Haihe River Basin Research Articles

Virtual water indicator for comprehensive water pressures: Model and case studies

Virtual scarce water (VSW) is an improved indicator of virtual water, enabling meaningful comparisons between water use of products from regions with different water resources and water scarcity. This study considers virtual water for comprehensive water pressures (VWCP) as an improvement to VSW, adding a more thorough consideration of available water resources, including the consideration of ecological water demand, like aesthetic water demand, shipping water demand, and unusable flood water. An input–output (IO) model is built to calculate the VWCP contained in the final product of industrial sectors. The Pearl River Basin (PRB) and Haihe River Basin (HRB) in China are used as study cases to evaluate the model. The results show that the pressure of water resource consumption in HRB has exceeded its carrying capacity, and the pressure of water resource consumption in the PRB, which is rich in water resources, is greater than current models report. These findings support the observations that VWCP can reveal more pressures of water resource usage than traditional VSW. Thus the new VWCP model can account for and improve environmental and social ethics in VW management and trading.

Risk assessment of heavy metals in suspended particulate matter in a typical urban river.

Suspended particulate matter (SPM) is a major source of contamination in urban rivers as it serves as a carrier for pollutants, such as heavy metals. In this study, the Beiyun River, northern China, was used as a case study to determine the characteristics of SPM-associated heavy metal spatial distribution, to evaluate the potential ecological risks and identify heavy metal sources. The concentrations of seven heavy metals and other associated indicators (TC, TN, TP, and OM) were measured at 12 sites and analyzed by Pearson correlation (PC) and principal component analyses (PCA). The average concentrations of Cr, Ni, Cu, Zn, As, Cd, and Pb were 70.72, 27.88, 31.35, 115.70, 27.77, 0.23, and 29.62mg/kg, respectively, with significant spatial differences occurring between some elements. Igeo values established the ranked order of heavy metal pollutant concentrations in SPM as As > Cd > Zn > Cu > Pb > Cr > Ni. [Formula: see text] analysis demonstrated that the ranked order of potential ecological risk from the seven metals was Cd > As > Cu > Pb > Ni > Cr > Zn. Potential ecological risk index (RI) results confirmed the high potential ecological risk in the study area. Among the measured heavy metals, Cd represented the highest pollution risk, as shown by its highest [Formula: see text] value. Correlation analysis (CA) showed that Zn had a strong correlation with Cu and Pb. Significant positive correlations were found between TC, TN, TP, and Cu. Three element pairs, Zn-Cd, Cr-Cu, and Cr-Ni, were also found to have strong correlations. Zn, Cu, and Ni were mainly introduced by human activities including urban industrial sewage discharge (such as metallurgy and electroplating industrial wastewater), agricultural drainage, and landfill wastewater, while Cr mainly originated from natural processes like mineral weathering and atmospheric precipitation. This information on the concentration, risk, and sources of SPM in Beiyun River provides an important reference for the reduction of heavy metal pollution in SPM in a typical river in the Haihe River Basin (China).

Runoff changes in the major river basins of China and their responses to potential driving forces

Identifying the effect of climate variability and human activities on runoff changes is scientifically essential for understanding hydrological processes and sustainable water resources management. This study selected 64 catchments located in the mainland of China to quantify the effects of different driving forces on runoff changes. Results showed that annual runoff in the Haihe river basin, Liaohe river basin, and Yellow river basin exhibited significantly decreasing trends from 1965 to 2018 (P < 0.05), whereas the Northwest river basin had positive trends in the annual runoff. Meanwhile, the Pettitt test method was applied to detect abrupt changes in annual runoff. Compared to the rivers in Southern China, the northern rivers had significant abrupt changes in annual runoff and mostly occurred in the 1990 s. The Choudhury-Yang equation based on the Budyko hypothesis was used to assess the sensitivity of runoff to precipitation (P), potential evapotranspiration (ET0), and the land surface (n) changes. The results showed that runoff was more sensitive to P and n, compared to ET0. Attribution analysis revealed that P was the dominant factor in the Northwest river basin, Southwest river basin, Yangtze river basin, Southeast river basin, and Pearl river basin, whereas the changes in n were responsible for runoff changes in the Liaohe river basin, Haihe river basin, Yellow river Basin, Songhuajiang river basin, and Huaihe river basin. The land surface changes (n) were resulted from vegetation restoration, urbanization expansion, construction of reservoirs/check dams, and surface water withdrawals, leading to significant changes in river runoff in recent years. The findings can provide good insight for water resources management across China.

