Observed and future shifts in climate zone of Borneo based on CMIP6 models.

Climate change has significantly altered the characteristics of climate zones, posing considerable challenges to ecosystems and biodiversity, particularly in Borneo, known for its high species density per unit area. This study aimed to classify the region into homogeneous climate groups based on long-term average behavior. The most effective parameters from the high-resolution daily gridded Princeton climate datasets spanning 65 years (1950-2014) were utilized, including rainfall, relative humidity (RH), temperatures (Tavg, Tmin, Tmax, and diurnal temperature range (DTR)), along with elevation data at 0.25° resolution. The FCM clustering method outperformed K-Mean and two Ward's hierarchical methods (WardD and WardD2) in classifying Borneo's climate zones based on multi-criteria assessment, exhibiting the lowest average distance (2.172-2.180) and the highest compromise programming index (CPI)-based correlation ranking among cluster averages across all climate parameters. Borneo's climate zones were categorized into four: 'Wet and cold' (WC) and 'Wet' (W) representing wetter zones, and 'Wet and hot' (WH) and 'Dry and hot' (DH) representing hotter zones, each with clearly defined boundaries. For future projection, EC-Earth3-Veg ranked first for all climate parameters across 961 grid points, emerging as the top-performing model. The linear scaling (LS) bias-corrected EC-Earth3-Veg model, as shown in the Taylor diagram, closely replicated the observed datasets, facilitating future climate zone reclassification. Improved performance across parameters was evident based on MAE (35.8-94.6%), MSE (57.0-99.5%), NRMSE (42.7-92.1%), PBIAS (100-108%), MD (23.0-85.3%), KGE (21.1-78.1%), and VE (5.1-9.1%), with closer replication of empirical probability distribution function (PDF) curves during the validation period. In the future, Borneo's climate zones will shift notably, with WC elongating southward along the mountainous spine, W forming an enclave over the north-central mountains, WH shifting northward and shrinking inland, and DH expanding northward along the western coast. Under SSP5-8.5, WC is expected to expand by 39% and 11% for the mid- and far-future periods, respectively, while W is set to shrink by 46%. WH is projected to expand by 2% and 8% for the mid- and far-future periods, respectively. Conversely, DH is expected to expand by 43% for the far-future period but shrink by 42% for the mid-future period. This study fills a gap by redefining Borneo's climate zones based on an increased number of effective parameters and projecting future shifts, utilizing advanced clustering methods (FCM) under CMIP6 scenarios. Importantly, it contributes by ranking GCMs using RIMs and CPI across multiple climate parameters, addressing a previous gap in GCM assessment. The study's findings can facilitate cross-border collaboration by providing a shared understanding of climate dynamics and informing joint environmental management and disaster response efforts.

Understanding Auger recombination in perovskite solar cells.

Enhanced radiative efficiency, long carrier lifetimes, and high carrier mobilities are hallmarks of perovskite solar cells. Considering this, complete cells experience large nonradiative recombination losses that restrict their VOC considerably below the Shockley-Queisser limit. Auger recombination, which involves two free photo-induced carriers and a trapped charge carrier, is one potential mechanism. Herein, the effects of Auger capture coefficients in mixed-cation perovskites are analyzed employing SCAPS-1D computations. It is demonstrated that VOC and FF are severely decreased with an increase in the acceptor concentration and Auger capture coefficients of perovskites, thus reducing the device performance. When the Auger capture coefficient is increased to 10-20 cm6 s-1 under the acceptor concentration of 1016 cm-3, the performance is drastically lowered from 21.5% (without taking Auger recombination into account) to 9.9%. The findings suggest that in order to increase the efficiency of perovskite solar cells and prevent the effects of Auger recombination, the Auger recombination coefficients should be less than 10-24 cm6 s-1.

The role of environmental trace element toxicants on autism: A medical biogeochemistry perspective.

