Study Area Research Articles

Expanding our knowledge of the coffee family in continental Africa: a synopsis of the genus Pyrostria (Rubiaceae)

SummaryA summary of the species of Pyrostria from continental Africa is presented, including full descriptions of three new species, P. makovui, P. namacubiensis and P. rovumensis from the coastal forests of the Tanzania-Mozambique border region, and one new subspecies, P. lobulata subsp. bridsoniae from Zambia. Pyrostria makovui is the first species with plurilocular (3-lobed) fruits and the second hermaphrodite species for the study area, characters that are otherwise found mainly in taxa of Pyrostria from Madagascar and surrounding islands. Five of the 16 species treated in this synopsis remain undescribed because they are known only from incomplete material. A key to the species of Pyrostria occurring in continental Africa is presented and notes on the distribution, habitat and relevant taxonomic considerations are provided, along with an assessment of the conservation status (extinction risk) for each species. The Coastal Forests of Eastern Africa Biodiversity Hotspot is found to be a centre of diversity for Pyrostria, with the Rovuma Centre of Plant Endemism in Mozambique and Tanzania being particularly rich in endemic species.

Quantifying Soil Erosion in Drought-Impacted Central Odisha, India, through Geospatial Mapping with RUSLE

Soil degradation in Odisha poses a significant conservation concern for the local environment. The present research focused on a region in central Odisha State, India, affected by drought conditions. Several models have emerged to assess soil loss, with the Revised Universal Soil Loss Equation (RUSLE) standing out as the most suitable option. The erosion computation process entails utilizing the digital elevation model (DEM), Landsat-9 imagery, and soil data from several sources accessible in different forms and scales. The present analysis took into account various elements, namely, crop management factor (C), practice management factor (P), slope length factor (LS), steepness factor of the slope (S), and rainfall factor (R). Multiplying these factors yielded the average rate of soil erosion. Areas with a high slope length factor, such as those in Kandhamal, Kalahandi, and Nuapada, have a high erosion rate. The study reveals that 57% of the land in the study area experiences very low to moderate soil erosion at a rate of 2–10 tons per hectare per year, while 43% faces moderately to very severe erosion at a rate of 10–25 tons per hectare per year. Erosion hotspots, covering 32,205 square kilometers, are mainly identified in agricultural and forested hilly areas where slopes exceed 10°, such as those in Kandhamal, Kalahandi, and Nuapada, which have a high erosion rate. These districts are especially vulnerable to soil loss and resulting climate action (Sustainable Development Goals-13) because of frequent and severe rainfall, shifting agricultural practices, a thin surface soil covering, natural erosion, and barren hills. The research emphasizes the urgent need for implementing conservation and management measures to protect high-risk areas from further degradation. In conclusion, the study underscores the effectiveness of the RUSLE-GIS model in conducting quantitative and spatial assessments of soil erosion on a river watershed scale. The model is deemed crucial in formulating conservation strategies to address the identified erosion issues in the tropical highlands of the area.

Large-scale Wi-Fi and Bluetooth data collection for reconstructing passenger flows

ABSTRACT For a public transport operator, estimating passenger flows is vital to plan inclusive and efficient service. Understanding where and when to optimally allocate resources results in better customer experiences, further shaping and optimizing public transport services. To this end, the project Mobile Data Fusion aims at developing a technique to collect, process, and merge data from different sources. The data are used to inform public transport operators about passenger demand, by combining Wi-Fi and Bluetooth signals from customer devices with time-of-flight counting sensors. The project had access to a large study area in Kassel, Germany, with a fleet of 50 vehicles servicing eight passenger lines, all equipped with automatic passenger counting sensors. In the course of this study, we found out that Bluetooth signals did not offer any significant insight , while Wi-Fi signals could be used to reconstruct a trend, albeit not the absolute occupancy level. In this paper, we present our hardware setup and a subset of our data set to show how the Wi-Fi and Bluetooth signals correlate with the measured occupancy. The ongoing data collection will provide additional material for future studies, and answer some of the pending questions discussed in this work.

