Integrated management of groundwater quantity, physicochemical properties, and microbial quality in West Nile delta using a new MATLAB code and geographic information system mapping.

Groundwater is an excellent alternative to freshwater for drinking, irrigation, and developing arid regions. Agricultural, commercial, industrial, residential, and municipal activities may affect groundwater quantity and quality. Therefore, we aimed to use advanced methods/techniques to monitor the piezometric levels and collect groundwater samples to test their physicochemical and biological characteristics. Our results using software programs showed two main types of groundwater: the most prevalent was the Na-Cl type, which accounts for 94% of the groundwater samples, whereas the Mg-Cl type was found in 6% of samples only. In general, the hydraulic gradient values, ranging from medium to low, could be attributed to the slow movement of groundwater. Salinity distribution in groundwater maps varied between 238 and 1350 mg L-1. Although lower salinity values were observed in northwestern wells, higher values were recorded in southern ones. The collected seventeen water samples exhibited brackish characteristics and were subjected to microbial growth monitoring. Sample WD12 had the lowest total bacterial count (TBC) of 4.8 ± 0.9 colony forming unit (CFU mg L-1), while WD14 had the highest TBC (7.5 ± 0.5 CFU mg L-1). None of the tested water samples, however, contained pathogenic microorganisms. In conclusion, the current simulation models for groundwater drawdown of the Quaternary aquifer system predict a considerable drawdown of water levels over the next 10, 20, and 30 years with the continuous development of the region.

Utilizing geospatial artificial intelligence to map cancer disparities across health regions.

We have developed an innovative tool, the Intelligent Catchment Analysis Tool (iCAT), designed to identify and address healthcare disparities across specific regions. Powered by Artificial Intelligence and Machine Learning, our tool employs a robust Geographic Information System (GIS) to map healthcare outcomes and disease disparities. iCAT allows users to query publicly available data sources, health system data, and treatment data, offering insights into gaps and disparities in diagnosis and treatment paradigms. This project aims to promote best practices to bridge the gap in healthcare access, resources, education, and economic opportunities. The project aims to engage local and regional stakeholders in data collection and evaluation, including patients, providers, and organizations. Their active involvement helps refine the platform and guides targeted interventions for more effective outcomes. In this paper, we present two sample illustrations demonstrating how iCAT identifies healthcare disparities and analyzes the impact of social and environmental variables on outcomes. Over time, this platform can help communities make decisions to optimize resource allocation.

GIS-based intelligent planning approach of child-friendly pedestrian pathway to promote a child-friendly city.

Pedestrian safety, particularly for children, relies on well-designed pathways. Child-friendly pathways play a crucial role in safeguarding young pedestrians. Shared spaces accommodating both vehicles and walkers can bring benefits to pedestrians. However, active children playing near these pathways are prone to accidents. This research aims to develop an efficient method for planning child-friendly pedestrian pathways, taking into account community development and the specific needs of children. A mixed-methods approach was employed, utilizing the Datang community in Guangzhou, China, as a case study. This approach combined drawing techniques with GIS data analysis. Drawing methods were utilized to identify points of interest for children aged 2-6. The qualitative and quantitative fuzzy analytic hierarchy process assessed factors influencing pathway planning, assigning appropriate weights. The weighted superposition analysis method constructed a comprehensive cost grid, considering various community elements. To streamline the planning process, a GIS tool was developed based on the identified factors, resulting in a practical, child-friendly pedestrian pathway network. Results indicate that this method efficiently creates child-friendly pathways, ensuring optimal connectivity within the planned road network.

[Related factors and equity of health status among floating population in China based on geographic information system analysis].

