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Noise pollution is a major challenge in urban contexts all around the world. The study was designed to assess road traffic noise pollution with possible health effects on those living in the study region. The IDW spatial interpolation approach and an ArcGIS-based evaluation were used to map the recorded noise levels in the research region. The noise descriptors including Noise Climate (NC), Traffic Noise Index (TNI), Equivalent Noise Level (Leq), and Noise Pollution Level (NPL) were computed. The required information has been collected through a questionnaire survey and previously published documents. The study reveals that the current noise level is higher than the recommended national threshold at every location. According to the study, the Nathullabad region had the highest level of noise pollution (86.5 dBA), while the Kaunia Abasik area had the lowest level (67.8 dBA). Study findings also show that in the area context, the highest levels of noise pollution are found in commercial areas (82 dBA), followed by industrial areas (80.4 dBA),mixed areas (81.3 dBA), and residential areas (72.7 dBA). The lowest level is found in sensitive areas (72.5 dBA). Statistical analyses, including one-way ANOVA, Tukey HSD post-hoc and LSD post-hoc test results, showed that there was no statistically significant difference (p > 0.05) between the noise pollution levels (NPL) in the morning, noon, and evening shifts. The results showed that 32 % of respondents stated they felt disturbed while working, and 27% of respondents said it was somewhat sensitive for them. As the last step in minimizing noise pollution in the research area, 37 % of respondents reported enforcing the regulations, 31% suggested making hydraulic horns illegally, and 21 % suggested raising public awareness. This study may contribute to academic knowledge and assist decision-makers of government officials in formulating appropriate local strategies to deal with this grave environmental problem.

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This dataset consists of 190,832 manually-digitized cropland field boundaries, with associated attributes, within Brazil, Ukraine, United States of America, Canada, and Russia. Specifically, 22 regions of various sizes (74km2 - 38,000km2) spanning 5 countries were digitized over a range of predominant crop types over different time periods. These field boundaries were drawn over 20 m Sentinel-2 imagery. This field boundary dataset is a byproduct of a larger effort to map cropland burned area (Global Cropland Area Burned: GloCAB product [1]), however, it has several benefits beyond its original intent, including as a training dataset for machine-learning field size analyses, or a dataset to derive cropland field characteristics across different predominant crop types and geographies.

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Three-dimensional (3D) simulations and precise landscape visualizations are crucial for various applications, like landscape management and planning, computer and connection of the landscape, evaluation, and tracking of land use. The consequences of several plans and a large scene cannot be communicated using older methods of comprehensive environmental planning and development in a timely, rational, and coordinated manner. Architects have trouble incorporating ideas into other comprehensive planning implementation processes. Architects did not thoroughly investigate the neighbourhood's demographics and matching behavioural needs and lacked critical thinking. The 3D dynamic landscape simulation is a detailed computerized three-dimensional simulation of the environment that can be dynamically presented. With the aid of Artificial Intelligence (AI) technology, the system possesses a strong sense of reality, a user-friendly interface, and interactive features that can be tailored to the requirements of the contemporary urban environmental landscape. Regarding exterior publicity, domestic assistance, environmental land use planning, and information systems. The novelty of the proposed Interactive Design System based on AI (IDS-AI) is to create a 3D dynamic landscape model based on a real-life environmental scene, utilizing a Geographic Information System (GIS) to optimize landscape vision. Secondly, 3D environmental landscape design simulation was implemented using GIS spatial analysis in conjunction with the Fuzzy Analytical Hierarchical Process (FAHP) to reduce the data overlap rate and help make an accurate decision. Finally, the design incorporates the development of the interactive interface system application of landscape design and environmental resources for viewing the landscape, the factors that affect them, and the area coverage ratio of various land cover types. The experimental outcomes show that the suggested IDS model increases the gradient sensitivity level of 98.3 % and area coverage ratio of 93.4 % compared to other existing models.

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This study investigated land use and land cover changes in the Gauteng Province of South Africa with emphasis on wetland ecosystems. Using Landsat images and various image manipulation software such as ArcGIS and ENVI, this study conducted a predictive analysis of the potential state of wetland ecosystems in the Gauteng Province of South Africa by year 2040, using illegal dumping trends in the last 20 years. Based on this methodological framework, it is found that continued illegal dumping trends would lead to an almost total disappearance of wetlands in the study area by 2040. It is argued that despite the pro-environmental initiatives introduced in the early 2000s to conserve wetlands in South Africa, wetland deterioration has increased rapidly due to the associated methodological flaws, which further supports the findings of this study. Ultimately, it is recommended that a collaborative approach to wetland conservation, and a robust methodological shift are required to mitigate the threats faced by wetlands in the Gauteng Province of South Africa, and the model can be deployed across developing countries.

