Search Results (2,934)

Aqueous precipitation and crystallization are central to impurity removal, product formation, and resource recovery in mineral and chemical processing, but robust inline monitoring remains challenging because supersaturation is not measured directly and conductivity signals are affected by temperature, composition drift, bubbles, solids, polarization, and fouling. Electrical conductivity (EC) is attractive as a low-cost, rugged process analytical tool, yet its usefulness depends on mechanistic interpretation: EC reflects charge-carrier concentration and mobility rather than supersaturation itself. This review organizes the literature into a layered framework covering (i) measurement integrity and deployment, (ii) bulk-signal extraction in multiphase media, (iii) estimation of latent variables such as dissolved concentration or supersaturation proxies, and (iv) control readiness based on conductivity-derived targets. Frequency-aware conductivity extraction, event-anchored verification, and observer-based estimation are treated as optional, complementary modules. A Ca-carbonate/CaCO₃ system is used as an illustrative case because its coupling among conductivity, pH/speciation, supersaturation, and precipitation is especially transparent, although the framework is intended for broader processing systems, including complex liquors and slurries. Opportunities are also highlighted for nanomaterials to improve both precipitation control and EC information content. Full article

To enhance the meteorological forecasting and early warning service capability for urban waterlogging risks in Jinan, this study aims to investigate the relationship between rainfall and urban waterlogging. Based on minute-scale precipitation observations from 38 automatic weather stations and records from 70 waterlogging monitoring sites in the urban area of Jinan from 2011 to 2024, this study systematically analyzes the spatiotemporal characteristics of precipitation and waterlogging events and quantifies their response relationship. The main findings are summarized as follows. Heavy precipitation and waterlogging events are strongly temporally coincident, primarily occurring during the main flood season from June to August. Regarding diurnal variation, short-duration heavy rainfall and waterlogging events are concentrated between 14:00 and 20:00. The water depth of most waterlogging events ranges from 0.11 m to 1.04 m, with a median of 0.26 m, and the distribution of waterlogging exhibits a pronounced right-skewed pattern. A moderate positive spatial autocorrelation was observed in waterlogging depth, suggesting that severe urban waterlogging events are more likely to occur in the northern region of Jinan. The precipitation preceding waterlogging events is predominantly short-duration heavy rainfall. A strong temporal relationship exists between peak precipitation and maximum waterlogging depth. In nearly 90% of the waterlogging events, peak precipitation occurs within 2 h before the maximum waterlogging depth, with an average lead time of approximately 55 min. The relationship between antecedent cumulative precipitation and peak waterlogging depth is strongest at the 120 min timescale. About 90% of maximum rainfall over 10 min, 1 h, and 2 h did not exceed the 1-year return period threshold, indicating that the precipitation causing waterlogging events in Jinan is generally non-extreme. Full article

Atmospheric wet deposition represents a major pathway for the transfer of organic micropollutants into terrestrial and aquatic ecosystems. This study investigates the occurrence and spatial distribution of polycyclic aromatic hydrocarbons (PAHs), phthalate esters (PAEs), and BTEX compounds in rainwater across Northern Serbia (Vojvodina region). Rainwater samples were collected during the 2025–2026 heating season at three locations: a petrochemical site in Kikinda, a traffic- and residentially influenced site in Sremska Mitrovica, and an urban background site in Sombor. Total concentrations showed pronounced spatial variability, with the highest ΣBTEX and ΣPAE levels recorded in Kikinda (∑BTEX = 2.818 μg L^∑1; ∑PAE = 0.930 μg L^∑1). Diagnostic ratios identified a dominant petrogenic signature in Kikinda (LMW/HMW > 1), while pyrogenic sources prevailed in Sremska Mitrovica and Sombor ((Fla/Fla + Pyr) > 0.5). BTEX profiles across all sites were characterised by the absence of benzene and elevated toluene and xylene levels (B/T ≈ 0; T/X > 1). Health risk assessment indicated an acceptable but non-negligible carcinogenic risk from PAHs, particularly for children in industrial areas. These findings highlight the role of precipitation as an efficient scavenger of organic pollutants and emphasise the need for integrated atmospheric–hydrological monitoring frameworks in industrialised regions. Full article

