Due to their rarity, large-magnitude hazards are often ignored in disaster risk analysis, leaving societies with unmitigated exposure and limited preparedness. Volcanic eruptions of Volcanic Explosivity Index (VEI) 7 magnitude occur once or twice per millennium, with the last in 1815. Due to increasing populations and interdependent infrastructure, such an event happening today would be catastrophic. Assessing exposure is challenging due to the lack of past event data, limiting hazard modelling potential. We have developed a framework to assess exposure of populations, buildings, infrastructure, and cropland to VEI 7 eruptions. We assessed exposure within 100 km of 136 VEI 7 potential volcanoes, the likely extent of caldera collapse and pyroclastic density currents. We find that approximately 312 million people live within 100 km of these volcanoes. Laguna Caldera, Taal, and Wilis have the highest exposures. For tephra fall, we quantified exposure within isopach footprints from five past VEI 7 eruptions, and rotated these around the volcano to account for wind-direction variability. Among case studies, Ilopango has the highest exposure in the direction of current and future wind direction, with 115 million people, 3.2 million buildings, ~4,000 km 2 cropland exposed. Our results show that exposure is highly sensitive to wind direction and highlight the scale of potential exposure. These findings can help prioritise preparation for catastrophic eruptions by integrating VEI 7 scenarios into disaster risk analysis. • Approximately 312 million people live within 100 km of 136 volcanoes with potential for VEI 7 eruptions. • Laguna Caldera, Taal, and Wilis volcanoes rank highest in combined population, infrastructure, and cropland exposure. • Exposure to tephra fall is sensitive to wind direction.

Wind effect and optimization of multi-span greenhouses with different roof

Quantifying the coupled effects of dust accumulation and wind direction on the thermal performance of photovoltaic modules

A novel optical-electrical-thermal coupled model by BP neural network algorithm: Applied to high-precision prediction for key performance parameters of photovoltaic modules

Near-field dispersion of radioactive nuclides from nuclear power plants under different meteorological conditions.

Machine learning-optimized interpretability analysis for identifying key drivers of NO3 lifetime variability.

A nonparametric framework for linear–circular regression: Applications in environmental and biological sciences

Billboard effects on idealized ground-heated street canyon ventilation across varied wind directions

Stratospheric airship flight trajectory envelope prediction based on wind field uncertainty model

Effects of wind direction and wind-thermal coupling on airflow and ventilation at different local spaces in strip-type residential areas

Maritime weather data from historical ship logbooks are used to assess 19th century surface wind conditions. Housed across several New England archives, logbooks of U.S. whaling voyages contain systematic weather observations (e.g., wind strength/direction, sea state, precipitation) at (sub-)daily temporal resolution. Here, qualitative wind descriptions by the whalers from ~200 ship logbooks are quantified to generate a dataset with ~81,000 daily records of wind strength and direction en route and covering key whaling grounds in the Atlantic, Pacific, Indian, and Southern Ocean during the period 1820-1900 CE. Following extensive quality control, we find good agreement in wind strength and direction for the whaling records when compared with 20th Century Reanalysis winds for mean and seasonal conditions. For the North Atlantic with the densest coverage of whaling records, interannual variations in the basin-wide wind field associated with different phases of the North Atlantic Oscillation are also captured in the whaling records. Our results demonstrate that the historical records provide an important long-term context for maritime wind patterns in ocean regions lacking direct observational data during the 19th century.

