Abstract: This study addresses the problem of computational cost of transient CFD simulations, which rely on iterative time-step calculations, by employing deep learning to generate optimized initial conditions for accelerating the Weather Research and Forecasting (WRF) model. To this end, we forecasted wind speed for short time frames over Houston region using the WRF model data from 2018 to 2022, training the models to predict the wind speed X-component (U). The so-called global FNO model, trained across all atmospheric heights, was first tested, achieving competitive results. A more refined approach was tested to improve it further, training separate models for each altitude level, enhancing accuracy significantly. These ad hoc models outperformed surface and middle atmosphere persistence, achieving 27.64% and 20.46% nRMSE, respectively, while remaining competitive at higher altitudes. Variable selection played a key role, revealing that different physical processes dominate at various altitudes, necessitating distinct input features. The results highlight the potential of deep learning, particularly FNO, in atmospheric modeling, suggesting that tailored models for specific altitudes may enhance forecast accuracy. Thus, this study demonstrates that a deep learning model can be designed to start the iterations of a transient simulation, reducing convergence time and enabling faster, lower-cost predictions.

Abstract: PDE-based hydrologic models demand extensive preprocessing, which can create a bottleneck and slow down the model setup process. Mesh generation typically lacks integration with hydrological features like river networks. To address this, we present GHOST Mesh (GMesh), an automated, watershed-oriented mesh generator built within the Watershed Modeling Framework (WMF). While primarily designed for the GHOST hydrological model, GMesh’s functionalities can be adapted for other models. GMesh enables rapid mesh generation in Python by incorporating Digital Elevation Models (DEM), flow direction maps, network topology, and online services. The software creates Voronoi polygons that maintain connectivity between river segments and surrounding hillslopes, ensuring accurate surface-subsurface interaction representation. Key features include customizable mesh generation and variable refinement to target specific watershed areas. We applied GMesh to Iowa’s Bear Creek watershed, generating meshes from 10,000 to 30,000 elements and analyzing their effects on simulated stream flows. Results show higher mesh resolutions enhance peak flow predictions and reduce response time discrepancies, while local refinements improve model performance with minimal additional computation. GMesh’s open-source nature streamlines mesh generation, offering researchers an efficient solution for hydrological analysis and model configuration testing.

Abstract: Pesticides are chemicals used in agriculture, industry, and households to control pests and enhance crop yields but have emerged as pollutants in soil and water due to their presence in domestic and agricultural wastewater effluents. The World Health Organization (WHO) has identified the development of pesticide resistance as a significant threat to global public health. Consequently, removing pesticides in aqueous environments has gained considerable attention. Numerous methodologies, including biological, physical, and chemical methods, have been employed for their treatment. Among these methods, advanced oxidation processes (AOPs) have garnered particular interest due to their fast reaction rates and strong oxidizing abilities. This review focuses on various AOPs such as Fenton and Fenton-like oxidation, Ozonation, UV/H2O2 process, electrochemical oxidation, Photocatalytic oxidation, and UV/O3 process. The review analyzes and summarizes the current applications of these AOPs for treating pesticides in in aqueous environment. It also compares various AOPs treatment methods and discusses the challenges, the drawbacks, advantages, strategies for addressing these issues, and provides insights into the future prospects. Finally, propose potential strategies and areas of improvement for future research to enhance the efficiency and sustainability of AOPs in practical application.

Abstract: Biomass burning (BB) results from complex interactions between ecosystems, humans, and climate, releasing large amounts of gases and particles. Accurate BB emission estimates are essential for air quality, climate studies, and impact assessments. Various existing bottom-up BB emission inventories show significant discrepancies, varying by factors of 2 to 4 due to uncertainties in burned area (BA), emission factors (EF), and vegetation parameters such as biomass density (BD) and burning efficiency (BE). Here, we investigate the role of vegetation parameters in these discrepancies in Africa. Two BB emission inventories, AMMABB-like (African Monsoon Multidisciplinary Analysis Biomass Burning) and GFED-like (Global Fire Emission Database) were developed for Organic Carbon (OC) and Black Carbon (BC). Both inventories used identical fire products, vegetation maps, and EF but different BD and BE values. Results highlight substantial differences in BD and BE, with relative gaps ranging from 44% to 85.12%, explaining the observed differences between BB emission inventories. Key vegetation classes responsible for BB emissions were identified. Discrepancies of 2.4 to 3.9 times were observed between AMMABB-like and GFED4-like, with higher values in the Southern Hemisphere. Better BD and BE estimates with regional distinctions for both hemispheres would improve BB emission accuracy in Africa.

