Permanganate Index Research Articles

Ecosystem-inspired model and artificial intelligence predicts pollutant consumption capacity by coagulation in drinking water treatment

Conventional methods for water and wastewater treatment are energy-intensive, notably at the stage of coagulation–flocculation, calling for new strategies to predict pollutant reduction because the amount of energy consumed is related to how much of the pollutant is treated. Here we developed a model, named Bio-logic, inspired by ecosystems, where pollutants represent organisms, coagulants are food, and the wider environmental conditions are the living environment. Artificial intelligence was used to learn the biological behavior, which enabled an accurate prediction of the amount of pollutant reduction. Results show that pseudo-biological objects that have a strong affinity for biological food, such as turbidity, total phosphorus, ammonia nitrogen and the potassium permanganate index, induced a strong correlation, between measured pollutant consumption capacity and predicted values. For instance, R2 correlation coefficients are 0.97 for turbidity and 0.92 for the potassium permanganate index in the laboratory; and 0.99 for turbidity, 0.90 for total phosphorus, 0.75 for ammonia nitrogen and 0.63 for the potassium permanganate index in water treatment plants. Overall, our findings demonstrate that artificial intelligence can use the water Bio-logic model to predict the pollutant consumption capacity.

Identifying the water quality variation characteristics and their main driving factors from 2008 to 2020 in the Yellow River Basin, China.

Understanding of the water quality dynamics and their main influence factors is crucial for sustainable water environment management especially in the sensitive ecosystem area. Here, the spatiotemporal dynamic of water quality in the Yellow River Basin from 2008 to 2020 and its relationship with physical geography, human activities, and meteorology were studied by using Pearson correlation test, and a generalized linear model. The results showed that water quality was significantly improved since 2008, which was reflected from the decreasing trend of the permanganate index (CODMn) and ammonia nitrogen (NH3-N), and increasing trend of the dissolved oxygen (DO). However, the total nitrogen (TN) remained severely polluted with average annual concentration inferior to level V. Spatially speaking, the water quality in the upper and lower reaches was better than that of the middle reaches. The whole basin was severely contaminated by TN with 2.62 ± 1.52, 3.91 ± 1.71, and 2.91 ± 1.20mg L-1 from upper, middle, and lower reaches, respectively. Thus, TN should be paid much attention in the water quality management of the Yellow River Basin. The water quality improvement could be attributed to the reduction of pollution discharges and ecological restoration. Further analysis found the variation of water consumption and increase of forest and wetland area contributed 39.90% and 47.49% for CODMn and 58.92% and 30.87% for NH3-N, respectively. Meteorological variables and total water resources contributed slightly. This study is expected to provide in-depth insights for the water quality dynamics and their response to human activities and natural factors in the Yellow River Basin, which could provide theoretical references for water quality protection and management.

Remote Sensing Inversion of Typical Water Quality Parameters of a Complex River Network: A Case Study of Qidong’s Rivers

The remote sensing inversion of the water quality parameters of a complex river network in the absence of historical ground data is a difficult problem in the field of remote sensing. In this paper, a sub-regional inversion method for typical water quality parameters is presented for a complex river network using Gaofen-1 satellite data. Qidong’s rivers were selected as the survey region, and different band combination models and datasets on different river sub-regions were used to perform the remote sensing inversion, which realized the inversion of the permanganate index (CODMn), ammonia nitrogen (NH3-N), total phosphorus (TP), and total nitrogen (TN) in the rivers. The results show that all the coefficients of determination (R^2) of the inversion models are larger than 0.5, indicating an increase of about 0.4 when compared with the inversion method of the whole region, indicating good relevance. Water quality data and satellite data collected at different times were used for validation, which showed good results. On the basis of the water quality inversion, the key polluted areas were extracted in combination with on-site surveys to find the pollution source in order to verify the results of the inversion. The sub-region inversion method proposed in this paper can be used for the remote sensing inversion of the water quality parameters of complex river networks in the absence of historical ground data.

