Prescribed fire is an effective tool for reducing wildfire risk but emits pollutants such as carbon monoxide (CO) and fine particulate matter (PM2.5) that negatively impact both indoor and outdoor air quality. While emissions from prescribed fires have been widely characterized, there remains limited understanding of how emissions vary across combustion conditions, fuel types, and regions. More specifically, there has been little work that has looked at both emissions of CO and PM2.5 as well as other compounds such as elemental carbon (EC), organic carbon (OC), and speciated organic compounds. This work quantifies the variability of emission factors (EFs) for CO, PM2.5, EC, OC, and speciated organic compounds across 19 prescribed fires in Colorado and southeastern Georgia using low-cost sensors. This allowed us to have highly temporally resolved EF datasets across multiple monitors and multiple burns. Results show that CO EFs are strongly driven by combustion phases, with higher emissions associated with lower modified combustion efficiency, or smoldering combustion (+3260%). However, PM2.5 EFs exhibit weaker and more variable relationships with combustion phase and are more strongly influenced by fuel type (+108% for Colorado-based fuels) and burn characteristics (+8.3%). Broadcast burns generally exhibited higher CO and PM2.5 EFs than pile burns, while daytime burning conditions were associated with lower emissions for both CO and PM2.5. The results provide improved emission factor estimates across multiple conditions and can inform both prescribed fire management practices and the development of more representative emissions inventories.
This article proposes an enhanced quality control (QC) method based on Spatiotemporal Graph Convolutional Networks (STGCN) to identify potential outliers in surface temperature observations. The STGCN model employs a graph structure to simultaneously capture temporal and spatial dependencies, with the adjacency matrix constructed using spatial distances and topography-assisted elevation priors. Compared to baseline methods, experimental results indicate that STGCN achieves superior overall performance across evaluation metrics, effectively balancing Type I and Type II errors. The findings demonstrate that the proposed framework is an effective QC method for detecting observational anomalies in surface temperature datasets.
Odor management requires consideration of both compound concentrations and olfactory impacts determined by odor threshold concentrations. This study evaluated missing-data structures, interpolation performance, odor activity value (OAV)-based odor contributions, and seasonal compositional changes using 20 min monitoring data for 22 designated odor compounds collected from a livestock farm, a wastewater treatment facility, and an anonymized organic waste treatment facility in Eumseong, Republic of Korea (Site C), from 1 August 2022 to 30 June 2023. Because the dataset contained multi-week to multi-month block missing periods, compound-specific interpolation performance was assessed using block holdout validation with BiLSTM, BiGRU, TCN, Transformer, and HistGradientBoostingRegressor models. OAV, summed odor activity value (SOAV), and odor contribution (OC) were calculated primarily from observed concentrations, while fully interpolated series were additionally used for sensitivity comparison to evaluate interpolation-induced bias. Ammonia was excluded from OAV analysis owing to its low valid observation rate. Among the analyzable compounds, odor contribution was dominated by volatile fatty acids and trimethylamine rather than hydrogen sulfide. The livestock farm and wastewater treatment facility showed n-valeric-acid-dominated profiles, whereas Site C showed a mixed profile involving valeric acids and trimethylamine. Seasonal analysis indicated relatively consistent fatty-acid-dominated compositions at the livestock farm and wastewater treatment facility, while Site C showed a possible shift from fatty-acid dominance in warm seasons to trimethylamine dominance in cold seasons. These results provide an OAV-based framework for identifying odor management priorities in long-term continuous odor datasets with severe missingness.