Since genetic factors alone cannot explain most cases of Autism, the environmental factors are worth investigating as they play an essential role in the development of some cases of Autism. This research is a review paper that aims to clarify the role of the macro elements (MEs), Trace elements (TEs) and ultra-trace elements (UTEs) on human health if they are greater or less than the normal range. Aluminium (Al), cadmium Cd), lead (Pb), chromium (Cr), zinc (Zn), copper (Cu), nickel (Ni), arsenic (As), mercury (Hg), manganese (Mn), and iron (Fe) have been reviewed. Exposure to toxicants has a chemical effect that may ultimately lead to autism spectrum disorder (ASD). The Cr, As and Al are found in high concentrations in the blood of an autistic child when compared to normal child reference values. The toxic metals, particularly aluminium, are primarily responsible for difficulties in socialization and language skills disabilities. Zinc and copper are important elements in regulating the gene expression of metallothioneins (MTs), and zinc deficiency may be a risk factor for ASD pathogenesis. Autistics frequently have zinc deficiency combined with copper excess; as part of the treatment protocol, it is critical to monitor zinc and copper levels in autistic people, particularly those with zinc deficiency. Zinc deficiency is linked to epileptic seizures, which are common in autistic patients. Higher serum manganese and copper significantly characterize people who have ASD. Autistic children have significantly decreased lead and cadmium in urine, whereas they have significantly higher urine Cr. A higher level of As and Hg was found in the ASD individual's blood.

Households' perceptions and socio-economic determinants of climate change awareness: Evidence from Selangor Coast Malaysia.

Households living in the close vicinity of shoreline are constantly threatened by various climate change impacts. Community awareness towards climate change is a subject of considerable study as adequate knowledge is a preliminary step for adaptation decision making. An important question is how coastal communities perceive climatic variation, sea level rise and coastal hazard impacts and the socio-economic factors that affect their level of awareness. Thus, this research measures the level of awareness and the factors influencing it based on a household survey (n = 1016) that was conducted 10 critically eroded coastal areas in Selangor. Descriptive statistical analysis reveals that more than half of the households have high level of awareness about climatic variation and sea level, however, there is moderate awareness about the coastal hazard impacts such as human causalities and disease transmission. Even though households are more aware of direct coastal hazard impact such as damages to properties and disruption of daily activities. An independent sample T test indicates that respondents who are male, at working age, educated, involve in natural resource dependent occupations, and had prior exposure to extreme coastal hazards have higher levels of awareness. Research indicated about 55% of all sampled households reflected awareness of climate change, 60% households were aware of sea level rise and 47% households were aware of coastal hazard impact. This study recommends that households in Selangor coast need capacity building and climate change awareness initiatives which would assist household to build adaptive capacity, increase resilience and reduce vulnerability to climate change.

Integrative artificial intelligence models for Australian coastal sediment lead prediction: An investigation of in-situ measurements and meteorological parameters effects.

Heavy metals (HMs) such as Lead (Pb) have played a vital role in increasing the sediments of the Australian bay's ecosystem. Several meteorological parameters (i.e., minimum, maximum and average temperature (Tmin, Tmax and TavgoC), rainfall (Rn mm) and their interactions with the other batch HMs, are hypothesized to have high impact for the decision-making strategies to minimize the impacts of Pb. Three feature selection (FS) algorithms namely the Boruta method, genetic algorithm (GA) and extreme gradient boosting (XGBoost) were investigated to select the highly important predictors for Pb concentration in the coastal bay sediments of Australia. These FS algorithms were statistically evaluated using principal component analysis (PCA) Biplot along with the correlation metrics describing the statistical characteristics that exist in the input and output parameter space of the models. To ensure a high accuracy attained by the applied predictive artificial intelligence (AI) models i.e., XGBoost, support vector machine (SVM) and random forest (RF), an auto-hyper-parameter tuning process using a Grid-search approach was also implemented. Cu, Ni, Ce, and Fe were selected by all the three applied FS algorithms whereas the Tavg and Rn inputs remained the essential parameters identified by GA and Boruta. The order of the FS outcome was XGBoost > GA > Boruta based on the applied statistical examination and the PCA Biplot results and the order of applied AI predictive models was XGBoost-SVM > GA-SVM > Boruta-SVM, where the SVM model remained at the top performance among the other statistical metrics. Based on the Taylor diagram for model evaluation, the RF model was reflected only marginally different so overall, the proposed integrative AI model provided an evidence a robust and reliable predictive technique used for coastal sediment Pb prediction.

