Untapped policy avenues to protect coral reef ecosystems.

Coral reefs are experiencing severe decline, and urgent action is required at local and global scales to curb ecosystem loss. Establishing new regulations to protect corals, however, can be time consuming and costly, and it is therefore necessary to leverage existing legal instruments, such as policies originally designed to address terrestrial rather than marine activities, to prevent coral reef degradation. Focusing on the United States, but drawing on successful examples worldwide, we present actionable pathways to increase coral protections under legislation that was originally designed to advance clean freshwater, safe drinking water, and emergency management. We identify specific legal policies and procedures (e.g., industrial permit limits, nonpoint source management incentives, and floodplain restoration programs) that can curb coral reef pollution and can be extended to other countries with similar regulations in place. Coral reef practitioners should consider a broad array of currently underused, actionable, and intersecting environmental policies that can be applied to mitigate coral stress.

Atmospheric Nitrogen Pollution Control Benefits the Coastal Environment.

Nitrogen is an essential nutrient and a major limiting element for the ocean ecosystem. Since the preindustrial era, substantial amounts of nitrogen from terrestrial sources have entered the ocean via rivers, groundwater, and atmospheric deposition. China serves as a key hub in the global nitrogen cycle, but the pathways, sources, and potential mitigation strategies for land-ocean nitrogen transport are unclear. By combining the CHANS, WRF-Chem, and WNF models, we estimated that 8 million tonnes (Tg) of nitrogen was transferred into the ocean in 2017 in China, with atmospheric deposition contributing 1/3. About half variation of the offshore chlorophyll concentration was explained by atmospheric deposition. The Bohai Sea was the hot spot of nitrogen input, estimated at 214 kg N ha-1, while other areas were around 25-51 kg N ha-1. The largest contributors are agricultural systems (4 Tg, 55%), followed by domestic sewage (2 Tg, 21%). Abatement measures could reduce nitrogen export to the ocean by 43%, and mitigating ammonia and nitrogen oxide emissions accounts for 33% of this reduction, highlighting the importance of addressing air pollution in resolving ocean pollution. The cost-benefit analysis suggests the priority of nitrogen reduction in cropland and transport systems for the ocean environment.

Fate and Transport of Mercury through Waterflows in a Tropical Rainforest.

Knowledge gaps of mercury (Hg) biogeochemical processes in the tropical rainforest limit our understanding of the global Hg mass budget. In this study, we applied Hg stable isotope tracing techniques to quantitatively understand the Hg fate and transport during the waterflows in a tropical rainforest including open-field precipitation, throughfall, and runoff. Hg concentrations in throughfall are 1.5-2 times of the levels in open-field rainfall. However, Hg deposition contributed by throughfall and open-field rainfall is comparable due to the water interception by vegetative biomasses. Runoff from the forest shows nearly one order of magnitude lower Hg concentration than those in throughfall. In contrast to the positive Δ199Hg and Δ200Hg signatures in open-field rainfall, throughfall water exhibits nearly zero signals of Δ199Hg and Δ200Hg, while runoff shows negative Δ199Hg and Δ200Hg signals. Using a binary mixing model, Hg in throughfall and runoff is primarily derived from atmospheric Hg0 inputs, with average contributions of 65 ± 18 and 91 ± 6%, respectively. The combination of flux and isotopic modeling suggests that two-thirds of atmospheric Hg2+ input is intercepted by vegetative biomass, with the remaining atmospheric Hg2+ input captured by the forest floor. Overall, these findings shed light on simulation of Hg cycle in tropical forests.

Small-Intensity Rainfall Triggers Greater Contamination of Rubber-Derived Chemicals in Road Stormwater Runoff from Various Functional Areas in Megalopolis Cities.

