Land Use Variables Research Articles

Determinants of persistent malaria transmission in the Arjo-Didessa farm area in Ethiopia

Background: In tropical and subtropical areas of the world, malaria is still a serious public health concern. Activities related to agricultural development that involve irrigation schemes likely increase the risk of malaria in tropical and sub-Saharan African regions. Ethiopia is a sub-Saharan country where malaria is endemic.Objectives: The aim of the study was to investigate the determinants related to the persistence of malaria transmission in the Arjo-Didessa sugarcane farm area in southwest Ethiopia.Method: The study employed a quantitative survey design. Quantitative data were collected from 397 households using structured questionnaires. SPSS Statistics version 26 was used to analyse the data. Z- and Chi-square tests were applied, and the data were analysed using logistic regression.Results: The determinants that were identified included variation in land use, water management practices, socioeconomic status and knowledge about the use of anti-malaria treatment. These were shown to contribute to increased malaria transmission and the rise in malaria cases in agricultural areas.Conclusion: The effectiveness of malaria control in agricultural settings can be improved through free access to malaria testing and treatment as well as knowledge about anti-malaria treatment among the residents in agricultural areas.Contribution: The study revealed key determinants, including the promotion of free access to anti-malaria treatment, which should be considered for the effective management of malaria in agricultural areas.

Identifying potential introduced and natural sources of pollution in Delaware watersheds.

Managing water quality with microbial impairment caused by Enterococcus poses unique challenges regarding the determination of fecal host origin. Most water monitoring is performed based on Enterococcus counts that neither detect the location of the introduction of pollution nor identify the type of contaminating Enterococcus. The use of sequenced-based microbial source tracking could allow for identification of fecal origin and potential remediation of pollution. The state of Delaware has numerous waterways with high microbial impairment from unknown sources, so we used sequence-based microbial source tracking to investigate potential microbial pollution in three watersheds with significant variation in land use and population density. In this study, we use a 16S rRNA sequence reference library of microbial communities from relevant fecal sources (wild animal, domestic animal, sediment, and septic/wastewater) to determine the most likely sources of microbial impairment in three Delaware watersheds. This study assigned sources of microbial contamination to mostly human-related sources (septic and wastewater) or unknown sources indicating that waste infrastructure may have a larger influence on microbial community structure in Delaware watersheds than previously considered. Our results suggest that long-term source tracking is valuable for ruling out native or domesticated animals as contributors to water pollution.IMPORTANCETraditional microbial pollution monitoring utilizes specific fecal indicator bacteria that need to grow in the laboratory for detection. Here, we show the use of sequence information from whole microbial communities and an expanded reference library in microbial source tracking. Expanding the host detection range by including the whole microbial community may allow for a wider range of potential fecal origin identification even when specific fecal indicators are absent or in low concentration. We show that many Delaware waterways bear the signature of human influence compared to natural sources. In addition, the robust reference library built in this study can be used to conduct source tracking studies in the mid-Atlantic USA.

An evaluation of intra-university campus temperature variability under variable synoptic weather conditions using mobile transects.

Intensive observations were collected in a wide range of synoptic weather conditions to evaluate variability in the intra-urban heat island on the campus of the University of North Carolina at Charlotte between February 2023 and June 2023. An easily reproducible bicycle-based mobile transit route around the university was traversed during 20 afternoon and 20 evening periods. The magnitude of observed temperature range from an individual data collection period is defined as the campus urban heat island intensity, with areas having more anthropogenic modification also having higher temperatures. While other papers have examined the relationship between the city-scale urban heat island intensity and the present weather conditions, this paper aims to disentangle the relationship between present weather conditions and the magnitude of thermal variability across a small intra-urban campus with diverse land use and land cover characteristics. This will contribute to a better understanding of intra-urban heat islands, particularly identifying days where conditions will be highly dangerous in more developed areas, and not in more natural environments. When comparing the standardized mobile-transit observations to the regionally present weather conditions it is evident that clear and calm conditions often enhance both city-scale and campus-scale heat islands, increasing temperature disparities. While the spatial distribution of warm and cool areas across campus remains relatively constant, the campus-scale heat island is significantly modulated by the present weather conditions.

