Clumped isotopes reveal relationship between mussel growth and river discharge.

Freshwater mussels preserve valuable information about hydrology, climate, and population dynamics, but developing seasonal chronologies can be problematic. Using clumped isotope thermometry, we produced high-resolution reconstructions of modern and historic (~ 1900) temperatures and δ18Owater from mussel shells collected from an impounded river, the Brazos in Texas, before and after damming. We also performed high-resolution growth band analyses to investigate relationships between mussel growth rate, rainfall, and seasonal temperature. Reconstructed δ18Owater and temperature vary little between the modern (3R5) and historic shell (H3R). However, a positive relationship between reconstructed δ18Owater and growth rate in H3R indicates that aside from diminished growth in winter, precipitation and flow rate are the strongest controls on mussel growth in both modern and pre-dam times. Overall, our results demonstrate (1) the impact, both positive and negative, of environmental factors such as flow alteration and temperature on mussel growth and (2) the potential for clumped isotopes in freshwater mussels as a paleohydrology and paleoclimate proxies in terrestrial environments.

Microplastic pollution in the Chapora River, Goa, Southwest India: spatial distribution and risk assessment.

The amount of information available on the microplastic (MP) contamination in Goa's riverine water systems is currently limited. The abundance, size, colour, and polymer composition of microplastics in Chapora River surface water were investigated in this study. MPs in Chapora River surface water ranged from 0.1 particles/L (station 13) to 0.47 particles/L (station 5). The mean (± standard deviation) MP concentration was 0.25 (± 0.13) particles/L. Fibre was the dominant shape (77.15%), followed by fragments (12.36%), films (9.36%), and foam (1.12%). Most MPs were found in the 0.1-0.3 mm size range, then in the 0.3-1 mm and 1-5 mm. The dominant type of polymer studied was polyethylene terephthalate (PET; 46%), followed by high-density polyethylene (HDPE; 14%), polypropylene (PP; 5%), and polystyrene (PS; 1%). The risk assessment study indicated high risk with respect to PHI, while PLI shows low risk in the area. The source of MPs was mostly anthropogenic in nature in the region. When compared with other tropical rivers, MP pollution was relatively lower in the Chapora River. Nevertheless, the baseline data will help the local administration take mitigation measures to reduce the impact of MP pollution in the region.

Climate warming shifts riverine macroinvertebrate communities to be more sensitive to chemical pollutants.

Freshwaters are highly threatened ecosystems that are vulnerable to chemical pollution and climate change. Freshwater taxa vary in their sensitivity to chemicals and changes in species composition can potentially affect the sensitivity of assemblages to chemical exposure. Here we explore the potential consequences of future climate change on the composition and sensitivity of freshwater macroinvertebrate assemblages to chemical stressors using the UK as a case study. Macroinvertebrate assemblages under end of century (2080-2100) and baseline (1980-2000) climate conditions were predicted for 608 UK sites for four climate scenarios corresponding to mean temperature changes of 1.28 to 3.78°C. Freshwater macroinvertebrate toxicity data were collated for 19 chemicals and the hierarchical species sensitivity distribution model was used to predict the sensitivity of untested taxa using relatedness within a Bayesian approach. All four future climate scenarios shifted assemblage compositions, increasing the prevalence of Mollusca, Crustacea and Oligochaeta species, and the insect taxa of Odonata, Chironomidae, and Baetidae species. Contrastingly, decreases were projected for Plecoptera, Ephemeroptera (except for Baetidae) and Coleoptera species. Shifts in taxonomic composition were associated with changes in the percentage of species at risk from chemical exposure. For the 3.78°C climate scenario, 76% of all assemblages became more sensitive to chemicals and for 18 of the 19 chemicals, the percentage of species at risk increased. Climate warming-induced increases in sensitivity were greatest for assemblages exposed to metals and were dependent on baseline assemblage composition, which varied spatially. Climate warming is predicted to result in changes in the use, environmental exposure and toxicity of chemicals. Here we show that, even in the absence of these climate-chemical interactions, shifts in species composition due to climate warming will increase chemical risk and that the impact of chemical pollution on freshwater macroinvertebrate biodiversity may double or quadruple by the end of the 21st century.

