Receiving international industrial transfer (mainly foreign direct investment, FDI) is extremely important for economic development but also brings negative environmental impacts for Southeast Asian developing countries (SEADCs). Due to relatively low labor costs and large market potential, SEADCs have become an attractive destination for industrial transfer after China, while studies were far from sufficient on the associated air pollutant emissions that would worsen air quality and threaten human health. We develop an exploratory framework to estimate the long-term trends of relevant air pollutant emissions in eight major SEADCs, including Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Thailand, and Vietnam. During 1990-2018, the emissions generally show a fluctuating upward trend and increased significantly in Cambodia, Laos, Philippines, and Vietnam. The total emissions of CO, NMVOC, SO2, NOX, PM2.5, and NH3 from the eight SEADCs increased from 19.0, 4.3, 3.6, 1.5, 0.5, and 0.4 kilotons (kt) to 391.6, 260.9, 271.1, 182.4, 48.4, and 12.2 kt, respectively. The emission growth in almost all SEADCs accelerated after 2008 and faster than FDI growth. The disparities in emissions among SEADCs basically grew first and then declined to a level lower than that of 1990, but generally exceeded the disparities in FDI. Productivity gain and emission intensity decrease primarily caused the emission growth and reduction, respectively. Relatively small reductions in emission intensity are found for NOX and SO2. In general, most SEADCs have utilized FDI for economic development without sufficient efforts on air pollutant emission controls. Our outcomes can inform the formulation and optimization of relevant policies reconciling economic development and air quality improvement in SEADCs.
Potentially toxic metal-polluted water resources are a heavily discussed topic the pollution by potentially toxic metals can cause significant health risks. Nanomaterials are actively developed towards providing high specific surface area and creating active adsorption sites for the treatment and remediation of these polluted waters. In an effort to tackle the limitations of conventional type adsorbents, nano-hydroxyapatite (HAp) was developed in this study by in situ generation onto wood powder, resulting in the formation of uniform hybrid powder (HAp@wood composite) structure consisting of HAp nanoparticles that showed the removal efficiency up to 80 % after 10 min; the maximum adsorption capacity for Cu(II) ions (98.95 mg/g-HAp) was higher compared to agglomerated nano-HAp (72.85 mg/g-HAp). The adsorption capacity of Cu(II) remained stable (89.85-107.66 mg/g-HAp) during the four adsorption-desorption cycles in multi-component system, thereby demonstrating high selectivity for Cu(II). This approach of using nanoparticle is relatively simple yet effective in improving the adsorption of potentially toxic metals and the developed approach can be used to develop advanced nanocomposites in commercial wastewater treatment.
Today, the limited sources of freshwater supply are a significant concern. Exploiting alternative sources, especially seawater, has been the focus, but purifying it is energy-intensive. Integrating desalination with renewable energy is a proposed solution, but it comes with high costs and environmental risks during construction. Hence, this study presents a framework to enhance the modeling, optimization, and evaluation of green water-power cogeneration systems to achieve the sustainability goals of cities and societies. An improved division algorithm (DA) determines the optimal component sizes based on criteria like minimal energy demand, reduced environmental and resource damage, low total life cycle cost (TLCC), and high reliability. Optimization considers varying loss of power supply probability (LPSP) levels (0 %, 2 %, 5 %, and 10 %). The environmental assessment utilizes a life cycle assessment (LCA) approach with IMPACT 2002+ and cumulative energy demand (CED) calculations. The study models the green cogeneration systems based on weather conditions, water demand, and power requirements of Al Lulu Island, Abu Dhabi, UAE. The system comprises photovoltaic panels, wind turbines, tidal generators, and backup systems (fuel cells). Results reveal that TLCC ranges from $186,263 to $486,876 for the highest LPSP. The solar-tidal-based configuration offers the lowest TLCC ($186,263) while substituting solar with wind energy increases TLCC by 160 %. The wind-tidal-based configuration has the lowest specific environmental impact (1020 mPt/yr) and cumulative energy demand (39.06 GJ/yr) for the highest LPSP. In contrast, the solar-tidal-wind-based configuration inflicts the most damage, with 62.63 GJ/yr and 1794 mPt/yr for the highest LPSP. The finding indicates that the DA is faster (100 iterations) than the genetic algorithm (1000 iterations), particle swarm optimization (400 iterations), and artificial bee swarm optimization (300 iterations). The study underscores the solar-tidal-based configuration as the optimal choice across multiple criteria, offering a promising solution for freshwater supply and environmental sustainability on Al Lulu Island.
