Assessing the Comparability of Ammonium,Nitrate and Sulfate Concentrations Measured by Three Air Quality Monitoring Networks

Airborne fine particulate matter across the United States is monitored by different networks, the three prevalent ones presently being the Clean Air Status and Trend Network (CASTNet), the Interagency Monitoring of PROtected Visual Environment Network (IMPROVE) and the Speciation and Trend Network (STN). If combined, these three networks provide speciated fine particulate data at several hundred locations throughout the United States. Yet, differences in sampling protocols and samples handling may not allow their joint use. With these concerns in mind, the objective of this study is to assess the spatial and temporal comparability of the sulfate, nitrate and ammonium concentrations reported by each of these networks. One of the major differences between networks is the sampling frequency they adopted. While CASTNet measures pollution levels on seven-day integrated samples, STN and IMPROVE data pertain to 24-hour samples collected every three days. STN and IMPROVE data therefore exhibit considerably more short-term variability than their CASTNet counterpart. We show that, despite their apparent incongruity, averaging the data with a window size of four to six weeks is sufficient to remove the effects of differences in sampling frequency and duration and allow meaningful comparison of the signals reported by the three networks of concern. After averaging, all the sulfate and, to a lesser degree, ammonium concentrations reported are fairly similar. Nitrate concentrations, on the other hand, are still divergent. We speculate that this divergence originates from the different types of filters used to collect particulate nitrate. Finally, using a rotated principal component technique (RPCA), we determined the number and the geographical organization of the significant temporal modes of variation (clusters) detected by each network for the three pollutants of interest. For sulfate and ammonium, the clusters’ geographical boundaries established for each network and the modes of variations within each cluster seem to correspond. RPCA erformed on nitrate concentrations revealed that, for the CASTNet and IMPROVE networks, the modes of variation do not correspond to unified geographical regions but are found more sporadically. For STN, the clustered areas are unified and easily delineable. We conclude that the possibility of jointly using the data collected by CASTNet, IMPROVE and STN has to be weighed pollutant by pollutant. While sulfate and ammonium data show some potential for joint use, at this point, combining the nitrate data from these monitoring networks may not be a judicious choice.     

云南东北部拱王山末次冰期

1 Introduction The Gongwang mountains constitute one of the few high m ountains of irrefutable late Pleistocene glaciation in eastern China. This area is one of the m ost extensively studied Quaternary geologicallocationsin eastern China and the interpret…     

Analysis on Bearing Capacity of Tunnel-Type Anchorage of a Long-Span Suspension Bridge

INTRODUCTION Tunnel typeanchoragehasobviousadvantages inbearingcapacityversusinvestment(LiuandHu,1996).However,itisrarelyusedinapracticalpro jectbecauseofitsrequirementofgoodrockcondi tions.Siduhebridge(Fig.1),whichliesintheBa dongmountainsinthewestplatea…     

北祁连山白山子花岗闪长岩成岩时代

寒山大型金矿是近年来在北祁连山西段发现的与侵人岩有关的构造蚀变岩型金矿。笔者首次利用锆石U-Pb方法测得白山子花岗闪长岩的形成年龄为370±25 Ma，属于华力西期。寒山金矿的成矿主要在213.95~339 Ma间。在多期热液叠加，多期成矿作用中，早期成矿的热液很可能是白山子花岗闪长岩（370±25 Ma) ,寒山辉长岩(347.1±6. 4 Ma）共同提供的。由于该区有较多的中酸性岩体存在，它们可能为金的主要来源，因而，确定这些侵入体的形成年龄，对于在该区寻找蚀变岩型金矿不仅有重要的理论意义，而且有重要的现实意义。     

基于YOLO算法的海洋中尺度涡三维结构构建

海洋中尺度涡是一种常见的中尺度海洋现象,研究海洋中尺度涡的分布及运动特性对航运、气候、军事等具有重要作用,海洋中尺度涡的识别是海洋学和计算机科学领域的一个热门研究课题。运用深度学习的方法和框架,对中尺度涡的二维识别和三维结构构建展开研究分析。首先,获取全球海洋再分析数据并进行流线可视化,构建涡旋流线数据集;其次,利用YOLO v5s卷积神经网络对涡旋流线数据集进行训练,并对南海区域中尺度涡进行有效检测。实验结果表明,YOLO v5s训练后得到最优模型经过测试,平均检测精度均值达到了86.10%;最后,根据涡旋检测结果,对检测出的同时刻不同深度的涡旋判断是否属于同一涡旋,确定后进行该涡旋的三维结构构建。     