Rubber-derived chemicals (RDCs) originating from tire and road wear particles are transported into road stormwater runoff, potentially threatening organisms in receiving watersheds. However, there is a lack of knowledge on time variation of novel RDCs in runoff, limiting initial rainwater treatment and subsequent rainwater resource utilization. In this study, we investigated the levels and time-concentration profiles of 35 target RDCs in road stormwater runoff from eight functional areas in the Greater Bay Area, South China. The results showed that the total concentrations of RDCs were the highest on the expressway compared with other seven functional areas. N-(1,3-Dimethylbutyl)-N'-phenyl-p-phenylenediamine (6PPD), 6PPD-quinone, benzothiazole, and 1,3-diphenylguanidine were the top four highlighted RDCs (ND-228840 ng/L). Seasonal and spatial differences revealed higher RDC concentrations in the dry season as well as in less-developed regions. A lag effect of reaching RDC peak concentrations in road stormwater runoff was revealed, with a lag time of 10-90 min on expressways. Small-intensity rainfall triggers greater contamination of rubber-derived chemicals in road stormwater runoff. Environmental risk assessment indicated that 35% of the RDCs posed a high risk, especially PPD-quinones (risk quotient up to 2663). Our findings contribute to a better understanding of managing road stormwater runoff for RDC pollution.

Inter-event and intra-event dynamics of microplastic emissions in an urban river during rainfall episodes.

Urban rivers represent the major conduits for land-sourced microplastics in the global oceans, yet the real-time dynamics of their emissions in rivers during rainfall (and runoff) events are poorly understood. Herein, we report the results of high-frequency sampling of microplastic particles (MPs) and fibers (MPFs) in the surface water of an urban river in Japan over the course of three rainfall events (i.e., light, moderate, and heavy rainfalls). The event mean concentrations (EMCs) of MPs amounted to 35,000 items/m3, 929,000 items/m3, and 331,000 items/m3; and the corresponding total loads were 0.5 kg, 19.8 kg, and 35.0 kg for light, moderate and heavy rainfalls, respectively. The inter-event total loads of MPs correlate well with the total rainfall, while the concentrations were linked with the number of antecedent dry days. The dynamic trends show that <2000 µm MPs displayed first flush effects during light to moderate rainfall events (>50% mass discharged with the initial 20-40% of flow). Small-sized MPs (10-40 µm) mobilized rapidly at lower rainfall intensities, whereas MPs over 2000 µm discharged immediately after the peak rainfall intensity. Moreover, <70 µm MPs depicted a surge following heavy rainfall events due to turbulent flow conditions reverting the deposited MPs into suspension. Overall, the three events increased the loads by 4-110 folds, and EMCs by 10-350 folds compared to the concentrations during dry weather while portraying a significant impact on 300-1000 µm MPs. The dynamics of MPs were correlated with those of suspended solids in river water, and the characteristics were comparable to the same of road dust sampled in Japan. Although the dynamic trends between MPs and MPFs in river water were comparable, MPFs were relatively less impacted by rain, likely due to the intervention of separate sewer systems in the study area.

Assessment of seasonal and annual patterns in phosphorus content in a monitored catchment through a partitioning approach based on hydrometeorological data.

High amounts of phosphorus (P) in rivers come mainly from two sources: fertilizers washed off from agricultural and urban areas by runoff water (non-point sources) and urban and industrial development which are translated in P discharges from wastewater treatment plants (WWTP). This work analyses the content of P in water for nearly 40 years inquiring into the origin of the sources, based on the hypothesis of runoff generation from the detection of river streamflow increases during the P contribution episode and the previous precipitation. For this purpose, the Guadaira River, which is located in the South of Spain and has a drainage surface of 1524 km2, was selected. In this watershed agricultural land use converges with numerous human activities resulting in high pressures on water quality. We found 40% of the P contribution episodes found seem to come from the runoff generated after the heaviest rainfall events, which normally occur between November and May. The remaining 60% of the P contribution episodes were found to be linked to point sources, which become more relevant from June to September, reaching the highest concentration values (6-17 mg/L). The results highlight that the target phosphate concentration value of 0.34 mg PO4/L imposed by the national legislation for a good state following the Water Framework Directive 2000/60/EC is exceeded by 96% of the measurements during the period from 1981 to 2022. On a monthly basis, PO4 loads showed a linear relationship with river streamflow (R2 = 0.94). However, on field measurements scale, a potential relationship between both variables was found, which changed according to the improvement in the wastewater treatment and facilities for 1982-1994, 1995-2017 and 2018-2022. In these three periods, different significant decreasing trends of the P content were found, mainly marked by the setup of each individual WWTP.

