Generation and classification of patch-based land use and land cover dataset in diverse Indian landscapes: a comparative study of machine learning and deep learning models.

In the context of environmental and social applications, the analysis of land use and land cover (LULC) holds immense significance. The growing accessibility of remote sensing (RS) data has led to the development of LULC benchmark datasets, especially pivotal for intricate image classification tasks. This study addresses the scarcity of such benchmark datasets across diverse settings, with a particular focus on the distinctive landscape of India. The study entails the creation of patch-based datasets, consisting of 4000 labelled images spanning four distinct LULC classes derived from Sentinel-2 satellite imagery. For the subsequent classification task, three traditional machine learning (ML) models and three convolutional neural networks (CNNs) were employed. Despite facing several challenges throughout the process of dataset generation and subsequent classification, the CNN models consistently attained an overall accuracy of 90% or more. Notably, one of the ML models stood out with 96% accuracy, surpassing CNNs in this specific context. The study also conducts a comparative analysis of ML models on existing benchmark datasets, revealing higher prediction accuracy when dealing with fewer LULC classes. Thus, the selection of an appropriate model hinges on the given task, available resources, and the necessary trade-offs between performance and efficiency, particularly crucial in resource-constrained settings. The standardized benchmark dataset contributes valuable insights into the relative performance of deep CNN and ML models in LULC classification, providing a comprehensive understanding of their strengths and weaknesses.

Comparative assessment of soil quality dynamics using SQI modelling approach: a study in rice bowl of West Bengal, India.

The study attempted to evaluate the agricultural soil quality using the Soil Quality Index (SQI) model in two Community Development Blocks, Ausgram-II and Memari-II of Purba Bardhaman District. Total 104 soil samples were collected (0-20 cm depth) from each Block to analyse 13 parameters (bulk density, soil porosity, soil aggregate stability, water holding capacity, infiltration rate, available nitrogen, available phosphorous, available potassium, soil pH, soil organic carbon, electrical conductivity, soil respiration and microbial biomass carbon) in this study. The Integrated Quality Index (IQI) was applied using the weighted additive approach and non-linear scoring technique to retain the Minimum Data Set (MDS). Principal Component Analysis (PCA) identified that SAS, BD, available K, pH, available N, and available P were the key contributing parameters to SQI in Ausgram-II. In contrast, WHC, SR, available N, pH, and SAS contributed the most to SQI in Memari-II. Results revealed that Ausgram-II (0.97) is notably higher SQI than Memari-II (0.69). In Ausgram-II, 99.72% of agricultural lands showed very high SQI (Grade I), whereas, in Memari-II, 49.95% of lands exhibited a moderate SQI (Grade III) and 49.90% showed a high SQI (Grade II). Sustainable Yield Index (SYI), Sensitivity Index (SI) and Efficiency Ratio (ER) were used to validate the SQIs. A positive correlation was observed between SQI and paddy ( R2 = 0.82 & 0.72) and potato yield (R2 = 0.71 & 0.78) in Ausgram-II and Memari-II Block, respectively. This study could evaluate the agricultural soil quality and provide insights for decision-making in fertiliser management practices to promote agricultural sustainability.

Multi-vehicle adaptive 3D mapping for targeted ocean sampling.

Expanding spatial presentation from two-dimensional profile transects to three-dimensional ocean mapping is key for a better understanding of ocean processes. Phytoplankton distributions can be highly patchy and the accurate identification of these patches with the context, variability, and uncertainty of measurements on relevant scales is difficult to achieve. Traditional sampling methods, such as plankton nets, water samplers and in-situ vertical sensors, provide a snapshot and often miss the fine-scale horizontal and temporal variability. Here, we show how two autonomous underwater vehicles measured, adapted to, and reported real-time chlorophyll a measurements, giving insights into the spatiotemporal distribution of phytoplankton biomass and patchiness. To gain the maximum available information within their sensing scope, the vehicles moved in an adaptive fashion, looking for the regions of the highest predicted chlorophyll a concentration, the greatest uncertainty, and the least possibility of collision with other underwater vehicles and ships. The vehicles collaborated by exchanging data with each other and operators via satellite, using a common segmentation of the area to maximize information exchange over the limited bandwidth of the satellite. Importantly, the use of multiple autonomous underwater vehicles reporting real-time data combined with targeted sampling can provide better match with sampling towards understanding of plankton patchiness and ocean processes.

