The evolution of drought events is a dynamic process with spatio-temporal continuity. Analyzing the migration characteristics of drought events from a three-dimensional perspective is significant to objectively identify the nature of droughts and improve our monitoring and projection capabilities. Existing three-dimensional drought identification methods focus on utilizing a simple overlapping logic to characterize the spatial and spatio-temporal continuity of drought patches, which ignores the dynamic of drought evolution. Here, we propose a three-dimensional drought identification method that considers both spatial autocorrelation and anisotropy. Subsequently, the spatio-temporal characteristics and migration patterns of meteorological drought events in China were identified during a historical period (1961-2010) and a future period (2031-2080, SSP2-4.5 and SSP5-8.5) using the ERA5 dataset and the multi-model ensemble mean (MEM) of CMIP6. The results suggest that, in the future, the number of meteorological drought events will decrease by over 70% in comparison with the historical period, but the severity, duration, affected area, and distance will increase significantly. The majority of future meteorological drought events will occur during spring and summer, specifically 96.3% under the SSP2-4.5 scenario and 95.0% under the SSP5-8.5 scenario. Future meteorological drought events will migrate predominantly to the north-east, with 33% (SSP2-4.5) and 38% (SSP5-8.5) being shown to do so. The hotspot area of the migration trajectory of meteorological drought events will shift from the upper Yangtze River Basin to the upper Yellow River Basin. These findings will help us implement drought prevention strategies and allocate water resources.

PM_2.5, PM₁₀, sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), and ozone (O₃) are six fundamental pollutants in atmospheric pollution, posing significant threats to human health and the ecological environment. Given the high spatiotemporal heterogeneity of atmospheric pollutants, there is a lack of in-depth exploration into the fine spatial variations and interactions among multiple atmospheric pollutants, at ultra-high spatiotemporal resolution. In addition, most of the current studies are single-pollutant predictions, which are deficient in time and resource consumption compared with multi-pollutant synergistic predictions. To address these issues, we integrated ground-measured pollutant data, top-of-atmosphere (TOA) radiation remote sensing data, Baidu Street View data, reanalysis data, and other relevant spatiotemporal data. Using a multi-output extremely randomized trees model, we collaboratively predicted six atmospheric pollutants, generating an ultra-high-spatiotemporal-resolution (temporal resolution: hourly; spatial resolution: 100 meters) dataset of atmospheric pollutants in Wuhan, where air quality still suffers from concentration exceedances in the year 2023. The ten-fold cross-validation R² for PM_2.5, PM₁₀, O₃, NO₂, CO, and SO₂ models were 0.71-0.95, respectively. Synergistic prediction models consume only one-fifth the time of single prediction models. The spatiotemporal analysis revealed that among them, the annual average values of PM_2.5 and PM₁₀ exceeded the first-level concentration limits in China. In addition, the annual average values of pollutants have obvious spatial and temporal heterogeneity, showing a distinct spatial pattern of higher concentrations in urban centers and decreasing outwards. Correlation analyses on annual and hourly scales showed some correlation between atmospheric pollutants. In addition, the correlation between them was found to be dynamic over time at the hourly scale. These findings provide a comprehensive, high-resolution dataset of atmospheric pollutants in Wuhan, offering valuable insights into their spatiotemporal distribution and interactions.

China has implemented a series of air pollution policies and measures to address its severe air pollution problem, particularly in relation to particulate aerosols. However, the temporal effectiveness of these policies remains unclear. This study investigates the impact of policy changes on aerosol pollution across China's seven major geographical regions from 2000 to 2022. The period is divided into four stages: 2000–2007, 2008–2014, 2015–2017, and 2018–2022. Using CALIPSO, MERRA2, and AERONET data, the study analyzes changes in the aerosol optical depth (AOD), an important indicator of aerosol loading, and the vertical distribution of aerosol types in the seven regions, as well as the long-range transport of aerosols at five sites during each stage. The results show that China's annual average AOD increased from 2000 to 2007, a period that was characterized by minimal air pollution restrictions and a focus on economic development. From 2008 to 2013, the increasing trend of the AOD was curbed due to the implementation of air pollution policies, resulting in relatively stable changes. From 2014 to 2017, the AOD exhibited a significant downward trend under the influence of the "Air Pollution Prevention and Control Action Plan." From 2018 to 2022, the AOD remained relatively stable. These results suggest that more stringent air pollution policies are needed to continue improving particulate aerosol pollution.

