海洋平台圆柱体坠落运动轨迹及落点分析

传统的角度偏差经验值方法导致落物风险评价过程中的落点和危险区预测存在较大误差，科学化、立体化、完整化、多因素概化的落物轨迹和落点分析方法及工具缺位。为此，以海洋平台圆柱体落物为研究对象开展先导研究，在2D运动理论模型基础上，考虑3维、6度受力与运动参数，引入粘性拖曳力系数经验公式、海流运动、旋转升力和力矩项，建立了具有普适性的落物水下3D运动轨迹预测模型，并以现场实验数据校验表明模型方法可行、结果更可信；据此以Matlab 2016为程序设计平台编制了海洋平台圆柱体坠落运动预测分析工具（MREDP），分析了坠落运动落点分布规律。研究结果表明:随着初始倾斜角度的增大，落点偏移距离呈现先增后降的趋势，于62°时达到最大值，运动轨迹整体呈现螺线曲率减小、向轨迹曲线外开口侧偏移趋势；落点分布变化可分为Y轴偏移稳定主导阶段、X轴偏移实力型快速主导阶段和X轴偏移机会型主导阶段3个阶段；相对MREDP预测结果，DNVGL-RP-F107经验性角度偏差推荐结果较为保守，MREDP可为DNVGL-RP-F107提供更加准确、可信的轨迹曲线和落点取值。基于分析结果，提出了采用Monte Carlo方法表征实际工程中起始运动参数随机性和入水撞击产生的不确定性等建议。     

安阳市冬季观测期间PM2.5污染特征及来源解析

为探究安阳市冬季PM_(2.5)的污染特征及来源,于2019年11月19—26日在安阳市3个站点(柏庄镇政府、红庙街小学、安阳师专)采集PM_(2.5)样品,并对PM_(2.5)质量浓度和无机元素、水溶性离子进行测定,利用正定矩阵因子模型(PMF)并结合大气污染源排放清单进行源解析。结果表明:观测期间安阳市的PM_(2.5)平均质量浓度为104.09μg/m~3,水溶性离子平均质量浓度为48.9μg/m~3,占PM_(2.5)质量浓度的46.9%。PMF解析结果为二次源58.9%、燃煤源15.7%、机动车排放源9.2%、扬尘源8.6%、工业源2.5%、其他源5.1%。结合2018年安阳市大气污染源排放清单对二次源贡献进行重新分配,得到安阳市PM_(2.5)主要贡献来自燃煤源29.8%、工业源28.5%、机动车源27.1%。后向轨迹聚类结果显示,安阳市气团输送路径主要有远距离传输、城市间输送和本地运输3类,其中本地运输占比最大,其次为正南和东南方向上的城市间输送。     

Ionic composition of submicron particles (PM1.0) during the long-lasting haze period in January 2013 in Wuhan, central China

In January 2013, a long-lasting severe haze episode occurred in Northern and Central China; at its maximum, it covered a land area of approximately 1.4 million km². In Wuhan, the largest city in Central China, this event was the most severe haze episode in the 21st century. Aerosol samples of submicron particles (PM_1.0) were collected during the long-lasting haze episode at an urban site and a suburban site in Wuhan to investigate the ion characteristics of PM_1.0 in this area. The mass concentrations of PM_1.0 and its water-soluble inorganic ions (WSIIs) were almost at the same levels at two sites, which indicates that PM_1.0 pollution occurs on a regional scale in Wuhan. WSIIs (Na⁺, NH₄⁺, K⁺, Mg²⁺, Ca²⁺, Cl^-, NO₃^- and SO₄^2-) were the dominant chemical species and constituted up to 48.4% and 47.4% of PM_1.0 at WD and TH, respectively. The concentrations of PM_1.0 and WSIIs on haze days were approximately two times higher than on normal days. The ion balance calculations indicate that the particles were more acidic on haze days than on normal days. The results of the back trajectory analysis imply that the high concentrations of PM_1.0 and its water-soluble inorganic ions may be caused by stagnant weather conditions in Wuhan.     

