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Sponge cities are disaster-resilient and sustainable infrastructure, and the emergence of impervious surfaces hinders the construction of sponge cities. In response to the United Nations' 2030 Sustainable Development Goals, the study of the spatio-temporal evolution and influencing factors of impervious surfaces provides an effective basis for the construction of sponge cities. In this paper, multi-source remote sensing images (Landsat 4–5 TM was used in 2000 and 2010, Landsat 8 OLI/TIRS was used in 2020) were used as data sources to construct a multi-feature impervious surface estimation model. By combining and refining the advantages of MISI, NDBI, and BUAI feature indices, we obtained the impervious surface cover of Nanchang City in 2000, 2010, and 2020. And its spatio-temporal evolution characteristics were analyzed by using the ESDA. The main factors of the impervious surface were analyzed by using the ordinary panel data model and the spatial durbin model. Results: (1) From 2000 to 2020, the impervious surface area of Nanchang City increased from 516.13 to 1075.12 km2. The overall impervious surface distribution in Nanchang City expressed a significant neighborhood distribution; (2) socio-economic factors had a positive role in promoting the impervious surface of Nanchang City. Among them, the correlation coefficient of the economic development index (0.2332), real estate investment (0.1518), and gross industrial output value (0.0453) were the most significant in the local areas; (3) the economic development index (0.2307), real estate investment (0.0251), and passenger volume (0.1679) stimulated the growth of impervious surfaces in adjacent areas, and the total population (-0.8074) had a buffering effect on adjacent areas. In order to promote the sustainable development of the region, it is necessary to propose corresponding measures and suggestions based on the impervious surface of Nanchang City. [ABSTRACT FROM AUTHOR]

Objective To study the epidemiological correlation and drug resistance of external factors of infection caused by open injury of limbs to pathogens. Methods: This experiment is a retrospective study. We took the geographical location and climate of Nanchang, Jiangxi Province, China as the background, analyzed 2017 strains of pathogens from 1589 patients with limb trauma infection in a University Affiliated Hospital from 2012 to 2017. Patients were divided into three groups according to the type of incision: I, In‐hospital infection of clean limb incision, II, In‐hospital infection with open injury, III, Community infection with open injury of the limb. Groups II and Groups III were divided into six subgroups according to the causes of trauma, including: accidents from non‐motor vehicles, machinery, cutting/piercing, pedestrian injuries, struck by/against, pedal cycles, and other injuries. We found eight common pathogens of orthopedic infection, which were mainly divided into Gram‐positive bacteria (G+, mainly including Staphylococcus) and Gram‐negative bacteria (G‐, mainly Enterobacteriaceae). The relationship between main pathogens and damage mechanism, apparent temperature and relative humidity was discussed in this study. SPSS v22.0 was used for statistical analysis of the data. Friedman's two‐way ANOVA was used to analyze the difference between the injury mechanism and incidence of pathogenic bacteria. Linear regression was used to determine the trend between the incidence of major pathogens and seasonal temperature and humidity. The level of significance was set as P < 0.05. Results: There was no significant difference in the distribution of pathogens between Groups II and Groups III (P>0.05). The drug resistance of Groups III was significantly higher than that of Groups II and Groups I. G+ bacteria were resistant to cephalosporin, ceftriaxone and other cephalosporins and erythromycin and other macrolides. They were sensitive to vancomycin and linezolid. G‐ were resistant to the first‐ and the second‐generation cephalosporins, including cefotetan and cefazolin, and ampicillin and other penicillins, while they were sensitive to third‐generation cephalosporins, such as ceftazidime, as well as to levofloxacin and other quinolones, meropenem, and other beta‐lactamases. The correlation between the injury mechanism and infection of pathogenic bacteria was not significant. The monthly average apparent temperature and relative humidity were correlated with the infection rate of pathogenic bacteria. Conclusion: In open injury of extremities, apparent temperature and relative humidity is an important risk factor for infection by pathogenic bacteria and the drug resistance of pathogenic bacteria in out‐of‐hospital infection was lower than that of hospital infection. [ABSTRACT FROM AUTHOR]

China is the largest CO 2 emitting country on Earth. During the COVID-19 pandemic, China implemented strict government control measures on both outdoor activity and industrial production. These control measures, therefore, were expected to significantly reduce anthropogenic CO 2 emissions. However, large discrepancies still exist in the estimated anthropogenic CO 2 emission reduction rate caused by COVID-19 restrictions, with values ranging from 10% to 40% among different approaches. Here, we selected Nanchang city, located in eastern China, to examine the impact of COVID-19 on CO 2 emissions. Continuous atmospheric CO 2 and ground-level CO observations from January 1st to April 30th, 2019 to 2021 were used with the WRF-STILT atmospheric transport model and a priori emissions. And a multiplicative scaling factor and Bayesian inversion method were applied to constrain anthropogenic CO 2 emissions before, during, and after the COVID-19 pandemic. We found a 37.1–40.2% emission reduction when compared to the COVID-19 pandemic in 2020 with the same period in 2019. Carbon dioxide emissions from the power industry and manufacturing industry decreased by 54.5% and 18.9% during the pandemic period. The power industry accounted for 73.9% of total CO 2 reductions during COVID-19. Further, emissions in 2021 were 14.3–14.9% larger than in 2019, indicating that economic activity quickly recovered to pre-pandemic conditions. [Display omitted] • A 40% decrease of anthropogenic CO 2 emissions was observed during the 2020 COVID-19 pandemic. • Power industry accounted for 73.9% of total CO 2 reductions during COVID-19. • Anthropogenic CO 2 emissions in 2021 were 14% larger than in 2019. • The recovery of CO 2 emissions between year 2021 and 2019 indicates a fast economic rebound. • Point source location mismatches should be evaluated in city scale emission inversion. [ABSTRACT FROM AUTHOR]