PM2.5 pollution is a global environmental problem, and its hourly exposure characteristics are closely related to short-term health risks. Traditional estimation methods are mainly based on satellite AOD, which are limited by AOD’s daily timescale and cloud/snow interference, resulting in difficulties in meeting the short-term prediction needs of PM2.5 pollution and in achieving high spatial resolution. This study proposes a spatiotemporal downscaling framework based on the Light-GBM (ST-Light-GBM) algorithm that integrates multi-source data. It innovatively integrated the daily 1 km PM2.5 data derived from AOD and other auxiliary predictors as a primary predictor for the hourly modeling instead of using the satellite AOD directly. Based on this, coupling with meteorological high-temporal-resolution data, this study successfully constructed a 1 km, hourly PM2.5 concentration prediction model. Testing on China in 2019, cross-validation results showed that the model was significantly superior to traditional methods in three dimensions (the random 10-fold cross-validation (10CV) R² reached 0.94, the spatial 10CV R² was 0.85, and the temporal 10CV R² was 0.92). The modeling process results indicated that incorporating the daily average variation in PM2.5 is important in capturing the hourly fluctuation characteristics, with a 0.84 correlation coefficient with hourly measurements and ranking top in variable importance analysis. The framework developed in this study realizes the importance of daily pm2.5 in the dynamic downscaling modeling of hourly concentration, providing a theoretical paradigm for building a "daily constraint-hourly response" PM2.5 prediction model, and produces gap-free pm2.5 data with both high spatial and temporal resolution for supporting refined pollution prevention and control and health risk assessment.

Accurately monitoring the spatiotemporal distribution of atmospheric CO₂ is essential for understanding the carbon cycle, formulating effective emission reduction strategies, and achieving carbon neutrality. However, research in this area is constrained by a lack of high-quality carbon monitoring data. While satellite remote sensing technologies can provide atmospheric CO₂ data with a high spatial resolution and broad coverage, inherent limitations often result in substantial data gaps. Addressing these gaps and generating high-resolution, gap-free CO₂ concentration datasets have thus become a critical research focus. This study utilizes column-averaged dry air CO₂ mole fraction (XCO₂) data retrieved from the OCO-2 satellite (2021–2022) as its observational input. It integrates XCO₂ data from the coarse-resolution CarbonTracker (CT) reanalysis ( 3° × 2°) and multiple environmental variables as the predictive inputs to develop and optimize an extreme random tree (ET) model. The goal is to generate a daily, high-resolution (0.01°) XCO₂ dataset with full spatial coverage across China. Spatiotemporal cross-validation demonstrates the model's high accuracy and stability, yielding an R² of 0.93 and an RMSE of 0.75 ppm. Independent validation using data from the TCCON and WDCGG sites further confirms the model’s effectiveness in capturing atmospheric CO₂ dynamics. This approach not only bridges critical gaps in the existing observational networks but could also enhance carbon cycle analyses and related research. Additionally, it can be extended to longer time series and broader regions, providing robust scientific support for policymakers in climate decision-making.