OBJECTIVE: To understand the health status, influencing factors and spatial distribution of the Chinese floating population and to evaluate the health equity of the floating population. METHODS: All the data were collected from the 2017 Migrant Population Dynamic Monitoring Survey in China, binary Logistic regression was used to analyze the factors that might affect the health of the floating population, and the concentration index method was used to evaluate the health equity of the floating population. Spatial autocorrelation analyses the spatial aggregation of health status and health equity. RESULTS: The unhealthy rate of the floating population in China was 2.71%. Age and gender show a statistically significant impact on self-rated health; that is, as age increases, the self-rated health of the migrant population gradually deteriorates, and women are more likely to think that they are unhealthy. Fairness analysis shows that the concentration index of the floating population is 0.021 7, the urban household registration floating population is 0.021 6, and the rural household registration floating population is 0.021 9. It is shown that the fairness of the health status of the floating population is biased towards the high-income class, and the rural household registration floating population' s health unfairness is greater than that of the urban household registration migration population. Moreover, Moran' s i=0.211 for self-rated health and Moran' s i=0.291 for the unhealthy rate indicate that self-rated health has a spatial aggregation trend. Moran' s i=0.136 showed the characteristics of spatial clustering, and the two-week prevalence fairness of the floating population was mainly in the northern and southeastern coastal areas. CONCLUSION: In general, the health status of the floating population in China is relatively good. The main influencing factors of health included gender and age. The central tendency of health inequity is reflected in the southeast coastal and northern regions, which are characterized by poverty. Attention to spatial aggregation is not only helpful to analyze the reasons of floating population, but also to study the health differences between different regions and health-related factors, to improve the overall health level of the whole population.

[Travel time to memory clinics in Bavaria: A geographical analyses within the framework of digiDEM Bayern]. / Räumliche Erreichbarkeit von Gedächtnisambulanzen ­ Eine geographische Analyse im Rahmen von digiDEM Bayern.

BACKGROUND: Memory clinics can contribute significantly to a qualified diagnosis of dementia. Since the accessibility of medical facilities is an important predictor for their utilisation, the aim of this study was to determine the accessibility of memory clinics for persons with dementia in Bavaria. METHODS: We used a Geographic Information System (GIS) to determine travel times to the nearest memory clinic for all Bavarian municipalities based on OpenStreetMap road network data. RESULTS: The majority of the modelled persons with dementia in Bavaria (40%; n = 93,950) live in communities with an average travel time of 20 to 40 minutes to the nearest memory clinic. Almost 7,000 (3%) require more than one hour. Especially persons from rural communities have to travel significantly longer distances than people from urban areas. CONCLUSION: In view of demographic developments, there is an urgent need for memory clinics to be accessible throughout the country for all persons with dementia, regardless of where they live. The systematic development of memory clinics in areas with long travel times or the establishment of mobile diagnostic services could help to improve dementia care.

Geospatial Science for the Environmental Epidemiology of Cancer in the Exposome Era.

Geospatial science is the science of location or place that harnesses geospatial tools, such as geographic information systems (GIS), to understand the features of the environment according to their locations. Geospatial science has been transformative for cancer epidemiologic studies through enabling large-scale environmental exposure assessments. As the research paradigm for the exposome, or the totality of environmental exposures across the life course, continues to evolve, geospatial science will serve a critical role in determining optimal practices for how to measure the environment as part of the external exposome. The objectives of this article are to provide a summary of key concepts, present a conceptual framework that illustrates how geospatial science is applied to environmental epidemiology in practice and through the lens of the exposome, and discuss the following opportunities for advancing geospatial science in cancer epidemiologic research: enhancing spatial and temporal resolutions and extents for geospatial data; geospatial methodologies to measure climate change factors; approaches facilitating the use of patient addresses in epidemiologic studies; combining internal exposome data and geospatial exposure models of the external exposome to provide insights into biological pathways for environment-disease relationships; and incorporation of geospatial data into personalized cancer screening policies and clinical decision making.

Geographic information system-aided evaluation of epidemiological trends of dengue serotypes in West Bengal, India.

BACKGROUND OBJECTIVES: West Bengal is a dengue-endemic State in India, with all four dengue serotypes in co-circulation. The present study was conceived to determine the changing trends of circulating dengue virus (DENV) serotypes in five consecutive years (2015-2019) using a geographic information system (GIS) during the dengue season in West Bengal, India. METHODS: Molecular serotyping of dengue NS1 sero-reactive serum samples from individuals with ≤5 days of fever was performed using conventional nested reverse transcriptase-PCR. GIS techniques such as Getis-Ord Gi* hotspot analysis and heatmap were used to elucidate dengue transmission based on the received NS1-positive cases and vector data analysis was used to point out risk-prone areas. RESULTS: A total of 3915 dengue NS1 sero-positive samples were processed from most parts of West Bengal and among these, 3249 showed RNA positivity. The major circulating serotypes were DENV 3 (63.54%) in 2015, DENV 1 (52.79%) in 2016 and DENV 2 (73.47, 76.04 and 47.15%) in 2017, 2018 and 2019, respectively. Based on the NS1 positivity, dengue infections were higher in males than females and young adults of 21-30 yr were mostly infected. Getis-Ord Gi* hotspot cluster analysis and heatmap indicate that Kolkata has become a hotspot for dengue outbreaks and serotype plotting on maps confirms a changing trend of predominant serotypes during 2015-2019 in West Bengal. INTERPRETATION CONCLUSIONS: Co-circulation of all the four dengue serotypes was observed in this study, but only one serotype became prevalent during an outbreak. Representation of NS1-positive cases and serotype distribution in GIS mapping clearly showed serotypic shift in co-circulation. The findings of this study suggest the need for stringent surveillance in dengue-endemic areas to limit the impact of dengue and implement better vector-control strategies.

