Spatial variations in daily average CO_2 concentrations above wetland surface of Xianghai National Nature Reserve, China

Horizontal and vertical variations of daily average CO 2 concentration above the wetland surface were studied in Xianghai National Nature Reserve of China in August, 2000 The primary purpose was to study spatial distribution characteristics of CO 2 concentration on the four levels of height(0 1 m, 0 6 m, 1 2 m and 2 m) and compare the differences of CO 2 concentration under different land covers. Results showed that daily average CO 2 concentration above wetland surface in Xianghai National Natural Reserve was lower than that above other wetlands in northeast China as well as the worldwide average, suggesting that Xianghai wetland absorbed CO 2 in August and acted as “sink” of CO 2 The horizontal variations on the four levels of height along the latitude were distinct, and had the changing tendency of “decreasing after increasing” with the increase of height. The areas with obvious variations were consistent on different levels of height, and those with the highest variations appeared above surface of shore, sloping field, Typha wetland and Phragmites wetland; the vertical variations were greatly different, with the higher variations in Phragmites wetland and Typha wetland, and the lands near the shore and the sloping field with the lower variations. Spatial variations of daily average CO 2 concentrations above wetland surface were affected by surface qualities and land covers.

Comparison of depth-averaged concentration and bed load flux sediment transport models of dam-break flow

This paper presents numerical simulations of dam-break flow over a movable bed. Two different mathematical models were compared: a fully coupled formulation of shallow water equations with erosion and deposition terms(a depth-averaged concentration flux model), and shallow water equations with a fully coupled Exner equation(a bed load flux model). Both models were discretized using the cell-centered finite volume method, and a second-order Godunov-type scheme was used to solve the equations. The numerical flux was calculated using a Harten, Lax, and van Leer approximate Riemann solver with the contact wave restored(HLLC). A novel slope source term treatment that considers the density change was introduced to the depth-averaged concentration flux model to obtain higher-order accuracy. A source term that accounts for the sediment flux was added to the bed load flux model to reflect the influence of sediment movement on the momentum of the water. In a onedimensional test case, a sensitivity study on different model parameters was carried out. For the depth-averaged concentration flux model,Manning's coefficient and sediment porosity values showed an almost linear relationship with the bottom change, and for the bed load flux model, the sediment porosity was identified as the most sensitive parameter. The capabilities and limitations of both model concepts are demonstrated in a benchmark experimental test case dealing with dam-break flow over variable bed topography.

Average cluster sizes and cluster size distributions of superfine nickel particles in light media

Effects of shear rates on average cluster sizes (ACSs) and cluster size distributions (CSDs) in uni- and bi-systems of partly charged superfine nickel particles were investigated by Brownian dynamics, and clustering properties in these systems were compared with those in non-polar systems. The results show that the ACSs in bi-polar systems are larger than those in the non-polar systems. In uni-polar systems the behavior of clustering property differs: at the lower ionic concentration (10%), repulsive force is not strong enough to break clusters, but may greatly weaken them. The clusters are eventually cracked into smaller ones only when concentration of uni-polar charged particles is large enough. In this work, the ionic concentration is 20%. The relationship between ACS and shear rates follows power law in a exponent range of 0.176-0.276. This range is in a good agreement with the range of experimental data, but it is biased towards the lower limit slightly.

Autonomous Changes in the Concentration of Water Vapor Drive Climate Change

When compared to the average annual global temperature record from 1880, no published climate model posited on the assumption that the increasing concentration of atmospheric carbon dioxide is the driver of climate change can accurately replicate the significant variability in the annual temperature record. Therefore, new principles of atmospheric physics are developed for determining changes in the average annual global temperature based on changes in the average atmospheric concentration of water vapor. These new principles prove that: 1) Changes in average global temperature are not driven by changes in the concentration of carbon dioxide;2) Instead, autonomous changes in the concentration of water vapor, ΔTPW, drive changes in water vapor heating, thus, the average global temperature, ΔTAvg, in accordance with this principle, ΔTAvg=0.4ΔTPW the average accuracy of which is ±0.14%, when compared to the variable annual, 1880-2019, temperature record;3) Changes in the concentration of water vapor and changes in water vapor heating are not a feedback response to changes in the concentration of CO2;4) Rather, increases in water vapor heating and increases in the concentration of water vapor drive each other in an autonomous positive feedback loop;5) This feedback loop can be brought to a halt if the average global rate of precipitation can be brought into balance with the average global rate of evaporation and maintained there;and, 6) The recent increases in average global temperature can be reversed, if average global precipitation can be increased sufficiently to slightly exceed the average rate of evaporation.