Water Quality Change Trend and Risk Analysis of Wuhan Hanjiang River Water Source

Hanjiang River is closely related to the middle route of the South-to-North Water Diversion Project, the Water Diversion Project from the Hanjiang River to the Wei River, and the Water Diversion Project in Northern Hubei. The Wuhan Hanjiang River water source is one of the important drinking water sources in China; its water quality safety is significant to living and production for millions of residents in Wuhan. Based on data from 2004 to 2021, the water quality variation trend and risk of Wuhan Hanjiang River water source were studied. The results showed that a certain gap existed between the concentrations of some pollutants such as total phosphorus, permanganate index, ammonia nitrogen, and correspondent water quality target, especially for the total phosphorus. The growth of algae in the water source was marginally limited by the concentrations of nitrogen, phosphorus, and silicon. When other factors remained unchanged, diatoms tended to grow rapidly when the water temperature was appropriate (6-12℃). The quality of water upstream had a great impact on the water quality of the Hanjiang water source. There may have been pollutants entering into the reach during the West Lake Water Plant and Zongguan Water Plant. There were differences in the temporal and spatial variation trend of concentrations between permanganate index, total nitrogen, total phosphorus, and ammonia nitrogen. Significant changes in the ratio of nitrogen and phosphorus in the water body will affect the population structure and quantity of planktonic algae and ultimately affect the safety of water quality. The water body in the water source area was generally in the state of medium nutrition to mild eutrophication, and middle eutrophication may have occurred in a few periods. In recent years, the nutritional level of the water source has been on the decline. It is necessary to make an in-depth investigation on the source, quantity, and change trend of pollutants in water sources in order to eliminate potential risks.

A hybrid approach based on Monte Carlo simulation-VIKOR method for water quality assessment

Under the dual influence of global climate change and human activities, river water environment is facing more and more serious problems and challenges. Assessing river water quality is of great significance for promoting regional sustainable development. Currently, traditional water quality assessment methods usually do not consider the uncertainty of water quality data in the collection process, which limits the application of these methods. In order to overcome the above shortcomings, this study constructed a water quality assessment method by integrating Monte Carlo method (MC), CRITIC and VIKOR methods, and applied it to assess the quality of water in the Songhua River tributary. Results indicate that: (1) The water quality assessment of the two sampling points in the study area is level III, which is consistent with the actual situation; (2) This method can overcome the uncertainty caused by sampling error and improve the credibility of water quality evaluation results; (3) Total nitrogen (TN), potassium permanganate index (PPI) and ammonia nitrogen (NH3-N) are more evaluation factors related to the evaluation results. When the decision coefficient mechanism λ is taken [0.1–0.5], the outcomes are in line with the real quality of the water. In addition, we recommend that the distribution profile generated based on the measured data should obey the probability distribution density curve that decreases from the middle to the tail of both sides. The findings of this paper can provide a scientific basis for decision makers to carry out water quality restoration and management.

High frequency variation characteristics of water quality and algae in Meiliang Bay of Taihu Lake in summer and the correlation analysis

This study aimed to observe the characteristics of water quality and algae in Meiliang bay of Taihu Lake, China, and probe the correlation. The water quality and algae were observed at fixed sites and high frequency (three times per day) for five days. The observation indexes included: (1) water temperature, dissolved oxygen percentage, dissolved oxygen concentration, electrical conductivity, TDS, salinity, pH and ORP. (2) Total nitrogen (TN), total dissolved nitrogen (TDN), total phosphorus (TP), total dissolved phosphorus (TDP), chlorophyll-a content, permanganate index (COD), NO3 −-N, NH4 +-N, and PO4 3-P. Then correlation analysis was conducted between the above indexes and algae growth. The results showed that:(1) The concentration of TN was 1.479±0.152mg/L. The concentration of TP was 0.113± 0.019mg/L. The COD concentration was 4.584±0.549mg/L. (2) A variety of algae were observed in the samples, among which microcystis was the most abundant. The concentration of chlorophyll-a was 28.509±7.421ug/L. (3) Chlorophyll-a content was significantly correlated with TDN, TDP, PO4 3-P (P < 0.001), NH4 +-N, NO3 −-N (P < 0.01), and TP (P < 0.05). Based on above results, we conclude that all the indexes in Meiliang Bay of Taihu Lake are in the normal range, indicating that the effects of ecological management is obvious. However, the water environment is still suitable for the growth of algae, especially cyanobacteria, so there is still potential danger of cyanobacteria bloom. It is necessary to strengthen monitoring and take timely countermeasures.