Modeling the effects of land use/land cover changes on river runoff using SWAT models: A case study of the Danjiang River source area, China.

Land use/land cover (LULC) is a crucial factor that directly influences the hydrology and water resources of a watershed. In order to assess the impacts of LULC changes on river runoff in the Danjiang River source area, we analyzed the characteristics of LULC data for three time periods (2000, 2010, and 2020). The LULC changes during these periods were quantified, and three Soil and Water Assessment Tool (SWAT) models were established and combined with eight LULC scenarios to quantitatively analyze the effects of LULC changes on river runoff. The results revealed a decrease in the cropland area and an increase in the forest, grassland, and urban land areas from 2000 to 2020. Grassland, forest, and cropland collectively accounted for over 94% of the total area, and conversions among these land types were frequent. The SWAT models constructed based on the LULC data demonstrated good calibration and validation results. Based on the LULC data in three periods, the area of each LULC type changed slightly, so the simulation results were not significantly different. In the subsequent LULC scenarios, we found that the expansion of cropland, grassland, and urban areas was associated with increased river runoff, while an increase in forest area led to a decrease in river runoff. Among the various LULC types, urban land exerted the greatest influence on changes in river runoff. This study establishes three SWAT models and combines multiple LULC scenarios, which is novel and innovative. It can provide scientific basis for the rational allocation of water resources and the optimization of LULC structure in the Danjiang River source area.

Efficient dissipation of acetamiprid, metalaxyl, S-metolachlor and terbuthylazine in a full-scale free water surface constructed wetland in Bologna province, Italy: A kinetic modeling study.

The study investigated the dissipation ability of a vegetated free water surface (FWS) constructed wetland (CW) in treating pesticides-contaminated agricultural runoff/drainage water in a rural area belonging to Bologna province (Italy). The experiment simulated a 0.1% pesticide agricultural water runoff/drainage event from a 12.5-ha farm by dissolving acetamiprid, metalaxyl, S-metolachlor, and terbuthylazine in 1000 L of water and pumping it into the CW. Water and sediment samples from the CW were collected for 4 months at different time intervals to determine pesticide concentrations by multiresidue extraction and chromatography-mass spectrometry analyses. In parallel, no active compounds were detected in the CW sediments during the experimental period. Pesticides dissipation in the wetland water compartment was modeled according to best data practices by fitting the data to Single First Order (SFO), First Order Multi-Compartment (FOMC) and Double First Order in Parallel (DFOP) kinetic models. SFO (except for metalaxyl), FOMC and DFOP kinetic models adequately predicted the dissipation for the four investigated molecules, with the DFOP kinetic model that better fitted the observed data. The modeled distribution of each pesticide between biomass and water in the CW highly correlated with environmental indexes as Kow and bioconcentration factor. Computed DT50 by DFOP model were 2.169, 8.019, 1.551 and 2.047 days for acetamiprid, metalaxyl, S-metolachlor, and terbuthylazine, respectively. Although the exact degradation mechanisms of each pesticide require further study, the FWS CW was found to be effective in treating pesticides-contaminated agricultural runoff/drainage water within an acceptable time. Therefore, this technology proved to be a valuable tool for mitigating pesticides runoff occurring after intense rain events.

The hidden threat of heavy metal leaching in urban runoff: Investigating the long-term consequences of land use changes on human health risk exposure.

This study evaluated the potential effects of long-term land use and climate change on the quality of surface runoff and the health risks associated with it. The land use change projection 2030 was derived from the main changes in land use from 2009 to 2019, and rainfall data was obtained from the Long Ashton Research Station Weather Generator (LARS-WG) model. The Long-Term Hydrological Impact Assessment (L-THIA) model was then utilized to calculate the rate of runoff heavy metal (HM) pollutant loading from the urban catchment. It was found that areas with heavy development posed a significantly greater public health risk associated with runoff, with higher risks observed in high-development and traffic areas compared to industrial, residential, and commercial areas. Additionally, exposure to Lead (Pb), Mercury (Hg), and Arsenic (As) was found to contribute significantly to overall non-carcinogenic health risks for possible consumers of runoff. Carcinogenic risk values of As, Cadmium (Cd), and Pb were also observed to increase, particularly in high-development and traffic areas, by 2030. This investigation offers important insight into the health risks posed by metals present in surface runoff in urban catchment areas under different land use and climate change scenarios.