Erratum to “Autonomous Changes in the Concentration of Water Vapor Drive Climate Change” [Atmospheric and Climate Sciences 10 (2020) 443-508]

A. Changes in average global temperature are not driven by changes in the concentration of carbon dioxide; B. Instead, autonomous changes in the concentration of water vapor, ΔTPW, drive changes in water vapor heating, thus, changes in the average global temperature, ΔTAvg, in deg. Celsius are calculated in accordance with this principle, measured in kg·m-2, the average accuracy of which is ±0.14%, when compared to the variable annual, 1880 - 2019, average global temperature record;

东北黑土区部分地域除草剂在不同作物类型土壤中残留及分布特征

为了解并掌握东北部分黑土区农田土壤除草剂残留情况,本研究调查了东北黑土区6个地区170份土壤样本,共检出17种除草剂。总体来看,乙草胺检出率最高,为74.7%,氟磺胺草醚平均残留量最高,为198.88μg·kg^(-1)。从省份来看,辽宁省除草剂检出种类最为丰富,黑龙江省除草剂残留量最高。其中,铁岭市、阜新市与长春市、四平市旱田除草剂残留情况更为相似,绥化市土壤除草剂残留情况与前者差异较大。从作物类型来看,水稻田土壤除草剂检出种类更多,旱田作物除草剂残留量更高。其中,水稻田共检出11种类型除草剂,氟磺胺草醚在玉米田和大豆田中的平均残留量分别高达68.77μg·kg^(-1)和409.30μg·kg^(-1)。研究表明,地理位置和作物类型是影响土壤除草剂残留的重要因素。

1979-2022年北极海冰变化及影响因素分析

海冰是海洋-大气交互系统的重要组成部分,是全球气候变化的指示器。深入分析海冰演变规律,探索全球气候变化与海冰范围之间的关联,对应对和减缓全球气候变化具有重要的理论意义。本文以北极海冰为研究对象,深入分析了1979-2022年北极海冰范围的季节、年际和年代际变化特征,并构建向量自回归模型(Vector AutoRegressive Model,VAR)检验全球平均气温、大气CO_(2)浓度与北极海冰范围之间的因果关系,并分析影响程度。结果表明:(1)北极海冰范围季节变化特征明显,一般在3月达到最大,9月达到最小,这主要与太阳辐射的年变化周期相关;(2)1979-2022年,北极海冰范围总体呈减小趋势,年变化量为5.3万km^(2);(3)北极海冰范围在年代际尺度上逐渐减小,2000-2009年,北极海冰范围较上一个十年减小最多(减少67万km^(2));(4)全球平均气温和大气CO_(2)浓度均对北极海冰范围变化造成了显著影响;(5)全球平均气温和大气CO_(2)浓度与北极海冰范围均有显著负相关关系,相关系数分别为-0.92和-0.95。

掺氢天然气管道泄漏危险介质激光检测扫描方案研究

为提高掺氢天然气管道危险介质检测效率,以流体力学为基础,进行不同工况下危险介质激光检测路径设置分析,并在不同环境风速下对危险介质流体力学进行仿真研究。研究结果表明:随着风速的不断增大,泄漏口与激光器间隔距离变宽,激光距离5 m时的扫描策略优势更明显。

基于ARIMA-IPOA-CNN-LSTM的太湖水体溶解氧浓度预测模型

为了提高太湖水体中溶解氧浓度(DOC)参数的预测准确性,设计了一种基于ARIMA-IPOA-CNN-LSTM的预测模型。首先,采用差分自回归移动平均模型(ARIMA)捕捉数据的时间序列趋势和季节性特征;其次,引入卷积神经网络(CNN)和长短期记忆网络(LSTM)模型,分别从数据中学习空间和时间特征;再次,提出了一种改进的鹈鹕优化算法(IPOA)来优化模型参数,算法增加了Logistic混沌映射种群初始化、反向差分进化、萤火虫扰动的方法,CEC2005函数的测试结果显著优于传统鹈鹕优化算法;最后,将“剪枝”模型部署于STM32嵌入式设备。试验结果表明,在溶解氧浓度预测方面,该模型具有高的准确性和鲁棒性,为水环境保护提供了一种高效、可靠的解决方案。

