Application of FTIR two-dimensional correlation spectroscopy (2D-COS) analysis in characterizing environmental behaviors of microplastics: A systematic review.

Microplastics (MPs) are ubiquitous in the environment, continuously undergo aging processes and release toxic chemical substances. Understanding the environmental behaviors of MPs is critical to accurately evaluate their long-term ecological risk. Generalized two-dimensional correlation spectroscopy (2D-COS) is a powerful tool for MPs studies, which can dig more comprehensive information hiding in the conventional one-dimensional spectra, such as infrared (IR) and Raman spectra. The recent applications of 2D-COS in analyzing the behaviors and fates of MPs in the environment, including their aging processes, and interactions with natural organic matter (NOM) or other chemical substances, were summarized systematically. The main requirements and limitations of current approaches for exploring these processes are discussed, and the corresponding strategies to address these limitations and drawbacks are proposed as well. Finally, new trends of 2D-COS are prospected for analyzing the properties and behaviors of MPs in both natural and artificial environmental processes.

Priority sources identification and risks assessment of heavy metal(loid)s in agricultural soils of a typical antimony mining watershed.

Heavy metal(loid) (HM) pollution in agricultural soils has become an environmental concern in antimony (Sb) mining areas. However, priority pollution sources identification and deep understanding of environmental risks of HMs face great challenges due to multiple and complex pollution sources coexist. Herein, an integrated approach was conducted to distinguish pollution sources and assess human health risk (HHR) and ecological risk (ER) in a typical Sb mining watershed in Southern China. This approach combines absolute principal component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) models with ER and HHR assessments. Four pollution sources were distinguished for both models, and APCS-MLR model was more accurate and plausible. Predominant HM concentration source was natural source (39.1%), followed by industrial and agricultural activities (23.0%), unknown sources (21.5%) and Sb mining and smelting activities (16.4%). Although natural source contributed the most to HM concentrations, it did not pose a significant ER. Industrial and agricultural activities predominantly contributed to ER, and attention should be paid to Cd and Sb. Sb mining and smelting activities were primary anthropogenic sources of HHR, particularly Sb and As contaminations. Considering ER and HHR assessments, Sb mining and smelting, and industrial and agricultural activities are critical sources, causing serious ecological and health threats. This study showed the advantages of multiple receptor model application in obtaining reliable source identification and providing better source-oriented risk assessments. HM pollution management, such as regulating mining and smelting and implementing soil remediation in polluted agricultural soils, is strongly recommended for protecting ecosystems and humans.

Exploring the diversity of dissolved organic matter (DOM) properties and sources in different functional areas of a typical macrophyte - derived lake combined with optical spectroscopy and FT-ICR MS analysis.

Lake Baiyangdian is one of China's largest macrophyte - derived lakes, facing severe challenges related to water quality maintenance and eutrophication prevention. Dissolved organic matter (DOM) was a huge carbon pool and its abundance, property, and transformation played important roles in the biogeochemical cycle and energy flow in lake ecosystems. In this study, Lake Baiyangdian was divided into four distinct areas: Unartificial Area (UA), Village Area (VA), Tourism Area (TA), and Breeding Area (BA). We examined the diversity of DOM properties and sources across these functional areas. Our findings reveal that DOM in this lake is predominantly composed of protein - like substances, as determined by excitation - emission matrix and parallel factor analysis (EEM - PARAFAC). Notably, the exogenous tyrosine-like component C1 showed a stronger presence in VA and BA compared to UA and TA. Ultrahigh - resolution mass spectrometry (FT - ICR MS) unveiled a similar DOM molecular composition pattern across different functional areas due to the high relative abundances of lignan compounds, suggesting that macrophytes significantly influence the material structure of DOM. DOM properties exhibited specific associations with water quality indicators in various functional areas, as indicated by the Mantel test. The connections between DOM properties and NO3N and NH3N were more pronounced in VA and BA than in UA and TA. Our results underscore the viability of using DOM as an indicator for more precise and scientific water quality management.

Development of a coupled model to simulate and assess arsenic contamination and impact factors in the Jinsha River Basin, China.

