A top-down spatial scenario approach for identifying the locations of rainwater harvesting sites in an urban region.

Alternative water sources are necessary in developing nations because surface water is not always accessible, and groundwater is depleted. In such situations, rainwater harvesting is considered a promising sustainable water resource management solution. Numerous studies have been conducted to determine suitable locations for rainwater harvesting (RWH) using bottom-up approaches applied to large watersheds. The bottom-up methods begin with various geographic criteria and end with regions suitable for RWH intervention, even considering the distance from settlements to be one of the criteria, excluding urban areas from RWH site identification. This study developed a top-down methodology that began with the distributed pinpoint locations of potential RWH sites, as determined by distributed flow accumulation values produced from a digital elevation model (DEM), and then filtered out the sites based on various criteria in the context of urban areas. The flow accumulation values were apportioned according to the flow-contributing area of each RWH site. Five flow-contributing areal scenarios corresponding to 1 km2, 2.5 km2, 5 km2, 7.5 km2, and 10 km2 were considered in this study, as it is challenging to choose a suitable location for RWH sites in urban zones for efficient water storage owing to a variety of land uses. Based on this technique, a case study was conducted in Jaipur, Rajasthan, India, where it was found that the volumetric potential of rainwater storage is maximum (403,679,424.9 cu. m) for 1 km2 and minimum (169,951,322 cu. m) for 10 km2 flow contributing areal distribution per RWH site.

Quantification and mapping of regulating ecosystem services in canola agroecosystems (case study: Gorgan County, Iran).

Regulating services are the advantages that humans receive from regulating ecosystem processes. These services include, but are not limited to pollination, climate regulation, water purification, carbon sequestration, and erosion control. Quantifying and mapping ecosystem services in agroecosystems is one of the main effective actions to increase pay attention to these services and adopt suitable approaches to direct sustainability. The purpose of the study was quantification, and mapping of regulating ecosystem services in canola agroecosystems of Gorgan County, north of Iran. For this purpose, some regulating services such as carbon sequestration, climate regulation, soil microbial respiration, soil aggregate stability, and pollination by insects were evaluated based on the Common International Classification of Ecosystem Services framework. The information and data required for each of these services were collected through field measurements, laboratory experiments, and field surveys. After quantifying, the surveyed services in canola agroecosystems were presented on geospatial maps generated by ArcGIS software, version 10.3. Results showed that agroecosystems in the west and north of the studied region provided the more regulating services. Also, the results of the pollination showed that pollinating insects belonged to four orders and 13 families. The majority of the pollinators were Hymenoptera (44.74%), especially honey bees (Apis mellifera L.), Diptera (5.26%), Butterflies (Lepidoptera; 25%), and the beetles (Coleoptera; 25%), and Anthophora sp. and Andrena sp. were the second and the third most abundant pollinating species after honey bees. Generally, the canola agroecosystems close to the rivers and the natural ecosystems provided more services than other regions.

Mapping new psychoactive substances: Leveraging GIS technology for advanced global surveillance and policy support.

The escalating challenge of New Psychoactive Substances (NPS) necessitates enhanced global monitoring and analysis capabilities. This study introduces an advanced interactive visualization tool that employs Geographic Information System (GIS) technologies to improve the functionality of the UNODC's Early Warning Advisory. The tool enables dynamic observation and analysis of NPS's geographical and temporal distribution, thereby facilitating a comprehensive understanding of their public health impacts. By incorporating detailed choropleth maps and annual and cumulative bar charts, the tool allows policymakers and researchers to visually track and analyze trends in NPS usage and control efforts across different regions. The results demonstrate the tool's effectiveness in providing actionable insights, which support the strategic development of public health policies and interventions to curb the global rise in NPS usage. This initiative illustrates the essential role of digital tools in enhancing public health strategies and responses to emerging drug trends. This rising challenge underscores the urgent need for innovative solutions in monitoring drug trends, a theme explored in this paper. The web tool is available at https://nps-vis.cmdm.tw, and the code is available at https://github.com/CMDM-Lab/nps-vis.

