Contamination in coastal regions attributed to fluoride and nitrate cannot be disregarded, given the substantial environmental and public health issues they present worldwide. For effective decontamination, it is pivotal to identify regional pollution hotspots. This comprehensive study was performed to assess the spatial as well as indexical water quality, identify contamination sources, hotspots, and evaluate associated health risks pertaining to nitrate and fluoride in the Al-Hassa region, KSA. The physicochemical results revealed a pervasive pollution of the overall groundwater. The dominant water type was Na-Cl, indicating saltwater intrusion and reverse ion exchange impact. Spatiotemporal variations in physicochemical properties suggest diverse hydrochemical mechanisms, with geogenic factors primarily influencing groundwater chemistry. The groundwater pollution index varied between 0.8426 and 4.7172, classifying samples as moderately to very highly polluted. Similarly, the synthetic pollution index (in the range of 0.5021–4.0715) revealed that none of the samples had excellent water quality, with various degrees of pollution categories. Nitrate health quotient (HQ) values indicated chronic human health risks ranging from low to severe, with infants being the most vulnerable. Household use of nitrate-rich groundwater for showering and cleaning did not pose significant health risks. Fluoride HQ decreased with age, and children faced the highest risk of fluorosis. The hazard index (HI) yielded moderate- to high-risk values. Nitrate risks were 1.21 times higher than fluoride risks, as per average HI assessment. All samples fell into the vulnerable category based on the total hazard index (THI), with 88.89% classified as very high risk. This research provides valuable insights into groundwater quality, guiding water authorities, inhabitants, and researchers in identifying safe water sources, vulnerable regions, and human populations. The results highlight the need for appropriate treatment techniques and long-term coastal groundwater management plans.

Toward Decontamination in Coastal Regions: Groundwater Quality, Fluoride, Nitrate, and Human Health Risk Assessments within Multi-Aquifer Al-Hassa, Saudi Arabia

Dielectric properties are crucial in understanding the behavior of water within soil, particularly the soil water content (SWC), as they measure a material’s ability to store an electric charge and are influenced by water and other minerals in the soil. However, a comprehensive review paper is needed that synthesizes the latest developments in this field, identifies the key challenges and limitations, and outlines future research directions. In addition, various factors, such as soil salinity, temperature, texture, probing space, installation gap, density, clay content, sampling volume, and environmental factors, influence the measurement of the dielectric permittivity of the soil. Therefore, this review aims to address the research gap by critically analyzing the current state-of-the-art dielectric properties-based methods for SWC measurements. The motivation for this review is the increasing importance of precise SWC data for various applications such as agriculture, environmental monitoring, and hydrological studies. We examine time domain reflectometry (TDR), frequency domain reflectometry (FDR), ground-penetrating radar (GPR), remote sensing (RS), and capacitance, which are accurate and cost-effective, enabling real-time water resource management and soil health understanding through measuring the travel time of electromagnetic waves in soil and the reflection coefficient of these waves. SWC can be estimated using various approaches, such as TDR, FDR, GPR, and microwave-based techniques. These methods are made possible by increasing the dielectric permittivity and loss factor with SWC. The available dielectric properties are further synthesized on the basis of mathematical models relating apparent permittivity to water content, providing an updated understanding of their development, applications, and monitoring. It also analyzes recent mathematical calibration models, applications, algorithms, challenges, and trends in dielectric permittivity methods for estimating SWC. By consolidating recent advances and highlighting the remaining challenges, this review article aims to guide researchers and practitioners toward more effective strategies for SWC measurements.

Recent Advances in Dielectric Properties-Based Soil Water Content Measurements

To comprehensively comprehend the hydrochemical features, spatial patterns, and controlling mechanisms of groundwater in the Longkou coastal plain (LCP), a prime coastal plain in northern China, a detailed study utilizing statistical analyses, Piper diagram, Gibbs model diagram, ion ratio methods, and reverse hydrogeochemical simulation was performed on 76 groundwater samples collected in 2021. The findings of this study reveal that the groundwater in the LCP has an average pH value of 7.6, indicating weak alkalinity. The groundwater types are predominantly mixed, followed by Cl-Ca. The NO3− concentration in groundwater is relatively high, with a mean value of 326 mg/L, originating mainly from agricultural production activities. In addition to NO3−, the spatial distribution analysis of the principal chemical constituents in the groundwater demonstrated a subtle north-south gradient. The chemical signatures of groundwater are primarily influenced by the process of rock weathering, resulting from the dissolution of silicates and carbonates minerals as well as gypsum. Indeed, cation exchange represents a pivotal hydrogeochemical reaction within the groundwater system of the region. The intrusion of seawater into groundwater primarily exhibits a mild nature, mostly in the form of mixing intrusion in the initial stage, affecting the western and northern regions of the LCP to a greater extent. The primary source of groundwater recharge is attributed to atmospheric precipitation; however, it is partially affected by evaporation. In addition, LCP groundwater is also being recharged by seawater intrusion, mainly in the western and northern parts of the study area. Overall, the findings of this research hold significant implications for the strategic management of groundwater resources and the implementation of measures to prevent and mitigate seawater intrusion not only in the LCP but also in analogous coastal regions across the globe. 

