科研成果

Surface ozone (O3) pollution affects air quality, human health, and the ecosystem. Understanding the complex non-linear relationship between ozone formation and its precursors, nitrogen oxides (NOx), and volatile organic compounds (VOCs) is critical for policymakers to mitigate the pollution. The Empirical Kinetic Modeling Approach (EKMA) based on classical observation-constrained zero-dimension box model provides the sensitivity of ozone production to precursor concentrations instead of emissions. This makes the box-model EKMA hard to apply in a real emission reduction scenario. Here, we developed an alternative box model approach driven by localized emissions, which are derived from the field-observed concentrations. This model approach reproduced the O3 variations well by capturing the short-term changes of NOx and AVOCs emissions among different phases of pollution control during the 31st World University Games in Chengdu in 2023. The EKMA analysis based on this model approach showed a different O3 response to precursor reductions from the concentration-constrained approach, which overestimated the baseline of O3 concentration. The result from the EKMA analysis demonstrated that the O3 level was most sensitive to NOx due to stringent control strategies during the event and rapidly rebounded to almost VOC-limited regime after the event. The effects of VOCs reduction on O3 control examined by concentration-constrained model approach were less pronounced than those by emission-driven approach due to the lack of consideration of the emission-to-reaction process. Our findings suggest that the emission-driven box model is applicable for developing O3 control strategy in the local scale.

In the context of supply chain digitization and green development in full swing, it is crucial to clarify the impact of the former on green energy innovation. Using exogenous shocks deriving from supply chain innovation and application pilot events, this study examines the impact of supply chain digitization on green energy innovation based on the data of Chinese listed companies from 2012 to 2021. The findings show that supply chain digitization significantly enhances corporate green energy innovation and that receivable asset management is a path mechanism for supply chain digitization to drive green energy innovation. Moreover, there is a significant positive intra-city spillover. Supply chain digitization contributes significantly to corporate green energy innovation in state-controlled manufacturing firms with effective internal controls in the eastern region. This study has important policy implications for promoting green energy innovation and accelerating the development of modern supply chain systems.

Class 1 integrons facilitate horizontal gene transfer, significantly influencing antibiotic resistance gene (ARG) dissemination within microbial communities. Wastewater treatment plants (WWTPs) are critical reservoirs of ARGs and integrons, yet the integron-mediated dynamics of ARG transfer across different WWTP types remain poorly understood. Here we show distinct ARG profiles associated with class 1 integrons in municipal and industrial WWTPs using a novel approach combining nested-like high-throughput qPCR and PacBio sequencing. Although industrial WWTPs contained higher absolute integron abundances, their relative ARG content was lower (1.27 × 107–9.59 × 107 copies/ng integron) compared to municipal WWTPs (3.72 × 107–1.98 × 108 copies/ng integron). Of the 132,084 coding sequences detected from integrons, 56.8 % encoded antibiotic resistance, with industrial plants showing lower ARG proportions, reduced ARG array diversity, and greater incorporation of non-ARG sequences. These findings suggest industrial WWTP integrons integrate a broader array of exogenous genes, reflecting adaptation to complex wastewater compositions. This work enhances our understanding of integron-driven ARG dynamics in wastewater and offers a robust strategy for environmental integron analysis.

Numerical resolution of moderately high-dimensional nonlinear PDEs remains a huge challenge due to the curse of dimensionality for the classical numerical methods including finite difference, finite element and spectral methods. Starting from the weak formulation of the Lawson-Euler scheme, this paper proposes a stochastic particle method (SPM) by tracking the deterministic motion, random jump, resampling and reweighting of particles. Real-valued weighted particles are adopted by SPM to approximate the high-dimensional solution, which automatically adjusts the point distribution to intimate the relevant feature of the solution. A piecewise constant reconstruction with virtual uniform grid is employed to evaluate the nonlinear terms, which fully exploits the intrinsic adaptive characteristic of SPM. Combining both, SPM can achieve the goal of adaptive sampling in time. Numerical experiments on the 6-D Allen-Cahn equation and the 7- D Hamiltonian-Jacobi-Bellman equation demonstrate the potential of SPM in solving moderately high-dimensional nonlinear PDEs efficiently while maintaining an acceptable accuracy

The ability to segment moving objects from three-dimensional (3D) LiDAR scans is critical to advancing autonomous driving technology, facilitating core tasks like localization, collision avoidance, and path planning. In this paper, we introduce a novel deep neural network designed to enhance the performance of 3D LiDAR point cloud moving object segmentation (MOS) through the integration of image gradient information and the principle of motion consistency. Our method processes sequential range images, employing depth pixel difference convolution (DPDC) to improve the efficacy of dilated convolutions, thus boosting spatial information extraction from range images. Additionally, we incorporate Bayesian filtering to impose posterior constraints on predictions, enhancing the accuracy of motion segmentation. To handle the issue of uneven object scales in range images, we develop a novel edge-aware loss function and use a progressive training strategy to further boost performance. Our method is validated on the SemanticKITTI-based LiDAR MOS benchmark, where it significantly outperforms current state-of-the-art (SOTA) methods, all while working directly on two-dimensional (2D) range images without requiring mapping.

The proliferation and spread of antibiotic-resistant bacteria (ARB) significantly threaten human health and ecosystem. Periodate (PI) based advanced oxidation process has potentials for water purification but limited by complex activators or activation process. Herein, we demonstrated that H2O2 could be used to activate PI, achieving efficient ARB disinfection performance. Particularly, we found that the PI/H2O2 system (0.1 mM for both oxidants) could inactivate ARB (Escherichia coli) within 35 min. The intracellular defense system attacked by HO· radicals generated in the disinfection system, resulting in the inactivation of ARB. Antibiotic resistance genes (ARGs) released with the lysis of cell membrane could be further degraded by HO· radicals. Moreover, we found that the PI/H2O2 system was effective to inactivate ARB in a broad range of ionic strengths, with coexisting common ions and humic acid, as well as in four typical actual water bodies. The PI/H2O2 system could also efficiently disinfect other types of bacteria and degrade typical organic contaminants. In addition, under sunlight irradiation, the ARB inactivation performance of the PI/H2O2 system could be greatly improved. This study provided a practical and efficient way for decontaminating ARB/ARGs-polluted water.

Coastal sediment cores provide important records of land-based antibiotics' deposition. This study examined sediment cores from the Hangzhou Bay, East China Sea, dating back to 1980–2020 using 210Pbex. The 40-year analysis revealed a mismatch between sediment depth and age. Wastewater treatment facilities have significantly reduced antibiotics discharge into the sea. We identified 27 antibiotics, with enrofloxacin (ERFX) and nadifloxacin (NDFX) exhibiting the highest average concentrations of 84.9 and 83.4 ng/g, respectively. Quinolones (QNs) were prominent, displaying strong co-occurrence and similar distribution patterns shaped by comparable soil-water distribution coefficient (Kd). QNs correlated positively with total antibiotic concentration, serving as indicators. We proposed a multi-dimensional risk assessment of antibiotics, encompassing ecological and antimicrobial resistance (AMR) risks, complementing each other. The assessment revealed antibiotics with distinct risks: sulfacetamide (SCM) and clindamycin (CLIN) exhibited high ecological risks, while ERFX, ciprofloxacin (CFX), norfloxacin (NFX), gatifloxacin (GTFX), moxifloxacin (MXFX), and marbofloxacin (MBFX) presented high AMR risks.