Molecular characteristics of dissolved organic phosphorus in watershed runoff: Coupled influences of land use and precipitation.

Land use and precipitation are two major factors affecting phosphorus (P) pollution of watershed runoff. However, molecular characterization of dissolved organic phosphorus (DOP) in runoff under the joint influences of land use and precipitation remains limited. This study used Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) to study the molecular characteristics of DOP in a typical P-polluted watershed with spatially variable land use and precipitation. The results showed that low precipitation and intense human activity, including phosphate mining and associated industries, resulted in the accumulation of aliphatic DOP compounds in the upper reaches, characterized by low aromaticity and low biological stability. Higher precipitation and widespread agriculture in the middle and lower reaches resulted in highly unsaturated DOP compounds with high biological stability constituting a higher proportion, compared to in the upper reaches. While, under similar precipitation, more aliphatic DOP compounds characterized by lower aromaticity and higher saturation were enriched in the lower reaches due to more influence from urban runoff relative to the middle reaches. Photochemical and/or microbial processes did result in changes in the characteristics of DOP compounds during runoff processes due to the prevalence of low molecular weight and low O/C bioavailable aliphatic DOP molecules in the upper reaches, which were increasingly transformed into refractory compounds from the upper to middle reaches. The results of this study can increase the understanding of the joint impacts of land use and precipitation on DOP compounds in watershed runoff.

A national-scale high-resolution runoff risk and channel network mapping workflow for diffuse pollution management.

Managing diffuse pollution from agricultural land requires a spatially explicit risk assessment that can be applied over large areas. Major components of such assessments are the precise definition of both channel networks that often originate as small channels and streams, and Hydrologically Sensitive Areas (HSAs) of storm runoff that occur on land surfaces. Challenges relate to regions of complex topography and land use patterns, particularly those which have been heavily modified by arterial drainage. In this study, a national scale, transferrable workflow and analysis were developed using a specifically commissioned LiDAR survey. Research on the first half of Northern Ireland (6927 km2) is reported where field-edge drain to major river channels were mapped from 1 m (16 points per metre) digital terrain models, and in-field HSAs were defined across over 400,000 fields with a median field size of 0.86 ha. Manual drainage mapping supplemented with a novel automated drainage channel correction process resulted in an unparalleled high-resolution national drainage network with 37,320 km of channels, increasing mapped channel density from 0.9 km km-2 to 5.5 km km-2. The HSAs were based on a Soil Topographic Index (STI) system using hillslope and contributing area models combined with soil hydraulic characteristics. In all, 249 km2 of runoff risk HSAs were identified by extracting the top 95th percentile of the modelled STI as the areas with the highest propensity to generate in-field runoff. At field and individual farm scale these targeted risk maps of diffuse pollution were delivered to over 13,000 farmers and form part of the nationwide Soil Nutrient Health Scheme programme.

Occurrence of specific pollutants in a mixture of sewage and rainwater from an urbanized area.

Urban runoff appears to be a pathway for transferring new emerging pollutants from land-based sources to the aquatic environment. This paper aimed to identify and describe the groups of pollutants present in rainwater surface runoff as well as their mixture with wastewater in the combined sewer system from urbanized catchments and to determine the correlations between these pollutants. Four leading groups of new emerging pollutants have been identified that may be present in rainwater and municipal wastewater mixtures. The samples were tested for microplastics, phthalic acid esters, pesticides, and polycyclic aromatic hydrocarbons as well as basic parameters. The pilot site was Slupsk (northwestern Poland). We conducted nine sampling campaigns at three points. The results of the present study revealed that (i) polycyclic aromatic hydrocarbons were not present in the tested samples; (ii) the selected organochlorine pesticides were detected during one campaign in the dry season and therefore were not of critical importance; (iii) out of the 11 analyzed phthalic acid esters, five selected substances released from commonly used plastic products were present; and (iv) the number of microplastics contained in the tested samples ranged from 1,400 to 14,036 pcs/L and even occurred during pure rainfall.

