River Health Research Articles

Inland river health assessment based on statistics and matter-element theory: a case study of the Shiyang River in China

ABSTRACT A method combining statistics and matter-element theory was proposed to evaluate the health status of the main stream of the Shiyang River in Gansu Province, China. The river health evaluation indicators and their weight values were derived, constructing an evaluation system with four criteria layers: ‘basin’, ‘water’, ‘biology’, and ‘social service function’, encompassing 11 indicators. The Shiyang River was divided into three sections: Xiying, Liangzhou, and Minqin. The fuzzy evaluation method and the matter-element model were used to assess health grades. The weights of the ‘basin’, ‘water’, ‘biology’, and ‘social service function’ criteria are 29, 15, 24, and 32%, respectively. The comprehensive evaluation results for the Xiying, Liangzhou, and Minqin sections are ‘very healthy’, ‘unhealthy’, and ‘sub-healthy’, respectively. The main factors leading to lower scores in the Liangzhou and Minqin sections are ‘ecological flow’, ‘macrobenthic integrity’, and ‘fish diversity’. This study provides a comprehensive, scientific method for assessing and maintaining the health of the Shiyang River.

Ecological flow research in response to hydrological variation: A case study of the Jinsha River Basin, China

Climate change and human activities have altered the natural state of rivers to a certain extent, leading to runoff variation and impacting the stability of riverine ecosystems. The ecological flow has emerged to balance the water relationship between humanity and riverine ecosystems and to safeguard the health and biodiversity of rivers. Presently, the scientific quantification of riverine ecological flow has become a focal point in water resource research. Addressing the quantification of ecological flow under hydrological variation conditions, the cumulative deviation method (CDM) and Pettitt methods were firstly employed to detect breakpoints in the runoff sequences. Then, the natural monthly runoff post-variation was restored using the Soil and Water Assessment Tool (SWAT) model, i.e., natural runoff restoration, and three testing methods were employed to determine the optimal hydrological distribution functions for natural monthly runoff. Subsequently, an improved frequency calculation method was used to calculate the ecological flow. The Jinsha River Basin was selected as the study area, and four crucial sections in the mainstream of the Jinsha River were studied, including Zhimenda (ZMD), Shigu (SG), Xiaodeshi (XDS), and Pingshan (PS). The results indicated: (1) the runoff sequences (1960–2017) at the four crucial sections exhibit different breakpoint times, with breakpoint years being 2004, 1986, 2005, and 1996, respectively; (2) the SWAT model, in simulating runoff at the four crucial sections, exhibited NSE values all exceeding 0.85, and r values all surpassing 0.9, demonstrating its effective ability to restore natural runoff in the Jinsha River basin; (3) the optimal hydrological distribution functions for natural monthly runoff at the four sections varied, and most natural monthly runoff conforms to the Generalized Extreme Value (GEV) distribution; (4) the average annual ecological flows for the ZMD, SG, XDS, and PS sections were 296 m3/s, 992 m3/s, 1238 m3/s, and 2755 m3/s, and the ecological flows derived using the improved frequency calculation method under the optimal hydrological distribution functions were more rational. This study can provide a scientific basis for the optimal allocation of water resources and the protection of riverine ecosystems in the Jinsha River basin.

The impact of wastewater treatment plants on the composition and toxicity of pollutants in urban rivers in Nanjing, China

Widespread wastewater pollution is one of the biggest challenges threatening the ecological health of rivers. It is crucial to identify the toxic changes of effluents after entering urban rivers as well as the toxic substances in the complex chemical mixtures found in these urban rivers. This study used HepG2 cell line for cytotoxicity test to evaluate the ecological impact of effluents on urban rivers. Water samples were collected from the Xingwu River and Yunliang River in Nanjing, China. The bacterial communities in the lower reaches of urban rivers were altered due to the differences in total nitrogen and nitrate nitrogen. The complex chemical mixtures collected in the urban rivers were divided into 10 fractions, >100 chemicals were screened in each fraction. The substances with LC50 < 1000 mg/L were listed as toxic substances, and the number of toxic substances dominated the toxicity of urban rivers. Our study highlights toxicity as a comprehensive indicator for assessing river pollutants and reveals relationship between the number of toxic substance and river toxicity. These findings have direct implications for the monitoring and management of environmental stressors and the protection of aquatic organisms and human health.

