北京地区明代、清代干旱灾害与气候事件研究

根据北京地区历史时期旱灾资料,利用数学统计方法中的参数区间估计研究了明清时期该地区旱灾的差异和气候的不同。结果表明:在北京地区明代(1368-1643年)276年内,共发生旱灾166次,平均1.66年发生1次;轻度旱灾47次,中度旱灾56次,大旱灾55次,特大旱灾8次。在清代(1644-1912年)的269年里,共发生旱灾62次,平均4.34年发生1次;轻度旱灾24次,中度旱灾25次,大旱灾10次,特大旱灾3次。北京地区明清旱灾差异显著,明代旱灾频次是清代的1.7倍;明代旱灾等级比清代显著高,前者的大旱灾与特大旱灾占旱灾总数的27.6%,后者的大旱与特大旱灾仅占5.7%。明代干旱气候事件频繁,清代干旱气候事件少见。北京地区明代旱灾发生频繁和旱灾等级高的原因是当时气候变干和干旱气候事件频繁出现引起的,指示明代是气候较为干旱的时期。北京地区清代旱灾频次低和等级低是当时降水量较多的结果,指示清代是较湿润的时期。明清时期大旱灾发生时的年均降水量为390mm左右,特大旱灾发生时为230mm左右。</p

青海湖西北部土壤入渗规律研究

通过对青海湖地区吉尔孟乡草地进行土壤入渗实验, 测定孔隙度与粒度, 研究了不同类型 草地土壤入渗特征和土壤蓄水性. 结果表明: 研究区内低草地土壤的稳定入渗率较小, 平均为 1. 5 mm/ m in, 高草地土壤的稳定入渗率较大, 平均为 3. 1 m m/ m in. 高草地达到稳定入渗的时间平均为 94 min, 低草地达到稳定入渗的时间为 174 min, 前者比后者短 80 min 左右. 霍顿公式对青海湖吉 尔孟乡低草地土壤入渗试验数据拟合最为适合, 而通用经验公式对高草地土壤入渗试验的数据拟 合最为适合. 高草根系发育深度大, 土质较低草地松软是造成高草地入渗率比低草地大的主要原 因. 青海湖土壤粒度组成较粗, 土层薄是该区土壤稳定入渗率较黄土大和在较短时间内能够达到稳 定入渗状态的主要原因

四川省国家Ⅱ级保护植物新记录——半枫荷

报道了四川省金缕梅科植物新纪录种半枫荷,该植物为国家Ⅱ级重点保护野生植物,同时半枫荷属也是四川省新纪录属。这一新发现,进一步丰富了四川省的生物多样性资料,也为华西雨屏带与云贵高原的植物区系联系提供了新资料

微生物混合培养生产油脂的研究进展 Progress on oil production by microbial mixed culture

产油微生物具有生长迅速、对环境适应能力强等诸多优点，是油脂生产领域的研究热点。与单一微生物培养相比，微生物混合培养对底物的选择性更广泛、利用效率更高，近几年越来越多地应用于工农业废弃物、城市废水和食物垃圾等的处理以及微生物油脂的生产。简要介绍了微生物油脂，综述了微生物混合培养生产油脂的现状以及发酵底物的研究情况，并提出了今后的研究方向。藻-菌、藻-藻和菌-菌混合培养是微生物混合培养生产油脂的主要研究对象，尤其是微藻与酵母混合培养利用废弃物生产油脂，对于降低生产成本和保护环境具有重要意义。今后应扩大混合培养微生物的种类，并筛选高产高附加值代谢产物的微生物作为混合培养的研究对象。Oleaginous microorganism which have many advantages, such as rapid growth and strong adaptability to the environment, has attracted intensive attention. Besides, compared with the monoculture, mixed culture exhibite wider selection for substrates as well as higher utilization efficiency. Recently, it has been increasingly used in the treatment of industrial and agricultural wastes, municipal wastewater and food wastes, as well as the microbial oil production. The microbial oil was simply introduced, the present situation of oil production by microbial mixed culture and the research status of substrates were reviewed, and the future research directions were proposed. The main object of microbial oil production by mixed culture was the combination of algae-yeast, algae-algae and yeast-yeast. Especially, oil production using wastes by mixed culture of microalgae and yeasts was of great significance in cost reduction and environment protection. Besides, it was necessary to enlarge the microorganism species as well as screening high-yield and high-value-added ones in the mixed culture in the future

元素分析-稳定同位素质谱法结合化学计量学 鉴别橄榄油掺假Olive oil adulteration identification using elemental analysis-stable isotope ratio mass spectrometer coupled with chemometrics

为建立快速鉴别橄榄油掺假的检测方法，以特级初榨橄榄油、玉米油、猪油、牛油和鸭油为实验材料，通过元素分析-稳定同位素质谱仪测定油脂的δ13C、δ18O和δ2H，并结合化学计量学鉴定橄榄油掺假。采用主成分分析（PCA）和正交偏最小二乘法判别分析（OPLS-DA）两种统计分析方法建立不同油脂的鉴别模型和橄榄油掺假鉴别模型。结果表明：橄榄油的δ13C、δ18O和δ2H范围分别在-30.411×10-3~-28.996×10-3、23.583×10-3~25.581×10-3、-163.611×10-3~-132.251×10-3 之间；橄榄油与玉米油、猪油、牛油和鸭油的OPLS-DA鉴别模型准确性稍好，3个变量δ13C、δ2H和δ18O对不同油脂区分的贡献度VIP值分别为1.056、0.997和0.943；使用其他4种油脂对橄榄油进行掺假时，OPLS-DA鉴别模型可明显区分橄榄油与掺假油；PCA-Class鉴别模型对橄榄油掺入玉米油、猪油、牛油和鸭油的检测限分别为5.8%、5.6%、6.4%和12.5%，盲样验证鉴别准确率可达100%。该方法所构建的鉴别模型准确可靠，可有效识别橄榄油中掺入玉米油、猪油、牛油和鸭油。 To establish a rapid detection method for the adulteration of olive oil，extra virgin olive oil, maize oil, lard, beef tallow and duck fat were used as materials, and an elemental analysis-stable isotope ratio mass spectrometer determining δ13C,δ18O and δ2H couple with chemometrics was established to identify olive oil adulteration. Two statistical analysis methods, principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA), were used to establish identification models for different oils and olive oil adulteration. The results showed that the δ13C,δ18O and δ2H of olive oil range from -30.411×10-3--28.996×10-3, 23.583×10-3-25.581×10-3 and -163.611×10-3--132.251×10-3, respectively. The accuracy of the OPLS-DA identification model of olive oil, maize oil, lard, beef tallow and duck fat was slightly better. The VIP values of δ13C, δ2H and δ18O were 1.056, 0.997 and 0.943, respectively. When adulterating olive oil with the other four kinds of oils, the OPLS-DA identification models could clearly distinguish olive oil from adulterated oil. The limits of determination of maize oil, lard, beef tallow and duck fat adulterated in olive oil were 5.8%, 5.6%, 6.4% and 12.5% respectively by PCA-Class model, and the test sample verification accuracy could reach 100%. The identification models constructed are accurate and reliable, and can effectively identify the adulteration of maize oil, lard, beef tallow and duck fat in olive oil