Harnessing urban analytics and machine learning for sustainable urban development: A multidimensional framework for modeling environmental impacts of urbanization in Saudi Arabia.

Sustainable urban development is crucial for managing natural resources and mitigating environmental impacts induced by rapid urbanization. This study demonstrates an integrated framework using machine learning-based urban analytics techniques to evaluate spatiotemporal urban expansion in Saudi Arabia (1987-2022) and quantify impacts on leading land, water, and air-related environmental parameters (EPs). Remote sensing and statistical techniques were applied to estimate vegetation health, built-up area, impervious surface, water bodies, soil characteristics, thermal comfort, air pollutants (PM2.5, CH4, CO, NO2, SO2), and nighttime light EPs. Regression assessment and Principal Component Analysis (PCA) were applied to assess the relationships between urban expansion and EPs. The findings highlight the substantial growth of urban areas (0.067%-0.14%), a decline in soil moisture (16%-14%), water bodies (60%-22%), a nationwide increase of PM2.5 (44 µg/m3 to 73 µg/m3) and night light intensity (0.166-9.670) concentrations resulting in significant impacts on land, water, and air quality parameters. PCA showed vegetation cover, soil moisture, thermal comfort, PM2.5, and NO2 are highly impacted by urban expansion compared to other EPs. The results highlight the need for effective and sustainable interventions to mitigate environmental impacts using green innovations and urban development by applying mixed-use development, green space preservation, green building technologies, and implementing renewable energy approaches. The framework recommended for environmental management in this study provides a robust foundation for evidence-based policies and adaptive management practices that balance economic progress and environmental sustainability. It will also help policymakers and urban planners in making informed decisions and promoting resilient urban growth.

Exposure Assessment of Pesticides in Surface Waters of Ontario, Canada Reveals Low Probability of Exceeding Acute Regulatory Thresholds.

The objectives of this study were to: (1) characterize the exposure of aquatic ecosystems in Southern Ontario, Canada to pesticides between 2002 and 2016 by constructing environmental exposure distributions (EEDs), including censored data; and (2) predict the probability of exceeding acute regulatory guidelines. Surface water samples were collected over a 15-year period by Environment and Climate Change Canada. The dataset contained 167 compounds, sampled across 114 sites, with a total of 2,213 samples. There were 67,920 total observations of which 55,058 were non-detects (81%), and 12,862 detects (19%). The most commonly detected compound was atrazine, with a maximum concentration of 18,600 ngL- 1 and ~ 4% chance of exceeding an acute guideline (1,000 ngL- 1) in rivers and streams. Using Southern Ontario as a case study, this study provides insight into the risk that pesticides pose to aquatic ecosystems and the utility of EEDs that include censored data for the purpose of risk assessment.

Investigation of organic micropollutant pollution in Izmit Bay: a comparative study of passive sampling and instantaneous sampling techniques.

In this study, we used a comprehensive array of sampling techniques to examine the pollution caused by organic micropollutants in Izmit Bay for the first time. Our methodology contains spot seawater sampling, semi-permeable membrane devices (SPMDs) passive samplers for time-weighted average (TWA), and sediment sampling for long-term pollution detection in Izmit Bay, together. Additionally, the analysis results obtained with these three sampling methods were compared in this study. Over the course of two seasons in 2020 and 2021, we deployed SPMDs for 21 days in the first season and for 30 days in the second season. This innovative approach allowed us to gather sea water samples and analyze them for the presence of polycyclic aromatic hydrocarbons (Σ15 PAHs), polychlorinated biphenyls (Σ7 PCBs), and organochlorine pesticides (Σ11 OCPs). Using SPMD-based passive sampling, we measured micropollutant concentrations: PAHs ranged from 1963 to 10342 pg/L in 2020 and 1338 to 6373 pg/L in 2021; PCBs from 17.46 to 61.90 pg/L in 2020 and 8.37 to 78.10 pg/L in 2021; and OCPs from 269.2 to 8868 pg/L in 2020 and 141.7 to 1662 pg/L in 2021. Our findings revealed parallels between the concentrations of PAHs, PCBs, and OCPs in both SPMDs and sediment samples, providing insights into the distribution patterns of these pollutants in the marine ecosystem. However, it is worth noting that due to limited data acquisition, the suitability of spot sampling in comparison to instantaneous sampling remains inconclusive, highlighting the need for further investigation and data collection.

