Regional Scale Research Articles

Chemical and isotopic exchanges in serpentinites during hydration and dehydration events in the Erro-Tobbio Massif (Italy): from ocean to subduction

Abstract Subducted serpentinites are important for recycling water and fluid mobile elements into the deep Earth. Here we present a texturally controlled in situ oxygen and boron isotope and trace element study of the Erro-Tobbio ultramafic rocks (Western Alps, Italy) that have experienced exhumation to the seafloor, hydration and subsequent dehydration upon subduction. High variability in δ18O from + 0.2 to + 12.5 ‰ in the low-temperature serpentine polymorph lizardite coupled to strong enrichment in fluid mobile elements are associated to variable serpentinisation conditions at the ocean floor during the opening of the Piemonte-Liguria Ocean. Incipient oceanic serpentinisation, i.e., mesh formation after mantle olivine, occurred at approximately 160–325 ± 20–60 °C. Subsequently, mantle pyroxene was transformed into serpentine bastite with decreasing temperatures down to 75–100 °C. The transition metals V, Sc, Co, Zn and Mn, and the Ni/Cr in lizardite are indicative of precursor olivine or orthopyroxene. Remarkably, some lizardite in partially serpentinised and undeformed mantle peridotites remained metastable during subsequent subduction. During early subduction, with increasing temperature, the serpentine polymorph antigorite replaced lizardite in highly serpentinised ultramafic rocks. Antigorite shows a narrower δ18O range from + 5.3 to + 7.5 ‰, indicating isotopic homogenisation at the regional scale. A redistribution of the transition metals is observed at the sample scale, and the fluid mobile elements B, Cl and Li are partly lost. The isotopic homogenisation and the element redistribution are likely the results of (i) sample internal equilibration during antigorite crystallisation and (ii) mobility of serpentinite-derived fluids during the lizardite-to-antigorite transition. During the peak subduction stage at 1.8–2.5 GPa and 550–650 °C, metamorphic olivine with δ18O of ~ + 4 to + 5 ‰ is formed from the dehydration of brucite + antigorite. This olivine is enriched in fluid mobile elements such as Li and B compared to primitive mantle, and is in oxygen isotopic equilibrium with the co-existing antigorite. Based on this equilibrium and the coupled trace element systematics, there is no evidence of large influxes of external slab fluids from other lithologies. Recrystallised mantle olivine has lower B and Li contents compared to metamorphic olivine formed during brucite dehydration, and has similar 𝛿18O values, XMg and Ni/Mn as mantle olivine, and is not in isotopic equilibrium with the antigorite. Such metamorphic olivine produced by recrystallisation, as well as the metastable lizardite during subduction, cannot be used as indicators of fluid production from serpentine dehydration.

The very-high-resolution configuration of the EC-Earth global model for HighResMIP

Abstract. We present the very-high-resolution (VHR) version of the EC-Earth global climate model, EC-Earth3P-VHR, developed for HighResMIP. The model features an atmospheric resolution of ∼16 km and an oceanic resolution of 1/12° (∼8 km), which makes it one of the finest combined resolutions ever used to complete historical and scenario-like CMIP6 simulations. To evaluate the influence of numerical resolution on the simulated climate, EC-Earth3P-VHR is compared with two configurations of the same model at lower resolution: the ∼100 km grid EC-Earth3P-LR (LR) and the ∼25 km grid EC-Earth3P-HR (HR). Of the three configurations, VHR shows the smallest drift in the global mean ocean temperature and salinity at the end of a 100-year 1950s control simulation, which points to a faster equilibrating phase than in LR and HR. In terms of model biases, we compare the historical simulations against observations over the period 1980–2014. In contrast to LR and HR, VHR shows a reduced equatorial Pacific cold tongue bias, an improved Gulf Stream representation with a reduced coastal warm bias and a reduced subpolar North Atlantic cold bias, and more realistic orographic precipitation over mountain ranges. By contrast, VHR shows a larger warm bias and overly low sea ice extent over the Southern Ocean. Such biases in surface temperature have an impact on the atmospheric circulation aloft, connected with a more realistic storm track over the North Atlantic yet a less realistic storm track over the Southern Ocean compared to the lower-resolution model versions. Other biases persist or worsen with increased resolution from LR to VHR, such as the warm bias over the tropical upwelling region and the associated cloud cover underestimation, a precipitation excess over the tropical South Atlantic and North Pacific, and overly thick sea ice and an excess in oceanic mixing in the Arctic. VHR shows improved air–sea coupling over the tropical region, although it tends to overestimate the oceanic influence on the atmospheric variability at midlatitudes compared to observations and LR and HR. Together, these results highlight the potential for improved simulated climate in key regions, such as the Gulf Stream and the Equator, when the atmospheric and oceanic resolutions are finer than 25 km in both the ocean and atmosphere. Thanks to its unprecedented resolution, EC-Earth3P-VHR offers a new opportunity to study climate variability and change of such areas on regional and local spatial scales, in line with regional climate models.