Designing Low-Cost Capacitive-Based Soil Moisture Sensor and Smart Monitoring Unit Operated by Solar Cells for Greenhouse Irrigation Management.

Precise and quick estimates of soil moisture content for the purpose of irrigation scheduling are fundamentally important. They can be accomplished through the continuous monitoring of moisture content in the root zone area, which can be accomplished through automatic soil moisture sensors. Commercial soil moisture sensors are still expensive to be used by famers, particularly in developing countries, such as Egypt. This research aimed to design and calibrate a locally manufactured low-cost soil moisture sensor attached to a smart monitoring unit operated by Solar Photo Voltaic Cells (SPVC). The designed sensor was evaluated on clay textured soils in both lab and controlled greenhouse environments. The calibration results demonstrated a strong correlation between sensor readings and soil volumetric water content (θV). Higher soil moisture content was associated with decreased sensor output voltage with an average determination coefficient (R2) of 0.967 and a root-mean-square error (RMSE) of 0.014. A sensor-to-sensor variability test was performed yielding a 0.045 coefficient of variation. The results obtained from the real conditions demonstrated that the monitoring system for real-time sensing of soil moisture and environmental conditions inside the greenhouse could be a robust, accurate, and cost-effective tool for irrigation management.

The assessment of emerging data-intelligence technologies for modeling Mg+2 and SO4-2 surface water quality.

The concentration of soluble salts in surface water and rivers such as sodium, sulfate, chloride, magnesium ions, etc., plays an important role in the water salinity. Therefore, accurate determination of the distribution pattern of these ions can improve better management of drinking water resources and human health. The main goal of this research is to establish two novel wavelet-complementary intelligence paradigms so-called wavelet least square support vector machine coupled with improved simulated annealing (W-LSSVM-ISA) and the wavelet extended Kalman filter integrated with artificial neural network (W-EKF- ANN) for accurate forecasting of the monthly), magnesium (Mg+2), and sulfate (SO4-2) indices at Maroon River, in Southwest of Iran. The monthly River flow (Q), electrical conductivity (EC), Mg+2, and SO4-2 data recorded at Tange-Takab station for the period 1980-2016. Some preprocessing procedures consisting of specifying the number of lag times and decomposition of the existing original signals into multi-resolution sub-series using three mother wavelets were performed to develop predictive models. In addition, the best subset regression analysis was designed to separately assess the best selective combinations for Mg+2 and SO4-2. The statistical metrics and authoritative validation approaches showed that both complementary paradigms yielded promising accuracy compared with standalone artificial intelligence (AI) models. Furthermore, the results demonstrated that W-LSSVM-ISA-C1 (correlation coefficient (R) = 0.9521, root mean square error (RMSE) = 0.2637 mg/l, and Kling-Gupta efficiency (KGE) = 0.9361) and W-LSSVM-ISA-C4 (R = 0.9673, RMSE = 0.5534 mg/l and KGE = 0.9437), using Dmey mother that outperformed the W-EKF-ANN for predicting Mg+2 and SO4-2, respectively.

Manganese (Mn) removal prediction using extreme gradient model.

Exploring the Manganese (Mn) removal prediction with several independent variables is tremendously critical and indispensable to understand the pattern of removal process. Mn is one of the key heavy metals (HMs) stipulated by the WHO for the development of many attributes of the ecosystem in controlled quantity. In the present paper, an extreme gradient model (XGBoost) is proposed for Mn prediction. A compressive statistical analysis reveals the stochastics behaviour of the data prior to the prediction investigation. The main goal is to determine the Mn predictability of XGBoost algorithm with influencing factors such as D2EHPA (M), Time (min), H2SO4 (M), NaCl (g/L), and EDTA (mM). The PCA biplot signifies the importance of the predictors. The XGBoost model validated against a diversity of data-driven models such as multilinear regression (MLR), support vector machine (SVM), and random forest (RF). The order of the applied models' performance are XGBoost > RF > SVM > MLR as per their R2 and RMSE metrics over testing phase i.e. 20.88, 0.75, 0.61, 0.40, and 2.23, 3.01, 3.51, 6.38, respectively. Moreover, the Taylor diagram and Radar chart have drown to emphasize the XGBoost model efficiency, stability, and reliability. In respect of XGBoost model prediction, 'Time' predictor outperforms D2EHPA, EDTA, H2SO4, and NaCl predictors in order.