Evaluation of urban flood susceptibility through integrated Bivariate statistics and Geospatial technology.

Flood disasters are frequent natural disasters that occur annually during the monsoon season and significantly impact urban areas. This area is characterized by impermeable concrete surfaces, which increase runoff and are particularly susceptible to flooding. Therefore, this study aims to adopt Bi-variate statistical methods such as frequency ratio (FR) and weight of evidence (WOE) to map flood susceptibility in an urbanized watershed. The study area encompasses an urbanized watershed surrounding the Chennai Metropolitan area in southern India. The essential parameters considered for flood susceptibility zonation include geomorphology, soil, land use/land cover (LU/LC), rainfall, drainage, slope, aspect, Topographic Wetness Index (TWI), and Normalized Difference Vegetation Index (NDVI). The flood susceptibility map was derived using 70% of randomly selected flood areas from the flood inventory database, and the other 30% was used for validation using the area under curve (AUC) method. The AUC method produced a frequency ratio of 0.806 and a weight of evidence value of 0.865 contributing to the zonation of the three classes. The study further investigates the impact of urbanization on flood susceptibility and is further classified into high, moderate, and low flood risk zones. With the abrupt change in climatic scenarios, there is an increase in the risk of flash floods. The results of this study can be used by policymakers and planners in developing a preparedness system to mitigate economic, human, and property losses due to floods in any urbanized watershed.

Assessment of land use and land cover change detection and prediction using deep learning techniques for the southwestern coastal region, Goa, India.

Understanding the connections between human activities and the natural environment depends heavily on information about land use and land cover (LULC) in the form of accurate LULC maps. Environmental monitoring using deep learning (DL) is rapidly growing to preserve a sustainable environment in the long term. For establishing effective policies, regulations, and implementation, DL can be a valuable tool for assessing environmental conditions and natural resources that will positively impact the ecosystem. This paper presents the assessment of land use and land cover change detection (LULCCD) and prediction using DL techniques for the southwestern coastal region, Goa, also known as the tourist destination of India. It consists of three components: (i) change detection (CD), (ii) quantification of LULC changes, and (iii) prediction. A new CD assessment framework, Spatio-Temporal Encoder-Decoder Self Attention Network (STEDSAN), is proposed for the LULCCD process. A dual branch encoder-decoder network is constructed using strided convolution with downsampling for the encoder and transpose convolution with upsampling for the decoder to assess the bitemporal images spatially. The self-attention (SA) mechanism captures the complex global spatial-temporal (ST) interactions between individual pixels over space-time to produce more distinct features. Each branch accepts the LULC map of 2 years as one of its inputs to determine binary and multiclass changes among the bitemporal images. The STEDSAN model determines the patterns, trends, and conversion from one LULC type to another for the assessment period from 2005 to 2018. The binary change maps were also compared with the existing state of the art (SOTA) CD methods, with STEDSAN having an overall accuracy of 94.93%. The prediction was made using an recurrent neural network (RNN) known as long short term memory network (LSTM) for the year 2025. Experiments were conducted to determine area-wise changes in several LULC classes, such as built-up (BU), crops (kharif crop (KC), rabi crop (RC), zaid crop (ZC), double/triple (D/T C)), current fallow (CF), plantation (PL), forests (evergreen forest (EF), deciduous forest (DF), degraded/scurb forest (D/SF) ), littoral swamp (LS), grassland (GL), wasteland (WL), waterbodies max (Wmx), and waterbodies min (Wmn). As per the analysis, over the period of 13 years, there has been a net increase in the amount of BU (1.25%), RC (1.17%), and D/TC( 2.42%) and a net decrease in DF (3.29%) and WL(1.44%) being the most dominant classes being changed. These findings will offer a thorough description of identifying trends in coastal areas that may incorporate methodological hints for future studies. This study will also promote handling the spatial and temporal complexity of remotely sensed data employed in categorizing the coastal LULC of a heterogeneous landscape.

