Buffer Distance Research Articles

Correlation analysis between urban environment features and crime occurrence based on explainable artificial intelligence techniques

ABSTRACT Crime and urban environments are considered to be closely related. However, there exists no clear understanding of this phenomenon. Therefore, this study analyzes the relationship between various urban environmental variables and crime occurrences and provides insights into the optimal placement of crime prevention facilities by developing a crime prediction map based on a paradigm of the Daegu city. To achieve this, we used 373,387 crime reports from Daegu as dependent variables and 370,000 random points. Independent variables included information such as the point of interest, land use, land cover, floating population, and card sales. The developed crime prediction map created using the model was used to evaluate the adequacy of CCTV installation locations and identify areas requiring new CCTV installations. The performances of various machine-learning models were compared and the XGBoost model (accuracy of 89.7 % and precision of 89.8 %) was selected. Key variables influencing crime report data were identified using the SHAP(SHapley Additive explanation) method. To analyze the spatial explanatory power of the relationship between crime and urban environmental variables, various buffer distances were tested, and a 20 m buffer distance was derived. The results of this study are expected to provide valuable data for crime prevention policies.

Distribution of Australian dental practices in relation to residential aged care facilities: A geographic analysis.

Limited access to oral health services contributes to poor oral health in institutionalised older adults. The objective of this study was to map and analyse the distribution of residential age-care facilities (RACFs) in relation to dental practices across Australia. Age-care data were sourced from the Australian Institute of Health and Welfare. The data were categorised according to the Australian Bureau of Statistics remoteness index in each state and territory, defined by a geographic coordinate system. The structure of remoteness area data was integrated into RACF data using a geographic information system. Buffer analysis in QGIS was employed to calculate the buffer distance surrounding RACFs by identifying dental practices relative to a measuring distance. In total, Australia had 2718 RACFs and 7379 dental practices (both private 95.5% and public 4.5%). In all States, more than a third of metropolitan RACFs were within accessible reach of a private practice (ranging from 37% of RACFs in NSW to 55% in WA). However, proximity to public clinics was low, ranging from only 4% (WA) to 9% (QLD). More than one-fifth of metropolitan RACFs in NSW, QLD, WA and ACT (ranging from 20% to 24%) were not within accessible proximity of either a public or private dental clinic/practice. While more than 70% of RACFs in regional and remote Australia are reasonably close to dental practices, areas with inadequate access exist, with the highest percentage recorded in WA (6%). Ensuring an equitable distribution of dental practices relative to RACFs is crucial in bridging the service access gap in underserved areas.

Including soil spatial neighbor information for digital soil mapping

Digital soil mapping (DSM) is transforming how we understand and manage soil resources, offering high-resolution spatial–temporal soil information essential for addressing environmental challenges. The integration of environmental covariates has advanced soil mapping accuracy, while the potential of neighboring soil sample data has been largely overlooked. This study introduces soil spatial neighbor information (SSNI) as a novel approach to enhance the predictive power of spatial models. Utilizing two open-access datasets from LUCAS Soil and Meuse, our findings showed that incorporating SSNI improved the accuracy of random forest models in mapping soil organic carbon density (reduced %RMSE of 3.1%), cadmium (reduced %RMSE of 3.6%), copper (reduced %RMSE of 5.9%), lead (reduced %RMSE of 11.5%), and zinc (reduced %RMSE of 7.4%). Compared to the inclusion of buffer distance or oblique geographic coordinates for modelling, SSNI also performed better for both LUCAS Soil and Meuse datasets. This study underscores the value of SSNI in improving digital soil maps by capturing the neighboring information. Embracing SSNI could lead to more informed decision-making in soil management and its potential applicability across other disciplines also remains open for exploration in future research endeavors.

Regional variation in the landscape ecology of West Nile virus sentinel chicken seroconversion in Florida.