Comparison of the performance of SWAT and hybrid M5P tree models in rainfall-runoff simulation.

Stream flow forecasting is a crucial aspect of hydrology and water resource management. This study explores stream flow forecasting using two distinct models: the Soil and Water Assessment Tool (SWAT) and a hybrid M5P model tree. The research specifically targets the daily stream flow predictions at the MH Halli gauge stations, located along the Hemvati River in Karnataka, India. A 14-year dataset spanning from 2003 to 2017 is divided into two subsets for model calibration and validation. The SWAT model's performance is evaluated by comparing its predictions to observed stream flow data. Residual time series values resulting from this comparison are then resolved using the M5P model tree. The findings reveal that the hybrid M5P tree model surpasses the SWAT model in terms of various evaluation metrics, including root-mean-square error, coefficient of determination (R2), Nash-Sutcliffe efficiency, and degree of agreement (d) for the MH Halli stations. In conclusion, this study shows the effectiveness of the hybrid M5P tree model in stream flow forecasting. The research contributes valuable insights into improved water resource management and underscores the importance of selecting appropriate models based on their performance and suitability for specific hydrological forecasting tasks.

Freeze tolerance influenced forest cover and hydrology during the Pennsylvanian.

The distribution of forest cover alters Earth surface mass and energy exchange and is controlled by physiology, which determines plant environmental limits. Ancient plant physiology, therefore, likely affected vegetation-climate feedbacks. We combine climate modeling and ecosystem-process modeling to simulate arboreal vegetation in the late Paleozoic ice age. Using GENESIS V3 global climate model simulations, varying pCO2, pO2, and ice extent for the Pennsylvanian, and fossil-derived leaf C:N, maximum stomatal conductance, and specific conductivity for several major Carboniferous plant groups, we simulated global ecosystem processes at a 2° resolution with Paleo-BGC. Based on leaf water constraints, Pangaea could have supported widespread arboreal plant growth and forest cover. However, these models do not account for the impacts of freezing on plants. According to our interpretation, freezing would have affected plants in 59% of unglaciated land during peak glacial periods and 73% during interglacials, when more high-latitude land was unglaciated. Comparing forest cover, minimum temperatures, and paleo-locations of Pennsylvanian-aged plant fossils from the Paleobiology Database supports restriction of forest extent due to freezing. Many genera were limited to unglaciated land where temperatures remained above -4 °C. Freeze-intolerance of Pennsylvanian arboreal vegetation had the potential to alter surface runoff, silicate weathering, CO2 levels, and climate forcing. As a bounding case, we assume total plant mortality at -4 °C and estimate that contracting forest cover increased net global surface runoff by up to 6.1%. Repeated freezing likely influenced freeze- and drought-tolerance evolution in lineages like the coniferophytes, which became increasingly dominant in the Permian and early Mesozoic.

A Novel Diastolic Doppler Index Less Affected by Aortic Arch Anomalies Co-existing with Coarctation.

Severity assessment for coarctation of the aorta (CoA) is challenging due to concomitant morphological anomalies (complex CoA) and inaccurate Doppler-based indices. Promising diagnostic performance has been reported for the continuous flow pressure gradient (CFPG), but it has not been studied in complex CoA. Our objective was to characterize the effect of complex CoA and associated hemodynamics on CFPG in a clinical cohort. Retrospective analysis identified discrete juxtaductal (n = 25) and complex CoA (n = 43; transverse arch and/or isthmus hypoplasia) patients with arm-leg systolic blood pressure gradients (BPG) within 24 h of echocardiography for comparison to BPG by conventional Doppler indices (simplified Bernoulli equation and modified forms correcting for proximal kinetic energy and/or recovered pressure). Results were interpreted using the current CoA guideline (BPG ≥ 20 mmHg) to compare diagnostic performance indicators including receiver operating characteristic curves, sensitivity, specificity, and diagnostic accuracy, among others. Echocardiography Z-scored aortic diameters were applied with computational simulations from a preclinical CoA model to understand aspects of the CFPG driving performance differences. Diagnostic performance was substantially reduced from discrete to complex CoA for conventional Doppler indices calculated from patient data, and by hypoplasia and/or long segment stenosis in simulations. In contrast, diagnostic indicators for the CFPG only modestly dropped for complex vs discrete CoA. Simulations revealed differences in performance due to inclusion of the Doppler velocity index and diastolic pressure half-time in the CFPG calculation. CFPG is less affected by aortic arch anomalies co-existing with CoA when compared to conventional Doppler indices.

