Polymer flooding is one of the most widely implemented chemical-enhanced oil recovery (EOR) techniques for improving sweep efficiency and mobilizing residual oil in mature reservoirs. However, the performance of partially hydrolyzed polyacrylamide (HPAM) flooding is strongly influenced by reservoir temperature, formation water salinity, and polymer concentration, particularly in carbonate formations where harsh reservoir conditions may significantly reduce polymer effectiveness. In this study, laboratory core flooding experiments combined with Response Surface Methodology (RSM) and Analysis of Variance (ANOVA) were employed to systematically investigate and optimize the effects of temperature, HPAM concentration, and salinity on the incremental recovery factor (RF) of matrix-type carbonate core samples. A total of 45 flooding experiments were conducted under temperatures ranging from 20 to 80 °C, polymer concentrations between 500 and 2500 ppm, and salinities from 1000 to 100,000 ppm. A highly significant quadratic model was developed, exhibiting excellent predictive capability (R2 = 0.9991, p < 0.0001) and accurately describing the individual and interactive effects of the investigated variables. Among the examined parameters, HPAM concentration was identified as the dominant factor controlling flooding performance, followed by salinity and temperature. The incremental recovery factor varied from approximately 6 to 19%, and optimization analysis predicted a maximum RF of 18.82% at 20 °C, 2500 ppm HPAM concentration, and 10,000 ppm salinity. Furthermore, optimization under high-temperature and high-salinity conditions revealed that a minimum HPAM concentration of about 2150 ppm is required to maintain RF values above 10%. The proposed experimental-statistical framework provides a reliable tool for predicting and optimizing HPAM flooding performance and offers practical guidance for polymer flooding design in carbonate reservoirs.
Extreme flooding driven by climate change demands strategies to enhance plant tolerance to low-oxygen stress. We investigated the ability of bioactive compounds to chemically prime plants for flooding events. A high-throughput chemical genetic screen of 2237 bioactive molecules was performed using an Arabidopsis thaliana luciferase-based reporter line driven by the ALCOHOL DEHYDROGENASE promoter, a hypoxia-inducible gene involved in anaerobic metabolism. The screen identified chlorquinaldol, an 8-hydroxyquinoline derivative, as a potent inducer of hypoxia responses. Chlorquinaldol inhibits the activity of PLANT CYSTEINE OXIDASES (PCOs), which are crucial oxygen-sensing enzymes in plants, resulting in the stabilization of the ETHYLENE RESPONSE FACTOR type VII and activation of hypoxia-responsive transcription under normoxic conditions. Plants pre-treated with chlorquinaldol showed enhanced tolerance to waterlogging and submergence, indicating that chemical priming of hypoxia responses improves plant tolerance to flooding. This study demonstrates that chemical inhibition of PCOs is an effective strategy for priming hypoxia responses and improving flooding tolerance in plants.
Child maltreatment is a major public health concern worldwide and may worsen during crises, particularly in low- and middle-income countries. Despite recurrent severe flooding in Brazil, evidence on disaster-related changes in police-recorded child maltreatment is limited. To evaluate the impact of the historic 2024 floods in Porto Alegre, Brazil, on official police-recorded child maltreatment crimes. All police-recorded child maltreatment cases involving individuals aged 0-17 years in Porto Alegre from January 2022 to December 2025 (7107 records). An ecological interrupted time-series analysis using quasi-Poisson regression was conducted to estimate immediate and gradual changes in reporting after the floods (May 1-June 30, 2024). Outcomes consisted of weekly counts of official police-recorded child maltreatment crimes. Subgroup analyses were performed by sex, age group, race/ethnicity, maltreatment type, and neighborhood flood severity. Police-recorded child maltreatment showed a significant upward secular trend over the study period (IRR per year = 1.0109; p < 0.001). At flood onset, weekly records dropped by 62.9% relative to the counterfactual expectation (IRR = 0.371; p < 0.001), followed by a significant recovery during the flood period (slope IRR = 1.180; p < 0.001). After floodwaters receded, no significant trend change was observed (p = 0.649). The immediate decline was larger among males, younger children, and physical abuse records. By neighborhood flood impact, severely affected areas showed a 39.6% reduction during the flood (RR = 0.604), moderately affected areas a 32.6% reduction (RR = 0.674), and less affected areas a non-significant 18.2% decline (RR = 0.817); all strata returned to near-baseline levels post-flood. The 2024 floods caused substantial, heterogeneous interruptions in police-recorded child maltreatment in Porto Alegre, with abrupt declines at onset, partial recovery during the flood, and post-flood stabilization. Strengthening administrative monitoring and protection structures is essential to maintaining child safety during climate-related emergencies. These findings reflect changes in formal police reporting rather than the true incidence of child maltreatment.
