Search Results (68)

Land use change is a critical factor influencing regional carbon emissions, and understanding its spatiotemporal variability is essential for supporting science-based emission-reduction strategies. In this study, we constructed an improved measurement framework by integrating high-resolution land use data, gridded anthropogenic carbon emission data, multi-source remote sensing indicators, and socioeconomic variables to quantify land use carbon emissions (LUCEs) in the Chengdu–Chongqing Urban Agglomeration (CCUA) from 2000 to 2022. We analyzed the temporal trends and spatial clustering of carbon emissions using the Mann–Kendall (MK) trend test and global/local Moran’s I statistics, and further explored the driving mechanisms through the Geodetector (GD) model, including both single-factor explanatory power and two-factor interaction effects. The results show that total LUCEs in the CCEC increased continuously during the study period, with significant spatial clustering characterized by high–high emission hotspots in the core areas of Chengdu and Chongqing and low–low clusters in western mountainous regions. Socioeconomic factors played a dominant role in shaping emission patterns, with construction land proportion, nighttime light intensity, and population density identified as the strongest drivers. Interaction detection revealed nonlinear enhancement effects among key socioeconomic variables, indicating an increasing spatial lock-in of human activities on carbon emissions. These findings provide scientific evidence for optimizing land use structure and formulating region-specific low-carbon development policies in rapidly urbanizing megaregions. Full article

The SDG 15.3.1 framework provides a standardized approach using land use/land cover (LULC) change, land productivity, and soil organic carbon (SOC) dynamics to assess land degradation. However, SDG 15.3.1. faces limitations like coarse resolutions of Landsat-8 and Sentinel-2, particularly for fine-scale studies. Accordingly, this paper integrates Very Deep Super-Resolution (VDSR) for downscaling Landsat-8 imagery to 1 m resolution and the Vegetation Health Index (VHI) into SDG 15.3.1 to enhance detection in the heterogeneous Loiret region, France—a temperate agricultural hub featuring mixed croplands and peri-urban interfaces—using 2017 as baseline and 2024 as target. Results demonstrated that 1 m resolution detected more degraded LULC areas than coarser scales. SOC degradation was minimal (0.15%), concentrated in transitioned zones. VHI reduced overestimation of productivity declines compared to the Normalized Difference Vegetation Index by identifying more stable areas and 2.69 times less degradation in integrated assessments. The “One Out, All Out” rule classified 2.6% (using VHI) and 7.1% (using NDVI) of the region as degraded, mainly in peri-urban and cropland hotspots. This approach enables metre-scale land degradation mapping that remains effective in heterogeneous landscapes where fine-scale LULC changes drive degradation and would be missed at lower resolutions. However, future ground validation and longer timelines are essential to enhance the presented methodology. Full article

Heavy metal contamination in agricultural soils poses serious threats to food safety, ecosystem integrity, and public health. This study investigates the concentrations, ecological risks, and human health impacts of nine heavy metals Cr, Mn, Co, Ni, Cu, Zn, Pb, As, and V in homestead agricultural soils collected from two depths, surface (0–20 cm) and subsurface (21–50 cm), across industrial and non-industrial regions of Bangladesh, using inductively coupled plasma mass spectrometry (ICP-MS). Results revealed that surface soils from industrial areas exhibited the highest metal concentrations in order of Mn > Zn > Cr > Pb > V > Ni > Cu > As > Co. However, maximum As levels were detected in non-industrial areas, suggesting combined influences of local geology, intensive pesticide application, and prolonged irrigation with As-contaminated groundwater. Elevated concentrations in surface soils indicate recent contamination with limited downward migration. Multivariate statistical analyses indicated that industrial and urban activities are the major sources of contamination, whereas Mn remains primarily geogenic, controlled by natural soil forming processes. Contamination factor (CF) and pollution load index (PLI) analyses identified Pb and As as the principal pollutants, with hotspots in Nairadi, Majhipara (Savar), Gazipur sadar, and Chorkhai (Mymensingh). Ecological risk (ER) assessment highlighted As and Pb as the dominant environmental stressors, though overall risk remained low. Human health risk analysis showed that ingestion is the primary exposure pathway, with children being more susceptible than adults. Although the hazard index (HI) values were within the acceptable safety limits, the estimated carcinogenic risks for As and Cr exceeded the USEPA thresholds, indicating potential long term health concerns. Therefore, the cumulative carcinogenic risk (CCR) results demonstrate that As is the primary driver of lifetime carcinogenic risk in homestead soils, followed by Cr, while contributions from other metals are minimal. These findings emphasize the urgent need for continuous monitoring, improved industrial waste management, and targeted mitigation strategies to ensure safe food production, a cleaner environment, and better public health. Full article