Atmospheric effects substantially influence remote-sensing reflectance retrieval in optically complex inland waters. This study evaluated seven atmospheric correction approaches (QUAC, FLAASH, Sen2Cor, LaSRC, 6S, C2RCC, and ACOLITE) for Sentinel-2 MSI and Landsat-8/9 OLI imagery over the Danjiangkou and Luhun reservoirs. The evaluation used 67 quality-controlled, temporally matched in situ spectral observations and satellite matchups. Performance was quantified using the squared Pearson correlation coefficient (r2), root mean square error (RMSE), and average unsigned relative error (AURE). Laboratory-measured chlorophyll-a (Chl-a) concentrations were used to develop sensor-specific retrieval models and to examine how atmospheric-correction differences propagated into Chl-a estimates and spatial patterns. Because residual aerosol and sun-glint effects may remain after atmospheric correction, an exploratory SWIR-based adjustment was evaluated for the C2RCC visible-band outputs. In the pooled-band analysis, C2RCC yielded the most favorable balance of the evaluated metrics for both sensor datasets. However, performance varied among bands, and the Landsat-8/9 B5 output showed very weak covariation with the in situ measurements. Within the model-development dataset, Sen2Cor achieved the highest Sentinel-2 r2 (0.762), whereas C2RCC achieved the lowest Sentinel-2 RMSE (2.30 mg/m3). C2RCC achieved both the highest Landsat-8/9 r2 (0.689) and the lowest RMSE (3.18 mg/m3). Independent temporal validation used 14 Luhun observations from 2024. Sen2Cor yielded the lowest Sentinel-2 RMSE and AURE (1.658 mg/m3 and 29.28%). For Landsat-8/9 OLI, C2RCC yielded the highest r2 (0.536), lowest RMSE (3.468 mg/m3), and lowest AURE (44.08%). Relative errors increased in weak-signal near-infrared bands, underscoring the need for band-specific interpretation. The SWIR-based adjustment improved both RMSE and AURE for Sentinel-2 MSI but did not provide a consistent improvement for Landsat-8/9 OLI. An exploratory comparison of quality-screened imagery from 2016 to 2025 showed broadly similar reservoir-scale Chl-a patterns in C2RCC-derived products from the two sensors. These results provide reservoir-specific evidence for atmospheric-correction selection and Chl-a retrieval under the sampled conditions.
Convection-permitting simulations resolve the deep convective cells that organise Mediterranean tropical-like cyclones. They also generate localised pressure minima that can capture a conventional cyclone tracker and pull it away from the synoptic-scale centre. We introduce High-Resolution Multilevel Python-Based Algorithm for Cyclones’ Centroid Tracking (HIMPACT), an open-source Python algorithm developed by the corresponding author within the CETEMPS framework, that stabilises cyclone-centre identification by combining three elements: a multi-level geopotential analysis restricted to the 800–950 hPa layer, a percentile-based threshold that isolates the vortex core from convective perturbations, and a convex-hull centroid that depends on the geometry of a percentile-defined core rather than on a single extreme grid point, so that an isolated convective pressure deficit cannot displace the estimate by more than a fraction of the core radius. HIMPACT was evaluated in four tracking experiments across three Mediterranean cyclones at grid spacings from 2 to 28 km using WRF, ICON-DREAM and ERA5, while MPAS was additionally used to test portability and computational scaling on an unstructured Voronoi mesh. Across the three experiments in which the driving data resolve a coherent lower-tropospheric cyclone structure, the best five-level configurations reduce root-mean-square displacement errors by approximately 16–48% relative to the corresponding single-level configurations. Activating the absolute minimum alongside the centroid more than doubles the error variance when the pressure field is multi-modal. The 800–950 hPa window avoids both surface extrapolation artefacts below 950 hPa and mid-tropospheric steering signatures above 800 hPa. A counterexample with an extratropical storm exposes a data-quality threshold: when the driving dataset does not resolve a vertically coherent cyclone structure, the multi-level weighted mean diverges, and single-level tracking becomes the safer choice. HIMPACT is model-agnostic, requires no format conversion, and runs on a single CPU core at approximately 9.8–41.3 s per time step for the recommended five-level configuration across the tested back-ends; substantially larger costs occur for high-level-count MPAS configurations.
Mutual interactions exist between tropical cyclones and the East Asian mid-latitude trough (EAMT). This study mainly used statistical analysis and numerical experiments to investigate the influence of TCs on the zonal movement of EAMT. Composite results indicate that TCs can induce an average of 11.82 degrees in the EAMT meridional displacement, and the anomalous remote geopotential height (HGT) triggered by TCs serves as an important factor driving the zonal movement of the EAMT. The EAMT tends to move towards the region of negative HGT difference and away from the region of positive HGT difference. The temperature anomalies induced by TCs are a critical factor leading to the HGT anomalies. For the TC Maria case, it induces a maximum meridional displacement of 0.76 degrees of the EAMT at 450 hPa. TC Maria first triggers anomalous cold advection in the mid-latitude regions of the East Asia–Northwest Pacific area, which then leads to an anomalous decrease in HGT within the EAMT trough region under the constraint of hydrostatic equilibrium. Consequently, the anomalous negative HGT caused by the TC results in the zonal movement of the EAMT line. The results of this study provide evidence that remote disturbances induced by TCs in the tropical WNP can affect weather circulation in the mid-latitudes of East Asia.