Spatial analysis of risk factors related to colorectal cancer in Iran: An ecological study.

Background and aims: Colorectal cancer is the third most common cancer worldwide, accounting for 10% of cancer deaths. Therefore, this study was performed with the aim of spatial analysis of risk factors for colorectal cancer in Iran. Method: This study was conducted ecologically using STEPS information (The WHO Stepwise Approach to NCD Risk Factor Surveillance) in Iran. To analyze the data, the researcher used cluster analysis and Geographically Weighted Regression methods with the help of ArcGIS version 10. Results: The results of OLS analysis showed that there was a significant relationship between tobacco consumption (B = 0.571, p-value = 0.044) and smoking (B = 0.772, p-value = 0.010) and the incidence of colon cancer (CC). There was also a significant relationship between abdominal obesity and the incidence of rectal cancer (RC) (B = 0.061, p-value = 0.027). Conclusion: This study showed that (CC) high-risk areas are located in central and northern parts of Iran, and the significant risk factors related to CC and RC were found to be tobacco use, cigarette smoking, and abdominal obesity. These findings are helpful to inform policymakers to plan screening services to reduce CC and RC, especially in high-risk populations.

Atlas of pacific sand lance (Ammodytes personatus) benthic habitat - Application of multibeam acoustics and directed sampling to identify viable subtidal substrates.

Defining and delineating species distribution and habitat is critical to informed management and conservation. This process is complicated in marine environments, where detection of marine taxa and characterization of marine habitat is more difficult. Small pelagic fishes and forage fishes are particularly challenging, though insights may be more accessible in species highly dependent on particular habitat. Pacific sand lance (Ammodytes personatus) is a common and ecologically-important pelagic fish that relies on specific benthic sediments for rest and refuge from predation. We applied multibeam echosounder (MBES) bathymetric data to develop high-definition benthic habitat maps and implemented multiyear sampling to assess potential habitat for sand lance via in situ sampling of sediments. We also applied acoustic Doppler current profilers (ADCP) data and tidally-driven volume-based ocean models to measure current strength and to visualize currents. We leveraged this data to further define and describe habitat for this important forage species. Sediment transport processes were mapped and areas of dispersal, embedment, and accumulation were evaluated. Dynamic bedform habitats, banner banks and glacial banks were identified as potential habitat and sampled for fish presence, density and sediment composition. In the central Salish Sea, approximately 25% of benthic substrates represent potential sand lance habitat. Sand lance prevalence and density correlated with substrate type and sediment coarseness. Densities were highest in areas of coarse grain sediments and presence was limited by fine particulates, such as silt and mud. Tidal currents appear important. Presence and densities of sand lance were correlated with current velocity and distance from current flow path. Nearly all viable sites were located on the immediate margins of high flow (<0.16 km from tidal currents with max speed of 1.72-2.58 m/s). While both flood and ebb were important, processes related to flood were dominant. Viable habitat was not constrained by depth. These results inform a developing atlas for sand lance in the central Salish Sea, provide new insights to subtidal sand lance habitat, characterize conditions that create and maintain subtidal benthic habitat, and provide a template for mapping habitat for this species in the coastal Pacific Ocean.

The Automatic Context Measurement Tool (ACMT) to Compile Participant-Specific Built and Social Environment Measures for Health Research: Development and Usability Study.

