Urban River Water Research Articles

An Enhanced Beetle Antennae Search Algorithm Based Comprehensive Water Quality Index for Urban River Water Quality Assessment

Urban river not only has the important function in urban hydrological environment, but also is an area for entertainment. Water quality assessment is the core technique in water resource management. As the typical urban river, water samples were collected at 5 sampling points in Xi’an moat from January 2018 to December 2020, and 10 physicochemical parameters were analyzed. In this paper, a comprehensive water quality index (WQI) is designed based on the criterion of water quality classes and entropy weight method firstly. Secondly, the crucial water quality parameters is determined by using mutual information, coefficient of variation and the water quality difference. Finally, an enhanced beetle antennae search algorithm is proposed to optimize the weight values of the crucial parameters in the range 0 to 1, which represent the ratio of the crucial parameter in the minimum WQI (WQImin) model. The WQImin models with different number of crucial water quality parameters are implemented for water quality assessment. The effectiveness and superiority of the proposed enhanced beetle antennae search algorithm are validated in comparison with other evolutionary algorithms. The results show that the proposed WQImin model can assess the water quality accurately.

Evaluation of polluted urban river water quality: a case study of the Xunsi River watershed, China.

Urbanization has caused severe negative impacts on intra-urban river water bodies. In this paper, 22 physicochemical parameters were measured at 20 locations in the level period and 29 locations in the wet and dry period using nearby urban area surface water samples from a medium-sized polluted river (a lake-river continuum, i.e., Xunsi River, Qingling River and Tangxun Lake) in the Yangtze River watershed in Wuhan, China. In this study, the environmental quality standards for surface water of China (GB3838-2002) and the WHO drinking water standard (2011) were used as reference standards. After the basic physicochemical parameters were evaluated, the possible sources of pollution in the study area under urban pressure were examined by applying multivariate statistical techniques. The factor analysis method and entropy weighting method identified three critical factors describing the type of surface water pollution in the watershed: (1) pollution from nutrients, mainly nitrogen pollution, (2) trace metals pollution, mainly from Fe and Pb, and (3) organic pollution. Based on multivariate analysis, the weighted arithmetic water quality index (WQI) was used to assess the water quality of each period. The WQI results show that the water near the urban area of the watershed temporally shows light to moderate pollution overall in the level and wet periods, and sever pollution in the dry period. The worst water quality indicating water that is not suitable for any practical use occurs in the dry period. Spatially distributed in clusters and strips visualized in kriging method, the water quality of the lake source and upstream is better than that of the downstream. In addition, Escherichia coli was detected at consistently high levels, and although the river is not a source of drinking water, it could pose a threat to downstream waterworks.

Monitoring SARS-CoV-2 in the Wastewater and Rivers of Tapachula, a Migratory Hub in Southern Mexico.

The COVID-19 pandemic has been monitored by applying different strategies, including SARS-CoV-2 detection with clinical testing or through wastewater-based epidemiology (WBE). We used the latter approach to follow SARS-CoV-2 dispersion in Tapachula city, located in Mexico’s tropical southern border region. Tapachula is a dynamic entry point for people seeking asylum in Mexico or traveling to the USA. Clinical testing facilities for SARS-CoV-2 monitoring are limited in the city. A total of eighty water samples were collected from urban and suburban rivers and sewage and a wastewater treatment plant over 4 months in Tapachula. We concentrated viral particles with a PEG-8000-based method, performed RNA extraction, and detected SARS-CoV-2 particles through RT-PCR. We considered the pepper mild mottle virus as a fecal water pollution biomarker and analytical control. SARS-CoV-2 viral loads (N1 and N2 markers) were quantified and correlated with official regional statistics of COVID-19 bed occupancy and confirmed cases (r > 91%). Our results concluded that WBE proved a valuable tool for tracing and tracking the COVID-19 pandemic in tropical countries with similar water temperatures (21–29 °C). Monitoring SARS-CoV-2 through urban and suburban river water sampling would be helpful in places lacking a wastewater treatment plant or water bodies with sewage discharges.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12560-022-09523-2.

A study on water quality parameters estimation for urban rivers based on ground hyperspectral remote sensing technology.

