不同季节气象条件对北京城区高黑碳浓度变化的影响

利用2013年至2015年北京城区黑碳气溶胶(下文统称为"BC")和PM2.5观测资料,结合地面气象观测资料、ECMWF边界层高度再分析资料和FNL/NCEP不同高度风速再分析资料,讨论了BC质量浓度及其在PM_(2.5)质量浓度中所占比例(下文统称"黑碳占比")的季节、月、日变化特征,并通过计算北京城区BC浓度与不同高度风速的相关矢量,分析了气象条件和外来输送对北京城区BC浓度变化的影响.结果发现:研究时段内北京城区BC浓度平均值为(4.77±4.49)μg·m~(-3);黑碳占比为8.23%±5.47%.BC浓度和黑碳占比在春、夏季低,秋、冬季高,其日变化特征在4个季节均为"白天低夜间高"的单峰型特征.随着PM_(2.5)浓度的升高,BC浓度增大,黑碳占比减小.当北京地区风向为东北、东北偏东、东南和西南偏西(主风向)时,BC浓度与风速和边界层高度均呈反向变化,即随风速和边界层高度的增大而减小.另外不同季节BC浓度随风速变化的临界值及其变化速率不同.冬季高BC浓度时段,北京城区BC浓度在低层大气的关键影响区分别位于河北南部与山东交界地区以及河北西北部与山西内蒙交界地区;高空关键影响区主要位于北京以西的河北西部、山西北部和内蒙古地区.     

2013年京津冀重污染特征及其气象条件分析

2013年中国东部地区多次发生持续的重霾污染事件.为探究其气象条件与重污染事件的关系,本文使用欧洲中心2013年东亚地区的逐日气象数据和北京、天津、石家庄的逐时PM_2.5浓度数据以及2013年MICAPS观测数据,分析了重污染事件对应的天气形势,并使用NAQMPS针对2013年1月的重污染事件进行情景模拟.研究结果表明：1北京、天津和石家庄地区PM_2.5浓度,夏秋季节日变化不显著,秋冬季节白天低夜间高;3地PM_2.5浓度均表现为12-1月浓度最高,7月最低;.2500 hPa平直西风气流,850 hPa弱暖平流,地面处于弱高压后部或高压底部高低空配置下的天气系统,对应着重污染事件的高发期;3源强不变的情况下,京津冀地区由弱高压前部控制转为弱高压控制时,地面温度升高0~5℃,相对湿度增加30%~50%,风速下降2~3 m·s^-1,PM_2.5浓度变化可达300 μg·m^-3.     

G20峰会期间杭州地区空气质量特征及气象条件分析

利用空气质量和气象要素的监测资料与再分析资料,分析了2016年G20峰会期间(2016年8月10日—9月20日)杭州及周边地区空气质量演变及区域特征,探讨了气象条件对G20峰会期间杭州空气质量的影响.结果表明:G20峰会管控期间,由于机动车排放大幅度降低,杭州NO_2浓度较管控前有所下降,对比周边城市降幅居于首位;而由于不利气象条件的影响,PM_(2.5)、PM_(10)、SO_2、CO和O_3浓度比管控前有不同程度的增长,但增幅相比周边城市较小,说明管控措施对杭州空气质量有一定的改善效果.9月7日管控措施结束后污染反弹现象明显.气象条件对杭州的空气质量有重要影响:在管控前,杭州晴热高温天气有利于O3的生成,偏东风相对洁净,污染传输较少;在管控期,杭州虽受到静稳天气和外来污染传输的影响,但得益于减排应急管控措施的有效实施,NO_2浓度下降幅度最大,其他污染物的增幅也较周边城市偏小;在管控后,气象条件不利于污染物的垂直扩散,受静稳天气和污染源恢复常态的影响,PM_(2.5)、PM_(10)、NO_2、SO_2和CO出现了整个研究时段的最大值,而台风"莫兰蒂"使得杭州PM_(2.5)、PM_(10)和O_3浓度出现了整个研究时段的最低值.     

大跨结构多维多点输入抗震研究进展

对于大跨度结构来说,应该考虑地震动的多维多点效应。从国内外发展现状及取得的主要研究进展和近期的发展动向等角度,对大跨度结构多维多点输入抗震的研究现状进行了系统的综述和总结。首先论述了多维地震动及其相关性,从地震动的各个分量之间的强度比、频率、持时及相关性等方面说明地震动分量的特性;然后论述地震动时空模型,侧重于反映地震动空间变化的相关函数模型的研究现状;最后比较了大跨度结构考虑多维多点输入的抗震计算方法,包括反应谱法、时程分析法以及随机振动分析法。对今后关于这一问题所应开展的研究提出了建议。     

Assessing Respiratory Morbidity Through Pollution Status and Meteorological Conditions for Delhi

