Integrated machine learning reveals aquatic biological integrity patterns in semi-arid watersheds.

Semi-arid regions present unique challenges for maintaining aquatic biological integrity due to their complex evolutionary mechanisms. Uncovering the spatial patterns of aquatic biological integrity in these areas is a challenging research task, especially under the compound environmental stress. Our goal is to address this issue with a scientifically rigorous approach. This study aims to explore the spatial analysis and diagnosis method of aquatic biological based on the combination of machine learning and statistical analysis, so as to reveal the spatial differentiation patterns and causes of changes of aquatic biological integrity in semi-arid regions. To this end, we have introduced an innovative approach that combines XGBoost-SHAP and Fuzzy C-means clustering (FCM), we successfully identified and diagnosed the spatial variations of aquatic biological integrity in the Wei River Basin (WRB). The study reveals significant spatial variations in species number, diversity, and aquatic biological integrity of phytoplankton, serving as a testament to the multifaceted responses of biological communities under the intricate tapestry of environmental gradients. Delving into the depths of the XGBoost-SHAP algorithm, we discerned that Annual average Temperature (AT) stands as the pivotal driver steering the spatial divergence of the Phytoplankton Integrity Index (P-IBI), casting a positive influence on P-IBI when AT is below 11.8 °C. The intricate interactions between hydrological variables (VF and RW) and AT, as well as between water quality parameters (WT, NO3-N, TP, COD) and AT, collectively sculpt the spatial distribution of P-IBI. The fusion of XGBoost-SHAP with FCM unveils pronounced north-south gradient disparities in aquatic biological integrity across the watershed, segmenting the region into four distinct zones. This establishes scientific boundary conditions for the conservation strategies and management practices of aquatic ecosystems in the region, and its flexibility is applicable to the analysis of spatial heterogeneity in other complex environmental contexts.

Weakened hydrological oscillation period increased the frequency of river algal blooms.

The evolution of riverine aquatic ecosystems typically exhibits notable characteristic with cumulative, enduring, and hysteresis. Exploring the non-linear response of riverine ecology to long-term hydrological fluctuations become a major challenge in contemporary interdisciplinary research. In response to the critical issue of frequent river algal blooms in the lower Han River, which is impacted by Asian largest inter-basin water diversion project. We identified the non-linear response of eco-hydrology across various time scales through the integration of Continuous Wavelet Transform (CWT) and Inverse Wavelet Transform (IWT). Our study revealed that: 1) Over the past half century, the hydrological regime in the lower Han river showed a significant downward trend, and existed three significant hydrological oscillation periods (HOPs), including the short-scale Intra-AC (180 days), the medium-scale AC (365 days, the first major period), and the long-scale Inter-AC (2500 days), the variation of Inter-AC changed most dramatically. 2) We further found that the Inter-AC variation of hydrology is more closely related to the formation of river algal blooms in the Han River, and when the hydrological Inter-AC shows steady state or downward trend, the frequency of algal blooms in the lower Han River increases significantly. 3) The river algal blooms in the lower Han River is a cumulative consequence to the long-term hydrological influences. Weakened hydrological Inter-AC is more likely to increase the frequency of river algal blooms, and 10-years Inter-AC cumulation increased the frequency by 60%. Therefore, the weaken of long-scale HOP will significantly increase the frequency of river algal blooms in the future. This study received a critical scientific insight and aimed at provide guidance for the optimization of ecological management within the framework of national large-scale water conservation.

IInception-CBAM-IBiGRU based fault diagnosis method for asynchronous motors.

Aiming at the problems of insufficient extraction of asynchronous motor fault features by traditional deep learning algorithms and poor diagnosis of asynchronous motor faults in robust noise environments, this paper proposes an end-to-end fault diagnosis method for asynchronous motors based on IInception-CBAM-IBiGRU. The method first uses a signal-to-grayscale image conversion method to convert one-dimensional vibration signals into two-dimensional images and initially extracts shallow features through two-dimensional convolution; then the Improved Inception (IInception) module is used as a residual block to learning features at different scales with a residual structure, and extracts its important feature information through the Convolutional Block Attention Module (CBAM) to extract important feature information and adjust the weight parameters; then the feature information is input to the Improved Bi-directional Gate Recurrent Unit (IBiGRU) to extract its timing features further; finally, the fault identification is achieved by the SoftMax function. The primary hyperparameters in the model are optimized by the Weighted Mean Of Vectors Algorithm (INFO). The experimental results show that the method is effective in fault diagnosis of asynchronous motors, with an accuracy rate close to 100%, and can still maintain a high accuracy rate under the condition of low noise ratio, with good robustness and generalization ability.

