Elevated ammonium (NH4-N) contents in groundwater are a global concern, yet the mobilization and enrichment mechanisms controlling NH4-N within riverside aquifers (RAS) remain poorly understood. RAS are important zones for nitrogen cycling and play a vital role in regulating groundwater NH4-N contents. This study conducted an integrated assessment of a hydrochemistry dataset using a combination of hydrochemical analyses and multivariate geostatistical methods to identify hydrochemical compositions and NH4-N distribution in the riverside aquifer within Central Yangtze River Basin, ultimately elucidating potential NH4-N sources and factors controlling NH4-N enrichment in groundwater ammonium hotspots. Compared to rivers, these hotspots exhibited extremely high levels of NH4-N (5.26 mg/L on average), which were mainly geogenic in origin. The results indicated that N-containing organic matter (OM) mineralization, strong reducing condition in groundwater and release of exchangeable NH4-N in sediment are main factors controlling these high concentrations of NH4-N. The Eh representing redox state was the dominant variable affecting NH4-N contents (50.17 % feature importance), with Fe2+ and dissolved organic carbon (DOC) representing OM mineralization as secondary but important variables (26 % and 5.11 % feature importance, respectively). This study proposes a possible causative mechanism for the formation of these groundwater ammonium hotspots in RAS. Larger NH4-N sources through OM mineralization and greater NH4-N storage under strong reducing condition collectively drive NH4-N enrichment in the riverside aquifer. The evolution of depositional environment driven by palaeoclimate and the unique local environment within the RAS likely play vital roles in this process.

Monitoring the land use/land cover (LU/LC) changes that have occurred with rapid population growth and urbanization since the Industrial Revolution is important for the optimal configuration of landscape patterns and ensuring the sustainability of ecological functions. Spatiotemporal dynamic pattern of LU/LC change using high-resolution land use data is an indicator to evaluate the landscape ecological risk through landscape pattern index analysis. In this study, the landscape ecological risk index (LERi) based on LU/LC change was calculated using remote sensing images of Landsat TM (Thematic Mapper) and OLI (Operational Land Imager) Rdata of a Gediz Mainstream Sub-basin in Turkiye between 1992 and 2022, and the spatial distribution regularity of LERi values was determined with spatial statistical analysis. According to the results, it was determined that the LERi values of the study area changed by 45% in 30 years. The highest change is in the very high-risk class, with an increase of 10.96%, and the least change occurred in the very low-risk class, with a decrease of 1.29%. According to the obtained statistical analysis results, it was determined that the global spatial autocorrelation values analyzed at different grain levels showed positive autocorrelation for both years and that the LERi values tended to have strong spatial clustering. As a result, it is emphasized that strict control measures should be taken for areas showing High-High (HH) autocorrelation type located in the southeast and north-southwest line of the study area at the local level, and ecological restoration applications should be given priority in these areas.

In order to explore the spatial differentiation characteristics and variation law of soil Cd content in a high geological background area, 14 421 topsoil samples were collected from topsoil in the karst area of Guiyang City. Global Moran\'s I index, cold hot spot analysis, semi-variance function, and Kriging interpolation were used to reveal the spatial structure and distribution law of soil Cd content. The influence of environmental factors on soil Cd content and its main controlling factors were analyzed through analysis of variance and geographic detector. The results showed that： ① The Cd content of karst surface soil in Guiyang varied from 0.03 to 1.36 mg·kg-1, with an average of 0.440 mg·kg-1, which was 1.77 times and 5.95 times the Guizhou soil Cd background value and Chinese soil Cd background value, respectively. The over-standard rate of soil Cd was 30%, which was 4.29 times that of 7% of soil Cd in China. ② There was a significant spatial positive correlation of soil Cd content, showing an aggregation trend in the global space, whereas in the local region, the northeast and southwest were hot spots, and the north was a cold spot. The nugget coefficient of soil Cd content was 10.37%, indicating that soil Cd was mainly affected by structural factors. ③ In terms of spatial distribution, soil Cd showed different accumulation trends. In some massive soils, such as Xifeng County, Xiuwen County, Qingzhen City, Huaxi District, and Nanming District, the soil ω（Cd）was less than 0.3 mg·kg-1. The soil ω（Cd）was between 0.3 and 0.6 mg·kg-1,and soil Cd in Baiyun District, Wudang District, Guanshan Lake area, and Yunyan area as a whole lied within this range. The soil ω（Cd）between 0.6 and 0.9 mg·kg-1 was concentrated in the southwest of Qingzhen City, the south of Huaxi District, and the north of Kaiyang County, whereas soil ω（Cd） between 0.9 and 1.2 mg·kg-1 was mainly concentrated in the southwest of Qingzhen City. The extreme value of soil Cd content （ > 1.2 mg·kg-1） was mostly distributed in Kaiyang County, Xiuwen County, Qingzhen City, and Huaxi District. ④ The results of analysis of variance and geo-detector showed that different environmental factors had significant effects on the spatial differentiation of soil Cd, but their explanatory power on soil Cd content varied： stratum （0.176 5） > soil type （0.026 0） > organic matter （0.025 1） > altitude （0.010 5） > parent rock （0.007 3） > land use （0.006 4） > pH （0.001 3）, and the interaction between stratum and arbitrary environmental factors was the greatest. Therefore, stratum was the main factor affecting the spatial differentiation of soil Cd content.