Diverging evolutionary trajectories for palm seed sizes in mainland Africa and Madagascar.

Seed size is a trait which determines survival rates for individual plants and can vary as a result of numerous trade-offs. In the palm family (Arecaceae) today, there is great variation in seed sizes. Past studies attempting to establish drivers for palm seed evolution have sometimes yielded contradictory findings in part because modern seed size variations are complicated by long-term legacies, including biogeographic differences across lineages. Here, we examined palm seed size evolution in two adjacent regions (mainland Africa and Madagascar) utilizing single- and multi-regime Brownian Motion and Ornstein-Uhlenbeck processes. We explicitly take into account species' evolutionary histories to investigate whether there may be shared evolutionary pressures over regional scales. We found that regional selection pressures do exist for palm seed lengths, but these pressures are distinct in mainland Africa and Madagascar, despite the two regions' proximity. Our study indicates that evolutionary drivers may differ in these two regions, highlighting the importance of re-considering the widespread assumption to view mainland Africa and Madagascar as a single evolutionary region.

Growth Response of Pinus tabuliformis and Abies fargesii to Climate Factors in Southern Slope of Central Qinling Mountains of China

The response of trees to climate is crucial for the health assessment and protection of forests in alpine regions. Based on samples of Pinus tabuliformis and Abies fargesii, two typical evergreen coniferous species with distinct elevation differences in the vertical vegetation zones of the Qinling Mountains, we have developed two tree-ring width chronologies for the southern slope of the central Qinling Mountains in central China. The correlation analysis results showed that the radial growth of P. tabuliformis and A. fargesii responded to different climatic factors. Water stress caused by temperature in May of the current year was the main limiting factor for radial growth of P. tabuliformis, while precipitation in September of the previous year and the current year had a negative impact on A. fargesii, with lag effects of temperature and precipitation during the previous growing season. Spatial correlation and comparative analysis indicated that the P. tabuliformis chronology responded to extreme dry and wet events on a regional scale. Interannual and multidecadal periodic signals recorded by tree rings suggested that the hydrological and climatic changes on the southern slope of the central Qinling Mountains were teleconnected with the Pacific and Atlantic Oceans, including El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO). Our results provide new evidence for a hydroclimatical response study inferred from tree rings on the southern slope of the central Qinling Mountains.

Challenges and Opportunities in Remote Sensing-Based Fuel Load Estimation for Wildfire Behavior and Management: A Comprehensive Review

Fuel load is a crucial input in wildfire behavior models and a key parameter for the assessment of fire severity, fire flame length, and fuel consumption. Therefore, wildfire managers will benefit from accurate predictions of the spatiotemporal distribution of fuel load to inform strategic approaches to mitigate or prevent large-scale wildfires and respond to such incidents. Field surveys for fuel load assessment are labor-intensive, time-consuming, and as such, cannot be repeated frequently across large territories. On the contrary, remote-sensing sensors quantify fuel load in near-real time and at not only local but also regional or global scales. We reviewed the literature of the applications of remote sensing in fuel load estimation over a 12-year period, highlighting the capabilities and limitations of different remote-sensing sensors and technologies. While inherent technological constraints currently hinder optimal fuel load mapping using remote sensing, recent and anticipated developments in remote-sensing technology promise to enhance these capabilities significantly. The integration of remote-sensing technologies, along with derived products and advanced machine-learning algorithms, shows potential for enhancing fuel load predictions. Also, upcoming research initiatives aim to advance current methodologies by combining photogrammetry and uncrewed aerial vehicles (UAVs) to accurately map fuel loads at sub-meter scales. However, challenges persist in securing data for algorithm calibration and validation and in achieving the desired accuracies for surface fuels.