GIS TECHNOLOGIES IN THE STUDY OF NATURAL RESULTS ESPECIALLY DANGEROUS DISEASES IN KAZAKHSTAN.

This study explores the application of GIS technologies in analyzing and visualizing spatial structures of especially dangerous infections (EPI) in Kazakhstan. International collaborations have facilitated projects studying the focal patterns of diseases, improving data analysis and visualization. Extensive electronic databases resulting from field research on EPI foci have elevated the study's depth. The dynamics of natural foci, influenced by intraspecific structures of infection carriers, are impacted by industrial and agricultural developments, urban expansions, and climate change. The study notes changes in the enzootic territory, affecting mammal migration and consequently altering natural focus boundaries. Industrial activities, rotational methods, and habitat changes contribute to the increased epidemic potential in enzootic areas. Despite anthropogenic and climatic influences, the prevalence of plague remains high in Kazakhstan, with a trend towards expanding enzootic territories. Unified electronic databases on plague, tularemia, anthrax, and other zoonoses, developed for GIS analysis, enable mapping and visualization of natural foci. Electronic maps aid in determining enzootic territory boundaries, assessing infectious disease activity, and planning preventive measures based on risk assessment. ESRI's ArcGIS Desktop 10.8 with Arc Toolbox modules facilitated data processing in the geoinformation environment. Data includes epidemiological examination results, species composition of carriers, and laboratory test outcomes, enhancing comprehensive analysis and decision-making for anti-epidemic measures. The study in Kazakhstan identifies and details six natural and twenty autonomous plague foci, categorizing them by main carriers and observing an expansion of natural hotspots. The enzootic territory is classified into four geographic zones, further divided into 105 landscape-epidemiological regions. Laboratory studies inform electronic maps for analyzing plague's dynamic situation. Anthrax prevalence, primarily in chernozem and chestnut soils, is assessed, revealing 1,778 unaffected settlements and spatially clustered points. An epidemiological index aids in zoning for anthrax trouble. Tularemia's landscape occurrence is classified into four types, with spatial analysis revealing clusters and potential epidemic danger in specific regions. Geographic information technologies highlight high-risk areas, justifying preventive measures for dangerous infections. The results obtained serve as a scientific justification for the priority of preventive measures within the boundaries of administrative territories characterized by a high degree of potential epidemic danger and objectively indicate the prospects for the introduction of GIS technologies into the practice of epidemiological surveillance of particularly dangerous infections.

Low-Cost Sensor Deployment on a Public Minibus in Fukushima Prefecture.

This study analyzed radiation dose data to observe the annual decline in ambient radiation doses and assess the factors contributing to fluctuations in reconstructed areas of the Fukushima prefecture. Utilizing a novel mobile monitoring system installed on a community minibus, the study employed a cost-effective sensor, namely, Pocket Geiger which was integrated with a microcontroller and telecommunication system for data transfer, access, visualization, and accumulation. The study area included the region between Okuma and Tomioka towns. The ambient dose rate recorded along the minibus route was depicted on a map, averaged within a 1 × 1 km mesh created with the Quantum Geographic Information System. To ensure accuracy, the shielding factor of the minibus material is determined to adjust the dose readings. A significant decrease (p < 0.001) in the radiation dose ranges from 2022 to 2023 was observed. The land use classification by the Advanced Land Observation Satellite revealed an ecological half-life ranging from 2.41 years to 1 year, suggesting a rapid radiation decay across all land types. This underscores the close connection between radiation attenuation and environmental factors, as well as decontamination efforts across diverse land categories.

Integrated application of multiple indicators and geographic information system-based approaches for comprehensive assessment of environmental impacts of toxic metals-contaminated agricultural soils and vegetables.