Evaluating Nature-based Solutions as urban resilience and climate adaptation tools: A meta-analysis of their benefits on heatwaves and floods.

Extreme weather events driven by climate change threaten the resilience of urban structures and urban dwellers. Nature-based Solutions (NbS) are an effective tool to reduce urban vulnerability to climate risks and, at the same time, develop more liveable urban areas. Despite the acknowledged positive impacts of individual observed NbS, numerous questions persist unanswered. While existing research supports NbS' positive influence on urban climate adaptation, the extent of their impact remains insufficiently studied. Understanding the magnitude of NbS impact is crucial for justifying their preference over non-NbS alternatives and, consequently, for securing public investment. Via a meta-analysis, this paper aims to contribute to research and practice by providing a more systematic assessment of NbS effects, offering urban planners and decision-makers a robust justification for their incorporation in climate change adaptation, urban resilience, and enhanced liveability. The results of the meta-analytic model indicate that the effect of NbS is indeed positive. When assessing the impact on temperature and flood protection, there is a general positive effect across the studied NbS. However, when evaluating an average effect, the task appears to be more complex due to methodological issues and limitations. The need to increase the formalisation of how the impact of NbS is measured and reported also emerges as a result. Replicable protocols would positively impact the formalisation of the literature on the topic and positively affect the evidence-based support for the implementation of NbS by urban decision-makers.

Predicting flood stages in watersheds with different scales using hourly rainfall dataset: A high-volume rainfall features empowered machine learning approach.

Accurate prediction of instantaneous high lake water levels and flood flows (flood stages) from micro-catchments to big river basins are critical for flood forecasting. Lake Carl Blackwell, a small-watershed reservoir in the south-central USA, served as a primary case study due to its rich historical dataset. Bearing knowledge that both current and previous rainfall contributes to the reservoirs' water body, a series of hourly rainfall features were created to maximize predicting power, which include total rainfall amounts in the current hour, the past 2 h, 3 h, …, 600 h in addition to previous-day lake levels. Notedly, the rainfall features are the accumulated rainfall amounts from present to previous hours rather than the rainfall amount in any specific hour. Random Forest Regression (RFR) was used to score the features' importance and predict the flood stages along with Neural Network - Multi-layer Perceptron Regression (NN-MLP), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and the ordinary multi-variant linear regression (MLR) together with dimension reduced linear models of Principal Component Regression (PCR) and Partial Least Square Regression (PLSR). The prediction accuracy for the lake flood stages can be as high as 0.95 in R2, 0.11 ft. in mean absolute error (MAE), and 0.21 ft. in root mean square error (RMSE) for the testing dataset by the RFR (NN-MLP performed equally well), with small accuracy decreases by the other two non-linear algorithms of XGBoost and SVR. The linear regressions with dimension reductions had the lowest accuracy. Furthermore, our approach demonstrated high accuracy and broad applicability for surface runoff and streamflow predictions across three different-sized watersheds from micro-catchment to big river basins in the region, with increases of predicting power from earlier rainfall for larger watersheds and vice versa.

Ultra-high Versus Standard Resolution Photon-Counting Detector CT Angiography for Imaging of Femoral Stents in a Cadaveric Perfusion Model.