Identifying areas that frequently experience post-rainfall ponding is essential for effective flood mitigation and planning. This study integrates Sentinel-1 radar imagery and the Topographic Control Index (TCI) to identify 378 flood-prone urban depressions in Beaumont, Texas. Out of 159 major rainfall events, only six had Sentinel-1 radar imagery acquired within six hours of peak rainfall, and these were used to generate the flood frequency map; the ground-based flood sensor data were used to verify that these selected events corresponded to actual peak rainfall and to validate radar-detected water pixels. Validation results showed 100% precision, 70.87% recall, an F1-score of 82.95%, and 71.32% overall accuracy. Approximately 84% of medium-to-high TCI depressions overlapped with Beaumont’s two-year inundation map, confirming a strong relationship between TCI and observed flooding. A total of 124 depressions retained significant water, and after excluding 25 engineered detention ponds, 99 natural depressions remained flood vulnerable. Among these, 74 depressions with medium or high TCI were identified as the highest-priority nuisance flooding hotspots. The results demonstrate that combining TCI with radar imagery provides a reliable and cost-effective approach for identifying areas prone to frequent urban ponding. This framework supports practical decision-making for drainage improvements, hotspot identification, and early-warning system development in urban flood-prone regions. Full article

Monitoring nitrate levels in water is critical to protect public health and ensure compliance with regulatory standards. This study provides a comprehensive evaluation of four analytical techniques—test strips, ion-selective electrodes (ISE), colorimetric methods, and titration—to assess nitrate levels in a variety of water sources, including standard solutions, rainwater, bottled water, and groundwater from both shallow and deep wells located in semi-urban regions of Saudi Arabia. Each method was assessed for sensitivity, accuracy, detection limits, reproducibility, and operational practicality. Test strips offer rapid, low-cost screening but consistently underestimate nitrate concentrations, particularly at low levels. The ISE demonstrated broad applicability and reliable performance across a wide concentration range when properly calibrated, making it suitable for both field and laboratory applications. Colorimetric methods provide excellent sensitivity for trace-level detection, whereas titration delivers the highest accuracy for high-nitrate samples despite its time-intensive nature. By calibrating and validating the methods against certified standards, we quantitatively demonstrated their reliability through statistical measures such as precision and accuracy rates. Moreover, the application of Geographic Information System (GIS) techniques in spatial analysis has revealed significant differences in the distribution of nitrates. Notably, shallow wells located in the northern regions surpass the 50 mg/L limit set by the World Health Organization (WHO), thereby indicating the presence of localized contamination hotspots. This study is among the first to systematically compare nitrate detection methods across a wide range of water types in a semi-urban area of Saudi Arabia. Building on a detailed analysis of each method, we underline the crucial need for the strategic selection of nitrate analysis techniques. This selection should be tailored to specific operational contexts, accuracy requirements, and concentration ranges to guide stakeholders towards more informed decision-making. These findings provide actionable guidance for public health officials and water managers to prioritize monitoring, safeguard drinking-water sources, and mitigate nitrate-related health risks in semi-urban communities. Full article