Integrating post-event very high resolution SAR imagery and machine learning for building-level earthquake damage assessment

AbstractEarthquakes have devastating effects on densely urbanised regions, requiring rapid and extensive damage assessment to guide resource allocation and recovery efforts. Traditional damage assessment is time-consuming, resource-intensive, and faces challenges in covering vast affected areas, often limiting timely decision-making. Space-borne synthetic aperture radars (SAR) have gained attention for their all-weather and day-night imaging capabilities. These advantages, coupled with wide coverage, short revisits and very high resolution (VHR), have created opportunities for using SAR data in disaster response. However, most SAR studies for post-earthquake damage assessment rely on change detection methods using pre-event SAR images, which are often unavailable in operational scenarios. Limited studies using solely post-event SAR data primarily concentrate on city-block-level damage assessment, thus not fully exploiting the VHR SAR potential. This paper presents a novel method integrating solely post-event VHR SAR imagery and machine learning (ML) for regional-scale post-earthquake damage assessment at the individual building-level. We first used supervised learning on case-specific datasets, and then introduced a combined learning approach, incorporating inventories from multiple case studies to assess generalisation. Finally, the ML model was tested on unseen study areas, to evaluate its flexibility in unfamiliar contexts. The method was implemented using datasets collected during the Earthquake Engineering Field Investigation Team (EEFIT) reconnaissance missions following the 2021 Nippes earthquake and the 2023 Kahramanmaraş earthquake sequence. The results demonstrate the method’s ability to classify standing and collapsed buildings, achieving up to 72% overall accuracy on unseen regions. The proposed method has potential for future disaster assessments, thereby contributing to more effective earthquake management strategies.

Traceability Analysis and Environmental Quality Assessment of Soil Heavy Metal Pollution in West Hunan Province

In order to explore the status of soil heavy metal pollution and environmental quality in west Hunan, relevant areas of Phoenix County were selected as the study area. Using data from 440 soil samples collected in the study area from June to August 2022, the pH value of the soil and contents of eight heavy metal elements, namely, As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn, were analyzed. The PMF model was used for traceability analysis and geochemical evaluation of soil environmental quality. The results showed that the average values of soil heavy metals ω(Zn), ω(Cr), ω(Pb), ω(Ni), ω(Cu), ω(As), ω(Cd), and ω(Hg) were 81.02, 64.67, 31.63, 29.27, 25.52, 9.93, 0.28, and 0.13 mg·kg-1, respectively. The soil in the study area was mainly weakly acidic, and the contents of the Cd and Hg elements were relatively high compared to the national soil background values and were highly variable. The contents of the Hg and Cd elements in forest land were higher than that in other land uses. The PMF model results showed that the contribution rates of heavy metal pollution sources in the study area were mining sources (37.4%), atmospheric sedimentation sources (7.7%), natural sources (41.1%), and agricultural activity sources (13.8%) and provided suggestions on pollution control measures according to the spatial distribution of the four types of pollution sources. Through the comprehensive assessment of soil environmental geochemistry, the study area was divided into three types of plots, namely, non-risk areas (94.27 km2), accounting for 76.38%; risk-controllable areas (27.45 km2), accounting for 22.24%; and high-risk areas (1.7 km2), accounting for 1.38%. This study provided data support for the prevention and control measures of land pollution in the research area, as well as the delineation of the prevention and control scope.

Ecological Environment Assessment and Driving Mechanism Analysis of Nagqu and Amdo Sections of Qinghai-Xizang Highway Based on Improved Remote Sensing Ecological Index

