River Surface Water Research Articles

Towards automation of river water surface detection

Towards automation of river water surface detection

Assessment of Changes in Water Quality of Enim River, Muara Enim, South Sumatera, Indonesia to Determine Environmental Designations

Enim river flows within several sub-districts in Muara Enim Regency, including Semendo Darat Laut, Semendo Darat Tengah, Semendo Darat Ulu, Tanjung Agung, Lawang Kidul, and Muara Enim. This study examines changes in the water quality of the Enim River due to domestic wastewater discharges and the presence of the coal mining industry and household industries. Water samples were taken from 4 sampling stations along the Enim River in 2018 and 2023. Data consisted of results of physical, chemical, and microbiological parameters. The status of river water quality was assessed from the pollution index. The results of Enim River surface water quality measurements showed that the parameters of total suspended solids, Dissolved Oxygen, iron (Fe), copper (Cu), oil, and grease concentrations had breached the criteria set for Class II water quality, referring to Government Regulation of the Republic of Indonesia Number 82 of 2001. The results showed that there has been a change in the water quality status of Enim River from 2018 to 2023. The river water pollution index was (1.43-2.47) in 2018 and (1.49-3.85) in 2023 (lightly polluted). Comprehensive water treatment and preventive measures are necessary to manage and mitigate additional pollution in the Enim River.

Assessment of Büyük Menderes River (West of the Türkiye) Surface Water Quality with Water Quality Index (WQI), Comprehensive Pollution Index (CPI) and Geographic Information Systems (GIS)

In the present study some of the water quality parameters were spatially examined with water quality and pollution indices in the Büyük Menderes River (BMR). In addition, the more effective parameters on the indices were determined among the parameters measured in the study. The measured water quality parameters were detected as WT:16.5-25.9 oC, pH:7.5-9.3, DO:nd-12.2 mgL-1, EC:312-30215 µScm-1, Salt:0.17-19.6 %o, NO2-N:nd-0.31 mgL-1, NO3-N:nd-1.08 mgL-1, NH4-N:0.035-25.2 mgL-1, TP:0.011-7.45 mgL-1, MBAS:nd-7.79 mgL-1 ve COD:nd-128.9 mgL-1. In the present study, several parameters were determined as the second class and third class of water quality according to the Turkish Water Pollution Control Regulation*. WQI value range was determined as from Bad (38.82) to Excellent (92.35), and CPI as from “Sub Clean (0.39) to Highly Polluted (9.62)” in the stream. It was determined that there was no compatibility between WQI and CPI used in determining the environmental risks in the study area. The GIS was found to be effective in interpreting the spatial distribution of the results obtained and in identifying the key areas that require control. Different statistical analyses were used to determine the relationships between variables. In multiple linear regression (MLR) analysis, it was determined that the most critical parameter affecting the indices was NH4-N. The results obtained from WQI and CPI in the present study indicate that anthropogenic activities are effective in the region. *:TWPCR, Quality criteria of inland surface water resources in terms of general chemical and physicochemical parameters by Class

Siting Considerations for Satellite Observation of River Discharge

AbstractWith growing global capability for satellite measurement of river discharge (flow) comes a need to understand and reduce error in satellite‐based discharge measurements. Satellite‐based discharge estimates are based on measurements of water surface width, elevation, slope, and potentially velocity. Site selection is important for reducing error and uncertainty in both conventional and satellite‐based discharge measurements because geomorphic river characteristics have strong control over the relationships between discharge and width, water surface elevation (or depth), slope, and velocity. A large ground‐truth data set of 8,445 conventional hydraulic measurements, collected by acoustic Doppler current profilers at 503 stations in the United States, was developed and quality assured to examine correlation between river discharge and water surface width, depth, velocity, and cross‐sectional area. A separate database of river surface slope and discharge time‐series was developed from paired continuous monitoring stations to examine slope‐discharge correlations. Results show that discharge correlates most strongly with velocity, cross‐sectional area, depth, width, and slope, in that order. Uncertainty of satellite discharge estimates is affected by observed hydraulic variable and reach‐specific variability in observed variable(s) characteristics including range of variability, georegistration accuracy, and stability over time of relationships between discharge and observed hydraulic variable.

