Seasonal Characteristics of Forecasting Uncertainties in Surface PM_(2.5)Concentration Associated with Forecast Lead Time over the Beijing-Tianjin-Hebei Region

Forecasting uncertainties among meteorological fields have long been recognized as the main limitation on the accuracy and predictability of air quality forecasts.However,the particular impact of meteorological forecasting uncertainties on air quality forecasts specific to different seasons is still not well known.In this study,a series of forecasts with different forecast lead times for January,April,July,and October of 2018 are conducted over the Beijing-Tianjin-Hebei(BTH)region and the impacts of meteorological forecasting uncertainties on surface PM_(2.5)concentration forecasts with each lead time are investigated.With increased lead time,the forecasted PM_(2.5)concentrations significantly change and demonstrate obvious seasonal variations.In general,the forecasting uncertainties in monthly mean surface PM_(2.5)concentrations in the BTH region due to lead time are the largest(80%)in spring,followed by autumn(~50%),summer(~40%),and winter(20%).In winter,the forecasting uncertainties in total surface PM_(2.5)mass due to lead time are mainly due to the uncertainties in PBL heights and hence the PBL mixing of anthropogenic primary particles.In spring,the forecasting uncertainties are mainly from the impacts of lead time on lower-tropospheric northwesterly winds,thereby further enhancing the condensation production of anthropogenic secondary particles by the long-range transport of natural dust.In summer,the forecasting uncertainties result mainly from the decrease in dry and wet deposition rates,which are associated with the reduction of near-surface wind speed and precipitation rate.In autumn,the forecasting uncertainties arise mainly from the change in the transport of remote natural dust and anthropogenic particles,which is associated with changes in the large-scale circulation.

Earthquake relocation using a 3D velocity model and implications on seismogenic faults in the Beijing-Tianjin-Hebei region

To enhance the understanding of the geometry and characteristics of seismogenic faults in the Beijing-Tianjin-Hebei region,we relocated 14805 out of 16063 earthquakes(113°E-120°E,36°N-43°N)that occurred between January 2008 and December 2020 using the double-difference tomography method.Based on the spatial variation in seismicity after relocation,the Beijing-Tianjin-Hebei region can be divided into three seismic zones:Xingtai-Wen'an,Zhangbei-Ninghexi,and Tangshan.(1)The Xingtai-Wen'an Seismic Zone has a northeastsouthwest strike.The depth profile of earthquakes perpendicular to the strike reveals three northeast-striking,southeast-dipping,high-angle deep faults(>10 km depth),including one below the shallow(<10 km depth)listric,northwest-dipping Xinghe fault in the Xingtai region.Two additional deep faults in the Wen'an region are suggested to be associated with the 2006 M 5.1 Wen'an Earthquake and the 1967 M 6.3 Dacheng earthquake;(2)The Zhangbei-Ninghexi Seismic Zone is oriented north-northwest.Multiple northeast-striking faults(10-20 km depth),inferred from the earthquake-intensive zones,exist beneath the shallow(<10 km depth)Xiandian Fault,Xiaotangshan Fault,Huailai-Zhuolu Basin North Fault,Yangyuan Basin Fault and Yanggao Basin North Fault;(3)In the Tangshan Seismic Zone,earthquakes are mainly concentrated near the northeast-striking Tangshan-Guye Fault,Lulong Fault,and northwest-striking Luanxian-Laoting Fault.An inferred north-south-oriented blind fault is present to the north of the Tangshan-Guye Fault.The 1976 M 7.8 Tangshan earthquake occurred at the junction of a shallow northwest-dipping fault and a deep southeast-dipping fault.This study emphasizes that earthquakes in the region are primarily associated with deep blind faults.Some deep blind faults have different geometries compared to shallow faults,suggesting a complex fault system in the region.Overall,this research provides valuable insights into the seismogenic faults in the Beijing–Tianjin–Hebei region.Further studies and monitoring of these faults are essential for earthquake mitigation efforts in this region.