Intelligent classification of water bodies with different turbidity levels based on Gaofen-1 multispectral imagery

The turbidity of water is crucial for the health of river and lake ecosystems, necessitating efficient monitoring for effective water management. Existing methods for studying water turbidity's spatial and temporal distribution rely mostly on measured data. There is limited research on the classification of water bodies with different turbidity levels. The main challenge lies in determining the boundaries of liquid water bodies at various turbidity levels, making it challenging to classify them accurately using traditional remote sensing image classification methods. This paper proposes and validates an intelligent turbidity classification method based on deep learning using GaoFen-1 multispectral remote sensing imagery. An adaptive threshold water extraction method based on the Normalized Difference Water Index is proposed to capture water boundaries more accurately to improve the accuracy of extracting nearshore water bodies. A semi-automatic semantic annotation method for water turbidity is introduced to reduce manual labeling costs. The paper applies mode filtering to address edge noise issues and establishes a high-quality training sample dataset. After comparing the accuracy of various neural network models, DeepLab V3+ is selected for intelligent turbidity classification. The results show high accuracy, with mean intersection over union (MIoU), mean F1 score (MF1), and overall accuracy (OA) reaching 94.73%, 97.29%, and 97.54%, respectively. The proposed method and experiments demonstrate the feasibility of intelligent classification of water bodies with different turbidity levels using deep learning networks. This provides a new approach for large-scale and efficient remote sensing water turbidity monitoring.

Temporal analysis of water chemistry and smallmouth bass (Micropterus dolomieu) health at two sites with divergent land use in the Susquehanna River watershed, Pennsylvania, USA

Monitoring wild fish health and exposure effects in impacted rivers and streams with differing land use has become a valuable research tool. Smallmouth bass (Micropterus dolomieu) are a sensitive, indicator species that exhibit signs of immunosuppression and endocrine disruption in response to water quality changes and contaminant exposure. To determine the impact of agriculture and development on smallmouth bass health, two sites (a developed/agriculture site and a forested site) in the Susquehanna River watershed, Pennsylvania were selected where bass and water chemistry were sampled from 2015 to 2019. Smallmouth bass were sampled for histopathology to assess parasite and macrophage aggregate density in the liver and spleen, condition factor (Ktl), hepatic gene transcript abundance, hepatosomatic index (HSI), and a health assessment index (HAI). Land use at the developed/agriculture site included greater pesticide application rates and phytoestrogen crop cover and more detections and higher concentrations of pesticides, wastewater-associated contaminants, hormones, phytoestrogens, and mycotoxins than at the forested site. Additionally, at the developed/agriculture site, indicators of molecular changes, including oxidative stress, immune/inflammation, and lipid metabolism-related hepatic gene transcripts, were associated with more contaminants and land use variables. At both sites, there were multiple associations of contaminants with liver and/or spleen macrophage aggregate density, indicating that changes at the molecular level seemed to be a better indicator of exposures unique to each site. The findings illustrate the importance of timing for land management practices, the complex mixtures aquatic animals are exposed to, and the temporal changes in contaminant concentration. Agricultural practices that affect hepatic gene transcripts associated with immune function and disease resistance were demonstrated which could negatively affect smallmouth bass populations.