BACKGROUND: The environment shapes health behaviors and outcomes. Studies exploring this influence have been limited to research groups with the geographic information systems expertise required to develop built and social environment measures (eg, groups that include a researcher with geographic information system expertise). OBJECTIVE: The goal of this study was to develop an open-source, user-friendly, and privacy-preserving tool for conveniently linking built, social, and natural environmental variables to study participant addresses. METHODS: We built the automatic context measurement tool (ACMT). The ACMT comprises two components: (1) a geocoder, which identifies a latitude and longitude given an address (currently limited to the United States), and (2) a context measure assembler, which computes measures from publicly available data sources linked to a latitude and longitude. ACMT users access both of these components using an RStudio/RShiny-based web interface that is hosted within a Docker container, which runs on a local computer and keeps user data stored in local to protect sensitive data. We illustrate ACMT with 2 use cases: one comparing population density patterns within several major US cities, and one identifying correlates of cannabis licensure status in Washington State. RESULTS: In the population density analysis, we created a line plot showing the population density (x-axis) in relation to distance from the center of the city (y-axis, using city hall location as a proxy) for Seattle, Los Angeles, Chicago, New York City, Nashville, Houston, and Boston with the distances being 1000, 2000, 3000, 4000, and 5000 m. We found the population density tended to decrease as distance from city hall increased except for Nashville and Houston, 2 cities that are notably more sprawling than the others. New York City had a significantly higher population density than the others. We also observed that Los Angeles and Seattle had similarly low population densities within up to 2500 m of City Hall. In the cannabis licensure status analysis, we gathered neighborhood measures such as age, sex, commute time, and education. We found the strongest predictive characteristic of cannabis license approval to be the count of female children aged 5 to 9 years and the proportion of females aged 62 to 64 years who were not in the labor force. However, after accounting for Bonferroni error correction, none of the measures were significantly associated with cannabis retail license approval status. CONCLUSIONS: The ACMT can be used to compile environmental measures to study the influence of environmental context on population health. The portable and flexible nature of ACMT makes it optimal for neighborhood study research seeking to attribute environmental data to specific locations within the United States.

Dynamics of Feline Sporotrichosis in Espírito Santo, Brazil: Impact of Urbanisation.

INTRODUCTION: Sporotrichosis is a chronic infectious mycosis caused by traumatic implantation of Sporothrix spp., which poses a significant challenge to public health because of its zoonotic nature. It affects humans and other animals, particularly cats. This study investigated epidemiological trends and spatial distribution of feline sporotrichosis in Espírito Santo between 2017 and 2022. METHODS: This study collected diagnostic data on sporotrichosis from the Veterinary Pathology Laboratory and Center for Infectious Diseases in Espírito Santo from 2017 to 2022. Statistical analyses were performed using STATA and spatial analyses were performed using ArcGIS software to create maps and identify hotspots. Kernel density estimation and directional mean statistics were applied to visualise the disease concentration and transmission trends. RESULTS: The results showed a significant increase in feline sporotrichosis cases in Espírito Santo from 2017 to 2022, with hotspots identified in urban and densely populated areas. Anchieta, Aracruz and Vila Velha reported the highest numbers of cases. Spatial analysis indicated a spread towards the central and northern coastal regions. CONCLUSION: This study highlights the growing threat of feline sporotrichosis in Espírito Santo, Brazil, particularly in densely populated urban areas. This underscores the urgent need for comprehensive public health strategies that address urbanisation, human-animal interactions and managing stray cat populations. Effective control measures and enhanced surveillance are crucial to mitigate the spread of this zoonotic disease. Importantly, this study provides valuable insights into the epidemiology of sporotrichosis in Brazil and offers a framework for other regions identified as emerging hotspots that face similar challenges.

Does geographic availability of alcohol influence drinking in adolescents? A systematic review of literature associations.

BACKGROUND: The role of alcohol geographic availability in influencing adolescent drinking has been debated. However, clear literature consensus has not been reached. OBJECTIVE: To provide a systematic review of the associations between geographic availability of alcohol outlets measured through different methodologies and drinking outcomes in adolescents. METHODS: We conducted a systematic search (PubMed/SCOPUS/Web of Science) for articles exploring associations between alcohol availability and adolescent drinking before 2023. Original articles written in English that evaluated adolescent populations (10-19 years old), included at least one quantitative alcohol consumption outcome and its relationship with geographic availability of alcohol, and declared no conflicts of interest were selected for the review. A quality assessment of the selected articles was made using the Newcastle-Ottawa Scale and descriptive analyses were carried out to summarize results. RESULTS: Thirty-one articles were reviewed (19 cross-sectional and 12 longitudinal studies), which included a total of 507336 participants. Alcohol availability was positively related to drinking prevalence and risky patterns in 53.3% and 60.5% of associations, respectively. Individual-level covariates, the type of alcohol outlets measured and the different methodological approaches to measure outlet density were related to differences in the direction and magnitude of these associations. CONCLUSION: Just over half of the studies in this review demonstrate a positive association between alcohol availability and adolescent alcohol consumption with no negative associations reported. The review highlights the mix of methodological approaches that are used, which made it difficult to conduct joint analyses. Additional research is needed to explore the appropriateness, effectiveness and reliability of these methods within various contexts.

