Eutrophication assessment of seasonal urban lakes in China Yangtze River Basin using Landsat 8-derived Forel-Ule index: A six-year (2013-2018) observation

A national-scale assessment on the spatial and temporal variations in water color for urban lakes in China

Monitoring the variations of lake water quality is essential for urban water security and sustainable eco-environment health. However, it is challenging to investigate the water quality of urban lakes at large scales due to the need for large-amount in situ data with diverse optical properties for developing the remote sensing inversion algorithms. Forel-Ule Index (FUI), a proxy of quantifying water color, whose calculation does not require in situ data of specific properties, can comprehensively reflect water quality conditions. However, the spatial and temporal distribution of water color in Chinese urban lakes is still poorly understood. To fill this research gap, this study investigated the spatial distribution of water color in 523 urban lakes (area > 0.5 km2) in China using the FUI derived from the high-quality Multi-Spectral Instrument (MSI) data onboard Sentinel-2 during the ice-free period (April-October) from 2019 to 2022. The monthly and seasonal variation patterns of water color in urban lakes were also analyzed. Our results show that green domain is the most common color of urban lakes, with about 86 % of urban lakes in China being green, and non-green lakes accounting for only 14 % of the total number of lakes. The monthly variation of FUI in urban lakes across the country and multiple geographic regions is basically the same. The monthly average FUI first increases, then decreases, and then rebounds. We also found that the seasonal variation of water color in most urban lakes in southern and northern China is opposite. This study helps to comprehensively understand the spatial and temporal variation of water color and quality of urban lakes in China, providing key basic information for the protection and governance of urban lakes.

Long-term spatiotemporal mapping in lacustrine environment by remote sensing:Review with case study, challenges, and future directions

Satellite remote sensing, unlike traditional ship-based sampling, possess the advantage of revisit capabilities and provides over 40 years of data support for observing lake environments at local, regional, and global scales. In recent years, global freshwater and coastal waters have faced adverse environmental issues, including harmful phytoplankton blooms, eutrophication, and extreme temperatures. To comprehensively address the goal of 'reviewing the past, assessing the present, and predicting the future', research increasingly focuses on developing and producing algorithms and products for long-term and large-scale mapping. This paper provides a comprehensive review of related research, evaluating the current status, shortcomings, and future trends of remote sensing datasets, monitoring targets, technical methods, and data processing platforms. The analysis demonstrated that the long-term spatiotemporal dynamic lake monitoring transition is thriving: (i) evolving from single data sources to satellite collaborative observations to keep a trade-off between temporal and spatial resolutions, (ii) shifting from single research targets to diversified and multidimensional objectives, (iii) progressing from empirical/mechanism models to machine/deep/transfer learning algorithms, (iv) moving from local processing to cloud-based platforms and parallel computing. Future directions include, but are not limited to: (i) establishing a global sampling data-sharing platform, (ii) developing precise atmospheric correction algorithms, (iii) building next-generation ocean color sensors and virtual constellation networks, (iv) introducing Interpretable Machine Learning (IML) and Explainable Artificial Intelligence (XAI) models, (v) integrating cloud computing, big data/model/computer, and Internet of Things (IoT) technologies, (vi) crossing disciplines with earth sciences, hydrology, computer science, and human geography, etc. In summary, this work offers valuable references and insights for academic research and government decision-making, which are crucial for enhancing the long-term tracking of aquatic ecological environment and achieving the Sustainable Development Goals (SDGs).

Impacts of rainfall and lakeshore soil properties on microplastics in inland freshwater: A case study in Donghu Lake, China

Microplastic (MP) pollution has garnered global attention in recent years. Although anthropogenic factors have been extensively studied for their impacts on MP pollution, there is still a lack of research on the relationship between non-anthropogenic factors and MP occurrence in inland freshwater. This study investigated MP pollution in Donghu Lake, the largest urban freshwater lake in China, to examine the effects of rainfall and lakeshore soil properties on MP pollution. The MP abundance in the surface water of Donghu Lake was 5.84 ± 2.95 items per L under the equilibrium state. However, during and after rainfall, the MP abundances significantly increased to 8.27 ± 5.65 items per L and 7.60 ± 4.04 items per L, respectively (p < 0.05). This increase could be attributed to an increase in the amount of MPs transported to the lake via atmospheric deposition and rainfall runoff, as well as the re-suspension of MP debris in sediment during stronger hydrodynamics. A statistically significant negative correlation was observed between MP abundance and lakeshore soil particle size. It suggested that a high proportion of large-sized soil particles created large pores that enabled MPs to be deposited in the surface layer of soil to migrate to deeper layers. As a result, the amount of MPs in the surface soil and transported to the lake via surface runoff was low. It is of practical significance to understand the sources and distribution impact factors of MPs in urban lakes. The fate and effects of MPs retained in the inland freshwater environments should receive more attention.