The safety of human health and agricultural production depends on the quality of farmland soil. Risk assessment of heavy metal pollution sources could effectively reduce the hazard of soil pollution from various sources. This study has identified and quantitatively analyzed pollution sources with geostatistical analysis and the APCS-MLR model. The potential ecological risk index was combined with the APCS-MLR model which has quantitatively calculated the source contribution. The results revealed that As, Cr, Cd, Pb, Zn, and Cu were enriched in soil. Geostatistical analysis and the APCS-MLR model have apportioned four pollution sources. The Mn and Ni were attributed to natural sources; As and Cr were from agricultural activities; Cu and Zn were originated from natural sources; Cd and Pb were derived from atmospheric deposition. Atmospheric deposition and agricultural activities were the largest contributors to ecological risk of heavy metals in soil, which accounted for 56.21% and 36.01% respectively. Atmospheric deposition and agricultural activities are classified as priority sources of pollution. The combination of source analysis receptor model and risk assessment is an effective method to quantify source contribution. This study has quantified the ecological risks of soil heavy metals from different sources, which will provide a reliable method for the identification of primary harmfulness sources of pollution for future studies.

We investigated the fluorescent dissolved organic matter (FDOM) composition in two watersheds with variable land cover and wastewater infrastructure, including sanitary sewers and septic systems. A four-component parallel factor analysis model was constructed from 295 excitation-emission matrices recorded for stream samples to examine relationships between FDOM and geospatial parameters. The contributions of humic acid- and fulvic acid-like fluorescence components (e.g., C1, C2, C3) were fairly consistent across a 12 month period for the 27 sampling sites. In contrast, the protein-like fluorescence component (C4) and a related ratiometric wastewater indicator (C4/C3) exhibited high variability in urban tributaries, suggesting that some sites were impacted by leaking sewer infrastructure. Principal component analysis indicated that urban areas clustered with impervious surfaces and sanitary sewer density, and cross-covariance analysis identified strong positive correlations between C4, impervious surfaces, and sanitary sewer density at short lag distances. The presence of wastewater was confirmed by detection of sucralose (up to 1,660 ng L-1) and caffeine (up to 1,740 ng L-1). Our findings not only highlight the potential for C4 to serve as an indicator of nearby, compromised sanitary sewer infrastructure, but also suggest that geospatial data can be used to predict areas vulnerable to wastewater contamination.

A spatiotemporal investigation of hematophagous fly prevalence was conducted over a 1-year period on 12 beef cattle farms located in major livestock areas of Bangkok, Thailand, using Vavoua traps. The survey revealed 5,018 hematophagous flies belonging to Muscidae and Tabanidae, with the 3 dominant species identified as Stomoxys calcitrans (Linnaeus) (2,354; 46.91%), Musca crassirostris Stein (1,528; 30.45%), and Haematobia exigua de Meijere (922; 18.37%). The abundance of S. calcitrans per trap per week was significantly higher during the rainy season (45.64 ± 14.10), followed by the cold and dry seasons (6.39 ± 2.16 and 3.04 ± 1.27, respectively). The relative abundance of S. calcitrans reached the highest apparent density per trap per day (ADT) index of 9.83 in August 2022 during the rainy season. Subsequently, there was a rapid decline, and the ADT index dropped to nearly zero in December 2022 during the cold season. This low abundance continued through the dry months from March to May 2023. The higher rainfall and relative humidity could significantly contribute to the high relative abundance of S. calcitrans. In contrast, M. crassirostris and H. exigua showed population fluctuations that were not significantly associated with seasonal changes and weather conditions. Remote sensing data and spatial regression analyses using ordinary least squares regression showed the high spatial density of S. calcitrans in the north direction of the Khlong Sam Wa district during the rainy season; it shifted toward the south in the cold and dry seasons, corresponding with rainfall.