With the increasing severity of arsenic (As) pollution, quantifying the environmental behavior of pollutant based on numerical model has become an important approach to determine the potential impacts and finalize the precise control strategies. Taking the industrial-intensive Jinsha River Basin as typical area, a two-dimensional hydrodynamic water quality model coupled with Soil and Water Assessment Tool (SWAT) model was developed to accurately simulate the watershed-scale distribution and transport of As in the terrestrial and aquatic environment at high spatial and temporal resolution. The effects of hydro-climate change, hydropower station construction and non-point source emissions on As were quantified based on the coupled model. The result indicated that higher As concentration areas mainly centralized in urban districts and concentration slowly decreased from upstream to downstream. Due to the enhanced rainfall, the As concentration was significantly higher during the rainy season than the dry season. Hydro-climate change and the construction of hydropower station not only affected the dissolved As concentration, but also affected the adsorption and desorption of As in sediment. Furthermore, As concentration increased with the input of non-point source pollution, with the maximum increase about 30%, resulting that non-point sources contributed important pollutant impacts to waterways. The coupled model used in pollutant behavior analysis is general with high potential application to predict and mitigate water pollution.

Rapid detection of colored and colorless macro- and micro-plastics in complex environment via near-infrared spectroscopy and machine learning.

To better understand the migration behavior of plastic fragments in the environment, development of rapid non-destructive methods for in-situ identification and characterization of plastic fragments is necessary. However, most of the studies had focused only on colored plastic fragments, ignoring colorless plastic fragments and the effects of different environmental media (backgrounds), thus underestimating their abundance. To address this issue, the present study used near-infrared spectroscopy to compare the identification of colored and colorless plastic fragments based on partial least squares-discriminant analysis (PLS-DA), extreme gradient boost, support vector machine and random forest classifier. The effects of polymer color, type, thickness, and background on the plastic fragments classification were evaluated. PLS-DA presented the best and most stable outcome, with higher robustness and lower misclassification rate. All models frequently misinterpreted colorless plastic fragments and its background when the fragment thickness was less than 0.1mm. A two-stage modeling method, which first distinguishes the plastic types and then identifies colorless plastic fragments that had been misclassified as background, was proposed. The method presented an accuracy higher than 99% in different backgrounds. In summary, this study developed a novel method for rapid and synchronous identification of colored and colorless plastic fragments under complex environmental backgrounds.

Assessment of heavy metal levels in polyculture fish farms and their aquatic ecosystems: an integrative study addressing environmental and human health risks associated with dam water usage.

This study examines the levels of heavy metals in polyculture fish (Labeo rohita, Cyprinus carpio, and Catla catla), water, and sediment in Tanda Dam, Kohat, Pakistan, aiming to understand environmental and health risks. Samples of fish, water, and sediment were collected from 3 fish farms, and heavy metal concentrations were measured using a Flame Atomic Absorption Spectrophotometer (AAS). Results reveal that C. catla exhibited significantly higher (p < 0.05) levels of Zn than other fish species. Conversely, C. carpio showed significantly higher (p < 0.05) concentrations of Pb, Cd, Cr, Mn, Cu, As, and Ni than other species. The heavy metal hierarchy in C. carpio was found to be Zn > Cu > Pb > Cr > Cd > Mn > As > Ni. While heavy metal levels in L. rohita and C. catla generally fell within reference ranges, exceptions were noted for Zn, Pb, and Cd. Conversely, in C. carpio, all metals exceeded reference ranges except for Cu and Ni. Principal Component Analysis (PCA) indicated a close relationship between water and sediment. Additionally, cluster analysis suggested that C. catla formed a distinct cluster from L. rohita and C. carpio, implying different responses to the environment. Despite concerns raised by the Geoaccumulation Index (Igeo) and Contamination Factor (CF), particularly for Cd, which exhibited a high CF. Furthermore, Hazard Index (HI) values for all three fish species were below 1, suggesting low health risks. However, elevated Igeo and CF values for Cd suggest significant pollution originating from anthropogenic sources. This study underscores the importance of monitoring heavy metals in water for both environmental preservation and human health protection. Future research efforts should prioritize pollution control measures to ensure ecosystem and public health safety.