Application Study of MIKE11 in Meiliang Lake Water Transfer Experiment

In order to strengthen the scientific and technological research work of cyanobacteria control in Taihu Lake, the "Taihu (Meiliang Lake) water transfer on the control of cyanobacteria, improve lake water quality" project declared by Wuxi Hydraulic Society was approved by the municipal Association for Science and Technology. By further studying the effect of pumping station water transfer on controlling cyanobacteria and improving the water quality of Meiliang Lake, it is of great practical significance to implement the comprehensive water environment management of Taihu Lake basin and give full play to the ecological and social benefits of Taihu Lake. This project mainly through the pump station water transfer test work, synchronously monitoring wind direction, water level, flow, water quality (dissolved oxygen, total phosphorus, total nitrogen, permanganate index, algae density, etc.) data, combined with the waterway section data measured in advance to prepare a river network file, reasonably determine the simulation calculation range, determine the boundary conditions and calculation period. MIKE11 hydrodynamic model and water quality convection and diffusion model were adopted for simulation calculation and analysis, and model verification was carried out, achieving ideal simulation results. The analysis showed that the water transfer test had little effect on pollutant concentration, and the comprehensive evaluation category of Meiliang Lake water quality did not change, still classified as V ~ poor V, and the main items exceeding the standard were total phosphorus and total nitrogen. During the experiment period of 8 days, the water transfer reduced the permanganate index by 147 tons, the total phosphorus by 5 tons, the total nitrogen by 42 tons, and the algal density by 2.45 million cells /L. If the water transfer rate of Meiliang Lake pumping station is 60 m³/s and that of Dashihe Pump station is 30 m³/s, the permanganate index of Meiliang Lake can be reduced by 440 tons, total phosphorus by 16 tons and total nitrogen by 119 tons, and the algae density can be reduced by 4.28 million cells /L. The water transfer increased the water flow speed of Meilang Lake, increased the self-purification capacity of the lake body, greatly reduced the water pollutants in Wuxi Taihu Lake key water source protection area and sightseeing area, and reduced the density of algae.

Dynamics of phytoplankton functional groups in river-connected lakes and the major influencing factors: A case study of Dongting Lake, China

Identifying the drivers that impact phytoplankton biomass and dynamics of functional groups (FGs) is crucial to the management of lake aquatic systems. To explore the interactive effects of hydrological characteristics, physicochemical parameters and phytoplankton FGs in the largest river-connected lake, a case study was carried out at Dongting Lake, China. We analyzed the changes in the hydrological characteristics of the lake as well as in the phytoplankton biomass throughout the year to explore the driving factors that influence phytoplankton FGs. Four periods were defined in our study based on water level (WL) fluctuations including the rising periods (RⅠ and RⅡ) and the falling periods (FⅠ and FⅡ). The phytoplankton biomass in Dongting Lake was the highest in RⅡ, followed by RⅠ, FⅠ, and FⅡ. And the trend of FGs dominance through the four periods were P/MP/D (RⅠ) → P/MP/J (RⅡ) → MP (FⅠ) → MP/P/Y (FⅡ). Changes in phytoplankton biomass and FGs were the result of a combination of hydrological characteristics and physicochemical parameters. Throughout the water period, the hydrological characteristics did not directly influence phytoplankton, instead exerting a more indirect effect on phytoplankton biomass and FGs by affecting the water’s physicochemical parameters, e.g., permanganate index (CODMn), nitrogen nutrients, dissolved oxygen (DO), water temperature (WT), and conductivity (Cond). CODMn might be a significant predictor of phytoplankton biomass and FGs in all water periods, explaining 36.6% of the variability, followed by Cond, WT, ammonia nitrogen (NH4+-N), and DO explaining 17.6%, 12.3%, 11.3%, and 7.2%, respectively. However, according to each water phase analysis, the WL was the key factor for phytoplankton growth in RⅡ. Nitrogen nutrients, WT and Cond had more pronounced effects on phytoplankton in RⅠ, FⅠ and FⅡ, respectively., Based on the findings of our study, we suggest that increased control of organic matter input into the lake and nitrogen fertilizer use at nearby agricultural surface sources are effective actions that are needed to prevent and control algal blooms in Dongting Lake.

