薄膜扩散梯度技术在重金属生物有效态监测中的应用

重金属生物有效性是评估重金属元素迁移性、生物可利用性和生态影响的关键参数。薄膜扩散梯度技术(DGT)是一种原位被动采样技术,因其具有原位富集性、形态选择性,可提供被监测物质在监测时间段内的平均浓度等优点,可作为生物对重金属摄取的模拟替代物对环境介质中重金属的生物可利用度进行预测,已被广泛应用于环境介质中重金属生物有效性的测定。研究主要介绍了DGT技术的原理、组成和特点,评述了其近年来在水体、土壤、沉积物中重金属生物有效态应用方面的新进展,提出了DGT技术未来要提高抗生物污染能力及寻找可与DGT技术联用的相关技术的观点。     

丹江湿地国家级自然保护区及其内外区域2000—2015年土地覆被变化分析

为研究2000—2015年丹江湿地国家级自然保护区及其内外生态状况变化和保护成效,基于高分1号数据生产的2m高分辨率遥感影像数据对丹江湿地国家级自然保护区2015年人类活动状况进行分析,基于30 m分辨率的4期TM遥感影像生产的土地覆被数据和基于Modis遥感影像生产的植被覆盖度数据,对淅川县、丹江湿地国家级自然保护区及其核心区的土地覆被状况、土地覆被转类指数及其土地覆被转类途径的主导程度和3个相关区域范围内的生态系统质量以及不同区域土地覆被变化的主要变化原因进行分析。结果表明,保护区核心区的格局和质量在该区域处于最优,且土地覆被变化状况也以核心区转类指数最高;丹江湿地国家级自然保护区内的主要人类活动影响为耕地,其次包括居民点、采石场、养殖场; 15年间,保护区内外土地覆被均呈现转好趋势,但是保护区内变化优于保护区外,保护区核心区优于整个保护区,且转好的主导因素均是耕地变为湿地; 15年间植被覆盖度变化较小。     

秦巴山区降雨侵蚀力时空变化特征

降雨侵蚀力时空变化特征的研究对区域土壤侵蚀风险评估及水土保持规划具有重要的意义。利用秦巴山区及周边地区共63个气象站1961~2016年的逐日降雨量数据计算各站的降雨侵蚀力,借助Kriging空间插值法、Mann-Kendall趋势检验、Pettitt突变检验等方法分析了秦巴山区降雨侵蚀力的时空变化特征。结果表明：秦巴山区年均降雨侵蚀力为3 696 MJ·mm/(hm²·h·a),年内变化呈单峰型,7月最大,占全年的26.6%;四季中,夏季最大,冬季最小。代际间,20世纪80年代的降雨侵蚀力最大,90年代最小。年际间,年降雨侵蚀力存在明显的阶段性,但未表现出显著的趋势性和突变性特征。秦巴山区多年平均降雨侵蚀力呈南高北低的分布格局,不同地区年均降雨侵蚀力变化于787~8 858 MJ·mm/(hm²·h·a)之间;整体而言,年降雨侵蚀力随纬度增加而减小,随海拔升高而减小。     

天然气开采废水处理技术综述

针对天然气开采废水量大、矿化度高、杂质含量多、处理难度大的问题,从预处理技术、COD处理技术、氨氮处理技术、脱盐处理到深度处理技术等对各种处理技术进行分析和对比,说明了各种方法的特点和适用情况等。根据对各种方法的特点总结,结合不同废水的水质特点,提出适宜的组合处理方法,并探讨了综合处理技术在部分地区的工程化应用情况。     

微波强化CWPO工艺CuO_x-CeO_2/GAC催化剂的制备及其对二甲亚砜的降解

以颗粒活性炭(GAC)为载体、铜为活性组分、铈为助剂组分、草酸钠为沉淀剂,采用浸渍焙烧法制得CuO_x-CeO_2/GAC催化剂。以H_2O_2为氧化剂,微波强化催化湿式过氧化氢氧化(CWPO)处理二甲亚砜(DMSO)初始质量浓度为1 000 mg/L的废水,处理3 min后DMSO去除率达93.8%。催化剂第7次使用时DMSO去除率仍保持在75%以上。初始废水pH在3~9范围内,DMSO去除率均在85%以上。助剂Ce的加入提高了催化剂表面活性组分的分散性和稳定性,使催化剂的活性稳定性和使用寿命显著提高。     