Water quality hotspot identification using a remote sensing and machine learning approach: A case study of the River Ganga near Varanasi

Turbidity (Turb) and Chlorophyll-a (Chl-a) are crucial indicators of water quality because they can reveal the presence of suspended particles and algae, respectively. Understanding the health of rivers and spotting long-term water quality changes can both benefit from monitoring these measures. Traditional methods of monitoring these parameters, like in-situ measurements, is time-consuming, expensive, and inconvenient in some places. Sentinel-2, a multispectral satellite, might offer a more workable and economical option for monitoring water quality, though. This study used 100 in-situ data collected from the Ganga River near Varanasi in the pre-monsoon season (pre-MS) and post-monsoon season (post-MS) in order to create a model for the prediction of optically active water quality parameters by combining Multispectral Instrument (MSI) data and machine learning method (Random Forest). To create spatial distribution maps for Chl-a and Turb, 14 spectral indices and band ratios were employed as independent variables. The results showed that the prediction accuracy for Turb (R2 = 0.91, MAE = 1.13 and MAPE=7.76 % during pre-MS and R2 = 0.93, MAE = 0.88 and MAPE=2.29 % during post-MS) and for Chl-a (R2 = 0.97, MAE = 0.59, and MAPE=2.07 % during pre-MS and R2 = 0.95, MAE = 0.61, and MAPE = 2.71 % during post-MS). The Ganga near Varanasi abruptly turned green due to an increase in algal bloom in May and June 2021. This study not only revealed the reasons behind the green appearance but also identified potential areas of concern or hotspots. In order to identify hotspot locations, drainage networks, point source discharge locations and LU-LC were used.

Towards sustainable river health: integrating hydrological modeling to predict nitrogen and phosphorus loads in the lower Sutlej sub-basin, Punjab, India

ABSTRACT This research evaluated the total nitrogen (TN), nitrate nitrogen (NO3-N), and total phosphorus (TP) loads of the lower Sutlej sub-basin using the Soil and Water Assessment Tool (SWAT) model. The model simulated TN, NO3-N, and TP successfully with satisfactory values of R2, NSE, PBIAS, and RSR. The streamflow and nutrient loss were found to be most sensitive to curve number and organic phosphorus enrichment ratio, with a sensitivity ratio of 0.661 and 0.556, respectively. Simulated TN, NO3-N, and TP loss within the basin vary from 6.32 to 14.23, 0.49 to 1.81, and 2.02 to 4.10 kg/ha/yr, respectively. Maximum and minimum TN losses were 14.23 and 6.32 kg/ha/yr in WS2 and WS5, respectively. Similarly, NO3-N losses were 1.81 and 0.49 kg/ha/yr in WS6 and WS10, respectively. WS2 and WS5 observed maximum and minimum TP loss of 4.10 and 2.02 kg/ha/yr, respectively. The watersheds located in the northeastern part of the basin contributed the highest nutrient load of TN (2,709 tons for WS12), NO3-N (214 tons for WS6) and TP (729 tons for WS12). By addressing nutrient losses and implementing suggested conservation measures, the present research findings can offer valuable guidance for managing sub-watersheds to reduce the nutrient loads and manage the health of the Sutlej River in Punjab.

Establishment of an Evaluation Indicator System and Evaluation Criteria for the Weihe River Ecological Watersheds

Ecological watersheds (eco-watersheds) are of great significance for boosting the construction of ecological civilization and realizing the ecological protection and high-quality development of watersheds. In order to establish a scientific eco-watershed evaluation index system, this paper refers to the river health evaluation index system, the water resources coupling evaluation index system, and the happy river evaluation index system, and reviews the development process of the ecological watershed evaluation index system. According to the eco-watershed theoretical system, combined with the relevant contents of policies and regulations, thousands of evaluation indicators that have been collected are screened using the theoretical analysis method and the frequency analysis method. Finally, a comprehensive evaluation index system of Weihe River eco-watersheds was constructed, including three first-level indicators: watershed water resources, socio-economic, and ecological. These were further subdivided into eight second-level indicators, namely water security, water resources, water economy, water management, water culture, water environment, water ecology, and water landscape. These second-level indicators were then further broken down into 60 third-level indicators. On the basis of determining the evaluation standards of Weihe River eco-watersheds, the evaluation weights were determined using the gray correlation method and the AHP-entropy comprehensive weight method (comprehensive weight), resulting in the establishment of an evaluation model and a coupling model for the eco-watersheds of the Weihe River. The results of the Weihe River eco-watershed evaluation model and the coupled coordination degree evaluation model show that, from 2019 to 2021, both the gray correlation analysis weights and comprehensive weight evaluation show a yearly increasing trend; the evaluation results are in the eligible status and below; and the evaluation grade is in the fourth-level eco-watersheds and below. Based on the evaluation results of each dimension, when comparing the gray correlation analysis weights with the evaluation of the comprehensive weights, the latter align more closely with the actual Weihe River Watershed. When comparing the evaluation results of the Henan Weihe River Watershed with those of the Handan Weihe River Watershed, the latter’s results, influenced by the scheduling of the Yuecheng Reservoir, are relatively better. Furthermore, in the evaluation of coupling coordination, the water resources subsystem is less coupled to other subsystems due to the 2021 flood. Therefore, in order to effectively improve the level of eco-watersheds, scientific and reasonable water resources scheduling programs should be developed.