An innovative method based on Gaussian cloud distribution and sample information richness for eutrophication assessment of Yangtze's lakes and reservoirs under uncertainty

The precise assessment of a water body's eutrophication status is essential for making informed decisions in water environment management. However, conventional approaches frequently fail to consider the randomness, fuzziness, and inherent hidden information of water quality indicators. These would result in an unreliable assessment. An enhanced method was proposed for the eutrophication assessment under uncertainty in this study. The multi-dimension gaussian cloud distribution was introduced to capture the randomness and fuzziness. The Shannon entropy based on various sample size and trophic levels was proposed to maximize valuable information hidden in the datasets. Twenty-seven significant lakes and reservoirs located in the Yangtze River Basin were selected to demonstrate the proposed method. The sensitivity and consistency were used to evaluate the accuracy of the proposed method. Results indicate that the proposed method has the capability to effectively assess the eutrophication status of lakes and reservoirs under uncertainty and that it has a better sensitivity since it can identify more than 33-50% trophic levels compared to the traditional methods. Further scenario experiments analysis revealed that the sample information richness, i.e., sample size and the number of trophic levels is of great significance to the accuracy/robustness of the method. Moreover, a sample size of 60 can offer the most favorable balance between accuracy/robustness and the monitoring expenses. These findings are crucial to optimizing the eutrophication assessment.

Multifunctionality promotes the prosperity of riverine planktonic diatoms in plateau

Interpreting the biogeographic distribution and underlying mechanisms of functional traits not only contributes to revealing the spatiotemporal dynamics of species biodiversity but also helps to maintain ecological stability during environmental variations. However, little is known about the functional profiles of diatom communities over large river systems. Herein, we provided the first blueprints about the spatiotemporal distributions and driving forces of functional traits for both planktonic and sedimentary diatoms over the 6030 km continuum of the Yangtze River, with the help of the high-throughput sequencing and functional identification. By investigating the 28 functional traits affiliated into five categories, we found that planktonic diatom functions showed clearer landform-heterogeneity patterns (ANOSIM R = 0.336) than sedimentary functions (ANOSIM R = 0.172) along the river, represented by life-forms and ecological-guilds prominent in water-plateau as well as cell-sizes and life-forms particularly in sediment-plateau. Planktonic diatom functions also displayed higher richness and network complexity in plateau (richness: 58.70 ± 9.30, network edges: 65) than in non-plateau regions (23.82 ± 13.16, 16), promoting the stability and robustness of diatom functions against the high-radiation and low-temperature plateau environment. Environmental selection (mainly exerted by PAR, UV, and Tw) played crucial roles in determining the functional variations of planktonic diatoms (explaining 80.5%) rather than sedimentary diatoms (14.5%) between plateau and non-plateau regions. Meanwhile, planktonic diatom traits within life-forms were identified to be well responsive to the ecological environment quality (r = 0.56-0.60, P < 0.001) in the Yangtze. This study provided comprehensive insights into the multifunctionality of diatoms and their responses to environmental disturbance and environment quality, which helps to develop effective strategies for maintaining ecological stability in changing river environments.

Increased dominance of terrestrial component in dissolved organic matter in Chinese lakes

The source composition of chromophoric dissolved organic matter (CDOM) in lakes is closely related to regional environmental changes, human activities, and the carbon cycle. The spectral slope ratio (SR) is an important parameter of CDOM optical components, and combined with remote sensing technology, the source composition of CDOM can be tracked comprehensively and efficiently in large regions. Here, we proposed a CDOM source tracking remote sensing model (CDOM-SR) based on the hue angle (α) to assess the spatial pattern and long-term trend of the CDOM source composition in Chinese lakes (surface area ≥ 1 km2) from 1986 to 2021. Validation results show that the CDOM-SR model has a good SR estimation performance with a median absolute percentage difference, root mean square deviation, median ratio, and median deviation of 17.91 %, 0.23, 1.02, and 0.03, respectively. We found that the average SR of Chinese lakes presents an obvious spatial pattern of high in the west and low in the east due to the difference in human activity intensity and the natural geographical environment. Additionally, we found that the average SR of Chinese lakes from 1986 to 2021 decreased at a rate of - 0.06/10 years, of which 64.37 % of lakes decreased significantly, 15.42 % of lakes had no significant change, and only 20.20 % of lakes increased. The widespread decrease in the average SR indicates that the increasing human activity discharge of terrestrial organic matter has had an important impact on the source composition of the CDOM in Chinese lakes. Our results provide a new resource for remote sensing monitoring of CDOM sources and important insights into lake carbon cycling under the influence of ongoing human activities.