The impact of environmental factors on the transport and survival of pathogens in agricultural soils from karst areas of Yunnan province, China: Laboratory column simulated leaching experiments.

Groundwater is considered the best candidate for drinking water supply in the karst area. The groundwater water resources, however, are vulnerable to pathogenic microorganism contamination because of the typically thin soil layers overlying aquifers and the high permeability of the aquifer host rock, resulting in short residence times and low natural attenuation capacities. Until now, little attention has been paid to the critical environmental factors affecting the pathogenic microorganism contamination in soil-groundwater systems in the karst area. In the study, orthogonality column experiments with controlling ambient temperatures, pH values of inlet water, and soil porosities were carried out to investigate the transport and lifespan of pathogenic microorganisms in the leachate of agricultural soils in the karst area of Yunnan province, China. The pathogenic indicators, i.e., total bacteria count (TBC) and total coliforms count (TCC), and hydrochemical parameters, i.e., pH and permanganate index (CODMn) in the leaching water, were systematically monitored. The results showed that bacteria including coliforms can survive for prolonged periods of time in karst soils. The soils overlying the karst rocks were unable to impede the bacteria from seeping into the groundwater. The soils, in turn, likely served as both reservoirs and incubators for pathogenic bacteria. The ambient temperature was the most predominant influential factor affecting both TBC and TCC. The bacteria concentrations were proportional to the temperature in the leachate. Therefore, more attention should be paid to temperature variations in protecting the water supply, particularly in the high-temperature period, such as during the summer months.

Research on Small-Scale Detection Instrument for Drinking Water Combined Laser Spectroscopy and Conductivity Technology.

In order to realize rapid and accurate evaluation of drinking water quality, a small-scale water quality detection instrument is designed in this paper that can detect two representative water quality parameters: the permanganate index and total dissolved solids (TDS). The permanganate index measured by the laser spectroscopy method can achieve the approximate value of the organic matter in water, and the TDS measured by the conductivity method can obtain the approximate value of the inorganic matter in water. In addition, to facilitate the popularization of civilian applications, the evaluation method of water quality based on the percent-scores proposed by us is presented in this paper. The water quality results can be displayed on the instrument screen. In the experiment, we measured the water quality parameters of the tap water as well as those after the primary and secondary filtration in Weihai City, Shandong Province, China. The testing results show that the instrument can quickly detect dissolved inorganic and organic matter, and intuitively display the water quality evaluation score on the screen. The instrument designed in this paper has the advantages of high sensitivity, high integration, and small volume, which lays the foundation for the popularity of the detection instrument.

Comprehensive Evaluation of Water Resource Characteristics in the Northern Yangtze River Delta, China

