Assessment of lake eutrophication using a novel multidimensional similarity cloud model

Trophic level plays an enhanced role in shaping microbiota structure and assembly in lakes with decreased salinity on the Qinghai-Tibet and Inner Mongolia Plateaus

Plateau lakes characterized by salinization and eutrophication are essential aquatic ecosystems. A myriad of microorganisms serve as crucial biological resources in plateau lakes and drive the elemental cycles of these ecosystems. Currently, there is a paucity of knowledge regarding the impacts of salinization and eutrophication dynamics on the microbiota in plateau lakes. Here, high-throughput sequencing of the 16S ribosomal RNA genes (V4 region) was used to characterize microbial community structure and assembly in plateau lakes with different salinities and trophic levels. Water samples were collected at 191 sites across 24 lakes on the Qinghai-Tibet and Inner Mongolia Plateaus in northern China. The results showed that high salinity considerably reduced microbial alpha-diversity and niche breadth while increasing within-group similarity among various lake types. High salinity additionally decreased the complexity of microbial networks and enhanced network robustness. The assembly of microbial communities was primarily governed by deterministic processes in high-salinity and eutrophic low-salinity lakes. At decreased salinity, trophic level played a leading role in shaping microbial community structure, and the ecological processes shifted from deterministic processes driven by high salinity to eutrophication-driven deterministic processes. The biomarkers also varied from taxa adapted to high-salinity environments (e.g., Nanoarchaeaeota, Rhodothermia) to those suited for living in freshwater and low-salinity habitats (e.g., Alphaproteobacteria, Actinobacteria). In the case of eutrophication, Actinobacteria, Chloroflexi, and Cyanobacteria became the dominant taxa. Our findings indicate that decreased salinity enables trophic level to play an enhanced role in shaping microbial community structure and assembly in plateau lakes. This study enriches our knowledge about the ecological impacts of salinization and eutrophication in plateau lakes.

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.

Screening the optimal modified biochar for nitrogen retention in black soil

Reducing the environmental problems caused by nitrogen loss and nitrogen pollution is of great significance. The addition of biochar to soil is a new method for increasing nitrogen interception due to the special structural and physicochemical properties of biochar. The optimal modified biochar was screened out after acid-base modification and batch adsorption test in this paper. And then the effects of different soil and biochar mixing methods on soil physicochemical properties and nitrogen adsorption and retention were explored through soil column leaching test. The results showed that the biochar with a pyrolysis temperature of 700 °C had the best adsorption effect on nitrogen after being modified by 0.1 mol/L HCI, and the adsorption capacity of nitrate nitrogen reached 121.46 mg/g. The adsorption process of ammonia nitrogen and nitrate nitrogen conformed to the Langmuir model and was mainly homogeneous monolayer. After mixing the selected modified biochar with black soil, the pH increased by 4.77%, the content of ammonia nitrogen increased by 4.89%, and the nitrate content increased by 16.62%. In this study, the adsorption effect of biochar on nitrogen in black soil was discussed, so as to explore the optimal use of biochar in soil, which provided some reference basis for the relevant research.

Using cyanobacteria and other phytoplankton to assess trophic conditions: A qPCR-based, multi-year study in twelve large rivers across the United States

Phytoplankton is the essential primary producer in fresh surface water ecosystems. However, excessive phytoplankton growth due to eutrophication significantly threatens ecologic, economic, and public health. Therefore, phytoplankton identification and quantification are essential to understanding the productivity and health of freshwater ecosystems as well as the impacts of phytoplankton overgrowth (such as Cyanobacterial blooms) on public health. Microscopy is the gold standard for phytoplankton assessment but is time-consuming, has low throughput, and requires rich experience in phytoplankton morphology. Quantitative polymerase chain reaction (qPCR) is accurate and straightforward with high throughput. In addition, qPCR does not require expertise in phytoplankton morphology. Therefore, qPCR can be a useful alternative for molecular identification and enumeration of phytoplankton. Nonetheless, a comprehensive study is missing which evaluates and compares the feasibility of using qPCR and microscopy to assess phytoplankton in fresh water. This study 1) compared the performance of qPCR and microscopy in identifying and quantifying phytoplankton and 2) evaluated qPCR as a molecular tool to assess phytoplankton and indicate eutrophication. We assessed phytoplankton using both qPCR and microscopy in twelve large freshwater rivers across the United States from early summer to late fall in 2017, 2018, and 2019. qPCR- and microscope-based phytoplankton abundance had a significant positive linear correlation (adjusted R2 = 0.836, p-value < 0.001). Phytoplankton abundance had limited temporal variation within each sampling season and over the three years studied. The sampling sites in the midcontinent rivers had higher phytoplankton abundance than those in the eastern and western rivers. For instance, the concentration (geometric mean) of Bacillariophyta, Cyanobacteria, Chlorophyta, and Dinoflagellates at the sampling sites in the midcontinent rivers was approximately three times that at the sampling sites in the western rivers and approximately 18 times that at the sampling sites in the eastern rivers. Welch's analysis of variance indicates that phytoplankton abundance at the sampling sites in the midcontinent rivers was significantly higher than that at the sampling sites in the eastern rivers (p-value = 0.013) but was comparable to that at the sampling sites in the western rivers (p-value = 0.095). The higher phytoplankton abundance at the sampling sites in the midcontinent rivers was presumably because these rivers were more eutrophic. Indeed, low phytoplankton abundance occurred in oligotrophic or low trophic sites, whereas eutrophic sites had greater phytoplankton abundance. This study demonstrates that qPCR-based phytoplankton abundance can be a useful numerical indicator of the trophic conditions and water quality in freshwater rivers.