Newly explored machine learning model for river flow time series forecasting at Mary River, Australia.

Hourly river flow pattern monitoring and simulation is the indispensable precautionary task for river engineering sustainability, water resource management, flood risk mitigation, and impact reduction. Reliable river flow forecasting is highly emphasized to support major decision-makers. This research paper adopts a new implementation approach for the application of a river flow prediction model for hourly prediction of the flow of Mary River in Australia; a novel data-intelligent model called emotional neural network (ENN) was used for this purpose. A historical dataset measured over a 4-year period (2011-2014) at hourly timescale was used in building the ENN-based predictive model. The results of the ENN model were validated against the existing approaches such as the minimax probability machine regression (MPMR), relevance vector machine (RVM), and multivariate adaptive regression splines (MARS) models. The developed models are evaluated against each other for validation purposes. Various numerical and graphical performance evaluators are conducted to assess the predictability of the proposed ENN and the competitive benchmark models. The ENN model, used as an objective simulation tool, revealed an outstanding performance when applied for hourly river flow prediction in comparison with the other benchmark models. However, the order of the model, performance wise, is ENN > MARS > RVM > MPMR. In general, the present results of the proposed ENN model reveal a promising modeling strategy for the hourly simulation of river flow, and such a model can be explored further for its ability to contribute to the state-of-the-art of river engineering and water resources monitoring and future prediction at near real-time forecast horizons.

Fusion-based approach for hydrometeorological drought modeling: a regional investigation for Iran.

The objective of this study was to model a new drought index called the Fusion-based Hydrological Meteorological Drought Index (FHMDI) to simultaneously monitor hydrological and meteorological drought. Aiming to estimate drought more accurately, local measurements were classified into various clusters using the AGNES clustering algorithm. Four single artificial intelligence (SAI) models-namely, Gaussian Process Regression (GPR), Ensemble, Feedforward Neural Networks (FNN), and Support Vector Regression (SVR)-were developed for each cluster. To promote the results of single of products and models, four fusion-based approaches, namely, Wavelet-Based (WB), Weighted Majority Voting (WMV), Extended Kalman Filter (EKF), and Entropy Weight (EW) methods, were used to estimate FHMDI in different time scales, precipitation, and runoff. The performance of single and combined products and models was assessed through statistical error metrics, such as Kling-Gupta efficiency (KGE), Mean Bias Error (MBE), and Normalized Root Mean Square Error (NRMSE). The performance of the proposed methodology was tested over 24 main river basins in Iran. The validation results of the FHMDI (the compliance of the index with the pre-existing drought index) revealed that it accurately identified drought conditions. The results indicated that individual products performed well in some river basins, while fusion-based models improved dataset accuracy more compared to local measurements. The WMV with the highest accuracy (lowest NRMSE) had a good performance in 60% of the cases compared to all other products and fusion-based models. WMV also showed higher efficiency in 100% of the cases than all other fusion-based and SAI models for simultaneous hydrological and meteorological drought estimation. In light of these findings, we recommend the use of fusion-based approach to improve drought modeling.

Multi-step ahead forecasting of electrical conductivity in rivers by using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model enhanced by Boruta-XGBoost feature selection algorithm.

