Improved comprehensive ecological risk assessment method and sensitivity analysis of polycyclic aromatic hydrocarbons (PAHs)

Predicting polycyclic aromatic hydrocarbons in surface water by a multiscale feature extraction-based deep learning approach

Accurate and effective prediction of polycyclic aromatic hydrocarbons (PAHs) in surface water remains a substantial challenge due to the limited understanding of the dynamic processes. To assist integrated surface water management, a novel hybrid surface water PAH prediction model based on a two-stage decomposition approach and deep learning algorithm was proposed. Specifically, a two-stage decomposition technique consisting of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) was first introduced to decompose the data into several subsequences to extract the main fluctuations and trends of the PAH sequence. Subsequently, the deep learning algorithm long short-term memory (LSTM) was employed to explore the latent dynamic characteristics of each subsequence. Finally, the predicted values of the subsequences were integrated to obtain the final predicted results. An empirical study was conducted based on PAH data of eight major rivers in Saxony, Germany. The empirical results proved that the CEEMDAN-VMD-LSTM model outperformed other benchmark data-driven methods in predicting PAHs in surface water because it combined the advantages of two-stage decomposition and deep learning methods. The mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²) of the model were 27.89, 37.92 and 0.85, respectively. The proposed hybrid method can achieve effective and accurate water quality prediction and is an effective tool for surface water management.

Distinct Functions and Assembly Mechanisms of Soil Abundant and Rare Bacterial Taxa Under Increasing Pyrene Stresses

Elucidating the relative importance of species interactions and assembly mechanisms in regulating bacterial community structure and functions, especially the abundant and rare subcommunities, is crucial for understanding the influence of environmental disturbance in shaping ecological functions. However, little is known about how polycyclic aromatic hydrocarbon (PAH) stress alters the stability and functions of the abundant and rare taxa. Here, we performed soil microcosms with gradient pyrene stresses as a model ecosystem to explore the roles of community assembly in determining structures and functions of the abundant and rare subcommunities. The dose–effect of pyrene significantly altered compositions of abundant and rare subcommunities. With increasing pyrene stresses, diversity increased in abundant subcommunities, while it decreased in the rare. Importantly, the abundant taxa exhibited a much broader niche width and environmental adaptivity than the rare, contributing more to pyrene biodegradation, whereas rare taxa played a key role in improving subcommunity resistance to stress, potentially promoting community persistence and stability. Furthermore, subcommunity co-occurrence network analysis revealed that abundant taxa inclined to occupy the core and central position in adaptation to the pyrene stresses. Stochastic processes played key roles in the abundant subcommunity rather than the rare subcommunity. Overall, these findings extend our understanding of the ecological mechanisms and interactions of abundant and rare taxa in response to pollution stress, laying a leading theoretical basis that abundant taxa are core targets for biostimulation in soil remediation.

Integrating ecosystem services and landscape ecological risk into adaptive management: Insights from a western mountain-basin area, China

There is an increasing interest in introducing ecosystem services (ESs) and landscape ecological risk (LER) into environmental policies and governance. Yet, we know little about how to integrate LER into real decision-making and ESs management. Using the ESs valuation method and the models of InVEST and LER, this study analyzed the spatiotemporal changes of cropland food production, carbon storage, water yield, biodiversity index and LER of Bailongjiang watershed (BLJW), China in 1990, 2002 and 2014, and the relationship between them. We found clear spatial differences in both ESs and LER levels in BLJW during the study period. The cropland food production service kept rising, and the areas of high yield mainly distributed in the loessal regions of BLJW with intensive human population. The carbon storage, water yield and biodiversity index first decreased and then increased. The LER was higher in the areas along the valleys with low elevation and intensive human activities. The regional ecological zoning based on overlay analysis of ESs with LER is effective for providing interactive spatial knowledge for adaptive landscape management. Our results illustrate the integrative approach on linking landscape ecological risk with ecosystem services is a comprehensive and helpful methodology for both regional risk reduction and ecosystem services enhancement at landscape scale.