This study leverages multi-source remote sensing data—Nighttime Light (NTL) imagery and POI (Point of Interest) datasets—to quantify the spatiotemporal interaction between urban spatial restructuring and logistics industry evolution in Zhengzhou, China. Using calibrated NPP/VIIRS NTL data (2012–2022) and fine-grained POI data, we (1) identified urban functional spaces through kernel density-based spatial grids weighted by public awareness parameters; (2) extracted built-up areas via the dynamic adaptive threshold segmentation of NTL gradients; (3) analyzed logistics agglomeration dynamics using emerging spatiotemporal hotspot analysis (ESTH) and space–time cube models. The results show that Zhengzhou’s urban form transitioned from a monocentric to a polycentric structure, with NTL trajectories revealing logistics hotspots expanding along air–rail multimodal corridors. POI-derived functional spaces shifted from single-dominant to composite patterns, while ESTH detected policy-driven clusters in Airport Economic Zones and market-driven suburban cold chain hubs. Bivariate LISA confirmed the spatial synergy between logistics growth and urban expansion, validating the “policy–space–industry” interaction framework. This research demonstrates how integrated NTL-POI remote sensing techniques can monitor policy impacts on urban systems, providing a replicable methodology for sustainable logistics planning. Full article

The main objective of this study was to select indigenous fungal species suitable for the potential mycoremediation of the soils polluted by organic pollutants. As a sampling area, Litvínov City (North Bohemia, Czech Republic) was selected. The city is characterized by intensive coal mining, coal processing, and the chemical industry, predominantly petrochemistry. The elevated contents of persistent organic pollutants (POPs) such as polyaromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) were identified in urban soils due to the long-term industrial pollution. The results confirmed elevated contents of PAHs in all the analyzed soil samples with high variability ranging between 0.5 and 23.3 mg/kg regardless of the position of the sampling area on the city map. PCBs and PCDD/Fs exceeded the detection limits in the soil at the sampling points, and several hotspots were revealed at some locations. All the sampling points contained a diverse community of saprotrophic and mycorrhizal fungi, as determined according to abundant basidiomycetes. Fungal species with a confirmed ability to degrade organic pollutants were found, such as species representing the genera Agaricus from the Agaricaceae family, Coprinopsis from the Psathyrellaceae family, Hymenogaster from the Hymenogasteraceae family, and Pluteus from the Pluteaceae family. These species are accustomed to particular soil conditions as well as the elevated contents of the POPs in them. Therefore, these species could be taken into account when developing potential bioremediation measures to apply in the most polluted areas, and their biodegradation ability should be elucidated in further research. The results of this study contribute to the investigation of the potential use of fungal species for mycoremediation of the areas polluted by a wide spectrum of organic pollutants. Full article

As urban traffic systems become increasingly complex, traditional traffic data collection methods based on fixed detectors face challenges such as poor data quality and acquisition difficulties. Traditional methods also lack the ability to capture complete vehicle path information essential for signal optimization. While vehicle trajectory data can provide rich spatiotemporal information, its sampling characteristics present new technical challenges for traffic parameter extraction. This study addresses the key issue of extracting traffic parameters suitable for signal timing optimization from sampled trajectory data by proposing a comprehensive method for traffic parameter extraction and analysis based on vehicle trajectory data. The method comprises five modules: data preprocessing, basic feature processing, exploratory data analysis, key feature extraction, and data visualization. An innovative algorithm is proposed to identify which intersections vehicles pass through, effectively solving the challenge of mapping GPS points to road network nodes. A dual calculation method based on instantaneous speed and time difference is adopted, improving parameter estimation accuracy through multi-source data fusion. A highly automated processing toolchain based on Python and MATLAB is developed. The method advances the state of the art through a novel polygon-based trajectory mapping algorithm and a systematic multi-source parameter extraction framework specifically designed for signal control optimization. Validation using actual trajectory data containing 2.48 million records successfully eliminated 30.80% redundant data and accurately identified complete paths for 7252 vehicles. The extracted multi-dimensional parameters, including link flow, average speed, travel time, and OD matrices, accurately reflect network operational status, identifying congestion hotspots, tidal traffic characteristics, and unstable road segments. The research outcomes provide a feasible technical solution for areas lacking traditional detection equipment. The extracted parameters can directly support signal optimization applications such as traffic signal coordination, timing optimization, and congestion management, providing crucial support for implementing data-driven intelligent traffic control. This research presents a theoretical framework validated with real-world data, providing a foundation for future implementation in operational signal control systems. Full article