Undervalued dry riverbeds: A key factor in equating intermittent river CO2 emissions to perennial rivers

Intermittent rivers in semiarid and arid regions, constituting over half of the world's rivers, alternate the carbon cycle interactions among the biosphere, hydrosphere, and atmosphere. Inadequate quantification of flow duration and river water surface area, along with overlooked CO2 emissions from dry riverbeds, result in notable inaccuracies in global carbon cycle assessments. High-resolution remote sensing images combined with intensive field measurements and hydrological modelling were used to estimate and extract the flow duration, river water surface area and dry riverbed area of Huangfuchuan, an intermittent river watershed that acts as a major tributary of the Yellow River in semiarid Northwest China. CO2 emission rates and partial pressures in water and air across the watershed were in-situ measured. In 2018, the flow duration of Huangfuchuan increased from less than 5 days in the first-order tributary to 150 days in the sixth-order mainstream. River water surface area estimated by remote sensing extraction plus the hydrodynamic model simulation varied from 3.9 to 88.6 km2 under 5 %–95 % discharge frequencies. CO2 emissions from the water-air interface and dry riverbed in 2018 were estimated at 582.3 × 103 and 355.2 × 103 ton, respectively. The estimated total annual emission (937.5 × 103 ton) aligns closely with the range of emissions (67.3 × 103–1377.2 × 103 ton) calculated for the water-air interface alone, derived using DEM river length and hydraulic geometry method. This similarity can be attributed to the overestimation of flow duration and flow velocity, as well as the over- or under-estimation of river water surface area and slope. The new method proposed in this study has large potential to be applied in estimating CO2 emissions from data-scarce intermittent rivers located in mountainous regions and provides a standardized solution in the estimation of CO2 emission. Results of this research reveal the spatiotemporal distribution of CO2 emissions along an intermittent river system and highlight the substantial role of dry riverbed in carbon cycle.

Heavy Metal Concentrations in Surface Water of the Lower Mekong River Basin (Vietnam)

Heavy Metal Concentrations in Surface Water of the Lower Mekong River Basin (Vietnam)

STRATEGI PEMBERDAYAAN EKONOMI MELALUI INOVASI TEKNOLOGI AIR BERSIH: STUDI KASUS PENERAPAN REVERSE OSMOSIS DAN PERINTISAN BISNIS

The helplessness of coastal communities in processing salt and brackish water into drinking water is a great irony of modern society, in the midst of technological advances and the industrial era 4.0. The problem in the coastal areas of Sungai Kakap subdistrict, Kubu Raya district, is the lack of availability of clean water and the absence of clean water infrastructure. Peat groundwater in coastal deltas cannot support the use of groundwater sources due to high levels of acid and organic substances. This can trigger problems of poor community sanitation and cause the community's inability to optimize the potential of villages that have abundant natural resources. This activity aims to empower and increase community participation in designing and processing brackish/salt water into drinking water using Reverse Osmosis (RO) technology to meet the need for hygienic ready-to-drink water. Small scale water treatment system design using ultrafiltration (UF) and reverse osmosis (RO) membranes. The raw water used comes from river surface water in coastal areas. The small-scale water treatment design consists of two FRP tubes containing filter media, three filter cartridges, and UF and RO membranes. Two thrust pumps, with a minimum pressure specification of 40 m, are used to provide pressure that exceeds osmosis pressure so that raw water can be filtered through the RO membrane. The results of the activity show that the implementation of the RO system has succeeded in increasing the availability of clean water in Sungai Itik Village, which previously experienced challenges in providing adequate water supply. In addition, the integration of local business startups with RO technology opens up new opportunities for local residents to participate in the local economy and increase family income. Key factors supporting the success of this economic empowerment strategy were identified, including active community involvement in planning and implementation, collaboration between local government and local stakeholders, and adoption of new technologies and skills by local communities. This activity provides an important contribution in understanding the role of clean water technology innovation as a catalyst for local economic transformation in coastal areas. The practical implications of these findings can help decision makers in designing sustainable and effective policies to improve the welfare of coastal communities through sustainable economic empowerment. The use of this technology can help increase the availability of clean water in areas that previously experienced difficulties in this regard.