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Generally, drought is influenced by both spatial characteristics and anthropogenic activities within an area. Drought vulnerability assessment is a critical tool that can be effectively used to develop proper drought mitigation strategies to prevent avoidable losses. To develop suitable drought mitigation strategies, the overall drought vulnerability must be assessed, and the interaction among drought-influencing factors in the area should be considered. Consequently, this study aimed to investigate the interactions among critical drought-influencing factors and drought vulnerability in the Lam Ta Kong Watershed via spatial analysis with the analytical hierarchy process (AHP) and geographical information system (GIS) technology. Ten drought-influencing factors were considered in the vulnerability assessment: slope, elevation, soil texture, soil fertility, stream density, precipitation, temperature, precipitation days, evaporation, and land use. The results indicated that the critical drought-influencing factors were precipitation, precipitation days, and land use, resulting in most of the watershed experiencing high drought vulnerability (35.1% of the watershed or 1810.83 km2). Moreover, this research highlighted the interactions among the critical drought-influencing factors. Precipitation interacted with precipitation days to cause drought vulnerability across the watershed, with a p-value <0.05. Similarly, the interactions between precipitation and land use and between precipitation days and land use, with p-values <0.05, showed that they were associated with and influenced by drought in the Lam Ta Kong Watershed. This study further indicated that appropriate drought mitigation strategies for this watershed must consider the interactions among these drought-influencing factors, as well as their specific interactions across the watershed.

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BACKGROUND: The impacts of prenatal maternal affective symptoms on the placental structure are not well-established. Employing Geographic Information System (GIS) spatial autocorrelation, Moran's I, can help characterize placental thickness uniformity/variability and evaluate the impacts of maternal distress on placental topography. METHODS: This study (N = 126) utilized cohort data on prenatal maternal affective symptoms and placental 2D and 3D morphology. Prenatal maternal depression, stress, anxiety and sleep quality were scored for each trimester using the Edinburgh Postnatal Depression Scale (EPDS), Stressful Life Event Scale (SLE), Penn State Worry Questionnaire (PSWQ), and Pittsburgh Sleep Quality Index (PSQI), respectively. Placental shape was divided into Voronoi cells and thickness variability among these cells was computed using Moran's I for 4-nearest neighbors and neighbors within a 10 cm radius. Sex-stratified Spearman correlations and linear regression were used to study associations between mean placental thickness, placental GIS variables, placental weight and the average score of each maternal variable. RESULTS: For mothers carrying boys, poor sleep was associated with higher mean thickness (r = 0.308,p = 0.035) and lower placental thickness uniformity (r = -0.36,p = 0.012). Lower placental weight (r = 0.395,p = 0.003), higher maternal depression (r = -0.318,p = 0.019) and worry/anxiety (r = -0.362,p = 0.007) were associated with lower placental thickness uniformity for mothers carrying girls. LIMITATIONS: The study is exploratory and not all GIS models were developed. Excluding high-risk pregnancies prevented investigating pregnancy complications related hypotheses. A larger sample size is needed for greater confidence for clinical application. CONCLUSIONS: Placental topography can be studied using GIS theory and has shown that prenatal maternal affective symptoms and sleep have sex-specific associations with placental thickness.

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Background: People are living longer but an increasing number of older people experience chronicity and disability in the latest years of their life. The Marche region is one of the Italian regions where people live the longest lives; therefore, the number of people with age-related chronic diseases is expected to be at least similar, if not higher, compared to the rest of Italy. The identification of the aging trajectories is of huge interest in the arena of public health. Administrative healthcare databases represent valuable reservoirs for reconstructing the trajectories of aging. Here, we present the protocol for a study (TREND project) aimed to integrate existing administrative databases into a Marche regional dataset in order to estimate the prevalence and incidence rates of age-related neurodegenerative diseases (ND), with a specific focus on Parkinsonism and Dementia. Methods: The TREND Project is a retrospective cross-sectional study. The source population includes permanent residents in the Marche region aged 40 years and older. A minimal dataset has been built up linking data on drug prescriptions, outpatient services, and diagnosis for hospital admission, from 2014 to 2021 in the Marche Region. Data on clinical outcomes (re-hospitalization, mortality, comorbidities), and therapeutic approaches (drugs and medicines) have been integrated with state-of-the-art statistical methods to define patients into different risk clusters and to analyze the aging trend by assessing the Comorbidity Index (CI) as a proxy for chronicity. Discussion: Our research contributes to the integration of existing administrative databases on ND to create a Marche regional ND database, support regional health policy, and better understand patients' needs and their aging trajectories. This approach could be implemented also at the National level. Moreover, by linking different administrative data sources, this study sheds light on important issues related to ND, such as early-onset dementia; ethical aspects such as anticipated wills; problems of dementia in patients still in the job market, etc. The results of this study will contribute to the successful implementation of integrated care for patients affected by ND at regional or national levels.