Abstract: Accurately understanding the morphological descriptions of plants by ancient people more than 1,000 years ago, and determining the plant species that people described, is of great value in understanding the natural geographic distribution of plant taxa, the var-iation of plant taxa, and climate change. The variation on the plant group and the climate at that time is critical for understanding the change of combination of plant taxa and the climate and the impact of human activity. However, there is limited research in this area. More studies have focused on plant taxa from billions of years ago or money millions of years ago. Research on the plants and flora of this period is limited. And little was known about climates prior to the millennium. In this study, the special text was selected, and the plant names, plants' morphological features were gathered and plant taxonomy were carried out. The study identified 3 species of gymnosperms, namely Pinaceae and Cu-pressaceae, one species of Tamaricaceae monocotyledon, and 19 species of dicotyledons. However, three plant groups could only be identified to the level of genus. In our study, we reconstruct the climate of 1.475 millennia ago through plant textual research and woody plant coexistence analysis in the western section of Henan Province in eastern Asia, in the middle and lower reaches of the Yellow River. The results showed that the mean temperature of the coldest month was around 1.3°C higher than that in modern times, the mean temperature of the warmest month and mean annual temperatures were lower than the corresponding modern values 1.475 millennia ago, implying that tem-peratures in the Luoyang region were slightly lower with a respective mild change at the time, which was supported by other studies of the same period. At the same time, the study concluded that 1.475 millennia ago, the ancient Luoyang region, located in the interior of central East Asia, had high temperatures and rainfall in the summer and low temperatures in the winter, but the mean annual precipitation, the hottest seasonal pre-cipitation, and the coldest seasonal precipitation were all higher than those in the modern Luoyang region. Despite East Asia's predominantly monsoonal climate, the water con-tent of air currents was significantly higher than it is today. This study provides high-resolution plant and climate background information for rebuilding the ecological environment in East Asia.

Abstract: Amidst the backdrop of the fossil fuel energy crisis, the development of renewable energy sources is experiencing an unprecedented acceleration in Spain and focusing in metropolitan areas. This study investigates the potential for photovoltaic energy development in Spanish metropolitan areas, specifically Madrid and its surrounding region. Recognizing the inherent challenges of land use and material scarcity associated with this development, the research aims to quantify the maximum achievable photovoltaic capacity for the region, along with the corresponding land occupation and material consumption requirements. A Material Flow Analysis (MFA) methodology is employed to project these parameters to 2050. The analysis estimates a potential production capacity of 32,163 GWh/year, representing 76.81% of the projected electricity consumption in 2050 (and 39.94% of final energy consumption). This capacity would necessitate the utilization of 32,169 hectares of land (4.01% of the regional area), including 7,139 hectares of rooftop space. Critically, 48% of the suitable land is classified as agricultural land, highlighting potential land-use competition. Furthermore, the study extrapolates the material requirements to a global scale, estimating the percentage of global mineral reserves required for a comparable energy transition. The analysis yields an estimate of 0.75% for aluminum, 17.69% for copper, and 34.89% for silver. These findings provide crucial insights into the material and geographical constraints impacting the feasibility of urban energy transitions.

Abstract:

Both natural gas production and fossil fuels production are the main sources to most of the energy consumption, this gas presented a series of impurities, i.e. CO2, which needed to be eliminated in order to prevent several concerns as the corrosion of equipments, greenhouse gas emissions and others. It is thus clear, that the development of efficient CO2 capture and storage processes are important to reduce both CO2 production and its contribution to global warming. CO2 can be capture from gas streams by three technologies: absorption, adsorption and membranes, however, they have some challenges in its utilization to be resolved, and some groups of scientist try to resolve it by the inclusion of deep eutectic solvents in them. In the present work, the most recent developments (2024 year) in CO2 capture using deep eutectic solvents (DESs) jointly to absorption, adsorption or membrane-based technologies have been reviewed.

