Biodegradation of Poly(Aspartic Acid-Itaconic Acid) Copolymers by Miscellaneous Microbes from Natural Water

Poly(aspartic acid-itaconic acid) copolymers (PAI) is a new scale inhibitor for water treatment. Thus, it is necessary to investigate its biodegradability. The biodegradability of PAI was investigated through CO₂ evolution tests under different conditions based on determination of carbon dioxide production. The investigation results showed that the degradation rate of PAI on day 10 and day 28 were respectively 38.7 and 79.5%, indicating that PAI was one kind of easily biodegradable scale inhibitors. With the increase in the content of itaconic acid in copolymerization process, the biodegradability of PAI was significantly reduced. In addition, the high biodegradability might be attributed to the existence of C–N bone-structure and more –COO–. Finally, Cu²⁺ could decrease the degradation percentage and the enzyme inhibition effect of Cu²⁺ was not the linear effect, but the “low-dosage effect”.     

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。     

不同浓度养殖废水对青萍生长能力的影响

为了探讨废水浓度对青萍(Lemna minor)净化能力、生物质和能量积累能力的影响,以猪场养殖废水为供试废水,分析了在不同废水浓度下青萍对废水中总氮(TN)、氨氮和总磷(TP)的净化能力,青萍的生长情况,以及青萍中碳(C)、氮(N)、磷(P)元素含量和热值的变化情况。研究表明,青萍在1%浓度的废水中表现出最高的污染物净化能力。虽然青萍的C、N、P含量和热值均随废水浓度的增加而增加,但是由于相对增长率以1%的废水中生长的青萍最高,青萍的最高生产力、C和能量的固定能力均出现在1%废水浓度培养的青萍中,其次是5%废水浓度培养的青萍。多项式回归分析表明,可以使青萍获得最大C和能量固定能力的废水浓度为3.4%,对应的氨氮浓度为26 mg/L,TP浓度为3.4 mg/L。研究结果为进一步优化高生物质生产、高污染物去除率的养殖废水-浮萍培养体系提供了一定的基础。