黄土高原近30年降水集中度分析及时空演变特征

选取近30年的黄土高原地区58个气象站点的降水数据,分析了黄土高原年降水以及降水集中度(PCD)和集中期(PCP)的时空变化特征,结果表明:(1)黄土高原地区年均降水量在50~900 mm,空间分布上东南高西北低。降水高值区分布在三门峡站与武功站附近以及五台山站,年均降水量为900 mm;低值区位于临河站附近,年均降水量为50 mm;(2)黄土高原地区的降水集中度分布于0.43~0.76,自东南向西北递增;而降水集中期的变化则不太明显,多集中于夏季的7-8月份。因此在降水集中的7-8月份,水土流失会比较加重,应加强水土流失防治工程,在降水量大的月份做好灾害防护,为工农业生产提供预警,减少工农业生产损失与经济损失。

祁连山北坡一次秋季对流性降水雨滴谱特征分析

利用Parsivel2激光雨滴谱仪观测数据,分析了祁连山北坡2020年9月21日发生的一次对流性降水天气的雨滴谱特征。结果表明:雨水含量W和降水强度R的时间演变一致,各站点雨滴数浓度N(D)最大时对应雨滴直径D均<1 mm,出现在层状云降水期间。在对流云雨增强至最大阶段,雨滴直径D迅速增大,各站点最大雨滴直径D在2.75~3.75 mm之间。高海拔站总数浓度NT平均值大于低海拔站,低海拔站质量加权平均直径D_(m)平均值大于高海拔站。对流云降水D_(m)显著大于层状云降水且分布相对集中,层状云降水D_(m)分布有更大谱宽。对流云降水logNw分布相对集中,层状云降水logNw分布有更大谱宽。Gamma函数能较好拟合祁连山区对流云和层状云降水平均谱。对流云、层状云μ-λ有很好的拟合关系。D_(m)-R拟合系数和指数均为正数,D_(m)随R增强而增大,并在雨强达到一定值后变化有变缓的趋势。层状云降水logNw随R增强变化较快,对流云降水logNw随R增大缓慢。业务雷达默认使用的夏季对流云降水和经典大陆性层状云降水Z-R关系对祁连山区对流云、层状云降水估测值偏低。

重型柴油车NO_(x)排放因子与其浓度相关性研究

重型柴油车排放的NO_(x)污染物对环境空气质量影响较大,受排放标准、排放控制技术水平、检测方法、驾驶工况等众多因素的影响,当前较难整体评估在用重型柴油车的实际排放状况.本研究首先通过发动机排放台架试验及实际道路排放(PEMS)循环试验,探究了重型柴油车NO_(x)排放因子与NO_(x)平均浓度之间的内在联系;其次,研究了远程监控有效数据筛选规则,提出了一种基于NO_(x)日均浓度评估远程监控重型柴油车NO_(x)排放因子的方法.结果表明:重型柴油车发动机排放台架、PEMS循环试验的NO_(x)排放因子均与NO_(x)平均浓度呈较强相关性,相关系数(R^(2))为0.99.通过远程监控平台监测数据验证了重型柴油车的NO_(x)排放因子与NO_(x)日均浓度有一定相关性,R^(2)高于0.9(p<0.01).因此,可用重型柴油车排放的NO_(x)日均浓度来表征整车污染物排放情况.研究显示,安装远程终端的第五和第六排放阶段的重型柴油车,分别按照NO_(x)日均浓度低于200×10^(−6)和50×10^(−6)来判定排放稳定达标重型柴油车,按照NO_(x)日均浓度高于900×10^(−6)和500×10^(−6)来判定高排放重型柴油车,监管部门和车辆生产企业可利用该指标快速筛选排放稳定达标以及高排放重型柴油车.