Short-term responses of littoral phytoplankton in a large shallow subtropical lake.

The littoral zone is an essential compartment for lake biota because of its high productivity and diversity. Moreover, phytoplankton is expected to have non-equilibrium dynamics on it. The study's aimed to explore phytoplankton in the littoral zone of a shallow lake over a short-term scale. Daily sampling was conducted for 25 consecutive summer days in 2016, at two marginal points of a continuously warm, polymictic, and oligo-mesotrophic subtropical lake (Lake Mangueira, Brazil). Cyanobacteria and Chlorophyta contributed 86% of total biomass. We observed high variability in phytoplankton structure, with species turnover over diel cycles. Redundancy analysis indicated spatial differentiation for phytoplankton structure in relation to abiotic conditions. Nutrient dynamics and humic substances were significant drivers for phytoplankton variability. Phytoplankton was positively correlated with SRP and negatively with humic substances. Our results showed a non- equilibrium state for the littoral phytoplankton of Lake Mangueira, given the high variability of abiotic conditions, even at short distances. Due to its high temporal and spatial variability, the littoralzone seems to contribute to the recruitment and maintenance of phytoplankton biodiversity in shallow lakes. Further studies should consider the functional attributes of species and the complex biological interactions of phytoplankton and macrophytes along the littoral zone.

Acoustic monitoring of artificial reefs reveals Atlantic cod and weakfish spawning and presence of individual bottlenose dolphins.

The artificial reefs in New York's waters provide structure in areas that are typically flat and sandy, creating habitat for a multitude of species as an area to spawn, forage, and reside. Passive acoustic data collected on the Fire Island and Shinnecock artificial reefs between 2018 and 2022 detected spawning-associated calls of weakfish (Cynoscion regalis) and Atlantic cod (Gadus morhua), as well as the presence of individual bottlenose dolphins (Tursiops truncatus) through their signature whistles. Weakfish and Atlantic cod were more vocally active on the Fire Island reef, where Atlantic cod grunts peaked during a new moon phase in December, and weakfish spawning experienced variable peaks between mid-July and mid-August on both reefs. Fifty-seven individual bottlenose dolphins were identified, with whistle repeats ranging from seconds to years apart. Passive acoustic monitoring allows for simultaneous collection of information on multiple species at different trophic levels as well as behavioral information that helps managers understand how these animals utilize these habitats, which can lead to improved conservation measures.

Toward a more realistic estimate of exposure to chromium and nickel in soils of geogenic and/or anthropogenic origin: importance of oral bioaccessibility.

To enhance risk assessment for contaminated sites, incorporating bioavailability through bioaccessibility as a corrective factor to total concentration is essential to provide a more realistic estimate of exposure. While the main in vitro tests have been validated for As, Cd, and/or Pb, their potential for assessing the bioaccessibility of additional elements remains underexplored. In this study, the physicochemical parameters, pseudototal Cr and Ni concentrations, soil phase distribution, and oral bioaccessibility of twenty-seven soil samples were analysed using both the ISO 17924 standard and a simplified test based on hydrochloric acid. The results showed wide variability in terms of the concentrations (from 31 to 21,079 mg kg-1 for Cr, and from 26 to 11,663 mg kg-1 for Ni) and generally low bioaccessibility for Cr and Ni, with levels below 20% and 30%, respectively. Bioaccessibility variability was greater for anthropogenic soils, while geogenic enriched soils exhibited low bioaccessibility. The soil parameters had an influence on bioaccessibility, but the effects depended on the soils of interest. Sequential extractions provided the most comprehensive explanation for bioaccessibility. Cr and Ni were mostly associated with the residual fraction, indicating limited bioaccessibility. Ni was distributed in all phases, whereas Cr was absent from the most mobile phase, which may explain the lower bioaccessibility of Cr compared to that of Ni. The study showed promising results for the use of the simplified test to predict Cr and Ni bioaccessibility, and its importance for more accurate human exposure evaluation and effective soil management practices.

Accumulation analysis and overall measurement to represent airborne toxic metals with passive tree bark biomonitoring technique in urban areas.