Electrical conductivity (EC) is widely recognized as one of the most essential water quality metrics for predicting salinity and mineralization. In the current research, the EC of two Australian rivers (Albert River and Barratta Creek) was forecasted for up to 10 days using a novel deep learning algorithm (Convolutional Neural Network combined with Long Short-Term Memory Model, CNN-LSTM). The Boruta-XGBoost feature selection method was used to determine the significant inputs (time series lagged data) to the model. To compare the performance of Boruta-XGB-CNN-LSTM models, three machine learning approaches-multi-layer perceptron neural network (MLP), K-nearest neighbour (KNN), and extreme gradient boosting (XGBoost) were used. Different statistical metrics, such as correlation coefficient (R), root mean square error (RMSE), and mean absolute percentage error, were used to assess the models' performance. From 10 years of data in both rivers, 7 years (2012-2018) were used as a training set, and 3 years (2019-2021) were used for testing the models. Application of the Boruta-XGB-CNN-LSTM model in forecasting one day ahead of EC showed that in both stations, Boruta-XGB-CNN-LSTM can forecast the EC parameter better than other machine learning models for the test dataset (R = 0.9429, RMSE = 45.6896, MAPE = 5.9749 for Albert River, and R = 0.9215, RMSE = 43.8315, MAPE = 7.6029 for Barratta Creek). Considering the better performance of the Boruta-XGB-CNN-LSTM model in both rivers, this model was used to forecast 3-10 days ahead of EC. The results showed that the Boruta-XGB-CNN-LSTM model is very capable of forecasting the EC for the next 10 days. The results showed that by increasing the forecasting horizon from 3 to 10 days, the performance of the Boruta-XGB-CNN-LSTM model slightly decreased. The results of this study show that the Boruta-XGB-CNN-LSTM model can be used as a good soft computing method for accurately predicting how the EC will change in rivers.

Suspended sediment load prediction using sparrow search algorithm-based support vector machine model.

Prediction of suspended sediment load (SSL) in streams is significant in hydrological modeling and water resources engineering. Development of a consistent and accurate sediment prediction model is highly necessary due to its difficulty and complexity in practice because sediment transportation is vastly non-linear and is governed by several variables like rainfall, strength of flow, and sediment supply. Artificial intelligence (AI) approaches have become prevalent in water resource engineering to solve multifaceted problems like sediment load modelling. The present work proposes a robust model incorporating support vector machine with a novel sparrow search algorithm (SVM-SSA) to compute SSL in Tilga, Jenapur, Jaraikela and Gomlai stations in Brahmani river basin, Odisha State, India. Five different scenarios are considered for model development. Performance assessment of developed model is analyzed on basis of mean absolute error (MAE), root mean squared error (RMSE), determination coefficient (R2), and Nash-Sutcliffe efficiency (ENS). The outcomes of SVM-SSA model are compared with three hybrid models, namely SVM-BOA (Butterfly optimization algorithm), SVM-GOA (Grasshopper optimization algorithm), SVM-BA (Bat algorithm), and benchmark SVM model. The findings revealed that SVM-SSA model successfully estimates SSL with high accuracy for scenario V with sediment (3-month lag) and discharge (current time-step and 3-month lag) as input than other alternatives with RMSE = 15.5287, MAE = 15.3926, and ENS = 0.96481. The conventional SVM model performed the worst in SSL prediction. Findings of this investigation tend to claim suitability of employed approach to model SSL in rivers precisely and reliably. The prediction model guarantees the precision of the forecasted outcomes while significantly decreasing the computing time expenditure, and the precision satisfies the demands of realistic engineering applications.

Vertical variations and environmental heterogeneity drove the symphony of periphytic protozoan fauna in marine ecosystems.

Periphytic protozoa are esteemed icons of microbial fauna, renowned for their sensitivity and role as robust bioindicators, pivotal for assessing ecosystem stress and anthropogenic impacts on water quality. Despite their significance, research exploring the community dynamics of protozoan fauna across diverse water columns and depths in shallow waters has been notably lacking. This is the first study that examines the symphony of protozoan fauna in different water columns at varying depths (1, 2, 3.5 and 5 m), in South China Sea. Our findings reveal that vertical changes and environmental heterogeneity plays pivotal role in shaping the protozoan community structure, with distinct preferences observed in spirotrichea and phyllopharyngea classes at specific depths. Briefly, diversity metrics (i.e., both alpha and beta) showed significantly steady patterns at 2 m and 3.5 m depths as well as high homogeneity in most of the indices was observed. Co-associations between environmental parameters and protozoan communities demonstrated temperature, dissolved oxygen, salinity, and pH, are significant drivers discriminating species richness and evenness across all water columns. Noteworthy variations of the other environmental parameters such as SiO3-Si, PO4--P, and NO2--N at 1 m and NO3--N, and NH4+-N, at greater depths, signal the crucial role of nutrient dynamics in shaping the protozoan communities. Moreover, highly sensitive species like Anteholosticha pulchara, Apokeronopsis crassa, and Aspidisca steini in varying environmental conditions among vertical columns may serve as eco- indicators of water quality. Collectively, this study contributes a thorough comprehension of the fine-scale structure and dynamics of protozoan fauna within marine ecosystems, providing insightful perspectives for ecological and water quality assessment in ever-changing marine environments.