秋季南通近海大气气溶胶水溶性离子粒径分布特征

2012年10~11月在南通近海设立观测点,利用Anderson分级采样器采集大气气溶胶样品,用离子色谱仪(Metrohm IC)分析其中10种水溶性离子组成.结果表明,南通秋季近海PM10和PM2.1中水溶性离子浓度分别为59.70,45.96mg/m3.PM2.1中主要离子质量浓度排列依次为SO42->NO3->NH4+>Ca2+. SO42-,NO3-和NH4+占PM10中离子浓度的80%以上,二次离子为近海区域气溶胶的主要成分.SO42,NH4+和NO3-均表现出单峰型分布,峰值区间均为0.43~1.1mm,Ca2+,Na+和Cl-表现为双峰型.Ca2+高浓度峰值出现4.7~5.8mm粒径段内;Na+和Cl-峰值出现在0.43~1.1mm和3.3~5.8mm内,但最大峰值浓度区间不一致.PM10中nss-SO42-/SO42-比值均高于90%,陆地源对近海硫酸盐的影响显著.nss-SO42-/NO3-的比值在<2.1μm的粒径段内均大于1,表明该区域固定源是大气细粒子中离子的重要贡献源,但移动源对粗粒子的影响值得重视.个例分析显示,稳定的天气系统,高污染排放内陆地区的污染物传输,是造成10月27日的严重污染过程的主要原因.     

Stepwise multiple regression method of greenhouse gas emission modeling in the energy sector in Poland

The energy sector in Poland is the source of 81% of greenhouse gas (GHG) emissions. Poland, among other European Union countries, occupies a leading position with regard to coal consumption. Polish energy sector actively participates in efforts to reduce GHG emissions to the atmosphere, through a gradual decrease of the share of coal in the fuel mix and development of renewable energy sources. All evidence which completes the knowledge about issues related to GHG emissions is a valuable source of information. The article presents the results of modeling of GHG emissions which are generated by the energy sector in Poland. For a better understanding of the quantitative relationship between total consumption of primary energy and greenhouse gas emission, multiple stepwise regression model was applied. The modeling results of CO₂ emissions demonstrate a high relationship (0.97) with the hard coal consumption variable. Adjustment coefficient of the model to actual data is high and equal to 95%. The backward step regression model, in the case of CH₄ emission, indicated the presence of hard coal (0.66), peat and fuel wood (0.34), solid waste fuels, as well as other sources (− 0.64) as the most important variables. The adjusted coefficient is suitable and equals R² = 0.90. For N₂O emission modeling the obtained coefficient of determination is low and equal to 43%. A significant variable influencing the amount of N₂O emission is the peat and wood fuel consumption.     

Stepwise multiple regression method of greenhouse gas emission modeling in the energy sector in Poland

The energy sector in Poland is the source of 81% of greenhouse gas (GHG) emissions. Poland, among other European Union countries, occupies a leading position with regard to coal consumption. Polish energy sector actively participates in efforts to reduce GHG emissions to the atmosphere, through a gradual decrease of the share of coal in the fuel mix and development of renewable energy sources. All evidence which completes the knowledge about issues related to GHG emissions is a valuable source of information. The article presents the results of modeling of GHG emissions which are generated by the energy sector in Poland. For a better understanding of the quantitative relationship between total consumption of primary energy and greenhouse gas emission, multiple stepwise regression model was applied. The modeling results of CO₂ emissions demonstrate a high relationship (0.97) with the hard coal consumption variable. Adjustment coefficient of the model to actual data is high and equal to 95%. The backward step regression model, in the case of CH₄ emission, indicated the presence of hard coal (0.66), peat and fuel wood (0.34), solid waste fuels, as well as other sources (-0.64) as the most important variables. The adjusted coefficient is suitable and equals R² = 0.90. For N₂O emission modeling the obtained coefficient of determination is low and equal to 43%. A significant variable influencing the amount of N₂O emission is the peat and wood fuel consumption.     

