Study of statistically correcting model CMAQ-MOS for forecasting regional air quality

Based on analysis of the air pollution observational data at 8 observation sites in Beijing including outer suburbs during the period from September 2004 to March 2005, this paper reveals synchronal and in-phase characteristics in the spatial and temporal variation of air pollutants on a city-proper scale at deferent sites; describes seasonal differences of the pollutant emission influence between the heating and non-heating periods, also significantly local differences of the pollutant emission influence between the urban district and outer suburbs, i.e. the spatial and temporal distribution of air pollutant is closely related with that of the pollutant emission intensity. This study shows that due to complexity of the spatial and temporal distribution of pollution emission sources, the new generation Community Multi-scale Air Quality (CMAQ) model developed by the EPA of USA produced forecasts, as other models did, with a systematic error of significantly lower than observations, albeit the model has better capability than previous models had in predicting the spatial distribution and variation tendency of multi-sort pollutants. The reason might be that the CMAQ adopts average amount of pollutant emission inventory, so that the model is difficult to objectively and finely describe the distribution and variation of pollution emission sources intensity on different spatial and temporal scales in the areas, in which the pollution is to be forecast. In order to correct the systematic prediction error resulting from the average pollutant emission inventory in CMAQ, this study proposes a new way of combining dynamics and statistics and establishes a statistically correcting model CMAQ-MOS for forecasts of regional air quality by utilizing the relationship of CMAQ outputs with corresponding observations, and tests the forecast capability. The investigation of experiments presents that CMAQ-MOS reduces the systematic errors of CMAQ because of the uncertainty of pollution emission inventory and improves the forecast level of air quality. Also this work employed a way of combining point and area forecasting, i.e. taking the products of CMAQ for a center site to forecast air pollution for other sites in vicinity with the scheme of model products "reanalysis" and average over the "area".     

超低频电磁声源非线性动力学建模与分析

电磁声源输出力大、体积小、易于实现超低频输出,在无人反水雷作战中具有显著优势。其动力学行为受到机械回复力和电磁力的耦合作用影响,当施加电流超过坍塌电流,电磁力将超过机械回复力, 就会发生吸和,造成声源的损坏。为准确描述电磁声源非线性动力学特性,预先评估坍塌电流,建立考虑动态漏磁系数的声源非线性动力学模型。通过三维有限元仿真计算动铁芯运动到不同位置处气隙的漏磁系数, 拟合得到动态漏磁系数。根据等效磁路法建立声源的改进电磁力模型,进而建立电磁声源非线性动力学模型。 通过 Runge-Kutta 算法计算得到阶跃激励下声源振动的位移和速度,绘制相平面图。研究稳定与失稳 2 种情况下的相轨迹的动态变化规律,为电磁声源的设计和控制提供理论支撑。     

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。     

Effect of the Initial Vortex Structure on Intensity Change During Eyewall Replacement Cycle of Tropical Cyclones: A Numerical Study

This study investigates the effect of the initial tropical cyclone (TC) vortex structure on the intensity change during the eyewall replacement cycle (ERC) of TCs based on two idealized simulations using the Weather Research and Forecasting (WRF) model. Results show that an initially smaller TC with weaker outer winds experienced a much more drastic intensity change during the ERC than an initially larger TC with stronger outer winds. It is found that an initially larger TC vortex with stronger outer winds favored the development of more active spiral rainbands outside the outer eyewall, which slowed down the contraction and intensification of the outer eyewall and thus prolonged the duration of the concentric eyewall and slow intensity evolution. In contrast, the initially smaller TC with weaker outer winds corresponded to higher inertial stability in the inner core and weaker inertial stability but stronger filamentation outside the outer eyewall. These led to stronger boundary layer inflow, stronger updraft and convection in the outer eyewall, and suppressed convective activity outside the outer eyewall. These resulted in the rapid weakening during the formation of the outer eyewall, followed by a rapid re-intensification of the TC during the ERC. Our study demonstrates that accurate in- itialization of the TC structure in numerical models is crucial for predicting changes in TC intensity during the ERC. Additionally, monitoring the activity of spiral rainbands outside the outer eyewall can help to improve short-term intensity forecasts for TCs experiencing ERCs.     