Sub-PPB Detection with Gas-Phase Multiphoton Electron Extraction Spectroscopy under Ambient Conditions.

Multiphoton electron extraction spectroscopy (MEES) is an advanced analytical technique that has demonstrated exceptional sensitivity and specificity for detecting molecular traces on solid and liquid surfaces. Building upon the solid-state MEES foundations, this study introduces the first application of MEES in the gas phase (gas-phase MEES), specifically designed for quantitative detection of gas traces at sub-part per billion (sub-PPB) concentrations under ambient atmospheric conditions. Our experimental setup utilizes resonant multiphoton ionization processes using ns laser pulses under a high electrical field. The generated photoelectron charges are recorded as a function of the laser's wavelength. This research showcases the high sensitivity of gas-phase MEES, achieving high spectral resolution with resonant peak widths less than 0.02 nm FWHM. We present results from quantitative analysis of benzene and aniline, two industrially and environmentally significant compounds, demonstrating linear responses in the sub-PPM and sub-PPB ranges. The enhanced sensitivity and resolution of gas-phase MEES offer a powerful approach to trace gas analysis, with potential applications in environmental monitoring, industrial safety, security screening, and medical diagnostics. This study confirms the advantages of gas-phase MEES over many traditional optical spectroscopic methods and demonstrates its potential in direct gas-trace sensing in ambient atmosphere.

Fluorescence Detection of Pb2+ in Environmental Water Using Biomass Carbon Quantum Dots Modified with Acetamide-Glycolic Acid Deep Eutectic Solvent.

In this study, a novel green fluorescent probe material, nitrogen-doped carbon quantum dots (N-CQDs), was prepared by a one-step hydrothermal synthesis method using walnut green skin as a carbon source and acetamide-glycolic acid deep eutectic solvent (AGADES) as a modifier. By covalent coupling, the amide chromophore in AGADES is designed to cover the surface of walnut green skin carbon quantum dots (W-CQDs), forming a fluorescence energy resonance effect and improving the fluorescence performance of the carbon quantum dots. The prepared N-CQDs have a uniform particle size distribution, and the fluorescence quantum efficiency has increased from 12.5% to 32.5%. Within the concentration range of 0.01~1000 µmol/L of Pb2+, the linear detection limit is 1.55 nmol/L, which can meet the trace detection of Pb2+ in the water environment, and the recycling rate reaches 97%. This method has been successfully applied to the fluorescence detection and reuse of Pb2+ in actual water bodies, providing new ideas and methods for the detection of heavy metal ions in environmental water.

Relationship between stream size, watershed land use, and pesticide concentrations in headwater streams.

A quantitative multiresidue study of current-use pesticides in multiple matrices was undertaken with field sampling at 32 headwater streams near Lac Saint-Pierre in Québec, Canada. A total of 232 samples were collected in five campaigns of stream waters and streambed sediments from streams varying in size and watershed land use. Novel multiresidue analytical methods from previous work were successfully applied for the extraction of pesticide residues from sediments via pressurized liquid extraction (PLE) and quantitative analysis using ultra high-performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) with online sample preparation on a hydrophilic-lipophilic balance (HLB) column. Of the 31 target compounds, including 29 pesticides and two degradation products of atrazine, 29 compounds were detected at least once. Consistent with other studies, atrazine and metolachlor were the most widely-detected herbicides. Detections were generally higher in water than sediment samples and the influence of land use on pesticide concentrations was only detectable in water samples. Small streams with a high proportion of agricultural land use in their watershed were generally found to have the highest pesticide concentrations. Corn and soybean monoculture crops, specifically, were found to cause the greatest impact on pesticide concentration in headwater streams and correlated strongly with many of the most frequently detected pesticides. This study highlights the importance of performing multiresidue pesticide monitoring programs in headwater streams in order to capture the impacts of agricultural intensification on freshwater ecosystems.