Appraisal of contamination, hydrogeochemistry, and Monte Carlo simulation of health risks of groundwater in a lithium-rich ore area.

This study incorporated hydrogeochemical facies, the entropy-weighted water quality index (EWQI), multivariate statistics, and probabilistic human exposure assessment to investigate hydrogeochemistry, analyze groundwater quality, and estimate potential risks to human health in a lithium-rich ore area (Jadar River basin, Serbia). The findings designated the Ca·Mg-HCO3 hydrogeochemical type as the predominant type of groundwater, in which rock weathering and evaporation control the major ion chemistry. Due to the weathering of a lithium-rich mineral (Jadarite), the lithium content in the groundwater was very high, up to 567 mg/L, with a median value of 4.3 mg/L. According to the calculated EWQI, 86.4% of the samples belong to poor and extremely poor quality water for drinking. Geospatial mapping of the studied area uncovered several hotspots of severely contaminated groundwater. The risk assessment results show that groundwater contaminants pose significant non-carcinogenic and carcinogenic human health risks to residents, with most samples exceeding the allowable limits for the hazard index (HI) and the incremental lifetime cancer risk (ILCR). The ingestion exposure pathway has been identified as a critical contaminant route. Monte Carlo risk simulation made apparent that the likelihood of developing cancerous diseases is very high for both age groups. Sensitivity analysis highlighted ingestion rate and human body weight as the two most influential exposure factors on the variability of health risk assessment outcomes.

Challenges and opportunities for implementing nature-based coastal protection in an urbanised coastal city based on public perceptions.

There has been an increase in recognition of the benefits of employing nature-based coastal protection strategies to adapt to the impacts of climate change (e.g., increased storminess, sea-level rise). To enable broader use of nature-based methods, coastal managers and policymakers need to consider the placement and social acceptance of any methods considered. Most published spatial planning models for nature-based coastal protection currently do not utilise social data during site selection. We conducted a public survey of 452 respondents from Singapore, a highly urbanised coastal city-state, to assess Singaporean's perceptions of climate change impacts on coastal areas and their support of nature-based coastal protection. We also assessed the respondents willingness to accept trade-offs for the prioritisation of nature-based coastal protection and subsequently spatially mapped them. The results showed a high awareness of the potential impacts of climate change on coastal areas. Nature-based coastal protection was highly supported as the associated benefits, such as protection of wildlife and their habitats and climate change reduction, were aligned with public values of coastal areas. However, nature-based coastal protection may not be prioritised over human-made structures due to the perception that they are less effective, and respondents may not be willing to undergo 1) replacement of their favourite coastal area, 2) increased taxes, and 3) relocation, for their prioritisation. Through spatially mapping the relevant trade-offs, we found that only 11.1% of coastal areas had moderate or high likelihood of social barriers to nature-based coastal protection, highlighting their potential in Singapore. These findings underscore the importance of incorporating social factors in government land use planning decisions.

Development and application of a GIS tool in the design of surface water quality monitoring networks: A micro-watershed-based approach.

The design of a representative surface water quality monitoring network is vital for accurately capturing the dynamics of water bodies and variability in pollution across a catchment. The representativeness of a surface water monitoring network refers to how well it reflects the characteristics of all monitored surface water bodies. In this study, using a micro-watershed-based approach, a Geographic Information System (GIS) tool (Surface Water Quality Monitoring Point Locations ANalysis (SWQM_PLAN)) has been developed to optimize the design of surface water quality monitoring networks. In the first stage of the two-stage study, a digital elevation model and minimum watershed area size were taken as input parameters and micro-watersheds with defined upstream-downstream relations were created. In the second stage, input parameters including land use data, pollution sources, and micro-watershed data, along with specific criteria, were used to identify the basins and determine the optimal locations for surface water monitoring stations. The developed GIS tool was then applied to evaluate the existing surface water monitoring network in the Gediz River Basin, designed by the Republic of Türkiye, Ministry of Agriculture and Forestry. The tool assessed the effectiveness if the existing monitoring network in terms of assessing agricultural pollution and provided potential revision suggestions to enhance the effectiveness of implemented pollution reduction measures.