Wings of Change Angola’s new international airport becomes a symbol of high-quality cooperation under the BRI

The new Dr.Antonio Agost inho Neto International Airport(AIAAN)infrastructure project is valued at around$3 billion.The project was fully funded by the Angolan government as a public investment,with lending from the Chinese government for the airport construction.China National Aero-Technology International Engineering Corp,the key construction subsidiary of AVIC International Holding,is the contractor for the project.

Investigating the Changes in Air Pollutant Emissions over the Beijing-Tianjin-Hebei Region in February from 2014 to 2019 through an Inverse Emission Method

In recent years,China has implemented several measures to improve air quality.The Beijing-Tianjin-Hebei(BTH)region is one area that has suffered from the most serious air pollution in China and has undergone huge changes in air quality in the past few years.How to scientifically assess these change processes remain the key issue in further improving the air quality over this region in the future.To evaluate the changes in major air pollutant emissions over this region,this paper employs ensemble Kalman filtering(EnKF)for integrating the national ground monitoring pollutant observation data and the Nested Air Quality Prediction Modeling System(NAQPMS)simulation data to inversely estimate the emission rates of SO_(2),NOX,CO,and primary PM_(2.5)over BTH region in February from 2014 to 2019.The results show that SO_(2),NOX,CO,and primary PM_(2.5)emissions in the BTH region decreased in February from 2014 to 2019 by 83%,37%,41%,and 42%,while decreases in Beijing during this period were 86%,67%,59%,and 65%,respectively.Compared with the prior emission inventory,the inversion emission inventory reduces the uncertainty of multi-pollutant simulation in the BTH region,with simulated root mean square errors of the monthly average concentrations of SO_(2),NOX,PM_(2.5),and CO reduced by 41%,30%,31%,and 22%,respectively.The average uncertainties of SO_(2),NOX,PM_(2.5),and CO inversion emissions in2014-19 are±14.03%yr^(-1),±28.91%yr^(-1),±126.15%yr^(-1),and±43.58%yr^(-1).Compared with the uncertainty of MEIC emission,the uncertainties of all species changed by+2%yr^(-1),-2%yr^(-1),-26%yr^(-1),and-4%yr^(-1),respectively.The spatial distribution results illustrate that air pollutant emissions are mainly distributed over the eastern and southern BTH regions.The spatial gap between the inversion emissions and MEIC emissions was further closed in 2019 compared to 2014.The results of this paper can provide a new reference for assessing changes in air pollution emissions over the BTH region in recent years and validating a bottom-up emission inventory.

Spatial Variability of PM_(2.5) Pollution in Imbalanced Natural and Socioeconomic Processes: Evidence from the Beijing-Tianjin-Hebei Region of China

Accurately identifying and quantifying the factors influencing PM_(2.5) pollution is of great significance for the prevention and control of pollution. However, the redundancy among potential factors of PM_(2.5) may be overlooked. Meanwhile, the inconsistent spatial distribution of the natural and socioeconomic conditions brings unique implications for the cities within a region, which may lead to an uncertain understanding of the relationship between pollution and environmental factors. This study focused on the Beijing-TianjinHebei(BTH) Region, China, which presents complex and varied background conditions. Potential impact factors on PM_(2.5) were firstly screened by combining systematic cluster analysis with a random forest recursive feature elimination algorithm. Then, the representative multi-factor responsible for PM_(2.5) pollution in the region during the key period of 2014–2018(when the strict national air pollution control policy was implemented). The results showed that the key driving factors of PM_(2.5) pollution in the BTH cities are different, indicating that the uniqueness of a city will have an impact on the leading causes of pollution. Further discussion shows that air control policy provides an effective way to improve air quality. This study aims to deepen the understanding of the risk drivers of air pollution within the BTH Region. In the future, it is recommended that more attention should be paid to the specific differences between the cities when formulating PM_(2.5) concentration control measures.

