Spatiotemporal Variability Research Articles

Exploring new methods for assessing landscape ecological risk in key basin

Scientific assessment of landscape ecological risk (LER) aims to optimize land use patterns and mitigate regional ecological hazards. However, accurate LER assessment requires the selection of a feasible spatial analysis scale. This study identified the optimal spatial scale by analyzing high-resolution data from 2006, 2012, and 2019. Within the “production-living-ecological” spatial (PLES) perspective, this study employed six methods to depict the pattern changes and spatiotemporal variations of LER. These included the Markov transfer matrix model, the geoscience information atlas–gravity center model (GIAGC), the LER index model, Min/Max Autocorrelation Factors, the Sequential Gaan simulation (MAF-SGS) multivariate geostatistical model, and spatial autocorrelation. The results are as follows: 1) A granularity of 10 m and a magnitude of 200 m are the most effective parameters for LER assessment. 2) From 2006 to 2019, there was a shift in the structure of the landscape pattern from “ecological space” to “productive space”, accompanied by a tendency for the center of gravity to shift towards the southwest. 3) The non-structural spatial factors contributing to LER are gradually increasing, and the maximum variability is steadily rising. Medium-low risk areas dominate the basin, and the spatial distribution of LER exhibits an overall pattern: high in the center, low in the southwest, and low in the northwest. Specifically, a low-low spatial clustering pattern is predominant; however, there is a general upward trend in LER. In summary, this study provides a scientific basis for adjusting basin land use structures, resolving ecological risks, and promoting high-quality sustainable development.

Biomechanical gait analysis in sheep: kinematic parameters.

Animals have been used as models to help to better understand biological and anatomical systems, and pathologies in both humans and non-human species, and sheep are often used as an in vivo experimental model for orthopedic research. Gait analysis has been shown to be an important tool in biomechanics research with clinical applications. The purpose of this study was to perform a kinematic analysis using a tridimensional (3D) reconstruction of the sheep hindlimb. Seven healthy sheep were evaluated for natural overground walking, and motion capture of the right hindlimb was collected with an optoelectronic system while the animals walked in a track. The analysis addressed gait spatiotemporal variables, hip, knee and ankle angle and intralimb joint angle coordination measures during the entire walking cycle. This study is the first that describes the spatiotemporal parameters from the hip, knee and ankle joints in a tridimensional way: flexion/extension; abduction/adduction and inter/external rotation. The results of this assessment can be used as an outcome indicator to guide treatment and the efficacy of different therapies for orthopedic and neurological conditions involving the locomotor system of the sheep animal model.

Machine Learning Classification of Air Quality Monitoring Stations to Achieve Ambient NO2 Objectives Using Emission Scenarios and Chemical Transport Model

Exceeding the latest Canadian Ambient Air Quality Standards (CAAQS) for NO2 concentration in Canadian cities motivates discussions on new policies and directives. Advanced air quality modeling and machine learning clustering algorithms integrating the Weather Research Forecast (WRF) and Community Multiscale Air Quality (CMAQ) models were used to classify air quality monitoring (AQM) stations based on pollution concentration modeling results. The sensitivity of ambient NO2 to the primary anthropogenic emission sources was investigated in Alberta, Canada. Emissions from two main sources, upstream oil and gas (UOG) and transportation sources, have been identified as the reason Alberta fails to meet the newly adopted NO2 CAAQS objectives. The air quality model was validated with ground-level observation data and the atmospheric model accurately replicates spatiotemporal NO2 variations. Despite contributing 62% of Alberta’s total NOx emissions, UOG influences ambient NO2 concentrations modestly in urban areas (<10%) but significantly affects rural regions. In contrast, transportation emission sources, responsible for 23% of NOx emissions, dominate ambient NO2 levels (up to 63%) in large cities. The discrepancy of emission contribution and ground-level concentrations, obtained from the chemical transport model, was resolved using a k-prototypes clustering algorithm to propose a new approach for categorizing AQM stations which led to improving the conventional classifications. The new approach considered the sensitivity of NO2 to emission reduction scenarios and provided an improved classification to be used for emission reduction interventions. Based on the updated classification, one set of AQM stations clearly showed sensitivity to NO2 emission reduction in the transportation sector despite their lower contributions to overall emissions. These stations were categorized as population exposure stations in large cities.