River Flashiness in Great Britain: A Spatio-Temporal Analysis

Flashiness refers to the rapidity and frequency of fluctuations in river flow. It can provide insights into flooding, by capturing dramatic increases in river flow, as well as contaminant transport, relating to concentrations of diffuse pollution. Despite a very well gauged river system, there is limited research in Great Britain targeting this component of river flow. This study addresses that gap in knowledge, with a detailed spatio-temporal analysis of river flashiness in Great Britain. Using 513 gauging stations, with historical records of at least 30 years, the average Richards–Baker flashiness index (RBI¯) was calculated for 1990–2020, showing an overall west- (0.6–0.8) to east-coast (0.1–0.2) gradient, being higher in the west (with the exception of some gauges in the south-east). Employing random forest models, the main predictor for flashiness was found to be soil composition, with some additional region-specific predictors. These include flood attenuation by reservoirs and catchment areas, affecting flashiness in the north and west of Great Britain. Additionally, using a subset of 208 gauging stations with data recorded from 1970 to 2020, a temporal analysis examined significant breakpoints and/or trends in yearly flashiness, using the Pettitt test and Mann–Kendall trend test, respectively. Increases in flashiness were found mainly in the north-east and south-west of Great Britain, with implications in flooding and river health. On a seasonal scale, and using a monthly RBI¯, the timing of flashy events was found to oscillate between autumn and spring over the 50 years, gravitating around winter.

Flowing Prosperity: Empowering Riverbank Communities in Rajshahi City

This study investigates strategies to enhance the quality of life for riverbank communities in Rajshahi City, Bangladesh, situated along the Padma River. A comprehensive analysis of socioeconomic, environmental governance, and community aspects, the study identifies key challenges and opportunities. By paying attention to the natural resources and cultural heritage of the riverbanks, the endeavor seeks to foster socio-economic growth, environmental sustainability, and cultural preservation. Findings reveal pervasive socio-economic disparities, exacerbated by environmental degradation and governance shortcomings. The study also indicates that only 78% of inhabitants take the polio vaccine and the Body Mass Index shows 72% of people are under-weight. Despite these challenges, the study highlights community resilience, and the potential of grassroots initiatives to effect positive change. The study emphasizes the importance of integrated and participatory approaches to address the multifaceted issues facing riverbank communities. By prioritizing socio-economic equity, environmental sustainability, and community empowerment, it is possible to create inclusive and resilient communities along the riverbanks of Rajshahi City in Bangladesh. Through collaborative efforts including local stakeholders, governmental bodies, and non-profit organizations, the study creates opportunities for inclusive participation, enhances livelihoods, and promotes resilience against socio-economic challenges. The authors identify why these (River-bank people) cannot manage the sustainability of river ecosystems, health problems, and sustainable development, which advocates for a harmonious balance between social, economic, and environmental dimensions. Finally, a well-thought-out recommendation has been drafted by the authors.