The purpose of this research is to seek a better inversion algorithm. And on this basis, it explores the feasibility of using hyperspectral monitoring technology instead of laboratory physical and chemical index test and evaluates the prediction effect of inversion model on water quality change. So as to be more convenient, more economical and extensive monitoring methods for water quality monitoring of urban internal river are provided. This paper takes the water samples collected in Fuyang River in downtown Handan as the research object and obtains original spectral data of the samples by the ASD FieldSpec 4 field hyperspectral spectrometer. After the smoothing filter pretreatment by the Savitzky-Golay (SG) method and specified mathematical transformations, the modeling spectral indicators of various water quality parameters are selected and determined by calculating the maximum mean of absolute values for correlation coefficients of various spectral indicators and measured values in the wavelength range from 400 to 950nm. By introducing partial least squares (PLS), random forest (RF), and Lasso (least absolute shrinkage and selection operator), six water quality parameter fitting models were constructed including turbidity (Turb), suspended substance (SS), chemical oxygen demand (COD), NH4-N, total nitrogen (TN), and total phosphorus (TP), which are also testified and evaluated through hyperspectral data. The results show that different spectral transformation methods highlight different information inversion effects. The first derivative of reciprocal logarithm of spectral data after SG smoothing has a good modeling effect on four water quality parameters including Turb, COD, NH4-N, and TP; and the first derivative of smoothed spectral data has a good modeling effect on both water quality parameters of SS and TN. Among the three models, the PLS model has a good prediction effect, with the [Formula: see text] for COD, TN, and TP ranging from 0.74 to 0.80, while that for Turb and SS shows relatively poorer prediction effect, followed by even worse effect on HN4-H. Both machine learning algorithms of RF and Lasso have respectively obtained the best prediction models for different water quality parameters. The Lasso model has a [Formula: see text] value above 0.8 for water body organic pollutants COD, TN, and TP, and the decrease value for [Formula: see text] and [Formula: see text] is below 0.1, which indicates that the model has high prediction accuracy and strong generalization ability, but the results of SS and NH4-N do not meet the expected accuracy. In the inversion model of RF for COD, [Formula: see text] is higher than [Formula: see text], which shows excellent performance, and has certain prediction ability for SS and NH4-N. The RF model and Lasso model complement each other effectively in applicability and prediction accuracy. Compared with the traditional regression model PLS, machine learning has obvious overall advantages, making it more suitable for classified inversion prediction of urban river water quality parameters.

Seasonal reduction of trace organic pollutants and biotoxicity in hybrid constructed wetlands

Little research has been performed on reducing trace organic pollutants (TOPs) and their biotoxic effects from contaminated river water in hybrid constructed wetlands (HCWs) at pilot-scale. This study used a combination of chemical and biotoxicity analyses to evaluate the effectiveness of HCWs on the removal of TOPs and the ecological risks in spring and summer. A total of 28 TOPs, including 16 pesticides, 8 pharmaceuticals, and 4 UV filters were detected in influent (contaminated urban river water) of HCWs. The total concentration of TOPs in influent was seasonal and was characterized by higher in spring and lower in summer. Nevertheless, the concentrations of some pesticides were higher in summer than in spring due to agricultural activities in the surrounding area. The HCWs significantly decreased the concentrations of most TOPs and were more effective during spring than summer. The HCWs were also effective for reducing the acute toxicity and genotoxicity, but the effluent still showed non-negligible acute toxicity to luminescent bacteria. Moreover, the ecological risk assessment based on the detected TOPs showed that the HCWs could effectively reduce the risk quotient (RQTotal) of river water to an acceptable level in spring, but not in summer, which was mainly due to the poor removal of chlorpyrifos in the HCWs during summer. Chlorpyrifos had the greatest contribution to RQTotal and was extremely toxic to Daphnia magna. This study helps to explain the important role of HCWs in the removal of TOPs and reducing the ecological impacts of river water on the aquatic environment.

