杭州地区大范围暴雨、局地暴雨和强对流的特征分析

本文根据杭州地区60个暴雨和强对流例子.通过对大范围暴雨、局地暴雨和强对流的对比分析,得到:风的垂直切变尤其是在边界层具有一定强度;三者湿度条件有一定差别,但差别比华北要小;强对流的不稳定度大于暴雨,而大范围暴雨与局地暴雨差别则不明显.     

基于土壤理化性质估计土壤水分特征曲线Van Genuchten模型参数

在东北黑土区采集了不同侵蚀强度黑土土样,测定其土壤水分、机械组成、有机质和容重等指标,利用Rosetta模型估计了Van Genuchten模型的参数,并将估算土壤水分与实测土壤水分对比,评价了选择不同土壤理化性质指标的模拟精度,及该方法对东北黑土的适宜性。结果表明：选择4个或6个土壤性质指标,尺度参数（α）和形状参数（n）的差异较大,采用6指标时,α增大,n减小。修正VG模型参数m与n的关系后,模型拟合精度明显提高,其中6指标的计算结果好于4指标,但拟合值偏大,需进一步较正。Rosetta模型适用于砂粒含量小于46%,粘粒含量大于28%的东北黑土。     

Tracking a Severe Pollution Event in Beijing in December 2016 with the GRAPES–CUACE Adjoint Model

We traced the adjoint sensitivity of a severe pollution event in December 2016 in Beijing using the adjoint model of the GRAPES–CUACE (Global/Regional Assimilation and Prediction System coupled with the China Meteorological Administration Unified Atmospheric Chemistry Environmental Forecasting System). The key emission sources and periods affecting this severe pollution event are analyzed. For comaprison, we define 2000 Beijing Time 3 December 2016 as the objective time when PM_2.5 reached the maximum concentration in Beijing. It is found that the local hourly sensitivity coefficient amounts to a peak of 9.31 μg m^–3 just 1 h before the objective time, suggesting that PM_2.5 concentration responds rapidly to local emissions. The accumulated sensitivity coefficient in Beijing is large during the 20-h period prior to the objective time, showing that local emissions are the most important in this period. The accumulated contribution rates of emissions from Beijing, Tianjin, Hebei, and Shanxi are 34.2%, 3.0%, 49.4%, and 13.4%, respectively, in the 72-h period before the objective time. The evolution of hourly sensitivity coefficient shows that the main contribution from the Tianjin source occurs 1–26 h before the objective time and its peak hourly contribution is 0.59 μg m^–3 at 4 h before the objective time. The main contributions of the Hebei and Shanxi emission sources occur 1–54 and 14–53 h, respectively, before the objective time and their hourly sensitivity coefficients both show periodic fluctuations. The Hebei source shows three sensitivity coefficient peaks of 3.45, 4.27, and 0.71 μg m^–3 at 4, 16, and 38 h before the objective time, respectively. The sensitivity coefficient of the Shanxi source peaks twice, with values of 1.41 and 0.64 μg m^–3 at 24 and 45 h before the objective time, respectively. Overall, the adjoint model is effective in tracking the crucial sources and key periods of emissions for the severe pollution event.     

Changes in Extreme Maximum Temperature Events and Population Exposure in China under Global Warming Scenarios of 1.5 and 2.0°C: Analysis Using the Regional Climate Model COSMO-CLM