Abstract:

The assessment of surface water quality of Danube river and Black Sea was performed taking into account the amounts of 9 heavy metals (As, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Zn), nutrients (6 N and P compounds, chlorophyll a), emerging contaminants (pharmaceutics and endocrine disruptors) and heterotrophic bacteria and total coliforms (fecal indicator bacteria) in thirty-two locations from the lower Danube sector (starting with km 375 up to the river mouths), the Danube Delta Biosphere Reserve (three Danube branches – Chilia, Sulina and Sf. Gheorghe) and the Romanian coastal area of the Black Sea. The results for heavy metals, nutrients and bacteria were compared with norms set up in the national legislation for good ecological status for surface water. The concentrations of pharmaceutics and endocrine disruptors from various classes (19 quantified compounds, out of 30 investigated chemicals) were compared with values reported for Danube River water in other studies performed in various river sectors. Correlations between contaminant levels and physicochemical parameters of water samples were studied. This is the first study carried out in the connected system Danube River–Danube Delta–Black Sea for a large palette of toxicants classes and microbial pollutants.

Abstract: Agriculture is increasingly challenged by soil nutrient depletion, climate variability, and resource inefficiency. Advanced technologies have to be incorporated into agriculture and this makes the present assessment of the possible revolution the AI and ML in developing the crop and fertilizer recommendation systems relevant. Drawing from global research and case studies, multiple methodologies are combined including the neural network, a decision tree, as well as complex ensemble models of Random Forest and XGBoost, which can determine the optimality of soil nutrient management and crop selection, while simultaneously ensuring balance between environmental sustainability and economic productivity through tailored recommendations based on soil properties that are detailed on nitrogen (N), phosphorus (P), potassium (K), and pH levels. Some of the existing gaps in pre-existing research, like limited adaptability to localized agricultural conditions, open avenues for future interdisciplinary innovations. Reviewing the book underlines how AI-driven insights can change traditional farming practices toward enabling sustainable agricultural systems that harmonize with the dynamics of nature.

Abstract: The North American Monsoon (NAM) in southern Arizona continues to be a topic of interest to many ecologists studying the triggers and characteristics of plant growth and reproduction in relation to the onset of the monsoon. The purpose of this article is to report interannual variation in the timing of NAM onset found while researching the phenology of Saguaro cactus (Carnegiea gigantea). Using a daily rainfall dataset from 33 stations located in Pima and Pinal Counties, Arizona, from 1990-2022, we analyzed monsoon onset, monsoon precipitation, annual precipitation, and the proportion of annual station precipitation received during the monsoon season. Onset was measured by the first day from 1 June to 30 September with precipitation ≥10 mm counted from the day of the vernal equinox of the year. Generalized Additive Models (GAMs) identified sinusoidal waves with period of 8.6 years and amplitudes of 14-29 days, providing frequency and amplitude estimates for Sinusoidal Regression Models (SRMs). Sinusoidal wave patterns found in the monsoon onset dataset are suggested in monsoon, annual, and proportion of monsoon in station-averaged annual precipitation although in and approximately mirror-image. These unexpected findings may have important implications for forecasters as well as ecologists interested in plant phenology.

Abstract: The air-sea transfer of gases is important within climate physics, biogeochemistry and the control of pollutants. A two-layer model of transfer directly across the sea surface underpins most discourse, but an expanding literature also features transfer mediated by “suspended fragments”, either bubbles in the upper ocean or drops and aerosol in the lower atmosphere. Here, we describe a categorization of process that elucidates departures from two-layer theory and is a starting point for quantification. Six distinct phenomena are identified that cause a transfer of gas across an interface such as the sea surface. A total of eight categories are identified. Each category has a distinct scaling with respect to the properties of the gas and this is key to the relative importance of different categories and processes. Transfer through sea spray can be an exchange process, but the evaporation of sea spray is more effective and is an ejection process. The reactivity of carbon dioxide in aqueous solution enhances the effect of spray. Exceptional levels of sea spray generation and evaporation are required to be significant for most gases, but moderate levels are sufficient for carbon dioxide and the most soluble pollutants.