Quantifying the Effects of Carbon Growth Grade and Structural Diversity on Carbon Sinks of Natural Coniferous–Broadleaved Mixed Forests Across the Jilin Province of China

Natural mixed forests’ carbon sequestration capacity is crucial for mitigating climate change and maintaining ecological balance. However, most of the current studies only consider the role of forest age, ignoring the influence of carbon growth grade and stand structural diversity, which leads to an increase in uncertainty in large-scale forest carbon sink assessment. The aim of this study was to quantify the effects of carbon growth grade and stand structure diversity on the carbon sink of natural mixed forests and to establish a more accurate stand carbon growth model. Based on sample data from the National Forest Inventory (NFI) of China, the stand carbon growth model was established based on Gompertz and Logistic theoretical growth models, and the forest carbon sink at the regional scale was predicted. It was found that the stand carbon growth model considering only the stand age as a single variable often had poor results, with R2 less than 0.36, while R2 values of the optimal model introducing carbon growth grade and stand structural diversity were 0.87 and 0.48, respectively, which significantly improved the prediction accuracy of the model, and both had significant effects on stand carbon stocks. By predicting the future forest carbon sink, it was found that the forest carbon sink of the natural coniferous–broadleaved mixed forests in Jilin Province would reach 791 (781–801) t c/a and 843 (833–852) t c/a in 2030 and 2060, respectively, which were 17% lower and 51% higher than that of the forest carbon sink estimated by considering only the age. Moreover, the model considering structural diversity predicted a more positive carbon sink trend, indicating that forest carbon stocks could be more effectively maintained and carbon sinks increased by increasing the complexity of stand diameter at breast height structure, which has important guiding significance for future forest carbon sink management. This study provides scientific support for achieving the goal of “carbon neutrality” proposed by China.

Factors controlling high elevations in the Canadian Cordillera: Isostatic analysis and thermal contributions

The Canadian Cordillera is characterized by higher mean elevation and thinner crust compared to the adjacent craton to the east, in apparent contradiction with the Airy isostatic model. Roy Hyndman hypothesized that the high Cordillera elevations may be supported by an overall hot, low-density Cordilleran backarc mantle, but this idea hasn't been tested at the regional scale. We use a new high-resolution crustal thickness model for western Canada derived from teleseismic receiver functions and active source data, to conduct a detailed analysis of elevation-crustal thickness trends in four distinct regions: forearc, backarc, foreland, and craton. The backarc and foreland regions have higher elevations relative to the forearc and craton, requiring additional buoyancy beneath these areas. Using a grid search, we find that unrealistically low crustal densities are required to explain the high elevations and, therefore, the support must come from below the crust. To isolate the mantle component, we use a global crustal density model to correct for the effects of crustal density variations. After correction, the elevation-crustal thickness trends in each area align with the Airy hypothesis. For a given crustal thickness, the elevation of the backarc region is ~700 m higher than that of the craton. This is consistent with uplift caused by a hot backarc mantle. However, the difference is lower than that determined for the entire North American Cordillera (~1600 m). This may suggest the mantle under the Canadian Cordillera is slightly cooler than regions to the adjacent US Cordillera, perhaps due to the absence of ongoing subduction.

Machine learning to identify environmental drivers of phytoplankton blooms in the Southern Baltic Sea

Phytoplankton blooms exhibit varying patterns in timing and number of peaks within ecosystems. These differences in blooming patterns are partly explained by phytoplankton:nutrient interactions and external factors such as temperature, salinity and light availability. Understanding these interactions and drivers is essential for effective bloom management and modelling as driving factors potentially differ or are shared across ecosystems on regional scales. Here, we used a 22-year data set (19 years training and 3 years validation data) containing chlorophyll, nutrients (dissolved and total), and external drivers (temperature, salinity, light) of the southern Baltic Sea coast, a European brackish shelf sea, which constituted six different phytoplankton blooming patterns. We employed generalized additive mixed models to characterize similar blooming patterns and trained an artificial neural network within the Universal Differential Equation framework to learn a differential equation representation of these pattern. Applying Sparse Identification of Nonlinear Dynamics uncovered algebraic relationships in phytoplankton:nutrient:external driver interactions. Nutrients availability was driving factor for blooms in enclosed coastal waters; nutrients and temperature in more open regions. We found evidence of hydrodynamical export of phytoplankton, natural mortality or external grazing not explicitly measured in the data. This data-driven workflow allows new insight into driver-differences in region specific blooming dynamics.