Feasibility of using Vis-NIR spectroscopy and PXRF spectrometry to estimate regional soil cadmium concentration

Conventionally,soil cadmium(Cd)measurements in the laboratory are expensive and timeconsuming,involving complex processes of sample preparation and chemical analysis.This study aimed to identify the feasibility of using sensor data of visible near-infrared reflectance(Vis-NIR)spectroscopy and portable X-ray fluorescence spectrometry(PXRF)to estimate regional soil Cd concentration in a time-and cost-savingmanner.The sensor data of Vis-NIR and PXRF,and Cd concentrations of 128 surface soils from Yunnan Province,China,were measured.Outer-product analysis(OPA)was used for synthesizing the sensor data and Granger-Ramanathan averaging(GRA)was applied to fuse the model results.Artificial neural network(ANN)models were built using Vis-NIR data,PXRF data,and OPA data,respectively.Results showed that:(1)ANN model based on PXRF data performed better than that based on Vis-NIR data for soil Cd estimation;(2)Fusion methods of both OPA and GRA had higher predictive power(R^(2))=0.89,ratios of performance to interquartile range(RPIQ)=4.14,and lower root mean squared error(RMSE)=0.06,in ANN model based on OPA fusion;R^(2)=0.88,RMSE=0.06,and RPIQ=3.53 in GRA model)than those based on either Vis-NIR data or PXRF data.In conclusion,there exists a great potential for the combination of OPA fusion and ANN to estimate soil Cd concentration rapidly and accurately.

基于MADF-DE-GRU的瓦斯浓度动态预测

为保障煤矿回采工作面安全生产,提高瓦斯浓度预测精度,本文提出一种基于门控循环单元(GRU)神经网络的瓦斯浓度动态预测模型。首先,用差分法改进的移动平均滤波器(MAF)去除原始数据中的噪声和趋势;其次,利用差分进化算法(DE)对GRU神经网络的隐藏层数、隐藏层神经元数量、时间步长和迭代次数等超参数进行寻优;最后,通过优化后的超参数搭建瓦斯浓度预测模型(MADF-DE-GRU)。通过仿真实验,并与多种预测模型进行对比,结果表明MADF-DE-GRU模型具有较高的预测精度,预测效果较好,能够应用到实际生产中。

淮安市公园绿地空间分布及其可达性分析

利用高德地图公开的公园兴趣点数据、天地图在线影像以及淮安市第七次人口普查数据,使用平均最近邻指数、标准差椭圆分析法、地理集中指数和网格化的高斯两步移动搜索法等多种方法研究淮安市绿地空间的分布、变化趋势,并对可达性进行评价。通过卫星影像解译提取研究区绿地面积,发现淮安市绿地数量上以小型点状绿地为主、面积上呈现以大型点状绿地占主导的分布格局;研究区绿地呈聚集态势;绿地呈现东北-西南方向的分布;各区的绿地分布不平衡,呈现集中特征;可达性是以公园绿地为中心,具有明显的“圈层式”分布特征,高值区出现在中间区域,即主城区,可达性范围在0—1303.56之间,仍有较高水平。认为淮安市绿地分布呈现明显的聚集特征,分布不均衡,且具有较高的可达性。

基于Airpak的幼儿园生活单元空气质量模拟分析

通过对幼儿园生活单元建立物理数学模型,基于Airpak模拟分析了排风量、新风量、新风口位置以及室外风速对室内空气品质的影响。结果表明,门窗关闭的室内,仅设置排风并不能有效降低室内CO_(2)平均浓度;引入新风对降低室内CO_(2)平均浓度效果显著;新风口距离人员越近,并非越有利于降低室内CO_(2)平均浓度;过渡季节,开启门窗室内CO_(2)平均浓度水平明显降低。

基于调峰场景下的亚临界切圆燃烧锅炉灵活性挖掘

为了提高燃煤发电机组锅炉低负荷稳燃、宽负荷脱硝、快速变负荷速率、快速启停和调峰辅助服务等能力,挖掘亚临界切圆燃烧锅炉灵活性潜力,以某电厂600 MW亚临界燃煤机组为例,采用直流燃烧器垂直浓淡改造技术、磨煤机动态分离器技术和省煤器旁路烟道技术对锅炉进行综合改造。通过锅炉性能试验,比较分析了改造前后锅炉的主要性能指标。试验结果表明:改造后机组最低稳定燃烧负荷由额定负荷的40%下降到30%;改造后,最低稳定燃烧负荷下脱硝系统入口NO_(x)平均质量浓度为239.73 mg/Nm^(3),比改造前低168.53 mg/Nm^(3);经过动态分离器改造的磨煤机煤粉细度R_(90)可实现25%~38%的调节范围,煤粉细度R_(200)可实现2%~5%的调节范围;改造后,30%额定负荷脱硝系统入口烟气温度高于300℃。600 MW亚临界机组灵活性改造的成功案例,可为火电机组柔性化转型提供借鉴和启示。

