Direct individual analysis using Scanning Electron Microscopy combined with online observation was conducted to examine the S-rich particles in PM2.5 of two typical polluted haze episodes in summer and winter from 2014 to 2015 in Beijing. Four major types of S-rich particles, including secondary CaSO4 particles (mainly observed in summer), S-rich mineral particles (SRM), S-rich water droplets (SRW) and (C, O, S)-rich particles (COS) were identified.We found the different typical morphologies and element distributions of S-rich particles and considered that (C, O, S)-rich particles had two major mixing states in different seasons. On the basis of the S-rich particles’ relative abundances, S concentrations and their relationships with PM2.5 as well as the seasonal comparison, we revealed that the S-participated formation degrees of SRM and SRW would enhance with increasing PM2.5 concentration. Moreover, C-rich matter and sulfate had seasonally different but significant impacts on the formation of COS.
输氧抽气技术是基于好氧降解原理对垃圾填埋场中固体废弃物实施原位处理的先进环保技术。采用该技术对垃圾填埋场中固体废弃物减量化研究,总结了技术应用中的关键参数:有效影响半径,最佳真空度等。结果表明抽气流量大小与影响半径密切相关,抽气影响半径建议取20~25 m,抽气系统最优负压为20 k Pa,同时在技术应用过程中应保证垃圾场表层覆盖层的密封性能,尽量减少空气的渗入。输氧抽气技术在垃圾场的成功实施为同类型场地治理提供工程技术参考。 相似文献
Precipitation is of great importance to agriculture, environment and ecosystem as a regular precipitation pattern is usually vital to healthy plants; excessive or insufficient rainfall can be harmful. Periodic patterns of precipitation can be studied based on regularly observed data over time. Since regularly observed precipitation data are generally skewed with many zeros, two common analysis approaches have been proposed recently. One approach investigates precipitation using a two-part model where the occurrence and positive amount of precipitation are analyzed separately (Piantadosi et al. in Environ Model Assess 14:431–438, 2009), whereas the other approach handles occurrence and amount simultaneously using a Tweedie’s compound Poisson model for independent observations (Hasan and Dunn in Int J Climatol 32:1006–1017, 2012). The former approach fails to maintain the regular temporal structure of serially observed precipitation, whereas the latter approach ignores serial dependence. As there is generally substantial serial correlation in the observed sequence of precipitation data over time, we introduce a compound Poisson state-space model with serially correlated random effects for daily precipitation data. This approach characterizes both occurrence and amount of precipitation simultaneously while accounting for the corresponding serial correlation. Our main results depend only on the first- and second-moment assumptions of unobserved random effects. We illustrate our method with the analysis of the daily precipitation data recorded at Mount Washington, NH, USA. 相似文献