An optimization method coupled the index-overlay method with entropy weighting model to assess seawater intrusion vulnerability

Enzymatic and ultrasound assisted β-cyclodextrin extraction of active ingredients from Forsythia suspensa and their antioxidant and anti-inflammatory activities

With the proposal of the 2030 Agenda for Sustainable Development, the Chinese medicine extraction technology has been innovatively improved to prioritize low energy consumption, sustainability, and minimized organic solvent utilization. Forsythia suspensa (FS) possesses favorable pharmacological properties and is extensively utilized in traditional Chinese medicine. However, due to the limitations of the composition and extraction methods, its potential has not been fully developed. Thus, a combination of ultrasound-assisted extraction (UAE), enzyme-assisted extraction (EAE), and β-cyclodextrin extraction (β-CDE) was employed to isolate and purify rutin, phillyrin, and forsythoside A from FS. The results demonstrated that the efficiency of extracting enzymatic and ultrasound assisted β-cyclodextrin extraction (EUA-β-CDE) was highly influenced by the temperature and duration of hydrolysis, as well as the duration of the extraction process. According to the results of the single-factor experiment, Box-Behnken design (BBD) in Response surface method (RSM) was used to optimize the experimental parameters to achieve the maximum comprehensive evaluation value (CEV) value. The EUA-β-CDE compared with other extraction methods, has good extraction effect and low energy consumption by high performance liquid chromatography (HPLC), scanning electron microscopy (SEM), calculation of power consumption and CO2 emission The EUA-β-CDE compared with other extraction methods, has good extraction effect and low energy consumption by HPLC, SEM, calculation of power consumption and CO2 emission. Then, the structural characteristics of EUA-β-CDE of FS extract had significant interaction with β-CD by Fourier infrared spectroscopy (FT-IR) and differential scanning calorimetry (DSC). In addition, EUA-β-CDE extract has good antioxidant and anti-inflammatory activities. The establishment of EUA-β-CDE of FS provides a new idea for the development and application of other sustainable extraction methods of traditional Chinese medicine.

Comparative study for coastal aquifer vulnerability assessment using deep learning and metaheuristic algorithms

Coastal aquifer vulnerability assessment (CAVA) studies are essential for mitigating the effects of seawater intrusion (SWI) worldwide. In this research, the vulnerability of the coastal aquifer in the Lahijan region of northwest Iran was investigated. A vulnerability map (VM) was created applying hydrogeological parameters derived from the original GALDIT model (OGM). The significance of OGM parameters was assessed using the mean decrease accuracy (MDA) method, with the current state of SWI emerging as the most crucial factor for evaluating vulnerability. To optimize GALDIT weights, we introduced the biogeography-based optimization (BBO) and gray wolf optimization (GWO) techniques to obtain to hybrid OGM-BBO and OGM-GWO models, respectively. Despite considerable research focused on enhancing CAVA models, efforts to modify the weights and rates of OGM parameters by incorporating deep learning algorithms remain scarce. Hence, a convolutional neural network (CNN) algorithm was applied to produce the VM. The area under the receiver-operating characteristic curves for OGM-BBO, OGM-GWO, and VMCNN were 0.794, 0.835, and 0.982, respectively. According to the CNN-based VM, 41% of the aquifer displayed very high and high vulnerability to SWI, concentrated primarily along the coastline. Additionally, 32% of the aquifer exhibited very low and low vulnerability to SWI, predominantly in the southern and southwestern regions. The proposed model can be extended to evaluate the vulnerability of various coastal aquifers to SWI, thereby assisting land use planers and policymakers in identifying at-risk areas. Moreover, deep-learning-based approaches can help clarify the associations between aquifer vulnerability and contamination resulting from SWI.

Assessing vulnerability of coastal aquifer to seawater intrusion using Convolutional Neural Network

This study examined coastal aquifer vulnerability to seawater intrusion (SWI) in the Shiramin area in northwest Iran. Here, six types of hydrogeological data layers existing in the traditional GALDIT framework (TGF) were used to build one vulnerability map. Moreover, a modified traditional GALDIT framework (mod-TGF) was prepared by eliminating the data layer of aquifer type from the GALDIT model and adding the data layers of aquifer media and well density. To the best of our knowledge, there is a research gap to improve the TGF using deep learning algorithms. Therefore, this research adopted the Convolutional Neural Network (CNN) as a new deep learning algorithm to improve the mod-TGF framework for assessing the coastal aquifer vulnerability. Based on the findings, the CNN model could increase the performance of the mod-TGF by >30 %. This research can be a reference for further aquifer vulnerability studies.

