The idea of systems analysis and mathematical modeling for formulating and resolving river pollution issues is of relatively recent vintage and has been applied widely in the last 3 decades. The present study illustrates the utility of Beck-modified Khanna–Bhutiani model (BMKB) to determine the pollution load due to the presence of organic matter in River Ganga from its course from Devprayag to Roorkee through the holy city of India, Haridwar. The study was conducted over a period of 3 years between 2010 and 2013. The study was aimed to verify the BMKB model for River Ganga. This model was simulated and calibrated through the data obtained by model by comparing it with the field data observed manually. Paired T-test were performed for dissolved oxygen (DO) and biochemical oxygen demand (BOD) between the titrated value and modelled value to determine if there was any statistically significant difference between the means of respective values. The results of T-test revealed statistically significant difference between DO and BOD, i.e., DO t (11)= 3.819, P= 0.003, BOD t(11)= 14.635, P= 0.000. The model presented with a good agreement between the calibrated and observed data, thereby actualizing the validity of the proposed model.

The present study evaluates the water quality of Dez River, a river 23 km long, via QUAL2Kw model, based on simulation of DO and BOD5 p98arameters, through considering water quality standards during six months in three stations of Kashefieh, Pole-Panjom, and Hamidabad. To determine the model’s validity and compare the observational data, the paper uses the square mean square error (RMES) and the squared mean square error coefficient (CV). The achieved results of the model largely indicate the actual conditions of the river, which represent the ability of QUAL2Kw model to simulate qualitative parameters. The main contamination of Dez River comes from municipal wastewater, either directly imported by river residents or collected by urban canals. It, then, enters the river at a certain point. Based on the simulation and observational results of DO at two stations of 5th and Hamidabad Bridge in all months of sampling, it is below 5 mg/L, regarded a threat to aquatic life. In addition, BOD5 parameter goes beyond 6 mg/L in Hamidabad station, being a threatening factor for aquatic life in this station. Critical conditions of Dez River, low discharge, and high loading of pollutants have increased the concentration of water quality parameters. Given the results of RMSE and CV parameters, the model has had the best conformity for DO parameter, followed by BOD5.

Xiangjiang (XJ) is a typical urban inland river that serves as a drinking water source, which may be affected by the currently used organochlorine pesticides (CUOCPs) originating from agricultural activities in the vicinity. On this basis, this study comprehensively explored the occurrence and distribution characteristics of CUOCPs in surface water and sediments under long-term precipitation and subsequent floods. Considering the low concentration of CUOCPs in water, a technique combining high-throughput organic analysis with high-volume solid phase extraction (High-throat/Hi-volume SPE) was introduced for effective analysis of CUCOPs. The results showed that the concentration of CUOCPs in the water and sediments of XJ ranged from 2.33 to 6.40 ng L⁻¹ (average of 3.93 ± 1.15 ng L⁻¹) and from 1.52 to 21.2 ng g⁻¹ (average of 6.60 ± 4.31 ng g⁻¹ dw), respectively. The distribution of CUOCPs in water was consistent throughout XJ, but that in sediments was not uniform, indicating a stronger impact of floods on water than on sediments. Water-sediment partition coefficients were generally >2 L g⁻¹, showing a tendency of CUOCP dominance in sediments. The results of principal component analysis and cluster analysis showed that the occurrence of CUOCPs is significantly affected by exogenous disturbance, which could be flood events; meanwhile, clusters of CUOCPs were found in both water and sediments in the source-limited middle reaches in urban areas. Redundancy analysis (RDA) showed that CUOCP occurrences were not positively correlated with nutrient elements (nitrogen and phosphorus), but related to pH and dissolved oxygen (DO), indicating complex sources.

Harmful algal blooms (HABs) are recurrent in the NW Patagonia fjords system and their frequency has increased over the last few decades. Outbreaks of HAB species such as Alexandrium catenella, a causal agent of paralytic shellfish poisoning, and Protoceratium reticulatum, a yessotoxins producer, have raised considerable concern due to their adverse socioeconomic consequences. Monitoring programs have mainly focused on their planktonic stages, but since these species produce benthic resting cysts, the factors influencing cyst distributions are increasingly gaining recognition as potentially important to HAB recurrence in some regions. Still, a holistic understanding of the physico-chemical conditions influencing cyst distribution in this region is lacking, especially as it relates to seasonal changes in drivers of cyst distributions, as the characteristics that favor cyst preservation in the sediment may change through the seasons. In this study, we analyzed the physico–chemical properties of the sediment (temperature, pH, redox potential) and measured the bottom dissolved oxygen levels in a “hotspot” area of southern Chile, sampling during the spring and summer as well as the fall and winter, to determine the role these factors may play as modulators of dinoflagellate cyst distribution, and specifically for the cysts of A. catenella and P. reticulatum. A permutational analysis of variance (PERMANOVA) showed the significant effect of sediment redox conditions in explaining the differences in the cyst assemblages between spring-summer and fall-winter periods (seasonality). In a generalized linear model (GLM), sediment redox potential and pH were associated with the highest abundances of A. catenella resting cysts in the spring-summer, however it was sediment temperature that most explained the distribution of A. catenella in the fall-winter. For P. reticulatum, only spring-summer sediment redox potential and temperature explained the variation in cyst abundances. The implications of environmental (physico-chemical) seasonality for the resting cysts dynamics of both species are discussed.