Global trends of dioxin and dioxin-like PCBs in animal-origin foods: a systematic review and gap areas.

Dioxins and dioxin-like polychlorinated biphenyls are a group of lipophilic compounds classified under persistent environmental pollutants (POPs). Significant sources of dioxin emissions include industrial effluents, open burning practices, and biomedical and municipal waste incinerators. These emissions will enter the food chain and accumulate in animal-origin foods (AOFs). A systematic review was conducted to analyze the global levels of dioxins and dioxin-like PCBs in AOFs using PRISMA guidelines 2020. The data on the dioxin contamination in AOFs were extracted from 53 publications based on their presence in eggs, meat and meat products, milk and dairy products, marine fish and fish products, and freshwater fish and crabs. A gap analysis was conducted based on the systematic review to understand the grey areas to be focused on the  future. No trend of dioxin contamination in AOFs was observed. A significant gap area was found in the need for nationwide data generation in countries without periodic monitoring of AOFs for dioxin contamination. Source apportionment studies need to be explored for the dioxin contamination of AOFs. Large-scale screening tests of AOFs using DR-CALUX based on market surveys are required for data generation. The outcomes of the study will be helpful for stakeholders and policyholders in framing new policies and guidelines for food safety in AOFs.

Comparison of numerical calculations and ALOHA modeling in consequence assessment of chlorine gas emissions from ethylene dichloride reactors.

Incidents involving chemical storage tanks in the petrochemical industry are significant events with severe consequences. Within the petrochemical industry, EDC is a sector that produces ethylene dichloride through the reaction of chlorine and ethylene. The present research was conducted to evaluate the consequences of chlorine gas released from the EDC reactor in a petrochemical industry in southern Iran. Data regarding reactor specifications were obtained from the factory's technical office, while climatic data was acquired from the Meteorological Organization. The consequences of chlorine gas release from the reactor were assessed in four predefined scenarios using numerical calculation methods and modeling with the ALOHA software. The numerical calculation method involved thermodynamic fluid path analysis, discharge coefficient calculations, and wind speed impact analysis. The hazard radius was determined based on the ERPG1-2-3 index. Results showed that in the scenario of chlorine gas release from EDC reactors, according to the ALOHA model, an increase in wind speed from 3 to 7 m/h led to an expanded dispersion radius. At a radius of 700 m from the reactor, the maximum outdoor concentration reached 3.12 ppm, decreasing to 2.27 ppm at 800 m and further to 1.53 ppm at 1000 m. The comparison of numerical calculations and modeling using the ALOHA software indicates the desirable conformity of the results with each other. The R2 coefficient for evaluating the conformity of the results was 0.9964, indicating the desired efficiency of the model in evaluating the consequences of the release of toxic gasses from the EDC tank. The results of this research can be useful in designing the site and emergency response plan.

Multispectral analysis-ready satellite data for three East African mountain ecosystems.

The East African mountain ecosystems are facing increasing threats due to global change, putting their unique socio-ecological systems at risk. To monitor and understand these changes, researchers and stakeholders require accessible analysis-ready remote sensing data. Although satellite data is available for many applications, it often lacks accurate geometric orientation and has extensive cloud cover. This can generate misleading results and make it unreliable for time-series analysis. Therefore, it needs comprehensive processing before usage, which encompasses multi-step operations, requiring large computational and storage capacities, as well as expert knowledge. Here, we provide high-quality, atmospherically corrected, and cloud-free analysis-ready Sentinel-2 imagery for the Bale Mountains (Ethiopia), Mounts Kilimanjaro and Meru (Tanzania) ecosystems in East Africa. Our dataset ranges from 2017 to 2021 and is provided as monthly and annual aggregated products together with 24 spectral indices. Our dataset enables researchers and stakeholders to conduct immediate and impactful analyses. These applications can include vegetation mapping, wildlife habitat assessment, land cover change detection, ecosystem monitoring, and climate change research.