Modeling and exploring the coordination relationship between green infrastructure and land use eco-efficiency: an urban agglomeration perspective

In limited land space, improving the construction of infrastructure with ecological services can help to achieve the goal of promoting land use eco-efficiency (LUEE). In view of this, this study constructed interactive coordination relationship model of green infrastructure (GI) and LUEE that involves entropy method model, super-efficiency slack-based measure (SBM) model with undesirable outputs, and coupling coordination degree model. The interactive coordination relationship model can help to study and reveal the mechanisms of interaction and the coordination relationship between GI and LUEE from a land benefit and ecological perspective. We took the Beijing-Tianjin-Hebei urban agglomeration as the study area. The results showed that the assessment results of GI showed a decreasing trend from 2000 to 2020. LUEE in different cities displayed obvious variability with efficiency values ranging from 0.5666 to 2.4437. The relationship between GI and LUEE is in the stage of uncoordinated development in 53.8% of cities, mainly concentrated in the eastern and southern parts of the study area. The unnatural human activities are the critical factors affecting interactive coupling coordination degree of LUEE and GI. It is clarified that the level of coordination relationship of the two can be used as an important indicator to judge the sustainable development of urban agglomerations. Intensive use of land, optimal connection of geographic information, and localization of policies would help improve the balance and coordination between the two. This study provides interesting research ideas and novel modeling approaches for the study of green and sustainable development of urban agglomerations.

Habitat suitability modeling of Descurainia sophia medicinal plant using three bivariate models

Climate change has caused medicinal plants to become increasingly endangered. Descurainia sophia (flixweed) is at risk of extinction in Fars Province, Iran, due to climate change and modifications of land use. Flixweed is highly valuable because of its medicinal properties. The conservation of this species using habitat suitability modeling seems necessary. In this research, the geographical locations of D. sophia's distribution in southern Iran were recorded and mapped using ArcGIS 10.2.2. Then, ten important variables affecting the growth of D. sophia medicinal plants were identified and prepared as thematic layers. These variables were, namely, "elevation," "slope degree," "slope aspect," "soil physical characteristics (sand, silt, and clay percentage)," "soil chemical properties (EC and pH)," "annual mean rainfall," "annual mean temperature," "distance to roads," "distance to rivers," and "plan curvature." In this study, three bivariate models, including the "index-of-entropy (IofE)," "frequency ratio (FR)," and "weight of evidence (WofE)," were used for mapping the habitat suitability of D. sophia. Moreover, the ROC curve and AUC index were used for evaluating the accuracy of the models. Based on the results, the IofE model ("AUC": 0.93) was the most accurate, while the FR ("AUC": 0.92) and WofE ("AUC": 0.90) models ranked second and third, respectively. The models in this study can be applied as tools for the protection of endangered medicinal plants. Furthermore, the map could assist planners, decision-makers, and engineers in extending study areas. By determining the habitat maps of medicinal plants, their extinction can be prevented. Such maps can also assist in the propagation of medicinal plants.

Integrated seawater intrusion and groundwater quality assessment of a coastal aquifer: GALDIT, geospatial and analytical approaches

The pressure and dependence on coastal aquifers are on the rise in many parts of the globe. These lead to overexploitation, aggravated levels of groundwater pollution, and seawater intrusion. Integrated analyses can create holistic insights into the quality and the vulnerability of aquifers to seawater intrusion. In this study, Mombasa North coast's coastal aquifer was characterized by integrating multiple approaches-GALDIT overlay index, seawater intrusion groundwater quality index GQI_SWI, total hardness, water quality index (WQI)-and the results were further explored and interpreted with geospatial analysis techniques. The study suggests that the predominant water type in areas under moderate or high vulnerabilities to seawater intrusion is the Na-Cl water type. However, similar Na-Cl water types can produce a range of total hardness from soft to hard. GQI_SWI classification can be used to narrow down the observations from Stuyfzand's TH-based classification system. In the aquifer studied, the results of the GALDIT overlay index, a weighted aggregation of intrinsic parameters contributing to seawater intrusion, show that 29%, 59%, and 12% of the aquifer have low, moderate, and high vulnerabilities, respectively. The GQI_SWI analysis indicates that the groundwater is largely brackish (68%) but saline towards the southern end of the aquifer at 32%. Total hardness values indicate that 67% of the aquifer's coverage falls under the "moderately hard" category. The geodatabase creation introduced in the study provides a template for similar studies and a baseline for future WQI and water quality monitoring. However, temporal studies on chronological timescales are recommended for sustainable management of the aquifer.

Vulnerability of a Tunisian Coastal Aquifer to Seawater Intrusion: Insights from the GALDIT Model

The Korba region in northwestern Tunisia has a coastal aquifer that is impacted by intensive irrigation, urban expansion, and sensitivity to SWI. We assessed the vulnerability extent of Korba’s GW to SWI. We utilized a parametric model for GW vulnerability assessment, the GALDIT, which considers six parameters to determine SWI effects. The GALDIT map has four rating categories (≥7.5, 7.5–5, 5–2.5, and <2.5), representing very high, high, moderate, and low vulnerability, respectively. Most of the region was found to be highly vulnerable (44.2% of the surface area), followed by areas characterized by very high (20.3%) and moderate (19.3%) vulnerability. Only 16.2% was found to have low vulnerability. A parameter sensitivity analysis showed that distance from shore and depth of GW represent the determining factors for SWI with variation index values of 24.12 and 18.02%, respectively. Inland advancement of seawater is causing GW salinity to rise, as indicated by a strong Pearson correlation coefficient of 0.75 between SWI indices and the electrical conductivity. Suitable areas for artificial recharge were mainly distributed in the alluvial plains, with a total area of 32.85 km2. Inhibiting SWI requires about 11.31 MCM of artificial recharge in the two most suitable recharge zones in the region.