How landscape composition and configuration impact the distribution of multi-vector and multi-host mosquito vector-borne disease systems, such as West Nile virus (WNV), remains challenging because of complex habitat and resource requirements by hosts and vectors that affect transmission opportunities. We examined correlations between landscape composition and configuration and 2018 WNV sentinel chicken seroconversion in Florida, USA across the state and within five National Oceanic Atmospheric Administration (NOAA) bioclimatic regions to understand strength and variation of landscape effects during an elevated transmission year. Although few landscape studies have examined WNV in Florida, we expected higher percentages of residential or medium-developed landscapes and more fragmented landscapes would be positively correlated with WNV seroconversion owing to the main mosquito vector habitats and avian host distributions. However, we expected to find variation in the importance of forest, wetland, and agriculture landscapes across bioclimatic regions in the state. WNV seroconversion rates were calculated using Florida 2018 Department of Health WNV sentinel chicken seroconversion data from 187 flocks maintained by mosquito control programs. Percent land cover and edge density metrics were calculated for multiple land cover classes and within multiple buffer distances from chicken coops using 2019 National Land Cover Data. We used binomial generalized linear mixed effects models to calculate the importance of landscape metrics to WNV seroconversion. We found no statewide predictors of seroconversion, but as expected, the importance of landscape varied across regions. In the north-central part of the state, we found higher seroconversion in less populated suburban areas while higher seroconversion in south-central Florida was correlated with fragmented forested areas within 0.5 km of coops and intact woody wetland areas within 2 km of coops. This work corroborates previous findings that consistent landscape predictors of WNV are difficult to identify across broader geographic areas and sets the stage for additional work that incorporates climate and landscapes interactions for a greater understanding of WNV ecology in this geographic region.

Modelling Blow Fly (Diptera: Calliphoridae) Spatiotemporal Species Richness and Total Abundance Across Land-Use Types.

Geographic Information Systems provide the means to explore the spatial distribution of insect species across various land-use types to understand their relationship with shared or overlapping spatiotemporal resources. Blow fly species richness and total fly abundance were correlated among six land-use types (residential, commercial, waste, woods, roads, and agricultural crop types) and distance to streams. To generate multivariate models of species richness and total fly abundance, blow fly trapping sites were chosen across the land-use gradient of Windsor-Essex County (Ontario, Canada) using a stratified random sampling approach. Sampling occurred in mid-June (spring), late August (summer), and late October (fall). Spring species richness correlated highest to residential (-), woods (-), distance to streams (+), and tomato fields (+) in models across all three land-use buffer scale distances (0.5, 1, 2 km), with waste (+/-), roads (-), wheat/corn (-), and commercial (-) correlating at only two of the three scales. Spring total fly abundance correlated with all but one land-use variable across all buffer scale distances, but the distance to streams (+), followed by orchards/vineyards (+) exhibited the greatest importance to these models. Summer blow fly species richness correlated with roads (-) and commercial (+) across all buffer distances, whereas at two of three buffer distances wheat/corn (-), residential (+), distance to streams (+), waste (-), and orchards/vineyards (+) were also important. Summer total fly abundance correlated to models with distance to streams (+), orchards/vineyards (+), and sugar beets/other vegetables (+) at the 2 km scale. Species richness and total abundance models at the 0.5 km buffer distance exhibited the highest correlation, lowest root mean square error, and similar prediction error to those derived at larger buffer distances. This study provides baseline methods and models for future validation and expansion of species-specific knowledge regarding adult blow fly relationships with spatiotemporal resources across land-use types and landscape features.

Mathematical Model of Horizontal Track Conflict Prevention Algorithm in Detect-and-Avoid Framework

With the proliferation of Unmanned Aerial Vehicle (UAV) technology, the demand for effective collision avoidance technology has intensified. The DAIDALUS algorithm, devised by NASA Langley Research Center under the Detect-and-Avoid (DAA) framework, provides conflict prevention bands for remotely piloted UAVs navigating in intricate airspace. The algorithm computes the bands in two distinct phases: Conflict and Recovery. The formal model for both phases has been established and implemented through iterative programming approaches. However, the mathematical model remains incomplete. Therefore, based on the model, this paper proposes the mathematical model for the two phases of the horizontal track conflict prevention algorithm. Firstly, Cauchy’s inequality is proposed to formulate the model that addresses trajectory conflicts considering the UAV non-instantaneous maneuvering dynamics model, and then a prudent maneuvering strategy is designed to optimize the model for the recovery phase. Finally, the execution procedure of the algorithm within the two-stage mathematical model is also detailed. The results demonstrate that the proposed model achieves a higher precision in the preventive bands, implements an effective collision avoidance strategy, and consistently aligns with the DAIDALUS model while offering a larger buffer time or distance. This work theoretically validates the formal model of the DAIDLAUS algorithm and provides insights for further refinement.