可解释性长短期记忆模型用于预测湖泊总磷浓度变化

对湖泊总磷的变化预测和来源识别对水资源调度和流域生态治理有着重要的意义,然而复杂的生化反应和水动力条件导致的非平稳性给湖泊总磷浓度的准确预测带来极大的困难。为克服这一挑战,本文引入了基于加权回归的季节趋势分解(seasonal and trend decomposition using Loess,STL)技术和夏普利加法(SHapley additive exPlanations,SHAP)结合长短期记忆网络(long short-term memory neural network,LSTM)和门控循环单元(gated recurrent unit,GRU)构建了一个可解释的预测框架,以增强对湖泊总磷浓度演变的预测并提高其可解释性。研究表明:(1)在骆马湖总磷浓度的预测中,该框架拥有较好的预报精度(R²=0.878),优于LSTM和卷积长短期记忆模型(convolutional neural networks and long short term memory network,CNN-LSTM)。当预测时间步长增加到8 h时,该框架有效提高了总磷浓度的预测精度,平均相对误差和均方根误差分别降低了47.1%和33.3%。从预测趋势来看,骆马湖在汛期的总磷平均浓度为0.158 mg/L,相较于非汛期的平均浓度,增加了202.1%。(2)运河来水是骆马湖总磷浓度最重要的影响因素,贡献权重为60.0%,并且不同断面(三湾、三场)的污染源受水动力、气象等因素的影响存在显著的时空差异。本文凸显了神经网络模型在预警水体污染方面的可实施性,并且为提高传统神经网络的学习能力和可解释性的开发与验证提供了重要方向。     

太湖表层沉积物多环芳烃的空间分布、来源及其风险

随着太湖流域社会与经济的发展,多环芳烃(PAHs)在各种环境介质中逐渐累积,污染日益严重,可能对太湖生态环境及周边人体健康构成威胁。为探究太湖沉积物PAHs的来源及生态风险,于2021年12月在太湖采集30个表层沉积物样品,利用气相色谱-质谱联用仪(GC-MS)检测样品中16种PAHs含量;利用受体模型和苯并[a]芘(BaP)毒性当量法进行来源解析及生态风险评估,并将各来源贡献与毒性当量浓度相结合,量化源风险。结果表明,太湖表层沉积物中16种PAHs总含量介于124~592 ng/g之间,平均值为294 ng/g,中值为279 ng/g;高环多环芳烃(HMW PAHs)为主要组分,占∑PAHs的67%。高含量区域位于竺山湾、梅梁湾、贡湖湾和西太湖,与国内外其他湖泊沉积物相比,太湖沉积物PAHs含量处于较低水平。源解析的结果表明,太湖表层沉积物中PAHs交通排放源贡献率为29.1%、煤炭燃烧源贡献率为26.7%、生物质燃烧源贡献率为28.7%、石油源贡献率为15.6%。生态风险评价结果表明,交通排放源、生物质燃烧源、煤炭燃烧源和石油源的BaP毒性当量含量(TEQ_BaP)均值分别为19.34、17.81、16.33和9.1 ng/g,均小于70 ng/g,几乎处于无风险水平。西太湖、贡湖湾和梅梁湾的部分区域ΣTEQ_BaP大于70 ng/g属于潜在风险区,具有一定潜在毒性。在后续的污染治理中应重点关注太湖西北部地区污染物的排放。本研究可为沉积物中PAHs污染的研究提供数据支撑,为地方政府精准、高效地管控PAHs污染提供理论依据。     