Drought monitoring in arid regions commonly relies on climatic indices, yet the accumulation timescale that best represents surface soil moisture drought may vary spatially. This study evaluated the Standardized Precipitation Evapotranspiration Index (SPEI) at 1-, 3-, and 6-month accumulation periods against satellite-derived surface soil moisture across Saudi Arabia during 2003–2024. Temporal correspondence, grid-cell Spearman correlation, and receiver operating characteristic area under the curve (ROC-AUC) were evaluated. The analysis included 1462 grid cells meeting the minimum paired-observation criterion, representing 53.4% of the 2736 cells in the national domain. SPEI-1 consistently showed the strongest overall performance, with the highest temporal correspondence with surface soil moisture drought extent (ρ=0.50, versus 0.37 for SPEI-3 and 0.30 for SPEI-6), median grid-cell correlation (ρ˜=0.290, versus 0.255 and 0.175), and median ROC-AUC (0.629, versus 0.618 and 0.580). SPEI-1 was also the nominal highest-AUC timescale in 52.5% of analyzed cells, compared with 29.5% for SPEI-3 and 17.9% for SPEI-6. However, the median AUC difference between the first- and second-ranked timescales was only 0.033, and paired bootstrap comparisons showed that 94.8% of cells had no uniquely supported AUC winner. Regionally, nine of the 13 administrative regions showed a nominal majority preference for SPEI-1, whereas four had no single-timescale majority. The overall SPEI-1 > SPEI-3 > SPEI-6 ordering was also preserved under alternative soil moisture drought thresholds and temporal out-of-sample validation. These findings indicate that shorter SPEI accumulation periods generally provide the closest representation of near-surface soil moisture drought within the observed Saudi domain. The results provide practical guidance for selecting SPEI accumulation periods according to the land-surface drought process being monitored.
Near-surface ozone (O3) pollution is a growing environmental concern, particularly in the Beijing–Tianjin–Hebei (BTH) region, one of China’s most densely populated megacity clusters experiencing increasingly severe O3 episodes. Existing data-driven forecasting models systematically underestimate high-concentration events and offer limited lead times. To reveal the meteorological drivers of extreme O3 episodes, we conducted composite anomaly analysis over 2019–2023 and identified the dominant meteorological mechanism as a coupled pattern of mid-tropospheric anticyclonic circulation with high temperature, low humidity, and deep subsidence inversion, which suppresses vertical diffusion while southerly advection drives rapid near-surface O3 accumulation. Motivated by meteorological diagnostics, we proposed ARC-Net, a Transformer-encoder-based Adaptive Residual Correction Network that ingests numerical weather prediction data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and air quality observations to produce hourly O3 forecasts up to 240 h (10 days) ahead. The model features a dual-branch regression-classification architecture enhancing feature discrimination at high concentrations and an Adaptive Residual Correction module that dynamically calibrates outputs through a triple-gating mechanism conditioned on pollution-level priors. In independent forecast tests for the year 2023 across 13 cities in the BTH region, ARC-Net achieved R2 = 0.879 and a root mean square error (RMSE) of 17.03 μg/m3 at 0–24 h, retaining R2 = 0.749 and RMSE = 24.57 μg/m3 at 0–240 h. For extreme episodes (maximum daily 8 h average ozone (MDA8_O3) ≥ 215 μg/m3), the Critical Success Index improved by 63.9% over the baseline, and RMSE decreased by 33.15% within the 215–265 μg/m3 range in a representative case. These results indicate that meteorology-guided predictors combined with adaptive residual correction can partially alleviate high-O3 underestimation and provide practically useful medium-range warning skill.