Anthropogenic climate change has intensified the frequency of catastrophic flooding, contributing to a complex array of public health challenges. Although acute trauma dominates the initial response, the post-disaster dermatological burden remains an under-reported driver of morbidity. This systematic review characterizes the pathophysiology of flood-associated dermatoses into three clinical categories: water-contact injuries, displacement-related conditions, and environmental aeroallergen-induced exacerbations. We conducted a systematic, mechanistic synthesis of 12 epidemiological studies (including retrospective cohorts, time-series, and case-crossover designs) from the USA, Brazil, France, Pakistan, China, Taiwan, Vietnam, Cambodia, and South Korea. Spanning over 71 million patient encounters, the data were rigorously appraised using JBI Critical Appraisal tools. Analysis reveals three distinct clinical phases. First, in high-resource settings, data indicate no significant increase in Staphylococcus aureus infections, although mucosal risks (e.g., leptospirosis, Aeromonas) and chemical irritant dermatitis are observed. Second, flood-associated humidity and wind independently drive acute atopic dermatitis flares (odds ratio [OR] 1.14) and allergic rhinitis, independent of direct water contact. Third, shelter overcrowding drives a delayed surge of communicable infestations (e.g., scabies, pediculosis) approximately 1 week post-disaster. Flood-associated skin conditions are heterogeneous. Preparedness requires moving from non-specific antibiotic prophylaxis to a stratified response involving targeted mucosal monitoring, ectoparasite control in shelters, and anti-inflammatory therapy for atopic populations.
Urban pluvial flooding (UPF) evolves rapidly in space and time, making physically realistic and near-real-time prediction difficult, particularly in urban settings where full-field observations are scarce. Mechanistic models can represent inundation processes with strong physical interpretability but remain computationally expensive, whereas purely data-driven models are efficient but depend heavily on large amounts of high-quality training data. To address this trade-off, this study develops a mechanism-data hybrid pretraining-finetuning framework for rapid UPF prediction. In the pretraining stage, hydrodynamic priors are learned from randomized environmental scenarios, mechanistic teacher signals, and conservation-based constraints. In the finetuning stage, limited real-world observations are used to adapt the pretrained model to local conditions. Applied to a real UPF event, the hybrid model achieved the highest site-scale simulation accuracy with NSE values above 0.9. It also produced physically coherent inundation patterns, completed a 24 h simulation 32 times faster than the mechanistic model, and remained robust under environmental and rainfall perturbations. These results demonstrate that mechanism-data hybrid learning can balance mechanistic fidelity, local observational adaptation, and computational efficiency, providing a practical route for rapid UPF prediction under limited observations.
Jakarta, the world's fastest-sinking city, faces complex urban challenges from the complexity of its urban morphology, infrastructure, and environmental conditions. This study presents a descriptor for a multidimensional database of Jakarta, Indonesia, that can be used to analyse the city's subsidence and understand the frequent flooding events throughout the city. The data comprise four different dataset modelled using satellite imagery: (1) land subsidence modelling to quantify subsidence rates in Jakarta based on Sentinel-1 SAR data processed using the open-source package LiCSBAS (2) spectral indices to highlight vegetation and built-up areas, namely the Normalised Difference Vegetation Index (NDVI) and the Normalised Difference Building Index (NDBI) using Landsat 5, Landsat 7, and Landsat 9 imagery, and urban morphology datasets, including (3) impervious surface areas using Sentinel-2 based on adaptation of the Enhanced Normalised Difference Impervious Surface Index (ENDISI) formulation and (4) the proportion of residential areas modelled using K-means clustering. Data processing was performed using Google Earth Engine (GEE) and Python to generate a critical dataset to support analysis and modelling of urban resilience, climate adaptation, and mitigation towards flooding in the context of sinking city. Robust technical validation techniques were performed to ensure data accuracy and validity, with detailed limitations presented. The result presents a series of modelled datasets that can inform further urban analysis and modelling related to flooding events in Jakarta, with a methodology that can be replicated globally, especially in other Global South cities.
