Airborne transboundary microplastics-A Swirl around the globe.

Microplastics are persistent pollutants discovered and extensively researched in marine, freshwater, and terrestrial ecosystems but have yet to receive attention in an atmospheric context. Although recent reports stated the presence of microplastics in the air, their global existence and distribution are not critically discussed to date. This review aimed to investigate the current status of research on atmospheric microplastics through bibliometric analysis and by comparing and summarising published research on global distribution. The review also provides a summary of methods that have been used to collect samples, identify microplastics, quantify their occurrence, and determine their transport mechanisms. The bibliometric analysis revealed that atmospheric microplastic studies predominantly originated in China. Clothing, vehicle, and tire materials were the major primary sources while house furniture, construction materials, landfills, urban dust, plastic recycling processes, and agricultural sludge were precursor secondary sources. Polyethylene, polypropylene, and polyethylene terephthalate microfibres have most frequently found in indoor and outdoor atmospheres. Level of urbanization and temporal or spatial distributions governs the fate of airborne microplastics, however, the knowledge gap in the retention and circulation of microplastics through the atmosphere is still large. Many challenges and limitations were identified in the methods used, presentation of data, aerodynamic processes facilitating atmospheric transport, and scarcity of research in spatially and temporally diverse contexts. The review concluded that there was a greater need for globalization of research, methods and data standardization, and emphasizes the potential for future research with atmospheric transportation modelling and thermochemical analysis.

Modeling, challenges, and strategies for understanding impacts of climate extremes (droughts and floods) on water quality in Asia: A review.

The increasing frequency and intensity of extreme climate events are among the most expected and recognized consequences of climate change. Prediction of water quality parameters becomes more challenging with these extremes since water quality is strongly related to hydro-meteorological conditions and is particularly sensitive to climate change. The evidence linking the influence of hydro-meteorological factors on water quality provides insights into future climatic extremes. Despite recent breakthroughs in water quality modeling and evaluations of climate change's impact on water quality, climate extreme informed water quality modeling methodologies remain restricted. This review aims to summarize the causal mechanisms across climate extremes considering water quality parameters and Asian water quality modeling methods associated with climate extremes, such as floods and droughts. In this review, we (1) identify current scientific approaches to water quality modeling and prediction in the context of flood and drought assessment, (2) discuss the challenges and impediments, and (3) propose potential solutions to these challenges to improve understanding of the impact of climate extremes on water quality and mitigate their negative impacts. This study emphasizes that one crucial step toward enhancing our aquatic ecosystems is by comprehending the connections between climate extreme events and water quality through collective efforts. The connections between the climate indices and water quality indicators were demonstrated to better understand the link between climate extremes and water quality for a selected watershed basin.

A multivariate Chain-Bernoulli-based prediction model for cyanobacteria algal blooms at multiple stations in South Korea.

Predicting the occurrence of algal blooms is of great importance in managing water quality. Moreover, the demand for predictive models, which are essential tools for understanding the drivers of algal blooms, is increasing with global warming. However, modeling cyanobacteria dynamics is a challenging task. We developed a multivariate Chain-Bernoulli-based prediction model to effectively forecast the monthly sequences of algal blooms considering hydro-environmental predictors (water temperature, total phosphorus, total nitrogen, and water velocity) at a network of stations. The proposed model effectively predicts the risk of harmful algal blooms, according to performance measures based on categorical metrics of a contingency table. More specifically, the model performance assessed by the LOO cross-validation and the skill score for the POD and CSI during the calibration period was over 0.8; FAR and MR were less than 0.15. We also explore the relationship between hydro-environmental predictors and algal blooms (based on cyanobacteria cell count) to understand the dynamics of algal blooms and the relative contribution of each potential predictor. A support vector machine is applied to delineate a plane separating the presence and absence of algal bloom occurrences determined by stochastic simulations using different combinations of predictors. The multivariate Chain-Bernoulli-based prediction model proposed here offers effective, scenario-based, and strategic options and remedies (e.g., controlling the governing environmental predictors) to relieve or reduce increases in cyanobacteria concentration and enable the development of water quality management and planning in river systems.

Strategic disposal of flood debris via CO2-assisted catalytic pyrolysis.

Recent abnormal climate changes resulted in the dramatic alternation of rainfall and flood patterns in many countries. The massive generation of flood debris, a mixture of soil (sediment), biomass, plastic, metal, and various hazardous materials, poses various environmental and public health problems. This study suggests a sustainable technical platform to convert the hazardous materials into value-added products. CO2-assisted pyrolysis was used to thermally convert flood debris into syngas (H2 and CO). CO2 enhanced the syngas production due to gas phase homogeneous reactions (HRs) between CO2 and volatile hydrocarbons evolved from pyrolysis of flood debris. For improvement of HRs in line with enhancement of syngas production, additional thermal energy and earth abundant catalyst were used. In particular, Ni/SiO2 catalyst increased more than one order of magnitude higher syngas production, comparing to non-catalytic pyrolysis. Synergistic effect of CO2 and Ni catalyst showed nearly 50% more production of syngas in reference to catalytic pyrolysis under N2. During flood debris pyrolysis, compositional matrix of flood debris was also determined by detecting index chemicals of waste materials that cannot be identified by naked eyes. Thus, this study confirmed that CO2-assisted pyrolysis is a useful tool for conversion of flood debris into value-added chemicals.