Significant regional disparities in riverine microplastics

Research on riverine microplastics has gradually increased, highlighting an area for further exploration: the lack of extensive, large-scale regional variations analysis due to methodological and spatiotemporal limitations. Herein, we constructed and applied a comprehensive framework for synthesizing and analyzing literature data on riverine microplastics to enable comparative research on the regional variations on a large scale. Research results showed that in 76 rivers primarily located in Asia, Europe, and North America, the microplastic abundance of surface water in Asian rivers was three times higher than that in Euro-America rivers, while sediment in Euro-American rivers was five times more microplastics than Asia rivers, indicating significant regional variations (p < 0.001). Additionally, based on the income levels of countries, rivers in lower-middle and upper-middle income countries had significantly (p < 0.001) higher abundance of microplastics in surface water compared to high-income countries, while the opposite was true for sediment. This phenomenon was preliminarily attributed to varying levels of urbanization across countries. Our proposed framework for synthesizing and analyzing microplastic literature data provides a holistic understanding of microplastic disparities in the environment, and can facilitate broader discussions on management and mitigation strategies.

Surface water quality prediction in the lower Thoubal river watershed, India: A hyper-tuned machine learning approach and DNN-based sensitivity analysis

The accurate and efficient monitoring, assessment, and prediction of water resources is crucial for the sustenance of life and health of the environment. Traditional methods for assessing water quality can be laborious and time-consuming, but the use of machine learning algorithms can improve the speed and precision of these predictions. The study aimed to evaluate the quality of surface water in the lower Thoubal river using a comprehensive approach that analyzed 12 physico-chemical properties of the water samples collected from 16 different sites. Four machine learning algorithms namely, deep neural network (DNN), gradient boost model (GBM), generalized linear model (GLM), and random forest (RF) were applied and compared for the prediction efficiency of a water quality index (WQI). Sensitivity and uncertainty analysis of the parameters were carried out using a DNN-based model. The study incorporated model evaluation using learning curve along with five key performance assessors. The findings revealed marked variation in the WQI in the watershed. Of the samples tested, about 70 % were found to have good water quality, 18 % had excellent water quality, 11 % had poor water quality, 1 % had very poor water quality, and less than 1 % were deemed unsuitable. Turbidity, BOD, and COD were found to be the most influential parameter towards WQI prediction. The effectiveness of the predictive models ranked as GLM>DNN>RF>GBM, GLM>GBM>DNN>RF, and GLM>DNN>RF>GBM; based on testing, training and 5-fold cross-validation. The GLM model consistently showed superior performance with the lowest RMSEs of 0.14783 in training, 0.15936 in validation, and 0.26115 in 5-fold cross-validation. Conversely, during the training, RF displayed the worst RMSEs of 5.38354, 4.79754 by GBM in validation, and 7.04887 by GBM in 5-fold cross-validation. The analysis using the Taylor diagram and learning curve further supported that GLM was the most effective model for predicting surface water quality. Biological oxygen demand, chemical oxygen demand, electrical conductivity, turbidity and total hardness were found to be the key parameters influencing the water quality. Unregulated and illegal sand mining from the riverbed, garbage dumping into the water bodies, runoffs from agricultural fields have significantly deteriorated the water quality in the study area. The scalable approach of the study and its results can benefit the local water managers and water research community.

Distribution and Risk Assessment of 80 Polycyclic Aromatic Compounds in Surface Water and Industrial Effluent in the Upper Yangtze River, China

Distribution and Risk Assessment of 80 Polycyclic Aromatic Compounds in Surface Water and Industrial Effluent in the Upper Yangtze River, China

Influence of ongoing discharge from multiple wastewater treatment plants on microplastic patterns in small-scale receiving rivers