Wildfires represent a major environmental hazard with significant impacts on ecosystems, climate, biodiversity, and human activities. The increasing frequency and intensity of wildfire events have highlighted the need for reliable and timely detection techniques based on satellite remote sensing. This study investigates the application of Proper Orthogonal Decomposition (POD) to thermal observations acquired from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellite for wildfire anomaly detection. A wildfire event that occurred on 8 August 2021 in Calabria, Southern Italy, was selected as the primary case study. To assess the consistency of the POD response beyond the primary case, the analysis was further extended to two additional wildfire events, Viggianello–Abate and Pazzano–Montestella, using the 15 × 15 pixel extraction window. Middle Infrared (MIR, 3.9 μm) observations collected at 15 min intervals over a complete day were analyzed using four different spatial extraction windows (3 × 3, 15 × 15, 30 × 30, and 45 × 45 pixels). POD was employed to separate dominant background thermal variability from localized fire-induced anomalies. The analysis focused on higher-order POD modes, particularly the 6th, 7th, and 8th modes, which exhibited enhanced sensitivity to wildfire activity. Results showed that POD successfully identified thermal anomalies corresponding to wildfire occurrence times independently detected by the RST-FIRES methodology. The comparison of extraction window sizes revealed that the 15 × 15 pixel window provided the best balance between anomaly enhancement, spatial localization, and noise reduction. Larger windows introduced excessive spatial smoothing and reduced localization capability, whereas the smallest window was more affected by noise. The findings demonstrate the potential of POD as an effective complementary approach for wildfire detection and monitoring using geostationary satellite observations.
Parsimonious emulators (PEs) trained on complex climate models (CCMs) are useful when global variables like global mean surface temperature and climate-system energy content are sought. CCM runs over millennia extracted from the LongRunMip repository are used to construct and test PEs for global mean temperature and net incoming radiation flux. For the temperature, the PE is a linear impulse response in the form of a superposition of k decaying exponentials, comprising k weight coefficients and k decay times to be estimated by least-square fitting to the temperature from CCM runs with abrupt step-function forcing. The model fit for k≥3 is good on all time scales, and the fitted model seems to perform even better for smoother forcing scenarios, suggesting that it reflects essential features of the CCM to which it is fitted. Data for radiation flux are combined with temperature data to produce low-order polynomial fits to Gregory plots and analytic expressions for the evolution of the effective feedback parameter, the radiation fluxes, the evolution of climate-system energy content, and an effective system heat capacity. The analysis reveals four stages of the ocean heat uptake, characterised by increasing effective heat capacity. From these Pes, one can compare the global performance of CCMs under different forcing scenarios, highlighting distinguishing features, such as evolution of albedo feedback and cloud radiative effect. Producing Gregory plots for all-sky and clear-sky outgoing long-wave and short-wave radiation, varying cloud albedo is identified as the main contributor to model spread of equilibrium climate sensitivity.
High-resolution wind-field prediction over complex terrain is important for wind-energy assessment, grid safety, and hazard mitigation. This study uses a coupled Weather Research and Forecasting–Computational Fluid Dynamics (WRF–CFD) workflow for the Askervein Hill benchmark. Driven by the fifth-generation European Centre for Medium-Range Weather Forecasts Reanalysis dataset, WRF provides mesoscale atmospheric conditions, and CFD resolves the terrain-induced flow features at high spatial resolution. Because repeated WRF and CFD simulations are computationally expensive, we develop a time-aware neural-network surrogate model, termed the multilayer perceptron-prior gated temporal correction (MLP-GTC) model, to predict the resulting three-component wind field. The model first learns the local wind response associated with terrain and inlet conditions, and it then applies a gated temporal correction based on recent inlet conditions and their corresponding pointwise predictions. The data are divided chronologically into 683 training, 146 validation, and 147 test time steps, with 5187 fixed spatial points evaluated at each time step. The coupled WRF–CFD simulations reproduce the observed wind structure with a maximum Pearson correlation coefficient of 0.96 and a 10 m wind-speed root mean square error of 0.84 m/s. For the three-component velocity field, MLP-GTC achieves validation/test root mean square errors of 0.282/0.310 m/s and mean absolute errors of 0.179/0.199 m/s. It improves on the multilayer perceptron model (0.486/0.516 m/s) and the matched five-step single-layer long short-term memory network (0.334/0.438 m/s).