Understanding uncertainties in the satellite altimeter measurement of coastal sea level: insights from a round-robin analysis

Abstract. The satellite radar altimetry record of sea level has now surpassed 30 years in length. These observations have greatly improved our knowledge of the open ocean and are now an essential component of many operational marine systems and climate studies. But the use of altimetry close to the coast remains a challenge from both a technical and scientific point of view. Here, we take advantage of the recent availability of many new algorithms developed for altimetry sea level computation to quantify and analyze the uncertainties associated with the choice of algorithms when approaching the coast. To achieve this objective, we did a round-robin analysis of radar altimetry data, testing a total of 21 solutions for waveform retracking, correcting sea surface heights and finally deriving sea level variations. Uncertainties associated with each of the components used to calculate the altimeter sea surface heights are estimated by measuring the spread of sea level values obtained using the various algorithms considered in the round-robin for this component. We intercompare these uncertainty estimates and analyze how they evolve when we go from the open ocean to the coast. At regional scale, complementary analyses are performed through comparisons with independent tide gauge observations. The results show that tidal corrections and uncertainties in the mean sea surface can be significant contributors to uncertainties in sea level estimates in many coastal regions. However, improving the quality and robustness of the retracking algorithm used to derive both the range and the sea state bias correction is today the main factor to bring accurate altimetry sea level data closer to the shore than ever before.

Magmatic evolution of the Kikai caldera revealed by zircon triple dating and its chemistry

Reconstructing the volcanic history of the Kikai caldera, a large active volcano that produced a ~ 160 km3 eruption at 7.3 ka off the southern coast of Kyushu Island (southwest Japan), is crucial to assess potential future volcanic hazards at both regional and global scales. However, revealing its volcanic history before the 7.3 ka eruption has been challenging due to the caldera being mostly submerged. In this study, we present evidence that the Kikai caldera erupted a geochemically distinct silicic lava at ~ 250 ka by using zircon triple (U-Pb, Th-Pb, U-Th) dating and its chemistry. The presence of 1.5–1.0 Ma zircons in the 7.3 ka eruption deposits suggests that zircon crystallization in the Kikai caldera began during this period. We further infer large eruptions occurred around 0.7–0.6 Ma, suggesting that the Kikai caldera may have experienced at least 5 major eruptions during its 1.0–1.5-million-year magmatic evolution.

A combined model of shoot phosphorus uptake based on sparse data and active learning algorithm

The soil ecosystem has been severely damaged because of the increasingly severe environmental problems caused by excessive application of phosphorus (P) fertilizer, which seriously hinders soil fertility restoration and sustainable farmland development. Shoot P uptake (SPU) is an important parameter for monitoring crop growth and health and for improving field nutrition management and fertilization strategies. Achieving on-site measurement of large-scale data is difficult, and effective nondestructive prediction methods are lacking. Improving spatiotemporal SPU estimation at the regional scale still poses challenges. In this study, we proposed a combination prediction model based on some representative samples. Furthermore, using the experimental area of Henan Province, as an example, we explored the potential of the hyperspectral prediction of maize SPU at the canopy scale. The combination model comprises predicted P uptake by maize leaves, stems, and grains. Results show that (1) the prediction accuracy of the combined prediction model has been greatly improved compared with simple empirical prediction models, with accuracy test results of R2 = 0.87, root mean square error = 2.39 kg/ha, and relative percentage difference = 2.71. (2) In performance tests with different sample sizes, two-dimensional correlation spectroscopy i.e., first-order differentially enhanced two-dimensional correlation spectroscopy (1Der-2DCOS) and two-trace 2DCOS of enhanced filling and milk stages (filling-milk-2T2DCOS)) can effectively and robustly extract spectral trait relationships, with good robustness, and can achieve efficient prediction based on small samples. (3) The hybrid model constrained by the Newton-Raphson-based optimizer’s active learning method can effectively filter localized simulation data and achieve localization of simulation data in different regions when solving practical problems, improving the hybrid model’s prediction accuracy. The practice has shown that with a small number of representative samples, this method can fully utilize remote sensing technology to predict SPU, providing an evaluation tool for the sustainable use of agricultural P. Therefore, this method has good application prospects and is expected to become an important means of monitoring global soil P surplus, promoting sustainable agricultural development.