Automated urban landscape design: an AI-driven model for emotion-based layout generation and appraisal

The evolution of a city is significantly shaped by the design of its urban landscape. The advancement of artificial intelligence has substantially increased convenience for individuals. This research proposes an urban landscape layout model powered by artificial intelligence that automatically generates urban landscape design based on deep learning (URDDL) with two dimensions: emotional tendency and urban landscape appraisal. The input image represents land use and surrounding road conditions, while the output image depicts the selection of the main entrance and the internal spatial function layout. The Pix2Pix model is trained to learn the internal function layout based on varying land use and road conditions. Additionally, a domain-specific dictionary is constructed using an existing semantic resource vocabulary, where positive and negative sentiment words are compared with their corresponding sentiment values, focusing on categories such as Stimulate, Sense, and Action. Experimental results indicate that the absolute average error of the URDDL model is 91.31%, with a maximum error of 96.87%. The degree of fit is highly appropriate for evaluating the emotional prediction of urban landscapes. The findings demonstrate that the URDDL model outperforms traditional design methods regarding generated results, suggesting its potential for future applications in automated landscape design.

Spatio-temporal variation and prediction of land use and carbon storage based on PLUS-InVEST model in Shanxi Province, China

AbstractCoal mining in Shanxi Province for many years has caused regional land-use changes, seriously affecting the local ecological environment. To promote the green and sustainable development of this area and achieve the goal of “double carbon”, the PLUS-InVEST model was used to analyze the nearly 20 year land-use types transfer and carbon storage changes in the region based on the five periods of land-use data of Shanxi Province from 2002 to 2022 in this study. Three scenarios were set up to predict the data in 2032, and the driving forces of spatial differentiation of the carbon storage were analyzed by geographical detector. The results showed that: (1) Land-use change had an important impact on carbon storage. Construction land in Shanxi Province expanded by 3068.47 km2 from 2002 to 2022, an increase of 59.33%, and the rest of the land types, except for forest, continued to decrease, resulting in a decline in total carbon storage of 228.88 × 104 t. (2) The natural development scenario shows the same transfer of land as before in 2032. Under the urban development scenario, the expansion of construction land in Shanxi Province is the largest, and carbon storage decreases instead of increasing; under the ecological protection scenario forest and grassland increase, and carbon storage increase the most, with a value of 738.67 × 104 t. (3) The dominant driving factors for the spatial differentiation of carbon storage in Shanxi Province were the land-use degree comprehensive index and night light index, with the explanatory power of 0.866 and 0.755, respectively, and the interaction of the land-use degree comprehensive index with other factors had the most significant effect on the change of carbon storage. The study suggested that the expansion of construction land into high carbon storage categories was the main reason for the loss of carbon storage, and Shanxi Province should optimize the land-use structure and adopt ecological protection works to increase carbon storage effectively.

Healthy urban blue space design: Exploring the associations of blue space quality with recreational running and cycling using crowdsourced data

Urban blue space offers substantial health benefits by encouraging population physical activity. Despite much evidence on the nature-health nexus, the relationship between blue space and recreational exercises remains under-studied, limiting the realisation of health benefits in blue space design. Using crowdsourced data, including volunteered geographic information and street view image data, this study investigates the associations of blue space quality with recreational running and cycling in Rotterdam, the Netherlands. Results show that recreational exercise levels on street segments vary based on the blue space type and design. Compared to inland canals and rivers, small-scale recreational waterbodies are more conducive to running but not cycling, while both activities tend to cluster around the Nieuwe Maas River. Interestingly, the Water View Index shows a general negative association with both activities after adjusting for the blue space type. Besides the waterbody characteristics, eye-level environmental factors, including higher Green View Index, lower building density, more diverse land use, greater connected street network and fewer traffic elements, are associated with more running and cycling exercises. Results for visual complexity and neighbourhood population composition are mixed depending on the exercise type. These findings are further translated into spatial design patterns for developing exercise-supportive and health-promoting blue spaces.