Flow backward alleviated the river algal blooms.

Mechanistic understanding and prediction of river algal blooms remain challenging. It is generally believed that these blooms are formed by the slowdown of water dynamics in tributaries due to the support of the main stream. However, few studies have investigated the impact of flow backward caused by the difference in water dynamics between the main stream and tributaries. Here, we focus on the eutrophication issue in the middle-lower reaches of the Han River, which is affected by the Middle Route of the South-to-North Water Diversion Project (SNWDP), the largest inter-basin water transfer project in Asia. We discover that the reversal of the Yangtze River water level could effectively alleviate the occurrence of Han River water blooms. The Yangtze River frequently back flows into the lower reaches of the Han River, with the probability of such events increasing as it nears the confluence (20 km from the Yangtze: 9.5 %, 10 km: 19.0 %, 8 km: 28.6 %). This flow backward carries nutrients that reduce the nitrogen to phosphorus ration (N:P), leading to a shift in the nutrient structure of the Han River. This change is concomitant with a significant decline in algae biomass (Chlorophyll-a = 11.19 µg·L-1 and algae density = 0.41×107 cells·L-1 under natural flow, Chlorophyll-a = 5.19 µg·L-1 and algae density = 0.18×107 cells·L-1 under flow backward), as well as a weakening of the correlation (R) between diatom density and chlorophyll-a concentration, i.e., R = 0.38 (p>0.05) under flow backward conditions versus R = 0.72 (p<0.01) under natural flow conditions. As phosphorus limitation typically suppresses algae growth, the correlation between diatom density and chlorophyll-a concentration can help to reveal the dominance of diatoms, with stronger correlations indicating greater diatom dominance. Consequently, our study provides evidence that the flow backward can alleviate river algal blooms by weakening the growth advantage of diatoms. This study could prove valuable in investigating the eutrophication mechanism within the complex hydrodynamic conditions of rivers. SYNOPSIS: Flow backward caused by the water level difference between the main streams and tributary alleviated the occurrence of river algal blooms in the confluence area.

Non-steady state fluctuations in water levels exacerbate long-term and seasonal degradation of water quality in river-connected lakes.

The hydrological regimes and environmental changes in large riverine lakes are known for their complexity and high level of uncertainty. Scientifically uncovering the response mechanisms of water environments under complex hydrological conditions has become a challenging research objective, in the interdisciplinary of environmental science and hydrology. This study delved into the unstable response process between water level and quality of Poyang Lake, the largest freshwater lake as well as one of the most intense hydrological variability water bodies in China. We developed a non-steady state identification approach incorporates Seasonal and Trend decomposition using Loess (STL) and Wavelet Correlation (WTC) methods. The results showed that there were remarkable alterations in the hydrological regime and water quality at both seasonal and long-term scale of Poyang Lake over the past nine years. These alterations were accompanied by significant non-steady state characteristics, reflecting the changes in the response between water level and quality. The employment of the STL-WTC method revealed a significant nonlinear response between the long-term trends of water level and quality, in both the 4-month and 12-month frequency bands. In particular, our findings showed an intriguing shift towards in-phase behavior between water level and quality in the 12-month frequency band, rather than the anti-phase pattern observed previously. This correlation changed more significantly in seasons where the fluctuation pattern of water level varied sharply, such as summer and winter in Poyang Lake. Our study underscored the hydrological conditions and water quality of large lakes connected to rivers do not exhibit a long-term stable unidirectional response state, alterations in hydrological rhythms may induce a transition in the relationship from negative correlation towards nonlinear positive correlation between water level and water quality. Finally, this non-steady state fluctuation of water conditions can further exacerbate long-term and seasonal degradation of water quality.

Supervised Attention-Based Bidirectional Long Short-Term Memory Network for Nonlinear Dynamic Soft Sensor Application.

Soft sensors are mathematical methods that describe the dependence of primary variables on secondary variables. A nonlinear characteristic commonly appears in modern industrial process data with increasing complexity and dynamics, which has brought challenges to soft sensor modeling. To solve these issues, a novel supervised attention-based bidirectional long short-term memory (SA-BiLSTM) is first proposed in this paper to handle the nonlinear industrial process modeling with dynamic features. In this SA-BiLSTM model, an attention mechanism is introduced to calculate the correlation between hidden features in each time step, thus avoiding the loss of important information. Furthermore, this approach combines historical quality information and a moving window through a supervised strategy of quality variables. Such manipulation not only extracts and exploits nonlinear dynamic latent information from the process and quality variables but also enhances the model's learning efficiency and overall prediction performance. Finally, two real industrial examples demonstrate the superiority of the proposed method compared to conventional methods.