The field of climate modeling is undergoing a significant transformation, moving away from the traditional General Circulation Models (GCMs) and toward the use of sophisticated artificial intelligence (AI)-based prediction systems. Research has shown that artificial intelligence (AI) has the potential to improve climate modeling's regional accuracy and computing efficiency. Nevertheless, these investigations have frequently functioned in discrete settings and oversimplified situations without a thorough connection with basic physical concepts. This drawback emphasizes the necessity of a more comprehensive strategy that can handle the intricacies of climatic variability and guarantee reliable model validation. In order to assess the possibilities and challenges of hybrid models in comparison to conventional GCMs, this study synthesizes proven climate models, AI methodologies, and their accuracy in climate predictions and analyzes existing climate models to evaluate the potential and limitations of hybrid models compared to traditional GCMs. Integrated AI-driven models show notable improvements in predicting regional variations in climate and accelerating simulation processes, especially when dealing with the growing presence of extreme weather occurrences. However, it is important to have consistent datasets and open evaluation procedures in order to guarantee accuracy and deal with the difficulties that come with model benchmarking. This research highlights how crucial it is to maintain interdisciplinary cooperation in order to properly utilize what artificial intelligence (AI) has to offer in climate modeling. This partnership is essential to creating more accurate and useful climate projections, which will eventually guide successful mitigation and adaptation plans for a changing global environment. In order to have a greater understanding of our climate's future, we must keep pushing the limits of the existing modeling tools.

Aerosol particles suspended in the atmosphere originate from a variety of natural and anthropogenic sources, with their optical, physical, and chemical properties often serving as indicators of their origin. However, long-range aerosol transport, ageing processes, and the mixing (external and internal) of various types in the atmosphere render aerosol-type identification a real challenge. Nevertheless, several techniques and classification matrices have been established for the better classification of aerosols into groups representative of their specific or dominant sources.

In this context, this study focuses on the first attempt at aerosol-type classification using in situ measurements from an aethalometer and a nephelometer at a continental background site in NW Greece (Kozani; 40.27 ^oN, 21.76 ^oE, 768 m). The classification matrix was based on the combined analysis of the Absorption Ångström Exponent (AAE) and the Scattering Ångström Exponent (SAE) values over a one-year period (2023).

Using appropriate threshold values, seven key aerosol types were identified and analyzed in terms of their seasonal, monthly and diurnal variations. The Black Carbon (BC)-dominated type was the most frequent, indicative of a regional background atmosphere influenced by fossil fuel combustion. A mixed Brown Carbon (BrC)–BC type was also frequently observed in winter, along with occasional occurrences of a pure BrC type, both of which are associated with biomass burning for residential heating in nearby villages. Another common category was a mix of large aerosols and BC, present throughout the year, while dust was detected episodically, primarily during Saharan dust transport events. Two types of aerosol, characterized by AAE values below 1 for fine (SAE>1) and coarse (SAE<1), were of lower frequency, indicating a possible mixing of carbonaceous aerosols with inorganic species of weaker spectral absorption. We analyzed the spectral absorption and scattering coefficients of each type of aerosol, as well as their single scattering albedo and PM_2.5 levels, which exhibit substantial seasonal variations.

Carbonaceous aerosols constitute a major component of the lower atmosphere in both urban and rural environments, originating from a variety of natural sources and anthropogenic combustion processes. This study examines the seasonal variability of BC and its fractions related to fossil fuel combustion (BC_ff) and biomass burning (BC_bb), along with the spectral absorption characteristics associated with BC and BrC. Aethalometer (AE-33) measurements were performed and analysed at a continental background site in NW mountainous Greece (Kozani, 40.27 ^oN, 21.76 ^oE, 768 m a.m.s.l) throughout the year 2023. The measuring station, located within the University of Western Macedonia campus, is mostly affected by regional background aerosol plumes with different optical and physicochemical characteristics. Major contributing sources include emissions from nearby lignite-fired power plants, the long-range transport of polluted air masses from the Balkan region, and, to a lesser extent, local emissions such as traffic within the university campus—where private vehicle use is restricted—and domestic heating in nearby villages (1–2 km away), particularly during the winter season. Furthermore, secondary aerosol formation plays a role in modifying the local aerosol burden. Hourly BC concentrations ranged from ~0.1 μg m^-3 to ~2.2 μg m^-3, with higher concentrations noted during winter due to enhanced residential biomass burning for heating. The BC_bb is about 50% during winter and much lesser (~25%) during summer, reflecting the absence of combustion processes and dominance of fossil-fuel sources, although the summer BC concentrations are low. Absorption due to BrC is mostly detected during winter, while its summer values are significantly lower. The contribution of BrC to the total absorption recorded is about 44% at 370 nm during winter, dropping to 16% during summer. This seasonal contrast reflects the influence of biomass combustion in the winter and the dominance of secondary organic aerosol formation and naturally occurring sources of BC during the summer.

