Chemical composition of precipitation and its sources in Hangzhou, China

To understand the origin and chemical characteristics of precipitation in Hangzhou, rainwater samples were collected from June 2006 to May 2008. All samples were analyzed for pH, electrical conductivity, and major ions (NH??, Ca2?, Mg2?, Na?, K?, SO?2?, NO??, F?, and Cl?). Acidification of precipitation in Hangzhou was serious with volume-weighted mean pH value of 4.5, while frequency of acid rain was 95%. The calculated SO?2?/NO?? ratio in Hangzhou precipitation was 2.87, which indicated that the precipitation of Hangzhou belonged to sulfate-based acid rain. The results of acid neutralization analysis showed that not all the acidity in the precipitation of Hangzhou was neutralized by alkaline constituents. The results of sea salt contribution analysis showed that nearly all SO?2?, Ca2?, and Mg2? and 33.7% of K? were of non-sea origins, while all Na? and Cl? and 66.3% of K? originated from sea sources. The principal component analysis which was used to analyze the sources of various ions indicated that chemical compositions of precipitation in Hangzhou mainly came from terrestrial sources, factory emissions, fuel wood burning, and marine sources.     

A large-scale outdoor atmospheric simulation smog chamber for studying atmospheric photochemical processes: Characterization and preliminary application

Understanding the formation mechanisms of secondary air pollution is very important for the formulation of air pollution control countermeasures in China. Thus, a large-scale outdoor atmospheric simulation smog chamber was constructed at Chinese Research Academy of Environmental Sciences (the CRAES Chamber), which was designed for simulating the atmospheric photochemical processes under the conditions close to the real atmospheric environment. The chamber consisted of a 56-m³ fluorinated ethylene propylene (FEP) Teflon film reactor, an electrically-driven stainless steel alloy shield, an auxiliary system, and multiple detection instrumentations. By performing a series of characterization experiments, we obtained basic parameters of the CRAES chamber, such as the mixing ability, the background reactivity, and the wall loss rates of gaseous compounds (propene, NO, NO₂, ozone) and aerosols (ammonium sulfate). Oxidation experiments were also performed to study the formation of ozone and secondary organic aerosol (SOA), including α-pinene ozonolysis, propene and 1,3,5-trimethylbenzene photooxidation. Temperature and seed effects on the vapor wall loss and SOA yields were obtained in this work: higher temperature and the presence of seed could reduce the vapor wall loss; SOA yield was found to depend inversely on temperature, and the presence of seed could increase SOA yield. The seed was suggested to be used in the chamber to reduce the interaction between the gas phase and chamber walls. The results above showed that the CRAES chamber was reliable and could meet the demands for investigating tropospheric chemistry.     

铝穹顶储罐雷击损伤及燃爆风险研究

针对铝穹顶储罐雷击问题,开展了回击后长持续时间雷电流分量作用下的铝合金板烧蚀损伤试验研究,分析了铝穹顶储罐损伤及油气燃爆风险。结果表明:当直流分量为164 C时,2 mm铝板发生穿孔,铝穹顶储罐存在雷击燃爆风险;当直流分量≤157 C时,未穿孔,背面温度最高为480℃,存在燃爆风险,铝穹顶储罐存在雷击燃爆风险;当直流分量196 C时,4 mm铝板发生穿孔;当直流分量为196 C时,6 mm铝板未发生穿孔,背面温升为125℃,小于205℃,铝穹顶储罐不存在雷击燃爆风险。     

A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning

• A spectral machine learning approach is proposed for predicting mixed antibiotic. • Pretreatment is far simpler than traditional detection methods. • Performance of the model is compared in different influencing factors. • Spectral machine learning is promising in the detection of complex substances. Antibiotics are widely used in medicine and animal husbandry. However, due to the resistance of antibiotics to degradation, large amounts of antibiotics enter the environment, posing a potential risk to the ecosystem and public health. Therefore, the detection of antibiotics in the environment is necessary. Nevertheless, conventional detection methods usually involve complex pretreatment techniques and expensive instrumentation, which impose considerable time and economic costs. In this paper, we proposed a method for the fast detection of mixed antibiotics based on simplified pretreatment using spectral machine learning. With the help of a modified spectrometer, a large number of characteristic images were generated to map antibiotic information. The relationship between characteristic images and antibiotic concentrations was established by machine learning model. The coefficient of determination and root mean squared error were used to evaluate the prediction performance of the machine learning model. The results show that a well-trained machine learning model can accurately predict multiple antibiotic concentrations simultaneously with almost no pretreatment. The results from this study have some referential value for promoting the development of environmental detection technologies and digital environmental management strategies.     

Spatiotemporal changes and driving factors of vegetation in 14 different climatic regions in the global from 1981 to 2018

Environmental Science and Pollution Research - Climate change affects the change of vegetation, and the analysis of vegetation change and its drivers in different globe climate zones is important...     

Spatiotemporal dynamics of vegetation in China from 1981 to 2100 from the perspective of hydrothermal factor analysis

Environmental Science and Pollution Research - The increased growth of vegetation has the potential to slow global climate warming. Therefore, analyzing and predicting the response assessment of...     

Numerical simulation and experimental research of fractal suspended carrier based on nonlinear equation

Environmental Science and Pollution Research - The geometric structure of the suspended carrier is an important factor that directly affects the effluent quality of the moving bed biofilm reactor,...     

Drivers and barriers to engage enterprises in environmental management initiatives in Suzhou Industrial Park, China

Small- and medium-sized enterprises (SMEs) play an important role in sustainable development not only for their significant contribution to China’s economy, but also for their large share of total discharged pollutants. Therefore, this research takes the enterprises in Suzhou Industrial Park, China as the case study to investigate the environmental management practices of SMEs, and identify drivers and barriers to engaging businesses in environmental management initiatives. It is shown that, as in other countries, SMEs are less active in adopting environmental management initiatives than larger companies. Legislation remains the key driver to engage SMEs in environmental management initiatives. Based on the analysis, policy recommendations are also presented.     

高含水油泥调质脱稳用剂

高含水油泥主要来自于页岩气田、油气田和炼厂的污水处理场（站）,含水率一般高达90%以上,成分复杂、乳化严重,是性质稳定的多相体系。破胶脱稳是高含水油泥处理处置与利用的前提,也是减量化的瓶颈,其核心是调质脱稳用剂的优选。介绍了高含水油泥的性质及调质脱稳的原理和方法,阐述了高含水油泥调质脱稳用剂的研究及应用现状,总结了助滤剂、氧化剂、电解质、破乳剂、絮凝剂和微乳化剂等常用药剂的调质机理、适应范围、研究实例和优缺点,并提出了调质脱稳用剂未来的发展方向。     

洛阳市大气污染特征与相关气象要素24小时变化分析

通过对洛阳市环境空气监测资料的统计,归纳出洛阳市区日内24小时环境空气污染变化特征,并结合同步气象观测资料综合分析,找出了二者之间的联系特征。引入气温日较差这一便于观测、预报的气象要素表征空气污染物垂直扩散特征,为今后进行污染预报提供了新的思路。