BACKGROUND AND AIMS: The aim of this study was to investigate the imaging performance and quality differences of PCD-CT in standard resolution mode (SR) versus ultra-high resolution mode (UHR) in the lower extremity runoff of dose-matched CTAs in a human cadaveric model. METHODS: Extracorporeal perfusion of the upper leg was established in one fresh-frozen human cadaver via inguinal and popliteal accesses using a peristaltic pump. Seven peripheral stents were deployed in the SFA. Photon-counting CTAs were performed under contrast perfusion in SR and UHR mode with dose-equivalent 120kVp acquisition protocols (low-/ medium-/ high-dose: CTDIVol=3, 5, 10 mGy) and reconstructed with four vascular convolution kernels. Lumen visibility and contrast-to-noise ratio were compared using analyses of variance. Subjective image quality was assessed using a pairwise, forced-choice comparison software. RESULTS: Lumen visibility was equal for SR and UHR at the used dose levels. CNR increase by UHR was significant for (ultra-)sharp convolution kernels BV60 (3 mGy; UHR vs. SR, 19.9 ± 1.9 vs. 15.7 ± 1.6, p < 0.046) and BV76 (8.0 ± 0.6 vs. 5.4 ± 0.3, p < 0.001). The relative CNR increase was higher for low-dose than high-dose scans (BV76: 48% vs. 36% at high dose, p < 0.033). The CNR of the low-dose scan in UHR mode was comparable to the high-dose scan in SR mode when the ultra-sharp kernel was used (8.0 ± 0.6 vs. 9.1 ± 1.1, p > 0.760). Among UHR examinations, a significant increase in CNR could only be measured in BV76 (8.0 ± 0.6 (3 mGy) vs. 12.4 ± 0.9 (10 mGy), p < 0.001). Readers preferred subjective image quality of UHR for all kernels with BV76 being ranked highest. CONCLUSION: The CNR increase in UHR mode is highest when combining low radiation dose and ultra-sharp reconstructions. Meanwhile, the subjective image quality in UHR mode generally supersedes SR images, suggesting further dose reduction potential.

Assessment of fine and coarse tyre wear particles along a highway stormwater system and in receiving waters: Occurrence and transport.

Tyre wear has been identified as a major road-related pollutant source, with road runoff transporting tyre wear particles (TWP) to adjacent soil, watercourses, or further through stormwater systems. The aim of this study was to investigate the occurrence and transport of TWP along a stormwater system. Water and sediment have been sampled at selected points (road runoff, gully pots, wells, outlet to a ditch, and stream) through a stormwater system situated along a highway in Sweden during November and December 2022, and March 2023. As there is limited data on the size distribution of TWP in different environmental media, especially in the size fraction <20 µm, the samples were fractioned into a fine (1.6-20 µm) and a coarse (1.6-500 µm) size fraction. The samples were analysed using a combination of marker compounds (benzene, α-methylstyrene, ethylstyrene, and butadiene trimer) for styrene-butadiene rubbers with PYR-GC/MS from which TWP concentration was calculated. Suspended solids were analysed in the water samples, and organic content was analysed in the sediment samples. TWP was found at nearly all locations, with concentrations up to 17 mg/L in the water samples and up to 40 mg/g in the sediment samples. In the sediment samples, TWP in the size fraction 1.6-20 µm represented a significant proportion (20-60%). Correlations were found between TWP concentration and suspended solids in the water samples (r = 0.87) and organic content in the sediment samples (r = 0.72). The results presented in this study demonstrate that TWP can be transported to the surrounding environment through road runoff, with limited retention in the studied stormwater system.

Effects of storm events on nutrient characteristics in a stratified drinking water reservoir: Behavior, transmission pathways and management strategy.

Storm events result in nutrient fluctuations and deterioration of reservoir water supply quality. Understanding of nutrient dynamics (e.g., concentration, composition, loads and transport pathways) and adoption of effective management strategies are critical for safeguarding water quality. A comprehensive monitoring was conducted for three storm events during the rainy season in 2023. Results showed nitrogen (N) and phosphorus (P) dynamics demonstrate a significant response to hydrological process. Rainfall resulted in the highest event mean concentrations (EMCs) of total nitrogen (TN), nitrate nitrogen (NO3--N), ammonia nitrogen (NH4+-N), total phosphorus (TP), and particulate phosphorus (PP) in the runoff being 1.97, 2.15, 2.30, 44.17, and 62.38 times higher than those observed in baseflow. On average, NO3--N/PP accounted for 82 %/96 % of N/P exports. Hysteresis analyses reveal that NH4+-N and PP were mainly transported by surface runoff from over-land sources, whereas TN and NO3--N were primarily delivered by subsurface runoff. Additionally, nutrient concentrations were significantly higher in the intrusive layer in reservoir compared to the pre-storm period, which gradually decreased from the tail to the head as particulate sedimentation and water column mixing occurred. Water-lifting-aerators (WLAs) were employed to alter the reservoir thermal stratification regime via artificial mixing to affect the intrusive layer of storm runoff. Comparison of the intrusive layer for three storms reveals that WLAs triggers the storm runoff to form an underflow via increasing the reservoir bottom water temperature above that the runoff, ensuring that water quality at the intake position remains unaffected by inflows. These findings serve as a reference for the response of reservoir eutrophication levels to storm events and present practical engineering experience for enhancing water quality safety during the rainy season.