The Yangtze River Delta is one of the most economically developed regions on the eastern coast of China. However, a local imbalance currently exists between its water resource environment and economic and social development due to its rapid urbanization. Thus, the reasonable evaluation and protection of local water resources are necessary. This study explores the northern Yangtze Delta, which is a more developed water system, as a pilot area. The temporal and spatial variation characteristics of rainfall and evaporation and their influencing factors were analyzed on the basis of 29 surface water sampling points, 16 rainfall stations, and three evaporation stations in the field from 1956 to 2019. Accordingly, the overall water supply quality of the river basin, the availability of different water resources, and the application of evaluation methods were assessed. Results show that local precipitation and evaporation are characterized by uneven spatial and temporal distributions in local areas, which, in turn, leads to the uneven temporal distribution of runoff, increasing the imbalance between the availability and demand of the limited local water resources. Nevertheless, the overall performance of local water quality is good. Surface water quality is mostly Ⅱ to Ⅲ, and locally Ⅳ. Most noncompliant months are during the non-flood season, and all values exceed the standard permanganate index. Groundwater is Class III or better, and the hydrochemistry type is predominantly calcium bicarbonate, sodium bicarbonate, and magnesium bicarbonate. By exploring the evaluation model of the Yangtze River Delta watershed characteristics, this study aims to provide a helpful reference for extending water resource evaluation in the Yangtze River Delta. Accordingly, this study can promote the sustainable development of the economic and social sectors of the Yangtze River Delta and the construction of its ecological environment.

Assessment of Water Eutrophication at Bao'an Lake in the Middle Reaches of the Yangtze River Based on Multiple Methods.

Based on the monthly monitoring of Bao'an Lake in Hubei Province from 2018 to 2020, the eutrophication level of Bao'an Lake in the middle reaches of the Yangtze River is investigated using the comprehensive trophic level index (TLI), chromophoric dissolved organic matter (CDOM) absorption coefficient, and the phytoplankton water quality biological method. The influencing factors are then identified. The results demonstrate that the overall water quality of Bao'an Lake remained at levels III-V during 2018-2020. Due to different eutrophication assessment methods, the results are different, but all show that Bao'an Lake is in a eutrophication state as a whole. The eutrophication level of Bao'an Lake is observed to vary with time, exhibiting an increasing then decreasing trend between 2018-2020, while levels are high in summer and autumn, and low in winter and spring. Moreover, the eutrophication level of Bao'an Lake presents an obviously varying spatial distribution. Potamogeton crispus is the dominant species of the Bao'an Lake, the water quality is good in spring when Potamogeton crispus vigorously grows, but poor in summer and autumn. The permanganate index (CODMn) and total phosphorous (TP), total nitrogen (TN), and chlorophyll a (Chl-a) contents are identified as the main influencing factors of the eutrophication level of Bao'an Lake, with a significant relationship observed between Chl-a and TP (p < 0.01). The above results provide a solid theoretical basis for the ecological restoration of Bao'an Lake.

Heavy metals in influent and effluent from 146 drinking water treatment plants across China: Occurrence, explanatory factors, probabilistic health risk, and removal efficiency

Heavy metals (HMs) in drinking water have drawn worldwide attention due to their risks to public health; however, a systematic assessment of the occurrence of HMs in drinking water treatment plants (DWTPs) at a large geographical scale across China and the removal efficiency, human health risks, and the correlation with environmental factors have yet to be established. Therefore, this study characterised the occurrence patterns of nine conventional dissolved HMs in the influent and effluent water samples from 146 typical DWTPs in seven major river basins across China (which consist of the Yangtze River, the Yellow River, the Songhua River, the Pearl River, the Huaihe River, the Liaohe River and the Haihe River) for the first time and removal efficiency, probabilistic health risks, and the correlation with water quality. According to the findings, a total of eight HMs (beryllium (Be), antimony (Sb), barium (Ba), molybdenum (Mo), nickel (Ni), vanadium (V), cobalt (Co) and titanium (Ti)) were detected, with detection frequencies in influent and effluent water ranging from 2.90 (Mo) to 99.30% (Ba) and 1.40 (Ti) to 97.90% (Ba), respectively. The average concentration range was 0.41 (Be)− 77.36 (Sb) μg/L. Among them, Sb (exceeding standard rate 8%), Ba (2.89%), Ni (21.43%), and V (1.33%) were exceeded the national standard (GB5749–2022). By combining Spearman’s results and redundancy analysis, our results revealed a close correlation among pH, turbidity (TURB), potassium permanganate index (CODMn), and total nitrogen (TN) along with the concentration and composition of HMs. In addition, the concentration of HMs in finished water was strongly affected by the concentration of HMs in raw water, as evidenced by the fact that HMs in surface water poses a risk to the quality of finished water. Metal concentration was the primary factor in assessing the health risk of a single metal, and the carcinogenic risk of Ba, Mo, Ni, and Sb should be paid attention to. In DWTPs, the removal efficiencies of various HMs also vary greatly, with an average removal rate ranging from 16.30% to 95.64%. In summary, our findings provide insights into the water quality and health risks caused by HMs in drinking water.