Physical activity, such as walking, plays a key role in preventing noncommunicable diseases. However, in urban environments, pedestrians are often exposed to elevated air pollution levels, especially fine particulate matter (PM_2.5), which poses significant health risks. This study investigates personal exposure to PM_2.5 while commuting on short walking routes in Gliwice, Poland, a city known for its high air pollution levels. It compares measurements made using a low-cost air quality sensor with data from stationary air quality monitoring stations and an air quality laboratory at the Silesian University of Technology (SUT). The aim of this study is to assess real-time PM_2.5 exposure and address data gaps for health risk assessments. This study was conducted between January and November 2022, focusing on the participants' daily walking routines to the university campus. Data analysis included pollutant concentrations, environmental conditions, and statistical comparisons between different seasons (heating and non-heating). The results indicated that PM_2.5 levels measured by the low-cost sensor were lower than those recorded at the stationary sites, with average concentrations of 7.3 µg/m³ during both seasons. The stationary data from the monitoring station and the SUT laboratory reported higher average concentrations of 12.3 and 20.1 µg/m³, as well as 16.0 and 29.1 µg/m³, during the non-heating and heating season, respectively, showing statistically significant seasonal variations, unlike the low-cost sensor. The results suggest that low-cost sensors, while useful for real-time individual exposure monitoring, may lack the sensitivity needed to detect seasonal variations compared to reference-grade instruments. Overall, this study emphasizes the importance of appropriate measurement methods for assessing air quality and highlights the potential role of low-cost sensors in personal exposure tracking, raising awareness about air pollution.

Introduction: Aerosols play a crucial role in Earth's climate system, influencing the balance of radiation and cloud formation. Antarctica, despite its remoteness, provides an ideal environment in which to study background aerosol composition and the long-range transport of pollutants. This study investigates size-segregated elemental composition, seasonal variations, and potential sources of atmospheric aerosols in Antarctica. Methods: Seven size-segregated aerosol samples were collected at Faraglione Camp (~3 km from Mario Zucchelli Station) between November 2019 and January 2020 using a high-volume cascade impactor. Samples were analyzed for major (Na, Ca, K, Mg), minor (Al, Fe, Mn), and trace elements (Cd, Cr, Cu, Hg, Ni, V) using ICP-OES, GF-AAS, and DMA after acid microwave digestion. Results: The elemental concentrations of PM10 followed the order K > Na > Al Ca > Mg Fe > Mn > Cr > Ni > Cu V > Cd Hg. Seasonal trends were element-specific, influenced by katabatic winds and pack ice melting. Notably, Mg, Cu, Cr, and Hg peaked in late November/mid-December, likely due to sea spray emissions, while Fe, V, and Mn showed a decreasing trend. The size distribution analysis identified three modes of particle dispersion: accumulation (0.1–1 µm) and two coarse fractions (~2.5–3 µm and ~9.5 µm). Crustal enrichment factors (EFs) indicated geogenic origins for Mn and Fe (EF < 1), while Cu, Ni, K, Ca, and V enrichment was linked to soil resuspension. Mg and Na showed moderate enrichment (EF ~10), associated with sea spray, while Cd, Cr, and Hg exhibited high EFs (10 < EF < 100), suggesting an anthropogenic influence. Conclusions: These findings underscore the importance of continuous monitoring in assessing the contributions of aerosols to polar environments and their potential climatic and ecological impacts.