Scale effect on the relationship between urban landscape patterns and land surface temperature

Great efforts have been made to examine the linkages between land surface temperature (LST) and urban landscape patterns (ULPs), which, however, focus on a single spatial scale and linear relationships. This study aims to examine the influence range of four key ULP indicators on LST, namely sky view factor (SVF) and three indices of built-up area, greens, and blue spaces. The analyses are conducted at street block level in 38 big cities in China, and leverage a multiscalar approach to investigate change patterns of indicators within multiple buffer zones and identify the buffer distance that yields the greatest influence. Results reveal that (1) the greatest local impacts are produced within 0−150 m buffer zones in most cities for all key ULP metrics; (2) the maximum impacts of most indicators in summer are greater than other seasons; and (3) the influence magnitude of key ULP indicators increase after considering scale effect, and the influence of SVF varies significantly across cities. Results suggest that the consideration of maximum influence ranges of ULP indicators can better explain LST spatial variations. Our findings offer evidence on the local impact of ULPs on LST and contribute to urban design in urban heat mitigation.

Spatiotemporal variability of the association between greenspace exposure and depression in older adults in South Korea

Abstract Background A number of studies based on young to middle aged adult and child samples have found that exposure to greenspace and bluespace can have a positive impact on mental health and well-being. However, there is limited research among older adults and the extant studies have provided mixed results. The present study was designed to examine how the association between these forms of exposure and depressive symptoms among older adults varies as a function of different spatially and temporally resolved exposure metrics. Methods The sample consisted of 617 individuals (46.19% female) aged ≥ 60 years of age. Depressive symptoms were measured using the 10-item Center for Epidemiological Studies Depression Scale (CES-D). Individuals’ greenspace exposure was quantified using spatially and temporally resolved metrics, including monthly and annual averaged satellite-derived normalized difference vegetation index (NDVI) across multiple buffer distances (250 m to 2,000 m) centered at participants’ home address. We also quantified exposure to blue-greenspace from a highly detailed land use and land cover dataset. A multivariable logistic regression model assessed the association between greenspace and blue-greenspace exposure and depressive symptoms, adjusting for age, sex, income, education, marital status, current smoking, alcohol status, medical conditions, temperature, crime rate, population density, and per capita park area. Results We found a significant association between exposures to greenspace and blue-greenspace and depressive symptoms (CES-D cutoff ≥ 4) among older adults. After adjusting for confounding variables, the odds of depressive symptoms were significantly decreased by an IQR increment in residential exposure to greenspace [odds ratio (OR) = 0.67; 95% confidence interval (95% CI), 0.49 ~ 0.91] and blue-greenspace (OR = 0.59; 95% CI, 0.41 ~ 0.84) measured nearby their home address (i.e., as close as 250 m). When stratified by household income level, the association was only significant among low-income individuals. We also found temporal variation in the association between depressive symptoms and monthly NDVI-based greenspace exposure, in which the odds of depressive symptoms were the lowest for greenspace in cold months (i.e., January, February, and March). Conclusions Our findings suggest that neighborhood greenspace may serve as a protective factor against depression among older adults, but the benefits may depend on the spatial and temporal context. More investigation is needed to replicate our findings on the spatial and temporal variations of greenspace exposure metrics and their effects on depressive symptoms.

Residential proximity to conventional and unconventional wells and exposure to indoor air volatile organic compounds in the Exposures in the Peace River Valley (EXPERIVA) study

BackgroundIn a previous study located in Northeastern British Columbia (Canada), we observed associations between density and proximity of oil and gas wells and indoor air concentrations of certain volatile organic compounds (VOCs). Whether conventional or unconventional well types and phases of unconventional development contribute to these associations remains unknown. ObjectiveTo investigate the associations between proximity-based metrics for conventional and unconventional wells and measured indoor air VOC concentrations in the Exposures in the Peace River Valley (EXPERIVA) study samples. MethodsEighty-four pregnant individuals participated in EXPERIVA. Passive indoor air samplers were analyzed for 47 VOCs. Oil and gas well legacy data were sourced from the British Columbia Energy Regulator. For each participant's home, 5 km, 10 km and no buffer distances were delineated, then density and Inverse Distance Square Weighted (ID2W) metrics were calculated to estimate exposure to conventional and unconventional wells during pregnancy and the VOC measurement period. Multiple linear regression models were used to test for associations between the well exposure metrics and indoor air VOCs. For exposure metrics with >30% participants having a value of 0, we dichotomized exposure (0 vs. >0) and performed ANOVAs to assess differences in mean VOCs concentrations. ResultsAnalyses indicated that: 1) conventional well density and ID2W metrics were positively associated with indoor air acetone and decanal; 2) unconventional well density and ID2W metrics were positively associated with indoor air chloroform and decamethylcyclopentasiloxane, and negatively associated with decanal; 3) drilling specific ID2W metrics for unconventional wells were positively associated with indoor air chloroform. ConclusionOur analysis revealed that the association between the exposure metrics and indoor air acetone could be attributed to conventional wells and the association between exposure metrics and indoor air chloroform and decamethylcyclopentasiloxane could be attributed to unconventional wells.

