Ecological thresholds are a useful indicator for implementing ecological management. Many studies determine the thresholds for nutrient loads in lakes based on the maximum allowable concentration of chlorophyll a (Chla), although this neglects the overall performance of the ecosystem. A PCLake model of Baiyangdian (BYD) Lake in northern China was constructed with six ecological network analysis (ENA) indicators that characterized the ecosystem function, system maturity, and food web structure to quantify the overall status of the BYD ecosystem. To my knowledge, this is the first study on the system level responses of the BYD Lake to phosphorus load interference. Different phosphorus load scenarios were designed to simulate the ecological responses of BYD Lake. The simulated results were employed to calculate the ENA indicators. Ecological thresholds were determined through the driving response relationship between the phosphorus load gradient and the ENA indicators. The results show a non-linear transition response of ENA indicator under phosphorus load gradient. As phosphorus load increases, D/H, SOI, and FCI decreases while A/DC, TPP/TR, and TPP/TB increases. This indicates that the overall structure and function of the ecosystem will deteriorate if phosphorus load increases. The phosphorus load thresholds for the overall performance of BYD Lake were 0.50-1.32 mg m^-2 d^-1, slightly wider than that of Chla (0.53-1.26 mg m^-2 d^-1). The model results clearly indicate that there is a time-lag phenomenon at the switch points in the response of ENA indicators compared to that of single functional group. In addition, the A/DC, TPP/TR, SOI, and FCI present more time-lag than that of other ENA indicators. These time-lag effects provide a particular opportunity for biodiversity conservation. Therefore, a possible management strategy is proposed to combine system-level and function group-level thresholds, with the ENA-based threshold as the bottom line and the phytoplankton's threshold as the early-warning indicator. This design is expected to be more precise and efficient, by exploiting the advantages of two thresholds, and may benefit for ecological management practices.

As the global third-largest stationary source of carbon emissions, petroleum refineries have attracted much attention. Many investigations and methodologies have been used for the quantification of carbon emissions of refineries at the industry or enterprise scale. The granularity of current carbon emissions data impairs the reliability of precise mitigation, so analysis and identification of influencing factors for carbon emissions at a more micro-level, such as unit level, is essential. In this paper, four typical units, including fluid catalytic cracking, Continuous Catalytic Reforming, delayed coking, and hydrogen production, were chosen as objects. A typical 5-million-ton scale Chinese petroleum refinery was selected as an investigating object. The Redundancy analysis and multiple regression analysis were utilized to explore the relationship between the process parameters and carbon emissions. Three types of influencing factors include reaction conditions, processing scale, and materials property. The most important mitigation of carbon emission, in this case, can be summarized as measures of improving energy efficiency via optimizing equipment parameters or prompting mass efficiency by upgrading the scale for material and energy flow.

This study presented a stochastic approach for evaluating algae bloom risk. The uncertainties associated with risk evaluation criteria were emphasized and effectively addressed through the development of a stochastic approach. This development was based on (a) simulate the distribution function for concentration of chlorophyll-a in an eutrophied urban lakes; (b) quantification of uncertainties in evaluation criteria using triangle distribution function; (c) examination of the risk; The developed risk assessed approach was applied to “Six seas” urban lakes in Beijing China. The method is useful for the decision maker to gain insight into the environmental risks by considering uncertainties.

Interactions between species and detritus in aquatic ecosystems involve unassimilated food, non‐predator mortality, and complex trophic relationships, making it challenging to quantify interaction strengths. This study utilized classic and revised Lotka–Volterra equations, combined with the food web of Baiyangdian Lake, to develop methods for measuring interaction strengths in a phytoplankton‐based and a detritus food web. The analysis relied on three types of species–detritus interactions and outputs from an Ecopath model (1958–2019). Loop weight and Diagonal strength ( S ) were employed to assess stability. Lighter loops weight and lower S value indicate higher stability. From 1958 to 2009, the stability of Baiyangdian Lake was limited by a three‐link omnivorous loop: Detritus > zooplankton > filter‐feeding fish. As the predator–prey biomass ratio (filter‐feeding fish/detritus) increased, instability increased, and vice versa. However, the new loop (detritus > zooplankton > phytoplankton) and corresponding new predator–prey biomass ratios (zooplankton/detritus) resulted in stability from 2009 to 2019. It inferred dominant top‐down trophic cascade effects changed to dominant bottom‐up trophic cascade effects. Besides focusing on the heaviest loop weight, it was necessary to examine the heavier loops that may have a chance of evolving into the heaviest ones following catastrophic or long‐term perturbations to the food web. To facilitate management, a geometric mean ratio of predator‐to‐ prey biomass was proposed as a simplified indicator. This metric correlates with diagonal strength ( R ² = 0.6645) and offers a practical tool for early‐warning assessments of food web stability, despite its moderate precision. This study highlights the importance of integrating detritus dynamics into stability analyses and using loop weight analysis to identify critical trophic interactions. The proposed empirical indicators provide a bridge between theoretical models and ecosystem management practices.

The causal relationships between external driving forces and the ecological degradation of lakes are characterized as complex and multidimensional, with multiple inputs and outputs, nonlinearity, and many interactions. Conventional parametric statistical methods such as correlation analysis and multiple linear regression cannot handle these characteristics simultaneously. Thus, we developed an integrated analytical framework to screen, identify, and predict the factors related to the ecological degradation of lakes based on redundancy analysis (RDA), variance partitioning analysis (VPA), and principal component analysis-based generalized additive models (PCA-based GAM). The RDA and VPA methods were employed to identify and rank the driving factors that explained the decrease in species richness (specifically of key aquatic organisms, including phytoplankton, submerged plants, zooplankton, benthic animals, and fish), which is a critical ecological indicator closely associated with lake ecological degradation. PCA-based GAM was used to explore the patterns associated with driving forces. The driving forces related to the changes in species richness during the 35 years from 1986 to 2020 were investigated in Baiyangdian (BYD) Lake, China. Three categories of driving forces were identified: anthropogenic pollution, climate change, and hydrological conditions. Significant detrimental changes in species richness were detected in the first decade, followed by relative stability in the next decade, and favorable changes since 2015. Anthropogenic pollution, climate change, and hydrological conditions explained 41%, 18%, and 13% of the total variance, respectively. The best predictive model structures included the water level (WL), air temperature (AT), total phosphorus (TP), and (WL*TP) interaction, and they explained 98.4% of the total data variance. The proposed method offers actionable solutions for lake management, including real-time ecological health monitoring, adaptive strategies and indicating ecological degradation.