Soil carbon sequestration potential of different land use systems: evidence from sub-humid southern plains and Aravalli hills of Rajasthan, India.

This study has assessed total soil organic carbon (TOC) fractions, carbon management index (CMI), and carbon sequestration economic value under diverse land use systems (LUSs) of sub-humid Southern plains of Rajasthan, India. The study dealt with six LUSs: barren land (BL), agricultural land (AL), agri-horticulture (AH), horticultural land (HL), grassland (GL), and natural forest land (FL) were selected for the study. FL contained the highest TOC (13.04 ± 0.74gkg-1), particulate organic carbon (POC) (2.2 ± 0.19gkg-1), mineral-associated organic carbon (MAC) (10.84 ± 0.54gkg-1), and CMI (230.36 ± 4.13), whereas BL had the lowest amount of TOC (3.53 ± 0.4gkg-1), POC (0.47 ± 0.06gkg-1), MAC (3.06 ± 0.32gkg-1), and CMI (54.60 ± 4.2). The impact of LUSs on soil carbon lability index (LI) was minimal in all LUSs except FL exhibited statistically insignificant variations in LI. TOC stock showed the highest decline in BL (71.04%), AL (55.29%), AH (44.38%), and HL (25.92%) uses compared with the FL system. Different LUSs result in varying amounts of carbon stocks, representing the relative carbon credit gain. The maximum carbon credit was achieved by FL, which was roughly US $49,303 and 245% higher than BL. These results indicate the reinstatement of BL and AL towards HL and AH systems, and effective recycling of residues could improve the TOC storage in the study region.

Agricultural legacy shapes plant diversity patterns in mountain grasslands of Maramureș and Bukovina: A cross‐border perspective (Ukraine, Romania)

Abstract We investigated farming practices and grassland plant diversity patterns in the historical regions of Maramureș and Bukovina, located in the central Eastern Carpathians of Romania and Ukraine. We explored the influence of social and historical factors on recent grassland plant diversity, including changes and modifications in land use and grassland management practices. We used stratified random sampling to select 48 grassland parcels in eight villages on the Ukrainian‐Romanian borderlands. We identified the factors that affect the composition and diversity of grassland vascular plants and bryophytes at multiple spatial scales and compared the frequency and timing of historical and recent grassland management practices to distinguish between preserved and modified agricultural traditions. In most of the grassland parcels studied (83% in Romania and 78% in Ukraine), traditional land use persisted. Local plant species richness was primarily influenced by management practices, outweighing the impact of topography and soil factors. Regular mowing at appropriate intensity increased the richness of vascular plants, while grazing promoted the richness of bryophyte species. Modifications to farming systems, observed in all studied villages, were mainly due to declining population and livestock numbers. Although the timing of spring and autumn grazing in hay meadows still adhered to tradition, mowing schedules shifted earlier, especially for the first cut of two‐cut hay meadows. Traditional corralling, fertilization and hayseed application have become rare or discontinued in certain villages, while controlled burning has increased. Past use of mineral fertilizers in Ukrainian villages during the Soviet era influenced the recent diversity and composition of vascular plant species, favouring nutrient‐demanding species. Social factors and agricultural policies indirectly affect biodiversity in semi‐natural grasslands, leaving a tangible footprint even in the most remote areas of the Carpathian Mountains. We provide basic recommendations to preserve the remarkable biodiversity of the region. Read the free Plain Language Summary for this article on the Journal blog.