Authorities have long proved the utility of bioindicators in monitoring the state of environmental pollution. Some biological indicators can measure environmental pollutant levels, and many tree species have been tested for suitability for monitoring purposes. The differences in morphological characteristics in the trees have demonstrated the effects of human activities on different materials. Measuring bark and wood biomass from contaminated sites was identified and directly compared with those from a clean site or areas characterized by distinct contamination sources. However, preliminary results demonstrate the approach's potential in the realization of strategies for disease control and promoting health to reduce environmental and health inequalities in at-risk urban areas. Picea orientalis L. and Cedrus atlantica Endl., especially their bark, can be regarded as a more robust storage of Cu (37.95 mg/kg) and Mn (188.25 mg/kg) than Pinus pinaster, Cupressus arizonica, and Pseudotsuga menziesii, which and is therefore a better bioindicator for Cu and Mn pollution. Considering the total concentrations as a result of the study, the pollution is thought to be caused by environmental problems and traffic in the region. The deposition of Cu, Mn, Ni, and Zn elements was found P. menziesii (60, 443, 58, and 258 mg/kg) and P. orientalis (76, 1684, 41, and 378 mg/kg) and seems to reflect atmospheric quite clearly compared to P. pinaster, C. arizonica, and C. atlantica. Ni and Zn concentrations have significantly increased since 1983, and P. menziesii and P. orientalis can be potentially valuable bioindicators for emphasizing polluted fields.

An ecohydrological approach to assess water provisioning and supporting ecosystem services in the Budhabalanga River Basin, India.

Rivers are vital and complex natural systems that provide a wide range of ecosystem services. This study presents a methodology for assessing the riverine provisioning and supporting ecosystem services, whose applicability has been demonstrated over the Budhabalanga River Basin of India. The Soil and Water Assessment Tool (SWAT) is used to generate streamflow time series at various ungauged sites, and then the streamflow is characterized for the evaluation of provisioning services. Further, the diversity and abundance of macroinvertebrates, along with the Lotic-invertebrate Index for Flow Evaluation (LIFE), is used to study the riverine supporting ecosystem services. The streams show intermittent behavior and strong seasonality for low flows, which limits the water availability, particularly during pre-monsoon season. The Baseflow Index (BFI) is greater than 0.6, indicating that groundwater contributes more than 60% of the total streamflow. Interestingly, despite the high BFI, the streams did not conform to the prevailing opinion that a greater baseflow contribution results in a later commencement of the low-flow period in the hydrological year. Furthermore, the study depicts significant variations in the diversity and abundance of the macroinvertebrates across the various sampling sites. However, the LIFE score across the sites remained consistent within a narrow range, i.e., 8 to 9, suggesting a steady supply of supporting ecosystem services. The results of the study can help the policymakers towards an informed decision making and the simplistic methodology proposed in this study can be replicated in other river basins for identifying vulnerable watersheds and prioritizing management actions.

Spatial distribution and health risks assessment of heavy metals in e-waste dumping sites from Pakistan.

The present study focused on to determine the concentration and health risk of heavy metals (Cu, Pb, Zn, Cd, Hg, Cr) in e-waste contaminated soils collected from different provinces of Pakistan. Further, the impact of heavy metals on soil enzyme activities and microbial community was also investigated. The concentration (mg/kg) of Hg, Zn, Fe, Cu, Pb, Cd, and Cr ranged between 0-0.258, 2.284-6.587, 3.005-40.72, 8.67-36.88, 12.05-35.03, 1.03-2.43, and 33.13-60.05, respectively. The results revealed that Lahore site of Punjab province indicated more concentration of heavy metals as compared to other sites. The level of Cr at all sites whereas Hg at only two sites exceeds the World Health Organization standards (WHO) for soil. Soil enzyme activity exhibited dynamic trend among the sites. Maximum enzyme activity was observed for urease followed by phosphatase and catalase. Contamination factor (Cf), Pollution load index (PLI), and geo-accumulation index (Igeo) results showed that all the sites are highly contaminated with Cu, Cd, and Pb. Hazard index (HI) was less than 1 for children and adults suggesting non-carcinogenic health risk. Principle component analysis results depicted relation among Cr, Fr, catalase, and actinomycetes; Cd, OM, urease, and bacteria, and Pb, Cu, Zn, Hg, and phosphatase, suggesting soil enzymes and microbial community profiles were influenced by e-waste pollution. Therefore, there is a dire need to introduce sustainable e-waste recycling techniques as well as to make stringent e-waste management policies to reduce further environmental contamination.