Cryptomeria japonica (Japanese cedar) is extensively planted as windbreaks in Jeju, Korea, producing highly allergenic pollen that significantly affects local populations. This study analyzed 10-year trends of airborne C. japonica pollen concentrations and their relationship with meteorological factors in Jeju to provide essential data for allergy management and climate adaptation strategies. Daily airborne pollen sampling was conducted using Burkard traps from 2015 to 2024 at a monitoring site in Jeju. Meteorological data, including temperature, wind speed, relative humidity, precipitation, solar radiation, and cloud amount, were obtained from the Korea Meteorological Administration. Temporal trends were analyzed using linear regression and the Mann–Kendall test, while correlations between pollen parameters and meteorological variables were calculated using Spearman’s correlation coefficients. Over the 10-year period, annual pollen integral (APIn) and peak concentrations showed statistically significant increasing trends. Pollen season start dates demonstrated a tendency toward earlier occurrence. Season onset was strongly negatively correlated with pre-season temperatures in January and February. January solar radiation showed positive correlations with both season end and period duration. C. japonica pollen concentrations in Jeju demonstrate significant increasing trends with earlier seasonal onset, primarily driven by pre-season warming in January and February. These changes may lead to prolonged allergen exposure periods, necessitating enhanced public health preparedness and adaptation of clinical management strategies for allergic populations. Full article

Climate change and habitat loss pose severe threats to the survival of alpine butterflies worldwide. Parnassius imperator is a rare, endemic, and endangered butterfly in China, yet the spatiotemporal dynamics of its suitable habitats under climate change remain largely unknown. In this study, we applied ensemble species distribution models to simulate the shifts of its current and future suitable habitats, incorporating bioclimatic variables, elevation, normalized difference vegetation index, and human footprint. Results showed that the current suitable habitats cover 185.87 × 10⁴ km² and are concentrated in western China, mainly regulated by elevation, temperature seasonality (BIO4), precipitation of the wettest month (BIO13), precipitation of the warmest quarter (BIO18), and precipitation of the driest month (BIO14). Under future climate change scenarios, suitable habitats will shrink drastically, even to only 82.16 × 10⁴ km² under SSP585 in the 2070s, with nearly a complete loss of highly suitable habitats. In addition, centroid shift analyses reveal that the distribution centroid will shift eastward. Our findings indicate that suitable habitats will contract significantly, and P. imperator will face a sharply increasing risk of extinction in the future. Considering the overlap between suitable habitats and existing nature reserves, we recommend implementing integrated conservation strategies, including expanding protected areas, establishing long-term monitoring programs, restoring habitats, and strengthening law enforcement and public education. This study provides a scientific basis for the climate-adaptive conservation of P. imperator and other vulnerable alpine insects. Full article

Phenylephrine is a widely used α₁-adrenergic agonist employed as a decongestant and vasoconstrictor in numerous pharmaceutical formulations. Considering its widespread use and its relevance in biological monitoring and anti-doping control, the development of rapid, sensitive, and reliable analytical methods for its determination has attracted significant attention. A paper-based colorimetric sensor based on Prussian blue nanoparticles was developed for the determination of phenylephrine. Prussian blue nanoparticles were synthesized by the precipitation method, and their structural, morphological, and surface properties were systematically characterized using complementary analytical techniques. The sensing mechanism is based on the reduction in Prussian blue to its colorless form in the presence of phenylephrine, resulting in a decrease in absorbance intensity. Under optimized conditions (pH 6.5 and 5 min incubation time), the colorimetric sensor exhibited a linear response toward phenylephrine over the concentration range of 5–150 µg mL⁻¹, with a limit of detection of 1.56 µg mL⁻¹ (R² = 0.9986). The sensing system was further integrated into a paper-based platform, enabling visual detection of phenylephrine. Digital image analysis using ImageJ showed a linear response over 5–150 µg mL⁻¹ (R² = 0.9884) and a detection limit of 5.37 µg mL⁻¹. The sensor’s practical applicability was validated using artificial urine samples, yielding recovery values of 95.87–97.5% and relative standard deviations of 1.15–2.13%. Unlike conventional methods requiring multi-step reactions, this study introduces, for the first time, a simple paper-based colorimetric sensor for phenylephrine detection based on the direct Prussian blue–Prussian white redox transition integrated with digital image analysis. Full article

