Investigation on sampling artifacts of particle associated PAHs using ozone denuder systems

Polycyclic aromatic hydrocarbons （PAHs） are complex organic compounds which are identified as significant carcinogenic to human health. PAHs （mainly in particle phase） are susceptible to atmospheric oxidant gases, especially ozone, nitrogen oxides （NOx）, hydroxyl radical （OH）, and could be degraded on filters during sampling process, leading to an underestimation of ambient PAH concentrations. The goal of this work was to investigate particle associated PAHs sampling artifacts caused by ozone in summer of Beijing. Comparative sampling systems were operated simultaneously during the whole campaign, one with activated carbon ozone denuder, the other being set as conventional sampling system. Activated carbon denuder was testified to be highly efficient to eliminate ozone from air stream. In general, nine particle-bound PAHs observed from conventional sampler were all lower than those from ozone denuder system. The total PAHs （particle phase） concentration was averagely underestimated by 35.9% in conventional sampling procedure. Benzo[a]pyrene （BaP） had the highest percentage of mass loss. founded to have influences Ambient temperature was on PAHs sampling artifacts. High temperature can increase loss of particle associated PAHs during sampling.     

双酚A对斑马鱼精巢性激素生成酶基因表达的影响

为了解环境雌激素对鱼类精巢发育的影响,将成年雄性斑马鱼(Danio rerio)暴露于不同浓度双酚A(0、500、1 000、2 000、4 000μg·L-1)下21 d,并在此基础上,进一步研究了暴露在相同浓度(BPA2 000μg·L-1)不同暴露时间下各基因表达的动力学模式。用荧光定量PCR(qRT-PCR)方法检测试验鱼精巢性激素合成相关细胞色素P450酶类基因(CYP17、CYP11B和CYP19A)以及雌、雄激素受体和促卵泡激素受体(ERα、ERβ、AR和FSHR)基因的表达,用常规组织学方法研究试验鱼精巢结构的变化。结果表明,BPA促进了精巢内源雌激素合成相关酶类基因CYP19A和雌激素受体ERαmRNA、ERβmRNA的表达,且BPA浓度为2 000μg·L-1时3者的表达量显著上调,同时随着暴露时间的延长,具有明显的时间累积效应。1 000μg·L-1BPA可导致斑马鱼精巢CYP17 mRNA的表达量显著下调,BPA2 000μg·L-1暴露12 d引起了CYP11B mRNA表达下调,而随着暴露时间的延长有所回升,但它对精巢AR mRNA的表达却无明显影响。同时,BPA 500μg·L-1引起了FSHR基因的显著上调,在时间动态学上,呈上升趋势。在组织学上,2 000μg·L-1BPA引起斑马鱼精巢生精小管内精子浓缩严重,精原细胞(Sg)和精母细胞(PS和SS)数目都有所减少,BPA 4 000μg·L-1组斑马鱼精巢退化。因此,BPA可诱导精巢内源雌激素合成和雌激素受体的表达,提高雌激素效应,通过抑制CYP17基因的表达,一定程度上抑制雄激素的合成,从而引起斑马鱼精巢组织的破坏。同时,据实验数据推测,雄激素的下调可能启动了下丘脑-垂体-性腺(HPG)轴的负反馈调节机制,而此作用机制还有待进一步研究。     