The ecological environment along the Qinghai-Xizang highway is an important part of the construction of the ecological civilization in the Xizang region, and current research generally suffers from difficulties in data acquisition, low timeliness, and failure to consider the unique "alpine saline" environmental conditions in the study area due to the unique geographical environment of the Qinghai-Xizang plateau. Based on the GEE platform and the unique geographical environment of the study area, the remote sensing ecological index (RSEI) was improved, and a new saline remote sensing ecological index (SRSEI) applicable to the alpine saline region was constructed by using principal component analysis as an ecological environment quality evaluation index. The spatial distribution pattern and temporal variation trend of ecological environment quality along the Qinghai-Xizang Highway Nagqu-Amdo section were analyzed at multiple spatial and temporal scales using the ArcGIS 10.3 platform and geographic probes, and the driving mechanisms of eight control factors, including natural and human-made, on the spatial and temporal changes in SRSEI were investigated. The results showed that:① compared with RSEI, SRSEI was more sensitive to vegetation and had a stronger discriminatory ability in areas with sparse vegetation and severe salinization, which is suitable for ecological quality evaluation in alpine saline areas. ② The spatial scale of ecological environment quality in the study area had obvious geographical differentiation, and the areas with poor ecological quality were mainly concentrated in the northern Amdo County, whereas the areas with excellent and good quality grades were mainly distributed in the central-western and southeastern Nagqu areas. On the temporal scale, the ecological environment of the study area as a whole showed an improvement trend over 32 years, and the vegetation cover in the central-western and southeastern areas increased significantly, which had a strong improvement effect on the ecological environment. The improvement area was 1 425.98 km2, accounting for 99.82%. The mean value of SRSEI was 0.49, with an overall fluctuating upward trend and an average increase of 0.015 7 a-1. ③ The land use pattern was the most driving influence factor in the change of ecological environment quality in the study area, with an average q value of 0.157 6 over multiple years, and the influence of environmental factors was low. The multi-factor interaction results showed that the ecological environment in the study area was the result of multiple factors acting together, all factors had synergistic enhancement under the interaction, the influence of human factors was gradually increasing, and the interaction of the net primary productivity (NPP) of vegetation and land use pattern was the main interactive control factor of ecological environment quality in the study area. This study can provide a theoretical basis for ecological environmental protection and sustainable development along the Nagqu to Amdo section.

Analysis of Spatio-temporal Distribution Characteristics and Influencing Factors of PM2.5 Concentration in Urban Agglomerations on the Northern Slope of Tianshan Mountains

Analysis of the spatial and temporal distribution characteristics and influencing factors of PM2.5 concentrations for the urban agglomeration on the northern slope of Tianshan Mountain is of positive significance for regional economic construction and environmental protection. The spatial and temporal distributions of PM2.5 concentrations in the Tianshan North Slope urban agglomeration from March to November 2015 to 2021 were obtained through the inversion of the MCD19A2 aerosol product combined with meteorological factors using a geographically weighted regression (GWR) model, followed by the analysis of change trends and influencing factors. The results were as follows:① the high PM2.5 concentrations in the study area were mainly distributed in the oasis city cluster between the northern foot of Tianshan Mountain and the Gurbantunggut Desert, showing the spatial distribution characteristics of being "low around and high in the middle" and "low in the west and high in the east." The annual average value of ρ(PM2.5) in the study area was 16.98 μg·m-3, with high values mainly concentrated in the urban part of Urumqi and decreasing towards Changji and Fukang. The monthly average ρ(PM2.5) distribution pattern was consistent with the annual average, but there were seasonal differences as follows:autumn (20.32 μg·m-3) > spring (18.25 μg·m-3) > summer (12.47 μg·m-3). The accumulation phenomenon was more pronounced in spring and winter. ② The study area's annual average PM2.5 concentration showed a decreasing trend from 2015 to 2021, and the average value from March to October also showed a decreasing trend, with only a slight increase in November. From the analysis of the spatial distribution of PM2.5 concentration trends, the decrease was concentrated in the urban parts of major cities, especially in the urban part of Urumqi and its surrounding areas, where the decrease was the largest and the change was the most drastic. ③ Temperature and air pressure were positively correlated with PM2.5 concentrations, whereas relative humidity, wind speed, atmospheric boundary layer height, and precipitation were negatively correlated with PM2.5 concentrations. The degree of influence of each factor was ranked from high to low as follows:atmospheric boundary layer height > relative humidity > air pressure > air temperature > wind speed > precipitation.

Distribution Characteristics, Source Analysis and Ecological Risk Assessment of Heavy Metals in the Typical Industry Reclaimed Soil

