Research and application of method of oxygen isotope of inorganic phosphate in Beijing agricultural soils

Phosphorus (P) in agricultural ecosystems is an essential and limited element for plants and microorganisms. However, environmental problems caused by P accumulation as well as by P loss have become more and more serious. Oxygen isotopes of phosphate can trace the sources, migration, and transformation of P in agricultural soils. In order to use the isotopes of phosphate oxygen, appropriate extraction and purification methods for inorganic phosphate from soils are necessary. Here, we combined two different methods to analyze the oxygen isotopic composition of inorganic phosphate (δ¹⁸O_P) from chemical fertilizers and different fractions (Milli-Q water, 0.5 mol L^?1 NaHCO₃ (pH = 8.5), 0.1 mol L^?1 NaOH and 1 mol L^?1 HCl) of agricultural soils from the Beijing area. The δ¹⁸O_P results of the water extracts and NaHCO₃ extracts in most samples were close to the calculated equilibrium value. These phenomena can be explained by rapid P cycling in soils and the influence of chemical fertilizers. The δ¹⁸O_P value of the water extracts and NaHCO₃ extracts in some soil samples below the equilibrium value may be caused by the hydrolysis of organic P fractions mediated by extracellular enzymes. The δ¹⁸O_P values of the NaOH extracts were above the calculated equilibrium value reflecting the balance state between microbial uptake of phosphate and the release of intracellular phosphate back to the soil. The HCl extracts with the lowest δ¹⁸O_P values and highest phosphate concentrations indicated that the HCl fraction was affected by microbial activity. Hence, these δ¹⁸O_p values likely reflected the oxygen isotopic values of the parent materials. The results suggested that phosphate oxygen isotope analyses could be an effective tool in order to trace phosphate sources, transformation processes, and its utilization by microorganisms in agricultural soils.     

Deep learning architecture for air quality predictions

With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed. A stacked autoencoder (SAE) model is used to extract inherent air quality features, and it is trained in a greedy layer-wise manner. Compared with traditional time series prediction models, our model can predict the air quality of all stations simultaneously and shows the temporal stability in all seasons. Moreover, a comparison with the spatiotemporal artificial neural network (STANN), auto regression moving average (ARMA), and support vector regression (SVR) models demonstrates that the proposed method of performing air quality predictions has a superior performance.     

Pollination ecology of <Emphasis Type="Italic">Eomecon chionantha</Emphasis> Hance (Papaveraceae), an endemic species in China

Eomecon chionantha Hance, the only species in the genus Eomecon, is an angiosperm species endemic to China. Here we investigated the pollination syndrome, pollinator generalization of E. chionantha. Our results showed that the individual flower longevity of E. chionantha was 3–5 days. The flowering phenology of E. chionantha can be divided into five phases. We found significant differences in the pollination efficiency of the different insects visiting E. chionantha with bees and flies being the most efficient pollinators. This, in addition to floral morphology and phenology, suggests that E. chionantha is generalized on bees and flies and possesses an outcrossing, partially self-compatible and pollinator-dependent breeding system.     

中国工业水污染及其成因的de Bruyn模型分析

选取废水、挥发酚、氰化物、COD、石油类和氨氮为中国工业水污染指标,利用分解分析方法将2004—2010年间的污染变化分解为规模效应、结构效应、污染治理效应、清洁技术效应和广义技术效应。结果显示,这5类效应的平均作用强度分别为2.08%、3.04%、15.61%、17.37%和32.88%,其中规模效应和广义技术效应是影响工业水污染的主导效应。各类效应对不同污染物的作用方向并不完全一致,规模效应促进污染物排放量的增加;结构效应以加重污染为主,污染治理效应和清洁技术效应以减轻污染为主;广义技术效应的平均作用强度和负向作用概率均最大,是现阶段中国工业水污染控制最为有效的手段。     