Global marine phytoplankton dynamics analysis with machine learning and reanalyzed remote sensing.

Phytoplankton are the world's largest oxygen producers found in oceans, seas and large water bodies, which play crucial roles in the marine food chain. Unbalanced biogeochemical features like salinity, pH, minerals, etc., can retard their growth. With advancements in better hardware, the usage of Artificial Intelligence techniques is rapidly increasing for creating an intelligent decision-making system. Therefore, we attempt to overcome this gap by using supervised regressions on reanalysis data targeting global phytoplankton levels in global waters. The presented experiment proposes the applications of different supervised machine learning regression techniques such as random forest, extra trees, bagging and histogram-based gradient boosting regressor on reanalysis data obtained from the Copernicus Global Ocean Biogeochemistry Hindcast dataset. Results obtained from the experiment have predicted the phytoplankton levels with a coefficient of determination score (R2) of up to 0.96. After further validation with larger datasets, the model can be deployed in a production environment in an attempt to complement in-situ measurement efforts.

Seabirds reveal mercury distribution across the North Atlantic.

Mercury (Hg) is a heterogeneously distributed toxicant affecting wildlife and human health. Yet, the spatial distribution of Hg remains poorly documented, especially in food webs, even though this knowledge is essential to assess large-scale risk of toxicity for the biota and human populations. Here, we used seabirds to assess, at an unprecedented population and geographic magnitude and high resolution, the spatial distribution of Hg in North Atlantic marine food webs. To this end, we combined tracking data of 837 seabirds from seven different species and 27 breeding colonies located across the North Atlantic and Atlantic Arctic together with Hg analyses in feathers representing individual seabird contamination based on their winter distribution. Our results highlight an east-west gradient in Hg concentrations with hot spots around southern Greenland and the east coast of Canada and a cold spot in the Barents and Kara Seas. We hypothesize that those gradients are influenced by eastern (Norwegian Atlantic Current and West Spitsbergen Current) and western (East Greenland Current) oceanic currents and melting of the Greenland Ice Sheet. By tracking spatial Hg contamination in marine ecosystems and through the identification of areas at risk of Hg toxicity, this study provides essential knowledge for international decisions about where the regulation of pollutants should be prioritized.

Cost-effort analysis of Baited Remote Underwater Video (BRUV) and environmental DNA (eDNA) in monitoring marine ecological communities.

Monitoring the diversity and distribution of species in an ecosystem is essential to assess the success of restoration strategies. Implementing biomonitoring methods, which provide a comprehensive assessment of species diversity and mitigate biases in data collection, holds significant importance in biodiversity research. Additionally, ensuring that these methods are cost-efficient and require minimal effort is crucial for effective environmental monitoring. In this study we compare the efficiency of species detection, the cost and the effort of two non-destructive sampling techniques: Baited Remote Underwater Video (BRUV) and environmental DNA (eDNA) metabarcoding to survey marine vertebrate species. Comparisons were conducted along the Sussex coast upon the introduction of the Nearshore Trawling Byelaw. This Byelaw aims to boost the recovery of the dense kelp beds and the associated biodiversity that existed in the 1980s. We show that overall BRUV surveys are more affordable than eDNA, however, eDNA detects almost three times as many species as BRUV. eDNA and BRUV surveys are comparable in terms of effort required for each method, unless eDNA analysis is carried out externally, in which case eDNA requires less effort for the lead researchers. Furthermore, we show that increased eDNA replication yields more informative results on community structure. We found that using both methods in conjunction provides a more complete view of biodiversity, with BRUV data supplementing eDNA monitoring by recording species missed by eDNA and by providing additional environmental and life history metrics. The results from this study will serve as a baseline of the marine vertebrate community in Sussex Bay allowing future biodiversity monitoring research projects to understand community structure as the ecosystem recovers following the removal of trawling fishing pressure. Although this study was regional, the findings presented herein have relevance to marine biodiversity and conservation monitoring programs around the globe.

Mapping soil trace metal distribution using remote sensing and multivariate analysis.