Feline Infectious Peritonitis (FIP) has been transformed from a fatal disease to a treatable condition following the introduction of GS-441524, a nucleoside analogue targeting feline coronavirus replication. However, the widespread use of unregulated compounded formulations and the absence of validated analytical tools for therapeutic drug monitoring (TDM) represent critical gaps in clinical FIP management. This study describes the development and full ICH M10-compliant validation of a high-throughput LC-MS/MS method for the quantification of GS-441524 in feline serum, incorporating an automated protein precipitation protocol and a PBS-BSA surrogate matrix in accordance with 3Rs principles. The method met all acceptance criteria across validated parameters, including linearity (0.1–50 µg/mL), accuracy (bias within ±12.5%), precision (CV ≤ 10.9%), selectivity, extraction recovery (87.5–107.9%), and stability under clinically relevant storage conditions. Matrix equivalence between PBS-BSA and authentic feline serum was confirmed, enabling routine calibration without animal-derived materials. The validated method was applied to clinical TDM in cats undergoing GS-441524 treatment for FIP, providing preliminary evidence of inter-individual pharmacokinetic variability. The compounded formulations administered to the TDM cohort were independently verified by LC-MS/MS, confirming drug content within ±15% of labelled claims and excluding pharmaceutical quality as a confounding factor in the interpretation of serum drug concentrations. Full article

This study presents an integrated framework for agricultural drought monitoring in data-scarce regions, utilizing the Google Earth Engine (GEE) platform to analyze multisource Earth observation data over the South Wollo highlands, Ethiopia, from 2001 to 2024. The analysis was complemented by Mann–Kendall trend testing, Sen’s slope estimation, and Pettitt change-point detection to identify and quantify long-term trends and abrupt shifts in drought dynamics. The methodology integrates climatic and satellite-derived indicators within a hybrid analytical framework. It incorporates the standardized precipitation evapotranspiration index (SPEI), vegetation condition index (VCI), vegetation health index (VHI), temperature condition index (TCI), and land surface temperature (LST), which are derived from MODIS (NDVI, LST, PET) and CHIRPS precipitation datasets. The analysis focused on the main growing season (June–September) to capture critical crop growth and moisture-sensitive periods for agricultural production in the study area. The findings reveal pronounced interannual variability in drought occurrence and intensity across the study period. Severe agricultural drought conditions were most extensive in 2009 and 2014, with VHIs indicating 15% and 4% of the area under severe and extreme drought in 2009, respectively, and 2.6% and 2% in 2014, respectively. In contrast, 2001, 2005, 2020, and particularly 2024 were characterized by predominantly no-drought to mild-drought conditions, with no-drought coverage increasing from 86.7% (2009) to 98.0% (2024). Vegetation-based indices demonstrate that drought impacts are episodic rather than persistent and strongly controlled by rainfall timing and early-season moisture availability. The LST exhibited marked year-to-year variability (28.8 °C to 33.8 °C), with elevated temperatures coinciding with drought periods and suppressed evaporative cooling. Correlation analysis confirmed a strong positive relationship between the SPEI and VHI (r = 0.77), with moderate correlations for the VCI (r = 0.40) and TCI (r = 0.36), underscoring the sensitivity of integrated vegetation health to the climatic water balance. The study concludes that combining the SPEI with satellite-derived vegetation and thermal indices provides a robust, scalable approach for agricultural drought assessment in regions with limited ground-based observations. The integrated framework effectively captures both moisture deficits and thermal stress components, offering a scientific basis for improving drought early warning systems and climate-resilient agricultural planning in Ethiopia and similar environments. Full article

Sustainable small hydropower (SHP) is central to the clean-energy transition of mountainous Central Asia, yet its long-term reliability depends on a rapidly changing cryosphere. Winter river-ice dynamics—an underappreciated control on run-of-river generation—remain poorly characterized owing to the collapse of in situ hydrometeorological networks since 1991. We use a 112-month Sentinel-1 C-band SAR time series (February 2017–May 2026) over a 5320 km² headwater catchment of the Chu River basin, northern Tien Shan, Kyrgyzstan, to quantify river-ice phenology at 20 m resolution using a per-pixel summer-baseline anomaly approach. Mid-winter (December–February) ice cover declined significantly at −0.51%·yr⁻¹ (p = 0.013; Mann–Kendall p = 0.029), with the 2026 winter recording an unprecedented 2.6–2.8 σ departure from the 2017–2025 climatology. Contrasting the cold 2022 and warm 2026 winters revealed bidirectional climate sensitivity—early breakup versus persistent thin ice—posing distinct SHP hazards. ERA5-Land reanalysis (1992–2026) showed significant winter warming with no precipitation or snowfall trend, indicating thermally forced ice decline. Interpreted within a conceptual Peak Water scenario, this signals a closing window of opportunity for SHP generation, with direct relevance to sustainable water–energy management and the UN Sustainable Development Goals (SDG 7; SDG 13). Our results provide the first decadal, satellite-based evidence of river-ice loss for Central Asian mountain rivers and a transferable monitoring framework to support climate-resilient, sustainable hydropower planning in ungauged basins. Full article