Liming acidic soils creates profits, land use options but often more emissions

Context Soil acidity constrains crop production in Australia. The practice of liming can reduce soil acidity but produces greenhouse gas emissions. Aims By examining land use sequences over three decades at a range of locations in Western Australia, this study aims to identify firstly where and when liming might boost farm profits and secondly, what emissions and land use management flexibilities are generated by liming. Methods Bioeconomic simulation modelling is used to identify the gross margins and emissions associated with liming in land use sequences at 14 locations in Western Australia. Three intensities of cropping and three different rotational sequences are considered. The simulations account for price and weather–year variations across a 30-year period of analysis. Key results Liming is profitable at almost all locations and across all rotation sequences examined. Where problematic soil acidity is a feature or is poised to soon become a problem at a location, liming is a profitable ameliorative practice that enables greater diversity in land use. For most situations assessed, liming increases emissions. The exceptions are at locations where liming prevents a switch away from a crop-dominant system, due to soil acidity reducing crop yields, into additional sheep production that increases emissions. Conclusions Liming is profitable in most acidic soil situations and preserves land use flexibility, although additional greenhouse gas emissions are often generated. Implications Liming acidic soils bolsters land use profitability and helps sustain biologically diverse land use sequences, despite often increasing greenhouse gas emissions.

Differences in Urban Vibrancy Enhancement among Different Mixed Land Use Types: Evidence from Shenzhen, China

Mixed land use has the advantages of promoting the economic and intensive utilization of land and improving the efficiency of land use, which can help alleviate the current urban problems and promote the sustainable development of cities. Existing studies have usually used quantitative indicators to reflect complex and diverse mixed land use situations, and the conclusions obtained usually cannot provide a basis for functional selection in mixed land use practices. Therefore, this study took Shenzhen as the study area to explore whether there are differences in the urban vibrancy enhancement among different mixed land use types. First, the block-scale mixed land use dataset of the study area was constructed. Second, the spatial distribution characteristics of the main functional types and urban vibrancy in the study area were explored. Finally, the impact of mixed land use types on urban vibrancy was explored by using a multiple linear regression model and setting land use type as the dummy variable. The results show that the number of mixed-function blocks in Shenzhen is relatively small, and the mixed land use degree still needs to be improved. Among the 12 main land use types in the study area, those containing industrial land are usually clustered in the northern industrial area of Shenzhen, those containing public or commercial service land are usually clustered in the city center, and those containing residential land are widely distributed in the study area. From the perspective of urban vibrancy, there is a phenomenon of “jobs–housing mismatch” in Shenzhen, as well as a problem of low urban vibrancy in the peripheral areas of the city. In addition, the urban vibrancy intensity of mixed land use types including residential or commercial land is higher, such as “administration+residential”, “residential+commercial”, “industrial+residential+commercial”, and “administration+residential+commercial” land, which includes residential or commercial land, is stronger, while. However, the urban vibrancy stability of mixed land use types including industrial land is higher, such as “industrial+residential” and “industrial+administration” land. The results of this study can provide a basis for future mixed land use practices in terms of land use type selection. For the urban central areas and subcenters in urban peripheral areas, mixed land use types such as “administration+residential”, “residential+commercial”, and “administration+residential+commercial” can be selected to enhance the urban vibrancy stability of the area. For industrial parks in urban peripheral areas, mixed land use types such as “industrial+residential”, “industrial+commercial”, “industrial+administration+residential”, and “administration+residential+commercial” can be selected to enhance the urban vibrancy intensity of the area.

Passenger origin–destination estimation in public transit using boarding count data

Passenger boarding and alighting data are critical for understanding travel patterns in transit systems, yet many systems only record boarding, making it difficult to track alighting. This study introduces a method to estimate alighting counts from boarding data using transit symmetry at a cluster level. Bus stops are grouped into clusters, assuming equal inflow and outflow, and a gravity model is calibrated to estimate an origin–destination (OD) matrix. Using data from three bus routes in Regina, Canada (2017), the method achieved a symmetry ratio of 60% across clusters and a mean structural similarity measure score of 0.84, validating the accuracy of the OD estimates. This approach provides a practical solution to the challenge of missing alighting data. While effective for single-mode transit systems, future research will expand its application to multi-modal systems and address factors like seasonal and land-use variations, offering a cost-effective alternative for OD estimation.