Precipitation contributes to alleviating pollution of rubber-derived chemicals in receiving watersheds: Combining confluent stormwater runoff from different functional areas.

The release of rubber-derived chemicals (RDCs) in road surface runoff has received significant attention. Urban surface runoff is often the confluence of stormwater runoff from specific areas. However, the impact of precipitation on RDCs contamination in confluent stormwater runoff and receiving watersheds remains poorly understood. Herein, we investigated the profiles of RDCs and their transformation products in confluent stormwater runoff and receiving rivers affected by precipitation events. The results showed that 34 RDCs are ubiquitously present in confluent stormwater runoff and surface water, with mean concentrations of 1.03-2749 and 0.28-436 ng/L, respectively. The most dominant target compounds in each category were N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine (6PPD), 6PPD-quinone, 2-benzothiazolol, and 1,3-diphenylguanidine. Total RDCs concentrations in confluent stormwater runoff decreased spatially from industrial areas to business districts to college towns. A significant decrease in RDCs levels in surface water after rainfall was observed (P < 0.01), indicating that precipitation contributes to alleviating RDCs pollution in receiving watersheds. To our knowledge, this is the first report of N,N'-ditolyl-p-phenylenediamine quinone (DTPD-Q) levels in surface waters in China. The annual mass load of ∑RDCs reached 72,818 kg/y in confluent stormwater runoff, while 38,799 kg/y in surface water. The monitoring of confluent stormwater runoff is an efficient measure for predicting contamination loads from RDCs in rivers. Risk assessment suggested that most RDCs posed at least medium risks to aquatic organisms, especially 6PPD-quinone. The findings help to understand the environmental fate and risks of RDCs in the confluent stormwater runoff and receiving environments after precipitation events.

The difference between tire wear particles and polyethylene microplastics in stormwater filtration systems: Perspectives from aging process, conventional pollutants removal and microbial communities.

Tire wear particles (TWPs) in stormwater runoff have been widely detected and were generally classified into microplastics (MPs). TWPs and conventional MPs can be intercepted and accumulated in stormwater filtration systems, but their impacts on filtration, adsorption and microbial degradation processes of conventional pollutants (organic matters, nitrate and ammonium) have not been clarified. TWPs are different from MPs in surface feature, chemical components, adsorption ability and leaching of additives, which might lead to their different impacts on conventional pollutants removal. In this study, five different levels of aged polyethylene MPs (PEMPs) and aged TWPs contamination in stormwater filtration systems were simulated using thirty-three filtration columns. Results showed that ultraviolet aging treatment was less influential for the aging of TWPs than that of PEMPs, the specific surface area of aged PEMPs (1.603 m2/g) was over two times of unaged TWPs (0.728 m2/g) in the same size. Aged PEMPs and aged TWPs had different impacts on conventional pollutants removal performance and microbial communities, and the difference might be enlarged with exposure duration. The intensified aged PEMPs contamination generally promoted conventional pollutants removal, whereas aged TWPs showed an opposite trend. Mild contamination (0.01% and 0.1%, wt%) of aged PEMP/TWPs was beneficial to the richness and diversity of microbial communities, whereas higher contamination of aged PEMPs/TWPs was harmful. Aged PEMPs and TWPs had different impact on microbial community structure. Overall, the study found that TWPs were more detrimental than PEMPs in filtration systems. The research underscores the need for more comprehensive investigation into the occurrence, effects and management strategies of TWPs, as well as the importance of distinguishing between TWPs and MPs in future studies.

