基于临近台站气象数据的参考作物蒸散量估算方法

参考作物蒸散量（ET₀）是评估区域植被耗水进而指导水资源优化管理所需的主要参数之一,但我国大部分地区标准化气象台站稀疏、部分研究点的气象资料通常难以获取,给ET₀的计算带来了很大困难。以地处内陆旱区的内蒙古河套灌区为例,利用该区4个标准气象站1981-2006年的气象资料,讨论了研究点在没有历史气象数据且现有气象数据不完备的情况下,采用临近台站气象数据估算ET₀的可靠性。估算方法分别为估算未知气象数据的FAO56 Penman-Monteith方程（PM56）、基于临近台站气象数据校正的经验公式以及利用临近台站气象数据训练的人工神经网络模型。结果表明：（1）在完全没有气象数据的条件下,可采用临近站点的气象数据估算研究点的ET₀,平均绝对误差（MAE）为0.43~0.52 mm d^-1,均方根误差（RMSE）为0.56~0.63 mm d^-1;估算精度与台站间的距离有关,利用维度信息校正太阳辐射值可提高估算精度。（2）仅有最高和最低气温数据时,估算气象数据的PM56方程计算误差较大,且站点之间表现不稳定,人工神经网络模型的估算精度最高,MAE和RMSE分别为0.14~0.22 mm d^-1和0.17~0.29 mm d^-1;校正后的Hargreaves公式的估算效果次之,MAE和RMSE分别为0.23~0.26 mm d^-1和0.30~0.31 mm d^-1。（3）在已知温度和辐射数据时,利用临近台站气象数据训练的人工神经网络模型依然表现最好,MAE和RMSE分别为0.13~0.19 mm d^-1和0.17~0.25 mm d^-1,其他两种方法误差较大。在内陆干旱条件下,利用研究点的气温数据结合临近台站的历史气象信息可有效估算参考作物蒸散发。     

16届亚运会期间广州城区PM2.5化学组分特征及其对霾天气的影响

于2010年11月4～30日在广州城区每天昼夜各采集一个PM2.5样品.对样品进行有机碳、元素碳和水溶性离子分析,同步收集了在线PM2.5浓度、大气消光系数(bext)以及气象因子,探讨了PM2.5特征及其与大气消光系数的关系,并利用修正后的IMPROVE消光系数方程重建大气消光系数.结果发现:亚运期间PM2.5日均值质量浓度为(77.0±24.4)μg·m-3,比亚运前低27.8%.PM2.5和相对湿度是导致霾天气的重要因素.亚运期间大气消光系数为418 Mm-1,比亚运前低28.3%,(NH4)2SO4、POM(particulate organic material)和LAC(light-absorbing carbon)是主要贡献因子,贡献率达到87.0%.广州及其周边城市采取的减排措施对于缓解亚运期间广州城区的霾天气作用明显.     

Element and mineral characterization of dust emission from the saline land at Songnen Plain, Northeastern China

Recent observations of Asian dust storms show an eastern expansion of the source area to degraded lands, where dust emissions have been little studied. The dust concentrations over the saline land of the western Songnen Plain (SSL), Northeastern China, are circumstantially higher than those from the northwestern Chinese deserts. These concentrations are sensitive to the surface soil conditions and wind velocity on the ground. The dust samples collected during dust storm events on the SSL contain abundant Na, Mg, Al, K, Ca, Fe and Ti, as well as toxic elements such as Cu, V, Zn and Ba. Individual particle analysis reveals that fine saline particles (< 10 μm in diameter) on the saline land, consisting largely of carbonate, halite and sulfate together with lithogenic minerals such as SiO₂ and aluminosilicate, are eventually uplifted during the interval from spring to autumn. The predominantly fine saline particles uplifted from the SSL are likely transported eastward by the winter monsoon circulation and westerlies. Recent degradation of saline lands in Northeastern China would not only increase the frequency of dust storm events in the downwind area, but also might change the chemical composition of the Asian dust emissions.     