常州市臭氧污染传输路径和潜在源区

利用NCEP全球再分析资料和HYSPLIT4模式,计算了2013—2015年常州市臭氧(O_3)超标日的气流后向轨迹。结合聚类分析方法和常州市PM2.5、PM10、SO2、NO2、O_3数据,分析了O_3超标日不同类型气团来源对各污染物浓度的影响,并利用引入权重因子后的潜在污染源贡献函数分析了影响常州市O_3超标的潜在污染源区分布特征。结果表明:常州市O_3超标期间易受到东南和西南方向气流影响,其中从东海和黄海途经浙江东北部、上海、江苏南部等地的东南气流占比达50%以上。自内陆途经黄山-湖州-宜兴到常州的气流对应的O_3平均质量浓度最高,为116μg/m3。自山东经枣庄-宿迁-淮安-泰州-苏州-无锡到常州的气流对应的O_3平均质量浓度最低,为78μg/m3,但该气流对应的SO2和NO2平均值为各聚类中的最高。影响常州市O_3的潜在污染源区主要在常州周边200 km以内的区域,且集中在从南京至上海的长江下游沿线区域和杭州湾区域;其中太湖湖区为重点污染源源区之一。O_3超标日影响常州NO2的潜在污染源区主要集中在江苏南部、浙江东北部和上海3个区域,太湖周边的常州、无锡、苏州和湖州等几个临近城市为潜在的重点污染源区。与影响常州O_3的WPSCF高值区相比,影响NO2的高值区分布范围更大、距离更远。影响常州O_3的潜在污染源区分布,与长江三角洲地区人为源大气污染物的高排放区域较为一致,说明长江三角洲地区的O_3污染与本区域的人为源大气污染物排放有着极为密切的关联。     

海南五指山大气气态总汞含量变化特征

利用高时间分辨率自动测汞仪(Tekran 2537B)于2011-05~2012-05对五指山大气气态总汞(TGM)进行了连续1 a的观测.结果表明,五指山TGM含量为(1.58±0.71)ng·m-3,与全球大气汞背景值相当,表明五指山大气几乎没有受到明显的大气汞污染.TGM的月变化特征表现为2011年6~8月和2012年3~5月TGM含量较低,2011年9月~次年1月含量较高,热带季风是影响TGM月变化的主要因素.五指山TGM具有明显的日变化特征,小时均值最低值出现在09:00,峰值出现在19:00,白天TGM浓度低于晚上.日变化受汞长距离迁移和本地气象条件的共同影响.后向轨迹分析结果表明大气汞从中国大陆的长距离迁移为五指山TGM浓度升高的主要原因.     

南昌市大气降水化学特征及来源分析

为分析南昌市大气降水离子来源和源区,对南昌市2016年4—9月大气降水样品主要阴阳离子的组成进行了测定分析,并运用PMF（正定矩阵因子分解）模型分析来源和TrajStat软件模拟后向轨迹.结果表明:NH₄+和Ca2+是南昌市大气降水中的主要阳离子,加权平均浓度为65.3和23.9 μmol/L,分别占阳离子总量的57%和21%;SO₄2-和NO₃-是主要阴离子,加权平均浓度为60.4和25.3 μmol/L,分别占阴离子总量的56%和23%.c（NH₄+）、c（Ca2+）、c（K+）、c（Mg2+）、c（Na+）、c（Cl-）、c（NO₃-）之间均存在着较为显著的相关性,说明它们之间可能有相似的来源或形成化合物共同存在.结合PMF模型分析结果表明,Na+、Cl-很明显受到了海盐的影响,也部分受土壤和二次污染影响;K+、Mg2+、Ca2+大部分来自于土壤,海盐、二次污染也贡献了一部分的K+;SO₄2-、NH₄+和NO₃-是组成大气二次颗粒物的主要成分,主要由二次污染源贡献;煤燃烧贡献了主要的F-和部分SO₄2-.后向轨迹模型分析表明,南昌市大气降水主要受局地降雨气团影响,5月、8月、9月受陆源及人为影响较大,海源性离子经过内陆上空时被稀释或沉降,导致6月、7月来自于海洋上空的降雨气团对南昌影响不大.研究显示,SO₄2-对南昌市大气降水的影响逐渐增大导致降雨类型逐渐由混合型向硫酸型转化,人为影响是造成大气污染的主要原因.      

远程火箭弹高弹道飞行温度环境分析

目的在不采用防热涂层的前提下,为满足远程火箭弹高弹道飞行的防热需求,提出增加壳体厚度的设计思路。方法通过弹道耦合的气动加热计算,分析不同材料、不同厚度壳体的弹头壁面在飞行过程中的温度变化情况。结果壳体厚度达到20 mm以上时,铝、钢、铜三种材料壳体的外壁面温度均低于150℃,而相同厚度的壳体,钢壳的降温能力最强。结论增加壳体厚度可以有效降低弹头壳体壁面温度。