Prediction of runoff characteristics in permeable pavements using experimental data and intelligent models.

Considering the growing use of permeable pavements, the prediction of runoff passing through this pavement model is of considerable importance. The prediction of rainfall-runoff relationships can be a challenge because of several factors including data uncertainty, non-linear relationships, and high temporal and spatial variability. To deal with these challenges, intelligent algorithms are often used to predict such complex phenomena. In this research, runoff control parameters were investigated in two types of permeable pavements (permeable interlocking concrete pavement (PICP) and high strength clogging resistant permeable pavement (CRP)) using support vector machine (SVM), support vector machine-bat (SVM-BA) and support vector machine-grasshopper (SVM-GOA). Variables used in the models included percentage of coverage by permeable pavement (A), rainfall intensity (I), slope (S), and pavement type coefficient (K) as input data, and runoff coefficient (C), time to runoff (Tr), and peak discharge (Qp) as output data. In this research, from the total of 108 data extracted from the experimental results, 86 data were used in the training period, and 22 data were used in the test period. The results of the test period show that the SVM-BA model has the best performance with values of MAE = 0.010 in predicting C, MAE = 1.330 min in predicting Tr, and MAE = 0.029 lit/min in predicting Qp. The SVM-GOA model is ranked second with values of MAE = 0.051 in predicting C, MAE = 3.285 min in predicting Tr, and MAE = 0.097 lit/min in predicting Qp. Also, the SVM model is ranked third with values of MAE = 0.063 in predicting C, MAE = 4.470 min in predicting Tr, and MAE = 0.121 lit/min in predicting Qp. In summary, the SVM-BA algorithm showed the best performance and the SVM algorithm showed the weakest performance in predicting runoff characteristics in permeable pavements.

Incorporating glacier processes into hydrological simulations in the headwaters of the Yangtze and yellow Rivers.

Contemporary hydrological models often oversimplify or neglect the effects of glacier ablation on watershed hydrological processes, leading to inaccurate simulations. To address this issue, we introduce a glacier ablation module that incorporates glacier ablation, sublimation, meltwater refreezing, and snow accumulation, integrated with the fully distributed hydrological model ESSI-3, forming the Glacier-ESSI-3 model. Application of the Glacier-ESSI-3 model in the headwaters of the Yangtze River (HYaR) and Yellow River (HYeR) demonstrates superior accuracy compared to the ESSI-3 model, effectively capturing the impact of glacier ablation on hydrological dynamics. Validation with remotely sensed data of snow cover and glacier dynamics confirms the model's efficacy in reproducing actual conditions in both watersheds. The results indicate that snow meltwater contributes more significantly to runoff than glacier meltwater, and the HYaR exhibiting a larger glacier meltwater contribution than the HYeR. Over time, the contribution rate of snow+glacier meltwater to runoff in the HYaR shows a fluctuating upward trend (10.04 % ±â€¯1.13 % to 25.02 % ±â€¯2.80 %), while it remains relatively stable in the HYeR (6.83 % ±â€¯1.13 % to 10.19 % ±â€¯0.89 %). This study highlights the critical role of glacier ablation in hydrological processes within glacierized watersheds. The Glacier-ESSI-3 model proves to be a robust tool for enhancing hydrological simulations in cold plateau regions, providing valuable insights into the intricate interactions between glaciers and hydrological dynamics.

Spatiotemporal evolution of runoff and sediment and their dominant driving factors in the Lower Jinsha River basin.