玉溪市2010—2019年流感流行特征与发病趋势预测

目的:分析玉溪市2010—2019年流感的流行特征,建立自回归综合移动平均(ARIMA)模型预测流感发病趋势。方法:采用描述性流行病学方法进行数据统计分析,用SPSS 25.0软件建立ARIMA模型。结果:玉溪市共检测流感样病例9925例,检出核酸阳性2849例,阳性率为28.71%,流感阳性病例数大体上呈逐年上升趋势,平均增长速度为2.01%;流感阳性病例中,男女比为1.25∶1,5~14岁儿童占比最高(占40.86%)。流感具有较强的季节性(集中度为0.60)。各年度优势流感病毒株逐年变化,2010年和2018年以A/H1N1 pdm为主,2011年和2012年以B/Victoria为主,2013年和2016年以B/Yamagata为主,2015年和2017年以A/H3N2为主,2014年以B/Yamagata和A/H1N1 pdm为主,2019年以B/Victoria和A/H3N2为主。最佳预测模型为ARIMA(1,0,0)(0,1,1)_(12),其标准化BIC值为5.873,Ljung-Box Q为21.258,P=0.169,预测值和实际值均在95%置信区间内,平均相对误差为10.14%,利用该模型预测2020年1—6月流感发病呈下降趋势。结论:玉溪市流感具有较强季节性且流感阳性病例数大体上呈逐年上升趋势,优势流感毒株逐年变化,ARIMA(1,0,0)(0,1,1)_(12)模型可以有效地预测玉溪市短期流感发病趋势。

基于机器学习的星载短波红外CO_(2)柱浓度估算

利用OCO-2卫星遥感数据、全球碳柱总量观测网(TCCON)站观测数据、NDVI归一化植被指数数据、ERA5大气数据,采用决策树和集成学习(XGBoost、普通随机森林、极端随机森林、梯度提升)对二CO_(2)平均柱浓度进行预测.通过相关性分析、特征选择与特征提取,建立模型预测CO_(2)平均柱浓度,再与TCCON站点的地基观测数据进行比对.通过分析不同模型(决策树、XGBoost、普通随机森林、极端随机森林、梯度提升)预测的结果,发现使用极端随机森林回归模型预测CO_(2)平均柱浓度的精度最高,R^(2)、均方根误差(RMSE)、平均绝对误差(MAE)、平均相对误差(MRE)分别为:0.953、0.492×10^(-6)、0.260×10^(-6)、0.063%,其余模型次之,因此对极端随机森林回归模型的预测性能随自身参数的影响进行了分析,结果表明,在误差允许的范围内(±2×10^(-6)),极端随机森林回归模型和梯度提升回归模型预测的准确率一样,都为98.10%.由于CO_(2)的背景浓度较高,而边界层内CO_(2)浓度的空间差异相对较小,因此需要进一步缩小误差的范围,在±1×10^(-6)误差范围内,极端随机森林回归模型和梯度提升预测的准确率分别为91.82%和90.51%.所以采用极端随机森林算法预测CO_(2)柱浓度的结果更好,精度更高,符合CO_(2)预测的精度要求.

时间序列模型预测大气臭氧浓度

为了给大气污染防治预警预报提供参考,利用时间序列模型对大气臭氧浓度进行预测;以2020年1月1日至2020年12月31日期间366个烟台市区大气臭氧日均质量浓度作为研究数据,建立自回归移动平均模型,引入广义自回归条件异方差模型消除时间序列自回归条件异方差效应,最终构建自回归移动平均-广义自回归条件异方差时间序列模型,并对2021年1月烟台市区的大气臭氧日均浓度进行预测。结果表明,所构建的时间序列模型对大气臭氧浓度的短期预测值与实测值基本一致,但随着预测期数的增加,预测值与实测值的相对误差逐渐增大。