In the present study, the status of water quality, environmental contamination in the lower stretch of Subarnarekha River with respect to potentially toxic elements (PTEs), its seasonal distribution, and ecotoxicological health impacts were investigated. For this purpose, a combination of indexing approaches and geospatial methods was used. The estimated water quality index (WQI) has shown that the river water falls under \"moderate to very poor\" category during the pre-monsoon and \"moderate to poor\" category in the post-monsoon season. The abundance of PTEs (Pb, Cu, Ni, Cd, Fe, and Cr) was on the higher side during the pre-monsoon in comparison with the post-monsoon season. The results of contamination index (Cd) and heavy metal evaluation index (HEI) explain that Subarnarekha River has low-to-moderate levels of contamination with PTEs in the majority of sampling sites. However, HPI indicated that the river water is moderate-to-highly contaminated with PTEs in both seasons. Principal component analysis (PCA) and cluster analysis (CA) reveal that anthropogenic sources are prime contributors to PTEs contamination in Subarnarekha River. The potential non-cancerous health concerns for child and adults due to Cr and Pb in some sampling stations along the river stretch have been observed. The carcinogenic risk (CR) has been established for Cr, Pb, and Cd in Subarnarekha River with Cr (> 10-4) as the most unsafe element. Monte Carlo simulation (MCS) indicates a high risk of cancer hazards due to Cr (values > 1E-04) in present as well as future for both child and adults.

The coastal areas of Bangladesh have poor accessibility to fresh drinking water and the groundwater is not suitable for drinking, cooking, and other domestic uses due to high levels of salinity and potentially toxic elements. The current study focuses on understanding of the distribution of some physicochemical parameters (temperature, pH, EC, TDS, and salinity) and chemical elements (Fe, Mn, Zn, Ca, Mg, Na, K, Cu, Co, Pb, As, Cr, Cd, and Ni) with health perspective in drinking water from the southwestern coastal area of Bangladesh. The physicochemical properties of the water samples were examined with a multiparameter meter, while the elemental concentrations were analyzed using atomic absorption spectrometer. Water quality index (WQI) and irrigation indices were utilized to determine the drinking water quality and irrigation feasibility, respectively, whereas hazard quotients (HQs) and hazard index (HI) were used to assess the probable pathways and the associated potential risks to human health. The concentrations of some toxic elements in measured samples were relatively higher compared to drinking water guidelines, indicating that ground and surface water are not apt for drinking and/or domestic uses. The multivariate statistical approaches linked the source of the pollutants in the studied water body mostly to the geogenic origin including saline water intrusion. WQI values ranged from 18 to 430, reflecting excellent to unsuitable categories of water quality. The assessment of human health risks due to exposure to contaminated water demonstrated both carcinogenic and non-carcinogenic health risks in the exposed residents of the study area. Therefore, appropriate long-term coastal area management strategies should be adopted in the study region for environmental sustainability. The findings of this research will be supportive in understanding the actual situation of fresh drinking water in the area for policymakers, planners, and environmentalists to take effective necessary measures to ensure safe drinking water in the study area.

The aim of this study was to investigate the geographical spatial distribution of creatine kinase isoenzyme (CK-MB) in order to provide a scientific basis for clinical examination. The reference values of CK-MB of 8697 healthy adults in 137 cities in China were collected by reading a large number of literates. Moran index was used to determine the spatial relationship, and 24 factors were selected, which belonged to terrain, climate, and soil indexes. Correlation analysis was conducted between CK-MB and geographical factors to determine significance, and 9 significance factors were extracted. Based on R language to evaluate the degree of multicollinearity of the model, CK-MB Ridge model, Lasso model, and PCA model were established, through calculating the relative error to choose the best model PCA, testing the normality of the predicted values, and choosing the disjunctive kriging interpolation to make the geographical distribution. The results show that CK-MB reference values of healthy adults were generally correlated with latitude, annual sunshine duration, annual mean relative humidity, annual precipitation amount, and annual range of air temperature and significantly correlated with annual mean air temperature, topsoil gravel content, topsoil cation exchange capacity in clay, and topsoil cation exchange capacity in silt. The geospatial distribution map shows that on the whole, it is higher in the north and lower in the south, and gradually increases from the southeast coastal area to the northwest inland area. If the geographical factors are obtained in a location, the CK-MB model can be used to predict the CK-MB of healthy adults in the region, which provides a reference for us to consider regional differences in clinical diagnosis.

Various inaccurate traditional models have resulted in major ambiguities and gaps in the interpretation of Anatolian plate deformation directions. To address this issue, a GIS-based spatial statistical analysis method was used for the first time to detect the directional distribution of deformation along the Anatolian Plate in Turkey. Two strategies were used in this study: firstly, identifying the abnormally active seismic areas by detecting significant hotspot and cold spot clusters and confirming this detection using optimized hotspot analysis for earthquake events that occurred from 1900 to the end of 2019. Secondly, detecting the directional distribution of deformation using a Standard Deviational Ellipse (SDE) by calculating the standard deviation of the x and y coordinates from the mean center for each set of earthquake events in the Anaconda Python Platform and ArcGIS 10.8 software. Our improved geostatistical analysis results confirmed the existence of abnormal seismic hazard zones within the study area and three deformation directions: the east-west trend, the southeast-northwest trend, and the south-north trend.