A Mobile Laboratory Enables Fecal Pollution Source Tracking in Catchments Using Onsite qPCR Assays

Onsite molecular diagnostics can revolutionize fecal pollution source tracking. We aimed to validate a method for onsite qPCR assays with a miniature speaker-sized Q qPCR instrument and other portable equipment items. We showed that marker genes for total bacteria (16S) and E. coli (rodA) in 100 mL of river water measured with this method agreed within ±0.3 log10 units with results obtained when using conventional laboratory equipment items. We then deployed the portable method in a mobile laboratory (‘lab in a van’) and quantified HF183 marker genes for human host associated Bacteroides in river water within 3 h of sampling. We also used the mobile laboratory to investigate urban river water and effluents from two storm drains and a retention pond and collected comprehensive microbial and physicochemical water quality data. We found significantly higher HF183 gene levels in the older storm drain compared to the river water (6.03 ± 0.04 vs. 4.23 ± 0.03 log10 gene copies per 100 mL), and a principal component analysis revealed that storm drain effluent retention in a pond beneficially altered water characteristics, making them more like those of the receiving river. In conclusion, onsite qPCR assays can be performed with portable equipment items to quickly test water.

Distinctive Microbial Processes and Controlling Factors Related to Indirect N2O Emission from Agricultural and Urban Rivers in Taihu Watershed.

Inland rivers are hotspots of anthropogenic indirect nitrous oxide (N2O) emissions, but the underlying microbial processes remain poorly understood. This study measured N2O fluxes from agricultural and urban rivers in Taihu watershed and investigated the microbial processes driving N2O production and consumption. The N2O fluxes were significantly higher in agricultural rivers (140.1 ± 89.1 μmol m-2 d-1) than in urban rivers (25.1 ± 27.0 μmol m-2 d-1) (p < 0.001). All wind-based models significantly underestimated N2O flux in urban rivers (p < 0.05) when using the Intergovernmental Panel on Climate Change method because they underestimated the N2O emission factor (EF5r). Wind speed and nitrate were the key factors affecting N2O fluxes in agricultural and urban rivers, respectively. NirK-type denitrifiers produced N2O in urban river water, while nirS-type denitrifiers consumed N2O in the sediments of all rivers. Co-occurrence network analysis indicated organics from Microcystis served as electron donors for denitrifiers (dominated by Flavobacterium) in water, while direct interspecies electron transfer between Thiobacillus and methanogens and between Dechloromonas and sulfate-reducing bacteria enhanced N2O reduction in sediments. This study advances our knowledge on the distinctive microbial processes that determine N2O emissions in inland rivers and illustrates the need to revise EF5r for N2O estimation in urban rivers.

Integrated metagenomic and metatranscriptomic analysis reveals actively expressed antibiotic resistomes in the plastisphere

The plastisphere is viewed as a reservoir for the antibiotic resistome in water environments and may pose health concerns. However, the expression profiles of the resistome in the plastisphere are largely unknown. Here, we profiled the occurrence, abundance, and transcriptional level of antibiotic resistance genes (ARGs), plasmid associated ARGs, microbial composition and ARG bacterial hosts in the plastisphere and urban river water using 16S rRNA gene sequencing, metagenomic sequencing, and metatranscriptomic sequencing methods. A total of 173 ARGs conferring resistance to 24 major classes of antibiotics commonly prescribed to humans and animals were detected in the plastisphere. Of these, 75 genes were observed with transcriptional activity, indicating that the antibiotic resistome in the plastisphere was not only present, but also actively expressed. Human pathogens belonging to family Enterobacteriaceae were identified as bacterial hosts of ARGs in the plastisphere. The opportunistic and multidrug resistant human pathogen Enterobacter cloacae was found to actively express tetG and confer tetracycline resistance to the plastisphere. Furthermore, 39 genes were identified as “plasmid associated ARGs” in the plastisphere, displaying a higher proportion of transcript abundance compared with water. The above results suggest that the plastisphere is a hotspot for antibiotic resistome acquisition, expression, and dissemination.