High-resolution, long-term gridded meteorological datasets from in situ observations are crucial for ecosystem monitoring, soil diagnostics, hydrological modelling, and Earth system model evaluation. This study presents two enhanced real-time adaptations of Thornton’s Daymet V4 interpolation method. Daymet4-r1 uses a traditional calibration strategy with exhaustive parameter search, while Daymet4-r2 applies a global optimization algorithm (find_min_global from the dlib library) to adjust parameters automatically at each time step. Both methods were tested over Tuscany using high-resolution terrain and a dense observation network. Validation with leave-one-out method was carried out for the period 1995–2011 for both versions, while Daymet4-r2 underwent extended evaluation from 1991 to 2024 to assess seasonal dynamics and long-term variability. Results show that Daymet4-r2 outperforms Daymet4-r1 and the original Daymet V4 for all variables (mean absolute error of 1.24 mm, 1.06 °C, 1.29 °C, 6.26%, 0.78 m/s, and 2.04 hPa for precipitation, maximum and minimum temperature, relative humidity, wind speed, and sea level pressure, respectively). The largest improvement was observed in minimum temperature due to an enhanced approach for detecting and modelling thermal inversions. The high performance, flexibility, and ability of Daymet4-r2 to operate without prior calibration highlight its potential for model verification, real-time environmental monitoring, and integration into climate services. Full article

This study adapted and applied a spatially distributed analytical model to estimate the annual representative leached fraction and the annual potential leached mass of atrazine in the Cauquenes catchment in Chile under contrasting Mediterranean hydroclimatic conditions. The model was based on van der Zee and Boesten and Rakonjac et al. and was modified to account for the strong seasonality of precipitation and evapotranspiration by using representative daily hydrological conditions derived from monthly averages. Spatially distributed soil, climate, land-cover, and atrazine application data were integrated at the pixel scale, including locally corrected soil organic carbon, hydraulic properties, precipitation, evapotranspiration, leaf area index, and annual atrazine dose. The model was applied to two contrasting years, 2018 and 2023, and outputs were aggregated at the pixel, land-cover, hotspot, and catchment scales. The results showed a marked hydroclimatic control on potential atrazine leaching. In the drier year, 2018, both the annual representative leached fraction and the annual potential leached mass were generally very low across the catchment, whereas in the wetter year, 2023, moderate-to-high leaching values became much more spatially extensive, and hotspot areas expanded substantially. At the catchment scale, potential leached mass increased from 0.088 kg in 2018 to 179.784 kg in 2023, while the percentage of applied mass potentially leached increased from 5.50 × 10⁻⁵% to 0.112%. Land-cover classes influenced the results both through the spatial allocation of atrazine application and through LAI-dependent partitioning of evapotranspiration. Global sensitivity analysis using the Morris method identified K_OC and DT50 as the dominant controls on annual potential leached mass, and spatial uncertainty propagation was performed. Overall, the proposed framework provides a potential annual screening estimate and may serve as a preliminary screening tool to prioritize areas for targeted monitoring and future model benchmarking in Chile. Full article

In arid and semi-arid regions, groundwater plays an important ecohydrological role in sustaining ecosystem stability under climate-warming-induced surface-water uncertainty. Disentangling precipitation and groundwater recharge effects on vegetation growth remains challenging, limiting robust identification of groundwater-dependent vegetation ecosystems (GDVEs) and quantitative ecological groundwater level estimation. Taking the Daihai Basin, a typical inland closed-lake basin, as a case study, we integrated multi-source remote-sensing data (2005–2025) with in situ groundwater monitoring to develop a comprehensive framework for ecohydrological response analysis and management quantification. Using an improved Mann–Kendall test together with spatiotemporal correlation analyses, we analyzed the spatial relationships between vegetation dynamics and groundwater depth. Results show: (1) basin-wide vegetation exhibits a greening trend (Sen’s slope = 0.00014) with spatial heterogeneity; (2) vegetation dependence on groundwater displays a clear threshold behavior, with low-cover areas (fractional vegetation cover, FVC < 0.3) showing relatively strong groundwater dependency (r = 0.698) whereas high-cover areas exhibit a weaker relationship; and (3) approximate ecological groundwater reference thresholds are estimated as 1.0 m (90% assurance) for forest land and 0.6 m for grass land (80% assurance). The proposed GDVE identification scheme provides a scientific reference for adaptive groundwater management and ecological assessment. Full article