The effects of urban green space and road proximity to indoor traffic-related PM2.5, NO2, and BC exposure in inner-city schools.

BACKGROUND: Since there are known adverse health impacts of traffic-related air pollution, while at the same time there are potential health benefits from greenness, it is important to examine more closely the impacts of these factors on indoor air quality in urban schools. OBJECTIVE: This study investigates the association of road proximity and urban greenness to indoor traffic-related fine particulate matter (PM2.5), nitrogen dioxide (NO2), and black carbon (BC) in inner-city schools. METHODS: PM2.5, NO2, and BC were measured indoors at 74 schools and outdoors at a central urban over a 10-year period. Seasonal urban greenness was estimated using the Normalized Difference Vegetation Index (NDVI) with 270 and 1230 m buffers. The associations between indoor traffic-related air pollution and road proximity and greenness were investigated with mixed-effects models. RESULTS: The analysis showed linear decays of indoor traffic-related PM2.5, NO2, and BC by 60%, 35%, and 22%, respectively for schools located at a greater distance from major roads. The results further showed that surrounding school greenness at 270 m buffer was significantly associated (p < 0.05) with lower indoor traffic-related PM2.5: -0.068 (95% CI: -0.124, -0.013), NO2: -0.139 (95% CI: -0.185, -0.092), and BC: -0.060 (95% CI: -0.115, -0.005). These associations were stronger for surrounding greenness at a greater distance from the schools (buffer 1230 m) PM2.5: -0.101 (95% CI: -0.156, -0.046) NO2: -0.122 (95% CI: -0.169, -0.075) BC: -0.080 (95% CI: -0.136, -0.026). These inverse associations were stronger after fully adjusting for regional pollution and meteorological conditions. IMPACT STATEMENT: More than 90% of children under the age of 15 worldwide are exposed to elevated air pollution levels exceeding the WHO's guidelines. The study investigates the impact that urban infrastructure and greenness, in particular green areas and road proximity, have on indoor exposures to traffic-related PM2.5, NO2, and BC in inner-city schools. By examining a 10-year period the study provides insights for air quality management, into how road proximity and greenness at different buffers from the school locations can affect indoor exposure.

Ultrasensitive detection of Ag+ and Ce3+ ions using highly fluorescent carboxyl-functionalized carbon nitride nanoparticles.

The fluorescence quenching of carboxyl-rich g-C3N4 nanoparticles was found to be selective to Ag+ and Ce3+ with a limit of detection as low as 30 pM for Ag+ ions. A solid-state thermal polycondensation reaction was used to produce g-C3N4 nanoparticles with distinct green fluorescence and high water solubility. Dynamic light scattering indicated an average nanoparticle size of 95 nm. The photoluminescence absorption and emission maxima were centered at 405 nm and 540 nm respectively which resulted in a large Stokes shift. Among different metal ion species, the carboxyl-rich g-C3N4 nanoparticles were selective to Ag+ and Ce3+ ions, as indicated by strong fluorescence quenching. The PL sensing of mixtures of Ag+/Ce3+ and Ce3+ followed static Stern-Volmer kinetics with the Stern-Volmer constants determined to be 74.18 ×10^3 M-1 and 27.2 ×10^3 M-1 respectively. The PL sensing of Ag+ followed modified Stern-Volmer kinetics. Sensing was also demonstrated using commercial filter paper functionalized with g-C3N4 nanoparticles, enabling practical on-site applications. .