Spatiotemporal variations, health risk assessment, and sources of potentially toxic elements in potamic water of the Anday Stream Basin (Türkiye), Black Sea Region.

Monitoring and protecting freshwater habitats are paramount for a sustainable water management perspective. This study investigated potentially toxic elements (PTEs) in the potamic water of the Anday Stream Basin (Türkiye), Black Sea Region, for a hydrological year (from May 2020 to April 2021). Among PTEs, the highest average values were recorded for sodium (Na) at 41.3 mg/L and the lowest for mercury (Hg) at 0.009 µg/L and noted under quality guidelines. The stream was found to be at the level of "Low Heavy Metal Pollution" and "Low Contamination" based on the ecotoxicological risk indices. The highest calculated hazard quotient (HQ) value of 1.21E-02 for Cd was noted in the children via the dermal pathway and the lowest of 6.91E-06 for Fe in adults via the ingestion pathway. Results revealed a higher hazard index (HI) value of 1.50E-02 for Cd to children and the lowest of 1.98E-05 for Fe to adults. As a result of applying agricultural risk indices, the stream showed sodium adsorption ratio values less than 6 and was found to be "Excellent" for agriculture. However, the sodium percentage values were less than 20 and found "Permissible" and the magnesium hazard > 50 and noted as "Unsuitable" for agriculture. Statistical analysis revealed that natural factors mainly attributed to PTE contamination of the Anday Stream Basin.

Effect of seasonality and estuarine waters on the phytoplankton of the Guamá River (Belém, Amazon, Brazil).

This study aimed to analyze the application of the Phytoplankton Community Index-PCI and Functional Groups-FG in determining the water quality of the Guamá River (Pará, Amazônia, Brazil). Samplings occurred monthly for analyses of phytoplankton and physical and chemical parameters, for two years, at the station where water was collected for human supply consumption. Seasonality influenced electrical conductivity, total suspended solids, dissolved oxygen, transparency, winds, true color, and N-ammoniacal. The ebb tide showed high turbidity and suspended solids. The density varied seasonally with the highest values occurring in September and December (61.1 ind mL-1 and 60.2 ind mL-1, respectively). Chlorophyll-a was more elevated in December (21.0 ± 4.7 µg L-1) and chlorophyll-c higher in relation to clorophyll- b indicated the dominance of diatoms. Functional Group P prevailed in the study months. Through the PCI índex the waters of Guamá River varied from reasonable to excellent and the TSI ranged from oligo to mesotrophic. The use of Functional Groups proved to be a promising tool in the determination of water quality since it covered the most abundant species in the Environment, but the PCI is not adequate to characterize Amazonian white-waters rivers, which have diatoms as the leading dominant group.

Influence of combined abiotic/biotic factors on decay of P. aeruginosa and E. coli in Rhine River water.

Understanding the dynamic change in abundance of both fecal and opportunistic waterborne pathogens in urban surface water under different abiotic and biotic factors helps the prediction of microbiological water quality and protection of public health during recreational activities, such as swimming. However, a comprehensive understanding of the interaction among various factors on pathogen behavior in surface water is missing. In this study, the effect of salinity, light, and temperature and the presence of indigenous microbiota, on the decay/persistence of Escherichia coli and Pseudomonas aeruginosa in Rhine River water were tested during 7 days of incubation with varying salinity (0.4, 5.4, 9.4, and 15.4 ppt), with light under a light/dark regime (light/dark) and without light (dark), temperature (3, 12, and 20 °C), and presence/absence of indigenous microbiota. The results demonstrated that light, indigenous microbiota, and temperature significantly impacted the decay of E. coli. Moreover, a significant (p<0.01) four-factor interactive impact of these four environmental conditions on E. coli decay was observed. However, for P. aeruginosa, temperature and indigenous microbiota were two determinate factors on the decay or growth. A significant three-factor interactive impact between indigenous microbiota, temperature, and salinity (p<0.01); indigenous microbiota, light, and temperature (p<0.01); and light, temperature, and salinity (p<0.05) on the decay of P. aeruginosa was found. Due to these interactive effects, caution should be taken when predicting decay/persistence of E. coli and P. aeruginosa in surface water based on a single environmental condition. In addition, the different response of E. coli and P. aeruginosa to the environmental conditions highlights that E. coli monitoring alone underestimates health risks of surface water by non-fecal opportunistic pathogens, such as P. aeruginosa. KEY POINTS: Abiotic and biotic factors interactively affect decay of E. coli and P. aeruginosa E.coli and P.aeruginosa behave significantly different under the given conditions Only E. coli as an indicator underestimates the microbiological water quality.