Residential proximity to agricultural herbicide and fungicide applications and dust levels in homes of California children.

BACKGROUND: Few studies of the relationship between residential proximity to agricultural pesticide applications and pesticide levels in the home have incorporated crop location or wind direction. We evaluated the relationship between agricultural pesticide applications using the California Pesticide Use Reporting (CPUR) database and pesticide concentrations in carpet dust accounting for land use and wind direction. METHODS: We measured concentrations (ng/g) of seven herbicides and two fungicides in carpet dust samples from 578 California homes (2001-2007). We created three metrics by computing the density (kg/km2) of use of each pesticide reported in CPUR within 0.5-, 1-, 2-, and 4-km buffers around homes 180- and 365-days before sampling (CPUR metric). We apportioned applications to the crop area within the buffers (CROP-A metric) and weighted CPUR applications by the proportion of days that the home was within ±45° of the downwind direction (W-CPUR metric). We modeled natural-log concentrations (Tobit regression) and dust detections (logistic regression) adjusting for season/year, occupation, and home/garden use. RESULTS: Detections were >90 % for glyphosate, 2,4-D, and simazine. Detection rates and dust concentrations increased with increasing CPUR densities for all herbicides and one fungicide. Compared to homes without applications within 4 km, the highest tertile of 365-day glyphosate use was associated with ∼100 % higher concentrations (CPURT3>9.2kg/km2 %change = 110, 95 %CI = 55, 183; CROP-AT3>13.4kg/km2 %change = 144, 95 %CI = 81, 229; and W-CPURT3>2.1kg/km2 %change = 102, 95 %CI = 50, 171). The highest density tertiles of 2,4-D, simazine, and trifluralin were associated with 2- to 6-times higher concentrations, respectively; that was similar across metrics. Across all metrics, agricultural use of dacthal, dicamba, and iprodione were associated with 5- to 10-times higher odds of dust detections. Associations were unclear for 2-methyl-4-chlorophenoxyacetic acid and null for chlorothalonil. CONCLUSIONS: Agricultural herbicide and fungicide use was an important determinant of indoor contamination within 4 km of homes. Accounting for crops and wind direction did not substantially change these relationships.

Strategic Enhancement of Healthcare Services During the Hajj Season in Makkah: A Comprehensive Geographic Information System (GIS) Analysis.

Annually, over two million international pilgrims embark on the Hajj pilgrimage to Makkah, presenting a significant challenge for healthcare services. This study analyzes the spatial distribution of healthcare facilities in the Al Mashaer area using Geographic Information System (GIS) technology to enhance healthcare during this religious gathering. It evaluates the accessibility and efficacy of healthcare facilities, including primary care centers, clinics, and hospitals, each addressing distinct medical needs to ensure a holistic approach for pilgrims. The study maps the distribution, service radius, and services offered by each facility, along with an analysis of travel distances and times, to evaluate the viability of healthcare services. Identifying coverage gaps and accessibility issues is critical for making strategic recommendations to enhance resource allocation and distribution. The research addresses challenges such as data precision, population density, infrastructural constraints, and resource limitations. The study offers recommendations to optimize resource distribution, improve transportation strategies, expand healthcare capacity, and enhance cultural competency, resulting in improved healthcare accessibility, reduced congestion, quicker medical responses, and a safer pilgrimage experience, promoting a world-class pilgrimage management system.

Phase Dependence of Surface Charge Measurement on Epoxy Insulator in C4F7N/CO2 under AC Voltage.

