This research addresses urban heat island intensification driven by urbanization using Dianchi Lake in Kunming, China, as a case study, aiming to quantitatively evaluate the spatial extent, intensity, and land cover sensitivity differences in the cooling effects of large urban water bodies across [...] Read more.

This research addresses urban heat island intensification driven by urbanization using Dianchi Lake in Kunming, China, as a case study, aiming to quantitatively evaluate the spatial extent, intensity, and land cover sensitivity differences in the cooling effects of large urban water bodies across dry/wet seasons and complex urban landscapes (forest, cropland, and impervious surfaces) to provide a scientific basis for optimizing thermal environments in low-latitude plateau cities. Based on Landsat 8/9 satellite data from dry (January) and wet (May) seasons in 2020 and 2023 used for land surface temperature (LST) retrieval combined with land use data, buffer zone gradient analysis was adopted to quantify the spatial heterogeneity of key cooling indicators within 0–1500 m lakeshore buffers. The results demonstrated significant seasonal differences. The wet season showed a greater cooling extent (600 m) and higher intensity (6.0–6.6 °C) compared with the dry season (400 m; 2.4–3.9 °C). The land cover responses varied substantially, with cropland having the largest influence (600 m), followed by impervious surfaces (400 m), while forest exhibited a minimal effective cooling range (100 m) but localized warming anomalies at 200–400 m. Sensitivity analysis confirmed that impervious surfaces were the most sensitive to water-cooling, followed by cropland, whereas forest showed the lowest sensitivity.Full article

Urban outdoor thermal comfort (OTC) has become an increasingly critical issue under the pressures of urbanization and climate change. Comparative analyses of urban blocks with distinct spatial morphologies are essential for identifying OTC issues and proposing targeted optimization strategies. However, existing studies predominantly [...] Read more.

Urban outdoor thermal comfort (OTC) has become an increasingly critical issue under the pressures of urbanization and climate change. Comparative analyses of urban blocks with distinct spatial morphologies are essential for identifying OTC issues and proposing targeted optimization strategies. However, existing studies predominantly rely on microclimate numerical simulations, while comparative assessments of OTC from the human thermal perception perspective remain limited. This study employs the thermal walk method, integrating microclimatic measurements with thermal perception questionnaires, to conduct on-site OTC investigations across three urban blocks with contrasting spatial morphologies—a business district (BD), a residential area (RA), and a historical neighborhood (HN)—in Beijing, a hot summer and cold winter climate city. The results reveal substantial OTC differences among the blocks. However, these differences demonstrated great seasonal and temporal variations. In summer, BD exhibited the best OTC (mTSV = 1.21), while HN performed the worst (mTSV = 1.72). In contrast, BD showed the poorest OTC in winter (mTSV = −1.57), significantly lower than HN (−1.11) and RA (−1.05). This discrepancy was caused by the unique morphology of different blocks. The sky view factor emerged as a more influential factor affecting OTC over building coverage ratio and building height, particularly in RA (r = 0.689,p < 0.01), but its impact varied by block, season, and sunlight conditions. North–South streets generally perform better OTC than East–West streets, being 0.26 units cooler in summer and 0.20 units warmer in winter on the TSV scale. The study highlights the importance of incorporating more applicable physical parameters to optimize OTC in complex urban contexts and offering theoretical support for designing climate adaptive urban spaces.Full article

The ionospheric response to the major geomagnetic storm (SYM-H = −518 nT) of 10–11 May 2024 was investigated using total electron content (TEC) observations from the Central Asian (CAR) and East Asian (EAR) regions. In the CAR region, shortly after the storm sudden [...] Read more.