Eutrophication is an important water environment issue facing global lakes. Diversion of water from external watersheds into lakes is considered as effective in ameliorating eutrophication and reducing algal blooms. Nevertheless, the changes in lake water environment caused by external water diversion, especially the influence of water diversion on the characteristics of dissolved organic matters (DOM), are still poorly understood. We therefore used a combination of EEM-PARAFAC, Principal Component Analysis (PCA), and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) to investigate the effects of water diversion from the Niulan River on DOM characteristics in Lake Dianchi. The results showed that the water diversion from the Niulan River significantly improved the water quality of Lake Dianchi, the concentrations of TN, TP, COD and Chla decreased rapidly, and the degree of humification of dissolved organic matter (DOM) increased, which was in sharp contrast with that of pre-implementation. Firstly, the diversion of water from the Niulan River mainly led to changes in the structure of pollution sources. The load of influent rivers and sewage treatment plants rich in lignin and tannins increased, and the input of terrestrial humus increased. Second, the improved water quality reduced algal enrichment and frequency of blooms, and reduced the release of lipid- and protein-riched algal-derived DOM. Finally, the hydraulic retention time of Lake Dianchi caused by water diversion was shortened, the hydrodynamic conditions were significantly improved, and the dissolved oxygen (DO) level gradually recovered, which played a positive role in improving the humification degree of DOM. Our findings provide new insights for exploring the improvement of eutrophic lake eco-environmental quality caused by water diversion projects.

The decay of algal biomass and aquatic plants in freshwater lakes leads to the overproduction of autochthonous organic matter (OM) and the exhaustion of dissolved oxygen, impacting the microbial community and subsequent biodegradation of emerging contaminants in sediment. This study explored how the microbial processing of aquatic plant- and algal-derived OM (POM and AOM) mediates 17α-ethinylestradiol (EE2) biodegradation in the anoxic sediments of Lake Taihu in China. In four months of microcosm incubations, the increased concentrations of protein-like substances in AOM and POM exhibited temporary activation on microbial metabolic enzyme activity (fluorescein diacetate hydrolase and dehydrogenase) and significantly promoted the carbon mineralization with iron reduction (P < 0.001). These in turn increased the EE2 biodegradation efficiency to 77–90 ng g⁻¹ in the anoxic sediment. However, a higher EE2 biodegradation of 109 ng g⁻¹ was achieved with the humic acid augmentation containing more quinone-like compounds, showing a weaker substrate-priming effect but accelerated redox cycling of iron and organic substrates in the later period of incubation. The microbial analysis further revealed that the quinone-like compounds in OM were more closely associated with microbial electron transfer and strengthened their interspecies syntrophic cooperation favorable to contaminant biodegradation, even though the connective members exposed to protein-like components upregulated more functional genes related to organic carbon and xenobiotics metabolism and biodegradation. Our findings will help predict the fate of estrogens in various sedimentary environments under increasing eutrophication and further climate change scenarios.

Dissolved inorganic nitrogen (DIN) is considered the main factor that induces eutrophication in water, and is readily influenced by hydrodynamic activities. In this study, a 4-year field investigation of nitrogen dynamics in a turbulent river was conducted, and a laboratory study was performed in the approximately homogeneous turbulence simulation system to investigate potential mechanisms involved in DIN transformation under turbulence. The field investigation revealed that, contrary to NO⁻₃ dynamics, the NH⁺₄ concentrations in water were lower in flood seasons than in drought seasons. Further laboratory results demonstrated that limitation of dissolved oxygen (DO) caused inactive nitrification and active denitrification in static river sediment. In contrast, the increased DO levels in turbulent river intensified the mineralization of organic nitrogen in sediment; moreover, ammonification and nitrification were activated, while denitrification was first activated and then depressed. Turbulence therefore decreased NH⁺₄ and NO⁻₂ concentrations, but increased NO⁻₃ and total DIN concentrations in the overlying water, causing the total DIN to increase from 0.4 mg/L to maximum of 1.0 and 1.7 mg/L at low and high turbulence, respectively. The DIN was maintained at 0.7 and 1.0 mg/L after the 30-day incubation under low and high turbulence intensities (ε) of 3.4 × 10⁻⁴ and 7.4 × 10⁻² m²/s³, respectively. These results highlight the critical role of DO in DIN budgets under hydrodynamic turbulence, and provide new insights into the DIN transport and transformation mechanisms in turbulent rivers.