Evaluating total nitrogen and phosphorous concentrations in a watershed impacted by diverse anthropic activities in a developing country

This research aims to identify critical contamination points by nutrients, their possible origin (point and nonpoint sources), their spatial distribution, and possible attenuation by natural and anthropogenic processes. The study area is the Velhas River Basin, located in the Southeast Region of Brazil (17.0°-20.5° S; 43.5°-45.0°W). A historical series of water quality monitoring, land cover map, demographic and agricultural censuses, sewage treatment diagnostics, and local hydrographic networks were used to achieve the objectives. In addition, the regions were divided into incremental areas, enabling individualized analyses of each sub-basin. Descriptive statistics, seasonality, categorized data tests, agglomerative hierarchical cluster analysis, and principal component analysis were used. There was a significant contribution of nutrients in the most important urban agglomeration of the basin, resulting in peak concentrations measured at that place. Although the values were reduced by the mouth (650 km), the percentage of legislation violations remained high. The effects of punctual contamination were intensified by the low percentage of treated sewage in the basin, the absence of adequate treatment technologies to remove nutrients, and the disorderly urbanization. Furthermore, it was estimated that the nutrient load from animal husbandry is approximately 75% of the load from domestic effluents due to the high number of cattle in the basin and the low percentage of forests.

Forel-Ule index extraction and spatiotemporal variation from MODIS imagery in the Bohai Sea of China

In large-scale water quality evaluation, traditional field-measured data lack spatial-temporal representativeness, and the role of conventional remote sensing parameters (SST, Chla, TSM, etc.) is controversial. By calculating and grading the hue angle of a water body, a Forel-Ule index (FUI) can be obtained, which provides a comprehensive statement of water condition. Using MODIS imagery, hue angles are extracted with better accuracy than the literature's method. It is found that FUI changes in the Bohai Sea have correlated consistently with water quality. The decreasing trend of non-excellent water quality areas in the Bohai Sea was highly correlated with FUI (R² = 0.701) during the government-dominated land-based pollution reduction program (2012-2021). FUI can monitor and evaluate seawater quality.

Long-term spatial-temporal monitoring of eutrophication in Lake Burdur using remote sensing data

Lake eutrophication continues to be one of the major environmental challenges in lakes worldwide. This study is about using a remote sensing approach to monitor long-term chlorophyll-a (Chl-a) concentrations and the trophic state index (TSI) in Lake Burdur, Türkiye. Surface reflectance from Landsat 5, Landsat 7, and Landsat 8 satellite image collections were evaluated with an empirical equation to estimate a 38-year-long (1984-2021) time series of the lake's median Chl-a concentrations. Pixel quality masking was applied to retain only acceptable quality information and exclude images affected by cloud cover, icing, and other environmental factors from the analysis. The results were analyzed based on annual, seasonal, and monthly temporal averages. Over a period of 38 years, Chl-a concentrations were found to vary between 58.7 and 196.4 mg/m³. Notably higher concentrations were recorded in 2010 and 2021, while lower concentrations were observed between 1991 and 1993. A substantial increase in Chl-a concentration was evident from 2002 to 2010. The maximum concentration of Chl-a during the first two decades of the analysis was 130 mg/m³, which increased to 200 mg/m³ during the last two decades. Landsat-derived TSI values suggested that Lake Burdur has been mostly hypertrophic for the entire study period.

Geographic pattern of phytoplankton community and their drivers in lakes of middle and lower reaches of Yangtze River floodplain, China

Disentangling the relative contributions of deterministic and stochastic processes was critical to compressive understanding of underlying mechanism governing geographic pattern and assembly of phytoplankton community, while it was seldom performed in connected lakes under human pressure. Here, we investigated phytoplankton community pattern in relation to environmental and spatial factors over 81 lakes located in the middle and lower reaches of Yangtze River (MLYR) floodplain, where many lakes suffered from eutrophication and cyanobacterial blooms. A majority of MLYR lakes had higher phytoplankton abundance surpassing 10⁷ cells/L and were dominated by common bloom-forming cyanobacterial genera, including Pseudanabaena, Microcystis, Merismopedia, Dolichospermum, Limnothrix, and Raphidiopsis. Phytoplankton community exhibited a striking geographical pattern both for taxonomic and functional compositions, while functional groups were less sensitive, and dissimilarity in communities displayed no significant increases with increasing geographical distance. Further, species richness explained much higher percentage of community variations than species turnover, indicating a reduced effect of environmental filtering of phytoplankton species with tolerance to similar environments in connected MLYR lakes. Both deterministic and stochastic processes governed assembly and biogeographic of phytoplankton community. Variation partition analysis showed that spatial factors exhibited greater influence on phytoplankton community compared to environmental variables. The stronger influence of spatial factors was further demonstrated by Mantel test and neutral community model. These findings indicate that deterministic and stochastic processes exhibited similar biogeographic patterns for phytoplankton community in MLYR lakes, but stochastic process was overwhelmingly dominated. Moreover, a large proportion of unexplained variation implies that complex interactions exist to shape assembly mechanism of phytoplankton community in MLYR lakes.