The ionospheric response to the major geomagnetic storm (SYM-H = −518 nT) of 10–11 May 2024 was investigated using total electron content (TEC) observations from the Central Asian (CAR) and East Asian (EAR) regions. In the CAR region, shortly after the storm sudden commencement (SC) (17:05 UT on 10 May), during a rapid decrease in SYM-H, a significant TEC decrease (~70%) and a subsequent formation of a prolonged TEC depletion phase on 11 May were observed. The duration of the phase’s maximum intensity seemed to agree with the duration of southward interplanetary magnetic field (IMF) Bz activity. The total duration of the negative phase exceeded 3 days and correlated with the duration of the Auroral Electrojet (AE) index activity. The ionospheric response in the EAR region differed significantly, exhibiting a secondary, deeper TEC decrease (termed “phase 2”) on 12 May, which occurred during a period of reduced AE and IMF Bz activity. The analysis of latitudinal TEC variations in the EAR region revealed that “phase 2” occurred across a geographic latitude range of 31.4° N to 43.9° N (approximately 21° N to 34° N dipole latitude). These results are discussed in the context of potential longitudinal variations in thermospheric composition and meridional circulation during the geomagnetic storm.Full article

The relentless challenge of air pollution, driven by industrialization and urbanization, demands increasingly sophisticated tools for accurate prediction, source attribution, and comprehensive monitoring [...]Full article

Extreme air temperatures along with the synoptic conditions leading to their appearance are examined for the Mediterranean region for the 85-year period of 1940–2024. The data used are daily (04UTC and 12UTC) grid point (1° × 1°) values of 2 m air temperature, [...] Read more.

Extreme air temperatures along with the synoptic conditions leading to their appearance are examined for the Mediterranean region for the 85-year period of 1940–2024. The data used are daily (04UTC and 12UTC) grid point (1° × 1°) values of 2 m air temperature, 850 hPa air temperature, and 1000 hPa and 500 hPa geopotential heights, obtained from the ERA5 database. For 12UTC and 04UTC, the 2 m air temperature anomalies are calculated and are used for the definition of Extremely High Temperature Days (EHTDs) and Extremely Low Temperature Days (ELTDs), respectively. Overall, 3787 EHTDs and 4872 ELTDs are defined. It is found that EHTDs are evidently more frequent in recent years (increased by 305% since the 1980s) whereas ELTDs are less frequent (decreased by 41% since the 1980s), providing a clear sign of warming of the Mediterranean climate. A multivariate statistical analysis combining factor analysis and k-means clustering, known as spectral clustering, is applied to the data resulting in the definition of nine EHTD and seven ELTD clusters. EHTDs are mainly associated with intense solar heating, blocking anticyclones and warm air advection. ELTDs are connected to intense radiative cooling of the Earth’s surface, cold air advection and Arctic outbreaks. This is a unique study for the Mediterranean region utilizing the high-resolution ERA5 data collected since the 1940s to define and investigate the variability of both high and low temperature extremes using a validated methodology.Full article

Accurate estimation of potential evapotranspiration (PET) is of paramount importance for water resource management, especially in Mediterranean mountainous environments that are often data-scarce and highly sensitive to climate variability. This study evaluates the performance of four machine learning (ML) regression algorithms—Support Vector Regression [...] Read more.

Accurate estimation of potential evapotranspiration (PET) is of paramount importance for water resource management, especially in Mediterranean mountainous environments that are often data-scarce and highly sensitive to climate variability. This study evaluates the performance of four machine learning (ML) regression algorithms—Support Vector Regression (SVR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and K-Nearest Neighbors (KNN)—in predicting daily PET using limited meteorological data from a high-altitude in Central Greece. The ML models were trained and tested using easily available meteorological inputs—temperature, relative humidity, and extraterrestrial solar radiation—on a dataset covering 11 years (2012–2023). Among the tested configurations, RFR showed the best performance (R² = 0.917, RMSE = 0.468 mm/d, MAPE = 0.119 mm/d) when all the above-mentioned input variables were included, closely approximating FAO56–PM outputs. Results bring to light the potential of machine learning models to reliably estimate PET in data-scarce conditions, with RFR outperforming others, whereas the inclusion of the easily estimated extraterrestrial radiation parameter in the ML models training enhances PET prediction accuracy.Full article

Supplementary material:
Supplementary File 1 (ZIP, 339 KiB)

Source inversion optimization using sensor observations is a key method for rapidly and accurately identifying unknown source parameters (source strength and location) in abrupt hazardous gas leaks. Sensor number and location distribution both play important roles in source inversion; however, their combined impacts [...] Read more.