To further explore the characteristics of heavy metal pollution and the ecological risk of typical industries in reclaimed soil, based on data from 315 different depth profiles of soil samples collected from 49 plots in Jiading District, Shanghai, the geo-accumulation index and potential ecological risk index were used to evaluate the contents and potential ecological risk of seven heavy metals, namely Cd, Pb, Cu, Zn, Ni, Hg, and As. The APCS-MLR receptor model and PMF positive matrix factorization model were employed to analyze the pollution sources. The results showed that:① except for As, the contents of other heavy metals in the soil of the study area exceeded the Shanghai soil background values to varying degrees. The contents of Cd, Pb, Cu, Zn, Ni, and Hg in the surface soil were 3.54, 2.34, 2.91, 1.20, 3.75, and 4.40 times the background values, respectively. The contents of heavy metals in the soil decreased with the increase in depth, and heavy metals were enriched to a certain extent in the surface soil, indicating that human activities had an impact on the distribution of heavy metals in the soil. ② The APCS-MLR and PMF receptor models identified four main sources of soil heavy metals in the study area. Source 1 (Cu, Zn, and Pb) was a mixture of metal products and automobile manufacturing, source 2 (Ni and Cd) was electroplating enterprises, source 3 (Hg) was mainly from chemical enterprises, and source 4 (As) was natural. The combined use of the two receptor models further improved the accuracy and credibility of source identification. ③ The geo-accumulation index in descending order was Hg(1.54)>Ni(1.32)>Cd(1.21)>Cu(0.96)>Pb(0.64)>Zn(-0.33)>As(-1.02). The potential ecological risk index showed that the comprehensive potential ecological risk index RI value in the study area ranged from 32.50 to 4 910.97, with a mean of 321.40, indicating a strong potential ecological risk. The pollution values of heavy metals Hg, Ni, and Cd in industrial site soil deserve further attention for re-development and utilization purposes.

Numerical Simulation of Storm Surge Inundation in Estuarine Area Considering Multiple Influencing Factors

With global climate change, the risk of extreme storms and storm surges in estuarine areas is increasing; thus, storm surge inundation research and prediction have become important issues to ensure sustainable development in estuarine areas. The Jitimen estuary in Guangdong Province, China, was chosen as our study area. In this study, a numerical model for simulating storm surge inundation in small regions based on unstructured triangular grids was established, and the model accuracy was validated. The typhoon characteristics in the study area were statistically analyzed based on historical data. Three experimental schemes, involving factors influencing storm surge inundation, such as typhoon landfall location, intensity, and direction, were used to evaluate the differences in the numerical results. The results showed that when the typhoon landfall direction remained unchanged and the highest tide levels at the Sanzao tide gauge station were similar, the differences between the numerical results for the typhoon landfall location and typhoon intensity schemes were less than 5%, and the inundation characteristics were similar. However, when the typhoon location and intensity were unchanged and the highest tide levels at the Sanzao tide gauge station were similar, the numerical results for the typhoon landfall direction scheme significantly differed; this result was caused by the difference in the duration of the high tide level (exceeding 3 m); these results indicated that the topographic characteristics and the typhoon landing direction had a greater impact on storm surge inundation. The results from this study can aid in the prediction of storm surge inundation information for the Jitimen estuary area when the typhoon landing direction and the maximum tide level are known.

Characteristics and Identification Priority Source of Heavy Metals Pollution in Farmland Soils in the Yellow River Basin

Assessments of the soil environmental quality of farmland and pollution source apportionment are the foundation for ensuring national food security and agricultural sustainable development, as well as an important prerequisite for the pursuit to keep our lands clean. This study evaluated the characteristics of heavy metal pollution in farmland soils in the Yellow River Basin from 2000 to 2023, based on the data of heavy metal contents including As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn, using the geo-accumulation index method. Source apportionment was conducted by employing a positive matrix factorization (PMF) model. The probabilistic health risks were evaluated by coupling Monte Carlo simulation with a human health risk assessment model, and priority pollution sources and elements were identified. The results showed that:① the average content of all heavy metals in farmland soils within the study area was lower than the screening values specified in the soil environment quality risk control standard for soil contamination of agriculture land (GB 15618-2018) (pH>7.5). However, the contents of Cd, As, and Zn in the samples exceeded their screening values, with percentages of 21.69%, 5.56%, and 1.23%, respectively, with Cd having the highest rate of exceedance. ② Hg and Cd were moderately polluted, Cu and Pb were slightly polluted, and the other elements were not polluted. ③ The main sources of heavy metals in farmland soil were traffic-industrial sources, natural-agricultural sources, industrial-natural sources, and agricultural-industrial sources, with contribution rates of 37.04%, 26.69%, 21.72%, and 14.55%, respectively. ④ Heavy metals in farmland soil posed carcinogenic health risks to adults and children but did not have non-carcinogenic risks; As and Cd were priority control elements for human health risks, and industrial-natural sources and agricultural-industrial sources were priority control sources in the study area.