The application of binary gas mixtures consisting of heptafluorobutyronitrile (C4F7N) and carbon dioxide (CO2) in AC GIS is currently attracting much attention. Therefore, the evaluation of the gas-solid interface charge distribution characteristics of epoxy resin is indispensable. Additionally, the phase-dependency of the charging behavior remains not fully understood. We simulated coaxial electrode structure in GIS and investigated the surface charge distribution on a down-scaled epoxy insulator. The influence of the truncated phase angle and duration of AC voltage on charge behavior were analyzed, and the charge transport mechanism under AC voltage was theoretically analyzed. The results showed that there was a noticeable negative charge speckle with the presence of the metal particle and the accumulated negative charge on the insulator surface far exceeded that of the positive charge. The maximum surface charge density and the amount of surface charge accumulated first increased and then decreased over time. It was found that the phase angle has a negligible influence on the surface charge distribution at the cut-off moment.

Delineation of fresh groundwater potentiality zones in saline coastal aquifers, Southwest Bangladesh using remote sensing and GIS approaches.

Bay of Bengal in southern Bangladesh is a major source of water from coastal aquifers, but prone to contamination by seawater intrusion, making climate-vulnerable populations and economies unfit for potable, agricultural water, adopting crops, etc. The study area located in Khulna district lies in the southwestern coast of the country is among the most vulnerable due to its salinity issues. Therefore, this study identified fresh groundwater potential zones in the southwestern coastal zones of the country suited for community usage helping coastal peoples meet their demands. This study focused on twelve thematic layers employing remote sensing and GIS with analytical hierarchy process. Here, groundwater salinity is 84% brackish to saline over 70% of the region [electric conductivity: 295-16,295 micro-Siemens/cm]. However, groundwater chloride in 88% signifies a slightly to medium salty zone. The annual average rainfall reduced surface water infiltration in 75% of the area with little to very slightly soil salinity. The fresh groundwater resource zone has classified based on its potentiality as: very high (0.52%)-for drinking, agricultural irrigation, or industrial work; high (25%)-marginal salinity suitable for agricultural or industrial uses; low (11%)-low salinity, but usable for high salt tolerant crops in irrigation; and very low (64%)-very high groundwater salinity, and not suitable for human consumption and community uses. Finally, this study will help develop sustainable groundwater resources in the coastal region and a fresh groundwater supply plan in saline-prone areas.

Phyto-ecological studies and distribution patterns of subfamily Polygonoideae in relation to edaphic factors across diverse ecological zones.

The species of the subfamily Polygonoideae is an essential component of temperate forests as well as the flora of the western Himalayan region. The aim of this research was to explore the taxonomic diversity, distribution patterns, and associated flora of Polygonoideae in relation to edaphic factors in various ecological zones in the Muzaffarabad division of the Kashmir Western Himalayan Region. We applied a random sampling approach for data collection from 10 different sites with a cumulative 780 quadrats to record the diversity of wild Polygonoideae species across the Muzaffarabad division between 2021 and 2022. This study revealed 279 plant species from 192 genera and 75 families associated with Polygonoideae, with the dominant families being Asteraceae, Poaceae, Lamiaceae, and Rosaceae. Herbs were predominant in the investigated area, with a proportion of 72.40 %, followed by shrubs (9.68 %) and pteridophytes (8.24 %). The flora was dominated by therophytes (37.35 %), whereas nanophylls (37.28 %) were the most dominant leaf form. Persicaria, Rumex, and Polygonum genera were observed and collected from various ecological zones, while Bistorta, Fagopyrum, Oxyria, and Rheum were only collected from a single zone, representing a restricted niche. A total of 28 taxa from 8 genera were studied in the investigative subfamily Polygonoideae, with the majority being therophytes (57.14 %), followed by hemicryptophytes (28.57 %), and leaf form dominated by microphylls (50 %) and nanophylls (17.85 %). The average values for Shannon and Simpson's diversity for the reported plant communities were 0.96 and 3.53, respectively, whereas species richness averaged 2.43 and species evenness 0.92. The vegetation exhibited a relatively lower (<50) maturity, averaging 32.08. Deforestation, overexploitation for medicinal purposes, soil erosion, overgrazing, forest fires, and the expansion of agricultural fields were identified as major threats to floral diversity. A significant correlation was observed between elevation and soil nutrient parameters, where moisture content, SOC, SOM, TK, and TN ratios showed a positive correlation, while pH and TP showed a negative correlation. Polygonum paronychioides and Rumex alpinus were the least observed of Polygonoideae taxa, whereas 39 species were found to be threatened, having low (<0.2) IVI values and seeking immediate conservation efforts. Climate change and anthropogenic pressure may lead to a change in the composition patterns and threaten the Polygonoidae species. We suggest community-based initiatives and sustainable conservation measures to safeguard the floral wealth of the Western Himalaya.