Source inversion optimization using sensor observations is a key method for rapidly and accurately identifying unknown source parameters (source strength and location) in abrupt hazardous gas leaks. Sensor number and location distribution both play important roles in source inversion; however, their combined impacts on source inversion optimization remain poorly understood. In our study, the optimization inversion method is established based on the Gaussian plume model and the generation algorithm. A research strategy combining random sampling and coefficient of variation methods was proposed to simultaneously quantify their combined impacts in the case of a single emission source. The sensor layout impact difference was analyzed under varying atmospheric conditions (unstable, neutral, and stable) and source location information (known or unknown) using the Prairie Grass experiments. The results indicated that adding sensors improved the source strength estimation accuracy more when the source location was known than when it was unknown. The impacts of sensor location distribution were strongly negatively correlated (r ≤ −0.985) with the number of sensors across scenarios. For source strength estimation, the impacts of the sensor location distribution difference decreased non-linearly with more sensors for known locations but linearly for unknown ones. The impacts of sensor number and location distribution on source strength estimation were amplified under stable atmospheric conditions compared to unstable and neutral conditions. The minimum number of randomly scattered sensors required for stable source strength inversion accuracy was 11, 12, and 17 for known locations under unstable, neutral, and stable atmospheric conditions, respectively, and 24, 9, and 21 for unknown locations. The multi-layer arc distribution outperformed rectangular, single-layer arc, and downwind-axis distributions in source strength estimation. This study enhances the understanding of factors influencing source inversion optimization and provides valuable insights for optimizing sensor layouts.Full article

The Brazilian savanna, South America’s second-largest biome, is vital to Brazil’s economy but has suffered from environmental degradation due to unregulated agricultural and urban expansion. This study assesses climate change in the biome from 1961 to 2021 using the Köppen climate classification, drought [...] Read more.

The Brazilian savanna, South America’s second-largest biome, is vital to Brazil’s economy but has suffered from environmental degradation due to unregulated agricultural and urban expansion. This study assesses climate change in the biome from 1961 to 2021 using the Köppen climate classification, drought indices, historical trend analyses, and the climatological water balance. Fourteen municipalities across the biome were analyzed. According to the Köppen classification, most municipalities were identified as Aw (tropical with dry winters) and Am (tropical monsoon), with Dourados, MS, and Sapezal, MT, alternating between Am and Aw. The standardized precipitation index (SPI) revealed changes in rainfall distribution. The Mann–Kendall test detected rising air temperatures in 13 of the 14 municipalities, with Sen’s slope ranging from 0.0156 to 0.0605 °C per year. Rainfall decreased in seven municipalities, with decreases from −4.54 to −12.77 mm per year. The climatological water balance supported the observed decrease in precipitation. The results indicated a clear warming trend and declining rainfall in most of the Brazilian savanna, highlighting potential challenges for water availability in the face of ongoing climate change.Full article

The 2019 Antarctic sudden stratospheric warming (SSW) is well captured by the specified dynamics Whole Atmosphere Community Climate Model with thermosphere and ionosphere eXtension (SD-WACCM-X). This SSW is dominated by a strong quasi-stationary planetary wave with zonal wavenumber 1 (SPW1) activity, and nonmigrating [...] Read more.