Strategizing Low Carbon Urban Planning Through Environmental Impact Assessment by Artificial Intelligence Driven Carbon Foot-print Forecasting

Addressing the associated rise in Carbon Emissions (CE) as smart cities expand becomes paramount. Effective low-carbon urban planning demands robust, precise assessments. This research introduces a cutting-edge solution via an Artificial Intelligence (AI) -driven Carbon Footprint (CF) impact assessment. A detailed dataset, collected over 3 years, was harnessed to gather insights into vital urban factors, including CE, Energy Consumption (EC) patterns, variations in land use, transportation dynamics, and changes in air quality. The cornerstone of this research is developing the Multi-modal Stacked VAR-LSTM model. This model proposes to provide accurate CF predictions for urban environments by merging the capabilities of Vector Autoregression (VAR) with Long Short-Term Memory (LSTM) neural networks. The process encompasses dedicated assessments for each data segment, harnessing VAR to delineate interdependencies and refining these predictions with the LSTM network using the residuals from the VAR analysis. By interweaving AI-driven carbon footprint impact assessments into the urban planning discourse, this study underscores the vast potential in sculpting future urban development strategies that are sustainable and sensitive to carbon impact.

Drivers of carbon stabilization and sequestration in Brazil’s black soils

Climate and land use are recognized as two of the main drivers of changes in soil organic carbon (SOC) on a global scale. Both factors play an important role in understanding SOC sequestration and mitigation of climate change. Particularly important, black soils are mineral soils with high SOC contents and high natural fertility and play an important role in national and global food and climate security. Here, we used a database of 90 black soils in Brazil − under different climate and land use conditions across the country − to test the hypothesis that C stock is richer in wetter climate conditions and that agricultural land use reduces C stock and the percentage of carbon saturation (PCS%). Climate data were obtained from the National Oceanic Atmospheric Administration (NOAA) and used to classify Thornthwaite’s climate. The land use information was obtained in the MapBiomas platform and was grouped into three major types: cropland, pasture, and native vegetation. The nonparametric Kruskal-Wallis test showed no differences for C stock, C/N ratio, and PCS% for both land use and climate. The low C/N ratio and the strong correlation between Ca2+, CEC, clay, and SOC suggest that organo-mineral interactions − which are stronger in soils with high-activity clays (e.g., Chernozems, Kastanozems, and Phaeozems) − promotes greater stabilization of the SOC and its long-term persistence and, thus being less sensitive to variations in climate and land use. Considering the total area of approximately 3.7 × 106 ha and the average value of C stock of 93.2 Mg/ha, the total SOC stored in Brazil’s black soils is in the order of 0.35 Gt, and the carbon stock stabilization potential is 0.25 Gt. Our results highlight the potential of Brazil’s black soils to promote carbon sequestration and climate change mitigation.

Assessing Land Use Ecological-Social-Production Functions and Interrelationships from the Perspective of Multifunctional Landscape in a Transitional Zone between Qinghai-Tibet Plateau and Loess Plateau

Investigating the evolution and drivers of multifunctional land use is essential for sustainable land management and regional biological conservation. This research focuses on the Hehuang Valley, where we developed an “ecological-social-production” evaluation system for assessing land use multifunctionality from the perspective of multifunctional landscape. Leveraging Geographic Information System technologies, we conducted a quantitative analysis of spatiotemporal variations in multifunctional land use across the valley in recently twenty years. Correlation coefficients were employed to identify trade-offs and synergies among various land use functions. Additionally, geographical detector and grey relational analysis models were utilized to pinpoint the factors influencing spatiotemporal changes in land use functions during the specified period. The results showed that: (1) During the period, the overall multifunctionality of land use in the Hehuang Valley exhibited an increasing trend. The economic production function of the land showed the highest growth, while the ecological and social functions showed lower growth. (2) In most areas of the Hehuang Valley, there was a positive correlation between social and economic production functions and a negative correlation between social and ecological functions, as well as between economic production and ecological functions. (3) Natural conditions were the main factors of spatial variation of land use comprehensive functions, but human factors, including land use intensity and the rate of farmland conversion to non-agricultural uses, were the primary drivers of temporal changes in multifunctional land use. The findings provide valuable references and scientific support for policymakers in optimizing land use and multifunctional landscape conservation.