Sustaining rice productivity under the dual constraints of freshwater scarcity and low-temperature stress represents a pressing challenge for high-latitude japonica rice systems worldwide. There is an urgent need to develop coupled irrigation-agronomic management strategies that jointly safeguard yield stability and water use efficiency (WUE) in cold-region rice production. In this study, a two-year field experiment was conducted in 2024-2025 on albic soil (Albic Luvisols, WRB; θfc 38.2% v/v, pH 5.80, clayey texture with poor permeability and a propensity for subsurface waterlogging) in the Sanjiang Plain, Heilongjiang Province, China (47°15' N, 133°28' E), with nine coupled "irrigation regime × auxiliary practice" treatments, comprising conventional continuous flooding, four-level controlled irrigation (CI) at lower thresholds of 60%, 70%, 75%, and 80% θfc, and their combinations with film mulching (FM) or a humic-acid-based soil amendment (SA). An interpretable machine-learning diagnostic framework was developed, with elastic net (EN) as the primary analytical model and random forest (RF) as a nonlinear control, to simultaneously identify core yield predictors and outlier treatments. The principal findings were: (i) The soil-amendment-coupled 75% θfc CI treatment (SACI) increased grain yield by 12.3% and reduced water input by 17.0% relative to conventional continuous flooding, with WUE reaching 1.801 kg m-3, a 35.3% gain over the control (p < 0.05); these improvements were consistent across both individual years (year × treatment interaction: p = 0.601; inter-year rank correlation ρ = 0.967). Lowering the CI threshold below 75% θfc significantly reduced grain yield through diminished effective-panicle retention. (ii) Multi-method consensus analysis (Kendall's W = 0.871, p < 0.01) identified root volume at the milk stage as the most strongly and consistently associated statistical predictor of yield formation, with convergent mechanistic support from independent rhizosphere evidence (Eh, TTC reductive activity). Definitive causal validation awaits isotope-tracing experiments. (iii) The film-mulching × continuous-flooding treatment (FMCG) was diagnosed as a yield-response outlier (permutation test p = 0.003), three in situ rhizosphere measurements (redox potential, root TTC-reducing activity, and rhizosphere temperature) supported the proposed mechanism of hot-anoxic rhizospheric inhibition. Methodologically, this study develops a four-level evidence convergence framework that integrates intra-model self-consistency, cross-model (EN vs. RF) consensus, independent rhizosphere evidence, and distribution-free permutation testing, with Jackknife+ conformal prediction and companion Monte Carlo simulations (1000 replicates) used to quantify the reliability boundaries under small-sample conditions (n = 27). These findings provide an evidence-based irrigation-soil co-management strategy for cold-region rice production in Northeast China, and the proposed diagnostic paradigm offers a generalizable, reliability-quantified methodological template for interpretable small-sample modeling in multifactorial coupled field experiments.