Evaluation of Groundwater Storage Depletion Using GRACE/GRACE Follow-On Data with Land Surface Models and Its Driving Factors in Haihe River Basin, China

Groundwater storage (GWS) in the Haihe River Basin (HRB), which is one of the most densely populated and largest agricultural areas in China, is of great importance for the ecosystem environment and socio-economic development. In recent years, large-scale overexploitation of groundwater in HRB has made it one of the global hotspots of GWS depletion. In this study, monthly GWS variations in HRB from 2003 to 2020 were estimated using the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) data in combination with three land surface models (LSMs) from the Global Land Data Assimilation System (GLDAS). The results show the following: (1) HRB suffered extensive GWS depletion from 2003 to 2020, which has been aggravated since 2014, with a mean rate of 1.88 cm·yr−1, which is equivalent to a volume of 6 billion m3·yr−1. The GWS depletion is more serious in the plain zone (−2.36 cm·yr−1) than in the mountainous zone (−1.63 cm·yr−1). (2) Climate changes are excluded from the reasons for GWS depletion due to annual precipitation and evaporation being close to normal throughout the period. In addition, GWS changes show a low correlation with meteorological factors. (3) The consumption of groundwater for irrigation and land use/cover changes have been confirmed to be the dominant factors for GWS depletion in HRB. (4) The effects of inter-basin water transfer projects cannot be obviously observed using the GRACE and GRACE-FO; more inter-basin water transfers are needed for recovering the GWS in HRB. Therefore, it is imperative to control groundwater exploitation and develop a more economical agricultural irrigation structure for the sustainability of groundwater resources in HRB.

Ammonium Nitrogen Streamflow Transport Modelling and Spatial Analysis in Two Chinese Basins

Ammonium nitrogen (NH4+-N), which naturally arises from the decomposition of organic substances through ammonification, has a tremendous influence on local water quality. Therefore, it is vital for water quality protection to assess the amount, sources, and streamflow transport of NH4+-N. SPAtially Referenced Regressions on Watershed attributes (SPARROW), which is a hybrid empirical and mechanistic modeling technique based on a regression approach, can be used to conduct studies of different spatial scales on nutrient streamflow transport. In this paper, the load and delivery of NH4+-N in Poyang Lake Basin (PLB) and Haihe River Basin (HRB) were estimated using SPARROW. In PLB, NH4+-N load streamflow transport originating from point sources and farmland accounted for 41.83% and 32.84%, respectively. In HRB, NH4+-N load streamflow transport originating from residential land and farmland accounted for 40.16% and 36.75%, respectively. Hence, the following measures should be taken: In PLB, it is important to enhance the management of the point sources, such as municipal and industrial wastewater. In HRB, feasible measures include controlling the domestic pollution and reducing the usage of chemical fertilizers. In addition, increasing the vegetation coverage of both basins may be beneficial to their nutrient management. The SPARROW models built for PLB and HRB can serve as references for future uses for different basins with various conditions, extending this model’s scope and adaptability.