Assessment of urban river water pollution with urbanization in East Africa

Anthropogenic pollution plays an important part in deteriorating the water quality of rivers all over the world, especially in urban areas of Africa where water quality monitoring is still seriously constrained by the limited test facility and capability. In this study, for evaluating the impact of urbanization on the river water quality, we investigated four typical urban rivers of Tanzania through the upper-urban-down gradient assessment approach and analyzed by water quality index (WQI) and statistical methods. The physicochemical indices monitored in these rivers revealed that the contents of those indicators of TN, TP, PO43−, NH4+, CODMn, and NO3− were accumulated significantly in the lower reaches of the cities, which indicated the life-type pollution characteristics in such urban rivers of Africa. The following main conclusions are achieved from this study. The water quality of 30% of the investigated river sections is in the medium to good status based on the subjective WQI with sensory factors included. Moreover, the sections with obvious water quality decline are mainly limited to the river segments within the urban central area, and severe pollution of water bodies is closely related to large cities, indicating an increasing pollution tendency with the quickly growing population. Therefore, to help formulate water pollution control policies in response to the rapid urban expansion in African countries, it is necessary to adopt an economical and feasible method to carry out early monitoring of surface water quality timely.

Assessment of Urban River Water Quality Using Modified NSF Water Quality Index Model at Siliguri City, West Bengal, India

Assessment of Urban River Water Quality Using Modified NSF Water Quality Index Model at Siliguri City, West Bengal, India

Using Machine Learning Algorithms With In Situ Hyperspectral Reflectance Data to Assess Comprehensive Water Quality of Urban Rivers

Remotely sensed hyperspectral data can support more effective water quality monitoring. Nevertheless, the variability and complexity of urban river water make it hard to retrieve comprehensive water quality characteristics directly, so that most current water quality assessments rely on semiempirical, semianalytical, or bio-optical approaches. In this study, we carried out simultaneous <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> hyperspectral data and water quality measurements. We used the 382 hyperspectral data from urban rivers of Zhongshan City in the Pearl River Delta to test how well the random forest (RF) and one-dimensional convolutional neural networks (1D-CNNs) algorithms retrieved the newly established water quality index (WQI). The RF algorithm also identified essential wavelengths for retrieving the WQI. Our results demonstrate that the RF and 1D-CNN algorithms performed well in WQI estimations. The 1D-CNN model performed significantly better than the RF model, especially on high WQI samples. Both models were insensitive to smoothing of the hyperspectral data, showing that the noise of the original hyperspectral reflectance data has a limited impact on the algorithms. In addition, when we used the essential wavelength data (mainly located between 580–590 nm and near 722, 751, 821, and 830 nm) as input data, we achieved better retrieval results. The 1D-CNN model performed the best with an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> of 0.87, RMSE of 0.574, and RPIQ of 3.082 when we used the top tenth percentile of the essential wavelength data. This study demonstrates the potential of the 1D-CNN algorithm for hyperspectral data analysis to retrieve comprehensive water quality.

Anthropogenic Gd in urban river water

As an emerging contaminant, rare earth elements (REEs) are becoming an environmental concern, especially in Chinese cities. This study investigated the distribution of REEs in river water and tap water samples in Guiyang, Southwest China. In all water samples, the concentrations of total dissolved REE (∑REE) ranged from 15.1 to 53.3 ng·L–1, with the heavy rare earth elements enriched than the light rare earth elements. Most of the water samples showed significant positive Gd anomalies, with the highest abnormal value calculated to be 29.23. The main reason for the positive Gd anomalies was found to be the release of medical wastewaters containing Gadopentetic acids (Gd-DTPA). Overall, anthropogenic Gd could contributed &amp;gt;60% of total Gd in river waters and tap waters. Because Gd-DTPA is commonly used in large Chinese hospitals, the results of this study implied that anthropogenic Gd release could be common in Chinese cities.