聚苯乙烯/CdS/TiO_2双壳层核壳纳米颗粒可见光催化降解甲基橙

采用溶胶-凝胶法,在聚苯乙烯(PS)/CdS核壳纳米颗粒表面包覆致密TiO_2层,制备出具有双壳层结构的PS/CdS/TiO_2纳米复合颗粒,考察了制备条件对材料结构的影响,并利用甲基橙溶液对其光催化性能进行了评价。SEM、TEM、XRD和FTIR分析结果表明,制得的微球单分散性良好,壳层包覆完整,厚度均匀。PS/CdS/TiO_2制备的适宜反应时间为12~18 h,适宜反应pH为7~9。PS/CdS/TiO_2较PS/CdS具有更为优异的可见光催化性能。     

Sb release characteristics of the solid waste produced in antimony mining smelting process

Sb release characteristics of blast furnace slag, mining waste rock and tailing sand were investigated in static immersion and dynamic leaching test. These three kinds of waste samples were collected from the antimony mine in Lengshuijiang, China, produced in mining smelting process. Effects of solid/liquid ratio, sample size and pH of leaching solution on Sb release characteristics were inspected based on the analysis of scanning electron microscope, pH and EC of leachate. The optimal parameters for Sb leaching of each sample were analyzed. For blast furnace slag and mining waste rock, Sb release contents increased along with the decline of solid/liquid ratio. The maximum accumulative release contents were 42.13, 34.26 mg/kg at the solid/liquid ratio of 1:20. While Sb release content for tailing sand decreased first and then increased with the reduction of solid/liquid ratio. When the solid/liquid ratio was 1:5, the accumulative Sb release content reached the most (24.30 mg/kg). Sb release content of mining waste rock increased with the drop of leaching solution pH, with the highest accumulative release content of 26.01 mg/kg at pH 2.0. Sb release contents of blast furnace slag and tailing sand showed positive correlation with the variation of leaching solution pH. The maximum accumulative release contents of these two samples were 215.91 and 147.83 mg/kg, respectively, when leaching solution pH was 7.0. In summary, Sb release capacity of the three samples in descending order was tailing sand, blast furnace slag and mining waste rock. pH and EC of the leachate in dynamic test varied independently with the initial pH of leaching solution while showing close relationship with mineral hydrolysis in the waste.     

Effects of humic acids derived from lignite and cattle manure on antioxidant enzymatic activities of barley root

To investigate the effects of humic acids (HAs) on the ability of plants to defend themselves against oxidative stress, barley was hydroponically cultured in the absence and presence of HAs, and the antioxidant enzymatic activities (catalase, superoxide dismutase, ascorbate and glutathione peroxidases) of root tissue were evaluated. Auxin-like structures in HAs, which were extracted from an oxidation product of lignite (LHA) and compost derived from cattle manure (CHA), were identified by pyrolysis-gas chromatography/mass spectrometry (GC/MS) with tetramethylammonium hydroxide. The LHA, which had the lower molecular weight, was more effective in promoting the growth of barley root than CHA. However, the amounts of auxin-like structures in the CHA were much higher than those for LHA. The antioxidant enzymatic activities were initially decreased in the presence of LHA and CHA at the first day after refreshing the culture solution, but were significantly increased on the second day. The CHA sample, which contained relatively high levels of phenolic acids that contained auxin-like structures, was effective in increasing four types of enzymatic activities, while the activities of catalase and ascorbate peroxidase were increased in the presence of LHA, which contains naphthalene derivatives. These results indicate that using HAs as a supplement can be effective in enhancing antioxidation enzymatic activities, while the appearance of the effects is retarded because of the decomposition and release of auxin-like compounds from HAs by organic acids from the plant roots.     

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.