Revolutionizing aquatic eco-environmental monitoring: Utilizing the RPA-Cas-FQ detection platform for zooplankton.

The integration of recombinase polymerase amplification (RPA) with CRISPR/Cas technology has revolutionized molecular diagnostics and pathogen detection due to its unparalleled sensitivity and trans-cleavage ability. However, its potential in the ecological and environmental monitoring scenarios for aquatic ecosystems remains largely unexplored, particularly in accurate qualitative/quantitative detection, and its actual performance in handling complex real environmental samples. Using zooplankton as a model, we have successfully optimized the RPA-CRISPR/Cas12a fluorescence detection platform (RPA-Cas-FQ), providing several crucial "technical tips". Our findings indicate the sensitivity of CRISPR/Cas12a alone is 5 × 109 copies/reaction, which can be dramatically increased to 5 copies/reaction when combined with RPA. The optimized RPA-Cas-FQ enables reliable qualitative and semi-quantitative detection within 50 min, and exhibits a good linear relationship between fluorescence intensity and DNA concentration (R2 = 0.956-0.974***). Additionally, we developed a rapid and straightforward identification procedure for single zooplankton by incorporating heat-lysis and DNA-barcode techniques. We evaluated the platform's effectiveness using real environmental DNA (eDNA) samples from the Three Gorges Reservoir, confirming its practicality. The eDNA-RPA-Cas-FQ demonstrated strong consistency (Kappa = 0.43***) with eDNA-Metabarcoding in detecting species presence/absence in the reservoir. Furthermore, the two semi-quantitative eDNA quantification technologies showed a strong positive correlation (R2 = 0.58-0.87***). This platform also has the potential to monitor environmental pollutants by selecting appropriate indicator species. The novel insights and methodologies presented in this study represent a significant advancement in meeting the complex needs of aquatic ecosystem protection and monitoring.

Exploring local perceptions and drivers of engagement in biodiversity monitoring among participants in payments for ecosystem services schemes in southeastern Mexico.

Payments for ecosystem services (PES) are widely applied incentive-based instruments with diverse objectives that increasingly include biodiversity conservation. Yet, there is a gap in understanding of how to best assess and monitor programs' biodiversity outcomes. We examined perceptions and drivers of engagement related to biodiversity monitoring through surveys among current PES participants in 7 communities in Mexico's Selva Lacandona. We conducted workshops among survey participants that included training and field deployment of tools used to monitor biodiversity and land cover, including visual transects, camera traps, acoustic recorders, and forest cover satellite images. We conducted pre- and postworkshop surveys in each community to evaluate changes in respondents' perceptions following exposure to biodiversity monitoring training and related field activities. We also reviewed existing research on participatory environmental management and monitoring approaches. One quarter of current PES participants in the study area participated in our surveys and workshops. The majority stated interest in engaging in diverse activities related to the procedural aspects of biodiversity monitoring (e.g., planning, field data collection, results dissemination) and acknowledged multiple benefits of introducing biodiversity monitoring into PES (e.g., knowledge and capacity building, improved natural resource management, and greater support for conservation). Household economic reliance on PES was positively associated with willingness to engage in monitoring. Technical expertise, time, and monetary constraints were deterrents. Respondents were most interested in monitoring mammals, birds, and plants and using visual transects, camera traps, and forest cover satellite images. Exposure to monitoring enhanced subsequent interest in monitoring by providing respondents with new insights from their communities related to deforestation and species' abundance and diversity. Respondents identified key strengths and weaknesses of applying different monitoring tools, which suggests that deploying multiple tools simultaneously can increase local engagement and produce complementary findings and data. Overall, our findings support the relevance and usefulness of incorporating participatory biodiversity monitoring into PES.