Climate change has intensified public health challenges in coastal Bangladesh, particularly increasing the prevalence of diarrheal diseases. This study assesses the vulnerability to diarrheal diseases in four coastal districts-Satkhira, Jhalakathi, Barguna, and Gopalganj-by examining the relationship between climate variability and disease occurrences. A mixed-methods approach was used, integrating hospital admission records (n = 46,741 diarrheal cases identified from 361,265 patients) from five government health facilities between January 2017 and December 2022 with meteorological data from the Bangladesh Meteorological Department. Key Informant Interviews (KIIs) and Focus Group Discussions (FGDs) were conducted in all four districts. A total of 28 KIIs and 9 FGDs were conducted across the selected upazilas to collect field-level information. Findings indicate a significant positive correlation between maximum temperature and diarrheal incidence (r = 0.228, p = 0.0012). Diarrhea cases were more prevalent at maximum temperatures of 30-35 °C. In contrast, rainfall showed no statistically significant association with diarrheal trends (r = 0.094, p = 0.235), although descriptively fewer cases were observed at rainfall levels above 60 mm. Seasonal analysis revealed peak diarrheal cases between March and May, coinciding with the hottest months of the year. Age-specific analysis showed that children aged 0-3 years accounted for 37.6% of total cases, and females represented 52.7% of reported cases (χ2 test, p < 0.001). Qualitative findings from KIIs and FGDs highlighted salinity intrusion, flooding, waterlogging, inadequate sanitation, and limited healthcare resources as key drivers of vulnerability in the study districts. Vulnerable groups, including children under five and women, are disproportionately affected due to exposure to contaminated water and healthcare access constraints. To mitigate climate-induced diarrheal diseases, the study recommends improvements in water and sanitation infrastructure, enhanced healthcare services, and community-based awareness programs. Integrating climate resilience into public health policies is essential to reducing disease vulnerability in coastal regions.Findings indicate a significant positive correlation between maximum temperature and diarrheal incidence (r = 0.228, p = 0.0012). Diarrhea cases were more prevalent at maximum temperatures of 30-35 °C. In contrast, rainfall showed no statistically significant association with diarrheal trends (r = 0.094, p = 0.235), although descriptively fewer cases were observed at rainfall levels above 60 mm. Seasonal analysis revealed peak diarrheal cases between March and May, coinciding with the hottest months of the year. Age-specific analysis showed that children aged 0-3 years accounted for 37.6% of total cases, and females represented 52.7% of reported cases (χ2 test, p < 0.001). Qualitative findings from KIIs and FGDs highlighted salinity intrusion, flooding, waterlogging, inadequate sanitation, and limited healthcare resources as key drivers of vulnerability in the study districts. Vulnerable groups, including children under five and women, are disproportionately affected due to exposure to contaminated water and healthcare access constraints. To mitigate climate-induced diarrheal diseases, the study recommends improvements in water and sanitation infrastructure, enhanced healthcare services, and community-based awareness programs. Integrating climate resilience into public health policies is essential to reducing disease vulnerability in coastal regions.
Rhamnolipids are attractive biosurfactants for enhanced oil recovery, but the commonly used producing strains may raise biosafety concerns. In this study, a rhamnolipid-producing isolate, designated Bacillus sp. DQ-4, was obtained from oily sludge and cultivated in a glucose-based fermentation medium. The purified product, DQ-Rha, was characterized by TLC, FTIR, MALDI-TOF MS, and 1D/2D NMR (1H, 13C, 1H-1H COSY, 1H-13C HSQC, and HMBC). The combined spectroscopic results were consistent with a rhamnolipid structure, and no obvious conflicting signals were detected. DQ-Rha reduced the surface tension to 33.4 mN/m, and the DQ-4 supernatant showed an oil-spreading diameter of 89.3 mm and an emulsification index of 72.3% against diesel. In oil-displacement-related evaluations, DQ-Rha gave an oil-washing efficiency of 54.62%. In etched micromodel experiments, the total recovery factor reached 60.06%, which was comparable to that of commercial rhamnolipid (59.53%). In core flooding experiments at 85°C, injection of DQ-Rha after primary water flooding further increased the recovery factor by 10.56%, again showing performance comparable to commercial rhamnolipid. Acute oral toxicity testing in ICR mice showed no mortality or obvious toxic symptoms at 5040.6 mg/kg, and the acute oral LD50 was greater than 5000 mg/kg under the test conditions. These results suggested that DQ-Rha was a rhamnolipid biosurfactant with favorable oil-displacement-related performance and low acute oral toxicity.