Multi-Phase Environmental Impact Assessment of Marine Ecological Restoration Project Based on DPSIR-Cloud Model

In order to achieve a comprehensive evaluation of the environmental impact of ecological restoration projects (ERP) under the current destruction and restoration of coastal ecological areas, this paper takes into account the impact of positive and negative indicators on the environment; analyzes the positive and negative benefits of ERP; and establishes a comprehensive environmental impact index system for marine ERP from ecological, economic, and social perspectives through the DPSIR model. On this basis, the cloud model and Monte Carlo simulation are used to obtain the comprehensive assessment grade of the construction period, short-term operation, and long-term operation in the project life cycle. The results show that the benefits of ERP, considering the impact of negative factors, are significantly reduced, and the benefits of ERP will increase remarkably in the long-term operation period. In engineering practice, the environmental pressure factor caused by excessive human activities during construction and operation periods is a key negative factor affecting the overall benefits of ERP. For project decision makers and other stakeholders, the comprehensive assessment grade considering negative impacts is more practical. At the same time, decision makers should take active response measures in the framework of long-term sustainable development, set a tolerance threshold for negative pressure indicators, and strengthen the management of ERP.

Lake ecosystem health assessment using a novel hybrid decision-making framework in the Nam Co, Qinghai-Tibet Plateau

Lake health assessment (LHA), a powerful tool for lake ecological protection, provides the foundation for sustainable water environment management. However, existing methods have not yet considered the effects of fuzziness and randomness on LHA. In addition, most of the current studies on LHA focus on the plain areas, lack of quantitative studies in mountain areas, such as the Qinghai-Tibet Plateau. The Pythagorean fuzzy cloud (PFC) integration algorithm drawing on the advantages of Pythagorean fuzzy sets (PFS) and cloud model was proposed. A novel hybrid decision-making framework combining PFC integration algorithm and TOPSIS model was developed to determine the lake health levels with fuzziness and randomness. An indicator system incorporating ecosystem integrity (physical habitat, water quantity and quality, aquatic life) and non-ecological performance (social services) was established. To comprehensively investigate the lake health level in the Qinghai-Tibet Plateau, the Nam Co was selected as study area. Our results confirm that the developed framework in this study can overcome the shortcomings of existing methods and provide a more effective approach for LHA with fuzziness and randomness. In Nam Co, the non-ecological performance was significantly better than the ecosystem integrity. Health levels exhibited a remarkable spatial variation influenced by tourism and grazing, with decreasing health status from the northwestern to southeastern Nam Co. Approximately 85% of the sampling sites were at excellent or healthy levels, 15% were subhealthy, and no sampling sites were unhealthy and sick. Our results highlight that tourism has affected health levels at Nam Co, and effective measures are needed to minimize the impact in ecological fragile areas.

Evaluation on the implementation effect of public participation in the decision-making of NIMBY facilities

The construction of NIMBY (Not in my backyard) facilities has caused many conflicts but is struggling to reduce it in China. With the background of public participation in social governance in the future, effective public participation is extremely helpful to solve this issue. Promoting public participation and scientifically evaluating the implementation effect of public participation are urgent problems to be solved at present. This study aims to analysis the factors hindering public participation and improve the implementation effect. Therefore, an evaluation system with 16 factors is established based on literature review and questionnaire survey, namely the basis of participation, participation process, external support, and cost-effectiveness. Interactions among the 16 factors are further evaluated by expert opinions. The objective and subjective weights of indicators are determined and combined by introducing Decision-Making Trial and Evaluation Laboratory (DEMATEL) and entropy weight method (EWM). Considering the uncertainty and randomness of subjective judgment, cloud model is introduced to evaluate the implementation effect of public participation. Finally, this framework is applied to evaluate the project of Jiu Feng waste-to-energy (WTE) plant in Hangzhou, China, which verifies the applicability of the evaluation framework for the implementation effect of public participation in NIMBY facilities. The results indicate that the implementation of public participation is between "average" and "good", with significant room for improvement in the involvement of NGOs and the influence of public opinion on decision-making. Additionally, the participation process has a significant impact on the whole system. The framework can provide government departments with guidance in implementing public participation.