Dasyhippus barbipes (Fischer von Waldheim, 1846) is an early dominant grasshopper in Chinese grasslands, and understanding its climatic niche is important for monitoring and early warning. Based on nationwide field surveys conducted from 2019 to 2024, 732 filtered occurrence records were used with an optimized MaxEnt model parameters (rm = 1.5, fc = LQ) to predict the current and future suitable habitat of D. barbipes in China. The model performed well (mean AUC = 0.962, mean TSS = 0.924). The mean temperature of the coldest quarter and precipitation seasonality were the most important limiting variables, while elevation and human footprint also contributed to habitat suitability. Under current climate conditions, suitable habitat is concentrated mainly in central and eastern Inner Mongolia, with additional suitable areas in the eastern Tianshan Mountains of Xinjiang and the central Qilian Mountains of Gansu; the highly suitable habitat covers 6.57 × 10⁴ km². Under all future climate scenarios, suitable habitat is projected to shift northward, and stable areas are mainly located in eastern Inner Mongolia. Habitat changes become more pronounced by the 2090s. These results improve understanding of the spatiotemporal dynamics of D. barbipes under climate change and provide a basis for future monitoring and management. Full article

Drought is a major global hazard, yet critical knowledge gaps persist regarding how the El Niño–Southern Oscillation (ENSO) modulates it in topographically complex equatorial regions. This study characterizes ENSO’s spatiotemporal influence on drought across Ecuador’s four principal Köppen–Geiger climate zones: Amazon, Andean highlands, temperate, and arid coastal. Using meteorological data (1985–2015), we computed the Standardized Precipitation Evapotranspiration Index (SPEI) across multiple timescales. Ten ENSO indices were evaluated using Wavelet Coherence analysis to identify non-stationary, scale-dependent correlations and phase dynamics. Results show that tropical, temperate, and Andean (polar tundra) climates exhibit prolonged climatic memory, with significant ENSO correlations across 1- to 24-month SPEI scales. Conversely, arid regions display shorter memory, with correlations dissipating at longer timescales due to limited moisture storage. Phase analysis reveals two high-coherence intervals (1995–2000 and 2007–2013) at the 3-year return period, in which ENSO indices led drought by 9–18 months, underscoring their predictive potential. At 6- and 11-year periods, ENSO signals generally lag SPEI, indicating prolonged drought retention. The Trans-Niño Index and Southern Oscillation Index proved particularly sensitive for the Amazon–Andes transition. These findings establish a robust framework for improving drought monitoring and climate adaptation in vulnerable equatorial regions. Full article

Baiyangdian Lake, the largest freshwater lake in North China, plays a critical role in the ecological security of the Beijing–Tianjin–Hebei urban agglomeration. This study conducted systematic monitoring of Baiyangdian Lake from April 2023 to November 2024. Utilizing the Trophic State Index (TSI) and principal component analysis (PCA), we elucidated the impact mechanisms of extreme precipitation events on the water quality of shallow lakes. The results indicate that: (1) During the study period, Baiyangdian Lake exhibited moderate to severe eutrophication. The average total nitrogen (TN) concentration was 2.13 mg/L, exceeding the Class V threshold of the national surface water quality standard. The average total phosphorus (TP) concentration was 0.05 mg/L, far surpassing the recognized eutrophication threshold for freshwater lakes. (2) The average TSI was 49.6 ± 4.0, indicating the lake is in a transitional state from mesotrophy to eutrophy, with 64% of sampling sites classified as eutrophic. Nitrogen was identified as the primary limiting nutrient. (3) The 2023 extreme precipitation event exerted a significant three-phase impact on water quality: “dilution–legacy–restoration”. A clear dilution effect was observed from the pre-flood to the flood period (TN decreased from 1.52 to 1.04 mg/L). A pronounced legacy effect emerged post-flood, with the TN concentration sharply increasing to 4.22 mg/L in September 2023, the highest value recorded during the study. (4) PCA identified two major pollution sources: agricultural non-point source pollution (PC2, contribution: 25.4%) and domestic sewage/livestock farming (PC1, contribution: 27.6%). Correlation analysis further revealed that the flood event significantly altered the intrinsic relationships among parameters like nitrogen and phosphorus, reinforcing the dominance of agricultural non-point source pollution. (5) Source analysis suggests that external inputs are the primary contributors, while the internal loading from sediments is relatively limited. This study enhances the understanding of how shallow lakes respond to extreme climatic events and provides a scientific basis for lake management in the Beijing–Tianjin–Hebei region. Full article