A critical review of biomonitoring in East African rivers: fostering community-based collaboration for environmental change observation

The contribution of citizen scientists to environmental monitoring is notably increasing significantly. Governments worldwide establish Water Users Associations (WUAs) as a good practice model to implement Integrated Water Resource Management (IWRM) at local levels. These associations target different stakeholders depending on the expected outcome. However, their effectiveness, especially in East Africa, can only be determined case by case. In Kenya, Water Resources Users Associations are registered, voluntary community groups whose members are water users and land owners. Similarly, in Uganda, community-based initiatives exist, especially along degraded rivers, involving cooperation between local communities and regional water authorities. On the contrary, it has been reported that in Tanzania, many community initiatives created become non-functional within 2–3 years of induction. In general, the main responsibility of Water Users Associations or its equivalent is to manage and conserve water resources for sustainable uses. In most cases, water quality monitoring by communities through citizen science is limited to the visual appearance of water in the river channel. While this can indicate the water’s esthetic value, it is insufficient for assessing the ecological status of rivers, which is influenced by a variety of physical, chemical, biological, and socioeconomic factors. The use of bioindicators has been advocated as a feasible method for community-based water quality river monitoring. Therefore, this review explores the commonly used bio-indicators and bio-assessment tools for river health assessment and their complexities when using a biomonitoring community-based approach through citizen science. Tolerance and intolerance macroinvertebrate and fish metric protocols that utilize identification keys have been recommended. These protocols are designed to be user-friendly and require minimal taxonomic expertise, making them easy for community volunteers to use with minimal basic training. The sustainability of these citizen science initiatives relies on the motivation of volunteers, the frequency of monitoring activities, and collaboration with researchers and government agencies. These initiatives not only facilitate environmental monitoring but also foster community engagement and awareness regarding the ecological status of rivers, thereby addressing knowledge and data gaps necessary for effective policy-making. This approach provides a practical model for environmental stewardship and participatory resource management in East Africa.

Fish Community Characteristics and River Health Assessment of Gayasan National Par

Fish Community Characteristics and River Health Assessment of Gayasan National Par

An extended novel approach of river health analysis using nonmetric scaling ordination of fish ecological entities and functional identity indices

As river ecosystems continue to face environmental pressures, it is crucial to develop and apply new biologically relevant statistical tools to support river health assessments. This study aimed to test the potential significance of nonmetric ordination scores and multidimensional functional indices of fish communities for analyzing river ecological health linked to variations in environmental factors. We conducted a nonmetric multidimensional scaling (NMDS) to derive ordination scores based on twenty fish ecological entities (FEs) that we newly defined, along with their relative abundance (RA). These FEs were determined through a unique combination of trophic, habitat, and tolerance guilds. Additionally, we calculated the functional diversity and identity (F-Ide) indices using the RA-weighted distances between FEs defined by the guild identities in a multidimensional space. The results showed that these novel analyses were helpful in assessing river ecological health and spatial changes in the environment. The dominant F-Ide in each community was largely responsible for the variation of river health scores, driven by the combined impact of chemical water quality, alterations in substrate composition, land use changes, and the gradient of elevation. These environmental factors significantly influenced the site scores in the first axis of NMDS, F-Ide indices, and river health scores. Therefore, our study highlights the practical value of utilizing nonmetric scaling ordination scores of FEs and F-Ide indices as tools for evaluating the health of river ecosystems. By elucidating fish community variance, these tools can aid in determining the extent of river health degradation attributable to environmental stressors, including chemical water pollution and changes in substrate resulting from changes in land use and at different elevations.

Delving into river health: unveiling microplastic intrusion and heavy metal contamination in freshwater