Conventional monitoring and mapping approaches are laborious, expensive, and time-consuming because they need a large number of data and consequently extensive sampling and experimental operations. Therefore, due to the growing concern about the potential of contamination of soils and agricultural products with heavy metals (HMs), a field experiment was conducted on 77 farm lands in an area of 2300 ha in the southeast of Shiraz (Iran) to investigate the source of metal contamination in the soils and vegetables and to model spatial distribution of HMs (iron, Fe; manganese, Mn; copper, Cu; zinc, Zn; cadmium, Cd; nickel, Ni, and lead, Pb) over the region using geographic information system (GIS) and geostatistical (Ordinary Kriging, OK) approaches and compare the results with deterministic approaches (Inverse Distance Weighting, IDW with different weighting power). Furthermore, some ecological and health risks indices including Pollution index (PI), Nemerow integrated pollution index (NIPI), pollution load index (PLI), degree of contamination (Cdeg), modified contamination degree (mCd), PIaverage and PIvector for soil quality, multi-element contamination (MEC), the probability of toxicity (MERMQ), the potential ecological index (RI), total hazard index (THI) and total carcinogenic risk index (TCR) based on ingestion, inhalation, and dermal exposure pathways for adults and children respectively for analyzing the noncarcinogenic and carcinogenic risks were calculated. Experimental semivariogram of the mentioned HMs were calculated and theoretical models (i.e., exponential, spherical, Gaussian, and linear models) were fitted in order to model their spatial structures and to investigate the most representative models. Moreover, principal component analysis (PCA) and cluster analysis (CA) were used to identify sources of HMs in the soils. Results showed that IDW method was more efficient than the OK approach to estimate the properties and HMs contents in the soils and plants. The estimated daily intake of metals (DIM) values of Pb and Ni exceeded their safe limits. In addition, Cd was the main element responsible for ecological risk. The PIave and PIvector indices showed that soil quality in the study area is not suitable. According to mCd values, the soils classified as ultra-high contaminated for Cu and Cd, extremely high for Zn and Pb, very high, high, and very low degree of contamination for Ni, Mn, and Fe, respectively. 36, 60, and 4 % of the sampling sites had high, medium, and low risk levels with 49, 21, and 9 % probability of toxicity, respectively. The maximum health risk index (HRI) value of 20.42 with extremely high risk for children was obtained for Ni and the HI for adults and children were 0.22 and 1.55, respectively. The THI values of Pb and Cd were the highest compared to the other HMs studied, revealing a possible non-cancer risk in children associated with exposure to these metals. The routes of exposure with the greatest influence on the THI and TCR indices were in the order of ingestion > inhalation > dermal. Therefore, ingestion, as the main route of exposure, is the route of greatest contribution to health risks. PCA analysis revealed that Fe, Mn, Cu, and Ni may originate from natural sources, while Fe was appeared to be controlled by fertilizer, and Cu primarily coming from pesticide, while Cd and Pb were mainly associated with the anthropogenic contamination, atmospheric depositions, and terrific in the urban soils. While, Zn mainly originated from fertilization. Findings are vital for developing remediation approaches for controlling the contaminants distribution as well as for monitoring and mapping the quality and health of soil resources.

Forest fire risk zoning based on fuzzy logic and analytical network process.

Forest fires have a significant impact on human life, property safety, and ecological environment. Deve-loping high-quality forest fire risk maps is beneficial for preventing forest fires, guiding resource allocation for firefighting, assisting in fire suppression efforts, and supporting decision-making. With a multi-criteria decision analysis (MCDA) method based on geographic information systems (GIS) and literature review, we assessed the main factors influencing the occurrences of forest fires in Youxi County, Fujian Province. We analyzed the importance of each fire risk factor using the analytic network process (ANP) and assigned weights, and evaluated the sub-standard weights using fuzzy logic assessment. Using ArcGIS aggregation functions, we generated a forest fire risk map and validated it with satellite fire points. The results showed that the areas classified as level 4 or higher fire risk accounted for a considerable proportion in Youxi County, and that the central and northern regions were at higher risk. The overall fire risk situation in the county was severe. The fuzzy ANP model demonstrated a high accuracy of 85.8%. The introduction of this novel MCDA method could effectively improve the accuracy of forest fire risk mapping at a small scale, providing a basis for early fire warning and the planning and allocation of firefighting resources.