Longitudinal stream synoptic (LSS) monitoring to evaluate water quality in restored streams.

Impervious surface cover increases peak flows and degrades stream health, contributing to a variety of hydrologic, water quality, and ecological symptoms, collectively known as the urban stream syndrome. Strategies to combat the urban stream syndrome often employ engineering approaches to enhance stream-floodplain reconnection, dissipate erosive forces from urban runoff, and enhance contaminant retention, but it is not always clear how effective such practices are or how to monitor for their effectiveness. In this study, we explore applications of longitudinal stream synoptic (LSS) monitoring (an approach where multiple samples are collected along stream flowpaths across both space and time) to narrow this knowledge gap. Specifically, we investigate (1) whether LSS monitoring can be used to detect changes in water chemistry along longitudinal flowpaths in response to stream-floodplain reconnection and (2) what is the scale over which restoration efforts improve stream quality. We present results for four different classes of water quality constituents (carbon, nutrients, salt ions, and metals) across five watersheds with varying degrees of stream-floodplain reconnection. Our work suggests that LSS monitoring can be used to evaluate stream restoration strategies when implemented at meter to kilometer scales. As streams flow through restoration features, concentrations of nutrients, salts, and metals significantly decline (p < 0.05) or remain unchanged. This same pattern is not evident in unrestored streams, where salt ion concentrations (e.g., Na+, Ca2+, K+) significantly increase with increasing impervious cover. When used in concert with statistical approaches like principal component analysis, we find that LSS monitoring reveals changes in entire chemical mixtures (e.g., salts, metals, and nutrients), not just individual water quality constituents. These chemical mixtures are locally responsive to restoration projects, but can be obscured at the watershed scale and overwhelmed during storm events.

Cyanobacterial Harmful Algal Mats (CyanoHAMs) in tropical rivers of central Mexico and their potential risks through toxin production.

Cyanobacteria inhabiting lotic environments have been poorly studied and characterized in Mexico, despite their potential risks from cyanotoxin production. This article aims to fill this knowledge gap by assessing the importance of benthic cyanobacteria as potential cyanotoxin producers in central Mexican rivers through: (i) the taxonomic identification of cyanobacteria found in these rivers, (ii) the environmental characterization of their habitats, and (iii) testing for the presence of toxin producing genes in the encountered taxa. Additionally, we introduce and discuss the use of the term "CyanoHAMs" for lotic water environments. Populations of cyanobacteria were collected from ten mountain rivers and identified using molecular techniques. Subsequently, these taxa were evaluated for genes producing anatoxins and microcystins via PCR. Through RDA analyses, the collected cyanobacteria were grouped into one of three categories based on their environmental preferences for the following: (1) waters with high ionic concentrations, (2) cold-temperate waters, or (3) waters with high nutrient enrichment. Populations from six locations were identified to genus level: Ancylothrix sp., Cyanoplacoma sp., and Oxynema sp. The latter was found to contain the gene that produces anatoxins and microcystins in siliceous rivers, while Oxynema tested positive for the gene that produces microcystins in calcareous rivers. Our results suggest that eutrophic environments are not necessarily required for toxin-producing cyanobacteria. Our records of Compactonostoc, Oxynema, and Ancylothrix represent the first for Mexico. Four taxa were identified to species level: Wilmottia aff. murrayi, Nostoc tlalocii, Nostoc montejanii, and Dichothrix aff. willei, with only the first testing positive using PCR for anatoxin and microcystin-producing genes in siliceous rivers. Due to the differences between benthic growths with respect to planktonic ones, we propose the adoption of the term Cyanobacterial Harmful Algal Mats (CyanoHAMs) as a more precise descriptor for future studies.

Intelligence level evaluation and influencing factors analysis of equipment manufacturing industry in the Yangtze River Delta.