The persistent use of plastics in chemical and industrial procedures has resulted in the contamination of the environment and human health via microplastics (MPs). This study investigates heavy metals, MP, pH, conductivity, and turbidity levels in the Nida River in Poland. The Nida River exhibited the existence of diverse coloured MPs in several physical forms, including fibres, fragments, pellets, and foams. Differences in the number of MPs were noted at five selected locations, with values ranging from 215 to 280 MPs per litre. The mean number of MPs observed was 245 ± 21 per litre. The study of water samples using a scanning electron microscope indicates the existence of various MPs. The morphological study of these MPs revealed patterns of degradation. The energy-dispersive X-ray examination indicates the existence of several elements, such as carbon, nitrogen, oxygen, sodium, magnesium, aluminium, silicon, phosphorus, and others. The water exhibited fluctuations in its levels of heavy metal content. The average zinc concentration in all samples was 96.86 ± 40.25 μg/L, which was the highest. It was followed by Pb 37.6 ± 31.9 μg/L concentration. The turbidity measurement was recorded as 46.38 ± 22.54, the pH level was 7.078 ± 0.33, and the conductivity value was determined to be 1338.8 ± 39.64. The HQ and HI values for adults and children in all age groups were less than one. The carcinogenic excess lifetime cancer risk (ELCR) exceeds the threshold value in most water samples. This study aims to fill the existing gap in understanding the precise measurement of MPs and heavy metals in freshwater sources in Poland. Health risk evaluations are the first step in developing pollution management, control, and water quality standards.Graphical

Comprehensive River Health Evaluation Indicator System and Its Application

Comprehensive River Health Evaluation Indicator System and Its Application

Microplastics pollution in the Surma River, Bangladesh: A rising hazard to upstream water quality and aquatic life

The ecological health of freshwater rivers is deteriorating globally due to careless human activities, for instance, the emission of plastic garbage into the river. The current research was the first assessment of microplastics (MPs) pollution in water, sediment, and representative organisms (fish, crustacean, and bivalve) from the Surma River. Water, sediment, and organisms were sampled from six river sites (Site 1: Charkhai; Site 2: Golapganj; Site 3: Alampur; Site 4: Kazir Bazar; Site 5: Kanishail and Site 6: Lamakazi), and major water quality parameters were recorded during sampling. Thereafter, MPs in water, sediment, and organism samples were extracted, and then microscopically examined to categorize selected MPs types. The abundance of MPs, as well as size, and color distribution, were estimated. Polymer types were analyzed by ATR-FTIR, the color loss of MPs was recorded, the Pollution Load Index (PLI) was calculated, and the relationship between MPs and water quality parameters was analyzed. Sites 4 and 5 had comparatively poorer water quality than other sites. Microplastic fibers, fragments, and microbeads were consistently observed in water, sediment, and organisms. A substantial range of MPs in water, sediment, and organisms (37.33–686.67 items/L, 0.89–15.12 items/g, and 0.66–48.93 items/g, respectively) was recorded. There was a diverse color range, and MPs of <200 μm were prevalent in sampling areas. Six polymer types were identified by ATR-FTIR, namely Polyethylene (PE), Polyamide (PA), Polypropylene (PP), Cellulose acetate (CA), Polyethylene terephthalate (PET), and Polystyrene (PS), where PE (41%) was recognized as highly abundant. The highest PLI was documented in Site 4 followed by Site 5 both in water and sediment. Likewise, Sites 4 and 5 were substantially different from other study areas according to PCA. Overall, the pervasiveness of MPs was evident in the Surma River, which requires further attention and prompt actions.

Spatial distribution of polycyclic aromatic hydrocarbons in water and sediment in the Ganga River: source diagnostics and health risk assessment on dietary exposure through a common carp fish Labeo rohita.

We evaluated spatial distribution and source apportionment of polycyclic aromatic hydrocarbons (PAHs) in water and sediments at four selected sites of the Ganga River. Also, we measured PAHs in muscle tissues of Rohu (Labeo rohita), the most common edible carp fish of the Ganga River and potential human health risk was addressed. Total concentration of PAHs (∑PAHs) in water was highest at Manika Site (1470.5ng/L) followed by Knuj (630.0ng/L) and lowest at Adpr (219.0ng/L). A similar trend was observed for sediments with highest concentration of ∑PAHs at Manika (461.8ng/g) and lowest at Adpr Site (94.59ng/g). Among PAHs, phenanthrene (Phe) showed highest concentration in both water and sediment. Of the eight major carcinogenic contributors (∑PAH8C), Indeno (1,2,3-C,D) pyrene (InP) did appear the most dominant component accounting for 42% to this group at Manika Site. Isomer ratios indicated vehicular emission and biomass combustion as major sources of PAHs. The ∑PAHs concentrations in fish tissue ranged from 117.8 to 758.0ng/g (fresh weight basis) where low molecular weight PAHs assumed predominance (above 80%). The risk level in fish tissues appeared highest at Manika Site and site-wise differences were statistically significant (p < 0.05). The ILCR (> 10-4) indicated carcinogenic risk in adults and children associated with BaP and DBahA at Manika Site and with BaP at Knuj Site. Overall, the concentrations exceeding permissible limit, carcinogenic potential and BaP equivalent all indicated carcinogenic risks associated with some individual PAHs. This merits attention because the Ganga River is a reservoir of fisheries.

