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.     

铜氰溶液的电子束辐照降解

研究了铜氰溶液中总氰初始浓度、pH、铜浓度等对总氰降解效率的影响,比较了KCN溶液和铜氰溶液的总氰降解率,初步推断了铜氰溶液在辐照过程中的降解机制.实验结果表明,在同样的辐照剂量下,随着总氰初始浓度的增加,总氰降解率降低,相应的总氰降解G值增大;铜浓度越低,总氰降解率越高;pH越小的铜氰溶液总氰降解率越高.电子束辐照KCN溶液和铜氰溶液的总氰降解反应属于表观一级反应.在相同的辐照剂量下,KCN溶液的总氰降解率比铜氰溶液要高得多.铜氰溶液的电子束辐照降解过程非常复杂,主要是铜氰络合离子逐步离解出的CN-与·OH的反应.     

碱激发胶凝材料及其固化Pb^2＋的试验研究

对碱激胶凝材料（碱-偏高岭土、碱-矿渣和碱-粉煤灰）与水-水泥体系固化Ph^2＋进行了试验研究,其中水-水泥体系为对比样。结果表明：对于相同稠度的碱激发胶凝材料,即使Pb^2＋含量达到2．4％,除了碱-粉煤灰的抗压强度略低外,其他都达到30MPa以上,满足填埋或做建筑基材的使用要求;与水泥相比,碱激发胶凝材料能显著降低重金属离子（Ph^2＋）浸出浓度,其规律性与其NH4交换容量大小的规律性一致,与其固化体的抗压强度的大小没有相关性。     

Carbon isotope analysis for source identification of atmospheric formaldehyde and acetaldehyde in Dinghushan Biosphere Reserve in South China

Formaldehyde and acetaldehyde are two most abundant carbonyls in ambient air. Biogenic emission has been proposed as a significant source other than anthropogenic emissions and atmospheric secondary formation. Here at a forest site in South China, the carbon isotopic compositions of formaldehyde and acetaldehyde emitted from leaves of three tree species (Litsea rotundifolia, Canarium album and Castanea henryi) were measured in comparison with the bulk carbon isotopic compositions of tree leaves. δ¹³C data of the emitted aldehydes (from ?31‰ to ?46‰) were quite different for tree species, which were all more depleted in ¹³C than the tree-leaf bulk δ¹³C values (from ?27‰ to ?32‰). Formaldehyde in ambient air at the forest site had δ¹³C values different from those of leaf-emitted formaldehyde, indicating other sources for ambient formaldehyde apart from direct emission from leaves, most probably the photooxidation of biogenic hydrocarbon like isoprene and monoterpene. The δ¹³C differences of acetaldehyde between ambient data and those of tree leaves emission were less than 1‰, implying direct biogenic emission as the dominant source.     

Observations of Atmospheric Nitrogen and Phosphorus Deposition During the Period of Algal Bloom Formation in Northern Lake Taihu,China

Cyanobacterial blooms in Lake Taihu occurred at the end of April 2007 and had crucial impacts on the livelihood of millions of people living there. Excessive nutrients may promote bloom formation. Atmospheric nitrogen (N) and phosphorus (P) deposition appears to play an important role in algal bloom formation. Bulk deposition and rain water samples were collected respectively from May 1 to November 30, 2007, the period of optimal algal growth, to measure the bulk atmospheric deposition rate, wet deposition rate, and dry deposition rate for total nitrogen (TN; i.e., all species of nitrogen), and total phosphorus (TP; i.e., all species of phosphorus), in northern Lake Taihu, China. The trends of the bulk atmospheric deposition rate for TN and the wet deposition rate for TN showed double peaks during the observation period and distinct influence with plum rains and typhoons. Meanwhile, monthly bulk atmospheric deposition rates for TP showed little influence of annual precipitation. However, excessive rain may lead to high atmospheric N and P deposition rates. In bulk deposition samples, the average percentage of total dissolved nitrogen accounting for TN was 91.2% and changed little with time. However, the average percentage of total dissolved phosphorus accounting for TP was 65.6% and changed substantially with time. Annual bulk atmospheric deposition rates of TN and TP during 2007 in Lake Taihu were estimated to be 2,976 and 84 kg km⁻² a⁻¹, respectively. The results showed decreases of 34.4% and 78.7%, respectively, compared to 2002–2003. Annual bulk deposition load of TN for Lake Taihu was estimated at 6,958 t a⁻¹ in 2007 including 4,642 t a⁻¹ of wet deposition, lower than the values obtained in 2002–2003. This may be due to measures taken to save energy and emission control regulations in the Yangtze River Delta. Nevertheless, high atmospheric N and P deposition loads helped support cyanobacterial blooms in northern Lake Taihu during summer and autumn, the period of favorable algal growth.     