Welding technology, extensively utilized in modern industry, poses significant health risks due to metal dust exposure, which can lead to respiratory discomfort, neurological issues, and an increased risk of lung cancer and pneumoconiosis. Enhancing ventilation within factory buildings has proven to be an economical approach to mitigating these risks. This study employs computational fluid dynamics (CFD) to model the airflow and dust transport within a large welding workshop measuring 300 m in length, 28 m in width, and 21 m in height. The impact of the exhaust-to-supply air ratio (ESR) and the height of the side exhaust port (SEP) on dust removal efficiency is investigated. Comparative analysis of transport dynamics between low-density aluminum alloy welding fume and high-density carbon steel welding fume reveals optimal dust exhaust designs. The study identifies two peaks in workshop air velocity at 0–2 m and 8–12 m above the ground, with the top exhaust port (TEP) outperforming the SEP in dust removal. An increased ESR accelerates the upward migration of welding fume, reducing lateral dispersion. An improperly set SEP height can lead to airflow short-circuiting or excessive lateral dispersion, hindering effective dust removal. Optimal SEP height for aluminum alloy and carbon steel dust are determined to be 5 m and 6 m, respectively.
Regional 10 m wind-speed products extend coverage beyond sparse station networks, but their errors vary with wind regime and location. This study evaluates a station-trained LightGBM correction of a 5 km WRF/HNR wind-speed product over Hainan Island. Raw and corrected fields were collocated with stations using a four-neighbour inverse-distance operator. A spatially separated 2022 holdout retained 67 stations after five coordinate-overlap exclusions. Across 582,610 common hourly pairs, corrected RMSE decreased from 3.198 to 1.211 m s−1 and Pearson R increased from 0.368 to 0.651. The paired RMSE reduction was 1.986 m s−1 [95% CI 1.839, 2.122]. Improvement was largest below 3.4 m s−1 (2.203 m s−1) and remained positive for 3.4–7.9 m s−1 (0.566 m s−1). In contrast, corrected RMSE increased by 0.990 m s−1 for 8.0–10.7 m s−1 and by 1.656 m s−1 for ≥10.8 m s−1. A time-only adjustment reduced pooled RMSE to 1.870 m s−1 but also degraded the two upper strata. The 2016–2022 maps show a lower corrected product climatology, with a mean corrected-minus-raw increment of −2.273 m s−1 across the Hainan buffer. Temporal partitions and AWS-selected station groups show variation across season, time of day, and setting. The correction reduces station-sampled errors for light-to-moderate wind speeds; it changes speed magnitude while retaining the raw-product wind direction.
Airborne microplastics (AMPs) are increasingly recognized as an emerging air pollutant. However, observational data remain scarce in Southeast Asia. This study provides the first observations of AMPs in Phnom Penh, Cambodia, using µFTIR-ATR imaging. Number concentration, morphology, polymer composition, aerodynamic size distribution, Feret diameter, and surface aging characteristics were investigated together with meteorological parameters, gaseous pollutants, water-soluble ionic tracers, and HYSPLIT backward trajectories to examine possible source attribution. AMPs were dominated by polyethylene (PE), polypropylene (PP), and polyethylene terephthalate (PET), with 52% classified as fragments and 82% having Feret diameter smaller than 30 µm. Across four independent 72-h sampling periods (n = 4), AMP concentrations ranged from 0.55 to 1.27 MP m−3 in TSP, with a mean ± standard deviation of 0.97 ± 0.30 MP m−3, and from 0.23 to 0.49 MP m−3 in the PM2.5 fraction, with a mean ± standard deviation of 0.33 ± 0.10 MP m−3. In total, 134 particles were identified in TSP, of which 46 were detected in the PM2.5 fraction. Carbonyl and hydroxyl indices indicated that PE and PP were relatively fresh and in low-to-moderate surface aging states. Pearson correlations suggested that the abundances of individual polymers were varied differently in relation to local environmental and precipitation-related variables; however, the limited number of sampling periods precludes source or process attribution. In addition, HYSPLIT backward trajectories showed that some air masses arriving in Phnom Penh had passed over marine regions under southwest monsoon flow. These findings provide the first baseline dataset for AMP pollution in Phnom Penh, Cambodia, and highlight the combined importance of local emissions and regional atmospheric transport in Southeast Asia.