广州市环境空气监测与预警体系在亚运会中的应用

本文以2010年广州亚运会空气质量监测保障为例,从广州市环境空气质量监测网络、空气质量预报、预警体系和环境质量会商及信息发布管理机制等方面,介绍了广州市环境空气监测与预警体系。通过2010年广州亚运会空气质量保障监测实践,证明广州市环境空气监测与预警体系是适应广州环境保护工作发展,并能满足特殊环境空气质量保障目标需求,具有示范作用和可供全国中心城市借鉴的先进的环境空气质量监测与预警体系。     

干旱内陆河流域人居环境适宜性评价——以石羊河流域为例

论文以干旱区内陆河石羊河流域为研究区,选取坡度、 坡向、 地形起伏度、 植被指数、 水文、 气候、 交通等因子作为评价指标,建立了人居环境指数(HEI),利用GIS的叠置分析、 缓冲区分析、 密度分析等空间分析技术对石羊河流域人居环境适宜性进行了定量综合评价分析。评价结果表明,石羊河流域人居环境指数介于17.13~84.32之间,整体分布格局为:从西南方向到东北方向适宜性依次降低,适宜、 较适宜居住区主要分布在中游和中下游地区,不适宜人类居住区占研究区面积的比例很大。石羊河流域人居环境适宜区主要以带状和斑块状分布在石羊河各支流周围,其他区域只有零星分布,与流域内居民点分布的空间格局相吻合。石羊河流域人居环境状况与人口密度呈对数关系,确定系数达到0.851 2,与经济发展水平呈现较明显的正相关性,人居环境评价值与单位面积GDP确定系数达到0.845 4,与道路网密度关系不显著,受道路制约程度较小。一些居民点分布在人居环境恶劣区域,一些村镇分布在靠近沙漠区域内,地形复杂,气候多变,水资源短缺,经济发展滞后,结合新农村建设规划和当地土地利用规划,这部分村落需要异地搬迁以便改善人居环境状况。     

模拟东亚夏季风对中国东部地区大气中α-六六六(α-HCH)时空分布的影响

以中国为研究区域,利用CanMETOP模型模拟了2005年大气中α-六六六(α-HCH)浓度的变化,并分析了东亚夏季风对我国东部地区大气中α-HCH浓度时空分布的影响.模拟结果表明,6月15日-8月15日期间,东北、华北和东南地区的α-HCH平均大气浓度分别为260 pg·m~(-3),74 pg·m~(-3)和41 pg·m~(-3).大气浓度具有明显的时空分布特征,从6月15日在我国东南地区开始出现了较高的大气浓度堆集,6月24日达到最大值(259 pg·m~(-3)).在向我国北部传输过程中,7月1日在东北形成的气旋区域出现最大值(1947pg·m~(-3)).此后,东南地区的大气浓度均处于较低的水平(<50 pg·m~(-3)),而东北地区的大气浓度一直处于100-350 pg·m~(-3)之间的较高水平.分析结果表明,东亚夏季风可以将东南地区大气中的α-HCH传输至东北地区,并在东北区域堆集.     

Geochemical and mineralogical characteristics of elephant geophagic soils in Udawalawe National Park,Sri Lanka

Geophagy or deliberate ingestion of soils was observed among Asian elephants (Elephas maximus) in the Udawalwe National Park, Sri Lanka, for several years. The geochemical and mineralogical composition of the clayey soil layers which are purposefully selected and eaten by elephants in the park were studied, in order to identify the possible reasons for elephant geophagy. The concentrations of major and trace elements were determined by means of X-ray fluorescence spectrometry in 21 soil samples from eight geophagic sites and six soil samples collected from four non-geophagic sites. The mineralogical composition of selected soil samples was investigated using X-ray diffractometry (XRD). These geochemical analyses revealed that geophagic soils in the study areas are deeply weathered and that most of the elements are leached from the soil layers under extreme weathering conditions. The XRD data showed that the soils of the area consisted mainly quartz, feldspar, and the clay minerals kaolinite, Fe-rich illite, and smectite. Although no significant geochemical differences were identified between geophagic and non-geophagic soils, a clear difference was observed in their clay mineralogical content. Soils eaten by elephants are richer in kaolinite and illite than non-geophagic soils, which contain a higher amount of smectite. It is suggested that elephants in Udawalawe National Park ingest soils mainly not to supplement the mineral contents of their forage but to detoxify unpalatable compounds in their diet.     