Endosulfan in China 1—gridded usage inventories

Background, aim, and scope  Endosulfan, an organochlorine pesticide (OCP), is genotoxic in mammalian cells and generally considered to be toxic and classified by the World Health Organization and the US Environmental Protection Agency as priority pollutants and a nominator for inclusion in a future iteration of the persistent organic pollutants treaty. Endosulfan is a currently used pesticide and still being used worldwide. The general trend of total global endosulfan use has increased continuously since the first year when this pesticide was applied. It is critical to create national endosulfan usage/emission inventories for China to carry out source–receptor relation analysis, risk assessment, and other research related to endosulfan in this country. Chinese inventories have been published for some OCPs, such as technical HCH and lindane, DDT, and chlordane; for endosulfan, however, there has not been any usage inventory available on any scales (national or provincial), although endosulfan has been widely used since 1994 in this country. This is the first part of the work. The goal of this paper is to quantify the historical production and usage of endosulfan in China and to compile gridded historical usage inventories of endosulfan for this country. Based on these usage inventories, emission and residue inventories will be created, which is the goal of the second part of the work. Materials and methods  Due to the lack of national production and usage information of endosulfan in China, a method to estimate the use of endosulfan was developed. First, information of crops on which endosulfan is applied and average endosulfan use and annual application frequencies of endosulfan on these crops were collected. Secondly, usage of endosulfan on each crop was estimated using the national cropland area for each province from Chinese government reports. Finally, with the help of GIS (geographic information system), the usage data of this insecticide was allocated to a grid system, with a 1/4° longitude by 1/6° latitude resolution, with a size for each grid cell of approximately 25 km by 25 km. Results and discussion  The use of endosulfan in agriculture in China started on cotton in 1994, and on wheat, tea, tobacco, apples, and other fruits in 1998. Endosulfan usage on cotton, wheat, tea, tobacco, and apples in China has been estimated to be approximately 25,700 t between 1994 and 2004. The province with the highest usage of endosulfan is Henan Province, with a total usage reaching 4,000 t, followed by the uses in Xinjiang Autonomous Region (3,200 t), Shandong Province (3,000 t), Hebei Province (2,100 t), and Anhui Province (1,900 t). Gridded usage inventories of endosulfan at a 1/4° longitude by 1/6° latitude resolution have been created, which indicate that the intensive endosulfan use was in the south of Hebei Province, west of Shandong Province, east of Henan Province, north of Anhui Province, east of Jiangsu Province, and some areas in Yunnan Province and Xinjiang Autonomous Region. General agreement has been found between the usage data from our estimation and the small amount of usage data published in China. Conclusions  This is the first national gridded endosulfan usage inventory produced for China. The annual applications of endosulfan from 1994 to 2004 in China were estimated based on the total areas of major crops, on which endosulfan was applied, and spatial distribution of the application was generated at provincial and prefecture levels. With the help of GIS, endosulfan usage based on prefecture was transferred to a 1/4° longitude by 1/6° latitude gridding system. The satisfaction of the inventories was supported by the consistence between the estimation of the annual usage and the reported annual production of endosulfan. Recommendations and perspectives  This gridded endosulfan usage inventory created in this study will be improved upon availability of new information of endosulfan. The usage inventories can be used to create gridded emission and residue inventories for this insecticide. It is believed that this work will pave the way for further endosulfan studies in China and beyond.     

利用中巴地球资源卫星数据反演武汉市湖泊营养状态指数

以武汉市主要湖泊为例,研究了利用中巴地球资源卫星（CBERS2）数据反演水体营养状态指数（TLI）。研究旨在评估利用中巴地球资源卫星数据来估算内陆水体富营养化程度的可能性。首先利用地面水质监测数据计算武汉市某些湖泊监测点的“真实的”营养状态指数（包括综合营养状态指数和修正的Carlson营养状态指数）,同时,在事先经过辐射校正和几何校正的CBERS2图像上,以9×9像元为采样窗口,提取各个对应地点的灰度值均值（从波段1至波段4）;然后,采用多元逐步回归分析,以各波段灰度值均值为自变量,建立营养状态指数经验遥感反演模型;最后,利用模型对整个湖泊水体的营养化状态指数进行反演,并绘制了其空间分布图。 结果显示,营养状态指数的自然对数值与CBERS2图像各波段灰度值之间存在较好相关关系,回归系数平方值(R²)为0.51。利用反演模型反演得到的湖区水质分布与实际情况基本相符。由于CBERS2图像数据可以从我国许多数据分发中心免费获取,这为低成本的水质遥感监测提供了一条途径。     

Incorporating layer- and local-scale heterogeneities in numerical simulation of unsaturated flow and tracer transport

This study characterizes layer- and local-scale heterogeneities in hydraulic parameters (i.e., matrix permeability and porosity) and investigates the relative effect of layer- and local-scale heterogeneities on the uncertainty assessment of unsaturated flow and tracer transport in the unsaturated zone of Yucca Mountain, USA. The layer-scale heterogeneity is specific to hydrogeologic layers with layerwise properties, while the local-scale heterogeneity refers to the spatial variation of hydraulic properties within a layer. A Monte Carlo method is used to estimate mean, variance, and 5th, and 95th percentiles for the quantities of interest (e.g., matrix saturation and normalized cumulative mass arrival). Model simulations of unsaturated flow are evaluated by comparing the simulated and observed matrix saturations. Local-scale heterogeneity is examined by comparing the results of this study with those of the previous study that only considers layer-scale heterogeneity. We find that local-scale heterogeneity significantly increases predictive uncertainty in the percolation fluxes and tracer plumes, whereas the mean predictions are only slightly affected by the local-scale heterogeneity. The mean travel time of the conservative and reactive tracers to the water table in the early stage increases significantly due to the local-scale heterogeneity, while the influence of local-scale heterogeneity on travel time gradually decreases over time. Layer-scale heterogeneity is more important than local-scale heterogeneity for simulating overall tracer travel time, suggesting that it would be more cost-effective to reduce the layer-scale parameter uncertainty in order to reduce predictive uncertainty in tracer transport.