充分利用海洋环境提升声呐装备性能的研究

声呐装备的性能发挥与海洋环境密切相关。研究海洋环境对声呐装备的影响,积极推动环境适配型声呐装备的发展,对于综合利用水下探测和通信手段,提升我国水下信息化能力具有重要意义。着重分析典型海洋环境和过程对声呐性能的影响,包括不规则海底边界、海水的介质特性以及多尺度海洋动力过程, 通过算例对比说明环境效应对声呐的影响。进而,针对声呐装备的使用和发展,围绕环境适应与适配提出相关建议与措施。     

安哥拉东北部地区Inkisi组碎屑锆石U- Pb年龄、地球化学特征及其地质意义

Inkisi组是刚果盆地西南缘最古老的沉积地层之一,岩性主要为一套紫红色、红褐色细粒、细中粒长石砂岩、杂砂岩。通过对安哥拉东北部Inkisi组长石砂岩开展碎屑锆石测年、岩石地球化学研究,探讨其沉积时代、物质来源、沉积环境和大地构造背景,为研究刚果盆地演化提供科学依据。结果表明,碎屑锆石206Pb/238U年龄主要集中于3个年龄峰值区间2350~1900 Ma、1150~850 Ma、850~500 Ma,最年轻的锆石峰值年龄为531±9 Ma,据此,笔者等认为Inkisi组的沉积时代上限应厘定为早寒武世。Inkisi组砂岩地球化学特征显示,物源具有长英质物源近源搬运特征,沉积时期的水体为陆相开阔的淡水环境。通过碎屑锆石年龄谱特征、主微量元素物源判别及构造判别图解,本文认为,West Congo构造带为安哥拉东北部地区Inkisi组主要物源区,Lufilian构造带、Angola 地盾等是其次要物质来源,物源区构造环境主要为活动大陆边缘和被动大陆边缘构造环境背景。     

基于ROSE2.0的普洱地区CINRAD/CC雷达冰雹探测算法评估及参数本地化

为提高冰雹探测算法（Hail Detection Algorithm,HDA）产品的可用性,针对2015—2020年普洱地区监测到的22次冰雹个例,利用新一代雷达业务应用软件ROSE2.0对相关雷达基数据进行回放及产品分析,以命中率、虚警率、临界成功指数为指标对HDA算法在普洱地区的识别效果进行评估并给出本地化参数配置方案。结果表明:HDA算法在普洱地区命中率接近100%,但虚警现象非常普遍,使用强冰雹概率（Probability of Severe Hail,POSH）的预警效果优于任意大小冰雹概率（Probability of Hail,POH）,且冰雹尺寸越大POSH虚警的概率越低。进一步使用模拟测评法对POSH算法的适配参数进行分析,发现正确输入降雹日当天的0 ℃层和-20 ℃层高度能有效减少POSH的虚警率及提高临界成功指数;同时使算法预测的最大冰雹直径普遍偏大的情况得到控制,其中,中小冰雹直径偏离百分比减小76.07%,改善效果显著高于大冰雹。此外,增大反射率因子及POSH阈值能有效控制虚警,但也导致漏报次数快速增加,当阈值太大时命中率明显降低,为了保证较高的命中率和临界成功指数,选择Z=50 dBz或POSH=70%为阈值能明显改善HDA算法的识别效果。     

Influence of Near Real-Time Green Vegetation Fraction Data on Numerical Weather Prediction by WRF over North China

The green vegetation fraction(GVF) can greatly influence the partitioning of surface sensible and latent heat fluxes in numerical weather prediction(NWP) models. However, the multiyear averaged monthly GVF climatology—the most commonly used representation of the vegetation state in models—cannot capture the real-time vegetation state well. In this study, a near real-time(NRT) GVF dataset generated from an 8-day composite of the normalized difference vegetation index is compared with the 10-yr averaged monthly GVF provided by the WRF model. The annual variability of the GVF over North China is examined in detail. Many differences between the two GVF datasets are found over dryland, grassland, and cropland/grassland mosaic areas. Two experiments using different GVF datasets are performed to assess the impacts of GVF on forecasts of screen-level temperature and humidity. The results show that using NRT GVF can lead to a widespread reduction of 2-m temperature forecast errors from April to October.Evaluation against in-situ observations shows that the positive impact on 2-m temperature forecasts in the morning is more distinct than that in the afternoon. Our study demonstrates that NRT GVF can provide a more realistic representation of the vegetation state, which in turn helps to improve short-range forecasts in arid and semiarid regions of North China. Moreover, our study shows that the negative effect of using NRT GVF is closely related to the initial soil moisture.