Comprehensive evaluation and prediction of groundwater quality and risk indices using quantitative approaches, multivariate analysis, and machine learning models: An exploratory study.

Assessing and predicting quality of groundwater is crucial in managing groundwater availability effectively. In the current study, groundwater quality was thoroughly appraised using various indexing methods, including the drinking water quality index (DWQI), pollution index of heavy metals (HPI), pollution index (PI), metal index (MI), degree of contamination (Cd), and risk indicators, like hazard quotient (HQ) and total hazard indicator (HI). The assessments were augmented through multivariate analytical techniques, models based on recurrent neural networks (RNNs), and integration of geographic information system (GIS) technology. The analysis measured physicochemical parameters across 48 groundwater wells from El-Menoufia region, revealing distinct water types influenced by ion exchange, rock-water interactions, and silicate weathering. Notably, the groundwater showed elevated levels of certain metals, particularly manganese (Mn) and lead (Pb), exceeding the drinking water limits. The DWQI deemed the bulk of the tested samples suitable for consumption, assigning them to the "good" category, whereas a small number were considered inferior quality. The HPI, MI, and Cd indices indicated significant pollution in the central study region. The PI revealed that Pb, Mn, and Fe were significant contributors to water pollution, falling between classes IV (strongly affected) and V (seriously affected). HQ and HI analyses identified the central area of the study as particularly prone to metal contamination, signifying a high risk to children via oral and dermal routes and to adults through oral exposure alone (non-carcinogenic risk). The adults had no health risks due to dermal contact. Finally, the RNN simulation model effectively predicted the health and water quality indices in training and testing series. For instance, the RNN model excelled in predicting the DWQI, with three key parameters being crucial. The model demonstrated an excellent fit on the training set, achieving an R2 of 1.00 with a very low root mean of squared error (RMSE) of 0.01. However, on the testing set, the model's performance slightly decreased, showing an R2 of 0.96 and an RMSE of 2.73. Regarding HPI, the RNN model performed exceptionally well as the primary predictor, with R2 values of 1.00 (RMSE = 0.01) and 0.93 (RMSE = 27.35) for the training and testing sets, respectively. This study provides a unique perspective for improving the integration of various techniques to gain a more comprehensive understanding of groundwater quality and its associated health risks, with a strong focus on feature selection strategies to enhance model accuracy and interpretability.

GIS-based multi-criteria decision analysis model for utility water demand: The case of Lodwar Municipality, Turkana County, Kenya.

Water scarcity is a global issue resulting from rapid urbanization, increasing population growth, industrial development and expansion of human activities over time and space. Water shortage affects every continent and is listed as one of the largest global risks hence the need for proper management of water resources. Municipalities and cities worldwide are struggling to maintain a steady supply of water to meet the increasing water demand. The study used Geographic Information System (GIS) techniques and Multi-Criteria Decision Analysis (MCDA) to develop a decision support model that can be applied to improve the utility water demand management for the Lodwar Municipality in Turkana Kenya. The data comprised remotely sensed data, population density, spatial plans, utility infrastructure maps and metered water connections data. The AHP pairwise comparison matrix was applied to assign weights for the 8 criteria influencing water demand in the area. The population density, proximity to water network facilities and land use criteria were equivalent to 30 %, 25 %, and 23 % respectively whereas 22 % of other criteria were dependent on each other. The analysis of satellite images showed the expansion of built-up areas and emerging human activities in regions towards the South and Western of Lodwar Town. The resulting model outcome identified the potential demand priority sites within the region of which some are underserved. The model efficiency was assessed through the application of statistical indicators as well as graphical and map presentations. Consequently, the addition of more input variables affecting demand is likely to improve the results over changing aspects within the zones. Municipality water utility managers and decision-makers can therefore employ the model information to highlight suitable areas for network expansion as well as infrastructure management planning within the municipality. This method offers an alternative hybrid technique for mapping potential utility water demand in rural municipalities with inadequate data.

