary airline network and an externally calibrated Ebola importation hazard. Effective-distance analyses identified major international hub countries, including Belgium, France, South Africa, Kenya, and the United Arab Emirates, as higher-probability gateways within 30 days. These early projections provide a reproducible framework for real-time international situational awareness, while emphasizing that importation risk does not imply local transmission risk.

Southwestern Europe faced an extreme wildfire season in 2025, with nearly 700,000 hectares burned in the Iberian Peninsula (IP) alone. Here, we analyze the drivers and impacts of the 2025 wildfire season in the IP and its significance within the ongoing global pyrocrisis. Decades-long declines in burned area, driven by increased fire suppression, ceased after an inflection point in 2022. Fire intensity has escalated over the last two decades, and the energy emitted in 2025 approached that produced annually by a 1,000MW nuclear reactor. Despite a historically wet spring, an extreme summer heatwave triggered a flash drought, dehydrating fuels below critical thresholds. Remarkably, 29-42% of all wildfires spread faster at night than during the day, a seldom-reported phenomenon likely arising from interactions between surface weather, atmospheric instability, and pyroconvective processes. Global change-induced increases in fire intensity facilitated the overwhelming of suppression efforts during simultaneous fire events that may have been manageable decades ago. Fire activity expanded into previously fire-free high-altitude regions, and there was a marked change in fire-size distributions, with the largest wildfire in record and the largest proportion of burned area by megafires (those burning over 5,000ha). Impacts included over 2,000 premature deaths from smoke exposure and significant effects on protected areas. These results indicate shifts in key components of anthropogenic fire regimes, including unprecedented nocturnal fire acceleration and increased burned area and fire intensity, with escalating impacts on human health and ecosystems.

Short-term environmental exposures have been linked to cognitive and behavioral outcomes, although many reported associations may reflect broader geographic and contextual differences. Using longitudinal data from the All of Us Research Program (2018--2024), we linked daily weather and air-pollution exposures to repeated attention-related and subjective cognitive outcomes. Associations were evaluated using pooled, fixed-effects, lagged, and event-study analyses. Additional machine-learning analyses were conducted to explore potential heterogeneity and latent psychosocial structure. Replication analyses were performed using the 2024 Behavioral Risk Factor Surveillance System (BRFSS). Several environmental exposure measures showed small associations with cognitive outcomes in pooled analyses, but most attenuated substantially after accounting for within-location temporal variation. Mediation, sensitivity, and machine-learning analyses yielded similar conclusions. In contrast, mental-health burden, loneliness, and social functioning were consistently associated with subjective cognitive difficulty and exhibited substantially larger effect sizes than environmental exposures. Similar patterns were observed in BRFSS. Exploratory AI-assisted analyses yielded findings broadly consistent with the primary longitudinal analyses. These findings suggest that short-term environmental perturbations may have limited associations with cognitive outcomes after accounting for within-location variation, whereas psychosocial factors appear to be more consistently associated with subjective cognitive burden.

Short-term environmental exposures have been linked to cognitive and attention-related outcomes, but the robustness of these associations remains uncertain. We linked daily weather and air-pollution exposures to repeated measures of subjective cognitive difficulties and attention-related outcomes among participants in the All of Us Research Program from 2018 to 2024. Associations were evaluated using complementary longitudinal and causal-inference approaches, including fixed-effects, lagged-exposure, and event-study analyses. Machine-learning methods were used to characterize heterogeneity and latent psychosocial structure, and findings were independently evaluated using 2024 Behavioral Risk Factor Surveillance System data. Several environmental exposure measures were associated with cognitive outcomes in pooled analyses; however, most associations attenuated substantially after accounting for within-location temporal variation. In contrast, mental-health burden, loneliness, and impaired social functioning remained consistently associated with subjective cognitive difficulty across analytical approaches. Similar patterns were observed in the validation dataset. These findings suggest that some observed environmental associations may reflect broader geographic and contextual differences rather than short-term environmental effects. Overall, psychosocial factors demonstrated more consistent associations with subjective cognitive difficulties than short-term environmental exposures across multiple analytical frameworks and independent datasets.