Explorando las percepciones locales y los impulsores de la participación en el monitoreo de la biodiversidad entre participantes de esquemas de pagos por servicios ambientales en el sureste de México Resumen Los pagos por servicios ambientales (PSA) son instrumentos basados en incentivos ampliamente aplicados cuyos diversos objetivos incluyen cada vez más la conservación de la biodiversidad. Sin embargo, existe una brecha en la comprensión de cómo evaluar y monitorear los resultados de los programas en materia de biodiversidad. Examinamos las percepciones y los impulsores de la participación relacionados al monitoreo de la biodiversidad a través de encuestas entre participantes actuales de PSA en siete comunidades de la Selva Lacandona de México. Realizamos talleres entre los participantes de las encuestas que incluyeron capacitación y despliegue en campo de herramientas utilizadas para monitorear la biodiversidad y la cobertura del suelo, incluidos transectos visuales, cámaras trampa, grabadores acústicos e imágenes satelitales de la cobertura forestal. Realizamos encuestas antes y después del taller en cada comunidad para evaluar los cambios en las percepciones de los participantes tras su involucramiento en la capacitación de monitoreo de biodiversidad y las actividades de campo relacionadas. También revisamos la investigación existente sobre enfoques de monitoreo y gestión ambiental participativa. Una cuarta parte de los participantes actuales de PES en el área de estudio participaron en nuestras encuestas y talleres. La mayoría declaró interés en participar en diversas actividades relacionadas con los aspectos procesales del monitoreo de la biodiversidad (por ejemplo, planificación, recopilación de datos de campo, difusión de resultados) y reconoció múltiples beneficios de introducir el monitoreo de la biodiversidad en los PSA (por ejemplo, creación de conocimientos y capacidades, mejora de la gestión de los recursos naturales, y mayor apoyo a la conservación). La dependencia económica de los hogares de los PSA se asoció positivamente con la voluntad de participar en el monitoreo. Los factores inhibidores fueron la experiencia técnica, el tiempo y las limitaciones monetarias. Los encuestados estaban más interesados en monitorear mamíferos, aves y plantas y en utilizar transectos visuales, cámaras trampa e imágenes satelitales de la cubierta forestal. La exposición al monitoreo aumentó el interés posterior en el monitoreo al brindar a los encuestados nuevos conocimientos de sus comunidades relacionados con la deforestación y la abundancia y diversidad de especies. Los encuestados identificaron fortalezas y debilidades clave al aplicar diferentes herramientas de monitoreo, lo que sugiere que implementar múltiples herramientas simultáneamente puede aumentar la participación local y producir hallazgos y datos complementarios. En general, nuestros hallazgos respaldan la relevancia y utilidad de incorporar el monitoreo participativo de la biodiversidad en los PSA.

Molecular characterization of cetacean poxviruses along the coast of mainland Portugal.

Cetacean poxvirus (CePV) is the causative agent of tattoo skin disease (TSD) in dolphins, porpoises and whales, a condition characterized by pinhole, ring-like lesions or generalized tattoo-like skin lesions. This study genetically characterized cetacean poxviruses from stranded animals along mainland Portugal. Samples from skin lesions compatible with TSD were obtained from 4 odontocete species (Delphinus delphis, Stenella coeruleoalba, Phocoena phocoena, and Tursiops truncatus) and analyzed using a conventional PCR assay targeting the DNA polymerase gene partially. Among the positive samples (n = 29, 65.9%), a larger DNA polymerase gene fragment was obtained, allowing a robust phylogenetic analysis. Nineteen samples (43.2%) were successfully amplified and sequenced using Sanger sequencing. By combining 11 of these sequences with those from public databases, a maximum likelihood phylogenetic tree was constructed, revealing high heterogeneity within the group. These findings contribute to a better understanding of the genetic diversity, epidemiology, phylogenetics, and evolution of CePV.

Advances in machine learning and IoT for water quality monitoring: A comprehensive review.