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Estimates of the land area occupied by wind energy differ by orders of magnitude due to data scarcity and inconsistent methodology. We developed a method that combines machine learning-based imagery analysis and geographic information systems and examined the land area of 318 wind farms (15,871 turbines) in the U.S. portion of the Western Interconnection. We found that prior land use and human modification in the project area are critical for land-use efficiency and land transformation of wind projects. Projects developed in areas with little human modification have a land-use efficiency of 63.8 ± 8.9 W/m2 (mean ±95% confidence interval) and a land transformation of 0.24 ± 0.07 m2/MWh, while values for projects in areas with high human modification are 447 ± 49.4 W/m2 and 0.05 ± 0.01 m2/MWh, respectively. We show that land resources for wind can be quantified consistently with our replicable method, a method that obviates >99% of the workload using machine learning. To quantify the peripheral impact of a turbine, buffered geometry can be used as a proxy for measuring land resources and metrics when a large enough impact radius is assumed (e.g., >4 times the rotor diameter). Our analysis provides a necessary first step toward regionalized impact assessment and improved comparisons of energy alternatives.

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BACKGROUND: Using SaTScan™ Geographical Information Systems (GIS), spatial cluster analysis was used to examine spatial trends and identify high-risk clusters of Coronavirus 2019 (COVID-19) incidence in response to changing levels of public health intervention phases including international and state border closures, statewide vaccination coverage, and masking requirements. METHODS: Changes in COVID-19 incidence were mapped at the statistical area 2 (SA2) level using a GIS and spatial cluster analysis was performed using SaTScan™ to identify most-likely clusters (MLCs) during intervention phases. RESULTS: Over the study period, significant high-risk clusters were identified in Brisbane city (relative risk = 30.83), the southeast region (RR = 1.71) and moving to Far North Queensland (FNQ) (RR = 2.64). For masking levels, cluster locations were similar, with MLC in phase 1 in the southeast region (RR = 2.56) spreading to FNQ in phase 2 (RR = 2.22) and phase 3 (RR = 2.64). All p values <.0001. CONCLUSIONS: Movement restrictions in the form of state and international border closures were highly effective in delaying the introduction of COVID-19 into Queensland, with very low levels of transmission prior to border reopening while mandatory masking may have played a role in decreasing transmission through behavioural changes. Early clusters were in highly populated regions, as restrictions eased clusters were identified in regions more likely to be rural or remote, with higher numbers of Indigenous people, lower vaccination coverage or lower socioeconomic status.

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BACKGROUND: Access to health care services is a critical determinant of population health and well-being. Measuring spatial accessibility to health services is essential for understanding health care distribution and addressing potential inequities. OBJECTIVE: In this study, we developed a geoprocessing toolbox including Python script tools for the ArcGIS Pro environment to measure the spatial accessibility of health services using both classic and enhanced versions of the 2-step floating catchment area method. METHODS: Each of our tools incorporated both distance buffers and travel time catchments to calculate accessibility scores based on users' choices. Additionally, we developed a separate tool to create travel time catchments that is compatible with both locally available network data sets and ArcGIS Online data sources. We conducted a case study focusing on the accessibility of hemodialysis services in the state of Tennessee using the 4 versions of the accessibility tools. Notably, the calculation of the target population considered age as a significant nonspatial factor influencing hemodialysis service accessibility. Weighted populations were calculated using end-stage renal disease incidence rates in different age groups. RESULTS: The implemented tools are made accessible through ArcGIS Online for free use by the research community. The case study revealed disparities in the accessibility of hemodialysis services, with urban areas demonstrating higher scores compared to rural and suburban regions. CONCLUSIONS: These geoprocessing tools can serve as valuable decision-support resources for health care providers, organizations, and policy makers to improve equitable access to health care services. This comprehensive approach to measuring spatial accessibility can empower health care stakeholders to address health care distribution challenges effectively.