This study proposes a novel urban heat island detection method implemented in a GIS-based framework, designed to identify the most critical urban areas during heatwave events. The framework employs the fuzzy C-means clustering algorithm with remotely sensed land surface temperature and normalized difference vegetation index data to delineate and visualize hotspots. The proposed approach is compared with other established methods for urban heat island detection to evaluate their relative accuracy and effectiveness. This methodology integrates advanced spatial analysis with environmental indicators such as vegetation cover and permeable open spaces to assess urban vulnerability. The city of Naples, Italy, serves as a case study for testing the framework. The results from the case study indicate that the proposed method outperforms alternative methods in identifying heat hotspots, providing higher accuracy and suggesting potential adaptability to other urban contexts. This GIS-based approach not only provides a robust tool for urban climate assessment but also serves as a decision support framework that enables urban planners and policymakers to identify critical areas and prioritize interventions for climate adaptation and mitigation. Full article

High concentrations of pollutants in urban areas generate cardiovascular and respiratory problems in citizens; these are aggravated by the persistence of summer heatwaves. For this reason, in this research, we propose a fuzzy-based method for detecting air pollutant hotspots and determining critical urban areas for air pollution during heatwaves. After acquiring the pollutant concentration values recorded by monitoring stations during heatwaves, a spatial interpolation method is applied to obtain the distribution of the pollutant concentration during heatwaves and, subsequently, a fuzzification process is performed to determine urban hotspots in which the pollutant concentration assumes critical values. Finally, the critical urban areas are determined, consisting of the areas within hotspots with a high population density exposed to health risks. The method was implemented in a GIS platform and tested on an urban study area in the Lombardy region, Italy, to determine the urban areas with high criticality during the heatwaves that occurred in the summer months of 2024. The test results show that the method can provide valid support for decision makers and local administrators when evaluating which urban areas are most critical for the population due to the high rate of air pollution during heatwaves. Full article

Forests provide essential ecosystem services but face increasing threats from invasive species like Toumeyella parvicornis (pine tortoise scale). Since its introduction to Italy in 2014, this pest has severely impacted Pinus pinea forests, with a major outbreak in 2019 affecting an urban forest in the Rome municipality area. This study aims to develop a tool for detecting forest dieback symptoms caused by the scale and assess the role of prevailing winds in its dispersal by integrating multispectral and hyperspectral earth observation systems, including Sentinel-2 and the Hyperspectral Precursor of the Application Mission (PRISMA). At a 6000-hectare protected area with diverse vegetation, a binary Random Forest classifier, trained on near-infrared and short-wave infrared reflectance data, identified symptomatic stands. A generalized linear mixed model compared uniform and wind-influenced probabilistic dispersal models, assessing the pest spread relative to the initial infestation hotspot. The results confirmed a sharp decline in near-infrared reflectance in 2019, indicating severe defoliation and a shift from evergreen to deciduous canopy phenology by 2021. The classifier achieved 82% accuracy, effectively detecting symptomatic pine forests (91% precision). The scale spread to 51% of the pine forest area by 2021, with no strong correlation to prevailing winds, suggesting other augmenting dispersal drivers, such as vehicles along congested routes, wind tunnels, pest-resistant forests, and the potential mitigating role of alternating coastal wind patterns that are effective in the study area. Full article

Leptospirosis is a zoonotic disease of global public health importance caused by bacteria of the genus Leptospira. Small non-flying mammals are important reservoirs of the pathogen. The Brazilian Atlantic Forest is a biodiversity hotspot located in a densely populated area and subject to intense degradation. Although documented through serosurveys and the detection of leptospiral DNA in wild small mammals, no study has performed a genetic characterization of the bacteria in the region. The present study aimed to evaluate the genetic diversity of pathogenic leptospires identified in small non-flying mammals in the Southeast Atlantic Forest and to perform intraspecific genetic inferences with other hosts. The studied area included five different conservation units. Molecular diagnosis was performed based on the lipl32 gene. The SLST typing method was applied based on the secY gene. In total, 56% of samples were lipL32-PCR-positive and identified as L. interrogans, with a high genetic identity among them, distributed in four main haplogroups. The largest haplogroup also included reference sequences from humans, dogs, and urban rats, all belonging to the Icterohaemorrhagiae serogroup. Our results reinforce the role of small mammals as important carriers of L. interrogans and highlight the Atlantic Forest as a significant environment for the circulation and dissemination of spirochetes with zoonotic potential. Full article