Water holds great significance as a vital resource in our everyday lives, highlighting the important to continuously monitor its quality to ensure its usability. The advent of the. The Internet of Things (IoT) has brought about a revolutionary shift by enabling real-time data collection from diverse sources, thereby facilitating efficient monitoring of water quality (WQ). By employing Machine learning (ML) techniques, this gathered data can be analyzed to make accurate predictions regarding water quality. These predictive insights play a crucial role in decision-making processes aimed at safeguarding water quality, such as identifying areas in need of immediate attention and implementing preventive measures to avert contamination. This paper aims to provide a comprehensive review of the current state of the art in water quality monitoring, with a specific focus on the employment of IoT wireless technologies and ML techniques. The study examines the utilization of a range of IoT wireless technologies, including Low-Power Wide Area Networks (LpWAN), Wi-Fi, Zigbee, Radio Frequency Identification (RFID), cellular networks, and Bluetooth, in the context of monitoring water quality. Furthermore, it explores the application of both supervised and unsupervised ML algorithms for analyzing and interpreting the collected data. In addition to discussing the current state of the art, this survey also addresses the challenges and open research questions involved in integrating IoT wireless technologies and ML for water quality monitoring (WQM).

A survey on autonomous environmental monitoring approaches: towards unifying active sensing and reinforcement learning.

The environmental pollution caused by various sources has escalated the climate crisis making the need to establish reliable, intelligent, and persistent environmental monitoring solutions more crucial than ever. Mobile sensing systems are a popular platform due to their cost-effectiveness and adaptability. However, in practice, operation environments demand highly intelligent and robust systems that can cope with an environment's changing dynamics. To achieve this reinforcement learning has become a popular tool as it facilitates the training of intelligent and robust sensing agents that can handle unknown and extreme conditions. In this paper, a framework that formulates active sensing as a reinforcement learning problem is proposed. This framework allows unification with multiple essential environmental monitoring tasks and algorithms such as coverage, patrolling, source seeking, exploration and search and rescue. The unified framework represents a step towards bridging the divide between theoretical advancements in reinforcement learning and real-world applications in environmental monitoring. A critical review of the literature in this field is carried out and it is found that despite the potential of reinforcement learning for environmental active sensing applications there is still a lack of practical implementation and most work remains in the simulation phase. It is also noted that despite the consensus that, multi-agent systems are crucial to fully realize the potential of active sensing there is a lack of research in this area.

Extremozyme-Based Biosensors for Environmental Pollution Monitoring: Recent Developments.

Extremozymes combine high specificity and sensitivity with the ability to withstand extreme operational conditions. This work presents an overview of extremozymes that show potential for environmental monitoring devices and outlines the latest advances in biosensors utilizing these unique molecules. The characteristics of various extremozymes described so far are presented, underlining their stability and operational conditions that make them attractive for biosensing. The biosensor design is discussed based on the detection of photosynthesis-inhibiting herbicides as a case study. Several biosensors for the detection of pesticides, heavy metals, and phenols are presented in more detail to highlight interesting substrate specificity, applications or immobilization methods. Compared to mesophilic enzymes, the integration of extremozymes in biosensors faces additional challenges related to lower availability and high production costs. The use of extremozymes in biosensing does not parallel their success in industrial applications. In recent years, the "collection" of recognition elements was enriched by extremozymes with interesting selectivity and by thermostable chimeras. The perspectives for biosensor development are exciting, considering also the progress in genetic editing for the oriented immobilization of enzymes, efficient folding, and better electron transport. Stability, production costs and immobilization at sensing interfaces must be improved to encourage wider applications of extremozymes in biosensors.

Wastewater-Based Epidemiology for Viral Surveillance from an Endemic Perspective: Evidence and Challenges.