As widely acknowledged, wastewater treatment plants (WWTPs) stand as significant contributors to the presence of microplastics in surface water. Nonetheless, there exists a notable research gap regarding the extent of potential pollution resulting from the concurrent and uninterrupted discharges originating from multiple WWTPs into small-scale receiving water bodies. This study endeavors to address this knowledge deficit by conducting a thorough investigation into the prevalence of microplastics in surface water. The research encompasses seven distinct locations within the Changzhou section of the Beijing-Hangzhou Grand Canal and the effluent of three WWTPs situated along the tributary. The results indicate differences in the distribution of microplastics in surface waters of mainstream and tributaries. While the microplastic abundance and composition showed little variation along the main stream, the tributaries displayed an overall increasing trend in microplastic abundance from upstream to downstream. Notably, the major contributors to this increase were fragments, fiber particles, and microplastics with particle sizes ranging from 100 to 300 μm. Considering that the primary distinction between the tributaries and the mainstream is the presence of the three WWTPs along the tributaries, the study conducted a correlation analysis between river surface water and effluents from these plants. The results indicated a stronger correlation between the tributaries and the effluents, suggesting that WWTPs are one of the primary factors contributing to the elevated levels of microplastics in the tributaries. Finally, a comparative analysis of microplastic abundance in the Beijing-Hangzhou Grand Canal's Changzhou section and other regions was conducted. The findings revealed that the microplastic pollution level in the Beijing-Hangzhou Grand Canal's Changzhou section is higher than that in most other rivers. Therefore, the issue of microplastic pollution in the Beijing-Hangzhou Grand Canal's Changzhou section warrants our attention, particularly with regard to the effectiveness of microplastic removal by the WWTPs along its course.

LRFD methodology for river embankments against non-stationary flooding under climate change

Riverine floods have become increasingly prevalent on a global scale, posing significant risks to infrastructure systems and communities. The escalating impacts of climate change associated with the increase in rainfall intensities and frequencies necessitate the improvement of the existing design methodologies to account for the non-stationary climate change effects to ensure that the reliability is above the target level and mitigate future flood disasters. This paper presents a novel LRFD approach for river embankments subjected to extreme rainfall under non-stationary climate change effects. This approach introduces an additional partial factor to account for the effects of climate change. Precipitation and temperature projections are collected from various climate models considering several cases of emission scenarios. An integrated hydrological and hydraulic modeling of the analyzed river is carried out to estimate the associated time-variant river discharge and water surface elevation. The non-stationary extreme value associated with the maximum flood level is leveraged using the peak-over-threshold approach. The embankment reliability and the corresponding most probable points are evaluated using limit states associated with overtopping and slope failures. Based on the estimated and target reliability indexes, the design point for each random variable is assessed considering the cases with and without climate change effects. Finally, the partial factors associated with climate change effects are determined. As an illustrative example, the proposed framework is applied to the Ashida River in Fukuyama city of Japan.

Ecological risk assessment of oil & grease (OG) and heavy metals in the surface water of Naf River, Bangladesh

This study aims to fill the gap in our understanding of the distribution and ecological risk of oil and grease (OG) and toxic heavy metals in the surface water of the Naf River, a major transportation route connecting Teknaf to Saint Martin's Island in Bangladesh. Altogether, 6 sampling stations in this river were assessed for OG and heavy metal pollution, revealing the presence of both at each station. The OG concentration is found ranging from 3.6 to 23.6 mg/L and the average concentration is 8.13 mg/L. On the other hand, the contamination factor (Cf) of the toxic heavy metals follows the descending order of Pb (63.97) > Cd (23.94) > Mn (0.94) > Ni (0.64) > Zn (0.22) > Cr (0.09) > Cu (0.04) > As (0.04) in the water samples. Heavy metal pollution index (HPI), Heavy metal evaluation index (HEI), and Nemerow pollution index (PN) indicate that the surface water of the study area includes high levels of pollution category due to the elevated levels of Pb, Cd, and Ni concentrations. The mean values of the single factor pollution index (Pi) for Pi(Pb), Pi(Cd) and Pi(Ni) are found 45.69, 39.41, and 6.43, which exceed the threshold limit. The ecological risk index indicates that around 25 % of the total heavy metals show a very high ecological risk and 75 % exhibit a lower ecological risk. Notably, within the very high ecological risk, Cd is responsible for 53 % of this risk, while Pb contributes the remaining 47 %. Increased OG and heavy metal concentrations in the Naf River are likely due to human activities like waste discharge from municipalities, solar power plants, pesticide use, and fishing trawlers. This research offers insights into the current state of the Naf River and guides policymakers toward more effective initiatives.

