放射性核素水环境质量标准研究进展

地球环境中广泛存在的放射性核素对人类和其他物种产生辐射安全风险,成为水环境质量的重要指标之一。随着人类生活水平和环境保护意识的提升,以及核与辐射安全复杂的国际形势,放射性核素水环境质量标准的关注度越来越高。饮用水水质标准中的放射性核素限值基于个人辐射剂量标准,评估方法已经建立,并在世界卫生组织、美国、加拿大和日本等国际组织和国家的饮用水水质标准中得到广泛应用。水环境质量标准中的放射性核素限值基于辐射环境、参考生物、个体单位时间内的辐射剂量限值,对此各国际组织和国家相继开展了放射性核素的生态风险评价研究,并逐步制定相关标准。与国际水平相比,我国水环境质量标准存在放射性核素指标数量少、修订频率滞后、科学适用性有待提升等问题。在我国核能发展与生态文明建设的新形势下,加强放射性核素的健康风险和生态风险评价研究,建立健全水环境质量标准中放射性核素指标体系成为我国水环境研究的紧迫任务。     

大气可降水量卫星遥感反演不确定性对太阳总辐射模拟的影响

辐射传输方程模拟法作为一种较为准确有效的太阳总辐射间接获得方法,其模拟精度受到代入方程的大气参数精度的影响。论文通过将香港2005—2013年卫星遥感反演的大气可降水量产品与地面站数据对比,以68.3%的置信水平分析了卫星遥感反演的大气可降水量的不确定性;并通过收集2005—2013年香港其他影响大气上界太阳辐射到达地表的大气参数,如气溶胶光学厚度、气溶胶单次散射反照率、臭氧含量综合模拟大气环境,确定了大气可降水量的不确定性在夏季和冬季对太阳总辐射模拟（250~2 800 nm）造成的相对误差。研究结果表明,受大气可降水量不确定性影响,使用辐射传输方程法模拟获得的太阳总辐射有1%~3%的相对误差。使用该方法冬季受大气可降水量不确定性影响程度比夏季大。决定相对误差大小的主要因素是波长,大气水汽吸收作用强的波长上,相对误差也大;其次因素是太阳天顶角,太阳天顶角越大,该方法模拟所得太阳总辐射相对误差越大。研究为大气可降水量参数不确定性影响下太阳总辐射模拟的误差评估提供了依据。     

化学品急性毒性藻红外测试技术的方法改进研究

在以往藻红外测试技术的急性毒性测试中,每次每个测试杯测试1个藻温,共2个测试组,并用藻最大响应温差1个指标进行毒性评价,测试结果的可行性和稳定性不理想,针对这个问题提出了改进方法:1)每次每个测试杯连测3个藻温;2)改为3个测试组;3)将单指标法改为三指标法;4)增加测试结果的重现性分析。通过蒸馏水毒性测试实验和重金属毒性测试实验,分析改进方法的效果。结果显示,不同指标方法中,三指标法控制假结果出现率为20%,控制效果最好;在测试4元重金属共存(总浓度0.066~0.156 mg·L~(-1))的毒性时,测试3个藻温的所有指标法的平均重现率(%)/重现性(%)均为100%/100%,测试1个藻温的三指标法的平均重现率(%)/重现性(%)为67%/100%,表明测试高浓度的重金属毒性时,不同指标法都有很好的评价效果;在测试一元重金属(0.001~0.1 mg·L~(-1))毒性时,只有三指标法的平均重现率(%)/重现性(%)是100%/100%,远高于其他指标法,表明只有三指标法才可准确测试低浓度重金属的毒性。在测试5种不同重金属共存的毒性时,三指标法的平均重现率(%)/重现性(%)平均为86.8%/100%。研究表明,改进后的技术用于化学品急性毒性测试,灵敏度高和稳定性好,结果可靠。     