The Jinsha River Basin (JRB) contributes a significant amount of sediment to the Yangtze River; however, an imbalance exists between runoff and sediment. The underlying mechanisms and primary factors driving this imbalance remain unclear. In this study, the Shapley Additive Explanation (SHAP) and Geographical Detector Model (GDM) were employed to quantify the importance of the driving factors for water yield (WYLD) and sediment yield (SYLD) using the Soil and Water Assessment Tool (SWAT) model in the JRB. The results indicated that the SWAT model performed well in simulating runoff and sediment, with R2 > 0.61 and NSE > 0.5. Based on the simulated data, SYLD exhibited strong spatiotemporal linkages with WYLD. Temporally, both sediment and runoff showed decreasing trends, with the sediment decrease being more pronounced. Spatially, WYLD and SYLD displayed similar distribution patterns, with low values in the southwest and high values in the northeast. By quantifying the driving factors, we found that climatic factors, including precipitation and potential evapotranspiration, were the main influencing factors for WYLD and SYLD across the entire region, though their contributions to the two variables differed. For WYLD, climatic factors accounted for 70 % of the total influencing factors, whereas their contribution to SYLD was 50 %. Furthermore, soil type and land-use type played significant roles in the SYLD, with importance values of 16 % and 12 %, respectively. Under the influence of surface conditions, the proportion of SYLD in the JRB to the total SYLD in the Yangtze River Basin was greater than that of WYLD. The findings of this study provide scientific evidence and technical support for local environmental impact assessments and the formulation of soil and water conservation plans.

[Effects of Straw and Fertilizer Managements on Nitrogen Loss in Rice Cultivation in Taihu Lake Basin].

Nitrogen loss from rice systems is an important source of agricultural non-point source pollution. Many studies revolve around reducing the rate of nitrogen fertilizer application. However, studies examining the characteristics of nitrogen loss in multiple loss paths （runoff, leaching, and lateral seepage） under different straw and fertilizer managements are lacking. Therefore, a study was carried out based on a rice field planted for more than 20 years with straw continuously returned to the field for more than 5 years in Taihu lake basin. The effects of straw and fertilizer managements on nitrogen loss in different paths during the whole growth period of rice were studied. Moreover, straw and fertilizer managements were evaluated by their production suitability and environmental friendliness based on crop yield, nitrogen use efficiency, and nitrogen loss. The results showed that straw removal from the field increased the response sensitivity of nitrogen accumulation in plant tissue to nitrogen application. The nitrogen loss in the rice season was 9-17 kg·hm-2, accounting for 5%-7% of the nitrogen application rate. Straw removal increased the risk of nitrogen loss when soaking water discharged. Straw returning could decrease the nitrogen loss by more than 15%, though the effect of straw on nitrogen loss via lateral seepage was not clear. Furthermore, the suitable substitution of organic fertilizer （30% in this study） could respectively reduce the amount of nitrogen loss via runoff, leaching, and lateral seepage by 16%, 26%, and 37% compared with the fertilizer application under the same nitrogen gradient. In conclusion, the implementation of straw returning and fertilizer type optimization measures effectively reduced the nitrogen loss for unit weight of rice production and realized the balance between agricultural production and environmental protection.

[Identification and Derivation of Emerging Contaminants in the Roof Rainwater Confluence].

To identify emerging contaminants （ECs） in rainwater is a topic that has gradually received widespread attention. Rainwater resources, specifically urban roofs, play a crucial role in utilizing rainwater efficiently by understanding the occurrence and migration characteristics of pollutants in precipitation. This study selected a typical roof and studied the differences in rainwater quality and pollution occurrence at different collection stages during six rainfall events from March to May in 2023. Principal component analysis （PCA） and correlation analysis were used to explore the distribution, migration, and transformation of ECs in the collection process of roof rainwater. The findings revealed the presence of 44/54 ECs in wet deposition, dry and wet deposition, and roof runoff processes, with a total concentration range of 63.0 to 432.4 ng·L-1 and an average concentration of 166.8 ng·L-1. Notably, bisphenol A （BPA） exhibited the highest concentration, ranging from 14.7 to 265.6 ng·L-1, with an average concentration of 62.5 ng·L-1, followed by ofloxacin （OFX） and ethylhexyl methoxycinnamate （EHMC）, with detected concentrations up to 45.5 ng·L-1 and 44.8 ng·L-1. Dissolved organic matter （DOM）, nitrogen pollutants, and particulate matter were important factors affecting the occurrence characteristics of ECs, with a mantel correlation coefficient of up to 0.98 （P<0.01）. Based on the analysis of different rainfall events and collection stages, variations were observed in the accumulation pathways and contribution ratios of different pollutants. The wet deposition exhibited the highest content of ECs in the initial stage, whereas the dry and wet deposition and roof runoff processes displayed higher ECs content in the later stages. Additionally, the average ECs contribution rates of dry and wet deposition to roof runoff were 21.48% and 78.52%, respectively. Due to the influence of roof material and surface roughness retention performance, over 30% of ECs, including pharmaceuticals and personal care products （PPCPs）, endocrine-disrupting compounds （EDCs）, and pesticides, were deposited on the roof during the runoff collection. The results of this research can provide the theoretical foundation and technical support for the identification and control of ECs in urban roof runoff and for the safe storage of rainwater.