Impounding Impacts of the Three Gorges Reservoir on Phytoplankton Function Groups and Its Relationship with Resource Use Efficiency

In order to investigate the relationship between phytoplankton community functional group compositions and resource use efficiency in important tributaries of the Three Gorges Reservoir, phytoplankton and environment parameters were sampled from five tributaries, the Xiangxi River, Daning River, Meixi River, Pengxi River, and Huangjin River, in August and November, 2020. There were 119 species (variants) belonging to 62 genera and 7 phyla identified in summer, whereas 118 species (variants) belonging to 7 divisions of 58 genera were found in winter. According to Padisak's theory, all phytoplankton were divided into 25 functional groups, of which there were six important functional groups in both summer and winter:L0, H1, D, Y, MP, and P in summer and L0, H1, A, M, MP, and Y in winter. The α-diversity of the phytoplankton functional group in summer was higher than that in winter. Moreover, a higher α-diversity was also found in downstream samples relative to that in upstream samples, indicating that the community structure was more complex, and the community stability was relatively better in downstream regions of the rivers. Redundancy analysis (RDA) showed that the environment factors, i.e., ν, pH, permanganate index, WT, and RUETN, significantly affected phytoplankton functional groups (P<0.05). Variance partitioning analysis (VPA) indicated that environmental factors had a higher explanatory degree for the change in functional group composition in summer (45.23%); on the contrary, resource use efficiency had a higher explanatory degree in winter (42.33%). The linear fitted model showed that functional groups L0, H1, D, and Y showed a significant positive correlation relationship with RUETN and RUETP in summer, whereas only four functional groups (M, MP, Y, and A) had a linear relationship with RUETP, and all function groups had a good linear relationship with RUETN in winter. These results indicated that the functional groups belonging to cyanobacteria, dinoflagellates, and cryptophyta were more efficient at using limited resources in summer, whereas the diatoms had a good linear relationship with resource use efficiency and formed a dominant group in the low temperature environment of winter. These results suggest that the impounding of the Three Gorges Reservoir area can significantly change the resource use efficiency of phytoplankton, resulting in changes in the phytoplankton functional group composition and community structure.

Multiplex Analysis of Fundamental Surface Water Quality Indicators by Multichannel Electrochemical Device Combined With MFCSC Model

Rapid and accurate detection of water quality is essential for environmental monitoring and management. Among them, permanganate index (CODMn), nitrate–nitrogen (NO3−-N), dissolvable inorganic phosphorus (DIP), and ammonia nitrogen (AN) are four important water quality indicators. A portable multichannel electrochemical device (MCED) combined with a sensor array composed of gold nanoparticles (AuNPs), silver nanodendritic structures (AgNDs), and reduced graphene oxide (rGO)-modified screen-printed electrodes (SPEs) was applied for facile, rapid on-site inspection. The sensor array exhibits good complementarity in voltammetric measurements of water quality indicators and has cross-sensitivity in multiplex measurement. A multiple fully connected shared convolution (MFCSC) model was constructed to process the voltammetric data, extracting unique features to resolve the overlap in voltammetric data of complex solutions for the quantitative determination of target analytes. Accurate predictions were successfully achieved for the four parameters, CODMn, NO3−-N, DIP, and AN in the mixture, where CODMn, NO3−-N, and AN have high coefficients of determination ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R} ^{{2}}$ </tex-math></inline-formula> ) and low mean absolute percentage errors (MAPE%). Using the migration ability of MFCSC, combined with MCED, it is further applied for real water sample testing, which proves the good prediction of the water quality indicators of real water samples in complex matrices. The results show that the sensing system developed in this work has wide application potential and can be used to rapidly analyze surface water quality and obtain water quality information in a wide range of water bodies for monitoring.