Studies of algal bloom early warning systems have rarely paid attention to the dynamics of excessive proliferation of phytoplankton (EPP), which occurs prior to algal blooms, or to the sensitivity of a lake to EPP based on multiple environmental factors. In this study, we investigated EPP dynamics in large lakes and identified major factors that influenced the lake's vulnerability to EPP, to improve algal bloom early warning systems. High temporal moderate resolution imaging spectroradiometer (MODIS) images and multi-source daily site monitoring data of large lakes in the middle–lower Yangtze River basin were analyzed. Then, the floating algal index (FAI) and resource use efficiency (RUE) by phytoplankton were used to investigate the EPP dynamics and lake's vulnerability to EPP, respectively. Moreover, generalized linear models were used to assess the relative importance of environmental factors on RUE. The results indicate that the lakes freely connected (FC) to the Yangtze River (Dongting Lake and Poyang Lake) had lower FAIs but higher RUEs than the non-connected lakes (NC; Chaohu Lake and Taihu Lake). The key factors affecting RUE-FC were standard deviation of water level within 30 days(WL30), particulate matter <10 μm(PM₁₀), and relative humidity(Hum), which explained 15.91% of the variations in RUE. The key factors affecting RUE-NC were ozone(O₃), basin normalized difference vegetation index standard deviation(BNDVISD), and dissolved oxygen(DO), which explained 35.28% of the variations in RUE. These results emphasize the importance of air quality in influencing or reflecting EPP risks in large lakes. In addition, basin vegetation and hydrological rhythms can influence NH₄⁺ through non-point source loading. Algal bloom early warning systems can be improved by routine monitoring and forecasting of potential environmental factors such as air quality and basin vegetation.

Phosphorus-inactivating agents (PIAs) as geoengineering tools in lakes have been investigated extensively, but PIA resuspension in the photic layer occurs frequently in shallow lakes and little is known about the influence of algae on PIA performance. Our results proved that algae increased the dissolved oxygen, pH and dissolved organic carbon concentration substantially. In the absence of sediment, lanthanum modified zeolite (LMZ) as a representative PIA and algae could deplete dissolved inorganic phosphorus (DIP) from water but the former was faster than the latter. When LMZ and algae coexisted, the amount of phosphorus that was captured by LMZ was 3.1 times greater than that taken up by algae. An increase in pH or dissolved organic carbon increased the zero-equilibrium phosphorus concentration (EPC₀) of the sediment but LMZ addition could lower the EPC₀ and reduce the risk of phosphorus release during the algal blooming season. In the presence of sediment, LMZ reduced the DIP concentration more rapidly and yielded a lower final DIP concentration compared with algae. In conclusion, the influence of algae on the performance of LMZ by (i) taking up DIP to reduce the availability of DIP and convert DIP into a releasable phosphorus form and (ii) increasing the pH and dissolved organic carbon concentration to hinder the adsorption ability of DIP were recognized. The LMZ performed well, even in the presence of algae.

Dissolved oxygen (DO) is an effective indicator for water pollution. However, since DO is a non-optically active parameter and has little impact on the spectrum captured by satellite sensors, research on estimating DO by remote sensing at multiple spatiotemporal scales is limited. In this study, the support vector regression (SVR) models were developed and validated using the remote sensing reflectance derived from both Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data and synchronous DO measurements (N = 188) and water temperature of Lake Huron and three other inland waterbodies (N = 282) covering latitude between 22–45 °N. Using the developed models, spatial distributions of the annual and monthly DO variability since 1984 and the annual monthly DO variability since 2000 in Lake Huron were reconstructed for the first time. The impacts of five climate factors on long-term DO trends were analyzed. Results showed that the developed SVR-based models had good robustness and generalization (average R² = 0.91, root mean square percentage error = 2.65%, mean absolute percentage error = 4.21%), and performed better than random forest and multiple linear regression. The monthly DO estimates by Landsat and MODIS data were highly consistent (average R² = 0.88). From 1984 to 2019, the oxygen loss in Lake Huron was 6.56%. Air temperature, incident shortwave radiation flux density, and precipitation were the main climate factors affecting annual DO of Lake Huron. This study demonstrated that using SVR-based models, Landsat and MODIS data could be used for long-term DO retrieval at multiple spatial and temporal scales. As data-driven models, combining spectrum and water temperature as well as extending the training set to cover more DO conditions could effectively improve model robustness and generalization.