Trace metal soil contamination poses significant risks to human health and ecosystems, necessitating thorough investigation and management strategies. Researchers have increasingly utilized advanced techniques like remote sensing (RS), geographic information systems (GIS), geostatistical analysis, and multivariate analysis to address this issue. RS tools play a crucial role in collecting spectral data aiding in the analysis of trace metal distribution in soil. Spectroscopy offers an effective understanding of environmental contamination by analyzing trace metal distribution in soil. The spatial distribution of trace metals in soil has been a key focus of these studies, with factors influencing this distribution identified as soil type, pH levels, organic matter content, land use patterns, and concentrations of trace metals. While progress has been made, further research is needed to fully recognize the potential of integrated geospatial imaging spectroscopy and multivariate statistical analysis for assessing trace metal distribution in soils. Future directions include mapping multivariate results in GIS, identifying specific anthropogenic sources, analyzing temporal trends, and exploring alternative multivariate analysis tools. In conclusion, this review highlights the significance of integrated GIS and multivariate analysis in addressing trace metal contamination in soils, advocating for continued research to enhance assessment and management strategies.

Framework development for the physical vulnerability assessment index of hand-dug Wells in Are-Ekiti, Southwestern Nigeria.

The article presents a comprehensive framework for assessing the physical vulnerability of hand-dug wells within the Are Community, Southwestern Nigeria. The study spans from March to April 2023 and meticulously examines 90 wells, focusing on critical parameters such as well collar, well cover, and well lining information. The analysis reveals significant variations in well collar construction materials and dimensions, emphasizing the community's adaptive strategies. The Well Collar Height Index (WCi), Well Cover Index (WCOi), Well Lining Index (WLi), and the derived Vulnerability Index categorize wells into vulnerability classes, offering a nuanced understanding of susceptibility levels. Notably, the study identifies wells with Very High vulnerability that demand urgent attention, as well as wells with effective protective measures categorized as Very Low vulnerability. The article emphasizes the need for a nuanced understanding of local practices and materials, highlighting the variability in well collar construction. It discusses the implications of well cover conditions and the critical role of well linings in assessing groundwater vulnerability. The Vulnerability Index combines these parameters, guiding targeted interventions based on risk severity. The study lays the groundwork for future interventions to enhance the safety and sustainability of water sources within the Are Community. It recommends immediate comprehensive measures for highly vulnerable wells, ongoing monitoring, community engagement, and knowledge sharing. The future scope includes incorporating geochemical analysis, targeted interventions, regular maintenance, community training, and exploring alternative water sources for sustainable improvements.

Short-term extinction predicted by population viability analysis for a Neotropical salt marsh endemic bird.

Salt marshes pose challenges for the birds that inhabit them, including high rates of nest flooding, tipping, and predation. The impacts of rising sea levels and invasive species further exacerbate these challenges. To assess the urgency of conservation and adequacy of new actions, researchers and wildlife managers may use population viability analyses (PVAs) to identify population trends and major threats. We conducted PVA for Formicivora acutirostris, which is a threatened neotropical bird species endemic to salt marshes. We studied the species' demography in different sectors of an estuary in southern Brazil from 2006 to 2023 and estimated the sex ratio, longevity, productivity, first-year survival, and mortality rates. For a 133-year period, starting in 1990, we modeled four scenarios: (1) pessimistic and (2) optimistic scenarios, including the worst and best values for the parameters; (3) a baseline scenario, with intermediate values; and (4) scenarios under conservation management, with increased recruitment and/or habitat preservation. Projections indicated population decline for all assessment scenarios, with a 100% probability of extinction by 2054 in the pessimistic scenario and no extinction in the optimistic scenario. The conservation scenarios indicated population stability with 16% improvement in productivity, 10% improvement in first-year survival, and stable carrying capacity. The disjunct distribution of the species, with remnants concentrated in a broad interface with arboreal habitats, may seal the population decline by increasing nest predation. The species should be considered conservation dependent, and we recommend assisted colonization, predator control, habitat recovery, and ex situ conservation.