Primates constitute an integral part of many African ecosystems. In some areas, primate species can be locally very abundant, while in others they are rare or absent. Our central question is: does the abundance of primate bones (or carcasses) reflect the abundance of local living populations? Past bone surveys in Amboseli National Park, Kenya, and Virunga National Park, Democratic Republic of Congo, revealed a positive correlation between carcass abundance and living mammal communities, with a notable exception: a low abundance or absence of primate carcasses when compared to other medium-sized and large mammals. Recent research conducted at Gorongosa National Park, Mozambique, revealed a similar discrepancy between the abundance of primate carcasses and mammalian live census data. This study aims to understand this mismatch between live and dead by examining the influence of ecological factors on primate carcass occurrence and bone distribution in the modern rift ecosystem of Gorongosa National Park. To address these issues, we investigated whether (1) landscape type, proximity to roads, and water sources (pans, rivers, and lakes) predict primate carcass occurrence relative to other mammal taxa, and (2) the accumulation of primate bones significantly differs from that of other mammals in annually flooding and non-annually flooding landscapes (broadly speaking, in the floodplain vs. higher areas). We analysed 26 bones from 18 chacma baboon individuals (Papio ursinus) and 255 bones from 20 other mammal species. Generalised Linear Model (GLM) outputs suggest that permanent water sources such as rivers and lakes are key drivers of primate bone accumulation (p < 0.05). A Fisher's Exact Test indicates that primate bones are deposited more frequently in Rift Valley Riverine & Floodplain landscapes (p < 0.05). Given that baboons regularly cross rivers in Gorongosa, these findings also suggest that crocodile predation may play an important role in determining where baboon carcasses ultimately accumulate, potentially contributing to the observed mismatch between living and dead communities. These findings have important implications for our understanding of primate bone accumulations in the African fossil record, where some palaeontological sites contain abundant primate fossils whereas others preserve few or none.
Flooding is a major natural hazard in Ghana, with the White Volta Basin (WVB) highly susceptible due to flat terrain, intense rainfall, and upstream dam releases. The September 2020 flood, caused by heavy rainfall and Bagre Dam spillage, inundated croplands and settlements, revealing the need for impact-based flood intelligence. This study uses the Hydrologic Engineering Center's River Analysis System to simulate flood dynamics and assess cropland exposure. Calibration and validation with Sentinel-1 Synthetic Aperture Radar-derived flood extent showed strong spatial agreement (61-77%). A composite flood hazard index, combining water depth and velocity, classified hazard intensity into seven levels, linking hydraulic severity with crop impacts. The framework guides community-level early warning and preparedness, supporting decision-making by the National Disaster Management Organization (NADMO), Ministry of Food and Agriculture (MoFA), Water Resources Commission (WRC), and Ghana Meteorological Agency (GMet). High-risk areas included Bawku West (22.7%), Binduri (15.1%), and Talensi (6.1%), aiding climate-resilient planning in transboundary basins.
Heavy oil accounts for approximately 70% of global remaining oil reserves, and supercritical CO2 (scCO2) injection is a promising technology for heavy oil viscosity reduction, enhanced oil recovery, and carbon sequestration. In this paper, molecular dynamics (MD) simulation was employed to construct an scCO2-heavy oil system based on the saturates-aromatics-reins-asphaltenes (SARA) components of ZD crude oil (a representative heavy oil used in this study). The radial distribution function (RDF), mean square displacement (MSD), diffusion coefficient, cohesive energy density (CED), and solubility parameter were used to analyze the swelling, extraction, and miscibility processes of scCO2 in heavy oil, as well as the effects of temperature and pressure on system phase behavior. Results show that scCO2 molecules preferentially aggregate at the oil-gas interface, penetrate into heavy oil droplets, induce volume swelling and phase separation, and selectively extract saturated and aromatic hydrocarbons. Temperature and pressure significantly affect scCO2 solubility, intermolecular interaction, and diffusion behavior, thereby dominating the viscosity reduction effect. This paper reveals the microscopic viscosity reduction mechanism of scCO2 on heavy oil and provides theoretical support for field applications of scCO2 flooding in heavy oil reservoirs.