We used daily maximum temperature data (1986–2100) from the COSMO-CLM (COnsortium for Small-scale MOdeling in CLimate Mode) regional climate model and the population statistics for China in 2010 to determine the frequency, intensity, coverage, and population exposure of extreme maximum temperature events (EMTEs) with the intensity–area–duration method. Between 1986 and 2005 (reference period), the frequency, intensity, and coverage of EMTEs are 1330–1680 times yr^–1, 31.4–33.3°C, and 1.76–3.88 million km², respectively. The center of the most severe EMTEs is located in central China and 179.5–392.8 million people are exposed to EMTEs annually. Relative to 1986–2005, the frequency, intensity, and coverage of EMTEs increase by 1.13–6.84, 0.32–1.50, and 15.98%–30.68%, respectively, under 1.5°C warming; under 2.0°C warming, the increases are 1.73–12.48, 0.64–2.76, and 31.96%–50.00%, respectively. It is possible that both the intensity and coverage of future EMTEs could exceed the most severe EMTEs currently observed. Two new centers of EMTEs are projected to develop under 1.5°C warming, one in North China and the other in Southwest China. Under 2.0°C warming, a fourth EMTE center is projected to develop in Northwest China. Under 1.5 and 2.0°C warming, population exposure is projected to increase by 23.2%–39.2% and 26.6%–48%, respectively. From a regional perspective, population exposure is expected to increase most rapidly in Southwest China. A greater proportion of the population in North, Northeast, and Northwest China will be exposed to EMTEs under 2.0°C warming. The results show that a warming world will lead to increases in the intensity, frequency, and coverage of EMTEs. Warming of 2.0°C will lead to both more severe EMTEs and the exposure of more people to EMTEs. Given the probability of the increased occurrence of more severe EMTEs than in the past, it is vitally important to China that the global temperature increase is limited within 1.5°C.     

First observation of domoic acid and its isomers in shellfish samples from Shandong Province,China

Journal of Oceanology and Limnology - A total of 133 shellfish samples were collected in seven cities of Shandong Province, China, from May to October, 2019. The domoic acid (DA) concentrations...     

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印度洋Carlsberg洋脊玄武岩岩石地球化学特征及其地质意义

本文对采自印度洋Carlsberg脊14个站位的新鲜玄武岩样品进行了常量和微量元素组成分析,旨在研究岩浆源区地幔的性质以及岩浆作用过程。研究结果表明:该区玄武岩为典型的源于亏损型地幔的大洋中脊玄武岩,不同样品经历了不同程度的结晶分异作用,演化过程主要受控于橄榄石的结晶分异作用,部分样品中有单斜辉石结晶分异作用的影响,斜长石的结晶分异作用不显著;玄武岩岩浆来源于亏损型尖晶石二辉橄榄岩地幔的熔融,主微量元素组成中尚未见到富集型组分混入的证据;源区地幔不同比例的熔融作用及其后岩浆演化过程的差异是造成不同样品间地球化学性质差异的主要原因,彼此独立的局部岩浆作用过程是岩浆作用差异的主控制因素。Carlsberg脊玄武岩整体与全球标准大洋中脊玄武岩(N-MORB)平均组分相近,不同脊段间岩浆源区地幔的组成、熔融程度(比例)和熔融深度等无明显差异,这种特征向南直到CIR的北段。     

“白鹿”台风(1911)残涡造成广西大范围暴雨成因分析

利用常规气象资料及EC再分析、卫星云图、雷达、中尺度自动站等资料,采用天气动力诊断分析方法,对"白鹿"台风(1911)残涡造成广西大范围暴雨的成因进行分析。结果表明,"白鹿"台风残涡暴雨是在有利的高低空环流形势和强水汽辐合条件共同作用下造成的。有利的环流条件使停编后的"白鹿"残涡强度维持、加强,卫星云图显示"白鹿"南半环云系发展旺盛、密实,雷达回波显示残涡螺旋云带的前端有新生对流发展、传播,从而造成广西大范围暴雨发生。     

桃山大布铀矿床绿泥石特征及其形成环境

花岗岩型铀矿中,绿泥石化作为热液蚀变的重要类型之一,其种类多样,据此可以推断其形成环境。笔者在岩石薄片观察的基础上,通过对绿泥石进行电子探针成分分析,认为桃山大布铀矿床的绿泥石主要为铁绿泥石、蠕绿泥石,其形成于中低温酸性热液作用环境。     

Study on soil heavy metals contamination of a lead refinery

The contents of Pb, Cd, Cr, As, and Hg was investigated in the neighboring areas of a lead smelting enterprise and the metals contamination of soil was evaluated with the methodologies of the single factor index and Nemerow integrated index. It is found that the content of heavy metals in the neighboring areas has no obvious difference from the reference points. And the soil contamination of heavy metals varies by elements, the contamination index follows the pollution order： Cd〉Hg〉Cr〉As〉Pb. The pollution degree of Cd is at high level and Hg at low level, but Cr and As reached the warning level. Therefore, the lead smelting enterprises should not only control lead but also other heavy metals as Cd.