Groundwater quality characterization using an integrated water quality index and multivariate statistical techniques.

This study attempts to characterize and interpret the groundwater quality (GWQ) using a GIS environment and multivariate statistical approach (MSA) for the Jakham River Basin (JRB) in Southern Rajasthan. In this paper, analysis of various statistical indicators such as the Water Quality Index (WQI) and multivariate statistical methods, i.e., principal component analysis and correspondence analysis (PCA and CA), were implemented on the pre and post-monsoon water quality datasets. All these methods help identify the most critical factor in controlling GWQ for potable water. In pre-monsoon (PRM) and post-monsoon (POM) seasons, the computed value of WQI has ranged between 28.28 to 116.74 and from 29.49 to 111.98, respectively. As per the GIS-based WQI findings, 63.42 percent of the groundwater samples during the PRM season and 42.02 percent during the POM were classed as 'good' and could be consumed for drinking. The Principal component analysis (PCA) is a suitable tool for simplification of the evaluation process in water quality analysis. The PCA correlation matrix defines the relation among the water quality parameters, which helps to detect the natural or anthropogenic influence on sub-surface water. The finding of PCA's factor analysis shows the impact of geological and human intervention, as increased levels of EC, TDS, Na+, Cl-, HCO3-, F-, and SO42- on potable water. In this study, hierarchical cluster analysis (HCA) was used to categories the WQ parameters for PRM and POR seasons using the Ward technique. The research outcomes of this study can be used as baseline data for GWQ development activities and protect human health from water-borne diseases in the southern region of Rajasthan.

High adsorptive performance of chitosan-microalgae-carbon-doped TiO2 (kronos)/ salicylaldehyde for brilliant green dye adsorption: Optimization and mechanistic approach.

A composite of chitosan biopolymer with microalgae and commercial carbon-doped titanium dioxide (kronos) was modified by grafting an aromatic aldehyde (salicylaldehyde) in a hydrothermal process for the removal of brilliant green (BG) dye. The resulting Schiff's base Chitosan-Microalgae-TiO2 kronos/Salicylaldehyde (CsMaTk/S) material was characterised using various analytical methods (conclusive of physical properties using BET surface analysis method, elemental analysis, FTIR, SEM-EDX, XRD, XPS and point of zero charge). Box Behnken Design was utilised for the optimisation of the three input variables, i.e., adsorbent dose, pH of the media and contact time. The optimum conditions appointed by the optimisation process were further affirmed by the desirability test and employed in the equilibrium studies in batch mode and the results exhibited a better fit towards the pseudo-second-order kinetic model as well as Freundlich and Langmuir isotherm models, with a maximum adsorption capacity of 957.0 mg/g. Furthermore, the reusability study displayed the adsorptive performance of CsMaTk/S remains effective throughout five adsorption cycles. The possible interactions between the dye molecules and the surface of the adsorbent were derived based on the analyses performed and the electrostatic attractions, H-bonding, Yoshida-H bonding, π-π and n-π interactions are concluded to be the responsible forces in this adsorption process.

Navigating the environmental dynamics, toxicity to aquatic organisms and human associated risks of an emerging tire wear contaminant 6PPD quinone.