Effects of the Construction of Granadilla Industrial Port in Seagrass and Seaweed Habitats Using Very-High-Resolution Multispectral Satellite Imagery

Seagrass and seaweed meadows hold a very important role in coastal and marine ecosystems. However, anthropogenic impacts pose risks to these delicate habitats. This paper analyses the multitemporal impact of the construction of the largest industrial port in the Canary Islands, near the Special Area of Conservation Natura 2000, on Cymodocea nodosa seagrass meadows (sebadales) of the South of Tenerife, in the locality of Granadilla (Canary Islands, Spain). Very-high-resolution WorldView-2 multispectral satellite data were used for the analysis. Specifically, three images were selected before, during, and after the construction of the port (2011, 2014, and 2022, correspondingly). Initially, advanced pre-processing of the images was performed, and then seabed maps were obtained using the machine learning K-Nearest Neighbors (KNN) supervised classification model, discriminating 12 different bottom types in Case-2 complex waters. The maps achieved high-quality metrics with Precision values of 85%, 81%, and 80%, recall of 76%, 77%, and 77%, and F1 scores of 80%, 79%, and 77% for 2011, 2014, and 2022, respectively. The results mainly show that the construction directly affected the seagrass and seaweed habitats. In particular, the impact of the port on the meadows of Cymodocea nodosa, Caulerpa prolifera, and maërl was assessed. The total maërl population was reduced by 1.9 km2 throughout the study area. However, the Cymodocea nodosa population was maintained at the cost of colonizing maërl areas. Furthermore, the port sedimented a total of 0.98 km2 of seabed, especially Cymodocea nodosa and maërl. In addition, it was observed that Caulerpa prolifera was established as a meadow at the entrance of the port, replacing part of the Cymodocea nodosa and maërl areas. As additional results, bathymetric maps were generated from satellite imagery with the Sigmoid model, and the presence of a submarine outfall was, as well, presented.

Bioaccumulation of metals in Spartina alterniflora salt marshes in the estuary of the World's Largest Choked Lagoon.

Salt marshes are capable of mitigating metal pollution in coastal environments, yet the efficacy of this remediation is contingent upon various environmental factors and the plant species involved. This study investigates the influence of different anthropogenic activities, including industrial, urban, recreational (in an insular area), and dredging operations, on the bioaccumulation of eight metals (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) within Spartina alterniflora Loisel. in the Patos Lagoon estuary, Brazil. The research aims to assess the pattern of metal bioaccumulation and distribution within the plant's leaves, stems, and roots while also examining metal presence in the sediment. Our main findings reveal that S. alterniflora exhibited elevated metal levels in its plant structure directly related with the metal concentrations in the surrounding sediment, which, in turn, is related to the different anthropogenic activities. The industrial area presented the highest metal levels in sediment and plant sections, followed by dredging, insular, and urban areas. This same pattern was mirrored for the bioconcetration factors (BCF), with the BCFs consistently indicating active metal bioaccumulation across all areas and for most of the metals. This provides evidence of the metal bioaccumulation pattern in S. alterniflora, with elevated BCFs in areas affected by activities with a higher degree of impact. Translocation factors (TF) showed varying metal mobility patterns within the plant's below-ground and above-ground sections across the different areas, with only Hg exhibiting consistent translocation across all study areas. Zn was the primary metal contributor in all plant sections, followed by Pb and Cu. It is worth noting that Pb is a non-essential metal for this plant, highlighting the relationship between elevated Pb contributions in the plant sections and the bioaccumulation of this metal within the plant's structure. Overall, this study emphasizes the bioaccumulation capacity of S. alterniflora and elucidate the intrinsic connection between different anthropogenic activities and their impact on the resultant availability and bioaccumulation of metals by this salt marsh plant.