Wastewater-based epidemiology (WBE) is currently used to monitor not only the spread of the viral SARS-CoV-2 pandemic but also that of other viruses in endemic conditions, particularly in the absence of syndromic surveillance. The continuous monitoring of sewage requires high expenditure and significant time investments, highlighting the need for standardized methods and structured monitoring strategies. In this context, we conducted weekly wastewater monitoring in northwestern Tuscany (Italy) and targeted human adenovirus (HAdV), norovirus genogroup II (NoVggII), enterovirus (EV), and SARS-CoV-2. Samples were collected at the entrances of treatment plants and concentrated using PEG/NaCl precipitation, and viral nucleic acids were extracted and detected through real-time reverse transcription qPCR. NoVggII was the most identified target (84.4%), followed by HAdV, SARS-CoV-2, and EV. Only HAdV and EV exhibited seasonal peaks in spring and summer. Compared with data that were previously collected in the same study area (from February 2021 to September 2021), the results for SARS-CoV-2 revealed a shift from an epidemic to an endemic pattern, at least in the region under investigation, which was likely due to viral mutations that led to the spreading of new variants with increased resistance to summer environmental conditions. In conclusion, using standardized methods and an efficient monitoring strategy, WBE proves valuable for viral surveillance in pandemic and epidemic scenarios, enabling the identification of temporal-local distribution patterns that are useful for making informed public health decisions.

Protecting the Last Line of Defense: Analytical Approaches for Sample Preparation and Determination of the Reserve Group of Antibiotics in the Environment.

Drug resistance in microorganisms is a serious threat to life and health due to the limited number of antibiotics that show efficacy in treating infections and the difficulty in discovering new compounds with antibacterial activity. To address this issue, the World Health Organization created the AWaRe classification, a tool to support global and national antimicrobial stewardship programs. The AWaRe list categorizes antimicrobials into three groups - Access, Watch, and Reserve - according to their intended use. The Reserve group comprises "last resort" medicines used solely for treating infections caused by bacterial strains that are resistant to other treatments. It is therefore necessary to protect them, not only by using them as prudently as possible in humans and animals, but also by monitoring their subsequent fate. Unmetabolized antibiotics enter the environment through hospital and municipal wastewater or from manure, subsequently contaminating bodies of water and soils, thus contributing to the emergence and spread of antibiotic resistance. This article presents a review of determination methods for the Reserve group of antimicrobials in water, wastewater, and manure. Procedures for extracting and determining these substances in environmental samples are described, showing the limited research available, which is typically on a local level.

Advances in Carbon Dot-Based Ratiometric Fluorescent Probes for Environmental Contaminant Detection: A Review.

Detecting environmental contaminants is crucial for protecting ecosystems and human health. While traditional carbon dot (CD) fluorescent probes are versatile, they may suffer from limitations like fluctuations in signal intensity, leading to detection inaccuracies. In contrast, ratiometric fluorescent probes, designed with internal self-calibration mechanisms, offer enhanced sensitivity and reliability. This review focuses on the design and applications of ratiometric fluorescent probes based on CDs for environmental monitoring. Our discussion covers construction strategies, ratiometric fluorescence principles, and applications in detecting various environmental contaminants, including organic pollutants, heavy metal ions, and other substances. We also explore associated advantages and challenges and provide insights into potential solutions and future research directions.

Assessing the cadmium content of cacao crops in Arauca, Colombia.

The district of Arauca is the second-largest producer of cacao in Colombia. However, despite its quality, it faces issues for export due to levels of cadmium (Cd) higher than the regulatory thresholds. A central question is how it may impact agricultural performance in the presence of Cd in cacao and chocolates. This study quantified Cd in cacao plantations from Arauca. Thus, 180 farms were assessed in the municipalities of Arauquita, Fortul, Saravena, and Tame. Five sample types (soil, irrigation channel sediment, soil litter, cacao seeds, and chocolates) were assessed for Cd. As a technological innovation, the new MXRF technology was used for Cd in chocolates. The sequence of Cd content was soil litter > chocolate > soils > cacao seeds > irrigation-channel sediment. A gradient north-south of Cd content in soil was observed, where highest content was found in farms near the Arauca River, and lower farther away. In irrigation channel sediment, Cd levels averaged 0.07 mg kg-1. The Cd content in cacao seeds was 0.78 mg kg-1 on average. Cd content in chocolates was above the threshold (1.10 mg kg-1 on average, including several cacao mass percentages). These artisanal chocolate bars produced by single farms were near the limit of Cd set by the European Union (up to 0.8 mg kg-1). Therefore, mixing beans from different farms could reduce their Cd content. The present study underscores the complexity of Cd distribution, emphasizing the importance of integrating soil, crop, and landscape features in managing and mitigating Cd levels in cacao.