Sediment analysis and water quality assessment in the Pixquiac basin: drinking water supply of Xalapa city (Veracruz, Mexico).

Fluvial sediment analysis and water quality assessment are useful to identify anthropic and natural sources of pollution in rivers. Currently, there is a lack of information about water quality in the Pixquiac basin (Veracruz state, Mexico), and this scarcity of data prevents authorities to take adequate measures to protect water resources. The basin is a crucial territory for Xalapa, the capital city of Veracruz state, as it gets 39% of its drinkable water from it. This research analyzed 10 physicochemical parameters and 12 metal concentrations in various rivers and sources during two seasons. Dissolved metals presented average concentrations (µg/L): Al (456.25) > Fe (199.4) > Mn (16.86) > Ba (13.8) > Zn (7.6) > Cu (1.03) > Pb (0.27) > As (0.12) > Ni (0.118) (Cd, Cr and Hg undetectable). Metals in sediment recorded average concentrations (ppm): Fe (38575) > Al (38425) > Mn (460) > Ba (206.2) > Zn (65.1) > Cr (29.8) > Ni (20.9) > Cu (16.4) > Pb (4.8) > As (2.1) (Cd and Hg undetectable). During the rainy season, Water Quality Index (WAWQI) classified stations P17 and P18's water as "unsuitable for drinking" with values of 110.4 and 117.6. Enrichment factor (EF) recorded a "moderate enrichment" of Pb in sediment in P24. Pollution was mainly explained by wastewater discharges in rivers but also because of erosion and rainfall events. Statistical analysis presented strong relationships between trace and major metals which could explain a common natural origin for metals in water and sediment: rock lixiviation.

Quantifying spatiotemporal dynamics in the Kolonnawa marsh of Colombo, Sri Lanka.

Kolonnawa marsh (KM) is an important wetland ecosystem in Colombo district, Sri Lanka that provides essential ecosystem services, and has undergone significant changes over recent decades due to continuous exploitation and reclamation. The values of wetlands are disregarded by decision-makers, despite the fact that they are crucial for improving the quality of water and offer chances for relaxation and amusement in metropolitan areas. Underestimation of the value of wetlands contributes to their continuing deterioration and inevitable loss. Investigating the changes in wetlands can provide crucial information for decision-making. This study aimed to monitor the spatiotemporal land-cover dynamics of KM with the prospect prediction as reduced total extent of KM gradually with time and marsh area being transformed into terrestrial vegetation with time. The collective images from Google Earth (2000 to 2021) and drone data (2022) were analyzed with the GIS application. Subsequently, 50-m2 grid squares with unique cell IDs are designed to link among land cover maps for spatiotemporal land-cover change analysis. Then, we calculate land cover category: surface water, marsh, and terrestrial vegetation proportions for each map in 50-m2 grid cells. Statistical comparison of the land cover changes in grid square cells shows that each land cover category has significant change with the time. The results showed that the reduction of KM marsh resulting in land cover changes has a positive implication on wetland degradation. Thus, interventions should be made for the restoration and sustainable management of KM.

Bioavailable fractions of heavy metals in the road dust during infrastructure construction at urban Coimbatore and its potential health implications, India.