Stochastic modeling of chlorophyll-a for probabilistic assessment and monitoring of algae blooms in the Lower Nakdong River, South Korea.

Eutrophication is one of the critical water quality issues in the world nowadays. Various studies have been conducted to explore the contributing factors related to eutrophication symptoms. However, in the field of eutrophication modeling, the stochastic nature associated with the eutrophication process has not been sufficiently explored, especially in a multivariate stochastic modeling framework. In this study, a multivariate hidden Markov model (MHMM) that can consider the spatio-temporal dependence in chlorophyll-a concentration over the Nakdong River of South Korea was proposed. The MHMM can effectively cluster the intra-seasonal and inter-annual variability of chlorophyll-a, thereby enabling us to understand the spatio-temporal evolutions of algal blooms. The relationships between hydro-climatic conditions (e.g., temperature and river flow) and chlorophyll-a concentrations were evident, whereas a relatively weak relationship with water quality parameters was observed. The MHMM enables us to effectively infer the conditional probability of the eutrophication state for the following month. The self-transition likelihood of staying in the current state is substantially higher than the likelihood of moving to other states. Moreover, the proposed modeling approach can effectively offer a probabilistic decision-support framework for constructing an alert classification of the eutrophication. The potential use of the proposed modeling framework was also provided.

Valorization of sewage sludge via non-catalytic transesterification.

To seek a way to valorize sewage sludge (SS), it was chosen as a raw material for biodiesel production. As such, non-catalytic transesterification of dried SS was carried out, to enhance its value. Note that picking a waste material such as SS as an inexpensive lipid feedstock for biodiesel production, without lipid extraction, greatly increases the economic viability of biodiesel. Also, to enhance biodiesel sustainability, ethanol (EtOH) was employed as the acyl acceptor, in this study, and this was experimentally justified by results showing that employing EtOH as an acyl acceptor provided an effective means for compensating for the lower heating value arising from the large amount of palmitic (C16) acid in SS. This study experimentally proved that the fatty acid ethyl ester (FAEE) yield at 380 °C reached up to 13.33 wt% of dried SS. Given that the lipid content of dried SS is 14.01 ±â€¯0.64 wt%, the FAEE yield of 13.33 wt% implied that 95.14 wt% of lipid in dried SS had been converted into FAEEs. The introduced SS valorization in this study offered an excellent opportunity to address diverse environmental hazards arising from SS and associated emerging contaminants. Given that the optimal temperature for the non-catalytic conversion for biodiesel production from SS was found to be 380 °C, emerging contaminants, such as microplastics and antimicrobials, were simultaneously degraded, due to their inferior thermal stabilities. Lastly, considering that the introduced biodiesel conversion process is thermally induced, the SS reside after the biodiesel conversion process can be further used in thermo-chemical processes as raw materials for gasification and pyrolysis (future work).

Integrating nonstationary behaviors of typhoon and non-typhoon extreme rainfall events in East Asia.

Extreme rainfall events in East Asia can be derived from the two subcomponents of tropical cyclones (TC) and non-TC based rainfall (mostly summer monsoons). Critical natural hazards including floods and landslides occur repeatedly due to the heavy rainfall associated with the two subcomponents, and disaster losses are increasing because global warming has caused changes in the extreme rainfall characteristics of two subcomponents. Subsequently, the frequency and intensity of extreme rainfall have reportedly become nonstationary. The majority of literature on nonstationary frequency analyses do not account for the different behaviors (stationarity or nonstationarity) of annual maximum rainfall (AMR) from the two subcomponents (PM TC and PM NTC ). To carry out a nonstationary frequency analysis considering the different behaviors of the PM TC and PM NTC series, this study proposes a novel approach of integrating the fitted PM TC and PM NTC series after modeling the nonstationarity of the PM TC and PM NTC series individually. The presented results conclude that the proposed approach provides more reliable estimates than existing nonstationary approaches by reflecting the different features of the PM TC and PM NTC series. We suggest that the proposed approach provides a reasonable design rainfall in constructing hydraulics to mitigate the different nonstationary effects of two TC and non-TC rainfall extremes.

Thermo-chemical process with sewage sludge by using CO2.

This work proposed a novel methodology for energy recovery from sewage sludge via the thermo-chemical process. The impact of CO2 co-feed on the thermo-chemical process (pyrolysis and gasification) of sewage sludge was mainly investigated to enhance thermal efficiency and to modify the end products from the pyrolysis and gasification process. The CO2 injected into the pyrolysis and gasification process enhance the generation of CO. As compared to the thermo-chemical process in an inert atmosphere (i.e., N2), the generation of CO in the presence of CO2 was enhanced approximately 200% at the temperature regime from 600 to 900 °C. The introduction of CO2 into the pyrolysis and gasification process enabled the condensable hydrocarbons (tar) to be reduced considerably by expediting thermal cracking (i.e., approximately 30-40%); thus, exploiting CO2 as chemical feedstock and/or reaction medium for the pyrolysis and gasification process leads to higher thermal efficiency, which leads to environmental benefits. This work also showed that sewage sludge could be a very strong candidate for energy recovery and a raw material for chemical feedstock.