N-1,3-Dimethylbutyl-N'-phenyl-p-quinone diamine (6PPDQ) is a derivative of 6PPD, a synthetic antioxidant used in tire manufacturing to control the degradation caused by oxidation and heat aging. Its discovery in 2020 has raised important environmental concern, particularly regarding its association with acute mortality in coho salmon, prompting surge in research on its occurrence, fate, and transport in aquatic ecosystems. Despite this attention, there remain notable gaps in grasping the knowledge, demanding an in depth overview. Thus, this review consolidates recent studies to offer a thorough investigation of 6PPDQ's environmental dynamics, pathways into aquatic ecosystems, toxicity to aquatic organisms, and human health implications. Various aquatic species exhibit differential susceptibility to 6PPDQ toxicity, manifesting in acute mortalities, disruption of metabolic pathways, oxidative stress, behavioral responses, and developmental abnormalities. Whereas, understanding the species-specific responses, molecular mechanisms, and broader ecological implications requires further investigation across disciplines such as ecotoxicology, molecular biology, and environmental chemistry. Integration of findings emphasizes the complexity of 6PPDQ toxicity and its potential risks to human health. However, urgent priorities should be given to the measures like long-term monitoring studies to evaluate the chronic effects on aquatic ecosystems and the establishment of standardized toxicity testing protocols to ensure the result comparability and reproducibility. This review serves as a vital resource for researchers, policymakers, and environmental professionals seeking appraisals into the impacts of 6PPDQ contamination on aquatic ecosystems and human health.

Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India.

This research was established to accurately forecast daily scale air quality index (AQI) which is an essential environmental index for decision-making. Researchers have projected different types of models and methodologies for AQI forecasting, such as statistical techniques, machine learning (ML), and most recently deep learning (DL) models. The modelling development was adopted for Delhi city, India which is a major city with air pollution issues simialir to entire urban cities of India especially during winter seasons. This research was predicted AQI using different versions of DL models including Long-Short Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM) and Bidirectional Recurrent Neural Networks (Bi-RNN) in addition to Kernel Ridge Regression (KRR). Results indicated that Bi-RNN model consistently outperformed the other models in both training and testing phases, while the KRR model consistently displayed the weakest performance. The outstanding performance of the models development displayed the requirement of adequate data to train the models. The outcomes of the models showed that LSTM, BI-LSTM, KRR had lower performance compared with Bi-RNN models. Statistically, Bi-RNN model attained maximum cofficient of determination (R2 = 0.954) and minimum root mean square error (RMSE = 25.755). The proposed model in this research revealed the robust predictable to provide a valuable base for decision-making in the expansion of combined air pollution anticipation and control policies targeted at addressing composite air pollution problems in the Delhi city.

Microplastics in agroecosystems: Soil-plant dynamics and effective remediation approaches.

Increasing microplastic (MP) pollution, primarily from anthropogenic sources such as plastic film mulching, waste degradation, and agricultural practices, has emerged as a pressing global environmental concern. This review examines the direct and indirect effects of MPs on crops, both in isolation and in conjunction with other contaminants, to elucidate their combined toxicological impacts. Organic fertilizers predominantly contain 78.6% blue, 9.5% black, and 8.3% red MPs, while irrigation water in agroecosystems contains 66.2% white, 15.4% blue, and 8.1% black MPs, ranging from 0-1 mm to 4-5 mm in size. We elucidate five pivotal insights: Firstly, soil MPs exhibit affinity towards crop roots, seeds, and vascular systems, impeding water and nutrient uptake. Secondly, MPs induce oxidative stress in crops, disrupting vital metabolic processes. Thirdly, leachates from MPs elicit cytotoxic and genotoxic responses in crops. Fourthly, MPs disrupt soil biotic and abiotic dynamics, influencing water and nutrient availability for crops. Lastly, the cumulative effects of MPs and co-existing contaminants in agricultural soils detrimentally affect crop yield. Thus, we advocate agronomic interventions as practical remedies. These include biochar input, application of growth regulators, substitution of plastic mulch with crop residues, promotion of biological degradation, and encouragement of crop diversification. However, the efficacy of these measures varies based on MP type and dosage. As MP volumes increase, exploring alternative mitigation strategies such as bio-based plastics and environmentally friendly biotechnological solutions is imperative. Recognizing the persistence of plastics, policymakers should enact legislation favoring the mitigation and substitution of non-degradable materials with bio-derived or compostable alternatives. This review demonstrates the urgent need for collective efforts to alleviate MP pollution and emphasizes sustainable interventions for agricultural ecosystems.