Climate, CO2, and Anthropogenic Drivers of Accelerated Vegetation Greening in the Haihe River Basin

Vegetation regulates the exchange of terrestrial carbon and water fluxes and connects the biosphere, hydrosphere, and atmosphere. Over the last four decades, vegetation greening has been observed worldwide using satellite technology. China has also experienced a notably widespread greening trend. However, the responsiveness of vegetation dynamics to elevated CO2 concentration, climate change, and human activities remains unclear. In this study, we attempted to explore the impact of natural (precipitation, air temperature), biogeochemical (CO2), and anthropogenic drivers (nighttime light, afforestation area) on changes in vegetation greenness in the Haihe River Basin (HRB) during 2002–2018 at the county-level. We further determined the major factors affecting the variation in satellite-derived normalized difference vegetation index (NDVI) from moderate resolution imaging spectroradiometer (MODIS) for each county. The results indicated that over 85% of the counties had a significantly increased NDVI trend, and the average linear trend of annual NDVI across the study region was 0.0037 per year. The largest contributor to the NDVI trend was CO2 (mean contribution 45%), followed by human activities (mean contribution of 27%). Additionally, afforestation was a pronounced driving force for NDVI changes in mountainous areas, resulting from ecosystem restoration efforts. Our findings emphasize the crucial role of CO2 fertilization in vegetation cover change, while considering CO2 concentration, climate change, and human activities, and shed light on the significant influences of afforestation programs on water resources, especially in mountainous areas.

Comparative Analysis of Water Quality Prediction Based on Lstm in Yangtze River Basin and Haihe River Basin, China

Comparative Analysis of Water Quality Prediction Based on Lstm in Yangtze River Basin and Haihe River Basin, China

Impacts of Vegetation Restoration on Water Resources and Carbon Sequestration in the Mountainous Area of Haihe River Basin, China

Impacts of Vegetation Restoration on Water Resources and Carbon Sequestration in the Mountainous Area of Haihe River Basin, China

Effects of vegetation restoration on evapotranspiration water consumption in mountainous areas and assessment of its remaining restoration space

In mountainous areas with inadequate precipitation, anthropogenic vegetation restoration would increase evapotranspiration water consumption and decrease regional water yield, affecting downstream water use. Assessing vegetation restoration-related water consumption and future vegetation restoration potential is important for vegetation management in mountainous areas. However, it is still difficult to evaluate the impact of vegetation restoration on evapotranspiration water consumption and determine the appropriate restoration level. This study focused on the Haihe River Basin as a typical afforestation area in China with growing competition for water use between the upstream afforested vegetation and the downstream urban development. This study adopted the Penman-Monteith-Leuning evapotranspiration model with a de-trending data series method to analyze actual evapotranspiration and vegetation restoration potential. The results show that 1) the average precipitation in the mountainous areas of the Haihe River Basin and the actual evapotranspiration (AET) between 2000 and 2019 were 508 and 317 mm, respectively, while the precipitation, leaf area index (LAI), and actual evapotranspiration all showed an increasing trend in the past two decades with growth rates of 3.8 mm/yr, 0.0015(cm3/cm3)/yr, and 1.37 mm/yr, respectively; 2) in the past 20 years, vegetation restoration has cumulatively led to an actual evapotranspiration increase by 314, 269, 175, 429, 277, and 187 mm in the Luan-He Mountainous Basin (LHMB), Yong-Ding-He Mountainous Basin (YDHMB), Da-Qing-He Mountainous Basin (DQHMB), Bei-San-He Mountainous Basin (BSHMB), Zi-Ya -He Mountainous Basin (ZYHMB), and Zhang-Wei-He Mountainous Basin (ZWHMB), respectively; 3) the LAI maximum recovery level in the six sub-basins was deduced by using the linear fitting formula. When considering the replacement water of over-drafting groundwater in the downstream plain, the LHMB, YDHMB, ZYHMB, ZWHMB still have the potential for vegetation restoration, and the maximum LAI could be restored to 1.83, 1.30, 1.09, and 1.6 cm3/cm3, respectively. In summary, large-scale anthropogenic afforestation should consider the decrease in regional water yield to avoid the imbalance of ecological and human water use. This research method can be extended to other areas of water scarcity and provide a reference for mountain vegetation restoration to set a reasonable threshold.