Methane (CH4) emissions from confined animal feeding operations are spatially heterogeneous, and uncrewed aerial vehicle (UAV)-based concentration measurements depend critically on atmospheric conditions. Yet the limits of spatial interpretability under varying wind regimes remain poorly characterized. This study integrates anisotropy-aware geostatistical analysis with atmospheric stability classification to evaluate the spatial interpretability of UAV-derived near-field methane enhancements (ΔCH4) over a commercial feedlot. Grid-based UAV surveys under contrasting wind regimes show that weakly unstable conditions (wind speed ≳ 2 m s-1, turbulence intensity ≲ 0.35) produce elongated, wind-aligned plume structures with strong directional coherence (D2 up to 69%; |Δφ| ≤ 13.5°) and low interpolation uncertainty (CV-RMSE = 0.062-0.101 ppm). Extremely unstable conditions yield fragmented, near-isotropic ΔCH4 patterns with substantially higher uncertainty (CV-RMSE = 0.215-0.367 ppm). High directional coherence (D2 > 36%) coincided with persistent wind direction and low coherence (D2 < 12%) occurred exclusively under highly variable, extremely unstable conditions. These results demonstrate that the wind regime and atmospheric stability govern the spatial interpretability of UAV-derived ΔCH4 fields, with direct implications for survey design and data quality assessment. Future work should incorporate onboard wind measurements, multialtitude sampling, and inverse dispersion modeling to enable quantitative flux estimation at the facility scale.

Distance and wind direction dependent patterns of soil multifunctionality around an open-pit coal mine in an arid region.

Although associations between airborne allergens, weather conditions and respiratory health have been established, the precise mechanisms that contribute to the onset and severity of epidemic thunderstorm asthma (ETSA) events remain poorly understood. One critical area of this research lies in understanding the role of fungal spores, which have not been extensively studied for their impact on asthma exacerbations compared to pollen, as well as the influence of certain pollutants like ozone on airborne allergens, which is not well documented. This study aimed to identify the environmental parameters that influence the dispersal & distribution of airborne allergens and how these factors are associated with asthma presentations. Analysis was conducted on 475 days of data collected over a 6-year period between October-December. Using eight global negative binomial generalised linear mixed model formulations combined with a model selection approach, we analysed airborne levels of grass pollen, Alternaria spp., and Cladosporium spp. from Metropolitan Melbourne, along with weather values from the Bureau of Meteorology (precipitation, temperature, humidity, dew point, wind direction, wind speed, air pressure) and pollution values from the Environmental Protection Authority (CO, NO2, O3, SO2) to identify effects on asthma presentations from five emergency departments. Rainfall and O3 levels had significant positive relationships with number of asthma presentations, with Incidence Response Ratios of 1.074-1.088 and 1.05-1.06, respectively. High allergen levels (allergenPC1, representing overall levels of grass pollen and fungal spores) had an interactive effect with high rainfall creating positive association with asthma cases. On days of low rainfall, allergen levels had minimal impact on asthma presentations, but on days of high rainfall, there were substantially greater numbers of presentations when allergen levels were also high (e.g. a predicted four-fold increase on days with 45mm of rain). This impact of overall allergen count highlights the role of several prominent allergens in seasonal asthma, as well as potential cross reactivity between pollen and spores. The results of this study will inform researchers of several environmental monitoring parameters that should be included in risk forecasts for ETSA to allow for a more robust and accurate detection system and help improve public health responses across Australia.

ABSTRACT Wind farm micro-siting in mountainous regions is significantly challenged by terrain-induced wind-direction deflection, an effect often overlooked compared to wind speed studies. In Piedmont plains, this deflection causes power estimation errors when uniform inflow is assumed for all turbines. To address this, a validated computational fluid dynamics model for complex terrain was developed. Simulations revealed that the wind deflection angle decays following a power-law with distance, with its key parameters systematically analyzed across varying slopes. An analytical deflection model was then established and generalized via sinusoidal parametrization to account for complex terrain effects. The study quantitatively assessed the impact on power output estimation. Case results show power estimation deviations of 10.72%–11.61% in the windward region and 3.73%–5.04% in the leeward region. The proposed model effectively captures windward deflection patterns but is more suitable for qualitative or preliminary estimates in leeward areas. This work provides a practical reference for wind resource assessment and micro-siting optimization in hilly plain regions under certain conditions.