Molecular insight into the release of phosphate from dissolved organic phosphorus photo-mineralization in shallow lakes based on FT-ICR MS analysis

Dissolved organic phosphorus (DOP) is a key factor in the water eutrophication process because of its high potential bioavailability and inorganic phosphate (Pi) compensation ability through bio- and photo-mineralization. However, the research on the characterization and transformation of DOP is insufficient owing to their complex composition. This study investigates the release of dissolved Pi from DOP photo-mineralization in Lake Dong based on Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS) analysis. The results showed that the photo-release of dissolved Pi is spatially heterogeneous in Lake Dong and is consistent with the distribution of DOP concentration. The FT-ICR MS results showed that the simulated irradiation decreased the relative abundance (RA) of the DOP molecular formulae with higher molecular weight (MW) and higher double bond equivalence values (DBE), while the RA of DOP molecular formulae with lower MW and lower DBE value increased or remained. Besides, the total RA of lipid-like formulae increased from 49.09% to 55.90%, while the oxy-aromatic-like formulae decreased from 50.91% to 44.10%, suggesting that simulated irradiation would influence the potential bioavailability of DOP. As the main photolysis medium during DOP photo-mineralization, the hydroxyl radicals (∙OH) are mainly derived from dissolved organic matter (DOM) compared to the nitrate (NO₃^-) and iron ion (Fe³⁺) in Lake Dong. These results are important in understanding the ability and mechanism of DOP photo-mineralization and provide suggestions for cycling phosphorus in eutrophic shallow lakes.

De novo transcriptome analysis reveals the molecular regulatory mechanism underlying the response to excess nitrogen in Azolla spp

Phytoremediation potential of Azolla in removal of nitrogen from wastewater has been promising. However, little is known about the response of Azolla to high concentrations of nitrogen. In this study, the responses of four Azolla species to different concentrations of total nitrogen ranging from 0 to 180 mg L^-1 were examined. The responses varied among different species, and the high nitrogen-tolerant species A. caroliniana and A. microphylla could remove nitrogen from aqueous solutions with higher efficiencies. We further performed transcriptome analysis to explore the molecular mechanism underlying the response to high nitrogen stress in Azolla. RNA-seq analysis revealed a synergistic regulatory network of differentially expressed genes (DEGs) involved in nitrogen transport and metabolism in A. microphylla, mainly in the roots. Under high nitrogen treatment, the DEGs encoding nitrate transporters or nitrate transporter 1/peptide transporters (NRTs/NPFs), ammonium transporters (AMTs), nitrate reductase (NIA), nitrite reductase (NIR) and glutamine synthetases/glutamate synthases (GSs/GOGATs) were down-regulated, and the DEGs encoding glutamate dehydrogenases (GDHs) were up-regulated, suggesting that A. microphylla possessed high tolerance against excess nitrogen through down-regulation of nitrate and ammonium uptake and fine regulation of nitrogen assimilation in the roots. Our results provided a theoretical foundation for better utilization of Azolla for wastewater treatment.

Combining hydrochemistry and hydrogen and oxygen stable isotopes to reveal the influence of human activities on surface water quality in Chaohu Lake Basin

The input of pollutants caused by human activities induces the deterioration of surface water quality. To reveal the characteristics of surface water quality in Chaohu Lake Basin and the influence of human activities, the hydrochemistry and stable isotope composition of hydrogen and oxygen in lake water and inflow river water were analyzed. The results show that the hydrochemical type of lake water is the Na-Cl type,while river water is the Na-Cl, Ca-Cl and mixed types. The ion proportional coefficient method and principal component analysis show that surface water is controlled by weathering of evaporated salt rocks and silicate rocks, in which Cl^- and SO₄^2- are affected by fertilizers and sewage to some extent. There is a strong correlation between conventional ions and nutrient indexes, which indicates that dissolved ions are affected not only by rock weathering but also by human activities (such as the discharge of domestic sewage or nitrogen-containing wastewater and the use of fertilizers). The stable isotope values of hydrogen and oxygen in surface water are distributed at the lower right portion of the local precipitation line and are close to it, indicating that surface water mainly originates from precipitation. The high value of d-excess values in surface water indicates that evaporation is weak. As pollution indicators, EC, Cl^- and NO₃^- indicates that the Nanfei River, Dianbu River, Shiwuli River and Pai River flow in northwestern of Chaohu Lake Basin through Hefei urban city are severely polluted, NO₃^- originates from manure and sewage. Rivers flowing through farmland areas are less polluted, and the use of agricultural fertilizer contributes greatly to NO₃^-.