Evaluating a river's ecological health: A multidimensional approach

Evaluating the health of river surface water is essential, as rivers support significant biological resources and serve as vital drinking water sources. While the Water Quality Index (WQI) is commonly employed to evaluate surface water quality, it fails to consider biodiversity and does not fully capture the ecological health of rivers. Here we show a comprehensive assessment of the ecological health of surface water in the lower Yangtze River (LYR), integrating chemical and biological metrics. According to traditional WQI metrics, the LYR's surface water generally meets China's Class II standards. However, it also contains 43 high-risk emerging contaminants; nitrobenzenes are found at the highest concentrations, representing 25–90% of total detections, while polycyclic aromatic hydrocarbons present the most substantial environmental risks, accounting for 81–93% of the total risk quotient. Notably, the plankton-based index of biological integrity (P-IBI) rates the ecological health of the majority of LYR water samples (59.7%) as ‘fair’, with significantly better health observed in autumn compared to other seasons (p < 0.01). Our findings suggest that including emerging contaminants and P-IBI as additional metrics can enhance the traditional WQI analysis in evaluating surface water's ecological health. These results highlight the need for a multidimensional assessment approach and call for improvements to LYR's ecological health, focusing on emerging contaminants and biodiversity rather than solely on reducing conventional indicators.

Occurrence, distribution, and ecological risk assessment of pharmaceuticals and personal care products in the surface water of Lipu River, China

Pharmaceuticals and Personal Care Products (PPCPs) are inadvertently released into the aquatic environment, causing detrimental effects on aquatic ecosystem. There is an urgent need of an in-deep investigation on contamination information of PPCPs in aquatic environment as well as the ecological risks to the aquatic ecosystem. This study was carried out in Lipu River basin, China, to investigate the distribution pattern and ecological risks of PPCPs. Results showed that PPCPs pollution is ubiquitous, 29 out of 30 targeted PPCPs were detected in Lipu River. Fourteen PPCPs were detected with a frequency of 100% in all water samples, and ten PPCPs were detected with a frequency of more than 80%. The cumulated PPCPs concentrations ranged from 33.30 ng/L to 99.60 ng/L, with a median value of 47.20 ng/L in Lipu River. Caffeine, flumequine, nifedipine, and lomefloxacin were the predominant PPCPs in study area. Caffeine showed high ecological risk, five and seven individual PPCP showed medium and low ecological risk to algae.

Increasing seasonal variation in the extent of rivers and lakes from 1984 to 2022

Abstract. Knowledge of the spatial and temporal distribution of surface water is important for water resource management, flood risk assessment, monitoring ecosystem health, constraining estimates of biogeochemical cycles and understanding our climate. While global-scale spatiotemporal change detection of surface water has significantly improved in recent years due to planetary-scale remote sensing and computing, it has remained challenging to distinguish the changing characteristics of rivers and lakes. Here we analyze the spatial extent of permanent and seasonal rivers and lakes globally over the past 38 years based on new data of river system extents and surface water trends. Results show that while the total permanent surface area of both rivers and lakes has remained relatively constant, the areas with intermittent seasonal coverage have increased by 12 % and 27 % for rivers and lakes, respectively. The increase is statistically significant in over 84 % of global water catchments based on Spearman's rank correlations (rho) above 0.05 and p values less than 0.05. The seasonal river extent is nearly 32 % larger than the previously observed annual mean river extent, suggesting large seasonal variations that impact not only ecosystem health but also estimations of terrestrial biogeochemical cycles of carbon. The outcomes of our analysis are shared as the Surface Area of Rivers and Lakes (SARL) database, serving as a valuable resource for monitoring and research of hydrological cycles, ecosystem accounting, and water management.

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 0.1-0.3mm size range, then in the 0.3-1mm and 1-5mm. 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.