Geographic Distribution and Temporal Trends of Zika Virus Outbreaks in India (2016-2023): Insights From the Integrated Disease Surveillance Programme Data.

Introduction This study examines the geographic distribution and temporal trends of Zika virus (ZIKV) outbreaks in India from 2016 to 2023 using data from the Integrated Disease Surveillance Programme (IDSP). The burden of ZIKV in India has risen due to its rapid spread and significant health impacts. Existing literature highlights seasonal and geographic patterns but lacks a comprehensive, long-term analysis specific to India. This study addresses this gap by analyzing trends over seven years to inform better public health responses. Methods A secondary data analysis was conducted using publicly available data from the IDSP on reported Zika cases from January 2016 to December 2023. Descriptive statistical methods and geographic information system (GIS) mapping techniques were employed to analyze the geographic distribution and temporal trends of ZIKV outbreaks in India. The data were analyzed and visualized using R software version 4.3.2 (R Foundation for Statistical Computing, Vienna, Austria), with heat maps and choropleth maps to identify hotspots, and line diagrams to identify temporal trends. Results Zika outbreaks predominantly occurred during the post-monsoon season, accounting for 47.62% (n = 10) of the total 21 outbreaks, followed by the monsoon season with 33.33% (n = 7), and summer with 19.05% (n = 4). Two deaths were reported during a significant outbreak in Madhya Pradesh in 2018. Temporal trends indicated notable spikes in cases in 2018 (131 cases) and 2021 (234 cases), with no cases reported in 2019 and 2020. The geographic distribution maps highlighted significant concentrations of ZIKV outbreaks in specific districts within Uttar Pradesh, Madhya Pradesh, and Kerala. Discussion The study identified seasonal patterns, with most cases occurring in the post-monsoon season. The geographic spread of the ZIKV was observed in eight states from 2016 to 2023. GIS identified three hotspots in Uttar Pradesh, Madhya Pradesh, and Kerala. Conclusion The study highlights the need for heightened surveillance and targeted intervention preparedness during high-risk seasons. Enhancing testing facilities and data reporting systems could improve outbreak identification, management, and response.

Prediction and interpretive of motor vehicle traffic crashes severity based on random forest optimized by meta-heuristic algorithm.

Providing accurate prediction of the severity of traffic collisions is vital to improve the efficiency of emergencies and reduce casualties, accordingly improving traffic safety and reducing traffic congestion. However, the issue of both the predictive accuracy of the model and the interpretability of predicted outcomes has remained a persistent challenge. We propose a Random Forest optimized by a Meta-heuristic algorithm prediction framework that integrates the spatiotemporal characteristics of crashes. Through predictive analysis of motor vehicle traffic crash data on interstate highways within the United States in 2020, we compared the accuracy of various ensemble models and single-classification prediction models. The results show that the Random Forest (RF) model optimized by the Crown Porcupine Optimizer (CPO) has the best prediction results, and the accuracy, recall, f1 score, and precision can reach more than 90 %. We found that factors such as Temperature and Weather are closely related to vehicle traffic crashes. Closely related indicators were analyzed interpretatively using a geographic information system (GIS) based on the characteristic importance ranking of the results. The framework enables more accurate prediction of motor vehicle traffic crashes and discovers the important factors leading to motor vehicle traffic crashes with an explanation. The study proposes that in some areas consideration should be given to adding measures such as nighttime lighting devices and nighttime fatigue driving alert devices to ensure safe driving. It offers references for policymakers to address traffic management and urban development issues.