Decoding the molecular concerto: Toxicotranscriptomic evaluation of microplastic and nanoplastic impacts on aquatic organisms.

The pervasive and steadily increasing presence of microplastics/nanoplastics (MPs/NPs) in aquatic environments has raised significant concerns regarding their potential adverse effects on aquatic organisms and their integration into trophic dynamics. This emerging issue has garnered the attention of (eco)toxicologists, promoting the utilization of toxicotranscriptomics to unravel the responses of aquatic organisms not only to MPs/NPs but also to a wide spectrum of environmental pollutants. This review aims to systematically explore the broad repertoire of predicted molecular responses by aquatic organisms, providing valuable intuitions into complex interactions between plastic pollutants and aquatic biota. By synthesizing the latest literature, present analysis sheds light on transcriptomic signatures like gene expression, interconnected pathways and overall molecular mechanisms influenced by various plasticizers. Harmful effects of these contaminants on key genes/protein transcripts associated with crucial pathways lead to abnormal immune response, metabolic response, neural response, apoptosis and DNA damage, growth, development, reproductive abnormalities, detoxification, and oxidative stress in aquatic organisms. However, unique challenge lies in enhancing the fingerprint of MPs/NPs, presenting complicated enigma that requires decoding their specific impact at molecular levels. The exploration endeavors, not only to consolidate existing knowledge, but also to identify critical gaps in understanding, push forward the frontiers of knowledge about transcriptomic signatures of plastic contaminants. Moreover, this appraisal emphasizes the imperative to monitor and mitigate the contamination of commercially important aquatic species by MPs/NPs, highlighting the pivotal role that regulatory frameworks must play in protecting all aquatic ecosystems. This commitment aligns with the broader goal of ensuring the sustainability of aquatic resources and the resilience of ecosystems facing the growing threat of plastic pollutants.

Deep learning versus hybrid regularized extreme learning machine for multi-month drought forecasting: A comparative study and trend analysis in tropical region.

Drought is a hazardous natural disaster that can negatively affect the environment, water resources, agriculture, and the economy. Precise drought forecasting and trend assessment are essential for water management to reduce the detrimental effects of drought. However, some existing drought modeling techniques have limitations that hinder precise forecasting, necessitating the exploration of suitable approaches. This study examines two forecasting models, Long Short-Term Memory (LSTM) and a hybrid model integrating regularized extreme learning machine and Snake algorithm, to forecast hydrological droughts for one to six months in advance. Using the Multivariate Standardized Streamflow Index (MSSI) computed from 58 years of streamflow data for two drier Malaysian stations, the models forecast droughts and were compared to classical models such as gradient boosting regression and K-nearest model for validation purposes. The RELM-SO model outperformed other models for forecasting one month ahead at station S1, with lower root mean square error (RMSE = 0.1453), mean absolute error (MAE = 0.1164), and a higher Nash-Sutcliffe efficiency index (NSE = 0.9012) and Willmott index (WI = 0.9966). Similarly, at station S2, the hybrid model had lower (RMSE = 0.1211 and MAE = 0.0909), and higher (NSE = 0.8941 and WI = 0.9960), indicating improved accuracy compared to comparable models. Due to significant autocorrelation in the drought data, traditional statistical metrics may be inadequate for selecting the optimal model. Therefore, this study introduced a novel parameter to evaluate the model's effectiveness in accurately capturing the turning points in the data. Accordingly, the hybrid model significantly improved forecast accuracy from 19.32 % to 21.52 % when compared with LSTM. Besides, the reliability analysis showed that the hybrid model was the most accurate for providing long-term forecasts. Additionally, innovative trend analysis, an effective method, was used to analyze hydrological drought trends. The study revealed that October, November, and December experienced higher occurrences of drought than other months. This research advances accurate drought forecasting and trend assessment, providing valuable insights for water management and decision-making in drought-prone regions.