Although large-scale solar (LSS) is a promising renewable energy technology, it causes adverse impacts on the ecosystem, human health, and resource depletion throughout its upstream (i.e., raw material extraction to solar panel production) and downstream (i.e., plant demolition and waste management) processes. The LSS operational performance also fluctuates due to meteorological conditions, leading to uncertainty in electricity generation and raising concerns about its overall environmental performance. Hitherto, there has been no evidence-backed study that evaluates the ecological sustainability of LSS with the consideration of meteorological uncertainties. In this study, a novel integrated Life Cycle Assessment (LCA) and Artificial Neural Network (ANN) framework is developed to forecast the meteorological impacts on LSS's electricity generation and its life cycle environmental sustainability. For LCA, 18 impact categories and three damage categories are characterised and assessed by ReCiPe 2016 via SimaPro v. 9.1. For ANN, a feedforward neural network is applied via Neural Designer 5.9.3. Taking an LSS plant in Malaysia as a case study, the photovoltaic panel production stage contributes the highest environmental impact in LSS (30 % of human health, 30 % of ecosystem quality, and 34 % of resource scarcity). Aluminium recycling reduces by 10 % for human health, 10 % for ecosystem quality, and 9 % for resource scarcity. The emissions avoided by the forecasted LSS-generated electricity offset the environmental burden for human health, ecosystem quality, and resource scarcity 12-68 times, 13-73 times, and 18-98 times, respectively. The developed ANN-LCA framework can provide LSS stakeholders with data-backed insights to effectively design an environmentally conscious LSS facility, considering meteorological influences.
A modeling framework utilizing the coactive neuro-fuzzy inference system (CANFIS) has been developed for multi-lead time groundwater level (GWL) forecasting in four different wells located in Texas and Florida, USA. Various model input combinations, including GWL, precipitation, temperature, and surface water level variables, have been derived based on proposed correlation analysis using singular spectrum analysis (SSA) remainders. The models have been trained on data subsets of varying lengths to identify the optimal training data duration. Additionally, we have introduced the bagging ensemble learning method to enhance the performance of the CANFIS model. As part of a comprehensive model evaluation process, the best-performing CANFIS model for each forecasting scenario has undergone uncertainty analysis using bootstrap sampling. Our results reveal that the CANFIS model performs satisfactorily for daily forecasting but leaves room for improvement in monthly forecasting, particularly for two-month and three-month ahead forecasts. Moreover, we have identified several optimal input combinations, highlighting the significance of the temperature variable in monthly forecasting. Furthermore, our findings indicate that additional training data does not necessarily lead to improved performance. The ensemble CANFIS model has demonstrated significant performance enhancement, particularly for monthly forecasting. Finally, the CANFIS model uncertainty analysis has shown satisfactory results for daily forecasting scenarios, while monthly forecasting models exhibit higher uncertainties, particularly during periods with distinctly different GWL fluctuation patterns.
The most recent set of General Circulation Models (GCMs) derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6) was used in this work to analyse the spatiotemporal patterns of future rainfall distribution across the Johor River Basin (JRB) in Malaysia. A group of 23 GCMs were chosen for comparative assessment in simulating basin-scale rainfall based on daily rainfall from the historical period of the Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS). The methodological novelty of this study lies in the application of relative importance metrics (RIM) to rank and select historical GCM simulations for reproducing rainfall at 109 CHIRPS grid points within the JRB. In order to choose the top GCMs, the rankings given by RIM were aggregated using the compromise programming index (CPI) and Jenks optimised classification (JOC). It was found that ACCESS-ESM1-5 and CMCC-ESM2 were ranked the highest in most of the grid. The final GCM was then bias-corrected using the linear scaling method before being ensemble based on the Bayesian model averaging (BMA) technique. The spatiotemporal assessment of the ensemble model for the different months over the near-future period 2021-2060 and far-future period 2061-2100 was compared with those under Shared Socioeconomic Pathways (SSPs), namely, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. Heterogeneous changes in rainfall were projected across the JRB, with both increasing and decreasing trends. In the near-future and far-future scenarios, higher rainfall was projected for December, indicating an elevated risk of flooding during the end of the North East monsoon (NEM). Conversely, August showed a decreasing trend in rainfall, implying an increasing risk of severe drought. The findings of this study provide valuable insights for effective water resource management and climate change adaptation in the region.