The Yangtze River Delta (YRD) bears the vital task of driving the growth of China's equipment manufacturing industry (EMI) intelligence as an advanced region. Fostering the transformation and upgrading of the EMI in the YRD and constructing a modern production mode is vital to developing and reforming China's manufacturing industry. This paper uses industrial robot data to assess the level of intelligence (LoI) in the EMI from 2016 to 2019. The OLS (ordinary least squares) model is used for the measurements, and the MQ (the modified contribution index) is used to estimate the degree of contribution from a host of variables. It is identified that the LoI is on the rise. However, excluding railways, aerospace, shipbuilding, and other transportation equipment manufacturing, the LoI is significantly higher than in other subsectors. It is also identified that technological innovation ability, human capital density, and enterprise cost pressure govern the industry's LoI. Moreover, while there is a difference in the main influencing factors in LoI within different industries, R&D investment, technological innovation ability, and enterprise cost pressure have the most significant impact across most equipment manufacturing sub-industries.

Digital economy, innovation factor allocation and industrial structure transformation-A case study of the Yangtze River Delta city cluster in China.

The attainment of regional high-quality development necessitates the critical role of the digital economy in facilitating the transformation of industrial structures. This study intends to investigate the effect of the digital economy on industrial structure transformation from the perspective of innovation factor allocation using a panel dataset of 41 cities in the Yangtze River Delta region for the period from 2011 to 2020. This paper considers four dimensions to measure the level of industrial structure transformation i.e. industrial structure servitization, industrial structure upgradation, service industry structure upgradation and industrial interaction level. The results of the study suggest that the digital economy can significantly improve industrial structure transformation. The results remain consistent even after several robustness checks. Further, the analysis of the mechanism of action shows that the digital economy can promote industrial structure transformation by optimizing the innovation factor allocation. The study provides several policy implications for the digital economy and its role in the promotion of industrial structure transformation.

Transient social-ecological dynamics reveal signals of decoupling in a highly disturbed Anthropocene landscape.

Understanding the transient dynamics of interlinked social-ecological systems (SES) is imperative for assessing sustainability in the Anthropocene. However, how to identify critical transitions in real-world SES remains a formidable challenge. In this study, we present an evolutionary framework to characterize these dynamics over an extended historical timeline. Our approach leverages multidecadal rates of change in socioeconomic data, paleoenvironmental, and cutting-edge sedimentary ancient DNA records from China's Yangtze River Delta, one of the most densely populated and intensively modified landscapes on Earth. Our analysis reveals two significant social-ecological transitions characterized by contrasting interactions and feedback spanning several centuries. Initially, the regional SES exhibited a loosely connected and ecologically sustainable regime. Nevertheless, starting in the 1950s, an increasingly interconnected regime emerged, ultimately resulting in the crossing of tipping points and an unprecedented acceleration in soil erosion, water eutrophication, and ecosystem degradation. Remarkably, the second transition occurring around the 2000s, featured a notable decoupling of socioeconomic development from ecoenvironmental degradation. This decoupling phenomenon signifies a more desirable reconfiguration of the regional SES, furnishing essential insights not only for the Yangtze River Basin but also for regions worldwide grappling with similar sustainability challenges. Our extensive multidecadal empirical investigation underscores the value of coevolutionary approaches in understanding and addressing social-ecological system dynamics.

Performance evaluation of deep learning based stream nitrate concentration prediction model to fill stream nitrate data gaps at low-frequency nitrate monitoring basins.