SPATIALLY AWARE LANDSLIDE SUSCEPTIBILITY PREDICTION USING A GEOGRAPHICAL RANDOM FOREST APPROACH

Abstract. Landslide susceptibility prediction practices have been increasingly reliant on non-geographically-oriented (i.e., aspatial) machine learning algorithms. While these approaches have exhibited increasing success, they have often faced criticism for their limited consideration of spatial autocorrelations and local variations across geographical space, thereby neglecting the concept of spatial non-stationarity. To fulfill the research gap, this work applies a geographical random forest (GRF) approach, contrasting it with the conventional random forest (RF) algorithm. To this end, the study area, encompassing the Lake Sapanca Basin and its surroundings, was subdivided into 4,452 slope-based mapping units. The effectiveness of both predictive models was then measured by using overall accuracy (OA) and area under the curve (AUC). The results revealed that the GRF (OA = 80.82% and AUC = 85.22%) outperformed the RF algorithm (OA = 75.34% and AUC = 82.50%) by approximately 5% in OA, and demonstrated a 3% improvement in AUC score. The Wilcoxon signed-rank test confirmed significant differences (95% level) between the predictions of both models. The slope parameter emerged as the globally most influential factor, but local interpretations disclosed notable variations in the importance of causative factors contingent upon location. For instance, the curvature parameter was the most important geospatial covariate in around one-third (34.23%) of the slope units, mostly concentrated in the northernmost zones of the study area. On the other hand, elevation was the most important factor for 14.67% of the slope units primarily located in the southern region.

Change and Prediction of Water Purification Function in the South Bank of Hangzhou Bay in the Past 20 Years

The investigation of regional water purification functionality and its influencing factors holds significant pragmatic implications in understanding the potential of regional water purification, guiding context-specific regional comprehensive planning schemes, and environmental conservation measures. The study site, situated along the southern coast of Hangzhou Bay, represents a prototypical region characterized by intricate land-sea interactions that bear substantial economic and ecological functions. By assimilating a meticulously collected topographical and land-use dataset, in conjunction with site-specific meteorological records, the water purification model embedded within the integrated valuation of ecosystem services and trade-offs (InVEST) framework was employed to scrutinize the spatiotemporal dynamics of nitrogen (N) and phosphorus (P) loads, discharges, and removals within the southern coast of Hangzhou Bay. The prime objective of this study was to unravel the differentials in water purification functionality under diverse developmental scenarios. The investigation unearthed distinct temporal discrepancies in N and P discharges and removals over two temporal dimensions. Relative to the benchmark year 2000, the total N load experienced a reduction of 276.72 t, whereas the N discharge and removals decreased by 140.86 and 137.86 t, respectively, in the year 2020. In contrast, the total P load observed an increase of 93.65 t, accompanied by a surge in P discharge and removals by 28.91 and 64.74 t, respectively. Spatially, the distribution pattern of N and P discharges exhibited a general inclination of elevated values in the northern region and subdued values in the southern region, with certain pockets in the southern region exhibiting pronounced peaks, intimately associated with land-use typologies. Simulation analyses conducted under distinct scenarios unveiled that under the natural development priority scenario, the N and P discharges within the study area amounted to 1 682.36 and 115.50 t, respectively. Conversely, under the scenario emphasizing economic development, the regional N and P discharges showed an approximate escalation of 83.02% and 79.93%, correspondingly. In contrast, under the scenario emphasizing environmental conservation, the regional N and P discharges exhibited a notable decline of approximately 79.96% and 56.44%, respectively. Hence, the scenario prioritizing the amalgamation of environmental conservation and development effectively reduced the N and P discharges within the region, bolstering the water purification functionality. The results derived from this study furnish a solid theoretical foundation for effectuating region-specific planning schemes fostering coordinated economic and ecological advancement within the study area.

Can land-use and land-cover change explain reduced resilience in forests?