Improved Ant Colony Algorithm for Vehicle Scheduling Problem in Airport Ground Service Support

Support vehicles are part of the main body of airport ground operations,and their scheduling efficiency directly impacts flight delays.A mathematical model is constructed and the responsiveness of support vehicles for current operational demands is proposed to study optimization algorithms for vehicle scheduling.The model is based on the constraint relationship of the initial operation time,time window,and gate position distribution,which gives an improvement to the ant colony algorithm(ACO).The impacts of the improved ACO as used for support vehicle optimization are compared and analyzed.The results show that the scheduling scheme of refueling trucks based on the improved ACO can reduce flight delays caused by refueling operations by 56.87%,indicating the improved ACO can improve support vehicle scheduling.Besides,the improved ACO can jump out of local optima,which can balance the working time of refueling trucks.This research optimizes the scheduling scheme of support vehicles under the existing conditions of airports,which has practical significance to fully utilize ground service resources,improve the efficiency of airport ground operations,and effectively reduce flight delays caused by ground service support.

Internet of Things Supported Airport Boarding System and Evaluation with Fuzzy

The existing systems sustained with the investments made require more automation and digital transformation with the continuous advancement of tech-nology.The aviation industry is a sector that is open to more automation and digi-tal transformation,mainly because of the intense competition and the analysis of a large variety of data.The long duration of operations in current airline processes and some processflows cause customer dissatisfaction and cost increase.In this study,the boarding process,which is one of the operational processes of airline transportation and is open to improvement,was discussed.The classical boarding process has been redesigned using Internet of Things technology a model called Boarding 4.0 was created.With Boarding 4.0,it is aimed to design a process where passengers can take their time before boarding more efficiently.In the study,the sub-processes of the Boarding 4.0 model,other processes that the sub-processes interact with,their activities,and data exchange passenger move-ments during the activities are explained in detail.Compared to the classical boarding process and Boarding 4.0 with the fuzzy ahp technique,it has been shown that boarding 4.0 is more advantageous and passenger movement times can be reduced during boarding.As a result of the evaluation made with the fuzzy ahp,it was determined that boarding 4.0 is more advantageous than the classical boarding process.In addition,when the total time of the sub-activities in the board-ing process is calculated,boarding activities for a passenger take 50 min with the classic boarding process and 20 min with Boarding 4.0.Thus,when Boarding 4.0 is used,the passenger gains 30 min.Furthermore,when the calculation is made concerning the airport’s current capacity,two passengers are hosted with the clas-sical boarding process,whilefive passengers are hosted with Boarding 4.0.This acquisition is significant for airports in terms of efficient use of resources.

Analysis of Continuous Thunderstorm in One Week at Qingdao Airport Based on Sounding Data

Continuous thunderstorm occurring at Qingdao Airport in China from August 7 to 13,2022 was analyzed based on sounding data.The weather was divided into thunderstorm gale,thunderstorm and heavy precipitation,and some physical quantities and time variables which can effectively identify severe convective weather types were preliminarily obtained.The results show that CAPE was sensitive to different types of weather,but the uncertainty was relatively large.Convective temperature T_(CON),temperature difference between 500 and 850 hPa,and vertical wind shear can distinguish thunderstorm gale,thunderstorm and heavy precipitation weather obviously.Besides,K index,Showalter index,θ_(se) difference between 500 and 850 hPa were also important basis to distinguish thunderstorm and heavy precipitation weather.Thunderstorm gale can be distinguished by the 24-hour variations of K index,and the difference of dew point between 500 and 850 hPa.The 24-hour variations of(T-T_(d))_(500) and vertical wind shear can be used to distinguish between heavy precipitation and thunderstorm weather;the 24-hour variation of stratification stability Δθ_(se) can distinguish the three kinds of weather well.For the wind field,the existence of vertical wind shear was required for strong convective weather,and the 24-hour increment of thunderstorm gale and thunderstorm was larger than that of heavy precipitation.