A pioneering study employed a holistic geostatistical approach to predict the spatial variability of a non sampled area in the Chenab River, Pakistan, using kriging interpolation for organochlorine pesticide (OCP)-polluted risk zones. The Present research intended to investigate the carcinogenic and non-carcinogenic human health risks, contamination levels, and spatial variation of OCPs in the Chenab River, Pakistan. The residual OCP content in sediment samples (n = 120) ranged from 0.056 to 32.14 ng/g. DDE and α-HCH were prevalent among all the samples analyzed, with mean concentrations of 15.84 ± 8.02 and 12.45 ± 6.72 ng/g, respectively. The order of magnitude of OCPs in sediment samples was DDTs > α-HCH > chlorothalonil > heptachlor > endosulfan > aldrin > dieldrin. The findings of the single (SPI) and Nemerow (Nel) pollution index of α-HCH, heptachlor, and aldrin depicted the Chenab River as a serious pollution risk zone. The outcomes of the Pearson correlation coefficient analysis represent the positive correlation among all OCPs, revealing the common origin. Distribution trends showed substantially higher (p 10-4 illustrated a substantial cancer health risk posed by α-HCH, heptachlor, aldrin, and dieldrin in the downstream zone. We recommend the urgent cessation of the ongoing discharge of OCPs into the Chenab River, which needs to be highlighted owing to the significant cancer risk to public health to ensure the good health and wellbeings.
Impetus to minimise the energy and carbon footprints of evolving wastewater resource recovery facilities has promoted the development of microbial electrochemical systems (MES) as an emerging energy-neutral and sustainable platform technology. Using separators in dual-chamber MES to isolate anodic and cathodic environments creates endless opportunities for its myriad applications. Nevertheless, the high internal resistance and the complex interdependencies among various system factors have challenged its scale-up. This critical review employed a systems approach to examine the complex interdependencies and practical issues surrounding the implementation and scalability of dual-chamber MES, where the anodic and cathodic reactions are mutually appraised to improve the overall system efficiency. The robustness and stability of anodic biofilms in large-volume MES is dependent on its inoculum source, antecedent history and enrichment strategies. The composition and anode-respiring activity of these biofilms are modulated by the anolyte composition, while their performance demands a delicate balance between the electrode size, macrostructure and the availability of substrates, buffers and nutrients when using real wastewater as anolyte. Additionally, the catholyte governed the reduction environment and associated energy consumption of MES with scalable electrocatalysts needed to enhance the sluggish reaction kinetics for energy-efficient resource recovery. A comprehensive assessment of the dual-chamber reactor configuration revealed that the tubular, spiral-wound, or plug-in modular MES configurations are suitable for pilot-scale, where it could be designed more effectively using efficient electrode macrostructure, suitable membranes and bespoke strategies for continuous operation to maximise their performance. It is anticipated that the critical and analytical understanding gained through this review will support the continuous development and scaling-up of dual-chamber MES for prospective energy-neutral treatment of wastewater and simultaneous circular management of highly relevant environmental resources.
In this study, we present the genome characterization of a novel chitin-degrading strain, KSP-S5-2, and comparative genomics of 33 strains of Cellvibrionaceae. Strain KSP-S5-2 was isolated from mangrove sediment collected in Balik Pulau, Penang, Malaysia, and its 16S rRNA gene sequence showed the highest similarity (95.09%) to Teredinibacter franksiae. Genome-wide analyses including 16S rRNA gene sequence similarity, average nucleotide identity, digital DNA-DNA hybridization, and phylogenomics, suggested that KSP-S5-2 represents a novel species in the family Cellvibrionaceae. The Cellvibrionaceae pan-genome exhibited high genomic variability, with only 1.7% representing the core genome, while the flexible genome showed a notable enrichment of genes related to carbohydrate metabolism and transport pathway. This observation sheds light on the genetic plasticity of the Cellvibrionaceae family and the gene pools that form the basis for the evolution of polysaccharide-degrading capabilities. Comparative analysis of the carbohydrate-active enzymes across Cellvibrionaceae strains revealed that the chitinolytic system is not universally present within the family, as only 18 of the 33 genomes encoded chitinases. Strain KSP-S5-2 displayed an expanded repertoire of chitinolytic enzymes (25 GH18, two GH19 chitinases, and five GH20 β-N-acetylhexosaminidases) but lacked genes for agar, xylan, and pectin degradation, indicating specialized enzymatic machinery focused primarily on chitin degradation. Further, the strain degraded 90% of chitin after 10 days of incubation. In summary, our findings provided insights into strain KSP-S5-2's genomic potential, the genetics of its chitinolytic system, genomic diversity within the Cellvibrionaceae family in terms of polysaccharide degradation, and its application for chitin degradation.