Accurate and frequent nitrate estimates can provide valuable information on the nitrate transport dynamics. The study aimed to develop a data-driven modeling framework to estimate daily nitrate concentrations at low-frequency nitrate monitoring sites using the daily nitrate concentration and stream discharge information of a neighboring high-frequency nitrate monitoring site. A Long Short-Term Memory (LSTM) based deep learning (DL) modeling framework was developed to predict daily nitrate concentrations. The DL modeling framework performance was compared with two well-established statistical models, including LOADEST and WRTDS-Kalman, in three selected basins in Iowa, USA: Des Moines, Iowa, and Cedar River. The developed DL model performed well with NSE >0.70 and KGE >0.70 for 67% and 79% nitrate monitoring sites, respectively. DL and WRTDS-Kalman models performed better than the LOADEST in nitrate concentration and load estimation for all low-frequency sites. The average NSE performance of the DL model in daily nitrate estimation is 20% higher than that of the WRTDS-Kalman model at 18 out of 24 sites (75%). The WRTDS-Kalman model showed unrealistic fluctuations in the estimated daily nitrate time series when the model received limited observed nitrate data (less than 50) for simulation. The DL model indicated superior performance in winter months' nitrate prediction (60% of cases) compared to WRTDS-Kalman models (33% of cases). The DL model also better represented the exceedance days from the USEPA maximum contamination level (MCL). Both the DL and WRTDS-Kalman models demonstrated similar performance in annual stream nitrate load estimation, and estimated values are close to actual nitrate loads.

Assessing watercourse quality using results-based indicators in an agri-environment scheme.

Small watercourses are essential contributors to catchment water quality, but they continue to suffer degradation across Europe. A results-based agri-environment scheme, aimed at improving watercourse quality in Ireland, developed a rapid drainage assessment to identify point source risks. The assessment uses a scoring system based on visual indicators of nutrient and sediment risk, linking the outcomes to farmer payments. To understand how this novel drainage risk assessment relates to instream watercourse quality, we used three macroinvertebrate-based biotic indices (Q-value, Small Stream Impact Score and Proportion of Sediment Sensitive Invertebrates). Macroinvertebrate kick-sampling and physiochemical analysis were completed in May and July 2021 for 12 'At Risk' and 12 'Not at Risk' small watercourses as identified by the results-based scheme. Results show that the scheme's drainage risk assessment can identify point source risks but we found it does not directly reflect local instream quality as assessed by the biotic indices. Unexpectedly, the biotic indices showed watercourse degradation in 58% of the upstream (control) sampling points, indicating impacts not captured by the drainage risk assessment. Small watercourses displayed high heterogeneity, with significant species turnover between the sampling months. The Small Stream Impact Score was less influenced by temporal change than the Q-value index. There was a significant relationship between instream watercourse quality and sedimentation, as quantified by the Proportion of Sediment Sensitive Invertebrates. Including a measurement of instream sedimentation in the drainage assessments would improve the identification of risks and management. These results show that by linking farmer payments to the drainage risk assessments results-based payment schemes could positively contribute to improving catchment scale watercourse quality, but further work is required to capture wider sources of freshwater impacts.

Prediction of microbial activity and abundance using interpretable machine learning models in the hyporheic zone of effluent-dominated receiving rivers.

Serving as a vital linkage between surface water and groundwater, the hyporheic zone (HZ) plays a fundamental role in improving water quality and maintaining ecological security. In arid or semi-arid areas, effluent discharge from wastewater treatment facilities could occupy a predominant proportion of the total base flow of receiving rivers. Nonetheless the relationship between microbial activity, abundance and environmental factors in the HZ of effluent-receiving rivers appear to be rarely addressed. In this study, a spatiotemporal field study was performed in two representative effluent-dominated receiving rivers in Xi'an, China. Land use data, physical and chemical water quality parameters of surface and subsurface water were used as predictive variables, while the microbial respiratory electron transport system activity (ETSA), the Chao1 and Shannon index of total microbial community, as well as the Chao1 and Shannon index of denitrifying bacteria community were used as response variables, while ETSA was used as response variables indicating ecological processes and Shannon and Chao1 were utilized as parameters indicating microbial diversity. Two machine learning models were utilized to provide evidence-based information on how environmental factors interact and drive microbial activity and abundance in the HZ at variable depths. The models with Chao1 and Shannon as response variables exhibited excellent predictive performances (R2: 0.754-0.81 and 0.783-0.839). Dissolved organic nitrogen (DON) was the most important factor affecting the microbial functions, and an obvious threshold value of ∼2 mg/L was observed. Credible predictions of models with Chao1 and Shannon index of denitrifying bacteria community as response variables were detected (R2: 0.484-0.624 and 0.567-0.638), with soluble reactive phosphorus (SRP) being the key influencing factor. Fe (Ⅱ) was favorable in predicting denitrifying bacteria community. The ESTA model highlighted the importance of total nitrogen in the ecological health monitoring in HZ. These findings provide novel insights in predicting microbial activity and abundance in highly-impacted areas such as the HZ of effluent-dominated receiving rivers.