The 2019 Antarctic sudden stratospheric warming (SSW) is well captured by the specified dynamics Whole Atmosphere Community Climate Model with thermosphere and ionosphere eXtension (SD-WACCM-X). This SSW is dominated by a strong quasi-stationary planetary wave with zonal wavenumber 1 (SPW1) activity, and nonmigrating tides show great variations. The nonlinear interactions between SPW1 and diurnal, semidiurnal and terdiurnal migrating tides triggered by this SSW also have significant impacts on the variabilities of corresponding nonmigrating tides. This is clearly proven by the fact that the variations of the secondary nonmigrating tides, generated by the nonlinear interaction, show higher correlation during this SSW than those during the non-SSW period. Meanwhile, the SPW1 dominates the nonlinear interactions with diurnal, semidiurnal and terdiurnal migrating tides, and the corresponding secondary nonmigrating tides show concurrent increases with SPW1. In the ionosphere, the nonmigrating tidal oscillations exhibit consistent temporal variabilities with those shown in the neutral atmosphere, which demonstrates the neutral–ion coupling through nonmigrating tides and that nonmigrating tides are significant sources for the short-term ionospheric variability during this SSW event. Specifically, the enhancement of the ionospheric longitudinal wavenumber 4 structure coincides with the increase of the eastward-propagating diurnal tide with zonal wavenumber 3 (DE3), semidiurnal tide with zonal wavenumber 2 (SE2) and terdiurnal tide with zonal wavenumber 1 (TE1). Also, DE3 dominates the influence of nonmigrating tides on the ionospheric longitudinal wavenumber 4 structure during this SSW.Full article

Cities house more than half of the world’s population and are responsible for more than 70% of the world anthropogenic CO${}_2$ emissions. Therefore, quantifications of emissions from major cities, which are only less than a hundred intense emitting spots across the globe, [...] Read more.

Cities house more than half of the world’s population and are responsible for more than 70% of the world anthropogenic CO${}_2$ emissions. Therefore, quantifications of emissions from major cities, which are only less than a hundred intense emitting spots across the globe, should allow us to monitor changes in global fossil fuel CO${}_2$ emissions in an independent, objective way. The study adopted a high-spatiotemporal-resolution regional assimilation method using satellite observation data and atmospheric transport model WRF-Chem/DART to assimilate CO${}_2$ concentration and fluxes in Lisbon, a major city in Portugal. It is based on Zhang’s assimilation method, combined OCO-2 XCO${}_2$ retrieval data, ODIAC 1 km anthropogenic CO${}_2$ emissions and Ensemble Adjustment Kalman Filter Assimilation. By employing three two-way nested domains in WRF-Chem, we refined the spatial resolution of the CO${}_2$ concentrations and fluxes over Lisbon to 3 km. The spatiotemporal distribution characteristics and main driving factors of CO${}_2$ concentrations and fluxes in Lisbon and its surrounding cities and countries were analyzed in March 2020, during the period affected by COVID-19 pandemic. The results showed that the monthly average CO${}_2$ and XCO${}_2$ concentrations in Lisbon were 420.66 ppm and 413.88 ppm, respectively, and the total flux was 0.50 Tg CO${}_2$. From a wider perspective, the findings provide a scientific foundation for urban carbon emission management and policy-making.Full article

Accurate drought monitoring depends on selecting an appropriate cumulative distribution function (CDF) to model the original data, resulting in the standardized drought indices. In the numerous research studies, while rigorous validation was not made by scrutinizing the model assumptions and uncertainties in identifying [...] Read more.

Accurate drought monitoring depends on selecting an appropriate cumulative distribution function (CDF) to model the original data, resulting in the standardized drought indices. In the numerous research studies, while rigorous validation was not made by scrutinizing the model assumptions and uncertainties in identifying theoretical drought CDF models, such oversights lead to biased representations of drought evaluation and characteristics. This research compares the parametric theoretical and empirical CDFs for a comprehensive evaluation of standardized Drought Indices. Additionally, it examines the advantages, disadvantages, and limitations of both empirical and theoretical distribution functions in drought assessment. Three drought indices, Standardized Precipitation Index (SPI), Streamflow Drought Index (SDI), and Standardized Precipitation Evapotranspiration Index (SPEI), cover meteorological and hydrological droughts. The assessment spans diverse applications, covering different climates and regions: Durham, United Kingdom (SPEI, 1868–2021); Konya, Türkiye (SPI, 1964–2022); and Lüleburgaz, Türkiye (SDI, 1957–2015). The findings reveal that theoretical and empirical CDFs demonstrated notable discrepancies, particularly in long-term hydrological drought assessments, where underestimations reached up to 50%, posing risks of misinformed conclusions that may impact critical drought-related decisions and policymaking. Root Mean Squared Error (RMSE) for SPI3 between empirical and best-fitted CDF was 0.087, and between empirical and Gamma it was 0.152. For SDI, it ranged between 0.09 and 0.143. The Mean Absolute Error (MAE) for SPEI was approximately 0.05 for all timescales. Additionally, it concludes that empirical CDFs provide more reliable and conservative drought assessments and are free from the constraints of model assumptions. Both approaches gave approximately the same drought duration with different intensities regarding drought characteristics. Due to the complex process of drought events and different definitions of drought events, each drought event must be studied separately, considering its effects on different sectors.Full article