Bioconcentration of pharmaceuticals by aquatic flora in an Australian river system.

Pharmaceuticals are emerging contaminants in the environment and are a ubiquitous presence in rivers downstream of wastewater treatment plant outfalls. Questions remain about the persistence of pharmaceuticals in rivers, and the uptake and bioconcentration of pharmaceuticals by aquatic plants. Our study took place in the Yarrowee/Leigh/Barwon River system in southeastern Australia. We quantified the concentrations of five pharmaceuticals (carbamazepine, primidone, propranolol, tramadol, and venlafaxine) in surface water at five sites along a 144-km stretch of river, downstream of the presumed primary point source (a wastewater treatment plant outfall). We quantified pharmaceuticals in the leaves of two aquatic plant species (Phragmites australis and Vallisneria australis) sampled at each site, and calculated bioconcentration factors. All five pharmaceuticals were detected in surface waters, and the highest detected concentration exceeded 500 ng.L-1 (tramadol). Four of the pharmaceuticals (all except tramadol) were detected and quantified at all sites, including the furthest site from the outfall (144 km). Carbamazepine showed less attenuation with distance from the outfall than the other pharmaceuticals. Carbamazepine and venlafaxine were quantified in the leaves of both aquatic plant species (range: 10-31 ng.g-1), and there was evidence that bioconcentration factors increased with decreasing surface water concentrations. The study demonstrates the potential long-distance persistence of pharmaceuticals in river systems, and the bioconcentration of pharmaceuticals by aquatic plants in natural ecosystems. These phenomena deserve greater attention as aquatic plants are a potential point of transfer of pharmaceuticals from aquatic ecosystems to terrestrial food webs.

A grid-wise approach for accurate computation of Standardized Runoff Index (SRI).

The Standardized Runoff Index (SRI) is a major indicator for evaluating hydrological drought conditions, accomplished by comparing the current runoff data with retrospective runoff conditions of an area for the same period. This hydrological drought indicator facilitates the characterisation of runoff variations across diverse regions. This study introduces a refined methodology for accurate computation of SRI by employing a grid-wise approach. Distinct probability distributions were fitted to each grid within the study area, diverging from the conventional practice of using a single probability distribution for the entire basin or sub-basin. The research endeavours to assess the efficacy of the grid-wise approach in improving the representation of drought characteristics when compared to the traditional areal approach. A comparative analysis between the performances of SRI computed through grid-wise fitting (where the probability distribution dynamically adapts to each grid) and the areal fitting approach (employing a uniform distribution across all grids) was conducted within the Godavari Basin, India. The findings in this study underscore that the misrepresentation of extreme events is inevitable for large heterogeneous basins like Godavari when the traditional areal approach was employed for SRI computation. Consequently, the grid-wise fitting emerges as a more accurate method for computing the SRI, particularly in characterising extreme dry or wet events.

A method to derive nitrogen transport factors for New Zealand's agricultural lands.