Integrative Review of Riparian Buffers Benefits in Urbanized Watersheds

Riparian buffers or riparian corridors are areas of vegetation in the floodplains and areas surrounding a stream. By the early 2000s, numerous national reports and studies of riparian buffer benefits established that vegetation near streams is helpful in protecting the stream from increasing urban runoff, minimizing bank erosion, reducing flooding, and improving overall water quality. However, influential studies concluded that buffer benefits dwindle as urbanization increases, eventually becoming ineffective. This study evaluates more recent research that suggests riparian buffers are more effective at countering urbanization impacts than previously understood, and considers the extent to which we can quantify these benefits and identify the factors that maximize their effectiveness (i.e. greater efficiency based on buffer distance from the stream, extent of stream setbacks, and percentage of impervious cover in the area). Much of the research has been conducted in the Kansas City area in the Blue River Watershed, which begins in Kansas and flows into Missouri River east of downtown Kansas City, Missouri. About 800,000 residents live in the watershed, which includes some of the region’s fastest growing areas. It is critical to protect this major resource, and other regional rivers and streams, for residents of the Kansas City Metropolitan area and the ecosystems that depend on them. It is also critical to provide the latest and best information to the community-of-practice currently updating regional stormwater management planning and design guidance.

On the effectiveness of multi-scale landscape metrics in soil organic carbon mapping

Soil organic carbon (SOC) mapping delivers invaluable information to the global carbon budget and climate change mitigation endeavour. Environmental variables at sample locations are frequently used as explanatory variables for simulating the spatial distribution of soil properties. However, these may not fully capture the spatial information generated by soil-forming processes. We applied multi-scale landscape metrics that can comprehensively characterize the surrounding landscape information of sample points. The metrics were extracted at two levels (landscape level and class level) and include the diversity, shape and area, fragmentation and connectivity with a buffer distance from 500 m to 5,000 m. We then investigated its effectiveness as environmental variables via recursive feature elimination, random forests, and quantile regression forests. The Jianghan Plain, China, was selected as the study area, where over 19,000 topsoil samples were collected. Results indicated that multi-scale landscape metrics enhanced the predictability of SOC mapping, with R2 increased by 43 %. Specifically, Shannon’s diversity index, the percentage of landscape index, interspersion and juxtaposition index, and patch cohesion index outperformed environmental variables that were extracted at the sample location. In addition, the relationships between SOC and landscape metrics were found to be scale-dependent. Landscape metrics demonstrated significant explanatory capacity for SOC across various spatial scales. Notably, as the scale surpassed 3,000 m, there was a discernible improvement in the explanatory effectiveness of the landscape metrics for SOC. Our findings highlight that landscape metrics are effective in characterising the soil-landscape relationship that is generated by multi-scale natural and anthropogenic soil-forming processes. Meanwhile, knowledge of the intricate relationship between landscape characteristics and SOC is crucial for informing land management decisions aimed at enhancing carbon sequestration, mitigating climate change, and maintaining soil fertility.

Do greenspaces really reduce heat health impacts? Evidence for different vegetation types and distance-based greenspace exposure