Spatiotemporal distributions of dissolved N2O concentration, diffusive N2O flux and relevant functional genes along a coastal creek in southeastern China

Increased anthropogenic input of nitrogen into coastal creeks make them potential hotspots for N2O production and emission, but they are often excluded from regional and global N2O budget, and high-resolution sampling is required to characterize the strong spatiotemporal heterogeneity within the creeks. In this study, we analyzed the N2O concentration and diffusive N2O flux within a coastal creek in the Shanyutan Wetland in southeastern China in high spatial resolution across four seasons. Ancillary hydrographical variables and N2O-related functional gene abundances were also measured. Results showed that the creek was consistently oversaturated in N2O, at a seasonal average of 5.6–14.2 nmol/L, relative to the overlying atmosphere. The spatial distribution of N2O followed the gradient of nitrogenous substrate but was inversely related to the salinity gradient, and the coefficient of spatial variation of N2O flux ranged from 66.3 % to 116.5 %. Nitrite reduction (based on nirK and nirS gene abundances) and ammonia oxidation (AOA amoA and AOB amoA) appeared to outpace N2O reduction (nosZ I and nosZ II), and these were the main microbial processes that determined N2O concentration and flux. Both N2O concentration and flux were substantially higher in autumn than those in the other seasons, but that did not appear to be related to precipitation. N2O diffusive flux from the creek averaged 322.2 nmol m−2 h−1, which was over 2 times higher than the global average for lakes and reservoirs. Our results highlight that coastal creeks are strong atmospheric N2O sources with high spatiotemporal variability.

Remote sensing-based seasonal surface urban heat island analysis in the mining and industrial environment.

Cooling spaces have an optimistic influence on surface urban heat islands (SUHI). Blue spaces benefit from balancing thechanging climate and heat variations. Because of the rapid deforestation and SUHI increase, the climate is gradually changing in Paschim Bardhhaman, West Bengal state, India. Paschim Bardhhaman has two sectors: specifically, Durgapur is the main industrial centre and Asansol has coal mines. This investigation aims to categorize spatiotemporal variations and seasonal differences in cooling spaces and their influence on SUHI, land use and land cover (LULC), and thermal differences using Landsat datasets for the years 1992, 2004, 2012, and 2022 in summer and winter. The coal mining and industrial range decreased from 10,391.92 (1992) to 3591.1ha (2022), respectively. Open pit mining distresses fresh water by heavy water uses in ore processing, and mining water was applied to excerpt minerals. Among the two sub-divisions, the blue space amount was higher in Asansol because mining actions were higher in Asansol than in Durgapur. The open vegetation volume has reduced from 46,441.03 (1992) to 25,827.55ha (2022) and dense vegetation has erased from 7368.02 (1992) to 15,608.56ha (2022). Dense vegetation improved because of heavy precipitation in those regions. Mostly, Raghunathpur, Saraswatiganja, Bhagabanpur, Bistupur, Paschim Gangaram, Garkilla Kherobari, and Gourbazar have dense vegetation. The outcomes similarly demonstrate that the total built-up part has increased by 8412.82ha in between 30years. The built-up zone changes near the southeast and western Paschim Bardhhaman district. Those region needs appropriate attention and planning to survive soon.

Dynamic analysis of carbon emissions from construction and demolition activities in Japan: Revealed by high-resolution 4D-GIS modeling

To meet the 2050 decarbonization target of the global buildings and construction sector, more attention is needed to reduce carbon emissions from construction and demolition. However, current national carbon accounting studies for these activities remain limited in spatial granularity and localized applicability. This study developed a bottom-up spatiotemporal database of carbon emissions from building construction and demolition in Japan via integrating a geographic information system-based building stock model, statistical data, and survey information. Focusing on municipal-level emissions, the Logarithmic Mean Divisia Index approach was used to decompose spatiotemporal variations and identify the contributing factors. Results indicate that carbon emissions from Japan's construction and demolition activities fell by more than 50% between 2005 and 2020, largely due to declining new/demolished-to-stock ratio, suggesting a transition to a stock-based society. Central cities' reliance on carbon-intensive buildings positively contributed to spatial variations in their construction emissions, underscoring the importance of sustainable materials and timber designs. Differences between prefectures in demolition emission intensity highlighted the strategic placement of recycling facilities in key regions to curb transportation-related emissions. Overall, these findings provided data reference for local governments to devise tailored policies for managing construction and demolition emissions.