Obtaining Patients' Perspective for Advancing Health Equity in Maternal Depression Screening for Black Women in a Federally Qualified Health Center [ID 2683414

INTRODUCTION: One in eight women experience symptoms of postpartum depression. At Sun River Health, efforts to improve postpartum depression screening rates led to an increase from 76% in January 2022 to 80% in January 2023. However, we continue to see a persistent racial disparity in postpartum depression screening: For Black/African American women, postpartum depression screening rates are 77.4%, whereas White women have screening rates of 88%. METHODS: A focus group of English-speaking Black/African American women (n=7) who were not screened for postpartum depression was conducted in May 2023. We administered a series of questions aimed at gathering insight into patients' understanding of and beliefs around postpartum depression, patients' experiences, and barriers to care. The responses were recorded and interpreted using qualitative data analysis. RESULTS: Eighty-three percent of group participants reported that they experienced postpartum depression. Fifty-seven percent reported not receiving depression screening, and 71% reported no discussion on mental health with their clinicians. Among the barriers to care the participants described, social stigma was a common influence and the majority described clinicians as dismissive of their symptoms. Only 14% of patients voiced comfort in discussing mental health with their clinician. CONCLUSION: Further work is needed to better educate clinicians on mental health conditions in pregnancy and postpartum and the importance of engaging patients in discussion on mental health issues. The organization should propagate the survey to obtain additional patients’ feedback and further our understanding of barriers to screening and treatment of depression in Black/African American pregnant women.

Fish Community Characteristics and the Effects of Water Quality on River Health in the Anyang Stream, Korea&lt;sup&gt;1a&lt;/sup&gt;

Fish Community Characteristics and the Effects of Water Quality on River Health in the Anyang Stream, Korea&lt;sup&gt;1a&lt;/sup&gt;

Analysis of irregularly sampled stream temperature time series: challenges and solutions

Water temperature is a key physical indicator of stream health, and as such, is commonly measured as part of long-term river health monitoring programs. However, analysis of long-term stream water temperature time series can be challenging, due to typically low frequencies of sampling combined with common characteristics of data collection from streams – such as observations unevenly spaced across seasons, changes to routine sampling frequencies, or improvements in the accuracy of measurements over time – known as sampling artifacts. While there are many models regularly used to estimate trends and summary statistics in long-term stream temperature datasets, there is limited understanding of the impact that commonly encountered sampling artifacts have on the accuracy and uncertainty of their estimates. This study constructed Monte-Carlo simulations to examine the influence that common sampling artifacts and the choice of analysis model can have on trend and mean estimates from long-term stream temperature time series covering tropical, temperate and cold climates. We found that, if not appropriately accounted for during analysis, sampling artifacts may obscure true trends or summary statistics, such as site means, and lead to inaccurate or misleading estimates. However, models that included components to account for seasonal variation within the model structure could estimate trends and means with high confidence, in the presence of almost all of the sampling artifacts commonly found in long-term stream temperature datasets. Structural biases in the time-of-day of sampling, such as always sampling in the morning, or where the start and end of the record are sampled at different times of day, could make estimates highly inaccurate and uncertain, and should be avoided in data collection strategies. This work aims to facilitate the analysis of historical stream temperature datasets with confidence, through identifying models that perform reliably in the presence of common sampling artifacts. The findings will enable further global insights into stream temperature, support management decisions based on accurate analysis and assist the design of future stream sampling programs using cost-effective, low-frequency sampling strategies.