Spatiotemporal convolutional long short-term memory for regional streamflow predictions.

Rainfall-runoff (RR) modelling is a challenging task in hydrology, especially at the regional scale. This work presents an approach to simultaneously predict daily streamflow in 86 catchments across the US using a sequential CNN-LSTM deep learning architecture. The model effectively incorporates both spatial and temporal information, leveraging the CNN to encode spatial patterns and the LSTM to learn their temporal relations. For training, a year-long spatially distributed input with precipitation, maximum temperature, and minimum temperature for each day was used to predict one-day streamflow. The trained CNN-LSTM model was further fine-tuned for three local sub-clusters of the 86 stations, assessing the significance of fine-tuning in model performance. The CNN-LSTM model, post fine-tuning, exhibited strong predictive capabilities with a median Nash-Sutcliffe efficiency (NSE) of 0.62 over the test period. Remarkably, 65% of the 86 stations achieved NSE values greater than 0.6. The performance of the model was also compared to different deep learning models trained using a similar setup (CNN, LSTM, ANN). An LSTM model was also developed and trained individually to predict for each of the stations using local data. The CNN-LSTM model outperformed all the models which was trained regionally, and achieved a comparable performance to the local LSTM model. Fine-tuning improved the performance of all models during the test period. The results highlight the potential of the CNN-LSTM approach for regional RR modelling by effectively capturing complex spatiotemporal patterns inherent in the RR process.

The investigation of the binding ability between sodium dodecyl sulfate and Cu (II) in urban stormwater runoff.

The simultaneous presence of heavy metals and surfactants in runoff induces complexation and ecological harm during migration. However, interactions between these pollutants are often overlooked in past studies. Thus, investigating heavy metal-surfactant complexes in runoff is imperative. In this work, Cu (II) and sodium dodecyl sulfate (SDS) were selected to investigate the interaction between heavy metals and surfactants due to the higher detected frequency in runoff. Through 1H NMR and FTIR observation of hydrogen atom nuclear displacement and functional group displacement of SDS, the change of SDS and Cu (II) complexation was obtained, and then the complexation form of Cu (II) and SDS was verified. The results showed that solution pH values and ionic strength had significant effects on the complexation of Cu (II). When the pH values increase from 3.0 to 6.0, the complexation efficiency of SDS with Cu (II) increased by 12.12% at low concentration of SDS, which may be attributed to the excessive protonation in the aqueous solution at acidic condition. The increase of ionic strength would inhibit the complexation reaction efficiency by 19.57% and finally reached the platform with concentration of NaNO3 was 0.10 mmol/L, which was mainly due to the competitive relationship between Na (I) and Cu (II). As a general filtering material in stormwater treatment measures, natural zeolite could affect the interaction between SDS and Cu (II) greatly. After the addition of SDS, the content of free Cu (II) in the zeolite-SDS-Cu (II) three-phase mixed system was significantly reduced, indicating that SDS had a positive effect on the removal of Cu (II) from runoff. This study is of great significance for investigating the migration and transformation mechanism of SDS and Cu (II) in the future and studying the control technology of storm runoff pollution.

Contributions of climate change and urbanization to urban flood hazard changes in China's 293 major cities since 1980.