Mosquito-borne diseases continue to exact a heavy toll on human health, particularly in sub-Saharan Africa. Yet in many environments, fundamental aspects of mosquito behavior and population dynamics remain poorly characterized. Here, we employ a well-established method for directly measuring mosquito dispersal and estimating population size: mark-release-recapture (MRR). We focused on the key malaria vector Anopheles coluzzii and conducted experiments in São Tomé and Príncipe, an island nation in the Gulf of Guinea under consideration as a site for the first field trial using gene drive mosquitoes for malaria elimination. Understanding mosquito dispersal, population size, and responses to environmental factors is essential for planning such releases. To assess these parameters in An. coluzzii, a total of four MRR experiments were conducted across both São Tomé and Príncipe islands during both wet and dry seasons. Population size estimates were higher during the wet season in both study areas, but seasonal fluctuation was more pronounced in São Tomé. Seasonal patterns of mosquito dispersal differed by location, with greater dispersal in São Tomé during the wet season and in Príncipe during the dry season. Mosquito flight direction was biased toward broad-scale wind direction in São Tomé, but not in Príncipe. Together, these results enhance our understanding of An. coluzzii behavior in island ecosystems and support the design of effective vector control approaches in this biogeographical context.

The coastal ocean in the northern Gulf of Mexico is highly productive and socioeconomically important. Here, stratification can inhibit vertical exchange, with consequences for ecosystem health and hypoxia. Previous work emphasized the role of wind-driven turbulent mixing as a mechanism to overcome the stratification barrier. We identify symmetric instability (SI) as an additional and more energy-efficient pathway linking surface and bottom waters. In high-resolution observations, diagonal bands of overturning motions, a telltale sign of SI, connect the sea surface to the bottom and produce intrusions of temperature and oxygen anomalies. Overturning persists for 2 days after instability-favorable wind ceases. During this time, vertical advective fluxes exceed turbulent fluxes by an order of magnitude, ventilating low-oxygen bottom waters and transporting surface heat downward. Our results show that SI facilitates vertical exchange that can outlive direct wind forcing, highlighting an instability-driven mechanism that may be important in coastal oceans more generally.

Ultrafine particles (UFPs) are a major air quality concern because their small diameter (<100 nm) allows them to reach the lungs' alveolar regions, causing adverse health effects. Civil aviation and airports are important sources of UFPs in urban areas. In this study, a one-month intensive measurement campaign in November 2022, 1 km downwind of Zurich Airport, revealed that high UFP number concentrations up to 300 000 cm-3 originate solely from aircraft operations. These emissions are either advected downwind of the airport or mixed downward during aircraft landings overhead. The measurements confirm that most aviation-related UFPs are volatile with geometric mean diameters <20 nm. Under certain conditions, they can rapidly grow up to ∼40 nm, while their volatile particle number fraction drops from ∼0.9 to ∼0.1. Online mass spectrometry shows that engine lubrication oil signals closely track aviation-related high UFP levels, enabling attribution of high UFP concentration events to aviation emissions. Multiyear measurements at the site further show that airport emissions dominate daytime UFP concentrations for ∼30% of the time across all wind directions. The widespread presence of UFPs and related organophosphate oil compounds poses a health concern for communities near airports that regulators should address.

Traditional wind farm control operates each turbine independently to maximize individual power output. However, coordinated wake steering across the entire farm can substantially increase the combined wind farm energy production. Although dynamic closed-loop control has proven effective in flow control applications, wind farm optimization has relied primarily on static, low-fidelity simulators that do not resolve critical dynamic turbulent fluctuations in the flow. In this work, we present a reinforcement learning controller trained using high-fidelity turbulence resolving simulations, enabling real-time response to atmospheric turbulence through collaborative, dynamic control strategies. In a three wind turbine test case, our reinforcement learning controller achieves a 4.30% (95% CI = [4.10%,4.49%]) increase in wind farm power output compared to baseline operation, nearly doubling the 2.19% (95% CI = [1.98%,2.39%]) gain from static optimal yaw control and a substantial increase over the gain from global wind direction based dynamic control obtained through Bayesian optimization of 2.67% (95% CI = [2.47%,2.87%]). These results establish that reinforcement learning is able to utilize the increased information available from turbulence resolved simulations to learn improved, dynamic flow-responsive control for wind farm power maximization, with direct implications for accelerating renewable energy deployment to net-zero targets.

Air quality and suicide.