Investigation of odor pollution by utilizing selected ion flow tube mass spectrometry (SIFT-MS) and principal component analysis (PCA).

Odor pollution, also referred to as odor nuisance, is a growing environmental concern that is significantly associated with mental health. Once emitted into the air, the concentration of odorous substances varies considerably with wind conditions, leading to difficulties in timely sampling. In the present study, we employed selected ion flow tube mass spectrometry (SIFT-MS) to measure 22 odor-producing molecules continuously in an urban-rural complex city. In addition, we applied statistical analyses, principal component analysis (PCA), and a conditional probability function (CPF) to the datasets obtained from SIFT-MS to identify the odor characteristics at two study sites. At site A, odorants related to livestock farming and industry showed high factor loadings on principal components (PCs) from the PCA. In contrast, we estimated that the odorous gaseous chemicals affecting site B were closely related to sewage treatment and municipal solid waste disposal. Similar CPF patterns of grouped substances from the PCA supported the association between potential odor sources and specific odorants at site B, which helped estimate possible source locations. Consequently, our findings indicate that continuous monitoring of odorous substances using SIFT-MS can be an effective way to provide sufficient information on odor-producing molecules, leading to the clear identification of odor characteristics despite the high variability of odorous substances.

Mobility, bioavailability and distribution of Fe and Cu in mangroves (Avicennia schaueriana and Rhizophora mangle) from a semiarid coast in NE Brazil.

Mangroves buffer metals transfer to coastal areas though strong accumulation in sediments making necessary to investigate metals' bioavailability to plants at the rhizosphere. This work evaluates the effect of mangrove root activity, through iron plaque formation, on the mobility of iron and copper its influence on metals' uptake, and translocation through simultaneous histochemical analysis. The Fe2+ and Fe3+ contents in porewaters ranged from 0.02 to 0.11 µM and 1.0 to 18.3 µg.l-1, respectively, whereas Cu concentrations were below the method's detection limit (<0.1 µM). In sediments, metal concentrations ranged from 12,800 to 39,500 µg.g-1 for total Fe and from 10 to 24 µg.g-1 for Cu. In iron plaques, Cu concentrations ranged from 1.0 to 160 µg.g-1, and from 19.4 to 316 µg.g-1 in roots. Fe concentrations were between 605 to 36,000 µg.g-1 in the iron plaques and from 2,100 to 62,400 µg.g-1 in roots. Histochemical characterization showed Fe3+ predominance at the tip of roots and Fe2+ in more internal tissues. A. schaueriana showed significant amounts of Fe in pneumatophores and evident translocation of this metal to leaves and excretion through salt glands. Iron plaques formation was essential to the Fe and Cu regulation and translocation in tissues of mangrove plants.

Establishing a river health evaluation index system for seven rivers in Jiamusi City of China.

The Jiamusi section of the Songhua River is one of the first 17 model river construction sections in China. The implementation of river health assessments can determine the health dynamics of rivers and test the management's effectiveness. Targeting seven rivers, this study conducted river zoning and monitoring point deployment to conduct sufficient field research and monitoring. The authors selected hydrological and water resources, physical structure, water quality, aquatic life, social service functions, and management as guideline layers and 15 indicator layers. Subsequently, the authors established an evaluation index system to evaluate and analyze the ecological status and social service status of each river. The results showed that the Yindamu, Alingda, and Gejie rivers scored well as healthy rivers, with health evaluation scores of 78.98, 76.06, and 75.83, respectively. The Wangsanwu, Lujiagang, and Lingdangmai rivers are generally sub-healthy rivers with scores of 71.55, 67.97, and 60.7, respectively. The Yinggetu River has a score of 54.52 and is therefore assessed as unhealthy. Based on the scientific evaluation index method, this study analyses the current river health state in Jiamusi City to provide the basis for the evaluation of the river chief's work and future river management.

A hybrid water quality prediction model based on variational mode decomposition and bidirectional gated recursive unit.