Assessment of the spatiotemporal water quality variations in the Middle Route of China's South-to-North Water Diversion Project by multivariate analysis.

As an important drinking water source for North China, the Middle Route of China's South-to-North Water Diversion Project (MRP) must provide high-quality water to maintain the health and safety of more than 60 million people. However, different water transfer operation modes may affect the water quality status, and the spatiotemporal characteristics of water quality in the MRP, with high water transfer volumes, remain poorly understood. In this study, the differences in water quality in the MRP between the initial stage (Nov. 2015 to Oct. 2017, low transfer volumes) and the current stage (Nov. 2017 to Oct. 2020, high transfer volumes) were compared, and the spatiotemporal water quality variations in the current stage were evaluated using multivariate statistical methods. For this purpose, approximately 12,528 observations, including the datasets of 12 water quality parameters collected from 29 monitoring sites, were used. The results showed that the water quality status improved significantly during the current stage. Based on principal component analysis (PCA), physical parameters (natural), nutrients, organic matter and microbes (anthropogenic), and heavy metals (natural and anthropogenic) were the key factors influencing water quality variations. Based on hierarchical cluster analysis, 12months were classified into two groups: the high-flow period (HFP, Jun.-Oct.) and the low-flow period (LFP, Nov.-May). Additionally, 29 sampling sites were grouped into three sections: the Henan section (HN; S1-S16), Hebei section (HB; S17-S24), and Tianjin-Beijing section (TB; S25-S29). From the perspective of water quality regulation, the total nitrogen concentration and permanganate index in the HB and TB sections of the MRP should be considered throughout the year, and the faecal coliform concentrations in these two sections should also be considered during the HFP. The results of this study could be helpful for local administrations to understand and control pollution and better protect the quality of water in the MRP.

Spatiotemporal variation and driving factors of water quality in Yunnan-Guizhou plateau lakes, China

The Yunnan-Guizhou Plateau (YGP) lakes are the typical plateau rift lakes and an important water source in southwest China. However, there is a scarcity of research on its spatiotemporal water quality variations and driving factors, especially on long-term scales. Herein, multiple water quality indicators for 11 natural lakes on the YGP were measured from 2005 to 2020. In this study, the effects of natural lake attributes, human activities, and meteorological conditions on water quality were also analyzed. The results showed that the water quality of the YGP lakes tended to degrade, and was divided into heavy, medium, and light pollution types. Total phosphorus (TP), total nitrogen (TN), permanganate index (CODMn), and biochemical oxygen demand (BOD5) increased by 14.69%, 14.44%, 22.61%, and 11.26%, respectively, from 2005 to 2020. Natural attributes of lakes and land use types were the main reasons for the spatial heterogeneity of water quality in YGP. In contrast, the temporal evolution of lake water quality was mainly related to human activities and climatic conditions. The smaller the water/ terrestrial area ratio, water storage capacity, and water depth, the easier the eutrophication and the worser the water quality of YGP lakes. Land degradation accelerated the deterioration of water quality in plateau lakes, while ecological land played an improving role. This study summarizes the water quality changes and influencing factors in YGP lakes over the past 15 years, which can provide a scientific database reference for water environment protection in YGP.