Existing levels of biodiversity and river location may determine changes from small hydropower developments.

Global ecosystems are facing anthropogenic threats that affect their ecological functions and biodiversity. However, we still lack an understanding of how biodiversity can mediate the responses of ecosystems or communities to human disturbance across spatial gradients. Here, we examined how existing, spatial patterns of biodiversity influence the ecological effects of small hydropower plants (SHPs) on macroinvertebrates in river ecosystems. This study found that levels of biodiversity (e.g., number of species) can influence the degrees of its alterations by SHPs occurring along elevational gradients. The results of the study reveal that the construction of SHPs has various effects on biodiversity. For example, low-altitude areas with low biodiversity (species richness less than 12) showed a small increase in biodiversity compared to high-altitude areas (species richness more than 12) under SHP disturbances. The increases in the effective habitat area of the river segment could be a driver of the enhanced biodiversity in response to SHP effects. Changes in the numerically dominant species contributed to the overall level of community variation from disturbances. Location-specific strategies may mitigate the effects of SHPs and perhaps other disturbances.

Harnessing market based mechanisms to improve water quality: Water quality trading policies in the river Alde, UK.

Nitrogen fertiliser in agriculture continues to be one of the largest contributors to water pollution driven by the global food demand. Consequently, policies designed to tackle nitrogen pollution tend to be focused on the farm level. Applying mitigation measures requires knowledge, local labour and financial investment to achieve desired goals. Influencing farming activity comes with challenges as policies result in economic losses. We propose Water Quality Trading (WQT) to minimize the cost of controlling water pollution and develop it for policy recommendations in the River Alde catchment in Suffolk. We apply WQT to three scenarios named Reference Pollution Target, Livestock Target Plan and Variation of Farming. Our findings demonstrate that WQT can reduce farmers nitrogen load by 8%, 7% and 18% respectively from the baseline of 6 mg/L. The scenario simulations show a net revenue increase of 6%, 5% and 18% respectively. Our study demonstrates the effectiveness of the WQT approach in reducing water pollution, promoting sustainable agriculture and meeting water management goals.

Responses of streamflow to forest expansion in a typical subhumid watershed under future climate conditions.

Water availability in the subhumid region is highly vulnerable to frequent droughts. Water scarcity in this region has become a limiting factor for ecosystem health, human livelihood, and regional economic development. A notable pattern of land cover change in the subhumid region of the United States is the increasing forest area due to afforestation/reforestation and woody plant encroachment (WPE). Given the distinct hydrological processes and runoff generation between forests and grasslands, it is important to evaluate the impacts of forest expansion on water resources, especially under future climate conditions. In this study, we focused on a typical subhumid watershed in the United States - the Little River Watershed (LRW). Utilizing SWAT + simulations, we projected streamflow dynamics at the end of the 21st century in two climate scenarios (RCP45 and RCP85) and eleven forest expansion scenarios. In comparison to the period of 2000-2019, future climate change during 2080-2099 will increase streamflow in the Little River by 5.1% in the RCP45 but reduce streamflow significantly by 30.1% in the RCP85. Additionally, our simulations revealed a linear decline in streamflow with increasing forest coverage. If all grasslands in LRW were converted into forests, it would lead to an additional 41% reduction in streamflow. Of significant concern is Lake Thunderbird, the primary reservoir supplying drinking water to the Oklahoma City metropolitan area. Our simulation showed that if all grasslands were replaced by forests, Lake Thunderbird during 2080-2099 would experience an average of 8.6 years in the RCP45 and 9.4 years in the RCP85 with water inflow amount lower than that during the extreme drought event in 2011/2012. These findings hold crucial implications for the formulation of policies related to afforestation/reforestation and WPE management in subhumid regions, which is essential to ensuring the sustainability of water resources.

A novel approach to identify priority areas for optimal nutrient management in mixed land-use watersheds through nutrient budget assessment.