Over the past two decades the Global South is witnessing unprecedented economic transformation and Asian Cities in particular have a remarkable upsurge. Coimbatore, an industrial city in Southern India with an estimated population of 2 million (in 2022) is witnessing a rapid transition in terms of infrastructure development. In this context, the present study attempts to assess the particulate matter (PM10 and PM2.5) emissions at road network construction sites and the heavy metal fractionation in the road dust/sediment samples with a core focus to quantify the bioavailable fraction of metals (Fe, Cu, Cr, Cd, Pb and Ni) and its source apportionment in the road side dust/sediment samples. About 60 composite road dust/sediment samples were collected for heavy metal fractionation analysis in the six arterial roads that undergo core developments like construction of road over bridges, additional road incorporation and street expansions. PM monitoring revealed that 24 h average PM2.5 (47 µg/m3) and PM10 (69 µg/m3) concentrations at many construction sites exceeded 24 h average recommended by WHO guidelines [PM2.5 (15 µg/m3) and PM10 (45 µg/m3), respectively]. The bioavailable fractions of Fe, Cu, Cr and Cd are notably higher in the roadside sediment samples at road construction sites. Health Risk assessment, such as carcinogenic risks (Children-4.41 × 10-2, Adult-3.598 × 10-6) and non-carcinogenic risks, inferred substantial risks at high intensity construction sites with statistical analyses, including PCA and cluster analysis, indicating considerable anthropogenic influences in the heavy metal fractions.

Estimation of methane greenhouse gas emissions from livestock in Egypt during 1989 to 2021.

This study investigates methane emissions from the livestock sector, representing by enteric fermentation and manure management, in Egypt from 1989 to 2021, focusing on spatial and temporal variations at the governorate level. Utilizing IPCC guidelines and emission factors, methane emissions were estimated for dairy and non-dairy cattle, buffalo, sheep and goat, poultry, and other livestock categories. Results reveal fluctuating emission patterns over the study period, with notable declines in certain governorates such as Kafr El-Sheikh and Red Sea, attributed to reductions in livestock populations. However, increasing trends were observed overall, driven by population growth in other regions. Hotspots of methane emissions were identified in delta governorates like Behera and Sharkia, as well as agriculturally rich regions including Menia and Suhag. While livestock populations varied between regions, factors such as water availability, climatic conditions, and farming practices influenced distribution. Notably, cluster analysis did not reveal regional clustering among governorates, suggesting emissions changes were not dependent on specific geographic or climatic boundaries. Manure management accounted for only 5-6% of total emissions, with emissions at their lowest in the last three years due to population declines. Despite the highest livestock populations being sheep and goats, emissions from enteric fermentation and manure management were highest from buffalo and cattle. This study underscores the importance of accurate data collection and adherence to IPCC recommendations for estimating GHG emissions, enabling the development of targeted mitigation strategies to address climate change challenges in the livestock sector.

Disturbance and vegetational structure in an urban forest of Indian Siwaliks: an ecological assessment.

Urban forests face multiple human-mediated pressures leading to compromised ecosystem structure and functioning. Therefore, understanding ecosystem structure in response to ongoing pressures is crucial for sustaining ecological integrity and human well-being. We aim to assess the disturbance and its effects on the vegetation structure of urban forests in Chandigarh using a combination of remote sensing techniques and vegetation surveys. The disturbance was evaluated as a change in NDVI (Normalised Difference Vegetation Index) from 2001 to 2021 by applying the BFAST (Breaks For Additive Season and Trend) algorithm to the MODIS satellite imagery data. A vegetation survey was conducted to compare the species composition, taxonomic and phylogenetic diversity as measures of forest vegetational structure. While signals of disturbance were evident, the changes in vegetation structure were not well established from our study. Further, this analysis indicated no significant differences in vegetation composition due to disturbance (F1,12 = 0.91, p = 0.575). However, the phylogenetic diversity was substantially lower for disturbed plots than undisturbed plots, though the taxonomic diversity was similar among the disturbed and undisturbed plots. Our results confirmed that disturbance effects are more prominent on the phylogenetic than taxonomic diversity. These findings can be considered early signals of disturbance and its impact on the vegetation structure of urban forests and contribute to the knowledge base on urban ecosystems. Our study has implications for facilitating evidence-based decision-making and the development of sustainable management strategies for urban forest ecosystems.

Anomaly detection in groundwater monitoring data using LSTM-Autoencoder neural networks.