Climate-Driven Escalation of Global PM2.5 Health Burden from Wildland Fires.

Wildland fires constitute a major source of ambient fine particulate matter (PM2.5), significantly impacting air quality and public health. As the climate becomes warmer and drier, fire frequency is projected to rise, yet how the associated health impacts of fire-sourced PM2.5 (FPM2.5) respond to climate change remains vague. In this study, we modeled the global concentration and associated premature deaths of FPM2.5 over the past two decades. Our results reveal an upward trend in FPM2.5 concentrations globally, contributing to an increase in premature deaths from 156,000 to 241,000 between 2000 and 2021, alongside population growth and aging. Substantial variations are observed on a regional scale, but most regions experience increasing exposure levels, suggesting an increased impact of FPM2.5. Further regression analysis indicates that climate change plays a critical role in these trends, accounting for 56% of the net changes in FPM2.5-related premature deaths. Warming and drought emerge as key drivers of climate-induced health risks, with slightly different contributions to the FPM2.5 concentration and the associated premature deaths. Our findings underscore the urgent need to integrate climate adaptation strategies with fire management to mitigate the growing health burden caused by FPM2.5 worldwide.

Exposure of insects to current use pesticide residues in soil and vegetation along spatial and temporal distribution in agricultural sites

Current use pesticides (CUPs) are recognised as the largest deliberate input of bioactive substances into terrestrial ecosystems and one of the main factors responsible for the current decline in insects in agricultural areas. To quantify seasonal insect exposure in the landscape at a regional scale (Rhineland-Palatine in Germany), we analysed the presence of multiple (93) active ingredients in CUPs across three different agricultural cultivation types (with each three fields: arable, vegetable, viticulture) and neighbouring meadows. We collected monthly soil and vegetation samples over a year. A total of 71 CUP residues in different mixtures was detected, with up to 28 CUPs in soil and 25 in vegetation in single samples. The concentrations and numbers of CUPs in vegetation fluctuated over the sampling period, peaking in the summer months in the vegetation but remaining almost constant in topsoil. We calculated in-field additive risks for earthworms, collembola, and soil-living wild bees using the measured soil concentrations of CUPs. Our results call for the need to assess CUP mixture risks at low concentrations, as multiple residues are chronically present in agricultural areas. Since this risk is not addressed in regulation, we emphasise the urgent need to implement global pesticide reduction targets.

Enhancing prediction and inference of daily in-stream nutrient and sediment concentrations using an extreme gradient boosting based water quality estimation tool - XGBest.

Estimating constituent loads in streams and rivers is a crucial but challenging task due to low-frequency sampling in most watersheds. While predictive modeling can augment sparsely sampled water quality data, it can be challenging due to the complex and multifaceted interactions between several sub-watershed eco-hydrological processes. Traditional water quality prediction models, typically calibrated for individual sites, struggle to fully capture these interactions. This study introduces XGBest, a machine learning-based tool, that integrates hydrological data, land cover, and physical watershed attributes at a regional scale to predict daily concentrations of Total Nitrogen (TN), Total Phosphorus (TP), and Total Suspended Solids (TSS). XGBest leverages 29 environmental variables, including daily and antecedent discharge, temporal features, and landscape characteristics, to comprehensively evaluate water quality dynamics across a large hydrologic region. To explore the robustness of the developed tool, XGBest was validated using observed water quality data in three different hydrologic regions in the eastern United States, encompassing 499 water quality monitoring sites characterized by diverse hydro-climatic conditions and watershed attributes. This study also employed the legacy United States Geological Survey (USGS) tools - LOADEST and WRTDS as benchmarks to evaluate the performance of XGBest in these regions. The results demonstrated that XGBest outperformed LOADEST and WRTDS in predictive accuracy and revealed critical insights into the spatial and temporal variability of nutrient and sediment loads. In addition, SHapley Additive exPlanations (SHAP) values highlighted the importance of integrating static and dynamic watershed attributes, such as land cover, antecedent discharge, and seasonality, in capturing the complex concentration-discharge (C-Q) relationships. This study positions XGBest as a robust and scalable water quality prediction tool that bridges the gap between hydrology and broader environmental management. By combining multiple environmental factors into a unified predictive framework, XGBest enhances our understanding of water quality and supports more effective environmental monitoring and management strategies.