Excessive nutrient supply in agricultural regions has led to various environmental issues, thereby requiring concentrated management owing to its persistent upward trend. Nutrient budgets (NBs), a vital agricultural environmental indicator, are employed for nutrient management in agricultural areas, using data surveyed by administrative agencies. However, the spatial extent of nutrient data for nutrient budgeting is limited by administrative boundaries according to the surveying organization, posing challenges in interpreting spatial patterns at the watershed level. In this study, a novel approach was developed to identify priority nutrient management areas by applying hot spot spatial analysis to watershed-level NBs, considering hydrological characteristics. This method was applied to approximately 850 subwatersheds across the Republic of Korea, where land cover characteristics are complex. Reassessing nutrient budgets at the watershed scale, accounting for overlapping administrative boundary areas and crop cultivation ratios, indicated similar levels between the two methods. Hot spot analysis revealed that watersheds with elevated NBs mirrored the spatial patterns of livestock excreta and cropland. The spatial distribution characteristics of watersheds with high nutrient levels in rivers corresponded with the concentration characteristics of industrial and commercial areas. Therefore, applying watershed-level NBs based on land cover ratios that consider nutrient input characteristics in agricultural regions is deemed appropriate for selecting priority nutrient management areas. Collectively, this study presents a method for selecting nutrient management priority areas by simultaneously considering the spatial characteristics of various environmental factors, such as land cover, livestock excreta, river water quality, and land area-based watershed-specific NBs. The proposed approach, considering mixed land cover characteristics, is anticipated to be valuable for selecting priority management areas in watersheds with diverse pollution sources. Future research is needed to explore nutrient budgets within watersheds, the influence of land use on pollution sources, and their correlation with water quality.

Analysis of dissolved oxygen influencing factors and concentration prediction using input variable selection technique: A hybrid machine learning approach.

Accurate quantification of dissolved oxygen (DO) is critically important for the protection and management of aquatic ecosystems. Successful applications have utilized mechanistic and data-driven models to simulate DO content in aquatic ecosystems. However, mechanistic models present challenges due to their complex and difficult-to-solve conditions, making them less portable. Additionally, data-driven model predictions are hindered by the challenge of numerous input variables, impacting both the running speed and prediction performance of the model. To address these challenges, water quality data and meteorological data of the Tanjiang River were obtained. The maximum information coefficient (MIC) input variable selection technique was employed to identify primary environmental factors influencing DO changes. Furthermore, coupled with support vector regression (SVR), two models (SVR and MIC-SVR) were employed to estimate the DO concentration of the Tanjiang River, and the optimal model was established. The results indicated a shift in the primary pollution factor from ammonia nitrogen to total phosphorus after recent treatment in the Tanjiang River. In comparison with the SVR model, the root mean square error (RMSE) of the MIC-SVR model was reduced by 4.46%, and the Nash-efficiency coefficient (NSE) was improved by 45.85%. In addition, study of kernel function selection revealed that considering as many kernel functions as possible is necessary for improving the performance of the SVR model. Conclusively, the proposed MIC-SVR model serves as an effective tool to analyze the relationship between DO and environmental factors, identifying the primary causes of low DO, and accurately predict the DO concentration in the Tanjiang River (especially in its middle and lower reaches), thus providing a reference for governmental decision-making on water environmental protection and water resource management.

RIvER Clinic: Rethinking Incarceration and Empowering Recovery-An Innovative Postincarceration Care Delivery Model Implemented During the COVID-19 Pandemic.

The Rethinking Incarceration and Empowering Recovery (RIvER) Clinic was launched in June 2021 to address the health disparities experienced during and after incarceration. The RIvER Clinic's multidisciplinary, community-centered team engages patients during jail detention and after release via telehealth, collocated in community locations, on a mobile van, and in clinic. The clinic serves as a bridge between incarceration and the establishment of permanent health care and social services in the community. In 2022, a total of 479 visits were completed. The clinic provided multidisciplinary substance use support to all eligible patients, paying for 104 medication for opioid use disorder (MOUD) prescriptions for uninsured patients. Twenty-five percent of patients were transitioned to community-based care, and less than 5% of patients were reincarcerated. Despite some limitations, results demonstrate that the RIvER Clinic is successfully reintegrating a marginalized population into its community. The purpose of this article is to describe the implementation and preliminary outcomes of this postincarceration clinic.