Abstract:

Noise is a sound wave that is generally aperiodic in nature, with random and undefined pitch, and which interferes with the quality or detection of other signals. Even so, noise is an unwanted sound that is regarded as an environmental hazard that affects animal and human health. The expansion of urban sprawl, transportation, economic, and development activities is thought to have a significant impact on noise pollution. This research focuses on level of noise in Chattogram Metropolitan Area (CMA) based on both qualitative and quantitative methods. Findings show that, noise pollution level in the study area is exceeded both national and international standard. It will not be an exaggeration to say that noise pollution is endangering city dwellers' quality of life. The noise pollution level is taken several time intervals. According to the study, the noise value is 86.3 dB(A), 87.23 dB(A), 94.07 dB(A) and 84.35 dB(A) at 10 am-12 pm, 2 pm-5 pm, 5 pm-7 pm and 8 pm-10 pm respectively in the industrial area during working day. 78.8 dB(A), 78.03 dB(A), 84.8 dB(A), 76.08 dB(A) at 10 am-12 pm, 2 pm-5 pm, 5 pm-7 pm and 8 pm-10 pm respectively in the industrial area during holiday. 64.0 dB(A), 60.58 dB(A), 62.21 dB(A), 55.95 dB(A) at 10 am-12 pm, 2 pm-5 pm, 5 pm-7 pm and 8 pm-10 pm respectively in the residential area during working day. 58.43 dB(A), 61.71 dB(A), 63.9 dB(A), 57.2 dB(A) at 10 am-12 pm, 2 pm-5 pm, 5 pm-7 pm and 8 pm-10 pm respectively in the residential area during holiday. Noise pollution level in the study area is exceeded both national and international standard. It can be said that city dwellers' quality of life is being endangered by the noise pollution.

Abstract:

A model for rapid detection of coastal mangrove cover was devised. The idea is that it can be applied by users with basic knowledge of remote sensing and GIS. The model is based on calculating the principal components (PC) from bands corresponding to the visible, near infrared, and shortwave infrared regions in Landsat Level-2 images. The model was tested for RAMSAR sites located Mexico: Laguna Guasima on the upper Gulf of California coast, Puerto Arista on the Pacific Ocean coast, and Laguna Madre on the Gulf of Mexico. It was found that the first PC in the three RAMSAR sites explains 80 to 90% of the variation and corresponds mainly to areas that include crop fields or urban infrastructure. The second PC, with cumulative variance of 8 to 14%, corresponds mainly to mangrove cover, and the PC with the lowest percentage of cumulative variance (< 5.0%) is invariably open water. The advantage of using Landsat Collection Level 2 is that there is an archive managed by the USGS of imagery from virtually all over the world that is over 50 years old.

Abstract:

In this study, we analyze the long-term climate dynamics in Poland (1901–2010), using Shannon entropy as a measure of uncertainty and complexity in the atmospheric system. We focus on the monthly distributions of precipitation and temperature, modeled using a bivariate Clayton copula with a normal marginal distribution for temperature and a gamma distribution for precipitation. The correctness of the selected distributions was confirmed by the Anderson-Darling test. The conducted analysis reveals distinct trends in entropy values, indicating an increase in climate instability, which may lead to a higher frequency of extreme weather events. Nonparametric tests enabled the identification of key patterns and potential critical points in the evolution of climate variables. The structure of entropy variability was described in phase space using an attractor, revealing both periodic and chaotic components in climate dynamics. The obtained results highlight the increasing complexity of the climate system and suggest that Shannon entropy can be an effective tool not only for analyzing historical trends but also for forecasting future climate variability. This study confirms that climate is a nonlinear, dynamic system susceptible to chaotic fluctuations, which has crucial implications for modeling and predicting extreme weather conditions.