Risk index tools have the potential to assist farmers in making strategic decisions regarding their farm design to manage losses of nutrients. Such tools require a vulnerability framework, and these are often based on scores or rankings. These frameworks struggle to take account of interactions between elements of the physical environment. Process-based simulation models inherently take account of interactions and may be a viable alternative to score-based methods. We describe the method to populate a database of transport factors that covers the agricultural lands of New Zealand that is designed for usage as the susceptibility framework within a risk index tool. The method gives both leaching and runoff transport factors and gives values by month. The simulation model used had already been validated for simulating water and nitrogen balances and the generated spatial patterns of the transport factors was validated via expert assessment. These features allow good representation of the risks posed across a wide range of farming activities.•Use of a simulation model to quantify transport factors.•Captures the interactions between soil and weather factors in the physical environment.•Produces a country-wide database intended as a susceptibility framework for a risk index tool.

Field performance of 15 rain gardens in different cities in Taiwan.

Rain gardens are widely used for low impact development (LID) or as a nature-based solution (NbS). They help to reduce runoff, mitigate hot temperatures, create habitats for plants and insects, and beautify landscapes. Rain gardens are increasingly being established in urban areas. In Taiwan, the Ministry of Environment (MoE) initiated a rain garden project in Taipei city in 2018, and 15 rain gardens have since been constructed in different cities. These Taiwanese-style rain gardens contain an underground storage tank to collect the filtrated rainwater, which can be used for irrigation. Moreover, the 15 rain gardens are equipped with sensors to monitor temperature, rainfall, and underground water levels. The monitoring data were transmitted with Internet of Things (IoT) technology, enabling the capture and export of real-time values. The water retention, temperature mitigation, water quality, and ecological indices of the rain gardens were quantified using field data. The results from the young rain gardens (1-3 years) showed that nearly 100 % of the rainfall was retained onsite and did not flow out from the rain gardens; however, if the stored water was not used and the tanks were full, the rainwater from subsequent storms could not be stored, and the tanks overflowed. The surface temperatures of the rain garden and nearby impermeable pavement differed by an average of 2-4 °C. This difference exceeded 20 °C in summer at noon. The water in the underground storage tanks had very low levels of SS and BOD, with averages of 1.6 mg/L and 5.6 mg/L, respectively. However, the E. coli concentrations were high, and the average was 6283 CFU/100 mL; therefore, washing or drinking water is not recommended. The ecological indices, i.e., the Shannon and Simpson indices, demonstrated the good flora status of the rain gardens after one year. Although the weather differed by city, the performance of the rain gardens in terms of water retention, temperature mitigation, rainwater harvesting, and providing biological habitats was consistent. However, maintenance influences rain garden performance. If the stored water is not frequently used, the stored volume is reduced, and the stored water quality degrades.

Precipitation input increases biodiversity of planktonic communities in the Qinghai-Tibet Plateau.

Planktonic communities in aquatic ecosystems are crucial water quality indicators, with their growth dependent on runoff chemical and hydraulic characteristics (e.g., nutrient availability and turbidity). Previous studies have indicated that runoff components (i.e., proportions of precipitation, groundwater, snowmelt, etc.) play a vital role in regulating runoff characteristics, potentially affecting planktonic communities. However, the response of these communities to runoff components, particularly in mountainous regions, remains underexplored. In this study, we conducted four sampling campaigns from 2017 to 2020 in a watershed on the Qinghai-Tibet Plateau. Combined with laboratory incubation experiments, we examined the impact of various runoff components on the diversity and abundance of phytoplankton and zooplankton. We found that a higher proportion of precipitation in runoff contributed to an increase in the diversity of plankton communities. Laboratory experiments with unified water samples incubated with different runoff components demonstrated that the significant influence of precipitation on planktonic diversity primarily stems from the influx of abundant exogenous particulate material into rivers. Using a path analysis, we further confirmed that the impact of precipitation on diversity is primarily through chemical pathways, notably by increasing nutrient concentrations. Our study enhances our understanding of the interactions between the hydrological cycle and aquatic ecosystems, offering valuable insights for effectively maintaining and managing these natural environments.