Increasing urban flood risk in China over recent 40 years induced by LUCC

Land use/cover change (LUCC) caused by urbanization has a great impact on urban hydrological systems, among which urban pluvial floods are particularly prominent and have attracted worldwide attention. Although some previous studies have shown that urban LUCC increases urban pluvial flood risk and reduces urban flood regulation service (UFRS) in individual cities, there has not been a nationwide quantitative assessment in China. Here, we use a data-driven hydrological model and constructed indicators to clarify the quantitative impact of LUCC on China's UFRS and its spatial pattern from 1977 to 2018. Our analysis revealed that LUCC has caused China's UFRS to decline by 13.39%, from 31.34% in 1977 to 17.95% in 2018. Spatially, the UFRS decline in all river basins in China was greater than 10%, and the UFRS in almost all cities was decreasing. Especially in the densely distributed eastern Yangtze River basin, Pearl River basin, Southeast basin, Haihe River basin and Huai River basin, their UFSR values decreased by 11.89%, 12.17%, 12.34% and 10.81%, respectively. This is a warning that China's urban land conversion has greatly increased the urban pluvial flood risk.

Evaluation of Six Satellite-Based Terrestrial Latent Heat Flux Products in the Vegetation Dominated Haihe River Basin of North China

In this study, six satellite-based terrestrial latent heat flux (LE) products were evaluated in the vegetation dominated Haihe River basin of North China. These LE products include Global Land Surface Satellite (GLASS) LE product, FLUXCOM LE product, Penman-Monteith-Leuning V2 (PML_V2) LE product, Global Land Evaporation Amsterdam Model datasets (GLEAM) LE product, Breathing Earth System Simulator (BESS) LE product, and Moderate Resolution Imaging Spectroradiometer (MODIS) (MOD16) LE product. Eddy covariance (EC) data collected from six flux tower sites and water balance method derived evapotranspiration (WBET) were used to evaluate these LE products at site and basin scales. The results indicated that all six LE products were able to capture the seasonal cycle of LE in comparison to EC observations. At site scale, GLASS LE product showed the highest coefficients of determination (R2) (0.58, p &lt; 0.01) and lowest root mean square error (RMSE) (28.2 W/m2), followed by FLUXCOM and PML products. At basin scale, the LE estimates from GLASS product provided comparable performance (R2 = 0.79, RMSE = 18.8 mm) against WBET, compared with other LE products. Additionally, there was similar spatiotemporal variability of estimated LE from the six LE products. This study provides a vital basis for choosing LE datasets to assess regional water budget.

Evaluating spatial differences in the contributions of climate variability and human activity to runoff change in the Haihe River basin

ABSTRACT Quantifying the effects of climate variability and human activities on runoff change has attracted widespread attention. However, very few researchers have used multiple methods to analyse the spatial differences in their relative contributions on the sub-basin scale. In this study, we used three main methods, multiregression, climate elasticity, and the Water and Energy transfer Process in Large river basins (WEP-L) hydrological model, to explore the spatial differences in the relative contributions of climate variability and human activities to runoff change in the Jumahe River Basin (JRB). The results indicate that the annual mean runoff decreased significantly in the JRB and its three sub-basins during 1964–2015. The three methods produced similar estimates in the JRB, and human activities were the main reason for runoff reduction. The WEP-L model provides the most satisfactory results compared with the other two methods. The influence of human activity increased from upstream to downstream, whereas that of climate change decreased. These results can help to provide a basis for sustainable water resource management.

Analysis of the distribution pattern of Chinese Ziziphus jujuba under climate change based on optimized biomod2 and MaxEnt models