Immobilization of microorganisms in activated zeolite beads and alkaline pretreated straws for ammonium-nitrogen removal from urban river water

AbstractThe non-treated wastewater from residential areas contains high concentrations of ammonium-nitrogen (NH4+-N). When discharged into the drainage water system, it deteriorates the water quality in urban rivers. This study used two types of materials to form eco-bags, using activated zeolite bead (AZB) and alkaline pretreated straw (APS), in geotextile bags for easy recovery and reuse. The AZB and APS provided the breeding habitat for the microorganisms that promoted biofilm formation on their surface. The immobilization of engineered denitrification microorganisms facilitated the removal of NH4+-N from the urban river water. The NH4+-N removal in the AZB and APS bags were in the range of 64–73%, and 56–61%, respectively, while the chemical oxygen demand (COD) removal in the AZB and APS bags ranged from 33–36%, and 30–31%, respectively. In addition, as evident from DNA and microbial community analysis, the microorganisms demonstrated a greater proclivity to grow and proliferate on the surface of AZB and APS and improved the water quality of urban rivers.

Distribution and Influencing Factors of DOM Components in Urban and Suburban Polluted Rivers

Dissolved organic matter(DOM) in water environments is an important component of the global carbon cycle. Under the current urgency to control the pollution of urban rivers in China, exploring the influence of different exogenous and endogenous secondary pollution and weather patterns on river DOM is the premise to better understand the causes of the pollution. In this study, a large city in China was established as the research area, and the underlying water and sediments from 21 sites along urban and suburban rivers, and other water sources were evaluated. The excitation-emission matrix-parallel factor analysis(EEM-PARAFAC) was used to analyze the difference in DOM composition and equivalent in urban rivers polluted by domestic sewage and suburban rivers polluted by aquaculture, agriculture, and livestock breeding. The results showed that:① DOM components in urban and suburban river waters were mainly protein compounds(tyrosine-and tryptophan-like), containing a small amount of humic acid. Humic acid components of anthropogenic origin were found in urban river water; ② The reasons for the formation of DOM components in urban and suburban rivers were completely different. Urban rivers are mainly polluted by domestic sewage and endogenous secondary pollution, resulting in an increase in anthropogenic humic acid components. Suburban rivers are mainly polluted by agricultural wastewater rich in N and P, which promotes endogenous metabolism of autotrophic bacteria and increases protein components, which may be related to the formation of existing DOM characteristics. ③ Rainfall runoff and urban overflow transported exogenous pollutants into rivers, while hydrodynamic factors such as hydraulic agitation affect the distribution of DOM components in underlying water and sediments through physical effects such as dilution.

Urban river water quality improvement in Bandung City, Indonesia

Bandung City is one of the big cities in Indonesia with a relatively poor river water quality. Several improvement efforts have been made, especially by the government. This study aims to review these improvement efforts and the results obtained. The rivers that are the leading review are the Cikapundung River, Citepus River, Cicadas River, and Cipamokolan River. The data was obtained from the monitoring results conducted by the government environmental service bureau of Bandung and the Province of West Java. Based on the study results, it was found that there was an improvement in the water quality of the Citepus River, but this river had a non-biodegradation capacity in 2020. The high TSS, COD, BOD, and MBAS detergents indicate the dominating domestic activity in urban watersheds. The most significant control effort to improve the quality of river water in the city of Bandung is the availability of waste treatment at the source, both household and regional scale. Onsite efforts to improve river water quality also have a positive impact, i.e., applying an eco-enzyme solution and developing structures in the river for the aeration process.

Short Term Real-Time Rolling Forecast of Urban River Water Levels Based on LSTM: A Case Study in Fuzhou City, China.

Water level management is an important part of urban water system management. In flood season, the river should be controlled to ensure the ecological and landscape water level. In non-flood season, the water level should be lowered to ensure smooth drainage. In urban areas, the response of the river water level to rainfall and artificial regulation is relatively rapid and strong. Therefore, building a mathematical model to forecast the short-term trend of urban river water levels can provide a scientific basis for decision makers and is of great significance for the management of urban water systems. With a focus on the high uncertainty of urban river water level prediction, a real-time rolling forecast method for the short-term water levels of urban internal rivers and external rivers was constructed, based on long short-term memory (LSTM). Fuzhou City, China was used as the research area, and the forecast performance of LSTM was analyzed. The results confirm the feasibility of LSTM in real-time rolling forecasting of water levels. The absolute errors at different times in each forecast were compared, and the various characteristics and causes of the errors in the forecast process were analyzed. The forecast performance of LSTM under different rolling intervals and different forecast periods was compared, and the recommended values are provided as a reference for the construction of local operational forecast systems.