Evidence of the Anthropic Impact on a Crustacean Zooplankton Community in Two North Patagonian Lakes

Lately, agriculture, livestock, forestry, and aquaculture activities have been greatly developed in Chilean North Patagonia, negatively impacting the balance of the environmental conditions in lakes and affecting the development and survival of several native species. The aim of this study was to assess the anthropic impact on a zooplankton community in two North Patagonian lakes. We collected samples from four sites belonging to Lake Icalma and Lake Llanquihue, including four replicates per site. Water samples were analyzed for physicochemical characteristics and zooplankton communities. We focused on the presence of Daphnia pulex, a species of zooplanktonic crustacean that performs a key role in capturing energy from primary producers to deliver it to final consumers such as fish. We found that Llanquihue showed higher total phosphorus, nitrogen, copper, iron, manganese, total dissolved solids (TDS), and conductivity (EC) than Icalma. Furthermore, ecological variables were greatly decreased due to total P, total N, manganese, copper, total dissolved solids, and conductivity, which changed the species dominance of the zooplankton community in Llanquihue, indicating some degree of anthropization. This study provides fundamental information on the anthropogenic impact on water quality, as well as on zooplankton diversity, highlighting the importance of monitoring the health of these North Patagonia freshwater ecosystems.

Perfluoroalkyl acids (PFAAs) in the aquatic food web of a temperate urban lake in East China: Bioaccumulation, biomagnification, and probabilistic human health risk

The bioaccumulation and biomagnification of perfluoroalkyl acids (PFAAs) in temperate urban lacustrine ecosystems is poorly understood. We investigated the occurrence and trophic transfer of and probabilistic health risk from 15 PFAAs in the food web of Luoma Lake, a temperate urban lake in East China. The target PFAAs were widely distributed in the water (∑PFAA: 77.09 ± 9.07 ng/L), suspended particulate matter (SPM) (∑PFAA: 284.07 ± 118.05 ng/g dw), and sediment samples (∑PFAA: 67.77 ± 17.96 ng/g dw) and occurred in all biotic samples (∑PFAA: 443.27 ± 124.89 ng/g dw for aquatic plants; 294.99 ± 90.82 for aquatic animals). PFBA was predominant in water and SPM, with 40.11% and 21.35% of the total PFAAs, respectively, while PFOS was the most abundant in sediments (14.11% of the total PFAAs) and organisms (14.33% of the total PFAAs). Sediment exposure may be the major route of biological uptake of PFAAs. The PFAA accumulation capacity was the highest in submerged plants, followed by emergent plants > bivalves > crustaceans > fish > floating plants. Long-chain PFAAs were biomagnified, and short-chain PFAAs were biodiluted across the entire lacustrine food web. PFOS exhibited the greatest bioaccumulation and biomagnification potential among the target PFAAs. However, biomagnification of short-chain PFAAs was also observed within the low trophic-level part of the food web. Human health risk assessment indicated that perfluorooctanesulfonate (PFOS) and perfluorooctanoic acid (PFOA) posed health risks to all age groups, while the other PFAAs were unlikely to cause immediate harm to consumers in the region. This study fills a gap in the knowledge of the transfer of PFAAs in the food webs of temperate urban lakes.

Review of the Forel-Ule Index based on in situ and remote sensing methods and application in water quality assessment

Water pollution is considered an acute worldwide environmental issue. At present, the commonly adopted method of water quality characterisation involves the retrieval of optically active water quality parameters based on remote sensing reflectance (R_rs), but this method is subject to the limitation that understanding local scatter and absorption characteristics of light is essential to precisely derive these parameters. Water colour primarily depends on water constituents and is traditionally gauged with the Forel-Ule (FU) scale. In recent years, R_rs within the visible region has been considered to determine the Forel-Ule Index (FUI) for water colour measurement. The FUI exhibits the advantages of remote sensing and does not rely on local retrieval algorithms. Therefore, this index can characterise natural waters in a simple and globally effective manner. As there exists a lack of review articles on the FUI, we present a comprehensive review of this index that may help researchers progress. First, we introduce the most recent techniques for FUI measurement, especially remote sensing-deriving methods. Then, we summarise FUI applications in water quality assessment of oceans and inland waters. Finally, FUI development trends, challenges and application perspectives are examined.

