Heavy metal-contaminated sediments posed a serious threat to both human beings and environment. A biosurfactant, rhamnolipid, was employed as the washing agent to remove heavy metals in river sediment. Batch experiments were conducted to test the removal capability. The effects of rhamnolipid concentration, washing time, solution pH, and liquid/solid ratio were investigated. The speciation of heavy metals before and after washing in sediment was also analyzed. Heavy metal washing was favored at high concentration, long washing time, and high pH. In addition, the efficiency of washing was closely related to the original speciation of heavy metals in sediment. Rhamnolipid mainly targeted metals in exchangeable, carbonate-bound or Fe-Mn oxide-bound fractions. Overall, rhamnolipid biosurfactant as a washing agent could effectively remove heavy metals from sediment.
Environmental Science and Pollution Research - Physiological responses of Echinodorus osiris Rataj plant under cadmium (Cd) stress (5 and 15 mg L?1) were studied by... 相似文献
Environmental Science and Pollution Research - Phytoremediation coupled with crop rotation (PCC) is a feasible strategy for remediation of contaminated soil without interrupting crop production.... 相似文献
Environmental Science and Pollution Research - Sediment samples were collected in five marshes (C1, Phragmites australis marsh; C2, P. australis and Cyperus malaccensis marsh; C3, C. malaccensis... 相似文献
Environmental Science and Pollution Research - Water-saving cultivation techniques have been attracting increased attention worldwide. Ridge-furrow mulching system (RFMS), as a prospective... 相似文献
Vehicle-specific power (VSP) has been found to be highly correlated with vehicle emissions. It is used in many studies on emission modeling such as the MOVES (Motor Vehicle Emissions Simulator) model. The existing studies develop specific VSP distributions (or OpMode distribution in MOVES) for different road types and various average speeds to represent the vehicle operating modes on road. However, it is still not clear if the facility- and speed-specific VSP distributions are consistent temporally and spatially. For instance, is it necessary to update periodically the database of the VSP distributions in the emission model? Are the VSP distributions developed in the city central business district (CBD) area applicable to its suburb area? In this context, this study examined the temporal and spatial consistency of the facility- and speed-specific VSP distributions in Beijing. The VSP distributions in different years and in different areas are developed, based on real-world vehicle activity data. The root mean square error (RMSE) is employed to quantify the difference between the VSP distributions. The maximum differences of the VSP distributions between different years and between different areas are approximately 20% of that between different road types. The analysis of the carbon dioxide (CO2) emission factor indicates that the temporal and spatial differences of the VSP distributions have no significant impact on vehicle emission estimation, with relative error of less than 3%.Implications: The temporal and spatial differences have no significant impact on the development of the facility- and speed-specific VSP distributions for the vehicle emission estimation. The database of the specific VSP distributions in the VSP-based emission models can maintain in terms of time. Thus, it is unnecessary to update the database regularly, and it is reliable to use the history vehicle activity data to forecast the emissions in the future. In one city, the areas with less data can still develop accurate VSP distributions based on better data from other areas. 相似文献
Fine particulate matter (PM2.5) levels, carbon dioxide (CO2) levels and particle-number concentrations (PNC) were monitored in train carriages on seven routes of the mass transit railway in Hong Kong between March and May 2014, using real-time monitoring instruments. The 8-h average PM2.5 levels in carriages on the seven routes ranged from 24.1 to 49.8 µg/m3, higher than levels in Finland and similar to those in New York, and in most cases exceeding the standard set by the World Health Organisation (25 µg/m3). The CO2 concentration ranged from 714 to 1801 ppm on four of the routes, generally exceeding indoor air quality guidelines (1000 ppm over 8 h) and reaching levels as high as those in Beijing. PNC ranged from 1506 to 11,570 particles/cm3, lower than readings in Sydney and higher than readings in Taipei. Correlation analysis indicated that the number of passengers in a given carriage did not affect the PM2.5 concentration or PNC in the carriage. However, a significant positive correlation (p < 0.001, R2 = 0.834) was observed between passenger numbers and CO2 levels, with each passenger contributing approximately 7.7–9.8 ppm of CO2. The real-time measurements of PM2.5 and PNC varied considerably, rising when carriage doors opened on arrival at a station and when passengers inside the carriage were more active. This suggests that air pollutants outside the train and passenger movements may contribute to PM2.5 levels and PNC. Assessment of the risk associated with PM2.5 exposure revealed that children are most severely affected by PM2.5 pollution, followed in order by juveniles, adults and the elderly. In addition, females were found to be more vulnerable to PM2.5 pollution than males (p < 0.001), and different subway lines were associated with different levels of risk. 相似文献