The primary aim of this study was to evaluate the spatial and temporal variation in human exposure to electromagnetic fields across different frequency bands within an urban area identified as the commercial zone of a medium-sized city. Central to this investigation was the use of an exposimeter, strategically positioned on the back of the operator and secured to the hip area via a belt, to ensure comprehensive and accurate field measurements. An initial analysis was conducted to determine the shielding coefficients of the human body, allowing for precise corrections of the electric field values used in the spatial assessment. To map power density across the study area for each frequency, kriging interpolation was applied. Furthermore, temporal variations in exposure levels were analyzed at three distinct times of day—morning business hours, afternoon business hours, and non-business hours—using robust statistical methods. The study’s innovative approach lies in the integration of GIS technology to uncover and visualize temporal patterns in exposure, particularly during periods of higher pedestrian density. This integration facilitated both the detection of temporal variations and the spatial representation of these changes, enabling rapid identification and assessment of exposure hotspots. Full article

We measured the spatiotemporal dynamics of habitat quality (HQ) in Sicily in two different reference years, 2018 and 2050, assuming a business-as-usual scenario. To estimate HQ and related vulnerability, we used the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) Habitat Quality model and data on land use/land cover provided by the Esri Land Cover 2050 project. We also implemented a Coarse–Filter approach to validate the reliability of HQ measures and detect biodiversity hotspots that require priority conservation. Further, we used spatial statistic tools for identifying clusters or hotspot/coldspot areas and uncovering spatial autocorrelation in HQ values. Finally, we implemented a geographically weighted regression (GWR) model for explaining local variations in the effects on HQ estimates. The findings reveal that HQ in Sicily varies across space and time. The highest HQ values occur in protected areas and forests. In 2018, the average HQ value was higher than it was in 2050. On average, HQ decreased from 0.29 in 2018 to 0.25 in 2050. This slight decline was mainly due to an increase in crop and urbanized areas at the expense of forests, grasslands, and bare lands. We found the existence of a positive spatial autocorrelation in HQ, demonstrating that areas with higher or lower HQ tend to be clustered, and that clusters come into contact randomly more often in 2050 than in 2018, as the overall spatial autocorrelation moved from 0.28 in 2018 to 1.30 in 2050. The estimated GWR model revealed the sign and the significance effect of population density, compass exposure, average temperature, and patch richness on HQ at a local level, and that such effects vary either in space and time or in significance level. Across all variables, the spatial extent of significant effects intensifies, signaling stronger localized influences in 2050. The overall findings of the study provide useful insights for making informed decisions about conservation and land planning and management in Sicily. Full article

The rational allocation of urban public open spaces (UPOS) is critical for creating a livable urban environment. Traditional Two-Step Floating Catchment Area (2SFCA) models often lack sufficient quantitative analysis regarding the supply of urban public service facilities and population demand. This study, taking the area within Chengdu’s Second Ring Road as an example, proposes a 2SFCA model that integrates both supply and demand improvements to evaluate UPOS accessibility. The accessibility results are further analyzed using hotspot analysis, and blind zone detection. In terms of supply improvements, the model incorporates additional indicators beyond the spatial area of UPOS, including service quality and the diversity of surrounding environmental service functions, to better evaluate the overall attractiveness of UPOS to residents. On the demand side, besides population size, the model incorporates the spatial distribution of residents and differences in social characteristics affecting UPOS demand. Results indicate that the improved 2SFCA model, which considers both the attractiveness of UPOS and residents’ demand, significantly enhances the accuracy of accessibility assessments. There are substantial differences in service quality among UPOS, while the diversity of surrounding environmental service functions remains generally high. UPOS demand follows a “high in the northeast—low in the southwest” spatial pattern. The spatial distribution of UPOS accessibility shows a “high in the west—low in the east” pattern, opposite to the demand distribution, indicating a supply–demand mismatch. UPOS accessibility identifies one hotspot cluster and four cold spot clusters, with large areas showing no significant characteristics. Additionally, 10.58% of the study area remains blind zones, requiring urgent attention. This study offers a more scientific method and framework for research on the spatial layout and supply–demand matching of UPOS. Full article