Phosphorus - The main limiting factor in riverine ecosystems in China.

River receive substantial nutrient inputs, and serve as the main channel for nitrogen and phosphorus to enter the lake, their nutrient control is of great significance to the alleviation of lake eutrophication. While nutrient limitation affects the primary productivity of water ecosystems and the biodiversity of aquatic communities, identifying the limiting factors in riverine ecosystems across China remains elusive. Here, we explore which nutrients have a stronger effect on nutritional balance and aquatic ecosystems in China's rivers based on the total nitrogen (TN) and total phosphorus (TP) observations from 1412 sampling sites in 2018. This study supports the following three main conclusions. Though the percentages of the sites with TN or TP exceeding the limits varied as per different mesotrophic targets, and TP (53.7 %) contributed more to nutrient enrichment than TN (46.3 %). In addition, the spatial distribution characteristics of river nutrients were high in the north (arid zone) and low in the south (humid zone) in China. According to four classification criteria of N:P ratio, 70.8 % of the sampling sites were attributed to phosphorus limiting, much higher than the sites with nitrogen limiting (4.1 %). TN and TP have a synergistic effect on river nutrients, while TP has a stronger regulation framework. Our results reveal that the nutrients in China's rivers are mainly phosphorus limiting, which implies that phosphorus-oriented best management practices are more likely to maintain the nutrient balance of rivers towards healthy aquatic ecosystems. Synopsis: Phosphorus is the key factor that affecting the stability and nutrient balance of riverine ecosystem.

Understanding China's industrialization driven water pollution stress in 2002-2015-A multi-pollutant based net gray water footprint analysis.

China produces a large amount of industrial effluent with multiple pollutants contained, along with a flourishing economy. This study aims to examine the dynamics between China's industrialization and accompanying environmental pressure based on the gray water footprint (GWF) concept. A newly proposed net GWF (NetGWF) and the decoupling index (DI) are applied to evaluate China's industrial activities during 2002-2015 in different modes considering typical, all, and individual pollutants. The NetGWF dynamics are further quantitatively decomposed into 17 effects of not only commonly assessed drivers but also industrial fixed capital formation, inventory variation, and import, using an advanced dynamic decomposition analysis approach. Results show NetGWF is an effective indicator measuring domestic water pollution stress from industrialization, with NetGWF-AllPlt (estimated using all pollutants) validated to be more reliable and sensitive than NetGWF-COD&NH3N (estimated using Chemical oxygen demand and Ammonia nitrogen). An overall decoupling between China's industrialization and wastewater pollution is identified with most of DIs less than 1.0 caused mainly by decreased (by around 40%) industrial NetGWFs for 2002-2015. Industrial fixed capital formation and export have caused main components of China's industrial GWF, with proportions of 37.3% and 30.8%, respectively, followed by urban household consumption (16.8%). Volatile phenol, Petroleum, and Ammonia nitrogen are recognized as three decisive contaminants to the industrial NetGWFs. Technological development is the dominant contributor (-50%) to decreasing China's industrial NetGWFs, while fixed capital formation (18%) and export (16%) are principal drivers increasing the NetGWFs. Based on these, we expect to provide informative findings for building a pollution-decoupled industrialization.

Urbanization and haze-governance performance: Evidence from China's 248 cities.

Using a panel data set of 248 Chinese cities at the prefecture level and above from 2004 to 2013, this study employs the data envelopment analysis (DEA) method based on a non-angular and non-radial directional distance function (DDF) combined with the overall technology, to measure the haze-governance performance. Furthermore, we construct a composite index based on the nighttime light (NTL) data to reflect the urbanization level, and use a spatial Durbin model (SDM) to investigate the effect and its mechanism of urbanization on the haze-governance performance. The results show a significant U-shaped curve relationship between urbanization and haze-governance performance for the samples of both the whole country and sub-regions. When urbanization exceeds a certain critical level, urbanization is conducive to the improvement of haze-governance performance. The proportion of cities exceeding the critical level in eastern China is higher than in central and western China. The mechanism analysis reveals that urbanization exerts a U-shaped influence on haze-governance performance via the effects of industrial structure, technological innovation, and human capital accumulation. In addition, as for the whole country, urbanization in neighboring regions also has a U-shaped spatial spillover effect on local haze-governance performance; however, the corresponding critical value is relatively small. In eastern China and in central and western China, urbanization in neighboring regions exhibits one-way positive and negative effects on local haze-governance performance, respectively.