Ziziphus jujuba has high ecological and economic value and is widely distributed in northern China. Reconstructing the historical geographic distribution patterns of Z. jujuba and determining the environmental distribution restrictions could provide a scientific basis for its germplasm conservation and management. Here, the R packages ENMeval and Biomod2 were used assess to 121 distribution records and 18 environmental variables and model and analyze the geographic distribution changes of Z. jujuba in different periods since the last interglacial period. Environmental anomalies and factors underlying the geographical distribution changes and migration trends in distribution areas under climate change were assessed. The optimized MaxEnt model generated the best predictions, and the modern highly suitable area was located in the Yellow and Haihe river basins. Climate anomalies in the historical periods ranged in decreasing order from the last interglacial > last glacial maximum > middle Holocene. The degree of climate anomalies under RCP (Representative Concentration Pathways) 2.6-2050s was the lowest, and the degree of climate anomalies under RCP8.5-2070s was the highest. From the last interglacial period to the 2070s, the most dissimilar variable was initially precipitation but gradually changed to temperature with climate change, which was the key factor for the geographical distribution of Z. jujuba. Although extensive habitat expansion occurred during the last interglacial period, the area suitable for Z. jujuba retreated to higher latitudes during the last glacial maximum and middle Holocene. As climate warming intensifies, suitable habitats will shift to higher latitudes while habitats in lower latitudes will become scarcer. The whole suitable region in China will migrate to a higher latitude area, indicating that the Z. jujuba distribution shows a strong response to climate change. This study provides a theoretical basis for establishing conservation measures for Z. jujuba genetic resources and studying trends in plant formation and evolution in North China.

Hazard Assessment of Peanut Drought and Flood Disasters in Huang-Huai-Hai Region

Peanut is an important kind of food, oil plants and cash crops which promotes the sustainable development in the modern agricultural economy. In recent years, the frequency and intensity of drought disaster and waterlogging disaster in the Huang-Huai-Hai Region has remarkably increased, which poses a huge impact on the production of spring peanut. Therefore, analyzing and making hazard assessment of drought disaster and waterlogging disaster during the growth period of spring peanut has a great significance for preventing drought disaster and waterlogging disaster, minimizing the damage, and taking disaster insurance in this region. Based on the daily meteorological data of 186 meteorological stations from 1960 to 2019 and combined with the growth data of spring peanut, the hazard assessment of drought disaster and waterlogging disaster is divided into 7 grades by using the standardized precipitation requirement index(ISPR), and the spatial and temporary distribution characteristics of drought disaster and waterlogging disaster in Huang-Huai-Hai Region are analyzed. Based on the probability and intensity of drought disaster and waterlogging disaster, hazard index is constructed to evaluate the hazard during the growth period of spring peanut. High incidence of drought disaster is found in the northwestern and central part of the Yellow River Basin, the northeastern part of the Huaihe River Basin and the northern part of the Haihe River Basin. While the areas with high incidence of waterlogging disaster are mainly concentrated in most areas of the Yellow River Basin, the northern, southern part of the Huaihe River Basin and eastern part of the Haihe River Basin, and mainly with moderate waterlogging disaster. The areas with high hazard of drought disaster during the growth period of spring peanut are scattered, mainly concentrated in the northwest of the Yellow River Basin. While the areas with medium and high hazard of waterlogging disaster during the growth period of spring peanut are mainly distributed in the Yellow River Basin. In the northern and central planting areas of the Yellow River Basin, the phenomenon of drought disaster and waterlogging disaster abrupt alternation often occurs. Therefore, it is necessary to reduce the hazard of drought disaster and waterlogging disaster abrupt alternation, regulate crop exposure in planting areas, reduce the vulnerability of crop, improve the overall disaster prevention and mitigation capabilities, and promote management level in planting areas. The above research results can provide a reference for the drought disaster and waterlogging disaster prevention and loss reduction during the growth period of spring peanut and the construction of security production guarantee method system in Huang-Huai-Hai Region.

Impact of Assimilating Advanced Himawari Imager Channel 16 Data on Precipitation Prediction over the Haihe River Basin

Assimilation of high-resolution geostationary satellite data is of great value for precise precipitation prediction in regional basins. The operational geostationary satellite imager carried by the Himawari-8 satellite, Advanced Himawari Imager (AHI), has two additional water vapor channels and four other channels compared with its predecessor, MTSAT-2. However, due to the uncertainty in surface parameters, AHI surface-sensitive channels are usually not assimilated over land, except for the three water vapor channels. Previous research showed that the brightness temperature of AHI channel 16 is much more sensitive to the lower-tropospheric temperature than to surface emissivity, which is similar to the three water vapor channels 8–10. As a follow-up work, this paper evaluates the effectiveness of assimilating brightness temperature observations over land from both the three AHI water vapor channels and channel 16 to improve watershed precipitation forecasting through both case analysis (in the Haihe River basin, China) and batch tests. It is found that assimilating AHI channel 16 can improve the upstream near-surface atmospheric temperature forecast, which in turn affects the development of downstream weather systems. The precipitation forecasting test results indicate that adding the terrestrial observations of channel 16 to the assimilation of AHI data can improve short-term precipitation forecasting in the basin.