Di-2-ethylhexyl phthalic acid (DEHP) is one of the most widely used plasticizers in the industry, which can improve the flexibility and durability of plastics. It is prone to migrate from various daily plastic products through wear and leaching into the surrounding environment and decompose into the more toxic metabolite mono-2-ethylhexyl phthalic acid (MEHP) after entering the human body. However, the impacts and mechanisms of MEHP on neuroblastoma are unclear. We exposed MYCN-amplified neuroblastoma SK-N-BE(2)C cells to an environmentally related concentration of MEHP and found that MEHP increased the proliferation and migration ability of tumor cells. The peroxisome proliferator-activated receptor (PPAR) β/δ pathway was identified as a pivotal signaling pathway in neuroblastoma, mediating the effects of MEHP through transcriptional sequencing analysis. Because MEHP can bind to the PPARβ/δ protein and initiate the expression of the downstream gene angiopoietin-like 4 (ANGPTL4), the PPARβ/δ-specific agonist GW501516 and antagonist GSK3787, the recombinant human ANGPTL4 protein, and the knockdown of gene expression confirmed the regulation of the PPARβ/δ-ANGPTL4 axis on the malignant phenotype of neuroblastoma. Based on the critical role of PPARβ/δ and ANGPTL4 in the metabolic process, a non-targeted metabolomics analysis revealed that MEHP altered multiple metabolic pathways, particularly lipid metabolites involving fatty acyls, glycerophospholipids, and sterol lipids, which may also be potential factors promoting tumor progression. We have demonstrated for the first time that MEHP can target binding to PPARβ/δ and affect the progression of neuroblastoma by activating the PPARβ/δ-ANGPTL4 axis. This mechanism confirms the health risks of plasticizers as tumor promoters and provides new data support for targeted prevention and treatment of neuroblastoma.
This correspondence critically examines and rectifies modeling deficiencies identified in a recent article published in this journal. Our analysis covers a range of models and issues, including the Temkin isotherm, the Flory-Huggins isotherm, the pseudo-first-order kinetic model, the pseudo-second-order kinetic model, the intraparticle diffusion model, the Elovich kinetic model, and the computation of thermodynamic parameters. The elucidation and correction of these modeling issues contribute to a more accurate and reliable understanding of the studied phenomena, thereby enhancing the scientific rigor of the subject paper.
Inverse vulcanized polysulfides (IVP) are promising sulfur-enriched copolymers with unconventional properties irresistible for diverse applications like Hg2+ remediation. Nevertheless, due to their inherent hydrophobic nature, these copolymers still offer low Hg2+ uptake capacity. Herein, we reported the synthesis of IVP by reacting molten sulfur with 4-vinyl benzyl chloride, followed by their functionalization using N-methyl D-glucamine (NMDG) to increase the hydration of the developed IVP. The chemical composition and structure of the functionalized IVP were proposed based on FTIR and XPS analysis. The functionalized IVP demonstrated a high mercury adsorption capacity of 608 mg/g (compared to <26 mg/g for common IVP) because of rich sulfur and hydrophilic regions. NMDG functionalized IVP removed 100 % Hg2+ from a low feed concentration (10-50 mg/l). A predictive machine learning model was also developed to predict the amount of mercury removed (%) using GPR, ANN, Decision Tree, and SVM algorithms. Hyperparameter and loss function optimization was also carried out to reduce the prediction error. The optimized GPR algorithm demonstrated high R2 (0.99 (training) and 0.98 (unseen)) and low RMSE (2.74 (training) and 2.53 (unseen)) values indicating its goodness in predicting the amount of mercury removed. The produced functionalized IVP can be regenerated and reused with constant Hg2+ uptake capacity. Sulfur is the waste of the petrochemical industry and is abundantly available, making the functionalized IVP a sustainable and cheap adsorbent that can be produced for high-volume Hg2+ remediation. ENVIRONMENTAL IMPLICATION: This research effectively addresses the removal of the global top-priority neurotoxic pollutant mercury, which is toxic even at low concentrations. We attempted to remove the Hg2+ utilizing an inexpensive adsorbent developed by NMDG functionalized copolymer of molten sulfur and VBC. A predictive machine learning model was also formulated to predict the amount of mercury removal from wastewater with only a 0.05 % error which shows the goodness of the developed model. This work is critical in utilizing this low-cost adsorbent and demonstrates its potential for large-scale industrial application.