内质网应激介导的凋亡在PBDE-47致大鼠海马损伤中的作用

为了明确内质网应激(endoplasmic reticulum stress,ERS)介导的凋亡在2,2’,4,4’-四溴联苯醚(2,2’,4,4’-tetrabromodiphenylether,PBDE-47)致大鼠神经毒性中的作用,在脑发育的突增期(出生第10天),分别用0 mg·kg~(-1)、1 mg·kg~(-1)、5 mg·kg~(-1)和10mg·kg~(-1)PBDE-47进行单次灌胃染毒,并从出生第8天开始每天给予150 mg·kg~(-1)ERS抑制剂4-苯基丁酸(phenylbutyric acid,PBA),持续3周。仔鼠出生第8周末,从对照组、10 mg·kg~(-1)PBDE-47处理组、150 mg·kg~(-1)PBA处理组和150 mg·kg~(-1)PBA+10mg·kg~(-1)PBDE处理组中随机选取8只大鼠进行水迷宫实验。然后将所有动物断头处死,分离大鼠海马组织,观察其海马组织形态学改变、测定ERS标志分子(GRP78、IRE1和CHOP)和凋亡相关蛋白Cyt c的表达水平。结果显示,10 mg·kg~(-1)PBDE-47处理可导致雌性大鼠海马CA4区细胞排列紊乱、锥形神经细胞数量减少甚至消失,尼氏小体数量减少,大鼠逃避潜伏期延长(P0.05)。5和10 mg·kg~(-1)PBDE-47处理可显著上调ERS相关蛋白IRE1和CHOP的表达水平(P0.05),并明显增强海马组织凋亡相关蛋白Cyt c的表达。PBA干预可明显降低PBDE-47诱导的IRE1和CHOP以及Cyt c蛋白的表达水平,提示PBDE-47可通过ERS介导凋亡,导致大鼠海马组织损伤,从而影响大鼠学习记忆功能。     

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.     

Long-run dynamics of renewable energy consumption on carbon emissions and economic growth in the European union

This paper examines long-run and short-run dynamics of renewable energy consumption on carbon dioxide (CO₂) emissions and economic growth in the European Union. This study employs cointegration tests, Granger causality tests and vector error correction estimates to examine the direction of Granger causality, the long-run dynamics of economic growth and energy variables on carbon emissions. This study analyses time series data from the World Development Indicators over the period from1961 to 2012. The results of this study support a link between renewable energy consumption, economic growth, industrialization, exports and CO₂ emissions in the long-run and short-run. The results support that the sign of the long-run dynamics from the endogenous variables to the CO₂ emissions variable is negative and significant, which implies that the energy and environmental policies of the European Union aimed at curbing CO₂ emissions must have been effective in the long-term. Furthermore, renewable energy consumption and exports have significant negative impact on CO₂ emissions in the short-run. However, industrialization and economic growth have positive impact on CO₂ emissions in the short-run. The results suggest that both economic growth and industrialization must have been achieved at the cost of harming the environment. The finding suggests that the increasing consumption of renewable energy tends to play an important role in curbing carbon emissions in the region.     

Compositional data analysis and geochemical modeling of CO2–water–rock interactions in three provinces of Korea

Environmental Geochemistry and Health - The CO2-rich spring water (CSW) occurring naturally in three provinces, Kangwon (KW), Chungbuk (CB), and Gyeongbuk (GB) of South Korea was classified based...     

典型类雌激素的降解方法及其影响因素研究进展

类雌激素是一类新兴污染物,目前在环境中普遍存在,且在极低浓度下可对生物体和环境产生重大威胁.本文重点介绍了酚类、天然类固醇类和邻苯二甲酸酯等3种典型类雌激素的来源、环境分布特征及其生物毒性,综述了典型类雌激素的最新化学降解法(直接光解法和高级氧化法)和生物降解法(菌类降解法和藻类降解法)及其特点.直接光解法操作简便,但降解效果不佳;高级氧化法能快速高效地去除类雌激素,但操作相对复杂、影响因素较多;生物降解法的应用性强,但处理周期长、受环境因素影响大.此外还阐述了类雌激素的降解机理及其影响因素,最后对典型类雌激素的多种高效联合降解技术提出了展望.     

武汉市湖泊蓝藻分布影响因子分析

对武汉市15个浅水湖泊在不同水期的浮游植物进行调查,同时监测相应的环境因子指标;以蓝藻物种多度及生物量数据和9个环境因子进行了典范对应分析(CCA).物种鉴定结果表明武汉市湖泊蓝藻的常见属有微囊藻(Microcystis)、螺旋藻(Spirulina)、平裂藻(Merismopedia)和色球藻(Chroococcus)等.CCA分析结果表明水温、水深、pH、浮游动物生物量是影响城市浅水小型湖泊蓝藻种类组成及分布的主要因子,同时绿藻生物量等对蓝藻组成分布也有一定的影响;由于武汉市浅水湖泊的高营养盐浓度,总磷及氮磷比不再是蓝藻生长的限制因子.＃