Intraspecific variation in the functional response of an invasive crayfish under different temperatures

Non-native species can react to changes to their thermal environment by altering their feeding behaviour, thereby potentially causing shifts in predator-prey dynamics and competitive dominance over native species. In this study, we measured intraspecific variation in the functional response (i.e., predation rate as a function of prey density) of the rusty crayfish Faxonius rusticus (Girard, 1852) at two temperatures (18˚C and 26˚C) in the laboratory. We compared six invasive populations spanning a 2˚ latitudinal gradient in eastern North America, to test the prediction that under warmer conditions individuals from more southerly populations exhibit a higher functional response than those from northern populations. Temperature, latitude, and the interaction between these two variables had significant effects on attack rates and handling times of individual crayfish from the tested populations. Contrary to our prediction, the attack rates of individuals from northern populations were consistently higher than those from southern populations at both temperatures. We propose that these interpopulation differences in functional response could arise, at least in part, from countergradient selection. Our results suggest that climate warming promotes spatiotemporal variation in per capita effects across latitudinally distributed populations of aquatic invasive species.

Influence of environmental filtering and spatial processes on macroinvertebrate community in urban lakes in the Taihu Lake Basin, China.

Studies identifying the relative importance of multiple ecological processes in macroinvertebrate communities in urban lakes at a basin scale are rare. In this study, 14 urban lakes in the Taihu Lake Basin were selected to explore the relative importance of environmental filtering and spatial processes in the assembly of macroinvertebrate communities. Our findings revealed significant spatiotemporal variations in macroinvertebrate communities, both between lakes and across seasons. We found that environmental filtering exerted a greater influence on taxonomic total beta diversity and its individual components (species turnover and nestedness) compared to spatial processes. Key environmental variables such as water depth, water temperature, total dissolved solids, chlorophyll a, and lake surface area were found to be crucial in shaping macroinvertebrate communities within these urban lakes. The observed high spatial heterogeneity in environmental conditions, along with intermediate basin areas, good connectivity and short distances between lakes, and the high dispersal ability of dominant taxa, likely contributed to the dominance of environmental filtering in macroinvertebrate community assembly. Our study contributes to a better understanding of the underlying mechanisms governing macroinvertebrate community assembly in urban lakes, thereby providing valuable insights for studies on community ecology and water environmental protection in urban lakes.

Investigation of the Historical Trends and Variability of Rainfall Patterns during the March–May Season in Rwanda

This study explores the spatiotemporal variability and determinants of rainfall patterns during the March to May (MAM) season in Rwanda, incorporating an analysis of teleconnections with oceanic–atmospheric indices over the period 1983–2021. Utilizing the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) dataset, the study employs a set of statistical tools including standardized anomalies, empirical orthogonal functions (EOF), Pearson correlation, the Mann–Kendall (MK) trend test, and Sen’s slope estimator to dissect the intricacies of rainfall variability, trends, and their association with large-scale climatic drivers. The findings reveal a distinct southwest to northwest rainfall gradient across Rwanda, with the MK test signaling a decline in annual precipitation, particularly in the southwest. The analysis for the MAM season reveals a general downtrend in rainfall, attributed in part to teleconnections with the Indian Ocean Sea surface temperatures (SSTs). Notably, the leading EOF mode for MAM rainfall demonstrates a unimodal pattern, explaining a significant 51.19% of total variance, and underscoring the pivotal role of atmospheric dynamics and moisture conveyance in shaping seasonal rainfall. The spatial correlation analysis suggests a modest linkage between MAM rainfall and the Indian Ocean Dipole, indicating that negative (positive) phases are likely to result in anomalously wet (dry) conditions in Rwanda. This comprehensive assessment highlights the intricate interplay between local rainfall patterns and global climatic phenomena, offering valuable insights into the meteorological underpinnings of rainfall variability during Rwanda’s critical MAM season.

Characterizing Spatiotemporal Variability in Phosphorus Export across the United States through Bayesian Hierarchical Modeling.

Phosphorus inputs from anthropogenic activities are subject to hydrologic (riverine) export, causing water quality problems in downstream lakes and coastal systems. Nutrient budgets have been developed to quantify the amount of nutrients imported to and exported from various watersheds. However, at large spatial scales, estimates of hydrologic phosphorus export are usually unavailable. This study develops a Bayesian hierarchical model to estimate annual phosphorus export across the contiguous United States, considering agricultural inputs, urban inputs, and geogenic sources under varying precipitation conditions. The Bayesian framework allows for a systematic updating of prior information on export rates using an extensive calibration data set of riverine loadings. Furthermore, the hierarchical approach allows for spatial variation in export rates across major watersheds and ecoregions. Applying the model, we map hotspots of phosphorus loss across the United States and characterize the primary factors driving these losses. Results emphasize the importance of precipitation in determining hydrologic export rates for various anthropogenic inputs, especially agriculture. Our findings also emphasize the importance of phosphorus from geogenic sources in overall river export.