Aquaculture is a highly important and expanding industry in Southeast Asia (SEA). An upcoming problem is the emergence of antibiotic resistant pathogens due to the unchecked use of antibiotics and human clinical practices. This review focused insight into the occurrence of antimicrobial resistance (AMR) and strategies from SEA aquaculture based on the original research publication over the period 2002 to 2023. Amongst the 11 SEA countries, the most AMR report has come from Vietnam, Malaysia, and Thailand, respectively. The AMR found in SEA aquaculture were classified into 17 drug classes. The most reported AMR are aminoglycosides, beta-lactams, (fluoro)quinolones, tetracycline, sulpha group and multi-drug. Beta-lactams, tetracycline, sulpha group are reported in each country with the reported frequencies higher than 40 %. Escherichia coli, Aeromonas and Vibrio are the most widely and frequently reported ARB in SEA aquaculture. Multiple antibiotic resistance (MAR) indexes for the sample containing multiple bacterial isolates were generally low, while the medium numbers of MAR indexes for the typical bacteria species were higher than 0.2 and showed higher MAR levels than the global mean. Most of the detected ARGs are related to beta-lactams, tetracycline, sulpha group, and aminoglycosides. Amongst the beta-lactam resistance genes, blaTEM, and blaSHV are the most frequently detected. Almost all the available information of antibiotics, ARB and ARGs in SEA aquaculture was consistent with the global scale analysis. In addition, factors that contribute to the development and spread of AMR in SEA aquaculture were discussed. Moreover, the national action plan to combat AMR in SEA countries and the available technologies that already applied in the SEA aquaculture are also included in this review. Such findings underline the need for synergistic efforts from scientists, engineers, policy makers, government managers, entrepreneurs, and communities to manage and reduce the burden of AMR in aquaculture of SEA countries.
Nanobubbles (NBs), given their unique properties, could theoretically be paired with rhamnolipids (RL) to tackle polycyclic aromatic hydrocarbon contamination in groundwater. This approach may overcome the limitations of traditional surfactants, such as high toxicity and low efficiency. In this study, the remediation efficiency of RL, with or without NBs, was assessed through soil column experiments (soil contaminated with phenanthrene). Through the analysis of the two-site non-equilibrium diffusion model, there was a synergistic effect between NBs and RL. The introduction of NBs led to a reduction of up to 24.3 % in the total removal time of phenanthrene. The direct reason for this was that with NBs, the retardation factor of RL was reduced by 1.9 % to 15.4 %, which accelerated the solute replacement of RL. The reasons for this synergy were multifaceted. Detailed analysis reveals that NBs improve RL's colloidal stability, increase its absolute zeta potential, and reduce its soil adsorption capacity by 13.3 %-19.9 %. Furthermore, NBs and their interaction with RL substantially diminish the surface tension, contact angle, and dynamic viscosity of the leaching solution. These changes in surface thermodynamic and rheological properties significantly enhance the migration efficiency of the eluent. The research outcomes facilitate a thorough comprehension of NBs' attributes and their relevant applications, and propose an eco-friendly method to improve the efficiency of surfactant remediation.