Generating signals of reduced resilience in ecosystems is crucial for conservation and management endeavors. However, the practical implications of such systems are still limited due to the lack of high-frequency data and uncertainties associated with predicting complex systems such as ecosystems. This study aims to investigate the potential of time series analysis of remote sensing data in detecting signals of reduced resilience in mangrove forest ecosystems. Using time series analysis of remote sensing images, the resilience of mangrove forests was explored across two distinct study sites. One site (Qeshm Island) has been adversely affected by land-use and land-cover changes, while the other (Gabrik) serves as a reference ecosystem. The study uses data from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite to quantify three remotely sensed indices: the Normalized Difference Vegetation Index (NDVI), the Modified Normalized Difference Water Index (MNDWI), and the Modified Vegetation Water Ratio (MVWR). In addition, Landsat data has been used to explore temporal alterations in land-use and land-cover change. To identify signals of reduced resilience, trend analyses of indicators such as autocorrelation (acf (1)) and standard deviation (SD) are applied. The findings revealed a notable decrease in resilience, signaled by significant upward trends in NDVI statistical metrics for Qeshm Island (Kendall’s τ of acf (1): 0.50 and SD: 0.90), contrasting with the pattern observed in Gabrik (Kendall’s τ of acf (1): −0.19 and SD: −0.19). These results align with our expectations derived from previous studies. Despite MNDWI significantly indicating reduced resilience in Qeshm Island (Kendall’s τ of acf (1): 0.86 and SD: 0.90), it also signaled decreased resilience in Gabrik (Kendall’s τ of acf (1): 0.79 and SD: 0.90). Moreover, MVWR failed to indicate signals of reduced resilience in both sites, specifically in Qeshm (Kendall’s τ of acf (1): −0.10 and SD: −0.07) and in Gabrik (Kendall’s τ of acf (1): −0.72 and SD: −0.12). These findings may be explained through quantitative analyses of land-use and land-cover change. While Qeshm Island and Gabrik share similarities in climate, geography, and annual rainfall, the analysis of land-use and land-cover change revealed significant differences between the two study areas. Qeshm Island underwent drastic increases in the built-up class by a 64.40% change between 1996 and 2014, whereas the built-up class expanded modestly by a 4.04% change in the Gabrik site. This study contributes to advancing our understanding of ecosystem dynamics. The findings of this study can be integrated with ecosystem management tools to enhance the effectiveness of conservation efforts. This is the first report of the successful application of remote sensing in generating signals of reduced resilience within mangrove forests in the Middle East.

Factors Explaining the Distribution of Physella acuta (Draparnaud, 1805) in Freshwaters of Morocco

Invasive species are a major threat to global biodiversity. Therefore, it is crucial to monitor their presence and expansion within invaded areas and carry out studies to improve our knowledge of their biology and ecology. One of the most effective and spectacular invaders among freshwater snails is the acute bladder snail (Physella acuta) (Draparnaud, 1805). This study aims to update the available data on P. acuta in Morocco and determine the main environmental factors that favor its distribution and expansion in this country. Field surveys were conducted in northern Morocco between 2014 and 2023, with a focus on protected areas such as Ramsar sites, and especially great geographical barriers such as the Middle Atlas Mountains and the Sebou and Moulouya River basins. The gastropods were collected using Surber samplers (20 × 25 cm surface area, 400 µm mesh), together with measurements of the physicochemical parameters of the water and other abiotic factors. The bladder snail is probably the most widespread freshwater snail in Morocco, where the species appears to be highly adaptable and can thrive in different habitats, including degraded ones, showing great plasticity in terms of the physicochemical parameters of the water. The main factor limiting the geographical distribution and abundance of P. acuta in the study area was water velocity and conductivity. However, further studies are required to address the future range of expansion of P. acuta in relation to climate change. Although one of the consequences of climate change is reduced water flow speed, which may promote its range of expansion in Morocco, salinization of streams may also reduce its ability to colonize new environments.

Appraisal of non-carcinogenic health risks posed by fluoride and iron in groundwater of a rural semi-arid part of India

ABSTRACT The study was initiated to evaluate groundwater quality in the post-monsoon season (2021) vis a vis chemistry and hydrochemical facies, assessment of probabilistic health risks posed by F− and Fe and quality appraisal of regional groundwater for irrigation in Kumarganj community development block. As per potable water quality standards of Bureau of Indian Standards (BIS), 50% and 13.3% of water samples were found to be unsafe due to F− and Fe, respectively. Fluoride concentration ranged from 0.1 to 3.62 mg l−1 (mean, 1.22 mg l−1). The northern, north-western and south-eastern parts of the block were found to have F− concentrations above the permissible safe limit of 1.0 mg F l−1 while a small area within southern region had >1 mg l−1 Fe. Factor analysis showed that four factors contributed to 38.21%, 19.67%, 10.62%, and 8.45%, respectively, to water quality. According to the human health risk assessment by Total Hazard Index, all age groups in the study area were at risk of exposure, in the order of THIchildren > THIinfant > THIadult due to consumption of excessive F− and Fe. This assessment was consistent with the results of probabilistic health risk assessment undertaken by Monte Carlo simulation (5000 trials) wherein maximum risk of 4.14 and 0.17 at 95th percentile to children was attributable to F− and Fe, respectively.