Abstract: This study evaluates the ICESat-2 ATL13 altimetry product for water level estimation in 182 Canadian lakes by integrating satellite-derived observations with in situ measurements and applying spatial filtering based on the HydroLAKES dataset. Statistical metrics, including root mean square error (RMSE), mean absolute error (MAE), and mean bias error (MBE), were employed to quantify the discrepancies between datasets. Notably, the application of HydroLAKES filtering reduced the mean RMSE from 1.53 m to 1.40 m, and further exclusion of high-error cases lowered the RMSE to 0.96 m. Larger, deeper lakes exhibited lower error margins, whereas smaller lakes with complex shorelines were more prone to variability. Regression analysis confirmed an excellent correspondence between satellite and gauge measurements (R² = 0.9999; Pearson’s r = 0.9999, p &lt; 0.0001). Temporal analysis revealed water level declines in 134 lakes and increases in 48 lakes, suggesting potential influences of climatic variability and anthropogenic activity. These findings underscore the promise of integrating ICESat-2 altimetry with HydroLAKES-based filtering for robust inland water monitoring, while also highlighting the need for further refinement in data-processing algorithms and site-specific calibration.

Abstract:

Artificial Intelligence (AI) and digital governance possess the ability to impact societies benefiting all people and nature especially in the context of rural regions in India. The presence of AI technologies available in the administration of regions and advancement of rural development suggests that there are great opportunities in agriculture, healthcare, education, and resource management. Integrating AI in governance has the possibility of integrating technology, improving rural livelihood via access to healthcare and the precision of agricultural practices, and even achieving sustainable development goals (SDGs). Nevertheless, better possibilities of employment of Ai are precluded by barriers such as lack of technological capabilities, deficits in the level of education and restrictions within the policies. Due to the effectiveness of AI in changing environments in rural areas, a mix of policy frameworks, enhancing resources on education, and collaboration between government bodies, business groups, and community organizations is practiced. Once implemented, such a strategy can further facilitate the embedding of AI in rural development, preparing the ground for future research and policy development.

Abstract: Detailed characterization of evapotranspiration (ET) patterns is crucial for optimizing irrigation scheduling and enhancing water use efficiency in the North China Plain. A two-season study conducted at the National Experimental Station for Precise Agriculture in Beijing compared summer maize varieties with contrasting canopy sizes (JK: large canopy; CF: small canopy) using 12 weighing lysimeters. The results revealed: (1)seasonal ET patterns: daily average ET rates showed consistent variation between cultivars, with JK exhibiting higher water consumption, the highest daily ET of JK and CF reach 5.91 mm/day and 5.52 mm/day at V13-R1 stage in the first growth season and reach 5.21 mm/day and 5.22 mm/day at R1-R3 stage in the second growth season, respectively. (2)Diurnal dynamics: hourly ET fluctuations displayed similar temporal patterns across growth stages, regardless of canopy size. The most pronounced inter-varietal ET differences occurred during the R1-R3 reproductive stages when both cultivars maintained peak canopy development (Leaf area index(LAI) > 4.5). Notably, ET differences between JK and CF followed characteristic diurnal "increase-decrease" pattern, peaking in mid-morning (09:00-11:00) and early afternoon (13:00-15:00), with minimal divergence at solar noon. (3)Transpiration differentiation: pooled data analysis identified two critical periods driving cumulative ET differences: 12:00-17:00 during R1-R3 (anthesis) and 08:00-16:00 during R3-R5 (grain filling). JK maintained significantly higher transpiration rates (Tr), particularly during morning hours (09:00-12:00), with mean Tr exceeding CF by 5.3% (pre-anthesis) and 16.0% (post-anthesis).The observed Tr differentials suggest that canopy architecture modulates stomatal regulation patterns, with large-canopy genotypes demonstrating greater morning photosynthetic activity but similar midday water conservation strategies. Our findings reveal the relationship between the leaf area index and evapotranspiration of summer maize across multiple timescales, including periodic, daily, and hourly variations. This study provides valuable data to support precise and quantitative irrigation strategies for maize production in the region.