This research provides the first quasi-experimental and longitudinal examination of the Social Identity Model of Traumatic Identity Change among individuals affected by the September 2024 flood in Poland. Advancing the state of the art, we analyzed the emergence of identification with flood-affected people and extended the model by incorporating the sense of shared experience (SSE) within this group. Surveys were completed 3 months (N = 630) and 7 months after the flood (N = 315). The results indicated that the severity of flood exposure was linked with higher post-traumatic stress (PTS) and post-traumatic growth (PTG). Repeated trauma strengthened both identification and SSE with those affected by the flood. Over time, SSE remained stable, whereas identification decreased, suggesting that SSE serves as a more enduring psychosocial resource than group identification. SSE predicted the subsequent PTG but not the PTS, highlighting its positive role in adaptive coping, while PTS contributed to later increases in SSE. Multi-group analyses comparing participants from flood-prone areas who were at risk but had not experienced flooding, those with less recent flood exposure, those with recent flood exposure, and those experiencing re-traumatization from both past and recent floods revealed that the pathways linking identification, SSE, and post-traumatic outcomes differed across these groups. These findings demonstrate the differential function of social processes under varying trauma contexts. Overall, this research underscores the importance of considering exposure history in post-disaster interventions and highlights how social identity and shared experience can both support resilience and shape recovery trajectories.
Hierakonpolis was a major population and political center in Upper Egypt during the Predynastic era (c. 3800-3100 BC). Its ancient remains are preserved mainly as negative archeological features (i.e., structural cuts that disrupt the natural soil), such as postholes and foundation trenches for buildings of wood. One of these constructions is a large palisade wall, of which over 50 m of its length has been uncovered by archaeologists. To trace the buried continuation of this structure without excavation, researchers conducted a ground-penetrating radar (GPR) survey using a high-frequency (900 MHz) antenna. Despite subtle physical contrasts between the remains and the soil, the arid conditions allowed the GPR to detect the foundation trench as a lateral discontinuity within the Nile silt layer that extends across the entire study area. GPR scans revealed that the wall continues westward along the same trajectory as its excavated segments for an additional 18 m before the signal terminated. Analysis of GPR facies suggests that repeated flooding from an ancient Nile channel compromised the site's stability, likely resulting in the destruction of the wall in the northwestern sector. Archaeological findings indicate the wall demarcated a large ceremonial and administrative complex, while the survey results also suggest it had a broader functional role as a defensive barrier against Nile floodwaters and other natural threats.
The electrocatalytic CO2 reduction reaction (CO2RR) in membrane electrode assemblies (MEAs) represents a pivotal technology for carbon neutrality, yet its industrial deployment is severely restricted by the carbonate dilemma. Traditional alkali-cation systems (e.g., K+ and Cs+) suffer from salting deposition and carbonate crossover, which lead to gas diffusion electrode flooding and abrupt mass-transport failure. This review provides a comprehensive and timely analysis of non-alkali cationic systems, including inorganic ammonium salts, quaternary ammonium salts, and cationic polyelectrolytes, as a superior alternative for high-performance CO2 electrolysis. We systematically elucidate the multifaceted roles of cations based on their molecular level involvement: (1) indirect mediators, cations modulate local pH and exert electrostatic repulsion on H+ (protons); (2) energetic modulators, cations modify the energy of intermediates and reorganize the hydrogen-bonding network; and (3) reaction participants, cations serve as proton sources and co-catalytic species in the reaction transition state. For each category, we assess their functional role in the reaction, highlighting unique advantages and inherent limitations. Furthermore, we summarize multidimensional characterization techniques and multiscale theoretical simulation tools employed to unravel the complex kinetic processes at these interfaces. Finally, we propose a strategic roadmap for transitioning from nanoscale molecular design to macroscopic, facilitating the eventual commercialization of CO2 valorization.