Impact of light on anoxic/oxic reactors: performance, quorum sensing, and metagenomic characteristics

The effect of light has raised attention on wastewater treatment. However, little research has concentrated on the influences of light on activated sludge. In this study, the influences of light on the performance, quorum sensing (QS) and metagenomic characteristics of anoxic/oxic reactors were investigated. The reactor without light (AO1) showed higher total nitrogen (TN) removal (79.15 ± 1.69%) than the reactor with light (AO2) (74.54 ± 1.30%), and significant differences were observed. It was observed that light facilitated the production of protein-like and tryptophan-like substances by employing parallel factor analysis for extracellular polymeric substance (EPS), resulting in more EPS production in AO2, indicating light was beneficial to EPS production. The concentrations of N-acyl-homoserine lactones (AHLs) were various in the two reactors, so the AHLs-mediated QS behaviors in both reactors were also different. These results revealed that light significantly influenced nitrogen removal, EPS, and QS. Metagenomic analysis based on Tax4Fun demonstrated that light reduced the denitrification, stimulated the polysaccharide and protein biosynthesis pathways and down-regulated the AHLs synthesis pathway, resulting in lower TN removal, more EPS production, and lower AHLs concentrations. Based on the above, the likely mechanism was proposed for the influences of light on the reactor.

Four-decade dynamics of the water color in 61 large lakes on the Yangtze Plain and the impacts of reclaimed aquaculture zones

The lakes on the Yangtze Plain, a critical source of freshwater and fisheries for hundreds of millions of people in China, have lost a considerable portion of their surface area due to reclamation since the 1950s. Landsat satellites can provide long-term collections of high-resolution images and thus offer great potential for hindcasting the lake reclamations of aquaculture zones and their long-term impacts on the lacustrine water color. Using Landsat observations from 1984 to 2018 and a Forel-Ule index (FUI) model, we studied the water color dynamics of 61 lakes on the Yangtze Plain. Three distinct change patterns were found among the 61 examined lakes, and 25 of the 61 lakes showed statistically significant changes in the annual hue angle values (P < 0.05). We further collected environmental parameter datasets (runoff, normalized difference vegetation index (NDVI), and wind speed) and a lacustrine reclamation dataset, and measured the concentrations of chlorophyll-a (Chl-a) and dissolved organic carbon (DOC) from two field trips. We investigated their correlations with water color change from different facets. The results showed that the long-term water color in 33 of the 61 lakes exhibited significant correlations with environmental factors. The reclaimed aquaculture zones in this region have caused differences in the water color between the reclaimed area and that in adjacent natural waters. The Chl-a and DOC levels derived from field surveys further confirmed that reclaimed aquaculture zones increased light-absorbing materials in the water and may deteriorate water quality. This study is an important step forward in understanding the water quality changes in lake ecosystems affected by human impacts and natural variability.

Recording and response of persistent toxic substances (PTSs) in urban lake sediments to anthropogenic activities

Owing to the intensification of human activities, urban lakes serving as important freshwater resources are becoming seriously deteriorated, especially due to persistent toxic substance (PTS) pollution. Therefore, the spatial distribution and sediment record of PTS in urban lake sediments in the middle Yangtze River Basin were investigated to indicate its response to anthropogenic emission and pollution reduction actions. Spatial distribution of typical PTSs (polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs) included) showed that pollutants were concentrated in the southeast and center of the urban lake due to riverine inputs suffering from both petrochemical and municipal wastewaters. The sedimentary record of PAH concentrations indicated an increase from the 1960s to a peak level in the 2000s, which was induced mainly by increased PAH emissions, with PAH levels decreasing subsequently due to craft improvement of wastewater treatment plants (WWTPs). Source apportionment results revealed that historical PAH emissions transferred from petrogenic sources to a mixture of energy combustion and petrochemical industry. Furthermore, OCP and PCB pollutions reached peak levels in 1980s, which is consistent with their historical usage for agricultural and industrial production. From the synthetic sediment quality index (SeQI) analysis, sediment quality in nearly half of sites was poor, while the sediment record suggested that sediment quality had turned better since 2000s maybe due to the WWTP improvement. Furthermore, significant correlations (p < 0.05) between PTS levels and the ratio of PAH emissions to the number of WWTPs documented the PTS levels in response to the surrounding anthropogenic pollution and WWTPs in urban lakes.