Improving the Accuracy of Water Storage Anomaly Trends Based on a New Statistical Correction Hydrological Model Weighting Method

The Gravity Recovery and Climate Experiment (GRACE) satellite solutions have been considerably applied to assess the reliability of hydrological models on a global scale. However, no single hydrological model can be suitable for all regions. Here, a New Statistical Correction Hydrological Model Weighting (NSCHMW) method is developed based on the root mean square error and correlation coefficient between hydrological models and GRACE mass concentration (mascon) data. The NSCHMW method can highlight the advantages of good models compared with the previous average method. Additionally, to verify the effect of the NSCHMW method, taking the Haihe River Basin (HRB) as an example, the spatiotemporal patterns of Terrestrial Water Storage Anomalies (TWSA) in HRB are analyzed through a comprehensive comparison of decadal trends (2003–2014) from GRACE and different hydrological models (Noah from GLDAS-2.1, VIC from GLDAS-2.1, CLSM from GLDAS-2.1, CLSM from GLDAS-2.0, WGHM, PCR-GLOBWB, and CLM-4.5). Besides, the NSCHMW method is applied to estimate TWSA trends in the HRB. Results demonstrate that (1) the NSCHMW method can improve the accuracy of TWSA estimation by hydrological models; (2) the TWSA trends continue to decrease through the study period at a rate of 15.7 mm/year; (3) the WGHM and PCR-GLOBWB have positive reliability with respect to GRACE with r &gt; 0.9, while all the other models underestimate TWSA trends; (4) the NSCHMW method can effectively improve RMSE, NES, and r with 3–96%, 35–282%, 1–255%, respectively, by weighting the WGHM and PCR-GLOBWB. Indeed, groundwater depletion in HRB also proves the necessity of the South-North Water Diversion Project, which has already contributed to groundwater recovery.

A novel hybrid model for water quality prediction based on synchrosqueezed wavelet transform technique and improved long short-term memory

Predicting water quality accurately is of utmost importance in water resource management. This study focuses on the prediction of water quality parameters such as the dissolved oxygen (DO) in the watershed system. The traditional recurrent neural networks (RNNs) suffer from gradient disappearance or explosion and inability to solve the problem of long-time dependence, and their practical applications are limited. To overcome this drawback, an improved long short-term memory (LSTM) model was proposed to improve the model’s performance. In addition, to overcome nonstationarity, randomness, and nonlinearity of the water quality parameters data, a hybrid model that recruits synchrosqueezed wavelet transform (SWT) to denoise the original data was adopted. A novel metaheuristic optimization algorithm, the improved sparrow search algorithm (ISSA) combining Cauchy mutation and opposition-based learning (OBL) was also implemented to compute the optimal parameter values for the LSTM model. The proposed hybrid model was evaluated using an original weekly water quality parameters series, from 1/2010–12/2016, measured at the Yongding River and Gangnan gauging stations in the Haihe River Basin, China. The standalone LSTM, ISSA-LSTM, SWT-LSTM, support vector regression (SVR), and back propagation neural network (BPNN) were adopted as comparative forecast models using the same dataset. The results demonstrate that the addressed model, combining the strong noise-resistant robustness of the SWT and the nonlinear mapping of the LSTM, has the best prediction performance among the peer models at two gauging stations. The proposed hybrid model can be used as an alternative framework for water quality prediction, which can provide decision-making basis for comprehensive water quality management and pollutant control in the basin.

