Air pollution has recently become China's highest environmental issue due to the rapid development of industry and urbanization. So far, the precise sources of air pollution of main cities are unknown. To identify sources, we studied air pollution in the Hangzhou city from November 25 to December 11, 2013, at eight monitoring stations. We analyzed PM 2.5 , PM 10 , O 3 , NO 2 , CO, SO 2 , and satellite observations for aerosol optical thickness (PM: particulate matter). Pollution sources were identified by trajectory clustering and receptor models. The results show that during the weekly heavy haze episode, December 3-9, mean concentrations were 293.4 ± 103.2 lg m -3 for PM 2.5 , 376.8 ± 119.4 lg m -3 for PM 10 , 58.0 ± 37.2 lg m -3 for SO 2 , 118.5 ± 39.3 lg m -3 for NO 2 , and 2,429 ± 740 lg m -3 for CO. The back trajectory cluster analysis indicates that the predominant clusters are south (37.1 %) and southeast (28.6 %) during the weekly heavy haze episode. The results of the receptor models show that the sources affecting formation of the extremely high PM 2.5 in Hangzhou are mainly located in the southeastern coast of Zhejiang and Fujian provinces, north part of Jiangxi, and central part of Jiangsu province. Rather than local emissions, it is also found that air mass pathways and cross-border transports control high PM 2.5 concentrations and formation in Hangzhou. Therefore, it is necessary to implement air pollution control for all industrial areas at local, regional, and national scales in China.

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Background Dynamic arterial elastance (Eadyn) has been extensively considered as a functional parameter of arterial load. However, conflicting evidence has been obtained on the ability of Eadyn to predict mean arterial pressure (MAP) changes after fluid expansion. This meta-analysis sought to assess the predictive performance of Eadyn for the MAP response to fluid expansion in mechanically ventilated hypotensive patients. Methods We systematically searched electronic databases through November 28, 2020, to retrieve studies that evaluated the association between Eadyn and fluid expansion-induced MAP increases in mechanically ventilated hypotensive adults. Given the diverse threshold value of Eadyn among the studies, we only reported the area under the hierarchical summary receiver operating characteristic curve (AUHSROC) as the primary measure of diagnostic accuracy. Results Eight observational studies that included 323 patients with 361 fluid expansions met the eligibility criteria. The results showed that Eadyn was a good predictor of MAP increases in response to fluid expansion, with an AUHSROC of 0.92 [95% confidence interval (CI) 0.89 to 0.94]. Six studies reported the cut-off value of Eadyn, which ranged from 0.65 to 0.89. The cut-off value of Eadyn was nearly conically symmetrical, most data were centred between 0.7 and 0.8, and the mean and median values were 0.77 and 0.75, respectively. The subgroup analyses indicated that the AUHSROC was slightly higher in the intensive care unit (ICU) patients (0.96; 95% CI 0.94 to 0.98) but lower in the surgical patients in the operating room (0.72; 95% CI 0.67 to 0.75). The results indicated that the fluid type and measurement technique might not affect the diagnostic accuracy of Eadyn. Moreover, the AUHSROC for the sensitivity analysis of prospective studies was comparable to that in the primary analysis. Conclusions Eadyn exhibits good performance for predicting MAP increases in response to fluid expansion in mechanically ventilated hypotensive adults, especially in the ICU setting.

As the largest Chinese city by population and the largest city proper by population in the world, Shanghai has frequently suffered the heavy haze in recent years. In this study, the observational data (PM 2.5 , PM 10 , O 3 , NO 2 , CO and SO 2 ) at the ten urban monitoring stations in Shanghai from November 25 to December 9, 2013, were used to analyze the haze pollution. The source contributions of PM 2.5 in Shanghai were identified by trajectory clustering and hybrid receptor models (potential source contribution function (PSCF) and concentration weighted trajectory (CWT)). The results showed that for the whole study period, the ranges of pollutant concentrations are 2. . It was found that PM 2.5 contributed more than 80% of PM 10 for the whole period except the relatively clean period in which only 45% of PM 10 is PM 2.5 . The model analyses show that clean air masses reaching at Shanghai were from the far away regions like Mongolia and Inner Mongolia with the high mean wind speed (fast air masses). On the other hand, the heavy haze air masses were mainly from the nearby industrialized and urbanized provinces with industrial cities. It was found that the formation of the extremely heavy haze from December 5 to 7 in Shanghai was mainly because of the air pollution transported from the nearby provinces (i.e., Anhui, Jiangsu, Zhejiang) and central part provinces (such as Shandong, Hebei) of eastern China. The correlation analyses among PM 2.5 and other pollutants show that the PM 2.5 formation in Shanghai is affected by the sources similar to those of CO such as combustion, industry, mobile and oxidation of hydrocarbons. Finally, the controlling strategies are discussed on the basis of this result.

BackgroundProton pump inhibitors (PPI) and histamine 2 receptor antagonists (H2RA) have been widely used as stress ulcer prophylaxis (SUP) in critically ill patients, however, its efficacy and safety remain unclear. This study aimed to assess the effect of SUP on clinical outcomes in critically ill adults.MethodsLiterature search was conducted in PubMed, EMBASE, Web of Science, and the Cochrane database of clinical trials for randomized controlled trials (RCTs) that investigated SUP, with PPI or H2RA, versus placebo or no prophylaxis in critically ill patients from database inception through 1 June 2019. Study selection, data extraction and quality assessment were performed in duplicate. The primary outcomes were clinically important gastrointestinal (GI) bleeding and overt GI bleeding. Conventional meta-analysis with random-effects model and trial sequential analysis (TSA) were performed.ResultsTwenty-nine RCTs were identified, of which four RCTs were judged as low risk of bias. Overall, SUP could reduce the incident of clinically important GI bleeding [relative risk (RR) = 0.58; 95% confidence intervals (CI): 0.42–0.81] and overt GI bleeding (RR = 0.48; 95% CI: 0.36–0.63), these results were confirmed by the sub-analysis of trials with low risk of bias, TSA indicated a firm evidence on its beneficial effects on the overt GI bleeding (TSA-adjusted CI: 0.31–0.75), but lack of sufficient evidence on the clinically important GI bleeding (TSA-adjusted CI: 0.23–1.51). Among patients who received enteral nutrition (EN), SUP was associated with a decreased risk of clinically important GI bleeding (RR = 0.61; 95% CI: 0.44–0.85; TSA-adjusted CI: 0.16–2.38) and overt GI bleeding (RR = 0.64; 95% CI: 0.42–0.96; TSA-adjusted CI: 0.12–3.35), but these benefits disappeared after adjustment with TSA. Among patients who did not receive EN, SUP had only benefits in reducing the risk of overt GI bleeding (RR = 0.37; 95% CI: 0.25–0.55; TSA-adjusted CI: 0.22–0.63), but not the clinically important GI bleeding (RR = 0.27; 95% CI: 0.04–2.09).ConclusionsSUP has benefits on the overt GI bleeding in critically ill patients who did not receive EN, however, its benefits on clinically important GI bleeding still needs more evidence to confirm.