Pesticides in surface water of the Ondas river watershed, western Bahia, Brazil: Spatial-seasonal distribution and risk assessment

This study investigated the occurrence and seasonal distribution of different classes of pesticides in surface waters of the Ondas River Watershed, as well as potential risks to the aquatic health and human water consumption in the western region of Bahia state, Brazil. Two gas chromatography-mass spectrometry analytical methods were applied to monitor 34 pesticides in water samples collected during both the dry and rainy seasons at 17 sites. Upon individual analysis, only γ-HCH, methoxychlor, demeton-S, methyl parathion, fenitrothion, chlorpyrifos, and azoxystrobin exhibited statistically significant differences between seasons. During rainy season, concentration medians of residues were higher for γ-HCH (74.7 ng L−1), methoxychlor (25.1 ng L−1), and azoxystrobin (47.2 ng L−1), potentially linked to historical contamination or illegal use. Conversely, pesticides like methyl parathion, fenitrothion, and chlorpyrifos, belonging to the organophosphate class, showed higher concentration medians in the dry period, measuring 75.1, 5.50, and 10.8 ng L−1, respectively, probably due to region crop activities. The risk quotient (RQ) assessment for aquatic life indicated that 59.0% of the samples in the dry season and 76.0% in the rainy season had RQ values greater than one, signifying a critical scenario for species conservation. Regarding human consumption, elevated risks were observed for heptachlor in both sampling periods and for azoxystrobin during the rainy season, surpassing RQ levels above 1, indicating danger in untreated water ingestion. Additionally, 24.0% and 53.0% of the samples in the dry and rainy seasons, respectively, contained at least one pesticide exceeding the EU resolution limit (100 ng L−1). Therefore, considering this information, implementing mitigation measures to avoid the river's contamination becomes imperative.

Microplastics in the Water Column of the Rhine River Near Basel: 22 Months of Sampling.

Measured microplastic concentrations in river surface waters fluctuate greatly. This variability is affected by season and is codriven by factors, such as sampling methodologies, sampling site, or sampling position within site. Unfortunately, most studies comprise single-instance measurements, whereas extended sampling periods are better suited to assessing the relevance of such factors. Moreover, microplastic concentrations in riverine water column remain underexplored. Similar to the oceans, however, this compartment likely holds significant amounts of microplastics. By representatively sampling the entire Rhine River cross-section near Basel through five sampling points over 22 months, we found a median microplastic (50-3000 μm) concentration of 4.48 n m-3, and estimated a widely ranging load between 4.04 × 102 n s-1 and 3.57 × 105 n s-1. We also show that the microplastic concentration in the water column was not well explained by river discharge. This suggests that although high discharge events as observed here can over short time periods lead to peak microplastic concentrations (e.g., 1.23 × 102 n m-3), microplastic load variance was not dominated by discharge in the study area.

Characteristics and Risk Assessment of Antibiotic Contamination in Oujiang River Basin in Southern Zhejiang Province

Antibiotic pollution in the environment has a negative impact on ecosystem security. Taking the Oujiang River Basin as an example,high-performance liquid chromatography mass spectrometry(LC-MS)was used to detect the concentration of six classes of 35 antibiotics in the surface water of the southern Zhejiang River Basin. The concentration level and spatial distribution of antibiotics were analyzed,the risk of antibiotics to ecology and human health were assessed using relevant models,and the sources of antibiotics were discussed. The results showed that in 20 sampling sites,a total of four classes of 12 antibiotics were detected,including sulfonamides,quinolones,tetracyclines,and lincosamides. The total concentration was ND-1 018 ng·L-1. The highest detection rate was that of Lincomycin(90.48%),followed by that of sulfapyridine(38.10%). The three antibiotics with the highest average concentrations were ofloxacin(12.49 ng·L-1),Lincomycin(11.08 ng·L-1),and difloxacin(7.38 ng·L-1). Antibiotics in the basin showed mainly spotty pollution,which had large spatial differentiation. The average concentration of antibiotics in the upstream(54.39 ng·L-1)was higher than that mid-downstream(46.64 ng·L-1). The degree of antibiotic pollution from upstream to downstream showed a characteristic of being "sparse in the upstream and dense in the downstream. " This indicated that the concentration of antibiotics in the upstream was significantly different,whereas the pollution degree of antibiotics in the downstream was uniform. The upstream was mainly polluted by health,livestock,and poultry breeding wastewater emissions,and downstream pollution was mainly caused by densely populated activities and the rapid development of economy,trade,and industry. The ecological risk assessment results showed that the upstream site H6 had the highest risk quotient,ofloxacin and enrofloxacin had high risk levels, and lincomycin had a moderate risk level. Health risk assessment results showed that the Oujiang River surface water antibiotics posed no risk to human health.