Development of a Low-Cost Optical Sensor to Detect Eutrophication in Irrigation Reservoirs

In irrigation ponds, the excess of nutrients can cause eutrophication, a massive growth of microscopic algae. It might cause different problems in the irrigation infrastructure and should be monitored. In this paper, we present a low-cost sensor based on optical absorption in order to determine the concentration of algae in irrigation ponds. The sensor is composed of 5 LEDs with different wavelengths and light-dependent resistances as photoreceptors. Data are gathered for the calibration of the prototype, including two turbidity sources, sediment and algae, including pure samples and mixed samples. Samples were measured at a different concentration from 15 mg/L to 4000 mg/L. Multiple regression models and artificial neural networks, with a training and validation phase, are compared as two alternative methods to classify the tested samples. Our results indicate that using multiple regression models, it is possible to estimate the concentration of alga with an average absolute error of 32.0 mg/L and an average relative error of 11.0%. On the other hand, it is possible to classify up to 100% of the samples in the validation phase with the artificial neural network. Thus, a novel prototype capable of distinguishing turbidity sources and two classification methodologies, which can be adapted to different node features, are proposed for the operation of the developed prototype.

Evaluation of mining capacity of mines using the combination weighting approach: A case study in Shenmu Mining Area in Shaanxi Province, China

Aiming at the low mining rate in mines, Xingelao, Dabianyao, and Dongliang Coal Mines in Shenmu Mining Area, Shaanxi Province, China were taken as research objects. Based on this, this study constructed an evaluation index system for the mining capacity of the mines from the perspectives of geological factors, mechanical equipment, humans, and mining design. Moreover, the factors influencing the mining capacity of the mines were evaluated using a combination weighting approach based on an improved analytic hierarchy process and an entropy weight method. A standard cloud was generated based on the mapping standards of each index and a comprehensive cloud was obtained according to comprehensive weight and a backward cloud algorithm. Finally, by combining the comprehensive cloud with local and overall scores of the mines, the mining capacities of the mines were evaluated. The research results demonstrate that the key factor restricting the mining capacity of the mines is the geological environment and five major third-grade indexes affecting mining capacity are igneous rock intrusion, collapse column, scouring zone of the ancient river bed, mechanization level and coal pillar width. In addition, the corresponding suggestions and measures were put forward according to the main factors influencing the mining rate of the mines. In accordance with the weights and scores of each index, the overall scores of the mines were calculated. Dongliang, Dabianyao, and Xingelao Coal Mines were ranked in order based on scores. The research results provide a theoretical basis for improving the mining capacity of the mines under similar geological conditions.

Approach based on TOPSIS and Monte Carlo simulation methods to evaluate lake eutrophication levels

This study presents an approach for eutrophication evaluation based on the technique for order preference by similarity to an ideal solution (TOPSIS) method and Monte Carlo simulation (MCS). The MCS is employed to produce a normally distributed dataset based on the observed data while the TOPSIS method and membership function are used to evaluate the level of eutrophication. Herein, a eutrophication problem in Lake Erhai is evaluated to check the performance of the proposed approach. The evaluation results were consistent with the real situation when the coefficient P in the membership function is equal to 1. Moreover, the developed approach is able to (i) deal with evaluation items with inherent fuzziness and uncertainties, (ii) improve the reliability of evaluation results via MCS, and (iii) raise the tolerance to errors in measured data. A global sensitivity analysis indicated that the potassium permanganate index (COD_Mn) and Secchi disc (SD) are the most sensitive factors in the developed approach. Finally, a range for the coefficient P value in the membership function was recommended.

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.

Establishing a time series trend structure model to mine potential hydrological information from hydrometeorological time series data

This paper addresses the problem of missing latent time series information caused by the differences in the analysis of time series data and non-time series data. A time series trend structure model (TSTM) was established using the analysis of time series patterns and rules, the trends of patterns and rules, and trends in confidence and support. Shandong Province was selected as the study area. Rainfall and evaporation time series data from this area were input into the TSTM. The results show that: (1) the structure of multi-year precipitation and evaporation trends of the meteorological stations in the study area have continuously increasing or decreasing characteristics. The TSTM can excavate the different trend structure characteristics of different meteorological elements and enables diversity in time series data analysis; (2) the evaporation trend structure tends to change synchronously with increases and decreases in precipitation and evaporation. The synchronous change frequency is essentially the same as that of the rainfall trend structure. This indicates that the TSTM has spatial and temporal characteristics for time series data analysis; and (3) from the maximal non-descending and non-ascending subsequence in the TSTM, it can be concluded that there exists continuity in the years when the trend structure of precipitation and evaporation increases and decreases synchronously. In addition, the degree of similarity in the model is well reflected in the spatial distribution characteristics of time series data, and the model provides clustering characteristics for time series data analysis. The TSTM proposed in this paper can effectively obtain the potential hydrological information contained in time series data, and provides a scientific and reliable basis for rules for the spatial optimization of watershed data and for the calibration of hydrological models.