What makes affirmative action‐based hiring decisions seem (un)fair? A test of an ideological explanation for fairness judgments

Studies show that Whites tend to show the lowest level of support for affirmative action (AA) policies. Opponents of AA often argue that this is because it violates principles of meritocracy. However, self‐interest (based on social identification with those adversely affected) could also explain their opposition. In three studies, we varied whether an Asian or White male is adversely affected by AA to test another explanation; namely, that Whites' fairness judgments are based on both the adversely affected person's race and the fairness evaluator's ideological beliefs. Although we found some support for the meritocratic explanation, this was not sufficient to explain why Whites view AA as (un)fair. Instead, we found strong support for our prediction that Whites who are opposed to equality perceive more unfairness when a White (vs. Asian) was harmed by AA, whereas Whites who endorse egalitarian ideologies perceive the opposite. This finding suggests that neither self‐interest nor meritocratic explanations can fully account for Whites' opposition to AA. Copyright © 2014 John Wiley & Sons, Ltd.     

干旱区流域生态需水量估算原则分析

运用河流廊道原理 ,提出了生态需水量计算公式。以额济纳地区为例 ,分析研究了该地区不同景观的生态需水量。结果表明 ,河流两岸的乔灌木林和灌丛草甸的生态需水量为 3 .91× 1 0 8m3,占总需水量的 39.388% ,加上河流和湖泊的蒸发耗水量 0 .55×1 0 8m3及河流自身生态用水 ,四者占总需水量的 73 .61 %。可见 ,只要保证了四者的生态需水量 ,即可保证现状河流廊道的基本功能。但从长远来考虑 ,河道的流量要确保抗性最小面积对生态需水量的要求。     

中亚五国气溶胶光学厚度时空分布特征研究

气溶胶时空变化是影响气溶胶气候效应不确定性的主要因素之一.在生态环境脆弱的中亚五国（哈萨克斯坦、乌兹别克斯坦、土库曼斯坦、吉尔吉斯斯坦和塔吉克斯坦）研究气溶胶光学厚度（Aerosol Optical Depth,AOD）的时空变化对中亚地区及全球生态环境和气候变化具有重要意义.基于2002—2019年MODIS 04_L2气溶胶产品数据集,利用标准差椭圆（Standard Deviation Ellipse,SDE）、加权回归的季节趋势分解（Seasonal Trend Decomposition Procedure based on Loess,STL）和奇异值分解（Singular Value Decomposition,SVD）等方法,在时间和空间两个维度上揭示了中亚五国AOD和气溶胶细模态比例（Aerosol Fine Mode Fraction,FMF）的时空演变特征,以及AOD对气象因子和土地利用/土地覆被变化（Land Use/Cover Change,LUCC）的响应.结果表明：①中亚五国AOD分布呈现出显著的空间异质性,高值区主要集中于咸海区域和费尔干纳盆地,低值区主要分布于海拔相对较高的山区和丘陵;AOD年平均中心在2009年之前分布于中亚东南部,受咸海盐尘的影响,2010年之后偏向于西北部向咸海附近移动.②2002—2019年中亚五国AOD年均值整体呈先增长后下降的态势,2012年达到最大值.③中亚地区AOD的变化受气温、降水、风速的共同影响,AOD与气温的空间变化特征呈基本一致的态势,但与降水和风速存在一定差异;不同土地覆被区对AOD的影响差异显著,城市建设用地、裸地及水域是AOD高值分布区,低值分布于郁闭灌木丛和混杂林等植被覆盖度高的土地利用/土地覆被区.④气溶胶细模态比例（FMF）空间分布格局由自然和人为因素共同作用,呈现出咸海和沙漠区域附近以粗模态气溶胶为主,人口密集区和工业区以细模态粒子占主导地位.