A transient gas pipeline network simulation model for decoupling the hydraulic-thermal process and the component tracking process

The metering mode is gradually changing from volumetric metering to energy metering under the open-access operation of gas pipelines. The gas component tracking by operating simulation is an effective method to realize the energy metering of gas networks. In this paper, coupling and decoupling transient component tracking models of gas networks are proposed. The pipeline governing equations, compressor equations, valve equations, and node relationship equations are considered in the proposed models. The implicit central difference method and the damped Newtonian method are adopted to solve the models. The coupling model considers the component tracking part and hydraulic-thermal simulation as a whole, while the decoupling model splits the whole process into the gas component tracking part, the parameters calculation part, and the hydraulic-thermal simulation part. In the component tracking part, the spatiotemporal variation of the gas mass fraction and density can be obtained through the input velocity of each node. Then, the parameters in the gas state equation can be solved, the hydraulic-thermal simulation can be performed, and the new velocity of each node can be calculated. The iteration for each time step ends if the deviation of flow velocity in the component tracking process and the hydraulic-thermal process satisfies the requirements. Two cases are studied to verify the effectiveness of the proposed models, and the boundary condition adaptability of the transient simulation models is analyzed. The proposed models can efficiently simulate the spatiotemporal variation of the operating variables and gas components, providing a reference in the operation of natural gas pipeline networks.

Living at depth: ecophysiological condition of Boreomysis arctica in autumn and winter in the St. Lawrence estuary and gulf.

Mysids, besides krill, play a significant role in energy transfer and carbon sequestration. The ecology of coastal species is better understood than that of deep dwelling species such as Boreomysis arctica. The objectives of this study were to quantify spatiotemporal variations in body condition and the trophic level of B. arctica in autumn and winter, under sea-ice conditions in the St. Lawrence system, using a multimarker approach. We sampled along a 1000km transect. Mean abundances in winter were higher in the estuary compared to the Gulf of St. Lawrence. Body condition, measured as total lipid content, was higher in winter than in autumn. Lipids of B. arctica were mainly composed of wax esters, thereby B. arctica is richer in energetic lipids compared to the three dominant krill species. We also observed seasonal differences in the trophic level of B. arctica, revealing carnivorous behavior in autumn compared to omnivory in winter. High intra-specific variability in both energetic strategy and feeding behavior was found that is potentially due to opportunistic feeding. Energy rich reserves suggest that B. arctica could act as a valuable prey for both benthic and pelagic consumers and thus playing a key role in bentho-pelagic energy transfer.

Factors controlling spatiotemporal variability of soil carbon accumulation and stock estimates in a tidal salt marsh

Abstract. Tidal salt marshes are important contributors to soil carbon (C) stocks despite their relatively small land surface area. Although it is well understood that salt marshes have soil C burial rates orders of magnitude greater than those of terrestrial ecosystems, there is a wide range in accrual rates among spatially distributed marshes. In addition, wide ranges in C accrual rates also exist within a single marsh ecosystem. Tidal marshes often contain multiple species of cordgrass due to variations in hydrology and soil biogeochemistry caused by microtopography and distance from tidal creeks, creating distinct subsites. Our overarching objective was to observe how soil C concentration and dissolved organic carbon (DOC) vary across four plant phenophases and across three subsites categorized by unique vegetation and hydrology. We also investigated the dominant biogeochemical controls on the spatiotemporal variability of soil C and DOC concentrations. We hypothesized that subsite biogeochemistry drives spatial heterogeneity in soil C concentration, and this causes variability in total soil C and DOC concentrations at the marsh scale. In addition, we hypothesized that soil C concentration and porewater biogeochemistry vary temporally across the four plant phenophases (i.e., senescence, dormancy, green-up, maturity). To test these interrelated hypotheses, we quantified soil C and DOC concentrations in 12 cm sections of soil cores (0–48 cm depth) across time (i.e., phenophase) and space (i.e., subsite), alongside several other porewater biogeochemical variables. Soil C concentration varied significantly (p &lt; 0.05) among the three subsites and was significantly greater during plant dormancy. Soil S, porewater sulfide, redox potential, and depth predicted 44 % of the variability in soil C concentration. There were also significant spatial differences in the optical characterization properties of DOC across subsites. Our results show that soil C varied spatially across a marsh ecosystem by up to 63 % and across plant phenophase by 26 %, causing variability in soil C accrual rates and stocks depending on where and when samples are taken. This shows that hydrology, biogeochemistry, and plant phenology are major controls on salt marsh C content. It is critical to consider spatiotemporal heterogeneity in soil C concentration and porewater biogeochemistry to account for these sources of uncertainty in C stock estimates. We recommend that multiple locations and sampling time points are sampled when conducting blue C assessments to account for ecosystem-scale variability.