Fine particulate air pollution (PM2.5) in the United States has fallen by roughly half since 2000, yet linked health outcomes such as diabetes and childhood ADHD have not improved in parallel. One reconciling possibility is that pollution exposure in early life produces health effects that emerge only years or decades later, after pollution itself has declined. Using two decades of U.S. county-level data, we relate annual PM2.5 estimates to birth outcomes, diabetes prevalence, and small-area estimates of childhood attention-deficit/hyperactivity disorder (ADHD) across short and long time scales. Within counties, changes in low birth weight rates are associated with changes in PM2.5 during the same year and the year prior to birth. At longer time scales, cross-county comparisons show that PM2.5 exposure is associated with higher prevalence of adult diabetes and ADHD after approximately a decade. Together, these patterns suggest that population-level health risks from air pollution may persist over decades, even as pollution itself declines.

Background: Surgical care centralization in the U.S. delays access and increases carbon emissions. Global targets suggest patients live within 2-hours of a surgical facility. This study quantifies the environmental impact of travel for cataract surgery in rural Michigan and models the potential emissions reductions from decentralizing surgical and follow-up services. Methods: A retrospective, cross-sectional study analyzed electronic medical records from a rural Michigan ophthalmology practice (March-November 2023). We calculated travel distances using population-weighted centroids and estimated emissions using U.S. Department of Energy vehicle data. A k-means clustering model optimized additional facility placement, and a gradient analysis identified optimal numbers for decentralization points, for emissions reductions. Results: The 920 patients traveled a median of 55.45 km (IQR: 43.33-88.20 km) for surgery and 55.07 km (IQR: 43.54-87.82 km) for follow-up visits, generating Total Surgical Access Emissions (TSAE) of 57,168 kgCO2; (median of 59.20 kgCO2; IQR: 32.31-81.87) under the centralized model. The k-means decentralization model and gradient analysis identified 7 hospitals and 9 clinics, respectively, as the optimal expansion points, reducing emissions by 34.07% (19,475 kgCO2 saved) and 39.52% (22,590 kgCO2; saved). The Surgical Access Carbon Impact (SACI) model demonstrated that achieving two-hour access to clinic services reduced excess emissions by 54.7%. Sensitivity analyses using fuel-efficient vehicles (Toyota Prius and Tesla Model 3) or reducing follow-up visit frequency reduced emissions by 54.03% (30,888 kgCO2) and 25.83% (14,768 kgCO2), respectively. Conclusion: Decentralizing surgical services in rural U.S. settings could cut travel-related emissions by up to 40%, significantly reducing healthcare-related carbon footprints while improving timely access to care. The SACI metric provides a novel framework for integrating environmental sustainability into U.S. health policy and service planning

Respiratory virus transmission occurs in indoor settings where ventilation, occupancy, and dwell time determine exposure levels. Improving indoor air quality (IAQ) therefore could help reduce disease burden associated with respiratory viruses, yet its population-level impact remains poorly quantified. Here, we develop an individual-based transmission modelling framework that links within-location airborne dynamics to individual infection risk and population-level spread, whilst explicitly incorporating heterogeneity in ventilation and baseline indoor air quality across locations. We use this modelling approach to evaluate IAQ-improving interventions (air-quality interventions or AQIs), using hypothetical endemic and pandemic pathogen archetypes with properties similar to SARS-CoV-2 and influenza, and evaluate how effects on key epidemiological metrics (such as annualized incidence and epidemic final size) depend on AQI coverage, efficacy and allocation strategy. At 20% AQI intervention coverage and 80% efficacy, annualized incidence was reduced by approximately 7.2% for an endemic 'SARS-CoV-2-like' respiratory virus, and 17.0% for an endemic 'influenza-like' virus; at 60% coverage (80% efficacy) the reductions were 26.3% and 56.4%, respectively. Targeting AQI installation to the highest-risk locations outperformed random allocation: for SARS-CoV-2-like transmission, 20% coverage at 80% efficacy cut absolute incidence by 10.8% when targeted versus 7.2% when random; for influenza-like transmission, this comparison was 28.9% versus 17.0%. In epidemic scenarios, random installation at 40% coverage and 60% efficacy reduced final size by 23.7% (influenza-like) versus 6.3% (SARS-CoV-2-like). These results support treating clean indoor air as core public-health infrastructure and prioritising risk-based deployment of IAQ-improving interventions to maximise population-level benefit within budgetary and operational constraints.