Water Quality Monitoring with the Multiplexed Assay MitoOxTox for Mitochondrial Toxicity, Oxidative Stress Response, and Cytotoxicity in AREc32 Cells.

Mitochondria play a key role in the energy production of cells, but their function can be disturbed by environmental toxicants. We developed a cell-based mitochondrial toxicity assay for environmental chemicals and their mixtures extracted from water samples. The reporter gene cell line AREc32, which is frequently used to quantify the cytotoxicity and oxidative stress response of water samples, was multiplexed with an endpoint of mitochondrial toxicity. The disruption of the mitochondrial membrane potential (MMP) was quantified by high-content imaging and compared to measured cytotoxicity, predicted baseline toxicity, and activation of the oxidative stress response. Mitochondrial complex I inhibitors showed highly specific effects on the MMP, with minor effects on cell viability. Uncouplers showed a wide distribution of specificity on the MMP, often accompanied by specific cytotoxicity (enhanced over baseline toxicity). Mitochondrial toxicity and the oxidative stress response were not directly associated. The multiplexed assay was applied to water samples ranging from wastewater treatment plant (WWTP) influent and effluent and surface water to drinking and bottled water from various European countries. Specific effects on MMP were observed for the WWTP influent and effluent. This new MitoOxTox assay is an important complement for existing in vitro test batteries for water quality testing and has potential for applications in human biomonitoring.

Clean and accurate soil quality monitoring in mining areas under environmental rehabilitation in the Eastern Brazilian Amazon.

Soil quality monitoring in mining rehabilitation areas is a crucial step to validate the effectiveness of the adopted recovery strategy, especially in critical areas for environmental conservation, such as the Brazilian Amazon. The use of portable X-ray fluorescence (pXRF) spectrometry allows a rapid quantification of several soil chemical elements, with low cost and without residue generation, being an alternative for clean and accurate environmental monitoring. Thus, this work aimed to assess soil quality in mining areas with different stages of environmental rehabilitation based on predictions of soil fertility properties through pXRF along with four machine learning algorithms (projection pursuit regression, PPR; support vector machine, SVM; cubist regression, CR; and random forest, RF) in the Eastern Brazilian Amazon. Sandstone and iron mines in different chronological stages of rehabilitation (initial, intermediate, and advanced) were evaluated, in addition to non-rehabilitated and native forest areas. A total of 81 soil samples (26 from sandstone mine and 55 from iron mine) were analyzed by both traditional wet-chemistry methods and pXRF. The available/exchangeable contents of K, Ca, B, Fe, and Al, in addition to H+Al, cation exchange capacity at pH = 7, Al saturation, soil organic matter, pH, sum of bases, base saturation, clay, and sand were accurately predicted (R2 > 0.70) using pXRF data, with emphasis on the prediction of Fe (R2 = 0.93), clay content (R2 = 0.81), H+Al (R2 = 0.81), and K+ (R2 = 0.85). The best predictive models were developed by RF and CR (86%) and when considering pXRF data + mining area + stage of rehabilitation (73%). The results highlight the potential of pXRF to accurately assess soil properties in environmental rehabilitation areas in the Amazon region (yet scarcely evaluated under this approach), promoting a more agile and cheaper preliminary diagnosis compared to traditional methods.