Interaction and bacterial effects of microplastics pollution on heavy metals in hyporheic sediments of different land-use types in the Beiluo River Basin

Microplastics (MPs) pollution is ubiquitous, causing serious ecological damage by threatening the growth and health of living organisms. This study investigated the vertical and horizontal distribution of MPs, MPs-heavy metals (MPs-HMs) accumulation, contamination assessment and microbial biodiversity in hyporheic sediments of different land-use types. MPs abundance in shallow sediments (0-30 cm) was significantly higher than that in deep sediments (30-60 cm), with fewer large MPs in the deep sediments. Blue, fiber, and <500 μm were the dominant MPs types, and polystyrene, polylactic acid, and polyvinyl chloride were the dominant polymers in the Beiluo River Basin. The average concentrations of HMs detected in MPs were all much higher than the same metals in the sediments. The pollution loading index of MPs was higher in areas with a greater proportion of anthropogenic land use, and MP-HM were present to varying degrees in the vertical distribution (PN>1). Critically, bacterial diversity of anthropogenic land use was smaller than that of natural land use. High MP-HM concentrations reduced the abundance of cyanobacteria, nitrospirota, acidobacteriota, and planctomycetota, whereas desulfobacterota, chloroflexi, myxococcota, actinobacteriota, and proteobacteria have developed tolerance to MP-HM. Overall, our findings contribute to the understanding of the relationship between different land-use types and the spatial distribution of MPs and MP-HM, which is critical to manage and mitigate the hyporheic zone pollution.

Investigating the role of groundwater in Ecosystem Water Use Efficiency in India considering irrigation, climate and land use

Terrestrial ecosystems (TEs) play a crucial role in carbon sequestration and climate regulation. While interactions between surface water and ecosystems are well-studied, groundwater-ecosystem relationships remain poorly understood, particularly in groundwater-dependent regions like India. This study investigates the relationship between water table depth (WTD) and ecosystem productivity across India, considering the variation in irrigation practices, land use and climate types, from 2000 to 2021. We employ Ecosystem Water Use Efficiency (WUEe), the rate of carbon uptake per unit of water consumed, to examine these interactions at different spatial scales. Our findings reveal a strong link between WUEe and WTD. Shallower WTD regions, such as the lower Himalayas and Northeast India with forests and dominated by a wet/humid subtropical climate, exhibit higher WUEe (1.5 – 3.5 g C/kg H2O). Whereas deeper WTD regions like northwest India, characterized by shrublands and an arid climate, display lower WUEe (< 1 g C/kg H2O). This suggests vegetation in arid/semi-arid regions shows higher sensitivity to water availability compared to wetter areas. This is also evident by a declining WUEe trend and increasing elasticity of WUEe () to interannual climatic variability with increasing WTD in these regions. Furthermore, the study identifies potential unsustainable groundwater use for irrigation in areas like the Trans Gangetic plains. Irrigation has a strong correlation with evapotranspiration (ET) (r = 0.4 - 0.6) in deep WTD zones, but no correlation with WUEe. This implies that intense and unsustainable irrigation might disrupt the natural water use strategies of vegetation. This research, by improving understanding of these interactions, aims to contribute to the sustainable management of India’s groundwater resources.

Nonlinear associations between design, land-use features, and active travel

The nonlinearity correlations of design and land-use features with active travel (AT) are yet to be systematically identified by recent studies. This study aims to apply the extreme gradient boosting approach to examine the nonlinear relationships between design and land-use built environment (BE) features and AT. Based on a comprehensive travel survey in Wuhan’s main agglomeration with various land use and urban functions, we collected 2073 valid samples to obtain residents’ daily travel patterns and socio-demographic attributes. We found that all BE features have nonlinear relationships with AT. The collective relative importance of design features has greater predictive power for AT than land-use features. The most effective range of high contributions of street connectivity and street greenery was 21–34/km2 and 0.22–0.34, respectively. Planners should improve the street-level features and build dense, diverse land use and high street connectivity urban environments to promote AT activity.