MFC强化同步短程硝化反硝化工艺的启动

微生物燃料电池(MFC)可在阴极实现反硝化、短程反硝化和同步硝化反硝化并产生电能,但在MFC阴极实现同步短程硝化反硝化的研究尚未见到报道。为了探讨MFC阴极同步短程硝化反硝化工艺的性能,将双室曝气阴极MFC与A/O脱氮工艺结合处理人工模拟低碳氮比废水。通过静置运行15 d使得MFC阴极室亚硝态氮得以积累,氨氧化菌得以富集。随即改为连续运行后第21天成功启动同步短程硝化反硝化MFC;阴极出水氨氮浓度为0.3 mg/L,亚硝态氮浓度为15.9 mg/L,硝态氮浓度为0.6 mg/L,亚硝化率达到95%以上,阴极电极自养反硝化去除率达到50%以上,COD去除率达到85%以上。结果表明,将MFC与同步短程硝化反硝化工艺结合,通过阴极室中氧气得电子获得高p H,可以强化同步短程硝化反硝化工艺,完成生物脱氮的同时回收电能,并具有减少外加碱度的优势。     

羟基磷灰石的制备及其对水中氟离子的吸附

本研究利用红外光谱(FTIR)、扫描电镜(SEM)和X射线粉末衍射(XRD)观察了共沉淀法合成羟基磷灰石(HAP)的形貌及其晶型结构,并探讨了Ca/P摩尔比、反应时间及反应温度等因素对羟基磷灰石吸附水中氟离子性能的影响。结果表明,n(Ca/P)=1.5/1、反应时间1 h、反应温度40℃、陈化时间48 h、煅烧温度200℃、煅烧时间2 h时,HAP除氟效果最佳,吸附效率和吸附容量分别达到68.8%和6.88 mg/g。实验数据Langmuir等温模式拟合效果优于Freundlich模式,热力学参数计算可知,HAP对氟离子的吸附是自发(ΔG00),吸热(ΔH00),熵增(ΔS00)的过程。HAP对氟离子的吸附符合拟二级反应动力学过程。     

γ-Al2O3负载多金属复合催化剂选择性催化还原烟气脱硝

以γ-Al₂O₃作为载体,先后负载CeO₂,MnC₂O₄,Fe（NO₃）₃,CrO₃,Ni（NO₃）₂,NH₄VO₃等多种金属组分制备γ-Al₂O₃负载多金属复合催化剂,并用于模拟烟气的选择性催化还原脱硝。通过SEM和XRD技术对催化剂进行了表征。表征结果显示：Fe,Mn,Cr的添加能增加催化剂的低温催化活性、提高催化剂的N₂选择性;γ-Al₂O₃对活性金属氧化物的负载效果良好。实验结果表明：各金属化合物的最佳加入量为 w（MnC₂O₄·2H₂O）=20%,w（Fe（NO₃）₃·9H₂O）=15%,w（CrO₃）=10%,w（Ni（NO₃）₂·6H₂O）=5%,w（NH₄VO₃）=10%,w（CeO₂）=5%,w（γ-Al₂O₃）=35%;以在最佳正交实验条件下制得的γ-Al₂O₃负载多金属复合物为催化剂,在脱硝反应温度为205 ℃的条件下,NO转化率为96.7%;γ-Al₂O₃负载多金属复合催化剂经5次重复使用,NO转化率仍可稳定在94%左右。     

选择性吸附-高效生物降解法处理含硝基苯与苯酚混合废水

结合NDA-150型树脂（简称树脂）选择性吸附和生物降解的优点,对含硝基苯和苯酚的模拟混合废水（简称混合废水）进行处理。通过树脂的选择性吸附,使混合废水中的硝基苯和苯酚分离,随后用高效菌对树脂所吸附的硝基苯进行生物降解,同时实现树脂的再生。实验结果表明：通过调节混合废水的pH,树脂可有效地将混合废水中的硝基苯和苯酚进行选择性吸附分离;树脂对硝基苯的吸附是可逆的;树脂的再生程度受微生物对可利用硝基苯质量浓度的下限（1．2mg／L）限制;吸附-生物再生循环实验结果表明,该树脂可有效抵抗微生物的生物降解与破坏。