Influence of spatiotemporal and meteorological variation on Norwegian atmospheric pollen seasonality

Climate change is and will continue to alter plant responses to their environment. This is especially prominent concerning the adaptive tracking in reproductive phenology. For wind pollinated plants, this will substantially influence their pollen seasonality, yet there are gaps in knowledge about how environmental variation influences pollen seasonality. To investigate this, we monitored daily atmospheric pollen concentrations of seven pollen types from ecologically, economically and allergenically important plants (alder, hazel, willow, birch, pine, grass and mugwort) in twelve Norwegian locations spanning the entire country for up to 28 years. Six daily meteorological variables (maximum temperature, precipitation, wind speed, relative humidity, solar radiation and atmospheric pressure) was obtained from the MET Nordic dataset with full data cover. The pollen seasonality was then modelled using four spatial, three temporal and the six meteorological variables in a generalized linear model approach with a negative binomial distribution to investigate how each variable group thematically and individually contribute to variation in pollen seasonality. We found that the full models explained the most variation, ranging from R2 = 20.3 % to 59.5 %. The models were also highly accurate, being able to predict 54.5 % to 99.1 % of daily pollen concentrations to within 20.1 pollen grains/m3. Overall, the temporal variables were able to explain more variation than spatial and meteorological variables for most pollen types. Month, altitude and maximum temperature were the most important single variables for each category. The importance of each variable could be traced back to their individual effects of reproductive phenology, plant metabolism, species distributions and pollen release processes. We further emphasise the importance of source maps and atmospheric regional transport models in further model improvements. By understanding the relevance of environmental variation to pollen seasonality we can make better predictions regarding the consequences of climate change on plant populations.

Taking shape: A new geometric morphometric approach to quantifying dental fluctuating asymmetry and its application to the evaluation of developmental stress

AbstractAlthough evaluating developmental stress is challenging, it is critical to understanding phenotypic adaptation and differentials in morbidity and mortality related to spatiotemporal variation in environmental and cultural factors. This paper presents a new, reproducible, and reliable geometric morphometric (GM) protocol through which stress‐induced deviations to symmetry, known as fluctuating asymmetry (FA), can be robustly quantified. A case study, in which maternally mediated early‐life stress in human skeletal remains is explored through first permanent molar (M1) FA, illustrates the method's effectiveness and wide‐ranging potential to revolutionise the investigation of themes such as stress intensity, developmental processes, and buffering mechanisms in past populations.

Impacts of Wildlife Artificial Water Provisioning in an African Savannah Ecosystem: A Spatiotemporal Analysis

The use of artificial water points for wildlife in African savannah ecosystems has been widely criticised for affecting the distribution of wildlife and initiating changes in the heterogeneity of natural landscapes. We examined the spatiotemporal variations in the landscape before and after the installation of an artificial water point by integrating the analysis of vegetation and soil spectral response patterns with a supervised learning random forest model between 2002 and 2022 in Chobe Enclave, Northern Botswana. Our results revealed that the study area is characterised by animal species such as Equus quagga, Aepyceros melampus, and Loxodonta africana. The findings also showed that the main vegetation species in the study area landscape include Combretum elaeagnoides, Vachellia luederitzii, and Combretum hereroense. The artificial water point induced disturbances on a drought-vulnerable landscape which affected vegetation heterogeneity by degrading the historically dominant vegetation cover types such as Colophospermum mopane, Dichrostachys cinerea, and Cynodon dactylon. The immediate years following the artificial water point installation demonstrated the highest spectral response patterns by vegetation and soil features attributed to intense landscape disturbances due to abrupt high-density aggregation of wildlife around the water point. Landscapes were strongly homogenised in later years (2022), as shown by overly overlapping spectral patterns owing to an increase in dead plant-based material and senescent foliage due to vegetation toppling and trampling. The landscape disturbances disproportionately affected mopane-dominated woodlands compared to other vegetation species as indicated by statistically significant land cover change obtained from a random forest classification. The woodlands declined significantly (p &lt; 0.05) within 0–0.5 km, 0.5–1 km, 1–5 km, and 5–10 km distances after the installation of the water point. The results of this study indicate that continuous nonstrategic and uninformed use of artificial water points for wildlife will trigger ecological alterations in savannah ecosystems.