In the mining process of shallow-buried and close-distance coal seam groups in western China, the interconnected collapse fractures between the overlying goaf and the surface form large-area composite goafs, which aggravate surface air leakage and elevate oxygen levels within the goaf. This, in turn, leads to hazardous conditions such as CO over-limits and O2 deficiency at the working face’s return air corner, which seriously threatens the respiratory health of underground operators and the safe production of mines. Taking the 104 working face of a coal mine in Shenfu-Dongsheng Mining Area as the engineering background, this paper comprehensively adopts SF6 tracer gas test, fuzzy cluster analysis, and CFD numerical simulation methods to systematically study the distribution characteristics of three-dimensional air leakage channels in composite goafs and their influence mechanism on gas migration in goafs, and proposes a dynamic pressure equalization ventilation (PEV) regulation technology system. The research results show that a multi-dimensional three-dimensional air leakage channel of “surface-interlayer-own layer-roadway” exists in the research area, in which the surface fracture air leakage velocity is about 0.068 m/s, and the interlayer and internal goaf air leakage velocity is about 0.384 m/s. The atmospheric pressure difference between the working face and the surface is the main controlling factor inducing the O2 deficiency disaster of the working face. Every 100 Pa change in atmospheric pressure difference causes an O2 concentration fluctuation of about 0.30% at the return air corner, and the critical pressure difference for activating PEV is determined to be 300 Pa. Setting the PEV regulation point at the return air outlet of the working face and adopting the combined dynamic regulation system of fans and air windows can realize accurate pressure balance between the working face and the overlying composite goaf.
The degradation formed during the gas-phase reaction of amyl acetate, CH3COO(CH2)4CH3, initiated by chlorine atoms (●Cl), was investigated under atmospheric conditions using gas chromatography–mass spectrometry. The main products identified were acetic acid, formaldehyde, acetaldehyde, butyraldehyde, and propionaldehyde. Calibration curves were established for each identified product at different concentrations to enable their quantification by gas chromatography coupled with flame ionization detection. Product yields were subsequently determined from the calibration data, allowing a quantitative evaluation of the formation of the major oxidation products. The results obtained contribute to a better understanding of the atmospheric degradation pathways of amyl acetate and related ester compounds, providing useful information for assessing the atmospheric processing of ester-containing emissions, including those associated with biofuel applications.
Brazil presents a distinctive convergence of continental-scale climatic diversity, extensive urbanization, large-scale biomass burning, rapid land-use change, persistent air-quality monitoring gaps, and deep social inequalities, producing highly heterogeneous and compound environmental health risks. In this context, treating air pollution and climate change as parallel environmental crises obscures their structural interconnections through shared emission sources, mutually reinforcing exposure pathways, and overlapping health and social consequences. In this narrative review, we critically synthesize scientific and institutional lines of evidence and argue that air pollution and climate risks can be more effectively addressed in Brazil through a single strategic agenda for science, public health, and governance. We first discuss why these challenges cannot be managed in isolation, emphasizing the effects of heat, drought, stagnation events, biomass burning, and extreme weather on pollutant formation, dispersion, and health burden. We then examine Brazil as a critical case where recent regulatory advances coexist with structural limitations in monitoring, data integration, and territorial coverage. Based on this diagnosis, we propose an integrated national agenda organized around five mutually reinforcing priorities: monitoring through hybrid networks; predictive science through climate-informed modeling and early warning; public health through the convergence of epidemiology, toxicology, and mechanistic research; equity-oriented research and action through the explicit incorporation of vulnerability, inequality, and climate justice; and policy appraisal through the assessment of disease burden, economic costs, mitigation co-benefits, and trade-offs. We further discuss the governance mechanisms needed to connect these priorities and translate evidence into coordinated action and adaptive public policies. We also argue that the Amazon should be approached not as an isolated ecological exception but as a central component of a broader Brazilian and Global South discussion on environmental health, land-use change, and climate justice. In this scenario, Brazil has the scientific capacity and regulatory momentum to become a reference in the integrated management of air pollution and climate risks, but this will depend on replacing fragmented approaches with a coordinated framework capable of linking exposure, mechanism, burden, inequality, and action.