Drought affects vegetation growth to a large extent. Understanding the dynamic changes of vegetation during drought is of great significance for agricultural and ecological management and climate change adaptation. The relations between vegetation and drought have been widely investigated, but how vegetation loss and restoration in response to drought remains unclear. Using the standardized precipitation evapotranspiration index (SPEI) and the normalized difference vegetation index (NDVI) data, this study developed an evaluation framework for exploring the responses of vegetation loss and recovery to meteorological drought, and applied it to the humid subtropical Pearl River basin (PRB) in southern China for estimating the loss and recovery of three vegetation types (forest, grassland, cropland) during drought using the observed NDVI changes. Results indicate that vegetation is more sensitive to drought in high-elevation areas (lag time 8 months). Vegetation loss (especially in cropland) is found to be more sensitive to drought duration than drought severity and peak. No obvious linear relationship between drought intensity and the extent of vegetation loss is found. Regardless of the intensity, drought can cause the largest probability of mild loss of vegetation, followed by moderate loss, and the least probability of severe loss. Large spatial variability in the probability of vegetation loss and recovery time is found over the study domain, with a higher probability (up to 50 %) of drought-induced vegetation loss and a longer recovery time (>7 months) mostly in the high-elevation areas. Further analysis suggests that forest shows higher but cropland shows lower drought resistance than other vegetation types, and grassland requires a shorter recovery time (4.2-month) after loss than forest (5.1-month) and cropland (4.8-month).
Research on renewable energy from microalgae has led to a growing interest in porous substrate photobioreactors, but their widespread adoption is currently limited to pure microalgal biofilm cultures. The behavior of microalgal-bacterial biofilms immobilized on microporous substrates remains as a research challenge, particularly in uncovering their mutualistic interactions in environment enriched with dissolved organic matter. Therefore, this study established a novel culture platform by introducing microalgal-derived bio-coating that preconditioned hydrophilic polyvinylidene fluoride membranes for the microalgal-bacterial biofilm growth of freshwater microalgae, Chlorella vulgaris ESP 31 and marine microalgae, Cylindrotheca fusiformis with bacteria, Escherichia coli. In the attached co-culture mode, the bio-coating we proposed demonstrated the ability to enhance microalgal growth for both studied species by a range of 2.5 % to 19 % starting from day 10 onwards. Additionally, when compared to co-culture on uncoated membranes, the bio-coating exhibited a significant bacterial growth promotion effect, increasing bacterial growth by at least 2.35 times for the C. vulgaris-E. coli co-culture after an initial adaptation phase. A significant increase of at least 72 % in intracellular biochemical compounds (including chlorophyll, polysaccharides, proteins, and lipids) was observed within just five days, primarily due to the high concentration of pre-coated organic matter, mainly sourced from the internal organic matter (IOM) of C. fusiformis. Higher accumulation of organic compounds in the bio-coating indirectly triggers a competition between microalgae and bacteria which potentially stimulate the production of additional intra-/extra-organic substances as a defensive response. In short, insight gained from this study may represent a paradigm shift in the ways that symbiotic interactions are promoted to increase the yield of specific bio-compounds with the presence of bio-coating.
The South China Sea (SCS) is a receptor of pollution sources from various parts of Asia and is heavily impacted by strong meteorological systems, which thus dictate aerosol variability over the region. This study analyzes long-term aerosol optical properties observed at Dongsha Island (a representative site in northern SCS) from 2009 to 2021 and Taiping Island (a representative site in southern SCS) from 2012 to 2021 to better apprehend the temporal evolution of columnar aerosols over the SCS. The noticeable difference in loadings, optical properties, and compositions of aerosols between northern and southern SCS was due to the influence of dissimilar emission sources and transport mechanisms. Column-integrated aerosol optical depth (AOD) over northern SCS (range of monthly mean at 500 nm; 0.12-0.51) was significantly greater than southern SCS (0.09-0.21). The maximum AOD in March (0.51 ± 0.28) at Dongsha was attributed to westerlies coupled with biomass-burning (BB) emissions from peninsular Southeast Asia, whereas the maximum AOD at Taiping in September (0.21 ± 0.25) was owing to various pollution from the Philippines, Malaysia, and Indonesia. Fine-mode aerosol dominated over northern SCS (range of monthly mean Angstrom exponent for 440-870 nm: 0.85-1.36) due to substantial influence from continental sources including anthropogenic and BB emissions while coarse-mode particles dominated over southern SCS (0.54-1.28) due to relatively more influence from marine source. More absorbing columnar aerosols prevailed over northern SCS (range of monthly mean single scattering albedo at 675 nm: 0.92-0.99) compared to southern SCS (0.95-0.98) owing to differences in aerosol composition with respect to sources. Special pollution events showcased possible significant impacts on marine ecosystems and regional climate. This study encourages the establishment of more ground-based aerosol monitoring networks and the inclusion of modeling simulations to comprehend the complex nature of aerosol over this vast marginal sea.