A Machine Learning Approach for Estimating the Trophic State of Urban Waters Based on Remote Sensing and Environmental Factors

To improve the accuracy of remotely sensed estimates of the trophic state index (TSI) of inland urban water bodies, key environmental factors (water temperature and wind field) were considered during the modelling process. Such environmental factors can be easily measured and display a strong correlation with TSI. Then, a backpropagation neural network (BP-NN) was applied to develop the TSI estimation model using remote sensing and environmental factors. The model was trained and validated using the TSI quantified by five water trophic indicators obtained for the period between 2018 and 2019, and then we selected the most appropriate combination of input variables according to the performance of the BP-NN. Our results demonstrate that the optimal performance can be obtained by combining the water temperature and single-band reflection values of Sentinel-2 satellite imagery as input variables (R2 = 0.922, RMSE = 3.256, MAPE = 2.494%, and classification accuracy rate = 86.364%). Finally, the spatial and temporal distribution of the aquatic trophic state over four months with different trophic levels was mapped in Gongqingcheng City using the TSI estimation model. In general, the predictive maps based on our proposed model show significant seasonal changes and spatial characteristics in the water trophic state, indicating the possibility of performing cost-effective, RS-based TSI estimation studies on complex urban water bodies elsewhere.

Distribution and Risk Assessment of Toxic Pollutants in Surface Water of the Lower Yellow River, China

The lower reaches of the Yellow River is known for the rapid development of industry and agriculture, which has also led to some pollution. However, information about the level of toxic contaminants in the surface waters is lacking in this area. Therefore, five sampling points were set in the lower Yellow River to investigate the distribution of various pollutants and analyze the potential risks. The presence of heavy metals (Heavy metals tested for in this study were: Mercury (Hg), Arsenic (As), Copper (Cu), Chromium (Cr), and Zinc (Zn)) and antibiotics (Antibiotics tested for in this study were: Enrofloxacin (ENR), Ciprofloxacin (CIP), and Norfloxacin (NOR)) in water samples taken from the lower Yellow River were measured to reveal the spatial distribution and risk potential of the compounds. Various water quality parameters (Water quality parameters used in this study were: chemical oxygen demand (COD), biological oxygen demand (BOD5), total phosphorus (TP), and total nitrogen (TN)) were also tested. Study results showed the main surface water pollution components were COD, BOD5, TN, and TP. The average levels were 37.79 mg/L, 16.64 mg/L, 4.14 mg/L, and 0.42 mg/L, respectively. Among the detected metals from the water samples, Hg (LOD-0.1 μg/L) levels were only in line with the surface water class III or worse. Both fish and water samples contained antibiotics. According to an ecological risk assessment conducted along the river, the distribution of pollutants in the waters exhibited a spatial relationship with the land-use pattern in the study region and the Kenli site was the most polluted. Research shows that up-to-date data on the residual levels and distribution characteristics of pollutants in the lower Yellow River could provide valuable baseline data and technical support for relevant government departments and their management going forward.

Remote Estimation of Trophic State Index for Inland Waters Using Landsat-8 OLI Imagery

Remote monitoring of trophic state for inland waters is a hotspot of water quality studies worldwide. However, the complex optical properties of inland waters limit the potential of algorithms. This research aims to develop an algorithm to estimate the trophic state in inland waters. First, the turbid water index was applied for the determination of optical water types on each pixel, and water bodies are divided into two categories: algae-dominated water (Type I) and turbid water (Type II). The algal biomass index (ABI) was then established based on water classification to derive the trophic state index (TSI) proposed by Carlson (1977). The results showed a considerable precision in Type I water (R2 = 0.62, N = 282) and Type II water (R2 = 0.57, N = 132). The ABI-derived TSI outperformed several band-ratio algorithms and a machine learning method (RMSE = 4.08, MRE = 5.46%, MAE = 3.14, NSE = 0.64). Such a model was employed to generate the trophic state index of 146 lakes (> 10 km2) in eastern China from 2013 to 2020 using Landsat-8 surface reflectance data. The number of hypertrophic and oligotrophic lakes decreased from 45.89% to 21.92% and 4.11% to 1.37%, respectively, while the number of mesotrophic and eutrophic lakes increased from 12.33% to 23.97% and 37.67% to 52.74%. The annual mean TSI for the lakes in the lower reaches of the Yangtze River basin was higher than that in the middle reaches of the Yangtze River and Huai River basin. The retrieval algorithm illustrated the applicability to other sensors with an overall accuracy of 83.27% for moderate-resolution imaging spectroradiometer (MODIS) and 82.92% for Sentinel-3 OLCI sensor, demonstrating the potential for high-frequency observation and large-scale simulation capability. Our study can provide an effective trophic state assessment and support inland water management.