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Landslide susceptibility zonation (LSZ) mapping is used to delineate areas prone to landslides and is critical for effective landslide hazard management. The existing methodologies for generating such maps tend to neglect the influence of dynamic environmental variables on landslide occurrences, which may lead to obsolete and erroneous estimates of landslide susceptibility (LS) for a concerned area. Although recent studies have started to report the effects of Land Use/ Land Cover (LULC) variation on LSZ mapping, variations in other dynamic variables like rainfall, soil moisture, and evapotranspiration apart from LULC may also influence slope stability in mountainous regions. The present study investigates the impact of variations in these four variables on the LS distribution, of a selected Indian Himalayan region between 2017 and 2021. Random Forest (RF) susceptibility models are utilized for evaluating the LS for the selected years and geospatial technologies are employed for LS change detection. The results indicate up to 19% variations in the spatial extent for some of the zones of the generated LSZ maps. The research findings of this study are crucial since they reveal the impact of dynamic behavior on LS, which has not been previously documented in the literature.

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Among all other valuable natural resources, groundwater is crucial for global economic growth and food security. This study aimed to delineate groundwater potential zones (GWPZ) in the Gidabo watershed of the Main Ethiopian Rift. The demand for groundwater supplies for various applications has risen recently in the watershed due to rapid population upsurge. An integrated Geographical Information System, Remote Sensing, and Analytical Hierarchy Process (AHP) has been utilized. Eight groundwater regulating factors, including rainfall, elevation, drainage density, soil types, lineament density, slope, lithology, and land use/land cover, have been taken in the analysis. To assign suitable weights to each factor, AHP was employed, as each element contributes differently to groundwater occurrence. The weighted overlay analysis (WOA) technique was then used in the ArcGIS environment to integrate all thematic layers and generate a GWPZ map. The delineated GWPZ in the watershed was classified into five categories. The poor GWPZ covered 18.7 %, the low GWPZ covered 33.8 %, the moderate GWPZ covered 23.4 %, the high GWPZ covered 18.1 %, and the very high GWPZ covered 5.8 % of the area. Well and spring data were used to validate the model, and the ROC (Receiver Operating Characteristic) curve method was applied. The results showed good accuracy of 76.8 %. The result of this research can be valuable for planning and managing groundwater resources in the Gidabo watershed.

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The study focuses on the chemistry of groundwater and if it is suitable for drinking and for use in agriculture using water quality indices, GIS mapping, and multivariate analyses in Sharsa Upazila, Jashore district, Bangladesh. In this study, the concentration of NH4+, K+, Ca2+, EC, Turbidity overstep BDWS drinking standards in 69 %, 14 %, 100 %, 40 % (WHO), 73 % of samples respectively. The value of Water Quality Indices (WQI) results inferred that the maximum specimen was held good quality for drinking uses, and the values distributed central eastern part to the south-eastern part were good quality water in the selected studied area. The study area's PH, EC, SAR, Na (%), TH, and NO3- values were mapped using GIS tools to show their spatial distribution. The cluster and correlation matrix analyses are used to validate for Principle Component Analysis (PCA). The five PCA results exhibited that the presence of EC, turbidity, K+, SO42- and NO3- was significant and was caused by both geogenic (rock weathering and cation exchange) and anthropogenic (agrochemicals, animal feedback) factor. According to the hydro-geochemical data, the maximum number of samples is of the Ca-Mg-HCO3-Cl type and is dominated by rocks. The irrigation water indices like MH, KR, SAR, and %Na indicate show high-quality groundwater for irrigation purposes. Most of the samples were satisfactory and compiled with WHO and Bangladeshi criteria for standard drinking water guideline values.

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Larger animal models with a well-developed Haversian system, as observed in humans, are ideal to analyze cortical bone remodeling in pharmacological studies of anti-osteoporosis drugs, although they have some limitations in controlling individual variability in size, weight, age, and number. This study aimed to morphometrically analyze cortical bone remodeling focusing on Haversian canals in dogs using four regimens of TPTD with daily and weekly administrations at lower and higher weekly doses (4.9 µg/kg/week and 19.8 µg/kg/week, respectively) for 9 months. A micro-computed tomography-based analysis showed no significant differences among regimen groups. By establishing artificial intelligence (AI)-driven morphometric analyses and geographical information system (GIS)-based spatial mapping of Haversian canals that does not require confocal microscopy but is possible with more commonly used wide field microscopes, we successfully observed significant morphometric distinctions among regimens applied even in dogs. Our analytical results suggested that the daily higher regimen specifically increased the number of eroded pores creating spaces between existing canals, thus stimulating cortical bone remodeling.