The growing incidence of urban flood disasters poses a major challenge to urban sustainability in China. Previous studies have reported that climate change and urbanization exacerbate urban flood risk in some major cities of China. However, few assessments have quantified the contributions of these two factors to urban flood changes in recent decades at the nationwide scale. Here, surface runoff caused by precipitation extremes was used as the urban flood hazard to evaluate the impacts of climate change and urbanization in China's 293 major cities. This study assessed the contributions of these drivers to urban flood hazard changes and identified the hotspot cities with increased trends under both factors during the past four decades (1980-2019). The results showed that approximately 70% of the cities analyzed have seen an increase of urban flood hazard in the latest decade. Urbanization made a positive contribution to increased urban flood hazards in more than 90% of the cities. The contribution direction of climate change showed significant variations across China. Overall, the absolute contribution rate of climate change far outweighed that of urbanization. In half of the cities (mainly distributed in eastern China), both climate change and urbanization led to increased urban flood hazard over the past decade. Among them, 33 cities have suffered a consecutive increase in urban flood hazard driven by both factors.

UV irradiation enhanced removal of colloidal phosphorus in agricultural runoff.

Colloidal phosphorus (P) is an important P form in agricultural runoff and can threaten water quality. However, up to date, there are few effective approaches to mitigate colloidal P pollution. This study investigated the effect of ultraviolet (UV) irradiation on medium-colloidal (MC; 220 nm-450 nm) and fine-colloidal (FC; 3 kDa-220 nm) P in agricultural runoff. Under 24 h of UV irradiation, as the most abundant colloidal P fraction, concentration of total P (TP) in FC consistently decreased by 81.0%, while TP concentration in MC first increased by 74.4% after 3 h and then decreased with irradiation time. At the same time, particulate TP (>450 nm) concentration was found to be increased from 0 to 14.7 µM. However, there were no obvious variations in TP concentrations in FC and MC fractions under dark conditions. In FC fraction, with the decrease of TP, the corresponding concentrations of iron (Fe), aluminum (Al), silicon (Si) declined synchronously, and ferric iron/ferrous iron (Fe(III)/Fe(II)) ratio and organic matter (OM) concentration were reduced as well. These results suggested that P in FC fraction was gradually transformed into particulate P during photoreduction of Fe(III) and photodegradation of OM under UV irradiation. Our study helps to understand the mechanism of the phototransformation of colloidal P, and propose an UV irradiation-based approach to remove colloidal P in agricultural runoff.

Coupling machine learning and physical modelling for predicting runoff at catchment scale.

In this paper, we present an approach that combines data-driven and physical modelling for predicting the runoff occurrence and volume at catchment scale. With that aim, we first estimated the runoff volume from recorded storms aided by the Green-Ampt infiltration model. Then, we used machine learning algorithms, namely LightGBM (LGBM) and Deep Neural Network (DNN), to predict the outputs of the physical model fed on a set of atmospheric variables (relative humidity, temperature, atmospheric pressure, and wind velocity) collected before or immediately after the beginning of the storm. Results for a small urban catchment in Madrid show DNN performed better in predicting the runoff occurrence and volume. Moreover, enriching the input primary atmospheric variables with auxiliary variables (e.g., storm intensity data recorded during the first hour, or rain volume and intensity estimates obtained from auxiliary regression methods) largely increased the model performance. We show in this manuscript data-driven algorithms shaped by physical criteria can be successfully generated by allowing the data-driven algorithm learn from the output of physical models. It represents a novel approach for physics-informed data-driven algorithms shifting from common practices in hydrological modelling through machine learning.

Microplastic contamination in urban aquatic environments: Occurrence characteristics in urban streams and stormwater runoff from urban surfaces.