Inversion of Chlorophyll-a Concentration in Donghu Lake Based on Machine Learning Algorithm

Machine learning algorithm, as an important method for numerical modeling, has been widely used for chlorophyll-a concentration inversion modeling. In this work, a variety of models were built by applying five kinds of datasets and adopting back propagation neural network (BPNN), extreme learning machine (ELM), support vector machine (SVM). The results revealed that modeling with multi-factor datasets has the possibility to improve the accuracy of inversion model, and seven band combinations are better than seven single bands when modeling, Besides, SVM is more suitable than BPNN and ELM for chlorophyll-a concentration inversion modeling of Donghu Lake. The SVM model based on seven three-band combination dataset (SVM3) is the best inversion one among all multi-factor models that the mean relative error (MRE), mean absolute error (MAE), root mean square error (RMSE) of the SVM model based on single-factor dataset (SF-SVM) are 30.82%, 9.44 μg/L and 12.66 μg/L, respectively. SF-SVM performs best in single-factor models, MRE, MAE, RMSE of SF-SVM are 28.63%, 13.69 μg/L and 16.49 μg/L, respectively. In addition, the simulation effect of SVM3 is better than that of SF-SVM. On the whole, an effective model for retrieving chlorophyll-a concentration has been built based on machine learning algorithm, and our work provides a reliable basis and promotion for exploring accurate and applicable chlorophyll-a inversion model.

The effect of secondary capping on the control of phosphorus release from sediment by activated thin-layer capping with Al-PIA

It is well-known that the activated thin-layer capping covering by secondary capping of contaminated sediment poses a threat to the inactivation of activated material. In this study, the static simulation experiment was conducted to study the effect of secondary capping thickness by sediment on the control of TP release from the sediment by aluminum-based P-inactivation agent (Al-PIA), and to propose the phosphorus adsorption pathway of Al-PIA. The results showed that Al-PIA could effectively reduce the release of phosphorus pollutants from the sediment at the capping intensity of 2 kg/m². When the secondary capping thickness of sediment were 0, 2, 4, 7, 10, and 15 mm, the average removal rates of TP were 87.57%, 76.39%, 61.22%, 51.32%, 41.93%, and 32.11%, respectively, indicating that the removal efficiency of phosphorus decreased with the increase of the secondary capping thickness of the sediment. The adsorbed phosphorus by Al-PIA was mainly non-apatite inorganic phosphorus (NAIP) in inorganic phosphorus. With the increase of the secondary capping thickness of sediment, the NAIP proportion of phosphorus adsorbed by Al-PIA increased. Meanwhile, the removal rate of phosphorus in the activated capping system showed a first increase and then decrease trend, and the removal rates of total phosphorus (TP), inorganic phosphorus (IP), and organic phosphorus (OP) were obvious except for that of organic phosphorus (OP).

Temporal and Spatial Variations of Chlorophyll a Concentration and Eutrophication Assessment (1987–2018) of Donghu Lake in Wuhan Using Landsat Images

Chlorophyll a (Chl-a) concentration, which reflects the biomass and primary productivity of phytoplankton in water, is an important water quality parameter to assess the eutrophication status of water. The band combinations shown in the images of Donghu Lake (Wuhan City, China) captured by Landsat satellites from 1987 to 2018 were analyzed. The (B4 − B3)/(B4 + B3) [(Green − Red)/(Green + Red)] band combination was employed to construct linear, power, exponential, logarithmic and cubic polynomial models based on Chl-a values in Donghu Lake in April 2016. The correlation coefficient (R2), the relative error (RE) and the root mean square error (RMSE) of the cubic model were 0.859, 9.175% and 11.194 μg/L, respectively and those of the validation model were 0.831, 6.509% and 19.846μg/L, respectively. Remote sensing images from 1987 to 2018 were applied to the model and the spatial distribution of Chl-a concentrations in spring and autumn of these years was obtained. At the same time, the eutrophication status of Donghu Lake was monitored and evaluated based on the comprehensive trophic level index (TLI). The results showed that the TLI (∑) of Donghu Lake in April 2016 was 63.49 and the historical data on Chl-a concentration showed that Donghu Lake had been eutrophic. The distribution of Chl-a concentration in Donghu Lake was affected by factors such as construction of bridges and dams, commercial activities and enclosure culture in the lake. The overall distribution of Chl-a concentration in each sub-lake was higher than that in the main lake region and Chl-a concentration was highest in summer, followed by spring, autumn and winter. Based on the data of three long-term (2005–2018) monitoring points in Donghu Lake, the matching patterns between meteorological data and Chl-a concentration were analyzed. It revealed that the Chl-a concentration was relatively high in warmer years or rainy years. The long-term measured data also verified the accuracy of the cubic model for Chl-a concentration. The R2, RE and RMSE of the validation model were 0.641, 2.518% and 22.606 μg/L, respectively, which indicated that it was feasible to use Landsat images to retrieve long-term Chl-a concentrations. Based on longitudinal remote sensing data from 1987 to 2018, long-term and large-scale dynamic monitoring of Chl-a concentrations in Donghu Lake was carried out in this study, providing reference and guidance for lake water quality management in the future.