Rapid urban growth, tourism pressure, and ecological sensitivity pose significant challenges to solid waste management in coastal districts. Alappuzha District of Kerala state of India, characterized by the Vembanad-Kol Ramsar wetland system, dense backwater networks, and recurrent flooding, represents a complex setting where conventional landfill siting approaches are often inadequate. This study applies and refines a Geographic Information System (GIS)-based Multi-Criteria Decision Analysis (MCDA) framework integrated with the Analytical Hierarchy Process (AHP) to identify suitable landfill sites in Alappuzha District, with emphasis on methodological transparency in a hydrologically constrained coastal environment. Ten criteria were integrated into the spatial model: distance from water bodies, settlements, road networks, agricultural land, and plantation areas; flood-prone areas; slope; groundwater depth; soil permeability; and population density. Seven primary environmental criteria were evaluated using expert-based pairwise comparisons (n = 12) following the AHP protocol (Consistency Ratio, CR = 0.003), while groundwater depth, soil permeability, and population density were incorporated through a structured supplementary elicitation process involving the same expert panel. Standardized criteria layers were integrated using a weighted-overlay approach in ArcGIS, and model stability was evaluated through sensitivity analysis applying ± 15% variation in criterion weights. Only 51.0 km² (3.6%) of the district was classified as moderately to highly suitable for landfill siting, reflecting substantial hydrological, hydrogeological, and environmental constraints. Three candidate sites ranging from 2.1 to 4.8 hectares were identified in Cherthala and Chengannur taluks, all requiring field verification and statutory Environmental Impact Assessment before implementation. Sensitivity analysis indicated strong model stability (Spearman's r ≥ 0.88) across all perturbation scenarios. This study refines GIS-MCDA-based landfill suitability assessment for flood-prone coastal environments and provides a transferable framework for hydrologically constrained regions where comparable spatial datasets are available.
N2/CO2 flooding coalbed methane storage technology is an effective technology to improve coalbed methane recovery and CO2 geological storage at the same time. In this paper, considering the influence factors such as heat conduction and coal deformation, the characterization volume element method is used to describe the adsorption, desorption and seepage of ternary gas in coal and the related changes caused by coal matrix. The coupling model of N2, CO2 and CH4 ternary gas displacement adsorption is established, and the reliability of the model is verified. The results show that CO2 is the main reason to strengthen CH4 production. Simple CO2 injection has a significant adsorption and expansion effect on coal body, and high-pressure N2 gas is needed to drive CO2 to migrate to the far field of coal body. However, too high N2 gas injection ratio is more likely to lead to production well breakthrough, which reduces the recovery rate of CH4. The higher the proportion of CO2 injection, the greater the total adsorption heat released, and the larger the range of coal temperature rise. When the proportion of CO2 injection is too low, the temperature near the injection well first decreases slightly and then increases rapidly due to the combined effects of gas expansion cooling and CO2 adsorption heat release. The permeability ratio on the route from the injection well to the production well increases rapidly and then decreases slowly. The lower the CO2 injection ratio, the farther away from the injection well, the lower the permeability of the coal body. However, with the increase of CO2 injection ratio, the permeability of the coal body shows a trend of increasing first and then decreasing. When the CO2 injection ratio increases to 60%, the change trend of permeability ratio at different injection time is highly similar. After that, it is necessary to increase the injection ratio of N2 to drive CO2 to migrate to the far field coal body. Considering the recovery rate of CH4 and the storage effect of CO2, 60% CO2 + 40% N2 is the best injection ratio. This study provides a theoretical basis for optimizing the N2/CO2 mixed injection ratio to balance CH4 recovery and CO2 storage in coalbed methane reservoirs, supporting sustainable energy development and greenhouse gas emission reduction.