BackgroundWhile vegetation type, population density and proximity to greenspaces have been linked to human health, what type and location of greenspace matter most have remained unclear. In this context, there are question marks over investment-style metrics. ObjectivesThis paper aims at establishing what vegetation type may matter most in modifying heat-mortality associations, and what the optimal buffer distances of total and specific types of greenspace exposure associated with reduced heat-related mortality risks are. MethodsWe conducted small-area analyses using daily mortality data for 286 Territory Planning Units (TPUs) across Hong Kong and 1 × 1 km gridded air temperature data for the summer months (2005–2018). Using a case time series design, we examined effect modifications of total and specific types of greenspaces, as well as population-weighted exposure at varying buffer distances (200–4000 m). We tested the significance of effect modifications by comparing relative risks (RRs) between the lowest and highest quartiles of each greenspace exposure metric; and explored the strength of effect modifications by calculating the ratio of RRs. ResultsForests, unlike grasslands, showed significant effect modifications on heat-mortality associations, with RRs rising from 0.98 (95 %CI: 0.92,1.05) to 1.06 (1.03, 1.10) for the highest to lowest quartiles (p-value = 0.037) The optimal distances associated with the most apparent effects were around 1 km for population-weighted exposure, with the ratio of RRs being 1.424 (1.038,1.954) for NDVI, 1.191 (1.004,1.413) for total greenspace, and 1.227 (1.024,1.470) for forests. A marked difference was observed in terms of the paired area-level and optimal distance-based exposure to total greenspace and forests under extreme heat (p-values < 0.05). DiscussionOur findings suggest that greenspace, particularly nearby forests, may significantly mitigate heat-related mortality risks.

A self-reinforced luminescence signal amplification strategy to construct electrochemiluminescence immunosensor for carcinoembryonic antigen sensing

Carcinoembryonic antigen (CEA) is a disease biomarker that reflects the presence of various cancers and tumors in the human body. Sensitive detection of CEA concentration in the blood is beneficial for the clinical diagnosis and treatment evaluation of cancer. Electrochemiluminescence (ECL) sensors used for disease pre-screening are usually subjected to various complex chemical modifications for real-time analysis and detection of CEA concentration in the blood. The ECL behavior of a novel self-reinforced ECL complexes Zn-MOF-Ru-TEA coreacting at the anode is reported in this paper. The self-reinforced ECL complexes Zn-MOF-Ru-TEA are synthesized using one-pot method. Compared with the redox reaction of TEA in the PBS buffer, the energy and charge transport distance provided by Ru(bpy)32+ and TEA molecules in the pore size of Zn-MOF are greatly shortened, so the ECL efficiency of Ru(bpy)32+ is effectively improved. The Cu+/Cu2+ ion pair cycle in the signal label CuNiOS/Au NPs can react with TEA enriched in Zn-MOF-Ru-TEA to produce more reactive radicals, which realizes the amplification strategy of the ECL signal caused by Cu+-triggered probes. Under optimal conditions, the linear range of CEA detection is 10−5 ng·mL−1 to 80 ng·mL−1, and the detection limit is 3.43 fg·mL−1. This is the first time that a Ru(bpy)32+/TEA self-reinforced ECL system has been proposed for CEA detection, which has the advantages of low limit of detection and high sensitivity and offers the potential for the application of sensitive determination of biomarkers.

Spatial instability of crash prediction models: A case of scooter crashes

Scooters have gained widespread popularity in recent years due to their accessibility and affordability, but safety concerns persist due to the vulnerability of riders. Researchers are actively investigating the safety implications associated with scooters, given their relatively new status as transportation options. However, analyzing scooter safety presents a unique challenge due to the complexity of determining safe riding environments. This study presents a comprehensive analysis of scooter crash risk within various buffer zones, utilizing the Extreme Gradient Boosting (XGBoost) machine learning algorithm. The core objective was to unravel the multifaceted factors influencing scooter crashes and assess the predictive model’s performance across different buffers or spatial proximity to crash sites. After evaluating the model’s accuracy, sensitivity, and specificity across buffer distances ranging from 5 ft to 250 ft with the scooter crash as a reference point, a discernible trend emerged: as the buffer distance decreases, the model’s sensitivity increases, although at the expense of accuracy and specificity, which exhibit a gradual decline. Notably, at the widest buffer of 250 ft, the model achieved a high accuracy of 97% and specificity of 99%, but with a lower sensitivity of 31%. Contrastingly, at the closest buffer of 5 ft, sensitivity peaked at 95%, albeit with slightly reduced accuracy and specificity. Feature importance analysis highlighted the most significant predictor across all buffer distances, emphasizing the impact of vehicle interactions on scooter crash likelihood. Explainable Artificial Intelligence through SHAP value analysis provided deeper insights into each feature’s contribution to the predictive model, revealing passenger vehicle types of significantly escalated crash risks. Intriguingly, specific vehicular maneuvers, notably stopping in traffic lanes, alongside the absence of Traffic Control Devices (TCDs), were identified as the major contributors to increased crash occurrences. Road conditions, particularly wet and dry, also emerged as substantial risk factors. Furthermore, the study highlights the significance of road design, where elements like junction types and horizontal alignments – specifically 4 and 5-legged intersections and curves – are closely associated with heightened crash risks. These findings articulate a complex and spatially detailed framework of factors impacting scooter crashes, offering vital insights for urban planning and policymaking.