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BACKGROUND: Geographical context is an important consideration for health system design to promote equality in access to care for patients with childhood heart disease (CHD), particularly those living in regional, rural, and remote areas. To help inform future policy and practice recommendations, this study aimed to (i) describe the geographic distribution of high-risk CHD patients accessing an Australian state-wide specialist service and (ii) estimate travel time for accessing healthcare via general practitioners (primary), nearest paediatric centre (secondary) and specialist paediatric cardiac centre (tertiary). METHODS: Participants included a cohort of children (0-18 year) who accessed state-wide specialist CHD services over a 3-year period (2019-2021) in Queensland, Australia. Locations for patient residence, general practitioner, closest paediatric centre and tertiary cardiac centre were mapped using geographical information system (GIS) software (ArcGIS Online). Travel distance and times were estimated using a Google Maps Application Programming Interface (API). RESULTS: 1019 patients (median age 3.8 years) had cardiac intervention and were included in the sample. Of this cohort, 30.2% lived outside the heavily urbanised South East Queensland (SEQ) area where the tertiary centre is located. These patients travel substantially further and longer to access tertiary level care (but not secondary or primary level care) compared to those in SEQ. Median distance for patients residing outside SEQ to access tertiary care was 953 km with a travel time of 10 h 43 min. This compares to 5.5 km to the general practitioner and 20.6 km to a paediatric service (8.9 and 54 min respectively). CONCLUSION: This geographical mapping of CHD services has demonstrated a key challenge inherent in providing specialist cardiac care to children in a large state-based healthcare system. A significant proportion of high-risk patients live large distances from tertiary level care. The greater accessibility of primary care services highlights the importance of supporting primary care physicians outside metropolitan areas to acquire or build the ability and capacity to care for children with CHD. Strengthening local primary and secondary services not only has the potential to improve the outcomes of high-risk patients, but also to reduce costs and burden associated with potentially avoidable travel from regional, rural, or remote areas to access specialist CHD services.

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Afghanistan has been in an active state of conflict and war for twenty continuous years. Social services like health and education have been badly affected, facing issues such as service disruption, brain drain, and generalized instability. Health indices that provide proxy indicators for general population wellness, such as maternal health, child mortality, and immunization coverage, show that the health services available to the Afghan population are sub-optimal. Investment in social service and interventions has increased. The World Bank and the United Nations through its agencies (The World Health Organization (WHO) and United Nations' Children's Fund (UNICEF) are providing social support through targeted and strategic programs. However, the topographic and environmental realities of Afghanistan, with its broad mountain coverage, propensity toward natural disasters, and latent conflict, has made data and information gathering arduous. Since data is essential for measurement and management, the WHO Health Emergencies (WHE) information management unit at WHO Afghanistan has delivered an innovative form of data analysis, specialized and targeted at providing improved information on communities that are not adequately covered by health services. Deploying a geographical information system (GIS) approach, the WHE team has collated primary and secondary data from a combination of datasets to produce a far-reaching piece of analysis. The analysis of underserved communities in hard to reach, remote locations, provides a live, evidence-based information product. This provides a working tool that is essential to primary health programming and intervention in Afghanistan. The estimates show that approximately 9.5 million individuals in 22,181 villages across 34 provinces are underserved by primary health services. This paper is presented to explain the underpinning methodology.

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The site selection of biomass energy facilities has always been a key part of energy spatial planning. The site suitability evaluation criteria of the existing studies are not comprehensive. On the other hand, most of the existing studies are to determine the only site, while less research on the multiple-facility planning. The aim of this paper is to identify the most effective number and location for biomass energy facilities. To achieve this objective, the Geographic Information System (GIS) is utilized to perform the following tasks: Generate a site suitability map for potential biomass energy facilities and identify suitable site candidates. The standardization of site suitability evaluation indicators is based on fuzzy logic, and indicator weights are determined based on the Analytic Hierarchy Process (AHP) evaluation of experts' opinions. 2. Develop planning schemes for biomass energy facilities for various number of proposed facilities, and subsequently determine the optimal scheme using multi-objective fuzzy comprehensive evaluation. The weight of each indicator is again determined using the AHP method. Following the analysis, it was found that in the case study of Fuxin City, the plans of 1 and 40 biomass energy facilities can achieve the lowest cost and the highest energy self-sufficiency level. However, both options have potential drawbacks that must be considered. The plan of 30 energy facilities has the highest comprehensive benefits, corresponding to the 30,919.75 yuan of transport cost (3748 yuan lower than the average transport cost) and 75.49% of energy self-sufficiency (67.21% of the average value). This work maximizes the comprehensive positive impacts in economic, environmental and social aspects.

