Analysis of metal(loid) pollution and possibilities of electrokinetic phytoremediation of abandoned coking plant soil.

In this study, the spatial distribution of eight metal(loid)s in the soil of an abandoned coking plant in Shanxi, China, was mapped, and the ecological and health risks of the coking plant were assessed. The results showed that the soil Pb content of the coking plant greatly exceeded the background value, and Hg, Cd and Pb were the most polluting factors contributing to the considerable ecological risk level. There was also a non-carcinogenic risk in the coking plant, in which oral intake was the main pathway, and As, Pb and Cr were the main contributors. As the main contributor to ecological risk and non-carcinogenic risks and the most polluting metal, Pb was selected as a priority pollutant in the coking plant. Based on the detected concentration of Pb in the coking plant soil and in consideration of phytostabilization, ryegrass, alfalfa and castor were employed to study the phytoremediation and electrokinetic-enhanced phytoremediation effect in a series of Pb-contaminated soils (0, 100, 200, 300 and 400 mg/kg). It was found that the underground parts of alfalfa and castor had stronger Pb enrichment ability, and their biomass and Pb absorption capacity were improved in electrokinetic remediation methods. The Pb absorption capacities of the tested plants and the promotion efficiencies of electrokinetic-enhanced phytoremediation followed the order castor > ryegrass > alfalfa. Under the optimal electrical conditions, the remediation efficiency of castor was increased by 106 %, 83 %, 51 % and 48 % in 100, 200, 300, and 400 mg/kg Pb-contaminated soils, respectively.

Prehospital Identification of Stroke Subtypes in Chinese Rural Areas.

BACKGROUND: Differentiating intracerebral hemorrhage (ICH) from cerebral infarction as early as possible is vital for the timely initiation of different treatments. This study developed an applicable model for the ambulance system to differentiate stroke subtypes. METHODS: From 26,163 patients initially screened over 4 years, this study comprised 1989 consecutive patients with potential first-ever acute stroke with sudden onset of the focal neurological deficit, conscious or not, and given ambulance transport for admission to two county hospitals in Yutian County of Hebei Province. All the patients underwent cranial computed tomography (CT) or magnetic resonance imaging to confirm the final diagnosis based on stroke criteria. Correlation with stroke subtype clinical features was calculated and Bayes' discriminant model was applied to discriminate stroke subtypes. RESULTS: Among the 1989 patients, 797, 689, 109, and 394 received diagnoses of cerebral infarction, ICH, subarachnoid hemorrhage, and other forms of nonstroke, respectively. A history of atrial fibrillation, vomiting, and diabetes mellitus were associated with cerebral infarction, while vomiting, systolic blood pressure ≥180 mmHg, and age <65 years were more typical of ICH. For noncomatose stroke patients, Bayes' discriminant model for stroke subtype yielded a combination of multiple items that provided 72.3% agreement in the test model and 79.3% in the validation model; for comatose patients, corresponding agreement rates were 75.4% and 73.5%. CONCLUSIONS: The model herein presented, with multiple parameters, can predict stroke subtypes with acceptable sensitivity and specificity before CT scanning, either in alert or comatose patients. This may facilitate prehospital management for patients with stroke.