Spatial Distribution of Mangrove Density in Gili Gede, West Lombok Regency

Gili Gede hosts a vital mangrove ecosystem, essential for the community and protecting coastal areas from large waves. However, increasing development and human activities in and around the mangrove areas threaten their distribution and extent. This study aims to map the spatial distribution, density, and area of mangroves on Gili Gede. Sentinel-2A satellite imagery was utilized with the Normalized Difference Vegetation Index (NDVI) method, and the image data were validated using hemispherical photography. Environmental parameters measured included sediment salinity, temperature, pH, humidity, NPK nutrients, and substrate type. Additional data were gathered on slope, buffer distances, and through semi-structured interviews using purposive sampling. The findings revealed that the mangrove density on Gili Gede is predominantly sparse, covering 15.56 hectares or 77.07% of the total mangrove area. Medium-density mangroves span 3.45 hectares or 17.09%, while dense mangroves occupy the smallest area, 1.18 hectares or 5.84%. Mangroves are distributed across the northern, western, and eastern sides of Gili Gede. The study identified 22 species of mangroves, both true and associated, across 14 families. Environmental factors influencing the distribution, density, and extent of mangroves across three locations include sediment salinity, temperature, pH, humidity, and nutrient content (NPK), as well as substrate type, slope, and anthropogenic influences such as human activities and the proximity of settlements to the mangrove ecosystem.

Analysis of Spatiotemporal Variation and Driving Factors of Water Quality in the Xiangjiang River Basin from 1990 to 2016

In order to explore the spatiotemporal variation characteristics and driving mechanism of water quality in the Xiangjiang River Basin, the data of 16 water quality parameters at 113 stations over 26 years from 1990 to 2016 in the Xiangjiang River Basin were collected for synthetically assessing the water quality and identifying its main pollutants through the water quality index and other methods. The causal mechanism of water quality, especially the driving effect of water level and land use pattern, was analyzed. The results showed that： ① The overall water quality grade of the Xiangjiang River Basin during the study period was "good." However, the water quality deteriorated first （from 1990 to 2003） and then improved （from 2004 to 2016）. The season variation in water quality was not obvious, but the water quality fluctuation of the wet season was larger. The water pollution load of the main stream decreased successively from the middle reaches, downstream reaches, and upstream reaches. The upstream tributaries had the best water quality, whereas areas with poor water quality were mainly distributed at the mouth of the middle and downstream tributaries, especially in areas where multiple tributaries converged. ② Toxic heavy metals had the characteristics of toxicity, persistence, and bioaccumulation. If they exceeded a certain concentration in water, they were difficult to purify, posing great harm to the natural environment and human health. The toxic metal index （CI1） was the leading factor affecting water quality, in which Hg and Cd were the main parameters affecting the overall water quality of the Xiangjiang River Basin. ③ The water level had a positive impact on the water quality of the Xiangjiang River by diluting environmental parameters. Land type had little effect on heavy metal concentration, whereas forest land could improve water quality. Grassland had a negative correlation with permanganate index over a large scale range （≥ 5 km）. The increase in water bodies, arable land, and impermeable surface areas within the watershed increased the probability of high fecal coliform concentration in the water body. ④ With the increase in buffer distance, the water quality explained by the land use pattern increased. On the scale of 10 km buffer zone in the riparian zone, the explanation degree by land use pattern on water quality was the highest, which was an effective scale for water quality control of the Xiangjiang River. This research showed that the driving factors of heavy metal pollution and other pollution were different, but their regional differences were all obvious in the Xiangjiang River Basin. Therefore, pollution control should be classified and taken according to local conditions.