A conceptional model integrating geographic information systems (GIS) and social media data for disease exposure assessment.

Although previous studies have acknowledged the potential of geographic information systems (GIS) and social media data (SMD) in assessment of exposure to various environmental risks, none has presented a simple, effective and user-friendly tool. This study introduces a conceptual model that integrates individual mobility patterns extracted from social media, with the geographic footprints of infectious diseases and other environmental agents utilizing GIS. The efficacy of the model was independently evaluated for selected case studies involving lead in the ground; particulate matter in the air; and an infectious, viral disease (COVID- 19). A graphical user interface (GUI) was developed as the final output of this study. Overall, the evaluation of the model demonstrated feasibility in successfully extracting individual mobility patterns, identifying potential exposure sites and quantifying the frequency and magnitude of exposure. Importantly, the novelty of the developed model lies not merely in its efficiency in integrating GIS and SMD for exposure assessment, but also in considering the practical requirements of health practitioners. Although the conceptual model, developed together with its associated GUI, presents a promising and practical approach to assessment of the exposure to environmental risks discussed here, its applicability, versatility and efficacy extends beyond the case studies presented in this study.

Regional gap and sustainable development of interpreting level in mainland China: A statistics and GIS-based study.

Against the backdrop of globalization, interpreting, a translation communicative activity in a verbal way, plays an increasingly important role in international communications and exchanges. In response to this world pattern, the Chinese government attaches great importance to the interpreting industry. However, due to the national condition of uneven regional development, the English interpreting level across China is also unbalanced. Confronting this circumstance, previous research only stagnates at the level of recognizing the problem, but very few studies have attempted to solve the problem. Thus, the current study aims to figure out the regional interpreting level in mainland China by establishing and utilizing an innovative indicator system based on statistics and geography technologies. Based on the literature review and empirical questionnaire survey from different stakeholders, the study proposes an indicator system containing 3 first-level factors and 7 second-level factors to measure regional English interpreting levels. The weight of each indicator and scoring method is laid down based on factor analysis and interval marking. In addition, putting the innovative indicator system into practice, a total of 38 groups of regional data are collected to rank the regional interpreting level across China. Integrating with GIS and statistical techniques, the result visually shows that the English interpreting level across China is uneven at present: higher in the southern and eastern parts of China compared to that of northern and western China, which is unfriendly to sustainable development in the future. Facing this reality, a following-up analysis has been made for offering explanations of the results and suggestions for regional interpreting sustainable development.

GIS-FA: an approach to integrating thematic maps, factor-analytic, and envirotyping for cultivar targeting.

KEY MESSAGE: We propose an "enviromics" prediction model for recommending cultivars based on thematic maps aimed at decision-makers. Parsimonious methods that capture genotype-by-environment interaction (GEI) in multi-environment trials (MET) are important in breeding programs. Understanding the causes and factors of GEI allows the utilization of genotype adaptations in the target population of environments through environmental features and factor-analytic (FA) models. Here, we present a novel predictive breeding approach called GIS-FA, which integrates geographic information systems (GIS) techniques, FA models, partial least squares (PLS) regression, and enviromics to predict phenotypic performance in untested environments. The GIS-FA approach enables: (i) the prediction of the phenotypic performance of tested genotypes in untested environments, (ii) the selection of the best-ranking genotypes based on their overall performance and stability using the FA selection tools, and (iii) the creation of thematic maps showing overall or pairwise performance and stability for decision-making. We exemplify the usage of the GIS-FA approach using two datasets of rice [Oryza sativa (L.)] and soybean [Glycine max (L.) Merr.] in MET spread over tropical areas. In summary, our novel predictive method allows the identification of new breeding scenarios by pinpointing groups of environments where genotypes demonstrate superior predicted performance. It also facilitates and optimizes cultivar recommendations by utilizing thematic maps.

Healthcare equity in focus: bridging gaps through a spatial analysis of healthcare facilities in Irbid, Jordan.