BackgroundIn September 2021, the U.S. Centers for Disease Control and Prevention (CDC) implemented the Traveler-based Genomic Surveillance (TGS) program, a surveillance system that leverages genomic sequencing of samples from international air travelers and aviation wastewater for early detection of infectious threats. MethodsDuring September 2021-August 2024, nasal samples were collected anonymously from volunteer international travelers arriving at eight U.S. airports. During February 2023- August 2024, aviation wastewater samples were collected from arriving flights. Nasal samples were pooled and sent to a laboratory for RT-PCR testing. Genomic sequencing was conducted for SARS-CoV-2 and respiratory, gastrointestinal (wastewater), and other pathogens of public health importance. FindingsNasal samples from 694,798 travelers were grouped into 67,308 pools and tested; 13,990 (20.8%) were positive for SARS-CoV-2. Over 80% (400/495) of airplane and 96{middle dot}6% (422/437) triturator (a wastewater collection point from multiple airplanes) samples were positive for SARS-CoV-2. Sequence results were made publicly available a median of 11 days (IQR 10- 13 days) after sample collection. Predominant SARS-CoV-2 variants changed over time. Positive tests for influenza virus and respiratory syncytial virus were high in December/January, and gastrointestinal viruses were detected in wastewater during all months. Monitoring was scaled in response to reported outbreaks of COVID-19 and Mycoplasma pneumoniae in China and clade 1 monkeypox virus in central Africa. InterpretationTraveler nasal and aviation wastewater sampling can provide critical early detection of infectious pathogens before widespread U.S. community transmission. The TGS program provides a model for integrated traveler-based genomic surveillance. FundingCDC Research in ContextO_ST_ABSEvidence before this projectC_ST_ABSWe searched PubMed for relevant studies published during December 1, 2020-August 31, 2024, using the terms "traveler surveillance", "wastewater monitoring", "SARS-CoV-2 genomics", and "airport-based surveillance", without language restrictions. Previous reports have shown the feasibility of using travelers as sentinel populations for disease surveillance. Modeling studies have proposed integrating genomic data into international travel surveillance systems to enhance early pathogen detection, and evidence from Australia, Canada, and the UK suggests such programs could be scalable and effective. Early pandemic-era wastewater surveillance, particularly aviation wastewater, demonstrated that air travel hubs can be used to monitor pathogen importation. Prior efforts largely focused on SARS-CoV-2, with limited integration of multi-pathogen surveillance or side-by-side comparisons of nasal and wastewater surveillance modalities. A limited number of public health reviews have examined the broader implications of airport-based surveillance, including novel methods like airplane wastewater testing. However, empirical data on sustained, large-scale implementation of these models especially outside of regulatory or mandatory testing frameworks have been sparse. Added value of this projectThis is the first real-world implementation and scale-up of an anonymized, multi-pathogen traveler-based surveillance system across multiple U.S. international airports. We developed a scalable framework that integrated nasal swab testing, airplane and airport wastewater sampling, with genomic sequencing into a unified pathogen surveillance platform. Unlike prior efforts which primarily focused on SARS-CoV-2, this program captured respiratory and gastrointestinal viruses simultaneously and tracked genomic variation in near-real time. The program transitioned from a pilot to a multi-modality national surveillance system in under four years, engaging nearly 700,000 international travelers, and nearly 1000 aviation wastewater samples. Our findings demonstrate the feasibility of rapidly adapting this infrastructure for emerging threats and underscores the importance of sentinel surveillance in addressing global sequencing blind spots. Implications of all the available evidenceThe successful scale-up and real-time application of the TGS program illustrates that traveler-based surveillance can serve as a critical global early warning tool. Data generated from this program have filled gaps in global pathogen tracking, informed public health responses to outbreaks, and demonstrated that surveillance of international travelers can be achieved without mandatory testing. The scalability, speed, and adaptability of the program offer a viable model for global replication, especially as routine surveillance capacities decline. Our findings suggest that integration of multi-modal, voluntary traveler surveillance including sequencing and wastewater-based epidemiology should be considered a core component of pandemic preparedness and response frameworks worldwide.