巴丹吉林沙漠腹地夏季不同天气条件下陆-湖面辐射收支与能量分配特征对比

论文以巴丹吉林沙漠腹地陆面和湖面能量平衡系统的定位观测数据为基础,对比分析了夏季不同天气条件下两种典型下垫面的辐射收支和能量分配之异同,评价了典型晴天的辐射收支与能量分配对季节平均气候态的代表性。结果显示,按“晴天-多云-阴天-降水”的顺序,陆面与湖面的总辐射、反射短波辐射和净辐射之日均值呈逐渐减小趋势,大气逆辐射则逐渐增大。湖面长波辐射亦随这一天气变化顺序逐渐增大,暗示地下水携带的热量和局地平流作用对湖水表面温度皆有影响。非降水天气时,陆面感热通量占净辐射的主要部分。云和降水皆会直接影响陆面的能量分配,感热通量一般随云量的增加而减小,潜热通量则逐渐增大。湖面典型晴天的午后存在逆温现象,然降水天气时,湖面空气温度降低较快,破坏了湖面上空的逆温层,即感热通量皆为正值。湖面的潜热通量主要决定于风速,故其对天气变化响应不明显。在地下水携带的热量和局地平流共同作用下,湖面的感热通量和潜热通量之和大于湖面的净辐射。不同天气下陆面和湖面的波文比差异较大。陆面典型晴天的辐射收支和能量分配与季节平均气候态相差不大,湖面的典型晴天则不具有季节平均气候态的代表性。     

巢湖流域氮磷面源污染与水华空间分布遥感解析

基于遥感监测手段,分别应用DPeRS模型和MODIS水华提取方法对巢湖流域氮磷面源污染特征和巢湖水体水华爆发规律进行遥感像元尺度解析,结果表明: 2010年巢湖流域总氮产生量为1900.3t,入河量为846.5t;总磷为244.1t,入河量为76t.巢湖流域农业面源污染对氮素污染贡献最大,而水土流失则对磷面源污染贡献最大;综合巢湖流域氮磷面源污染和水华爆发的时空特征分析,明确氮磷面源污染与巢湖水华具有相关性,并且时间上水华爆发频率较氮磷面源污染具有先滞后后同步的特征,且面源污染负荷与水华爆发面积的相关系数为0.45;在空间上,面源污染负荷较大区域与水华爆发频度较高区域也有较好的匹配性;基于这种相关性,应用DPeRS模型对巢湖流域进行氮磷减排情景分析,结果表明在施肥量减少30%,农村生活垃圾处理率提高到60%,畜禽粪便处理率和城市垃圾处理率提高到80%的情况下,氮磷面源污染平均削减率可以达到50%.     

Application of a three-dimensional model for assessing effects of small clear-cuttings on radiation and soil temperature

A three-dimensional model Mixfor-3D of soil–vegetation–atmosphere transfer (SVAT) was developed and applied to estimate possible effects of tree clear-cutting on radiation and soil temperature regimes of a forest ecosystem. The Mixfor-3D model consists of several closely coupled 3D sub-models describing: forest stand structure; radiative transfer in a forest canopy; turbulent transfer of sensible heat, H₂O and CO₂ between ground surface and the atmospheric surface layer; evapotranspiration of ground surface vegetation and soil; heat and moisture transfer in soil. The model operates with the horizontal grid resolution, 2 m × 2 m; vertical resolution, 1 m and primary time step, 1 h.     

山西省能源圈辐射的环境效应

根据历年<山西统计年鉴>提供的能源输出数据,分析了1985-2007年间山西省煤炭的输出情况,并借助于GIS软件,分析了2006年山西省煤炭的流向及流量.山西省是一个大的能源辐射基地,每年产出煤炭的三分之二左右供应外省市,在外省市的经济水平、人民生活质量不断得到提升的同时,对于为周边地区的发展做出巨大贡献的能源输出地--山西来说,人均地区生产总值却低于全国平均水平,更低于其能源辐射到的其它省市区.山西省在不断向外省市输出能源的同时,也产生了巨大的环境辐射效应,但这种效应对于山西这个能源输出地来说却是负向的.主要表现在:山西省由于煤炭开采导致空气污染、地下水位下降、地面塌陷等地质灾害,而这些环境压力有三分之二是替煤炭输入地所承担的.输送电力,山西省不仅要承担煤炭开采造成的环境破坏,还要承担火力发电所造成的巨大环境二次污染,仅2007年,山西省生产外调电量就产生381.75×10~4 tSO_2,139.98×10~4 tNO_2,2799.51×10~4 t灰粉.     