This study investigates microplastics in urban environments, focusing on their abundance, types, and relationships with hydrological parameters. Microplastic analyses encompassed two steps: (1) examining urban streams including discharges from a wastewater treatment plant (WWTP) during non-rainy seasons, and (2) analyzing stormwater runoff from urban surfaces for microplastic content during rainy seasons. In urban streams, WWTP discharge exhibited higher microplastic concentrations compared to other streams, indicating WWTP discharge is a dominant source of microplastic pollution. The most prevalent microplastics detected were polypropylene, polyethylene, and their copolymer, although a variety of other types were also found. Concentrations of microplastics were notably influenced by specific urban land uses, as evidenced by a strong correlation (0.95) between microplastic concentrations and areas characterized by industrial and transportation activities. During rainy seasons, microplastics followed the pattern of stormwater runoff, but the highest concentrations, significantly exceeding those in urban streams, were observed before the peak runoff. These maximum concentrations and their timing of occurrence were linked to antecedent dry days, rain intensity, and runoff rate, showing significant statistical correlations. Regardless of their sizes, a diverse range of microplastic types was identified in these conditions, with no consistent pattern across different rain events. This highlights the complex nature of urban microplastic pollution. This study reveals that aquatic ecosystems are significantly affected by two primary factors: (1) the consistent contribution of microplastics from WWTP discharges, and (2) the short-term, but severe, impacts of microplastic pollution associated with stormwater runoff. Furthermore, it suggests the development of alternative strategies to mitigate microplastic pollution in aquatic ecosystems, informed by the findings on the characteristics of microplastics in urban environments. This research underscores the urgent need for integrated urban environmental management strategies, paving the way for future studies to further explore and address the multifaceted challenges posed by microplastic pollution in aquatic ecosystems.

Research on machine learning hybrid framework by coupling grid-based runoff generation model and runoff process vectorization for flood forecasting.

One of the important non-engineering measures for flood forecasting and disaster reduction in watersheds is the application of machine learning flood prediction models, with Long Short-Term Memory (LSTM) being one of the most representative time series prediction models. However, the LSTM model has issues of underestimating peak flows and poor robustness in flood forecasting applications. Therefore, based on a thorough analysis of complex underlying surface attributes, this study proposes a framework for distinguishing runoff models and integrates a Grid-based Runoff Generation Model (GRGM). Simultaneously considering the time series characteristics of runoff processes, including rising, peak, and recession, a runoff process vectorization (RPV) method is proposed. In this study, a hybrid deep learning flood forecasting framework, GRGM-RPV-LSTM, is constructed by coupling the GRGM, RPV, and LSTM neural network models. Taking the Jialu River in the Zhongmu station control basin as an example, the model is validated using 18 instances of measured floods and compared with the LSTM and GRGM-LSTM models. The study shows that the GRGM model has a relative error and average coefficient of determination for simulating runoff of 8.41% and 0.976, respectively, indicating that considering the spatial distribution of runoff patterns leads to more accurate runoff calculations. Under the same lead time conditions, the GRGM-RPV-LSTM hybrid forecasting model has a Nash efficiency coefficient greater than 0.9, demonstrating better simulation performance compared to the GRGM-LSTM and LSTM models. As the lead time increases, the GRGM-RPV-LSTM model provides more accurate peak flow predictions and exhibits better robustness. The research findings can provide scientific basis for coordinated management of flood control and disaster reduction in watersheds.

Distribution of microplastics in rainfall and their control by a permeable pavement in low-impact development facility.

Microplastics (MPs) released from plastic products in daily life are present in the air and could be transported to freshwater environments along with rain. Recently, low-impact development (LID) facilities, such as permeable pavements, have been used to treat non-point source pollutants, including rainfall runoff. While runoff is treated by LID facilities, the periodic monitoring of MPs in rainfall and the efficiency of removal of MPs through LID facilities have rarely been investigated. Therefore, this case study focused on monitoring MPs in rainwater runoff and permeate from a permeable pavement in Busan, South Korea, thus evaluating the removal efficiency of MPs by a LID system. The initial rainfall runoff and permeate through the LID system were sampled, and the amounts, types, sizes, and shapes of MPs in the samples were analyzed using micro-Fourier Transform Infrared (FTIR) spectroscopy. The results showed that the distribution of MPs in the initial rainfall was affected by population in tested area. Polyethylene was the most common type of MPs in all the samples. Polyamide was only found in the LID samples because of the pollution caused by water flows and pavement materials. Fragment type MPs was most commonly observed and consisted of relatively small-sized (under 100 µm) particles. LID facilities were able to capture approximately 98% of MPs in the rainfall through a filtration process in the permeable pavement.