Background Wildfire activity and smoke exposure are increasing worldwide because of climate and land-use change. Although fine particulate matter (PM2.5) may impair pulmonary immune defences against tuberculosis (TB), population-level evidence remains limited. We estimated the effect of wildfire-related PM2.5 exposure on TB notification rates in Brazil. Methods We conducted a nationwide panel study linking municipality-level monthly TB notifications from Brazil's SINAN system with wildfire-related PM2.5 estimates from GEOS-Chem simulations across 5,545 municipalities (2003-2023). We estimated the impact of high-exposure days (PM2.5 > 25 g/m3) on monthly TB notifications using Poisson regression with fixed effects for municipalities, state-by-year, and state-by-month, controlling for time-invariant differences, secular trends, and seasonality. Distributed lag effects were estimated over 1-24 months before notification. Models accounted for meteorological conditions, GeneXpert diagnostic coverage, and spatial correlation using Conley standard errors. We computed attributable fractions among exposed municipality-months (AFE). Sensitivity analyses evaluated alternative PM2.5 thresholds (15 and 35 g/m3), co-pollutants, and agricultural expansion. Findings From Jan 1, 2003 to Dec 1, 2023, 1,758,982 TB cases were reported. Of these, 353,319 (20.1%) had at least one high-exposure day (PM2.5 > 25 g/m3) 1-24 months before notification. An additional 14 high-exposure days over the 24-month lag period was associated with an average monthly increase of 2.9% [95% CI: 0.9-4.9%] in TB notification rates. Effects peaked at 13 months (IQR: 11-14) prior to notification. Results showed a dose-response relationship across PM2.5 thresholds and were robust to controlling for NO2, O3, and agricultural expansion. Overall, wildfire-related PM2.5 exposure accounted for 2.1% [0.7-3.5%] of TB notifications in exposed municipality-months, corresponding to 7,802 [2,612-12,544] attributable cases. The AFE reached 10.7% [7.1-14.0%] in Pantanal and 7.3% [6.1-8.5%] in Amazonia, areas most impacted by wildfires. Interpretation Wildfire-related PM2.5 exposure may represent an increasingly important and modifiable risk factor for TB. As wildfire activity increases across many regions of the world, these findings highlight the need for integrating air quality into climate adaptation and TB control strategies.

It remains challenging to quantify recent changes in forest carbon due to lags in forest inventory measurements. The national U.S. forest inventory remeasures plots every five to ten years, so quantifying current carbon stocks using inventory data requires extrapolating from the last time plots were measured. We address this extrapolation challenge by fusing spatially explicit fire disturbance and canopy cover data from Landsat with forest inventory data using a statistical model. We produce annual estimates of live forest carbon across the Western U.S. from 2005 to 2022, and find that live forest biomass increased from 2005 to 2015, and then declined by 5% from 2015 to 2022 -- a signal missed by both official U.S. reporting and Earth system models. The trend reversal was driven primarily by increasing tree mortality from wildfire, and secondarily by slowing rates of carbon accumulation in undisturbed areas. Our results highlight the importance of accounting for rapidly changing disturbance regimes, and can help to improve jurisdictional carbon accounting and inform the extent to which federal and state climate mitigation strategies can rely on land to achieve net-zero emissions targets. Significance statementPolicy makers need to accurately and rapidly assess the status of the land carbon sink in order to make land management decisions and to assess progress towards climate commitments. However, lags in on-the-ground measurements make it challenging to do so, and it remains an open question whether Western U.S. forests are a net sink or a source of carbon. We fuse on-the-ground forest measurements with remote sensing data to show that live biomass is net declining in Western U.S. forests, and that this trend is driven primarily by increasing wildfire activity. This result challenges the idea that jurisdictions can rely on the land to offset fossil emissions, and supports tracking land carbon trends separately from fossil emissions inventories.