V4RIN: visual analysis of regional industry network with domain knowledge.

The regional industry network (RIN) is a type of financial network derived from industry networks that possess the capability to describe the connections between specific industries within a particular region. For most investors and financial analysts lacking extensive experience, the decision-support information provided by industry networks may be too vague. Conversely, RINs express more detailed and specific industry connections both within and outside the region. As RIN analysis is domain-specific and current financial network analysis tools are designed for generalized analytical tasks and cannot be directly applied to RINs, new visual analysis approaches are needed to enhance information exploration efficiency. In this study, we collaborated with domain experts and proposed V4RIN, an interactive visualization analysis system that integrates predefined domain knowledge and data processing methods to support users in uploading custom data. Through multiple views in the system panel, users can comprehensively explore the structure, geographical distribution, and spatiotemporal variations of the RIN. Two case studies were conducted and a set of expert interviews with five domain experts to validate the usability and reliability of our system.

Deep ocean hydrographic variability estimated from distributed geodetic sensor arrays off northern Chile

Observations of spatio-temporal variability of the deep ocean are rare and little is known about occurrence of deep ocean mesoscale dynamics. Here, we make use of 2.5 years of time series data from three distributed sensor arrays, which acquired high-resolution temperature, pressure and sound speed data of the bottom layer offshore northern Chile. Estimating salinity and density from the direct observations enable access to the full spectrum of hydrographic variability from a multi-hourly to annual time scale and with average inter-station distances of less than 1 km. Analyses revealed interannual warming over the continental slope of 0.002 °C yr−1–0.003 °C yr−1, and could trace periodic hydrographic anomalies, likely related to coastal-trapped waves, as far as to the lower continental slope. A concurrent change in the shape of the warm anomalies and the rate of deep-sea warming that occurs with the crossing of the deep-sea trench suggests that the abyssal part of the eastern boundary current system off Chile does not extend past the deep sea trench. Furthermore, the comparison of anomaly timing and shape in between stations implies southwards flow over the mid to lower continental slope, centred closer to the trench.

Assessing the spatiotemporal variability of dissolved organic matter fluorescence composition in the Lake George, NY watershed

Lake George (LG) is a temperate, oligotrophic, medium-sized lake (114 km2) located in northeastern New York State (U.S.). Lakes are highly understudied environments where extensive dissolved organic matter (DOM) processing occurs. With this study we establish the foundation for researching the organic biogeochemistry of the LG watershed, in particular, the numerous tributaries flowing into the lake. Collected were 213 samples from 64 tributaries and 12 lake locations. Some of the tributaries had unique wastewater, agricultural, or wetland influences. We employed fluorescence spectroscopy, a common biogeochemical technique, to characterize the fluorescent DOM (FDOM) component. We developed a parallel factor analysis (PARAFAC) model for the deconvolution of FDOM data allowing to depict six underlying FDOM constituents, which varied in source and biogeochemical reactivity on spatiotemporal scales. Tributary DOM, in comparison to lake DOM, was much more aromatic, of larger molecular weight, more humic, and contained less protein-like material. The distribution of humic and protein-like PARAFAC components was impacted by land-use and wastewater influences. Supporting characterization of the chromophoric DOM (CDOM) and total DOM (on dissolved organic carbon basis) allowed differentiating the influence of wetlands, which could not be depicted by spatiotemporally assessing the variability of PARAFAC components. Temporal assessment revealed minor variabilities in tributary DOM quantity and quality except in cases of point sources such as wastewater treatment facilities. Overall, this primer study establishes baseline understanding of the baseflow levels of DOM constituents in the LG watershed, and more broadly, presents a PARAFAC model for the deconvolution of fluorescence spectra of DOM from temperate and oligotrophic lake watersheds such as LG.

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

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.