We studied the biochemical and anaerobic degradation characteristics of 29 types of materials to evaluate the effects of a physical composition classification method for degradable solid waste on the computation of anaerobic degradation parameters, including the methane yield potential (L0), anaerobic decay rate (k), and carbon sequestration factor (CSF). Biochemical methane potential tests were conducted to determine the anaerobic degradation parameters of each material. The results indicated that the anaerobic degradation parameters of nut waste were quite different from those of other food waste and nut waste was classified separately. Paper was subdivided into two categories according to its lignin content: degradable paper with lignin content of <0.05 g g VS?1, and refractory paper with lignin content >0.15 g g VS?1. The L0, k, and CSF parameters of leaves, a type of garden waste, were similar to those of grass. This classification method for degradable solid waste may provide a theoretical basis that facilitates the more accurate calculation of anaerobic degradation parameters. 相似文献
Environmental Science and Pollution Research - Groundwater pollution seriously threatens water resource safety due to high-intensity land use throughout the world. However, the relationship between... 相似文献
Nanoplastics are widely distributed in freshwater environments, but few studies have addressed their effects on freshwater algae, especially on harmful algae. In this study, the effects of polystyrene (PS) nanoplastics on Microcystis aeruginosa (M. aeruginosa) growth, as well as microcystin (MC) production and release, were investigated over the whole growth period. The results show that PS nanoplastics caused a dose-dependent inhibitory effect on M. aeruginosa growth and a dose-dependent increase in the aggregation rate peaking at 60.16% and 46.34%, respectively, when the PS nanoplastic concentration was 100 mg/L. This caused significant growth of M. aeruginosa with a specific growth rate up to 0.41 d?1 (50 mg/L PS nanoplastics). After a brief period of rapid growth, the tested algal cells steadily grew. In addition, the increase in PS nanoplastics concentration promoted the production and release of MC. When the PS nanoplastic concentration was 100 mg/L, the content of the intracellular (intra-) and extracellular (extra-) MC increased to 199.1 and 166.5 μg/L, respectively, on day 26, which was 31.4% and 31.1% higher, respectively, than the control. Our results provide insights into the action mechanism of nanoplastics on harmful algae and the potential risks to freshwater environments.
The Asian Network on Climate Science and Technology (www.ancst.org), in collaboration
with Tsinghua University, held a conference on environmental and climate science, air
pollution, urban planning and transportation in July 2015, with over 40 Asian experts
participating and presentation. This was followed by a meeting with local government and
community experts on the practical conclusions of the conference. Of the papers presented
at the conference a selection are included in this special issue of Journal of Environmental
Science, which also reflects the conclusions of the Paris Climate meeting in Dec 2015, when
the major nations of the world agreed about the compelling need to reduce the upward
trend of adverse impacts associated with global climate change. Now is the time for urban
areas to work out the serious consequences for their populations, but also how they should
work together to take action to reduce global warming to benefit their own communities
and also the whole planet! 相似文献
The status of energy consumption and air pollution in China is serious. It is important to analyze and predict the different fuel consumption of various types of vehicles under different influence factors. In order to fully describe the relationship between fuel consumption and the impact factors, massive amounts of floating vehicle data were used. The fuel consumption pattern and congestion pattern based on large samples of historical floating vehicle data were explored, drivers'' information and vehicles'' parameters from different group classification were probed, and the average velocity and average fuel consumption in the temporal dimension and spatial dimension were analyzed respectively. The fuel consumption forecasting model was established by using a Back Propagation Neural Network. Part of the sample set was used to train the forecasting model and the remaining part of the sample set was used as input to the forecasting model. 相似文献