Epidemiological and comparative genomic analyses of Multidrug-Resistant Acinetobacter baumannii collected between 2020 and 2022 in Liaocheng City, Shandong Province, China

The emergence and prevalence of multidrug-resistant Acinetobacter baumannii (MRAB) poses a huge challenge to clinical treatment. To investigate the genetic and epidemiologic characteristics of MRAB in Liaocheng, China, and to explore potential resistance mechanisms, 56 MRAB strains were collected from the clinical departments of seven hospitals in Liaocheng between 2020 and 2022. Molecular typing, antimicrobial resistance patterns, and epidemiological characteristics were determined by genome sequencing and comparative genome analysis. Sequence type (ST) 540 was the most prevalent ST of the 56 MRAB in Liaocheng, and most strains (92.86%) were grouped into CC92. Core genome multilocus sequence typing subdivided the strains according to the number of allelic differences and could distinguish different outbreaks caused by ST540 isolates in the hospitals. All the isolates harbored blaOXA-23 and blaADC-25, and at least 92.86% of the isolates were resistant to 10 antibiotics. The major MRAB epidemic clone detected in Liaocheng was ST540, which was different from the results reported in other regions in China. Furthermore, several inter-hospital transmissions of ST540 isolates were observed. The findings highlight the urgent need for effective nosocomial infection control measures and the continuous surveillance of ST540 MRAB in Liaocheng

Operational Data-Driven Intelligent Modelling and Visualization System for Real-World, On-Road Vehicle Emissions—A Case Study in Hangzhou City, China

On-road vehicle emissions play a crucial role in affecting air quality and human exposure, particularly in megacities. In the absence of comprehensive traffic monitoring networks with the general lack of intelligent transportation systems (ITSs) and big-data-driven, high-performance-computing (HPC) platforms, it remains challenging to constrain on-road vehicle emissions and capture their hotspots. Here, we established an intelligent modelling and visualization system driven by ITS traffic data for real-world, on-road vehicle emissions. Based on the HPC platform (named “City Brain”) and an agile Web Geographic Information System (WebGISs), this system can map real-time (hourly), hyperfine (10~1000 m) vehicle emissions (e.g., PM2.5, NOx, CO, and HC) and associated traffic states (e.g., vehicle-specific categories and traffic fluxes) over the Xiaoshan District in Hangzhou. Our results show sharp variations in on-road vehicle emissions on small scales, which even fluctuated up to 31.2 times within adjacent road links. Frequent and widespread emission hotspots were also exposed. Over custom spatiotemporal scopes, we virtually investigated and visualized the impacts of traffic control policies on the traffic states and on-road vehicle emissions. Such results have important implications for how traffic control policies should be optimized. Integrating this system with chemical transport models and air quality measurements would bridge the technical gap between air pollutant emissions, concentrations, and human exposure

Operational Data-Driven Intelligent Modelling and Visualization System for Real-World, On-Road Vehicle Emissions—A Case Study in Hangzhou City, China

On-road vehicle emissions play a crucial role in affecting air quality and human exposure, particularly in megacities. In the absence of comprehensive traffic monitoring networks with the general lack of intelligent transportation systems (ITSs) and big-data-driven, high-performance-computing (HPC) platforms, it remains challenging to constrain on-road vehicle emissions and capture their hotspots. Here, we established an intelligent modelling and visualization system driven by ITS traffic data for real-world, on-road vehicle emissions. Based on the HPC platform (named “City Brain”) and an agile Web Geographic Information System (WebGISs), this system can map real-time (hourly), hyperfine (10~1000 m) vehicle emissions (e.g., PM2.5, NOx, CO, and HC) and associated traffic states (e.g., vehicle-specific categories and traffic fluxes) over the Xiaoshan District in Hangzhou. Our results show sharp variations in on-road vehicle emissions on small scales, which even fluctuated up to 31.2 times within adjacent road links. Frequent and widespread emission hotspots were also exposed. Over custom spatiotemporal scopes, we virtually investigated and visualized the impacts of traffic control policies on the traffic states and on-road vehicle emissions. Such results have important implications for how traffic control policies should be optimized. Integrating this system with chemical transport models and air quality measurements would bridge the technical gap between air pollutant emissions, concentrations, and human exposure