Communities with histories of industrial contamination may face heightened exposures when compound climate events interact with persistent organic pollutants. Limited multi-matrix data exist are available to evaluate how sequential wildfire and flood disturbances may influence contaminant distribution in environmental justice (EJ) communities. This study characterized polycyclic aromatic hydrocarbons (PAHs) in nonresidential soil/sediment (n = 75), residential surface soil (n = 35), control soil (n = 24), and residential indoor dust (n = 35), and polychlorinated dibenzo-p-dioxins/dibenzofurans (PCDD/Fs) in selected 2-15 cm nonresidential and control soils, in the Globe-Miami area of Arizona, USA following the 2021 Telegraph and Mescal wildfires and subsequent flash floods. PAH diagnostic ratios classified 91-100% of samples across matrices as pyrogenic, while residential soils showed strong correlations among high molecular weight PAH co-correlations (r = 0.87-0.97) consistent with co-deposition/redistribution of combustion-derived particles. Residential soil ΣPAH16 ranged from 0.017 to 15,860 μg kg-1 and benzo[a]anthracene and benzo[a]pyrene exceeded USEPA soil-to-groundwater soil screening levels (SSL) in 40% and 20%, respectively. Indoor dust ΣPAH16 ranged from 1.667 to 167.5 μg m-2, with greater lower-molecular-weight contributions than outdoor soil, suggesting indoor-specific sources and partitioning processes in addition to possible outdoor-to-indoor transport. For PCDD/Fs, 16 of 17 congeners detected in flooded, nonresidential soils compared with 13 in controls; however, total PCDD/F concentrations and overall congener composition did not differ significantly (p = 0.22). Nonresidential soil toxic equivalency quotient (TEQ) values ranged from 0.192 to 29.6 ng kg-1, with the highest value observed at Icehouse Canyon, consistent with localized legacy dioxin contamination, including historical Agent Orange-related herbicide application as one plausible contributor. All samples exceeded the 2,3,7,8-Tetrachlorodibenzo-p-dioxin USEPA soil-to-groundwater SSL of 0.059 ng kg-1. Overall, findings support a layered-source interpretation in which combustion-derived PAHs and persistent legacy PCDD/Fs may coexist and be redistributed through post-fire runoff and flooding in these climate-vulnerable EJ communities.
Phytohormones act as key endogenous factors and signaling molecules that mediate abiotic stress responses in plants and are the integration centers of plant responses to environmental stimuli, playing an important role in plant resistance to drought, salt, cold, and other stresses. Stress responses are finely regulated through a complex network of different classes of phytohormone signaling pathways. Many transcription factors are able to regulate the content of endogenous plant hormones by influencing hormone synthesis and metabolic gene and stress-related gene expression, which in turn affects plant growth and development and improves plant tolerance to abiotic stresses. Signaling molecules in plant stress responses, such as abscisic acid, ethylene, gibberellin, jasmonic acid, and salicylic acid. Their roles in orchestrating plant responses to abiotic stresses. With global climate change, abiotic disasters have become increasingly frequent in recent years, severely hindering crop growth and development. Nanomaterials have attracted widespread attention from researchers because they can significantly alleviate abiotic stress in crops caused by factors such as salinity, drought, flooding, and heavy metals. This paper reviews recent research progress on the use of phytohormones and nanomaterials to alleviate abiotic stress in plants and elaborates on their underlying mechanisms of action. In the future, we will focus on investigating the roles of phytohormones and nanomaterials in modulating plant responses to abiotic stress, thereby enhancing plant tolerance to such stresses and increasing crop yields to address food security challenges.
Riparian wetlands play a crucial role in nutrient retention and water quality maintenance in agricultural watersheds. Restoring wetland function in these systems is becoming increasingly important as negative impacts of eutrophication continue to increase in both local and downstream ecosystems. This study identified factors regulating wetland soil denitrification rates, a major nitrogen (N) removal pathway, across various wetland restoration practices (based on hydrology and plant structure) in restored agricultural bottomland hardwood forested wetlands. Soil cores from five distinct restoration practices, natural vegetation regeneration, remnant forest, tree planting areas, and constructed shallow water areas (wet and dry), were collected in 23 restored wetlands in Kentucky and Tennessee, USA. Flow-through soil core incubations were used to estimate denitrification as nitrogen gas (N2) flux during a simulated 2-day flood event. All restoration practices produced N2 at each timepoint, and the rates were greater at 48 h for all practices. Mean N2 production was highest in natural regeneration and lowest in shallow water-wet areas throughout the 48 h incubation period. However, shallow water-wet areas exhibited the greatest percentage increase between 24 and 48 h, increasing by 48%. The predicted N2 production was correlated with sediment oxygen demand (SOD), initial soil moisture, and extractable soil phosphorus (P). These results suggest that all restoration practices efficiently remove N over a 48 h flood period; however, the highest removal rates can depend on the vegetation type, flooding duration, and site-specific soil properties.