In the Irbid Governorate, Jordan, equitable healthcare facility distribution is vital to ensuring healthcare accessibility and improving public health outcomes. This study investigated the spatial distribution, accessibility, and conformity of healthcare facilities to the Ministry of Health standards to identify areas requiring improvement. Using geographic information systems (GIS), three spatial analyses were conducted: nearest neighbor analysis, buffer analysis, and service area analysis. These analyses comprehensively assessed the healthcare landscape, revealing a random spatial distribution pattern of healthcare facilities; and indicating an absence of structured organization. The buffer analysis revealed concentrations in specific regions, while others were underserved. The Service Area Analysis revealed significant healthcare access challenges, especially in remote areas. The healthcare resource distribution of the Irbid governorate fell short of national and international standards, emphasizing the need for improvements. To address these disparities, policymakers and healthcare authorities should focus on equitably redistributing resources, tailoring allocation to local needs, improving remote area infrastructure, and refining government policies. Continuous monitoring and evaluation are imperative to ensure alignment with international standards and achieve healthcare equity. The insights from this case study provide valuable guidance for regions facing similar healthcare distribution challenges.

Comparing malaria risk exposure in rural Cambodia population using GPS tracking and questionnaires.

BACKGROUND: The Great Mekong Subregion has attained a major decline in malaria cases and fatalities over the last years, but residual transmission hotspots remain, supposedly fueled by forest workers and migrant populations. This study aimed to: (i) characterize the fine-scale mobility of forest-goers and understand links between their daily movement patterns and malaria transmission, using parasites detection via real time polymerase chain reaction (RT PCR) and the individual exposure to Anopheles bites by quantification of anti-Anopheles saliva antibodies via enzyme-linked immunosorbent assay; (ii) assess the concordance of questionnaires and Global Positioning System (GPS) data loggers for measuring mobility. METHODS: Two 28 day follow-ups during dry and rainy seasons, including a GPS tracking, questionnaires and health examinations, were performed on male forest goers representing the population at highest risk of infection. Their time spent in different land use categories and demographic data were analyzed in order to understand the risk factors driving malaria in the study area. RESULTS: Malaria risk varied with village forest cover and at a resolution of only a few kilometers: participants from villages outside the forest had the highest malaria prevalence compared to participants from forest fringe's villages. The time spent in a specific environment did not modulate the risk of malaria, in particular the time spent in forest was not associated with a higher probability to detect malaria among forest-goers. The levels of antibody response to Anopheles salivary peptide among participants were significantly higher during the rainy season, in accordance with Anopheles mosquito density variation, but was not affected by sociodemographic and mobility factors. The agreement between GPS and self-reported data was only 61.9% in reporting each kind of visited environment. CONCLUSIONS: In a context of residual malaria transmission which was mainly depicted by P. vivax asymptomatic infections, the implementation of questionnaires, GPS data-loggers and quantification of anti-saliva Anopheles antibodies on the high-risk group were not powerful enough to detect malaria risk factors associated with different mobility behaviours or time spent in various environments. The joint implementation of GPS trackers and questionnaires allowed to highlight the limitations of both methodologies and the benefits of using them together. New detection and follow-up strategies are still called for.

Spatio-temporal evaluation of the impact of anthropogenic stressors on physico-chemical characteristics and water quality of the River Ganga using GIS-based approach in the middle Gangetic Plains at Patna, Bihar, India.

The present study aimed to assess the impact of anthropogenic stressors on the physico-chemical characteristics and water quality of the River Ganga employing a GIS-based approach in the middle Gangetic Plain at Patna, India. After the survey, sand mining, bridge construction, and disposal of untreated domestic and sewage wastes were selected as major anthropogenic stressors. A total of 48 samples were collected in pre-monsoon and post-monsoon seasons of 2022 and were analyzed for 16 physico-chemical parameters, namely water temperature (WT), pH, electrical conductivity (EC), total dissolved solids (TDS), turbidity, dissolved oxygen (DO), biological oxygen demand (BOD), total hardness (TH), Ca2+, Mg2+, Na+, K+, Cl- and SO42- ions, following standard protocols. The WQI was calculated using the Weighted Arithmetic Water Quality Index (WAQWI) method and spatial maps were created using ArcGIS software. The result revealed significant seasonal variation in several physico- chemical parameters except for Ca2+, K+ and TA (p > 0.05). ANOVA revealed significant variation for BOD and COD at Ghagha and Triveni, whereas for nitrate at Gai Ghat reference and impact sites, respectively. The Water Quality Index (WQI) revealed a deterioration in water quality by 60% in post-monsoon season. HCA revealed that the WQI was mostly governed by TDS, TH, TA, and EC.

Navigating Aged Care Services with GIS: Trends, Developments, and Future Directions.