Implications of human–wildlife conflict on the diet of an endangered avian top predator in the northern Andes

Conflicts between rural people and the Endangered Black-and-chestnut Eagle (Spizaetus isidori) are a prominent conservation concern in the northern Andes, as at least 60 eagles were poached between 2000 and 2022 in response to poultry predation. Here, we conducted direct observations to analyze the Black-and-chestnut Eagle diet and evaluated how forest cover affects the feeding habits of the species during nestling-rearing periods in 16 nests located in different human-transformed Andean landscapes of Ecuador and Colombia. We analyzed 853 prey items (46 species) delivered to nestlings. We used Generalized Linear Models to test whether the percent forest cover calculated within varying buffer distances around each nest and linear distances from the nest to the nearest settlement and pasture areas were predictors of diet diversity and biomass contribution of prey. Forest cover was not a factor that affected the consumption of poultry; however, the eagle regularly preyed on chickens (Gallus gallus) (i.e., domestic Galliformes) which were consumed by 15 of the 16 eagle pairs, with biomass contributions (14.57% ± 10.55) representing 0.6–37% of the total prey consumed. The Black-and-chestnut Eagle is an adaptable generalist able to switch from mammalian carnivores to guans (i.e., wild Galliformes) in human-dominated landscapes, and eagles nesting in sites with low forest cover had a less diverse diet than those in areas with more intact forests. Management actions for the conservation of this avian top predator require studies on the eagle’s diet in areas where human persecution is suspected or documented, but also maintaining forest cover for the wild prey of the species, development of socio-economic and psychological assessments on the drivers behind human-eagle conflicts, and the strengthening of technical capacities of rural communities, such as appropriate poultry management.

Multicriteria GIS-based assessment of biomass energy and hydropower potentials in Nigeria

The deployment of hybrid renewable energy systems remains the quickest way to salvage the dwindling grid supply in Nigeria and improve energy access. The assessment of biomass and hydro potentials across the geopolitical zones of the country to determine the optimal locations for a Biogas fired thermal plant for energy generation is thoroughly addressed in this paper. Multiple Geographical Information System (GIS) approaches were used to assess the energy potentials of crop and forest residues for biomass, as well as waterbodies, and waterlines for a hydrothermal power plant. The study applies the inverse distance weighting and weighted overlay multicriteria decision analysis. Forest areas, waterbodies, distance from water sources and road accessibility were considered for locating the biomass and hydrothermal facilities within multiple ring buffer distances of 200 m–2000 m from the combined biomass and hydropower resources. The result shows four states with potentials to host biomass-hydrothermal power plants in Nigeria; Niger, Kaduna, Bauchi and Taraba states with biomass potentials of 831.9 tonnes/yr, 1173.27 tonnes/yr, 322 tonnes/yr and 739.48 tonnes/yr, respectively and inland waterways. It further shows that these plants can be optimally located within 2000 m from the source of the biomass and hydro resources. This paper recommends the utilization of these results to carry out feasibility studies and make informed decisions on the exploration of these resources for power generation. It also advocates for a control in the commercialization of forest residues to sustain the production from these hybrid plants and supports the Nigerian bio-energy policy that proposes to effectively utilize the Nigeria's non-fuelwood as substitute to felling of trees.

Alternate method for resolving particle collisions in PRS of freely evolving particle suspensions using IBM

Particle Resolved simulations using the Immersed Boundary Method (IBM) are performed for freely evolving sphere suspensions in the Reynolds number range of [10, 300], solid volume fraction (φ) of [0.1, 0.4] with solid to fluid density ratio of 2, 10 and 100. Collisions between particles are initiated at an inter-particle buffer distance and solved between subsequent fluid time steps using a soft sphere model. Guidelines for permissible buffer distances are developed as a function of solid fraction. Viscous damping effects in the unresolved thin lubrication layer between particles near collision is modeled using a wet coefficient of restitution. Finally, a new correlation for particle fluctuating velocities is proposed and the predicted drag force is compared to previously proposed drag correlations.

An Adaptive Simplification Method for Coastlines Using a Skeleton Line “Bridge” Double Direction Buffering Algorithm

Aiming at the problem that the current double direction buffering algorithm is easy to use to seal the “bottleneck” area when simplifying coastlines, an adaptive simplification method for coastlines using a skeleton line “bridge” double direction buffering algorithm is proposed. Firstly, from the perspective of visual constraints, the relationship between the buffer distance and the coastline line width and the minimum recognition distance of the human eye is theoretically derived and determined. Then, based on the construction of the coastline skeleton binary tree, the “bridge” skeleton line is extracted using the “source tracing” algorithm. Finally, the shoreline adaptive simplification is realized by constructing a visual buffer of “bridge” skeleton lines to bridge the original resulting coastline and the local details. The experimental results show that the proposed method can effectively solve the problem that the current double direction buffering algorithm has, which can significantly improve the quality of simplification.