English

English

Real-time forecasting of river water level in urban based on radar rainfall: A case study in Fuzhou City

Urban river network plays an important role in the process of drainage. The real-time prediction system of river water level can provide early warning information to the decision-makers of relevant departments in time, which plays an important role in reducing flood risk. Aiming at the rapid and strong response of urban river to rainfall, a real-time rolling prediction system of urban river water level is proposed based on radar rainfall prediction products and coupling model of hydrology and hydrodynamics. In the system, each simulation is divided into two parts: the review period and the prediction period. In the review period, the river model is driven by the observed rainfall to obtain the latest simulation state to avoid cumulative error, while in the prediction period, the radar rainfall is used to drive in the short-term prediction of river water level. Taking Fuzhou as the research area, a long series review of the process from April 1, 2020 to October 1, 2020 was carried out. The accuracy of the river model, radar-rainfall and prediction system during the study period are evaluated and discussed in detail. The results show that the river model can simulate the process of water level change in rainfall and rain free period. The performance of radar rainfall to total rainfall prediction is better than that of process prediction. The real-time prediction system can give the water level risk according to each radar forecast rainfall process, and has good performance in judging the peak water level and peak time, which can provide reference for urban river dispatching decision-makers.

Performance of hybrid-constructed floating treatment wetlands in purifying urban river water: A field study

Floating treatment wetlands (FTWs) are a new management method for improving urban stormwater runoff. However, most studies have been performed at laboratory, micro, and meso scales. Therefore, it is necessary to evaluate the purification effect of the ecological floating bed in urban water bodies under in situ conditions. The purpose of this study was to compare the treatment potential of two types of hybrid-constructed FTWs for urban river water, and to quantify the effects and concentration radii of dissolved oxygen (DO), water temperature (T), nitrate nitrogen (NO3−-N), ammonia nitrogen (NH4+-N), and total phosphorus (TP) under different rainfall conditions. The difference analysis in the first rainfall stage (p < 0.05) indicated that FTWs with multiple plant species (FTW-A) and FTWs with a single species (FTW-B) had significantly better treatment potential than the blank control for all water quality parameters except T. The second stage study showed that the hybrid system could effectively purify the river water during the rainfall period, and the effect of FTW-A was higher than that of FTW-B. After the initial rainfall, DO decreased, NH4+-N and TP concentrations increased, the removal rate of NH4+-N decreased to 55.88%–58.33%, and the removal rate of TP decreased to 5.00%–10.53%. During continuous rainfall, the removal rate of NH4+-N remained between 16.28% and 63.89%, while the removal rate of TP fluctuated, reaching 26.32%, although the final concentration of TP was not removed during the experimental period. Meanwhile, the hybrid constructed FTW continuously increased the DO concentration; however, denitrification was inhibited, resulting in ineffective removal of nitrate and nitrogen in the water body. Further study on the influence radius of two types of FTWs on DO, T, NO3−-N, NH4+-N, and TP found that the influence radius of FTWs varied with the concentration of each parameter. For the same parameter, the influence radius of FTWs with multiple plant species and FTWs with a single species was RFTW-A ≥ RFTW-B. In this study, the purification effect of hybrid-constructed FTW in urban water was evaluated, which provided guidance and support for the FTW site layout of actual river courses in the future.

Study on effect of different reservoir water diversion modes on river water quality improvement

Urban river water pollution is a serious environmental issue and tends to become worse. One of the key measures to improve urban river water quality is to divert water from upstream reservoir to downstream river course to flush pollutant. In this paper, a one-dimensional hydrodynamic and water quality model of Hun River network was developed based on HEC-RAS model. Different reservoir water diversion modes for sewage discharge were simulated and their effect on the water quality improvement of downstream river course was analysed. The results show that under different diversion flow rates, the larger the diversion flow rate, the better the effect on water quality improvement, and that when total water diversion volume is fixed, the effect of long-term diversion with a low flow rate is better than that of short-term diversion with a high flow rate on downstream water quality improvement. The research results can provide scientific support for optimization of the operation of water diversion from reservoir for sewage discharge.