Groundwater monitoring data can be prone to errors and biases due to various factors like borehole and equipment malfunctions, or human mistakes. These inaccuracies can jeopardize the groundwater system, leading to reduced efficiency and potentially causing partial or complete failures in the monitoring system. Traditional anomaly detection methods, which rely on statistical and time-variant techniques, struggle to handle the complex and dynamic nature of anomalies. With advancements in artificial intelligence and the growing need for effective anomaly detection and prevention across different sectors, artificial neural network methods are emerging as capable of identifying more intricate anomalies by considering both temporal and contextual aspects. Nonetheless, there is still a shortage of comprehensive studies on groundwater anomaly detection. The intricate patterns of sequential data from groundwater present numerous challenges, necessitating sophisticated modeling techniques that combine mathematics, statistics, and machine learning for viable solutions. This paper introduces a model designed for high accuracy and efficient computation in detecting anomalies in groundwater monitoring data through a probabilistic approach. We employed the Monte Carlo method and SEAWAT numerical simulation to ascertain the uncertainty in groundwater salinity. Subsequently, a Long Short-Term Memory (LSTM)-Autoencoder model was trained and evaluated, forming the basis of an anomaly detection framework. Each piece of training data was assessed by the LSTM-Autoencoder using the Negative Log Likelihood (NLL) score and a predefined threshold to determine the data's abnormality percentage. The accuracy evaluation of the proposed LSTM-Autoencoder algorithm revealed that this approach achieved commendable performance, with an accuracy of 98.47% in anomaly detection.

Towards a low-carbon and beautiful world: assessing the impact of digital technology on the common benefits of pollution reduction and carbon reduction.

When ecology thrives, civilization thrives, and when ecology declines, civilization declines. Based on panel data from 30 provinces in China from 2000 to 2021, this study used marginal abatement costs to estimate the co-benefits of pollution reduction and carbon reduction. Two-way fixed effect and two-stage intermediary effect models were used to evaluate the impact of digital technology on co-benefits and its indirect channels. The results indicated that China's total carbon emissions maintained a steady growth trend, while air pollution showed a fluctuating declining trend. Reaching peak carbon neutrality calls for more innovative solutions. Under joint emission reduction efforts, the study revealed marginal abatement cost savings of 535.8 million yuan/million tons and 6216.5 million yuan/µg/m3 for carbon reduction and pollution reduction, respectively. Most importantly, the study confirmed that joint emission reduction programs can reduce environmental governance costs more than individual emission reductions can, and the co-benefits increased from 37.983 to 44.757. The co-benefits generally showed a trend of fluctuation and increases and had the characteristics of phased transformation. Intragroup differences and cross-overlapping between regions made regional differences in co-benefits obvious. The subversive, permeable, and integrated features of digital technology have resulted in the all-around transformation of the economy and society, and the new technology-economy paradigm has significantly improved co-benefits. The conclusion remains valid after robustness testing and controlling for endogeneity problems. The results of the mechanism analysis suggest that digital technology can indirectly improve synergies through the intermediary channels of fostering green technology innovation, reducing energy consumption intensity and improving the energy structure.

Innovative and practical tools for monitoring and assessing biodiversity status and impacts of multiple human pressures in marine systems.

Human activities at sea can produce pressures and cumulative effects on ecosystem components that need to be monitored and assessed in a cost-effective manner. Five Horizon European projects have joined forces to collaboratively increase our knowledge and skills to monitor and assess the ocean in an innovative way, assisting managers and policy-makers in taking decisions to maintain sustainable activities at sea. Here, we present and discuss the status of some methods revised during a summer school, aiming at better management of coasts and seas. We include novel methods to monitor the coastal and ocean waters (e.g. environmental DNA, drones, imaging and artificial intelligence, climate modelling and spatial planning) and innovative tools to assess the status (e.g. cumulative impacts assessment, multiple pressures, Nested Environmental status Assessment Tool (NEAT), ecosystem services assessment or a new unifying approach). As a concluding remark, some of the most important challenges ahead are assessing the pros and cons of novel methods, comparing them with benchmark technologies and integrating these into long-standing time series for data continuity. This requires transition periods and careful planning, which can be covered through an intense collaboration of current and future European projects on marine biodiversity and ecosystem health.