Abstract: Urban planning has become increasingly complex, necessitating the use of digitized data, large-scale city scans, and advanced tools for planning and development. Recent advancements in open-source 3D modeling software – Blender, have introduced powerful procedural editing tools like geometry nodes, alongside robust mesh and curve manipulation capabilities. These features position Blender as a viable and cost-effective alternative to proprietary solutions in urban planning workflows. This study identifies common requirements, tasks, and workflows associated with cityscape transformation and visualization, implementing them within Blender’s environment. Documented working examples are provided, including procedural editing, cloud painting, and mesh transformation operations, demonstrating Blender’s versatility. To evaluate its practicality and performance, we conducted a comparative analysis with Rhinoceros Grasshopper, a widely used tool in urban planning. Metrics such as computational efficiency, workflow adaptability, and user experience were analyzed across multiple scenarios involving point cloud processing, mesh editing, and visualization tasks. Finally, we suggest further potential improvements aided by Blender’s powerful scripting framework.

Abstract: In Central Asian countries (CACs) atmosphere pollution is increasing due to population growth, economic growth, agricultural development, energy consumption and climate change. The countries of region developed climate change adaptation strategies - Nationally Determined Contributions (NDCs) under the UN Framework Convention on Climate Change (UNFCCC). At the same time, regional integration, which should be a necessary condition for achieving the Sustainable Development Goals (SDGs) in the solving of general environmental problems, is not involved. This article shows the importance of a comprehensive analysis of greenhouse gas (GHG) and non-greenhouse emissions into the atmosphere for the entire Central Asian region as a single ecosystem. The energy intensity of national economies structure was chosen as the main factor determining the level of pollution. The analysis shows that over the past 30 years, the main part of the commodity exports (73.6 - 81.4%) of Kazakhstan and Turkmenistan has been fossil natural resources. There is a strong economic dependence on coal and other types of fuel, which leads to the atmospheric emissions. The analysis shows that limited financial resources, lack of effective systemic monitoring and control of air quality that meets modern international requirements and standards, leads to absence of tangible changes in practice yet. Over 30 years in CACs, the share of CO2 emissions associated with the fossil fuels combustion has not decreased and amounts to 78%. The key mechanisms for reducing atmospheric emissions are significantly increase investments in the transformation of the economies in the context of regional development, interstate cooperation, the introduction of environmental norms, standards harmonized with world ones, green technologies based on alternative energy, sustainable transport and logistics infrastructure.

Abstract: As urban expansion continues, understanding water resource dynamics in urban areas is crucial for sustainable city planning, flood risk mitigation, and long-term water security. Accurately identifying spatiotemporal changes in urban water resources is important for environmental monitoring and management. Geospatial and remote sensing tools and techniques enable efficient monitoring of water resources in rapidly growing cities, aiding in decision-making for sustainable urban planning. By leveraging advanced geospatial tools and techniques, this research examined spatiotemporal changes in 17 lakes within the rapidly expanding Dallas-Fort Worth (DFW) metropolitan area in the United States from 1984 to 2021, using the Global Surface Water (GSW) dataset via cloud-based remote sensing (Google Earth Engine, GEE) and non-cloud-based remote sensing (ArcGIS Pro). The research employed iso-cluster unsupervised classification and deep learning-supervised classification methods for land cover analysis. The study utilized ArcGIS Pro’s pre-trained deep learning model, based on the National Land Cover Database (NLCD), to classify Landsat images. A comparative analysis was conducted to statistically compare the classification outcomes of the Landsat/NLCD-ArcGIS Pro and GSW-GEE methods in estimating changes in urban lakes. The findings indicate that there is no statistically significant difference between the two datasets and methods. This suggests that both the Landsat/NLCD-ArcGIS Pro and GSW-GEE methods can effectively detect changes in lakes over space and time, although their accuracy may vary slightly depending on the specific lake and classification approach used. Moreover, the total surface area of all lakes estimated using the GSW-GEE method is slightly larger than that estimated using the Landsat-ArcGIS Pro method. Overall, this study highlights the importance of selecting the appropriate dataset and method for analyzing spatiotemporal changes in urban lake environments. The findings have practical implications for environmental researchers, managers, and policymakers involved in urban lake conservation and management.