Assessing the impact of manufacturing agglomeration on environmental pollution in Beijing-Tianjin-Hebei region using spatial panel data model

The Beijing-Tianjin-Hebei region is the vanguard of economic development in northern China.Its manufacturing industry is more and more developed,but environmental pollution is also more serious.Based on the data of 13 cities in Beijing-Tianjin-Hebei region from 2017 to 2021,the paper verifies the impact of manufacturing agglomeration on environmental pollution.Both manufacturing agglomeration and environmental pollution are dependent on spatial distribution.Therefore,the paper selects spatial econometric model to study.First,the spatial lag model and spatial error model are constructed,and then the spatial lag model is selected through the results of OLS regression,LM Test and Hausman test,and the empirical process is carried out.Finally,the empirical results are analyzed and the conclusion is drawn.

临空经济对区域创新的结构性影响研究

临空经济在引导区域发展模式向创新驱动型转变中发挥着重要作用,以其独特的“技术—制度—产业—空间”属性,通过技术支撑、制度安排、产业延伸、空间极核等新的范式结构,为区域创新发展提供跃升条件。发展临空经济,能提升城市通达度和交通枢纽地位,影响城市创新层级;缩小城市间知识密集型服务业的差距,使城市群向创新多中心空间结构演化;为区域创新发展提供集聚与扩散叠加动力、制度创新变迁动力、政府与市场双向互动激活动力、中心极核与外围互补引领协同动力,影响区域创新的动力结构。各级政府应高度重视临空经济区在改革创新、先行先试中的引领作用,完善现代综合交通运输体系,差异化构建跨空间创新协作网络,促进创新要素流动和知识溢出,激发临空经济在区域创新中的功能。

New International Airport Launched

Angolan President Joao Lourenco offcially opened the new Dr.Antonio Agostinho Neto International Airport at an opening ceremony here on 10 November,launching the airport’s cargo operations.Lourenco expressed his gratitude for all the project’s builders,including the Chinese contractor,China National Aerotechnology International Engineering Corp.

Assessment of the Impacts of Thunderstorm on Flight Operations at Murtala Mohammed International Airport,Ikeja,Lagos State,Nigeria

The inefficiency of the aviation industry and the persistent rise in aviation hazards have been linked to weather phenomena.As a result,researchers are looking for better solutions to the problem.The study examined the impact of thunderstorms on flight operations at Murtala Mohammed International Airport,Lagos.The data on thunderstorms and flight operations were sourced from Nigerian Meteorological Agency(NiMet)and Nigerian Airspace Management Agency(NAMA)respectively.In order to meet the research target,descriptive statistics(mean,standard deviation,and charts)and inferential statistics(Pearson’s Product Moment Correlation(PPMC)and Regression)were used.The significance level for all inferential analyses was set at 5%(0.05).The study revealed that 77.4%of thunderstorms occurred during the rainy season(April-October)while 22.6%occurred during the dry season(November-March).It also revealed some fluctuating movements of a thunderstorm in the study area.According to the findings,thunderstorms occur most frequently at the airport in June and less frequently in January and December.The study also discovered that thunderstorms at the airport are positively and significantly related to flight delays and cancellations,while the association between flight diversions and thunderstorm occurrence is positive but statistically insignificant.Furthermore,flight delays,flight diversions,and flight cancellations interact positively among themselves.The regression result of the study revealed that a 1%increase in thunderstorm occurrence leads to a 19.4%increase in flight delay,a 7.1%increase in flight cancellation,and a 4.3%increase in flight diversion.As a result,the study presented various regression models that may be utilized to make predictions.The study proposes consistent thunderstorm observation at the airport and steady forecasts using the regression models,based on the findings.However,it further recommends that pilots,air traffic controllers,and meteorologists be trained and retrained so that they can provide better and more efficient services.