Impact of generated solar radiation on simulated crop growth and yield

The availability of observed daily solar radiation (OSR) is restricted to recent years. Its estimation through different methods is necessary to develop long-term data sets for agricultural and environmental applications. The objective of this study was to analyze the impact of using generated daily solar radiation (GSR) on simulated growth and yield of cotton, maize, and peanut. Nine locations representing Georgia's major crop belt were selected. Daily weather data from the Georgia Automated Environmental Monitoring Network (AEMN), including solar radiation, maximum and minimum temperature, and precipitation, were duplicated. The OSR was removed from one set and then generated using a stochastic procedure. The Cropping System Models (CSM)-CROPGRO-Cotton, CERES-Maize, and CROPGRO-Peanut of the Decision Support System for Agrotechnology Transfer (DSSAT) v4 were used to simulate crop growth and yield at each location with both OSR and GSR and for rainfed and irrigated conditions. The statistical analysis included summary statistics, Pearson's coefficient of correlation, mean squared deviation (MSD) and its components, namely: squared bias (SB), squared difference between standard deviations (SDSD), lack of correlation weighted by the standard deviations (LCS), and regressions. Within locations, for the three crops under rainfed and irrigated conditions, GSR did not significantly affect simulated total evapotranspiration and aboveground biomass and yields. For the three crops, deviations of simulated water use and yields from GSR with respect to simulated water use and yields from OSR were lower for the rainfed than for the irrigated conditions. Yields from the CSM-CROPGRO-Cotton and -Peanut models had lower deviations than yields from the CSM-CERES-Maize model. LCS was the major component of the MSD suggesting that the extent of the difference between standard deviations of GSR and OSRG could affect the outputs of the crop models. Nevertheless, for most locations none of the MSD components of the GSR showed significant correlation with simulated yields and the overall performance of the models was not affected. It can be concluded based on the results of this study that GSR can be used as an input for crop model simulation models when OSR is not available.     

Comparison of sensitivity analysis techniques: A case study with the rice model WARM

The considerable complexity often included in biophysical models leads to the need of specifying a large number of parameters and inputs, which are available with various levels of uncertainty. Also, models may behave counter-intuitively, particularly when there are nonlinearities in multiple input-output relationships. Quantitative knowledge of the sensitivity of models to changes in their parameters is hence a prerequisite for operational use of models. This can be achieved using sensitivity analysis (SA) via methods which differ for specific characteristics, including computational resources required to perform the analysis. Running SA on biophysical models across several contexts requires flexible and computationally efficient SA approaches, which must be able to account also for possible interactions among parameters. A number of SA experiments were performed on a crop model for the simulation of rice growth (Water Accounting Rice Model, WARM) in Northern Italy. SAs were carried out using the Morris method, three regression-based methods (Latin hypercube sampling, random and Quasi-Random, LpTau), and two methods based on variance decomposition: Extended Fourier Amplitude Sensitivity Test (E-FAST) and Sobol’, with the latter adopted as benchmark. Aboveground biomass at physiological maturity was selected as reference output to facilitate the comparison of alternative SA methods. Rankings of crop parameters (from the most to the least relevant) were generated according to sensitivity experiments using different SA methods and alternate parameterizations for each method, and calculating the top-down coefficient of concordance (TDCC) as measure of agreement between rankings. With few exceptions, significant TDCC values were obtained both for different parameterizations within each method and for the comparison of each method to the Sobol’ one. The substantial stability observed in the rankings seem to indicate that, for a crop model of average complexity such as WARM, resource intensive SA methods could not be needed to identify most relevant parameters. In fact, the simplest among the SA methods used (i.e., Morris method) produced results comparable to those obtained by methods more computationally expensive.