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.     

Suitable error evaluation criteria selection in the wind energy assessment via the K-means clustering algorithm

In this paper, wind energy potential of four locations in Xinjiang region is assessed. The Weibull distribution as well as the Logistic and the Lognormal distributions are applied to describe the distributions of the wind speed at different heights. In determining the parameters in the Weibull distribution, four intelligent parameter optimization approaches including the differential evolutionary, the particle swarm optimization, and two other approaches derived from these two algorithms and combined advantages of these two approaches are employed. Then the optimal distribution is chosen through the Chi-square error (CSE), the Kolmogorov–Smirnov test error (KSE), and the root mean square error (RMSE) criteria. However, it is found that the variation range of some criteria is quite large, thus these criteria are analyzed and evaluated both from the anomalous values and by the K-means clustering method. Anomaly observation results have shown that the CSE is the first one should be considered to be eliminated from the consequent optimal distribution function selection. This idea is further confirmed by the K-means clustering algorithm, by which the CSE is clustered into a different group with KSE and RMSE. Therefore, only the reserved two error evaluation criteria are utilized to evaluate the wind power potential.     

基于效果-效率-适应性的养殖废弃物资源化利用管理模式评价框架构建及初步应用

由于产业发展正处于转型阶段,我国畜禽养殖污染治理中的管理问题比技术问题更加突出,选择适宜的组织管理模式尤其重要。融合了效果分析、效率评价和适应性分析,尝试构建一个养殖废弃物资源化利用管理模式综合评价框架,并选择太湖流域4个不同类型的典型案例进行方法例证。研究表明,传统的养殖企业主导型管理模式应用于小型、分散养殖废弃物资源化利用时面临着经济效率低、适应性差难题;农村废弃物处理中心、种植企业和有机肥企业的参与可以改善小型、分散养殖废弃物资源化利用的经济效率;综合考虑效果、效率与适应性,种植企业、有机肥企业主导型模式的适宜推广范围更广。应根据不同地区种植-养殖产业关系、污染治理需求、经济发展水平与政府投入能力等,灵活组合应用不同管理模式。     

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.     

Comprehensive Environmental Investigation at Former Industrial/Petroleum Underground Storage Tank Sites in Long Beach,CA: A Forensic Perspective

Two industrial sites were investigated based on years of available hydrogeologic information and monitoring data for soil and groundwater. Collected data were forensically evaluated using age-dating and fingerprinting methods. The previous business uses of the project sites were as a gas station, laundry/dry-cleaning service, and car wash with petroleum underground storage tanks (USTs). As a result, these sites were exposed to a number of toxic contaminants at relatively high concentrations. Source control was necessary for successful remediation and the ultimate removal of the remaining compounds from these industrial sites. Although contaminated soil around the source was excavated during the remedial action and the high concentrations of contaminants were reduced, typical groundwater contaminants such as petroleum hydrocarbons as gasoline (TPH-G), benzene, toluene, ethylbenzene, xylenes (BTEX), and oxygenates including methyl tert-butyl ether (MTBE), diisopropyl ether (DIPE), ethyl tert-butyl ether (ETBE), tert-amyl methyl ether (TAME), and tert-butyl alcohol (TBA) were persistently found at the studied sites around the source points. The plume and concentration of contaminants had changed their shapes and strength for all monitoring periods. Thus, additional source control seems to be a requirement for the complete removal of source contamination, which must be ascertained with groundwater and soil monitoring on a regular time base. For the study sites, monitored natural attenuation was relatively feasible for the long-term plan; however, it did not offer a perfect remediation solution for an ultimate goal because of residual toxic compounds that might have affected the surrounding residential areas at higher concentrations than their health limits. Therefore, as a remediation strategy, the combination of clean-up technology and natural attenuation with monitoring activities are more highly recommended than either clean-up or natural attenuation used separately.     

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。     

阳宗海沉积物中磷的稳定性

絮凝剂(Fe Cl3)治理阳宗海砷污染的过程中,水体中的磷被大量沉积进入底泥,弄清其是否会在环境条件变化后发生反溶形成二次污染对湖泊富营养化防治具有重要意义。通过模拟实验模拟泥-水界面p H变化、物理扰动和微生物活动的变化,旨在分析这些环境因子变化对沉积物中磷溶出的影响。结果表明,阳宗海水体在目前这种磷浓度下,底泥的磷会随时间的增加而溶出再次造成水体磷污染,当暴露在清洁水条件下时磷的释放率会增加;这种由于絮凝剂吸附沉淀到湖底的磷,在上覆水p H增加0.5时底泥磷解吸附释放速率增加;每年秋季湖泊上下层水的交换活动会造成水体磷浓度的季节性升高,形成不可忽视的磷内源。而夏季水温分层导致湖泊底部厌氧,使厌氧微生物活性增强,这将进一步促进底泥中磷的释放。     

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。