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Landfilling is the least preferred method in the hierarchy of solid waste management options, but it is the most widely practiced option. Thus, identification of environmentally and economically suitable landfill sites should be of prime importance. The main objective of this study is to identify environmentally and economically suitable landfill sites using fuzzy analytical hierarchy process-based weighted linear combination model within a GIS environment. This study also used the DRASTIC-based groundwater vulnerability index and distance of landfills from densely populated areas to protect groundwater and reduce cost of transportation of solid waste which were not considered by the previous studies. Using the previously reported methods, a total of 132 landfill sites were found environmentally suitable in the study area. But, after applying DRASTIC-based groundwater vulnerability index, the number of environmentally suitable sites reduced to 95. When the proximity of the 95 sites to densely populated areas was considered to reduce waste transportation cost, the number of selected sites further reduced to 21 site and they can be considered the most environmentally and economically suitable landfill sites. This study will help the policy makers and the concerned SWM authorities to construct the engineered landfills at environmentally and economically suitable landfill sites in the study area and in other similar areas.

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Due to climate change and rapid urbanisation, many Norwegian cities and urban areas suffer from pluvial flooding caused by intense rainfall exceeding the capacity of the stormwater management system. This results in increased runoff rates, volumes and peak flows in the drainage network. In response to these challenges, the authors explore the potential of utilising the urban surface's ability to transport floodwater as an integral component of the stormwater infrastructure. When the capacity of the stormwater drainage system is exceeded, the overland flow paths transporting floodwater are considered a part of the stormwater management system, as floodways. The study proposes a spatial GIS method to map existing drainage lines and identify existing surface areas that function as floodways, combined with an automated process to identify which drainage lines could be implemented as stormwater management measures. Critical points are introduced to assess the floodways' potential hazards, combined with a classification method to evaluate and sort floodways. A case study from Trondheim, Norway, was used to demonstrate how drainage lines can be identified as floodways using the proposed method. The case study is also used to illustrate how a GIS-based analysis can be extended from identifying to evaluating floodways and whether GIS is sufficient for floodway evaluation. The method enables urban planners and municipalities to identify which areas of the urban surface already function as floodways during extreme events, and to prioritise measures to secure such areas and increase the city's flood resilience. The results highlight the need to assess existing areas that function as floodways, and to implement and design needed areas as floodways. GIS-based methods combined with an evaluation scheme can be an adequate tool to map and evaluate floodways in urban areas. When using GIS-based methods, however, the corresponding hazard potential, and also the uncertainty of the floodway's spatial placement, should be considered.

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Potentially toxic elements (PTEs) occur naturally in most geologic materials. However, recent anthropogenic disturbances such as ore mining have contributed significantly to their enrichment in soils. Their occurrence in soil may portend a myriad of related risks to the environment and biota. Most traditional soil quality evaluation methods involve comparing the background values of the elements to the established guideline values, which is often time-consuming and fraught with computational errors. As a result, to conduct a comprehensive and unbiased evaluation of soil quality and its effects on the ecosystem and human health, this research combined geochemical, numerical, and GIS data for a composite health risk zonation of the entire study area. Furthermore, the multilayer perceptron artificial neural network (MLP-NN) was used to forecast the most important toxic components influencing soil quality. Geochemical, statistical, and quantitative soil pollution evaluation (pollution index and ecological risk index) showed that apart from mining, the spread and association of trace elements and oxides occur as a consequence of surface environmental conditions (e.g., leaching, weathering, and organo-metallic complexation). The hazard quotients (HQs) and hazard index (HI) of all PTEs were greater than one. This indicates that residents (particularly children) are more susceptible to risks from toxic element ingestion than dermal exposure and inhalation. Ingestion of As and Cr resulted in higher cancer risks and lifetime cancer risk levels (> 1.0E 04), with risk levels increasing toward the northeastern, western, and southeastern directions of the study area. The low modeling errors observed from the sum of square errors, relative errors, and coefficient of determination confirmed the efficiency of the MLP-NN in pollution load prediction. Based on the sensitivity analysis, Hg, Sr, Zn, Ba, As, and Zr showed the greatest influence on soil quality. Focus on remediation should therefore be placed on the removal of these elements from the soil.

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