Fine-tuning the use of moss transplants to map pollution by Potentially Toxic Elements (PTEs) in urban areas

Mosspheres are a kind of moss transplants which offer a novel approach for detecting atmospheric pollution using devitalized mosses, as they reflect the atmospheric deposition of certain elements and polycyclic hydrocarbons. However, due to the unique features of the mosspheres such as the low elemental concentrations in the cultured material, the data treatment needs to be different from that of conventional biomonitoring studies. In this article, our objectives are to identify the best parameter for expressing the levels of chemical elements accumulated by mosspheres, and to apply a recently developed method to assess the probability of pollution of each sample and of the study area. To do this, we used data from a study in which 81 mosspheres were exposed in a medium-sized city in southwestern Europe.Comparing different pollution indices, we selected the enrichment rate (ER) as the most useful, as it is resilient to fluctuations in the initial concentrations and takes into account the time factor, allowing for greater comparability among studies. Then, we determined that the statistical distribution of the ERs of most elements fitted a normal distribution, showing that most samples did not differ significantly from the background concentrations for these elements. On the other hand, for Ni, Pb and Zn there was a subpopulation of samples above background values. In these cases, we determined the probability of pollution of each sample. Finally, we used indicator kriging to calculate the probability of pollution across the study area, identifying the polluted areas, which for some elements match the distribution of the main industries and highways, indicating that this is a suitable protocol to map elemental pollution in urban areas.

The effect of litter decomposition mostly depends on seasonal variation of ultraviolet radiation rather than species in a hyper-arid desert

Introduction: Ultraviolet (UV) radiation is believed to play a significant role in accelerating litter decomposition in water-limited ecosystems. Litter traits also influence the decomposition. However, the dominance of litter traits and ultraviolet radiation on litter decomposition in hyper-arid deserts (annual precipitation: potential evaporation < 0.05) with diverse species and seasonal variations remain unclear.Methods: To address this knowledge gap, we examined the decomposition of three dominant litter species (Karelinia caspia, Alhagi sparsifolia, and Populus euphratica) in the southern edge of the Taklimakan Desert, Northwest China.Results: Our results revealed that under UV radiation conditions, K. caspia, A. sparsifolia, and P. euphratica experienced mass losses of 45.4%, 39.8%, and 34.9%, respectively, and 20%, 22.2% and 17.4%, respectively under UV filtering treatment. Specifically, the loss rate of carbon and lignin under UV radiation, was 2.5 and 2.2 times higher than under UV filtering treatment, respectively.Conclusion: UV radiation did not dominate decomposition throughout the year in our study area, and the loss rate of litter traits was significantly higher in summer than in winter under UV radiation. Moreover, this photodegradation is related to the intensity of UV exposure, but not to precipitation or temperature. Surprisingly, species type had no significant effect on litter decomposition. However, when we applied a UV filtering treatment, we observed higher loss rates of nitrogen compared with the ambient treatment, suggesting the involvement of other spectra in the litter decomposition process. Overall, our findings elucidate that UV radiation is a crucial factor that affects litter mass loss. The magnitude of this effect mostly varies with the season rather than the species of litter.

Occurrence Characteristic and Risk Assessment of Microplastics in Sishui River (Xingyang Section)

Urban rivers are the main receptors and transporters of microplastic pollution. Understanding the occurrence and environmental risk of microplastics in urban rivers can provide theoretical basis for further control of microplastic pollution. The Sishui River, a tributary of the Yellow River, was selected as the research object. A total of nine water samples were collected from sewage outlets of the Sishui River (Xingyang section). The microplastics in the collected samples were characterized by their sizes, shapes, and colors using a microscope. It was found that microplastics were mostly in the form of transparent fibers and fragments in the water body of sewage outlets, of which the size below 500 μm was relatively high. In addition, PET and PE polymers were identified as the main types using a laser infrared imager. The correlation analysis showed that there was a significant correlation between the PET and PE, indicating that they were similar in origin. The results of the environmental risk assessment showed that the type of microplastics was the main factor affecting the assessment results, whereas the risk values of six sewage samples containing PVC were high. However, the value of pollution load index revealed a low risk level of pollutants in the study area.