A time-varying index for agricultural suitability across Europe from 1500–2000

Throughout the last centuries, European climate changed substantially, which affected the potential to plant and grow crops. These changes happened not just over time but also had a spatial dimension. Yet, despite large climatic fluctuations, quantitative historical studies typically rely on static measures for agricultural suitability due to the non-availability of time-varying indices. Relying on recent advances in paleoclimatology, we bridge this gap by constructing a spatio-temporal measure for agricultural suitability across Europe for a period of 500 years. Our gridded index has a 0.5° resolution and is available at a yearly level. It relies on a simple surface energy and water balance model, focusing only on so-called exogenous geographic and climatic features. Our index captures not just long-term trends, such as the Little Ice Age, but also short-term climatic shocks. It will empower researchers to explore the interplay between climatic fluctuations and Europe’s agricultural landscape, analyze human responses at a local and regional scale, and foster a deeper understanding of the region’s historical dynamics.

Using satellite imagery to assess the glacier retreat in King George Island, Antarctica

In recent decades, remote sensing has become a powerful tool for continuously monitoring glacier dynamics in remote areas, enabling the identification of significant spatiotemporal changes due to its capacity to provide multitemporal information at regional and global scales. In this study, Landsat satellite images (1989–2020) were used to quantify glacier retreat in the ice cap of King George Island (KGI), located in the Antarctic Peninsula, and to evaluate the teleconnections of El Niño – Southern Oscillation - ENSO (ONI and SOI indices) with climaticvariables (temperature and precipitation) in this region. Our findings reveal a 10% loss in glacier coverage over the last 31 years, with a slower glacier retreat observed since 2008. Glaciers with smaller areas and marine terminating were the most affected. Of the 73 glaciers on KGI, 42% had continental terminating, 21% had marine terminating, and 37% had mixed terminating (continental and marine). Of the total glacier area lost, 35% corresponds to glaciers with marine terminating, while 16% corresponds to glaciers with continental terminating. Furthermore, climatic variables exhibited heterogeneous responses during ENSO events, with a significant correlation between mean temperature and ONI at the annual level and during the austral spring, which may be influencing glacier retreat in the study area to some extent.

A New Pabs Model for Quantitatively Diagnosing Phosphorus Nutritional Status in Corn Plants

Accurate diagnosis of plant phosphorus nutritional status is critical for optimizing agricultural practices and enhancing resource efficiency. Existing methods are limited to qualitatively assessing plant phosphorus nutritional status and cannot quantitatively estimate the plant’s phosphorus requirements. Moreover, these methods are time-consuming, making them impractical for large-scale application. In this study, we developed an advanced phosphorus absorption model (Pabs) that integrates the phosphorus nutrition index (PNI) and phosphorus use efficiency (PUE). The PUE, a critical metric for assessing phosphate fertilizer use efficiency, was quantified by comparing yields under fertilized and unfertilized conditions. Utilizing the Agricultural Production Systems Simulator (APSIM) model, we simulated maize (Zea mays L.) phosphorus concentration (P) and aboveground biomass (Bio) under varying phosphorus application rates. The model exhibited robust performance, achieving an R2 above 0.95 and an RMSE below 0.22. Based on the APSIM model simulations, a phosphorus dilution curve (Pc = 3.17 Bio−0.29, R2 = 0.98) was established, reflecting the dilution trends of phosphorus across growth stages. Furthermore, the use of vegetation indices (VIS) to evaluate phosphorus nutritional status also showed promising results, with inversion accuracies exceeding 0.70. To validate the model, field sampling was conducted in maize-growing regions of Changchun. Results demonstrated a correct diagnosis rate of 75%, underscoring the model’s capacity to accurately estimate phosphorus requirements on a regional scale. These findings highlight the Pabs model as a reliable tool for precision phosphorus management, offering significant potential to optimize fertilization strategies and support sustainable agricultural systems.

Network-Based Identification of Key Toxic Compounds in Airborne Chemical Exposome.