Boao Jade Beach, on the east coast of Hainan Island, is a typical sandy beach and is one of the areas where typhoons frequently land in Hainan. This study examined wind speed, wind direction, and sediment transport data obtained from field meteorological stations [...] Read more.

Boao Jade Beach, on the east coast of Hainan Island, is a typical sandy beach and is one of the areas where typhoons frequently land in Hainan. This study examined wind speed, wind direction, and sediment transport data obtained from field meteorological stations and omnidirectional sand accumulation instruments from 2020 to 2024 to study the coastal aeolian environment and sediment transport distribution characteristics in the region. The findings provide a theoretical basis for comprehensive analyses of the evolution of coastal aeolian landforms and the evaluation and control of coastal aeolian hazards. The research results showed the following: (1) The annual average threshold wind velocity for sand movement in the study area was 6.13 m/s, and the wind speed frequency was 20.97%, mainly dominated by easterly winds (NNE, NE) and southerly winds (S). (2) The annual drift potential (DP) and resultant drift potential (RDP) of Boao Jade Belt Beach from 2020 to 2024 were 125.99 VU and 29.59 VU, respectively, indicating a low-energy wind environment. The yearly index of directional wind variability (RDP/DP) was 0.23, which is classified as a small ratio and indicates blunt bimodal wind conditions. The yearly resultant drift direction (RDD) was 329.41°, corresponding to the NNW direction, indicating that the sand on Boao Jade Belt Beach is generally transported in the southwest direction. (3) When the measured data from the sand accumulation instrument in the study area from 2020 to 2024 were used for a statistical analysis, the results showed that the total sediment transport rate in the study area was 39.97 kg/m·a, with the maximum sediment transport rate in the S direction being 17.74 kg/m·a. These results suggest that, when sand fixation systems are constructed for relevant infrastructure in the region, the direction of protective forests and other engineering measures should be perpendicular to the net direction of sand transport.Full article

Meteorological observations are essential for climate modelling, prediction, early warning systems, decision-making processes, and disaster management. These observations are critical to societal development and the safeguarding of human activities and livelihoods. Spatial interpolation techniques play a pivotal role in addressing gaps between observation [...] Read more.

Meteorological observations are essential for climate modelling, prediction, early warning systems, decision-making processes, and disaster management. These observations are critical to societal development and the safeguarding of human activities and livelihoods. Spatial interpolation techniques play a pivotal role in addressing gaps between observation sites, enabling the generation of continuous meteorological datasets. However, due to the inherent complexity of atmosphere–surface interactions, no single interpolation technique has proven universally effective in achieving consistently accurate results for meteorological variables. This study proposes a novel interpolation model based on Fuzzy Adaptive Optimal Fusion (FAOF). The FAOF model integrates fuzzy theory by constructing station-specific fuzzy sets and sub-method element pools, employing a nonlinear membership function with error as the independent variable. An iterative accuracy index is used to identify the optimal parameter combination, facilitating adaptive data fusion and interpolation optimisation. The model’s performance is evaluated against 10 individual methods from the method pool. Experimental results demonstrate that FAOF effectively combines the strengths of multiple methods, achieving significantly enhanced interpolation accuracy. Additionally, the model consistently performs well across diverse regions and meteorological variables, underscoring its robustness and strong generalisation capability.Full article

The evaluation of climate change effects on airport infrastructures is important to maintain safety and flexibility in air travel operations. Airports are particularly vulnerable to extreme weather events and temperature fluctuations, which can disrupt operations, compromise passenger safety, and cause economic losses. Issues [...] Read more.