A carbon-dot fluorescence capillary sensor for the determination of chemical oxygen demand

The development of a highly sensitive, reusable, and microportable sensor for determining the chemical oxygen demand (COD) is very important for performing in situ measurements. A reusable carbon-dot fluorescence capillary sensor is reported here. Citric acid/urea-carbon dots were synthesized by hydrothermal treatment and immobilized on the inner surface of a capillary using sol–gel technology. The sensor were used in conjunction with a recirculating-flow system to establish a set of methods for the indirect determination of COD in water by reaction with KMnO4. The carbon dots reduce KMnO4 to produce MnO2, thereby quenching the fluorescence of the sensor through the adsorption/inner filter effect. We harnessed this mechanism to successfully determine the CODMn (permanganate index) in the range of 0.2–40 mg/L (ΔF = -48.32c + 678.25, r = 0.9996, 0.2–12 mg/L; ΔF = -16.31c + 792.61, r = 0.9998, 8–40 mg/L). The sensor was subsequently regenerated by using ascorbic acid to reduce the carbon dots and remove the MnO2 adsorbed on the inner surface. The sensor could be reused at least 20 times with a relative standard deviation of less than 0.59 %. The proposed method was applied to quantify the COD in potable, waste and natural water. Combining the sensor with the automated flow system is a potential method for in situ real-time monitoring of COD in water.

Spatiotemporal distribution and prediction of chlorophyll-a in Ulansuhai lake from an arid area of China

Lake Ulansuhai, a typical shallow lake in an arid area that is economically and ecologically important along the Yellow River, is currently eutrophic. Long-term (2010–2020) data on chlorophyll-a, nutrient, and environmental factors were obtained from three Lake Ulansuhai monitoring stations. The temporal and spatial distribution characteristics of Chl-a were analyzed. Additionally, a hybrid evolutionary algorithm was established to simulate and predict Chl-a, and sensitivity analysis revealed the interaction between environmental factors and eutrophication. The results indicated that (1) the seasonal variation of eutrophication showed an obvious trend of spring &amp;gt; summer &amp;gt; autumn &amp;gt; winter, and the concentration of Chl-a in the inlet was significantly higher than that in the outlet; (2) The inlet, center, and outlet of Ulansuhai Lake are satisfactorily affected by HEA in the best suited method. The fitting coefficients (R2) of the optimal models were 0.58, 0.59, and 0.62 for the three monitoring stations, and the root mean square errors (RMSE) were 3.89, 3.21, and 3.56, respectively; (3) under certain range and threshold conditions, Chl-a increased with the increase of permanganate index, water temperature, dissolved oxygen concentration, and ammonia nitrogen concentration, but decreased with the increase of water depth, Secchi disk depth, pH, and fluoride concentration. The results indicate that the HEA can simulate and predict the dynamics of Chl-a, and identify and quantify the relationships between eutrophication and the threshold data. The research results provide theoretical basis and technical support for the prediction and have great significance for the improvement of water quality and environmental protection in arid and semi-arid inland lakes.

A soft sensor for simulating algal cell density based on dynamic response to environmental changes in a eutrophic shallow lake

There is a great need for timely monitoring and rapid water quality assessment to control the algal blooms that often occur in eutrophic lakes. While algal cell density (ACD) is a critical indicator of algal growth, field monitoring is laborious and time-consuming, and rapid assessment of algal blooms based on ACD is often not possible. To address the limitations of conventional ACD detection, we proposed a soft sensor approach that uses surrogate indicators to simulate ACD in machine learning models. We conducted a case study using monitoring data from Chaohu Lake collected between 2016 and 2019. We found that ensemble learning models, especially extreme gradient boosting (XGBoost), outperformed traditional machine learning algorithms by comparing various machine learning algorithms. Also, considering the influence of input variable selection on model performance, we combined the results of different filter methods in the multi-stage variable selection process. Finally, we screened out seven key variables out of the 43 initial candidate variables, including dissolved oxygen (DO), chlorophyll-a (Chl-a), Secchi disk depth (SD), pH, permanganate index (CODMn), week of the year (WOY), and wind velocity (WV). Their inclusion substantially improved data accessibility and supported the development of a rapid simulation model. The final model was capable of reliable spatiotemporal generalization, with an overall R2 value of 0.761. On the theoretical side, our study makes a new attempt to simulate ACD values in a eutrophic lake. For practical purposes, the soft sensor can facilitate the rapid assessment of bloom conditions, which helps the local administration with emergency prevention and control.