Water quality predicted accuracy is beneficial to river ecological management and water pollution prevention. Owing to water quality data has the characteristics of nonlinearity and instability, it is difficult to predict the change of water quality. This paper proposes a hybrid water quality prediction model based on variational mode decomposition optimized by the sparrow search algorithm (SSA-VMD) and bidirectional gated recursive unit (BiGRU). First, the sparrow search algorithm selects fuzzy entropy (FE) as the fitness function to optimize the two parameters of VMD, which improves the adaptability of VMD. Second, SSA-VMD is used to decompose the original data into several components with different center frequencies. Finally, BiGRU is employed to predict each component separately, which significantly improves predicted accuracy. The proposed model is validated using data about dissolved oxygen (DO) and the potential of hydrogen (pH) from the Xiaojinshan Monitoring Station in Qiandao Lake, Hangzhou, China. The experimental results show that the proposed model has superior prediction accuracy and stability when compared with other models, such as EMD-based models and other CEEMDAN-based models. The prediction accuracy of DO can reach 97.8% and pH is 96.1%. Therefore, the proposed model can provide technical support for river water quality protection and pollution prevention.

Source identification and driving factor apportionment for soil potentially toxic elements via combining APCS-MLR, UNMIX, PMF and GDM.

The contamination and quantification of soil potentially toxic elements (PTEs) contamination sources and the determination of driving factors are the premise of soil contamination control. In our study, 788 soil samples from the National Agricultural Park in Chengdu, Sichuan Province were used to evaluate the contamination degree of soil PTEs by pollution factors and pollution load index. The source identification of soil PTEs was performed using positive matrix decomposition (PMF), edge analysis (UNMIX) and absolute principal component score-multiple line regression (APCS-MLR). The geo-detector method (GDM) was used to analysis drivers of soil PTEs pollution sources to help interpret pollution sources derived from receptor models. Result shows that soil Cu, Pb, Zn, Cr, Ni, Cd, As and Hg average content were 35.2, 32.3, 108.9, 91.9, 37.1, 0.22, 9.76 and 0.15 mg/kg in this study area. Except for As, all are higher than the corresponding soil background values in Sichuan Province. The best performance of APCS-MLR was determined by comparison, and APCS-MLR was considered as the preferred receptor model for soil PTEs source distribution in the study area. ACPS-MLR results showed that 82.70% of Cu, 61.6% of Pb, 75.3% of Zn, 91.9% of Cr and 89.4% of Ni came from traffic-industrial emission sources, 60.9% of Hg came from domestic-transportation emission sources, 57.7% of Cd came from agricultural sources, and 89.5% of As came from natural sources. The GDM results showed that distance from first grade highway, population, land utilization and total potassium (TK) content were the main driving factors affecting these four sources, with q values of 0.064, 0.048, 0.069 and 0.058, respectively. The results can provide reference for reducing PTEs contamination in farmland soil.

Rapid seagrass meadow expansion in an Indian Ocean bright spot.

The areal extent of seagrass meadows is in rapid global decline, yet they provide highly valuable societal benefits. However, their conservation is hindered by data gaps on current and historic spatial extents. Here, we outline an approach for national-scale seagrass mapping and monitoring using an open-source platform (Google Earth Engine) and freely available satellite data (Landsat, Sentinel-2) that can be readily applied in other countries globally. Specifically, we map contemporary (2021) and historical (2000-2021; n = 10 maps) shallow water seagrass extent across the Maldives. We found contemporary Maldivian seagrass extent was ~ 105 km2 (overall accuracy = 82.04%) and, notably, that seagrass area increased threefold between 2000 and 2021 (linear model, + 4.6 km2 year-1, r2 = 0.93, p < 0.001). There was a strongly significant association between seagrass and anthropogenic activity (p < 0.001) that we hypothesize to be driven by nutrient loading and/or altered sediment dynamics (from large scale land reclamation), which would represent a beneficial anthropogenic influence on Maldivian seagrass meadows. National-scale tropical seagrass expansion is unique against the backdrop of global seagrass decline and we therefore highlight the Maldives as a rare global seagrass 'bright spot' highly worthy of increased attention across scientific, commercial, and conservation policy contexts.