Air pollution is a leading contributor to the global disease burden. However, the complex nature of the chemicals to which humans are exposed through inhalation has obscured the identification of the key compounds responsible for diseases. Here, we develop a network topology-based framework to identify key toxic compounds in the airborne chemical exposome. Using cardiovascular diseases (CVDs) as a model disease, we found that toxic network modules of various compounds are closely linked to the modules of CVDs. The proximity of compound target modules to disease modules can indicate the extent of toxicity induced by the compounds. By integrating mass spectrometry-based external exposure concentrations and machine learning-predicted internal exposure concentrations, we established a comprehensive linkage connecting exposure to disease-related risk for the identification of toxic compounds. These findings were subsequently validated using exposure and disease data on the regional scale. This work provides an effective strategy for identifying key compounds within environmental exposomes and establishes a new paradigm for understanding the pathogenicity of air pollution.

Temporal trends of community and climate changes in the anthropocene: 21-year dynamics of four major functional groups in a rocky intertidal habitat along the Pacific coast of Japan

The influence of climate change on marine organism abundance has rarely been assessed (1) at the functional-group level; (2) simultaneously in major functional groups within the same ecosystem; (3) for >10 years; and (4) at metapopulation/community scales. A study simultaneously addressing these gaps would greatly enhance our understanding of the influence of climate change on marine ecosystems. Here, we analyzed 21 years of abundance data at the functional-group and species levels on a regional scale for four major functional groups (benthic algae, sessile animals, herbivorous benthos, and carnivorous benthos) in a rocky intertidal habitat along the northeastern Pacific coast of Japan. We aimed to examine the 21-year trends in regional abundance at both functional-group and species levels, plus their driving mechanisms and their dependence on species properties (thermal niche, calcification status, and vertical niche). Significant temporal trends in abundance were detected at functional-group levels for benthic algae (increasing) and herbivores and carnivores (both decreasing); they followed the temporal population trends of the dominant species. At species level, the metapopulation size of 12 of 31 species were increasing and 4 of those were decreasing, depending on the thermal niche and species calcification status. At both functional-group and species levels, temporal trends in abundance are caused by the direct or indirect influence of warming and ocean acidification. Comparing these results with community responses to marine heat waves in the same study area offered two implications: (1) long-term ecosystem changes associated with global warming will be unpredictable from the community response to marine heat waves, possibly owing to a lack of knowledge of the influence of calcifying status on species’ responses to climate change; and (2) thermal niches contribute greatly to predictions of the influence of warming on population size, regardless of the time scale.

Interdecadal Variation of Springtime Compound Temperature‐Precipitation Extreme Events in China and Its Association With Atlantic Multidecadal Oscillation and Interdecadal Pacific Oscillation

AbstractThe concurrent occurrence of temperature and precipitation extremes, known as compound temperature‐precipitation extreme events (CTPEEs), leads to more pronounced consequences for human society and ecosystems than when these extremes occur separately. However, such compound extremes have not been sufficiently studied, especially during boreal spring. Spring is an important transition season, during which the CTPEEs plays a pivotal role in plant growth and revival of terrestrial ecosystems. This study investigates the spatio‐temporal variation characteristics of spring CTPEEs in China, including warm‐dry, warm‐wet, cold‐dry, and cold‐wet combinations. The compound cold‐wet extreme events occur most frequently, followed by warm‐dry, warm‐wet, and cold‐dry events. The frequency of CTPEEs associated with warm (cold) extremes shows a marked interdecadal increase (decrease) around the mid‐to‐late 1990s. It is found that the interdecadal change in CTPEEs is primarily determined by the variation in temperature extremes. This interdecadal shift coincides with the phase transitions of the Atlantic Multidecadal Oscillation (AMO) and the Interdecadal Pacific Oscillation (IPO). After the mid‐to‐late 1990s, the configuration of a positive AMO and a negative IPO excited atmospheric wave trains over mid‐high latitudes, causing high‐pressure and anticyclonic anomalies over East Asia. This leads to less cloudiness, allowing an increase in downward solar radiation, which enhances surface warming and contributes to an increase (decrease) in warm‐dry and warm‐wet extremes. The above observations are confirmed by the Pacemaker experiments. The results of this study highlight a significant contribution of internal climate variability to interdecadal changes in CTPEEs at the regional scale.