Per- and polyfluoroalkyl substances (PFAS) are also known as "forever chemicals" due to their persistence and ubiquitous environmental distribution. This review aims to summarize the global PFAS distribution in surface water and identify its ecological and human risks through integrated assessment. Moreover, it provides a holistic insight into the studies highlighting the human biomonitoring and toxicological screening of PFAS in freshwater and marine species using quantitative structure-activity relationship (QSAR) based models. Literature showed that PFOA and PFOS were the most prevalent chemicals found in surface water. The highest PFAS levels were reported in the US, China, and Australia. The TEST model showed relatively low LC50 of PFDA and PFOS for Pimephales promelas (0.36 and 0.91 mg/L) and high bioaccumulation factors (518 and 921), revealing an elevated associated toxicity. The risk quotients (RQs) values for P. promelas and Daphnia magna were found to be 269 and 23.7 for PFOS. Studies confirmed that long-chain PFAS such as PFOS and PFOA undergo bioaccumulation in aquatic organisms and induce toxicological effects such as oxidative stress, transgenerational epigenetic effects, disturbed genetic and enzymatic responses, perturbed immune system, hepatotoxicity, neurobehavioral toxicity, altered genetic and enzymatic responses, and metabolism abnormalities. Human biomonitoring studies found the highest PFOS, PFOA, and PFHxS levels in urine, cerebrospinal fluid, and serum samples. Further, long-chain PFOA and PFOS exposure create severe health implications such as hyperuricemia, reduced birth weight, and immunotoxicity in humans. Molecular docking analysis revealed that short-chain PFBS (-11.84 Kcal/mol) and long-chain PFUnDA (-10.53 Kcal/mol) displayed the strongest binding interactions with human serum albumin protein. Lastly, research challenges and future perspectives for PFAS toxicological implications were also discussed, which helps to mitigate associated pollution and ecological risks.
Gully erosion possess a serious hazard to critical resources such as soil, water, and vegetation cover within watersheds. Therefore, spatial maps of gully erosion hazards can be instrumental in mitigating its negative consequences. Among the various methods used to explore and map gully erosion, advanced learning techniques, especially deep learning (DL) models, are highly capable of spatial mapping and can provide accurate predictions for generating spatial maps of gully erosion at different scales (e.g., local, regional, continental, and global). In this paper, we applied two DL models, namely a simple recurrent neural network (RNN) and a gated recurrent unit (GRU), to map land susceptibility to gully erosion in the Shamil-Minab plain, Hormozgan province, southern Iran. To address the inherent black box nature of DL models, we applied three novel interpretability methods consisting of SHaply Additive explanation (SHAP), ceteris paribus and partial dependence (CP-PD) profiles and permutation feature importance (PFI). Using the Boruta algorithm, we identified seven important features that control gully erosion: soil bulk density, clay content, elevation, land use type, vegetation cover, sand content, and silt content. These features, along with an inventory map of gully erosion (based on a 70 % training dataset and 30 % test dataset), were used to generate spatial maps of gully erosion using DL models. According to the Kolmogorov-Smirnov (KS) statistic performance assessment measure, the simple RNN model (with KS = 91.6) outperformed the GRU model (with KS = 66.6). Based on the results from the simple RNN model, 7.4 %, 14.5 %, 18.9 %, 31.2 % and 28 % of total area of the plain were classified as very-low, low, moderate, high and very-high hazard classes, respectively. According to SHAP plots, CP-PD profiles, and PFI measures, soil silt content, vegetation cover (NDVI) and land use type had the highest impact on the model's output. Overall, the DL modelling techniques and interpretation methods used in this study proved to be helpful in generating spatial maps of soil erosion hazard, especially gully erosion. Their interpretability can support watershed sustainable management.