Human health risk assessment of selected pharmaceuticals in the five major river basins, China

Pharmaceuticals in aquatic environment have raised wide attention in recent years due to their potential adverse effects and bioaccumulation in biota. China has been a major producer and consumer of pharmaceuticals, however, the potential human health risk of these chemicals is yet to be determined in China. In this study, we evaluated available exposure data for twenty pharmaceuticals in surface waters from Chinese five major river basins (the Yangtze, Haihe, Pearl, Songliao, and Yellow River Basins), and human health risk assessment was performed. Based on the concentration data and risk data, we conducted research on the source, cause, and control measures of the pharmaceuticals. The twenty pharmaceuticals were found to be ubiquitous in China with median concentrations between 0.09 and 304 ng/L. The estimated daily intake of pharmaceuticals from drinking water and eating fish was calculated. The intake via drinking water was significantly lower than that via eating fish. The risk quotients via water intake and fish consumption ranged from 0 to 17.2, with estrogen and sulfapyridine highest among the twenty pharmaceuticals. High risks of exposure were mainly in North China, including the Haihe and Songliao River Basins. This is the first analysis in Chinese major river basins that has filled the gaps in the research on the human health risks of pharmaceuticals. The results of the study provide basic information of pharmaceutical intake from drinking water and eating fish in China and provide insights into the risk management guidance of pharmaceuticals, and will facilitate the optimization of health advisories and policy making.

Changes in turbidity and human activities along Haihe River Basin during lockdown of COVID-19 using satellite data.

During the outbreak of the COVID-19, China implemented an urban lockdown in the first period. These measures not only effectively curbed the spread of the virus but also brought a positive impact on the ecological environment. The water quality of urban inland river has a significant impact on urban ecology and public health. This study uses Sentinel-2 visible and near-infrared band reflectance and the Normalized Difference Turbidity Index (NDTI) to analyze the water quality of the Haihe River Basin during the control period of COVID-19. It is found that during the lockdown period, the river water quality was significantly improved compared to the same period in 2019. The average NDTI of the Haihe River Basin in March decreased by 0.27, a decrease of 219.06%; in April, it increased by 0.07, that is 38.38%. Further exploration using VIIRS lights found that the brightness of the lights in the main urban area was significantly lower in February, the beginning of the lockdown. However, as the city was unblocked, the lights rose sharply in March and then recovered to normal. There is obvious asynchrony in changes between river turbidity and light. The results can help understand the impact of human activities on the natural environment.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11356-021-15928-6.

Ecological and health risk assessments and water quality criteria of heavy metals in the Haihe River

Heavy metal pollution is an issue of wide concern owing to the toxic and bioaccumulative properties of many heavy metals and their tendencies to persist in the environment. The Haihe River is an important river in the Beijing–Tianjin–Hebei region, and heavy metal pollution of the basin has attracted considerable attention. This study determined the concentrations of 14 heavy metals (As, Ba, Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb, Sb, Se, Sn, and Zn) in water, sediments, and fish samples from the Haihe River basin. The results showed that the concentrations ranged from 0.08 μg L−1 to 60.49 μg L−1 in water, 0.11 mg kg−1 to 229.20 mg kg−1 in sediments, and 0.01 mg kg−1 to 11.72 mg kg−1 in fish. We derived the ambient water quality criteria (AWQC) value of each heavy metal with respect to human health, and then performed a comprehensive risk assessment according to the native parameters. The human health AWQC values for the assessed 14 heavy metals ranged from 0.16 μg L−1 to 726.53 μg L−1. The health risks posed by As, Cr, Hg, and Sb and the ecological risks associated with Ni, Cu, Cr, Zn, Cd, Co, Hg, and Sn were found to be issues of concern. The results of a sensitivity analysis revealed that the highest contributing parameter was i) the concentration in water (Cw) for Cd, Co, Mn, Sb, and Sn; ii) the intake rate of water (IRw) for As, Ba, Cr, Hg, Ni, Pb, and Se; and iii) the concentration in fish (Cf) for Cu and Zn. The results of this research could contribute to the information required for water quality assessments and the development of water quality standards.