The evaluation of climate change effects on airport infrastructures is important to maintain safety and flexibility in air travel operations. Airports are particularly vulnerable to extreme weather events and temperature fluctuations, which can disrupt operations, compromise passenger safety, and cause economic losses. Issues such as flooded runways and the disruption of power supplies highlight the need for strong adaptation strategies. The study focuses on the application of the high-resolution regional model COSMO-CLM to assess climate change impacts on Roma Fiumicino airport (Italy) under the IPCC RCP8.5 scenario. The complex topography of Italy requires fine-scale simulation to catch localized climate dynamics. By employing advanced statistical methods, such as fractal analysis, this research aims to increase an understanding of climate change and improve the model prediction capability. The findings provide valuable insights for designing resilient airport infrastructures and updating operational protocols in view of evolving climate risks. A consistent increase in daily temperatures is projected, along with a modest positive trend in annual precipitation. The use of advanced statistical methods revealed insights into the fractal dimensions and frequency components of climate variables, showing an increasing complexity and variability of future climatic patterns.Full article

Low-cost air temperature sensors are an emerging theme in environmental monitoring. These sensors offer the advantage of making microclimate monitoring feasible due to their affordability. However, they are limited by the quality of the data they provide; in many cases, they have been [...] Read more.

Low-cost air temperature sensors are an emerging theme in environmental monitoring. These sensors offer the advantage of making microclimate monitoring feasible due to their affordability. However, they are limited by the quality of the data they provide; in many cases, they have been reported to have presented errors in the sensor readings. These errors have been shown to improve after calibration was applied. The lack of a comprehensive understanding of the available calibration techniques, models, and sensor types has led to studies presenting heterogeneity in models and techniques alongside different performance metrics. To address this gap, this study conducted a systematic review following the PRISMA guidelines, reviewing studies from 2015 to 2024 across the databases Web of Science and Scopus, alongside the search engine Google Scholar. The aim was to identify the calibration techniques and models, the commercially available low-cost air temperature sensors used, the performance metrics utilised, and the calibration settings. The findings presented three main categories of calibration models utilised in the collected studies: linear, polynomial, and machine learning. Twenty-two commercially available low-cost sensors were identified, with the DHT22 sensor being the most utilised. Indoor settings were identified as the most preferred for conducting calibrations. Key challenges included limitations in reported results for calibration by the studies, the use of different performance metrics across studies, insufficient studies conducting calibration, and the diversity in sensor types utilised.Full article

Supplementary material:
Supplementary File 1 (ZIP, 1180 KiB)

Air pollution is a known contributor to cancer risk, although its specific impact on endometrial cancer (EC) remains unclear. This study integrates network toxicology, transcriptomics, molecular docking, and machine learning to investigate pollutant–gene interactions in EC. We identify 83 air pollution-associated EC genes [...] Read more.

Air pollution is a known contributor to cancer risk, although its specific impact on endometrial cancer (EC) remains unclear. This study integrates network toxicology, transcriptomics, molecular docking, and machine learning to investigate pollutant–gene interactions in EC. We identify 83 air pollution-associated EC genes (APECGs), with TNF, ESR1, IL1B, NFKB1, and PTGS2 as the hub genes. A 13-gene RSF-SuperPC model, including CCNE1, SLC2A1, AHCY, and CDC25C, shows effective prognostic stratification. Molecular docking reveals strong binding between pollutants (e.g., benzene, toluene, and ethylbenzene) and key APECGs. The enrichment and SHAP analyses suggest that pollutant-driven EC progression involves DNA damage, metabolic reprogramming, epigenetic dysregulation, immune suppression, and inflammation. These findings reveal potential mechanisms linking air pollution to EC and support the development of biomarkers for high-exposure populations. Further experimental and epidemiological validation is needed to enable clinical translation.Full article

Supplementary material:
Supplementary File 1 (ZIP, 1499 KiB)

The impact of black carbon (BC) emissions on climate change, human health, and the environment is well-documented in the scientific literature. Although BC still remains largely unregulated at the international level, efforts have been made to reduce emissions of BC and Particulate Matter [...] Read more.