The study focuses on assessing the status of respiratory morbidity in Delhi over a four years period from 2000–2003. An attempt was made to investigate the role of important pollutants (SO₂, NO₂, SPM and RSPM) and various meteorological factors (temperature minimum & maximum, relative humidity at 0830 and 1730 hrs. and wind speed) in being responsible for respiratory admissions on account of COPD, asthma and emphysema. The study showed that winter months had greater exposure risk as pollutants often get trapped in the lower layers of atmosphere resulting in high concentrations. Statistical analysis revealed that two pollutants have significant positive correlation with the number of COPD cases viz., SPM (r = 0.474; p < 0.01) and RSPM (r = 0.353; p < 0.05), while a meteorological factor temperature (minimum) has a significant negative correlation (r = −0.318; p < 0.05) with COPD. Stepwise multiple regression analysis was performed for COPD as dependent variable and R² value of 0.33 was obtained indicating that SPM and RH(1730) were able to explain 33 percent variability in COPD. The partial correlation of SPM and RH(1730) on COPD was higher than any other combination and therefore they can be regarded as important contributing variables on COPD.     

冬季动态槽式堆肥温度的空间变异

以鲜鸡粪、蘑菇渣和污泥按照体积比3∶1∶1混合进行动态堆肥模拟试验.堆肥槽沿物料前进方向分为7个部分,对每个部分按等间距分别做5个水平方向上切分和5个垂直方向上切分,在形成的125个交叉点上进行温度监测.研究结果表明,第1天的混合物料温度在同一层中变异很小,不同层之间略有差异.随着动态过程的进行,同一层温度变异逐渐增大,从第一天相差1～3℃,增加到相差30～40℃,靠近墙体的堆料温度较低,远离墙体的温度较高.随着堆肥时间延长,差异增大.机械翻堆能起到通风的作用,同时使每一个堆方的堆料在纵向方向上上下混合,但达不到横向混匀,因此,靠近墙体两侧的堆料始终处于较低的温度,只有中部能达到较高的温度,以堆肥温度50℃作为无害化指标,自墙体向中心方向的1m为没有达到无害化厚度,无害化体积占堆肥总体积50%.整个动态堆肥过程符合二级动力学方程.     

Determination of Frequency for Remeasuring Ground and Vegetation Cover Factor Needed for Soil Erosion Modeling

Determining a remeasurement frequency of variables over time is required in monitoring environmental systems. This article demonstrates methods based on regression modeling and spatio-temporal variability to determine the time interval to remeasure the ground and vegetation cover factor on permanent plots for monitoring a soil erosion system. The spatio-temporal variability methods include use of historical data to predict semivariograms, modeling average temporal variability, and temporal interpolation by two-step kriging. The results show that for the cover factor, the relative errors of the prediction increase with an increased length of time interval between remeasurements when using the regression and semivariogram models. Given precision or accuracy requirements, appropriate time intervals can be determined. However, the remeasurement frequency also varies depending on the prediction interval time. As an alternative method, the range parameter of a semivariogram model can be used to quantify average temporal variability that approximates the maximum time interval between remeasurements. This method is simpler than regression and semivariogram modeling, but it requires a long-term dataset based on permanent plots. In addition, the temporal interpolation by two-step kriging is also used to determine the time interval. This method is applicable when remeasurements in time are not sufficient. If spatial and temporal remeasurements are sufficient, it can be expanded and applied to design spatial and temporal sampling simultaneously.     

南方冰冻雨雪灾害年的环流及气象要素异常的统计分析

利用NCEP/NCAR的2.5°×2.5°格点的逐日再分析资料,通过分析我国南方19个冰冻雨雪灾害年的36个冰冻雨雪天气个例的环流形势,总结了发生冰冻雨雪天气的4种天气类型:北脊南槽型、乌山脊型、横槽南支型和高纬低涡型,后两种天气类型持续时间长、影响范围广,极易造成南方严重的持续冰冻雨雪灾害;进一步分析南方持续冰雪天气的物理量场和要素场特征,结果表明,对流层低层850~700 hPa辐合中心较垂直上升速度中心提前1~2 d出现,预示低温雨雪增强;冻雨严重年份在700 hPa维持大于0℃的融化层,南支槽前的西南急流为雨雪天气提供了水汽和热量输送,这种配置利于冻雨天气的发展和维持。天气类型的建立及气象要素的统计有利于为南方冰雪灾害天气预测预警服务。     

Variability and Trends in Runoff Efficiency in the Conterminous United States

Variability and trends in water‐year runoff efficiency (RE) — computed as the ratio of water‐year runoff (streamflow per unit area) to water‐year precipitation — in the conterminous United States (CONUS) are examined for the 1951 through 2012 period. Changes in RE are analyzed using runoff and precipitation data aggregated to United States Geological Survey 8‐digit hydrologic cataloging units (HUs). Results indicate increases in RE for some regions in the north‐central CONUS and large decreases in RE for the south‐central CONUS. The increases in RE in the north‐central CONUS are explained by trends in climate, whereas the large decreases in RE in the south‐central CONUS likely are related to groundwater withdrawals from the Ogallala aquifer to support irrigated agriculture.