Abstract BACKGROUND West Nile virus (WNV) is a growing health burden in Italy. Anticipating human infection risk is hampered by the pathogen's complex ecology, highlighting the need for comprehensive early-warning tools. AIM We aimed to model municipal-level WNV risk in Italy and characterize its decadal expansion in Italy, providing a comprehensive ecological understanding of viral emergence. METHODS We applied a machine learning framework to annual human WNV case data from 2014 to 2024. The model integrated a suite of environmental, socio-economic, and macroecological predictors to generate risk projections. We evaluated the model's performance through multiple validation settings. We also performed an anticipation test for the 2025 epidemic season, using 2024 environmental data to assess the model's predictive accuracy against observed 2025 human cases. RESULTS Our model achieved robust performance (True Skill Statistic > 0.4) and captured WNV progressive expansion from 184 predicted positive municipalities in 2014 to 2,012 in 2024 (an 11-fold increase in 11 years). Seasonal minimum temperature was the primary risk driver, followed by monitoring year and population density, indicating active spatial spread. Environmental suitability consistently preceded clinical detection. Municipalities with cases in 2023-2024 exhibited significantly higher predicted suitability during 2018-2022 than those without cases (average risk 0.58 vs 0.20). Our model successfully identified emerging risk hotspots along the Adriatic coast and southern Italy before the official human spillover of 2025. CONCLUSION Embedding macroecological drivers into WNV risk modelling provides an improved understanding of drivers of rapid WNV expansion. Our model enables proactive risk mapping, surveillance efforts, and targeted public health measures.

Traditional agricultural safety assessments often rely on raw incident counts that emphasize exposure but underrepresent outcome severity. This study presents a multi-criteria impact framework to distinguish frequency-driven activity patterns from severity-driven risk across the U.S. Midwest. Agricultural incident records from 2012 to 2023 across seven states were analyzed using descriptive statistics, county-level mapping, and quartic kernel density estimation. Comparative impact indices were constructed using Analytic Hierarchy Process (AHP) and Geometric-Fuzzy AHP weighting schemes to integrate incident frequency, outcome severity, and post-incident survivability. Results indicate that while overall incident frequency is strongly concentrated in northwestern Iowa, reflecting intensive agricultural activity, fatal outcomes exhibit a broader spatial footprint extending across central and northern Iowa and into central-southern Minnesota. Severity-weighted mapping further consolidates northwestern Iowa and the Minnesota-Iowa corridor as dominant high-impact zones. At the regional scale, Geometric-Fuzzy AHP produced consistently lower mean scores and reduced dispersion than AHP, yielding smoother spatial gradients while preserving the primary hotspot structure. These findings demonstrate that frequency-based mapping alone fails to capture the multi-dimensional nature of agricultural risk. By explicitly linking incident locations with survival infrastructure, this research provides an evidence-based framework for targeting safety interventions and improving rural emergency medical service planning.

Tropical dryland ecosystems are highly biodiverse and fragmented and are experiencing significant anthropogenic and climatic changes. With increasing extremes in temperature and precipitation, coupled with significant alteration, these ecosystems are at greater risk of increased exposure and vulnerability to climatic change; however, little work has quantified the climatic shifts occurring within these ecosystems globally. Here, we aim to fill this gap by using the ERA-5 reanalysis and CHIRPS precipitation data to quantify changes in essential climatic variables in tropical drylands since 2000. Overall, we find that regional pressures differ, with tropical dry forests, savannas, and shrublands becoming hotter and drier in the Neotropics and parts of the Afrotropics and Australasia. By contrast, the tropical dry forests in the Indomalayan, Oceania, and Nearctic are experiencing hotter and wetter conditions. Globally, though, these ecosystems are experiencing more change than the global average, suggesting they may be approaching tipping points in their resilience, ultimately shrinking the area where they can survive.