The impact of black carbon (BC) emissions on climate change, human health, and the environment is well-documented in the scientific literature. Although BC still remains largely unregulated at the international level, efforts have been made to reduce emissions of BC and Particulate Matter (PM_2.5), particularly in sectors such as energy production, industry, and road transport. In contrast, the maritime shipping industry has made limited progress in reducing BC emissions from ships, mainly due to the absence of stringent BC emission regulations. While the International Maritime Organization (IMO) has established emission limits for pollutants such as SO_x, NO_x, and VOCs under MARPOL Annex VI, as of today, BC emissions from ships are still unregulated at the international level. Whereas it was anticipated that PM_2.5 and BC emissions would be reduced with the adoption of the SO_x regulations, especially within the sulfur emission control areas (SECA), this study reveals that BC emissions are only partially affected by the current MARPOL Annex VI regulations. Based on 886 real-world black carbon (BC) emission measurements from ships operating in the southern North Sea, the study demonstrates that SECA-compliant fuels do contribute to a notable decrease in BC emissions. However, it is important to note that the average BC emission factors (EFs) within the SECA remain comparable in magnitude to those reported for non-compliant fuels in earlier studies. Moreover, ships using exhaust gas cleaning systems (EGCSs) as a SECA-compliant measure were found to emit significantly higher levels of BC, raising concerns about the environmental sustainability of EGCSs as an emissions mitigation strategy.Full article

Climate change is one of the most important current threats to global health. Outdoor workers are among the most vulnerable people to its effects. The aim of this systematic review is to assess the occupational risks related to climate change, investigating health outcomes [...] Read more.

Climate change is one of the most important current threats to global health. Outdoor workers are among the most vulnerable people to its effects. The aim of this systematic review is to assess the occupational risks related to climate change, investigating health outcomes in outdoor workers and estimating its impact in the occupational context. The review was performed following PRISMA guidelines, screening three databases (PubMed, Web of Science, and Scopus). Studies written in English or Italian languages, performed on outdoor workers, assessing occupational risks linked to climate change, and reporting on health outcomes were included. A quality assessment was performed using the Newcastle–Ottawa Scale. Thirteen studies were included in the review, performed mostly on construction (seven studies, 53.8%) and agricultural (five studies, 38.5%) workers. Twelve of the included studies (92.3%) reported on occupational risks related to heat stress, one on the effects of cold weather. Four studies (30.8%) reported a high prevalence of heat-related symptoms, ranging from 64.0% to 90.3% of workers. This systematic review highlights heat-related stress in outdoor workers as an important occupational risk, but it also underlines an important gap in scientific knowledge regarding other occupational risks relating to climate change.Full article

Meiyu is a critical component of the summer rainy season over the Yangtze–Huaihe River Basin (YHRB) in China, and the Meiyu onset date (MOD), serving as a key indicator of Meiyu, has garnered substantial attention. This article demonstrates an in-phase relationship between MOD [...] Read more.

Meiyu is a critical component of the summer rainy season over the Yangtze–Huaihe River Basin (YHRB) in China, and the Meiyu onset date (MOD), serving as a key indicator of Meiyu, has garnered substantial attention. This article demonstrates an in-phase relationship between MOD and the preceding spring Barents–Kara Seas ice concentration (BKSIC) during 1979–2023. Specifically, the loss of spring BKSIC promotes an earlier MOD. Further analysis indicates that decreased spring BKSIC reduces the reflection of shortwave radiation, thereby enhancing oceanic solar radiation absorption and warming sea surface temperature (SST) in spring. The warming SST persists into summer and induces significant deep warming in the BKS through enhanced upward longwave radiation. The BKS deep warming triggers a wave train propagating southeastward to the East Asia–Northwest Pacific region, leading to a strengthened East Asian Subtropical Jet and an intensified Western North Pacific Subtropical High in summer. Under these conditions, the transport of warm and humid airflows into the YHRB is enhanced, promoting convective instability through increased low-level warming and humidity, combined with enhanced wind shear, which jointly contribute to an earlier MOD. These results may advance the understanding of MOD variability and provide valuable information for disaster prevention and mitigation.Full article