Conflicts in Land Use and Management

As land is limited, conflicts between land uses, and, consequently, conflicts between land users about land use inevitably arise. The objective of this paper is to provide a broad, cross-sectional overview of land use conflicts as perceived by different actors and to explore the actors’ experiences with these conflicts. We conducted 45 face-to-face interviews with key land use actors in the Republic of North Macedonia. Then we applied a qualitative text analysis or identification of keywords to identify the most relevant conflicts across all land use sectors (agriculture, forestry, nature protection, etc.) and their impacts on local actors’ daily experiences. The results show that actors are aware of many diverse land use conflicts, most frequently regarding land uses for environmental/species conservation, forestry, and agriculture. Thus, land use conflicts play an important and mostly negative role in the experiences of land use actors. These findings fill current gaps in the literature on land use conflicts regarding the types of conflicts of which actors are aware and the consequences of these conflicts. The results also underline the relevance of addressing conflicts in land use planning and governance, the need for appropriate conflict management, and the necessity of providing local actors with sufficient resources to deal with land use conflicts.

Causality analysis and prediction of soil saturated hydraulic conductivity by combining empirical modeling and machine learning techniques

Soil saturated hydraulic conductivity (Ks), as a fundamental property governing the behavior of water in the soil environment, is important to agriculture, environmental quality, water resources management, and engineering practices. However, the intricate interplay between Ks and environmental factors leads to substantial variability, making direct measurement costly and challenging. To provide a cost-effective indirect method with regard to Ks estimation, we proposed a portable modeling framework combing partial least squares structural equation modeling and artificial neural network (PLS-SEM-ANN). The framework was applied to a mountainous watershed characterized by humid climate, steep topography, and diverse land use. This approach demonstrated that the PLS-SEM-ANN was efficient in Ks modeling with a prediction accuracy of more than 80 % (R2 of 0.989 and 0.862 for model training and validation), which is comparable to that of the conventional artificial neural network (R2 of 0.993 and 0.843 for model training and validation). According to structural equation modeling, land use significantly affected Ks through direct (β = 0.237, p < 0.05) and indirect (β = 0.263, p < 0.05) ways, and soil properties, especially soil particle composition, were the most direct factor affecting Ks (β = 0.410, p < 0.01), while topography had a lesser effect on Ks (β = -0.023, p > 0.05). However, Shapley additive explanations (SHAP) analyses of PLS-SEM-ANN revealed significant nonlinear effects of topography on Ks, especially slope (SHAP mean absolute value = 0.92). Moreover, threshold testing showed that there was an abrupt change in the slope-Ks relationship at approximately 21°, with the effect of slope on Ks turning from positive to negative. This study provides an approach with explainable and transparent modeling results for Ks.

Role of the Built Environment, Roadway Characteristics, and Socioeconomic Factors in Keeping Teen Drivers Safe: An Investigation of the Factors Most Related to Teen Driver Fatalities

The role of engineering, through infrastructure design, is known to influence safe driving behavior generally, yet the areas of possible intervention that could improve safe outcomes for teens are less understood. It is critical to identify interventions specific to teens, as they have different driving risk factors than more seasoned drivers, and motor vehicle crashes are the second leading cause of death among teens in the United States. We examined 5,210 fatal crashes from 2015 to 2019 in Ohio using Fatality Analysis Reporting System data. We employed multinomial logistic regression to associate the likelihood of fatal crashes involving younger teens (16 to 17 years old) or older teens (18 to 19 years old) relative to adults (20 years and older) with built environment, socioeconomic, and roadway characteristics at crash locations. Separating crashes into the two groups of teens allowed us to distinguish the possible impact of training, as younger teens are mandated to undergo driving education and behind-the-wheel training under Ohio’s graduated driver licensing laws. We found that compared with neighborhood socioeconomics, built environment factors were more significant predictors of the likelihood of fatal crashes involving younger teens compared with adults. Specifically, fatal crashes at locations with lower road density and more diverse land use were more likely to involve younger teens. Although these results could be related to a mix of younger teen driver behavior and exposure factors, the finding highlights that improving the safety of local roads in urban and rural settings could help stem teen driving fatalities.