BACKGROUND: With the growing challenge of an aging population, emerging technologies are increasingly being integrated into the production, organization, and delivery of aged care services. Geographic Information System (GIS), a computer-based tool for spatial information analysis and processing, has made significant strides in the allocation of care recources and service delivery for older adults, a notably vulnerable group. Despite its growing importance, cross-disciplinary literature reviews on this theme are scare. This scoping review was conducted to encapsulate the advancements and discern the future trajectory of GIS applications in aged care services. METHODS: A comprehensive search across nine databases yielded 5941 articles. Adhering to specific inclusion and exclusion criteria, 61 articles were selected for a detailed analysis. RESULTS: The 61 articles span from 2003 to 2022, with a notable increase in publications since 2018, comprising 41 articles (67% of the total) published between 2018-2022. Developed countries contributed 66% of the papers, with 45% focusing on accessibility issues. In the domain of aged care services, GIS has been predominantly utilized for model construction, mapping, and site selection, with a growing emphasis on addressing the unique needs of different subgroups of older adults. CONCLUSION: The past two decades have seen substantial growth in the application of GIS in aged care services, reflecting its increasing importance in this field. This scoping review not only charts the historical development of GIS applications in aged care services but also underscores the need for innovative research approaches. Future directions should emphasize the integration of GIS with diverse methodologies to address the heterogeneous needs of older adults and improve the overall delivery of aged care services. Such advancements in GIS applications have the potential to significantly enhance the efficiency, accessibility, and quality of care for the aging population.

The Ratio between Weekly Training and Match External Physical Loads in U17 Elite Youth Soccer Players: Implications for the Training Process.

Research on the external physical load on elite youth soccer players during the weekly training microcycle in competitive periods and official matches is limited. The aims of this study were twofold: a) investigate possible differences in external physical load (PL) across player positions in U17 elite youth soccer players during official matches; b) determine the weekly training to match physical load ratio (WTMLr) across player positions. The sample included 20 outfield players from an elite soccer academy (mean age 15.94 ± 0.25 years) playing in four positions: central defender (CD), full-back (FB), central midfielder (CM) and Striker (S). Data were collected during the spring in-season period for 17 official matches played in a 4-3-3 game format. Indicators of external physical load monitored were: total distance (TD); total distance in high-speed running (HSR; > 16.1 km.h-1); total distance in sprint running (SPR; > 21.6 km.h-1); and relative physical load intensity (%HSR). The WTMLr was calculated for TD, HSR, SPR and %HSR as the ratio of the average weekly sum of training PL to the average sum of PL in an official match for a given players' position. Collectively, the training intensity during a one-week microcycle (%HSR in WTMLr) achieved only 76 % of match demands. CD performed significantly lower in all measured indicators of external PL during the official match than all other positions (p < 0.05; g > 0.80) except for TD in S. S achieved significantly higher SPR during official matches compared to CD (p < 0.05; g > 0.80), CM, and FB (g > 0.80). In contrast, CD reported higher WTMLr (medium-large effect size) in HSR and SPR indicators than all other positions. CM performed significantly higher %HSR in WTMLr than S and FB (p < 0.05; g > 0.80). Results revealed insufficient training intensity relative to match demands and, at the same time, weekly training PL did not meet match demands (especially in HSR and SPR) for players across the different positions. Therefore, practitioners should select appropriate training methods (drills and games) to ensure sufficient training intensity (HSR and SR metrics) and consider using the WTMLr, which can be used to help optimise and individualise training PL for different player positions.

Towards more realistic measures of accessibility to emergency departments in Sweden.

BACKGROUND: Assuring that emergency health care (EHC) is accessible is a key objective for health care planners. Conventional accessibility analysis commonly relies on resident population data. However, the allocation of resources based on stationary population data may lead to erroneous assumptions of population accessibility to EHC. METHOD: Therefore, in this paper, we calculate population accessibility to emergency departments in Sweden with a geographical information system based network analysis. Utilizing static population data and dynamic population data, we investigate spatiotemporal patterns of how static population data over- or underestimates population sizes derived from temporally dynamic population data. RESULTS: Our findings show that conventional measures of population accessibility tend to underestimate population sizes particularly in rural areas and in smaller ED's catchment areas compared to urban, larger ED's-especially during vacation time in the summer. CONCLUSIONS: Planning based on static population data may thus lead to inequitable distributions of resources. This study is motivated in light of the ongoing centralization of ED's in Sweden, which largely depends on population sizes in ED's catchment areas.