Comprehending hydrochemical fingerprint, spatial patterns, and driving forces of groundwater in a topical coastal plain of Northern China based on hydrochemical and isotopic evaluations

Wetlands, vital ecosystems that support 40 % of the world's species and serve as nature's water filters, are disappearing three times faster than forests. While global research extensively examines the increasing degradation of wetland health, there exists a significant research gap concerning its impact on livelihoods and the achievement of Sustainable Development Goals (SDGs). To address this gap, a systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, utilising data from 1270 database records and 350 research studies spanning 1990 to 2023. The study reveals alarming annual wetland health loss rates ranging from 0.02 % to 3.14 %, driven globally by built-up areas, agricultural expansion, climate change, and pollution. Notably, developing countries and those with lower development indices exhibit the highest rates of wetland health degradation, primarily attributed to agricultural and urban expansion, as well as pollution. The analysis establishes a negative correlation between wetland health degradation rates, driving factors, and key indicators such as the Sustainable Development Goal Index (SDGI) (r = −0.38), Environmental Performance Index (EPI) (r = −0.34), Income Classification (r = −0.42), and Human Development Index (HDI) (r = −0.38). The study emphasizes the imperative of improving economic and socio-ecological conditions to enhance conservation and restoration efforts in wetland areas, thereby contributing to the achievement of SDGs. The interconnectedness of wetland health with broader SDGs underscores the need for targeted interventions. Recommendations include prioritizing comprehensive strategies for environmental and societal well-being, urging policymakers and practitioners to consider the holistic implications of wetland health degradation in their decision-making processes. 

Wetland degradation and its impacts on livelihoods and sustainable development goals: An overview

Advanced stacked integration method for forecasting long-term drought severity: CNN with machine learning models

Global Sensitivity Analysis (GSA) is key to assisting appraisal of the behavior of hydrological systems through model diagnosis considering multiple sources of uncertainty. Uncertainty sources typically comprise incomplete knowledge in (a) conceptual and mathematical formulation of models and (b) parameters embedded in the models. In this context, there is the need for detailed investigations aimed at a robust quantification of the importance of model and parameter uncertainties in a rigorous multi‐model context. This study aims at evaluating and comparing two modern multi‐model GSA methodologies. These are the first GSA approaches embedding both model and parameter uncertainty sources and encompass the variance‐based framework based on Sobol indices (as derived by Dai & Ye, 2015, https://doi.org/10.1016/j.jhydrol.2015.06.034) and the moment‐based approach upon which the formulation of the multi‐model AMA indices (as derived by Dell'Oca et al., 2020, https://doi.org/10.1029/2019wr025754) is based. We provide an assessment of various aspects of sensitivity upon considering a joint analysis of these two approaches in a multi‐model context. Our work relies on well‐established scenarios that comprise (a) a synthetic setting related to reactive transport across a groundwater system and (b) an experimentally‐based study considering heavy metal sorption onto a soil. Our study documents that the joint use of these GSA approaches can provide different while complementary information to assess mutual consistency of approaches and to enrich the information content provided by GSA under model and parameter uncertainty. While being related to groundwater settings, our results can be considered as reference for future GSA studies coping with model and parameter uncertainty.

Comparative Assessment of Two Global Sensitivity Approaches Considering Model and Parameter Uncertainty

Distribution characteristics and factors influencing microbial communities in the core soils of a seawater intrusion area in Longkou City, China

Process-based nitrogen reactive transport models (NRTMs) are vital tools for simulating complex nitrogen reactive transport processes in soils and groundwater to reduce nutrient loss and contamination risk. To develop and improve these models, global sensitivity analysis (GSA) is used to identify key model parameters and processes. Because existing GSA approaches focus mainly on model parameters and not model processes, in this study, we developed a two-step GSA method to improve our understanding of model processes. First, we applied the Morris method to identify less influential parameters. Then, we developed a Bayesian network (BN)-based GSA method to identify the dominant processes for different model predictions. The BN-GSA method accounts for the hierarchical uncertainty in the structure of the model system and flexibly groups uncertain inputs into processes based on their characteristics. The two-step GSA method was applied to a real-world nitrogen reactive transport model with 68 uncertain parameters in three different climate scenarios. The results show that the dominant processes differed for various nitrogen and agricultural outputs; thus, different processes should be emphasized based on specific modeling purposes. 

A two-step Bayesian network-based process sensitivity analysis for complex nitrogen reactive transport modeling

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Yujiao Liu

Author Tools

Chat about Author

Papers

Comparative Assessment of Two Global Sensitivity Approaches Considering Model and Parameter Uncertainty

Distribution characteristics and factors influencing microbial communities in the core soils of a seawater intrusion area in Longkou City, China

A two-step Bayesian network-based process sensitivity analysis for complex nitrogen reactive transport modeling