Salt dust storms are a distinct and highly hazardous type of dust storm in arid and semi-arid regions. Salt crusts commonly develop on the surfaces of desiccated lake beds, and variations in their structure and properties directly influence dust release. To investigate how wetting–drying alternation affects the erodibility of sodium sulfate salt crusts with varying salt contents, four crust types with 0%, 1%, 3%, and 5% sodium sulfate were prepared under controlled laboratory conditions. A combination of wind-tunnel tests, direct shear tests, and surface morphology observations was employed to evaluate changes in mechanical properties and wind-erosion responses before and after wetting–drying treatment. The results showed that wetting–drying alternation induced pronounced cracking, salt crystallization, and the formation of a loose surface layer in salt-bearing crusts, with structural damage severity increasing with salt content. In contrast, the physical crust without added salt exhibited minimal surface deterioration. Direct shear tests revealed that after wetting–drying, the internal friction angle of salt-bearing crusts first decreased and then increased with salt content, while cohesion declined markedly; the 5% salt crust showed a 32.4% reduction in cohesion, indicating substantial structural degradation. Wind-tunnel tests further demonstrated that wind-erosion intensity increased significantly after wetting–drying treatment across all salt contents, with the largest relative increase observed in the 1% salt crust. Wind-erosion intensity also scaled approximately as a power function of salt content. These findings demonstrate that wetting–drying alternation is a critical trigger for the degradation of sodium sulfate salt crusts and for enhancing their erodibility. Post wetting–drying, salt crusts may evolve into highly erodible surfaces, becoming major potential sources of salt dust storms. This study provides a theoretical foundation for understanding salt dust release from desiccated lake beds and for improving early warning of ecological hazards in arid regions.
Quantifying the large-scale impact of combined ozone (O3) and nitrogen dioxide (NO2) pollution on terrestrial carbon sinks remains a major challenge. Here, we develop a parsimonious yet robust empirical framework that leverages high-resolution remote sensing datasets (CHAP O3/NO2 and MODIS NPP, 2008–2021) to characterize nonlinear threshold responses of terrestrial net primary productivity (NPP) across China’s diverse ecosystems. Our observational analysis identifies only associative temporal relationships between annual NPP variability and pollutant concentrations, with NPP positively correlated with O3 (Pearson’s r = 0.714, p < 0.01) and negatively correlated with NO2 (r = −0.599, p < 0.05). Notably, the ecosystem-specific threshold values (O3: 28,324–34,391 μg m−3 yr−1; NO2: 3646–4968 μg m−3 yr−1) are statistically derived from spatially aggregated pixel-level records across the full 14-year period, independent of the national annual time-series correlation analyses. Distinct from previous single-pollutant national evaluations, our study advances a novel analytical framework focusing on the interactive and combined impacts of O3 and NO2 co-exposure. The results demonstrate that NPP displays an increasing trend under low-level pollutant exposure but declines substantially once pollutant loads exceed the identified threshold ranges. Based on K-means clustering and segmented regression analyses, we estimate a national average NPP reduction of 17.4% per year (−0.68 Pg C yr−1), resulting in a cumulative carbon loss of −9.48 Pg C over the 14-year study period—equivalent to 2.45 years of China’s total terrestrial carbon uptake. Among all ecosystem types, forestlands experience the largest cumulative carbon loss (−4.22 Pg C), with prominent loss hotspots concentrated on the Tibetan Plateau and Northwest China. This refined national-scale assessment of dual-pollutant impacts provides observation-based evidence of substantial terrestrial carbon sink degradation, underscoring the necessity of combined air pollution mitigation strategies to sustain ecosystem stability and climate mitigation targets.
Over the past decade, stringent emission control policies have been implemented in the North China Plain, fundamentally altering regional air pollution profiles. This study investigates the long-term temporal evolution (2015–2025) and chemical reconstruction of air pollutants in Jinan, China. Benefiting from rigorous emission controls, annual median concentrations of PM2.5, SO2, and CO decreased significantly by 64%, 81%, and 54%, respectively. Conversely, NO2 exhibited a slower decline (47%), and the maximum 8-h daily average (MDA8) ozone (O3) increased by 23%. Generalized additive model (GAM) analysis identified solar radiation, temperature, relative humidity, and NO2 as the primary factors strongly associated with O3 variations. The amplified atmospheric oxidation capacity driven by O3 has triggered a profound chemical reconstruction of secondary inorganic aerosols (i.e., SO42−, NO3−, and NH4+), with a significant increase in sulfur and nitrogen oxidation ratios (SOR and NOR), which may continue to intensify the combined pollution trend of ozone and PM2.5. These findings emphasize that coordinated reductions in nitrogen oxides and volatile organic compounds must be achieved in future air quality management while also taking into account the promoting effect of climate warming on ozone generation.