葛洲坝至胭脂坝长江干流段江豚活动规律

长江江豚是我国一级保护野生动物,也是长江生态系统的指示性物种,其种群数量恢复情况及活动规律一直备受关注。本文选择长江宜昌段江豚活动频繁的葛洲坝下游至胭脂坝江段为研究区域,于2021年6月-2022年5月采用定点目测、水上流动监测与无人机监测相结合的方法分区监测长江江豚的游泳行为,分析长江江豚的活动规律,构建了长江江豚出水头次的零膨胀泊松回归模型,识别影响长江江豚出水头次的关键因素,建立长江江豚出水头次与各因素间的相关关系。结果表明:(1)葛洲坝至胭脂坝长江干流段监测到最大江豚出水头次为19头次,长江江豚集群规模以2~3头最为常见,占比达58.1%;长江江豚主要表现出4种行为特征,各行为占比从大到小依次为:玩耍＞休息＞摄食＞抚幼。(2)长江江豚在葛洲坝下游近坝区(A区,葛洲坝至至喜长江大桥)出水头次最多,且在秋冬季节累计出水头次多于春夏季节,冬季累计出水头次最高,达252头次。(3)长江江豚出水头次与电站下泄流量呈显著负相关;浊度增大,长江江豚出水的概率减小。本文研究结果对长江江豚生态保护策略及长江十年禁渔效果评估具有重要参考价值。     

湘中南二叠系孤峰组硅质岩的成因属性及其地球动力学指示意义

华南二叠系底部的硅质岩及同一层位对应于华南重要的地幔强活动期和成矿期，有非常重要的动力学指示意义。研究表明，孤峰组剖面的岩性可划分为三段：泥页岩段、厚层状硅质岩段和薄层状硅质岩段。沿剖面自下而上，Al2O3、K2O和TiO2含量逐渐降低，MnO、TFe等组分逐渐升高；稀土元素显示出一定的铈负异常，∑REE偏低，且向上∑REE逐渐减少而HREE／LREE逐渐增高；一些热水沉积诊断性微量元素如As、Sb、Hg等也显示出一定程度的异常。这些特征均表明硅质岩的成岩过程中包含有正常沉积和热水沉积的共同作用，且自下而上热水沉积作用逐渐增强的趋势。这种成岩作用的演化特征，代表了下扬子地台拉张裂陷盆地对晚古生代伸展背景的沉积响应，对于探讨裂谷盆地阶段性发育的动力学特征有一定的指示意义。     

川西理塘第四纪盆地沉积体系、演化及古气候分析

理塘第四纪盆地以河湖相沉积为主，其形成于747ka，消亡于179～180ka。盆地内不同地点的沉积物的时空分布，揭示出古湖泊演化属于一种不连续的扩展型，在其演化过程中存在着多次的消失或萎缩时期；盆地内孢粉组合表明，其形成于一种温热湿润的气候环境，当时的海拔高度在2000m左右，以后高原气候由热变温；冰川发育加剧了古无量河的溯源侵蚀作用，最终切穿盆地，致使湖水外泄，湖泊消亡。理塘盆地的演化历程为研究青藏高原的古气候变化提供了重要证据。     

OC4漂浮式风电平台的水弹性响应及波浪载荷研究

漂浮式风电平台在波浪中的摇晃运动会导致塔架根部产生很大的弯矩,极大地威胁平台结构安全。同时流体的附连水质量效应对平台塔架结构的动响应有着较为显著的影响,因此充分考虑这种流固耦合效应有利于更加准确地评估平台塔架的结构动响应。采用三维频域线性水弹性方法计算塔架在流体作用下的结构共振频率特征以及各阶模态振动的主坐标响应特性,然后通过弹性模态叠加法获得塔架的弯矩载荷。并结合概率学手段,预报了短期极限海况下塔架弯曲载荷响应,为新一代漂浮式风电平台结构安全性评估和方案设计提供了技术支撑。