京津多机场系统容量损失影响分析

全球范围内多机场系统数量和规模都在不断增长。由于终端区内有限资源的限制,邻近机场的运行存在一定的相关性,这导致多机场系统运行效率低下。为了估计多机场运行产生的限制,以北京终端区为例提出一种统计分析框架对多机场系统的容量损失进行整体分析。首先,根据理论容量模型分别计算出北京终端区各机场的理论容量,并进一步确定各机场独立运行下的实际容量;其次,以北京终端区的历史运行数据为基础,估计出3个机场及终端区系统的具有鲁棒性的Pareto容量包络曲线;最后,基于独立运行容量与相关运行容量量化出终端区各机场及多机场系统的实际容量损失。为进一步提高多机场终端区的运行效率提供建议与参考。

机场飞行区多层异质链序风险传播模型研究

为探究机场飞行区风险发生机制及风险传播特性,增强机场飞行区安全管控能力,采用复杂网络理论,依据风险致因间的因果链序关系,构建多层异质网络风险传播模型。采用多层次因果图(AcciMap)理论分析机场飞行区运行风险传播的致因链;基于风险致因特征构建3层异质风险传播网络并根据网络特征设计概率-触发传播模型、容量-阻抗传播模型、容量-负荷传播模型;利用复杂网络理论设计风险传播评价指标,对风险网络传播特征进行分析。结果表明:多层风险网络中节点风险传播能力与节点度弱相关,节点风险灵敏指数可提升风险节点排序准确性;对风险灵敏度指数排序前15%的节点进行风险控制,可有效降低风险扩散近32%;网络风险传播结构存在高连通和松散状态,对风险扩散指数排序前15%的节点进行控制,可以有效降低网络结构鲁棒性指数使风险网络结构由高连通进入到松散状态;所建模型可有效识别风险扩散过程并进行精准控制,以提升机场飞行区风险控制水平。

考虑机场、航空公司与空管需求的机场群离场航班时刻优化

随着中国航空流量的不断增加,机场群内航班延误时间长、延误架次多、机场时刻紧张等问题逐渐暴露,这些问题主要是航班时刻设置不合理所导致。为解决这些问题,需要对机场群内航班时刻进行优化。通过从机场、航空公司、空管3个角度综合考虑,分别以延误时间、航空公司航班时刻调整总方差、管制员总调整量作为优化目标,建立了机场群航班时刻优化模型,并使用权重线性递减的粒子群优化算法实现对模型的求解。以京津冀机场群为例进行分析,使用MATLAB对模型进行寻优。结果表明优化后机场群内总延误时间由77580 min减少至46260 min,航空公司航班时刻调整总方差由447.076减少至63.141,管制员总调整量由467次减少至253次,3个目标均得到了优化。可见该模型权衡了机场、航空公司与空管之间的公平性,为机场群航班时刻的优化提供了理论参考。

成渝机场群运行效率的测度与评价研究

成渝机场群是中国重要的航空交通枢纽以及连接西南地区的重要节点,探讨成渝机场群的运行效率对成渝地区经济和交通发展的重要意义。明确了成渝机场群的投入指标和产出指标,使用Kendall和Spearman相关性分析法对指标间的关系进行了分析,并利用层次分析法对数据进行了处理。运用数据包络分析法(Data Envelopment Analysis,DEA)对机场群的运行效率进行了深入研究,通过DEAP 2.1软件对三个模型进行求解,从静态、动态和Malmquist指数方面评价了成渝机场群的运行效率。研究结果显示,投入与产出指标之间存在较强的正相关性;成渝机场群整体效率偏低,特别是在货邮吞吐量方面存在严重的产出不足问题;各个机场之间的效率差距较大,存在资源利用不足的问题。

航空器场面滑行关键路径识别与抗毁性研究

为准确辨识机场地面滑行系统重要路段,提升拥塞风险控制能力,通过构建机场活动区交通网络模型,从节点近邻、全局路径、特征向量和网络位置4个角度挖掘关键节点,提出改进介数中心性算法,得到节点重要度排序样例集。综合鲁棒性、性能下降速率以及单位时间性能损失等网络性能指标,通过抗毁性实验分析性能变化曲线,确定综合抗毁性衡量指标ki测度下复杂网络节点重要度排序结果,实现关键路径识别。以华北某机场为例开展实证研究,结果表明:机场活动区交通网络具有较小的聚类系数,在随机攻击下抗毁性较强,基于度中心性的节点序列为所有样例中最满足实际运行中的关键路段,管制员可实施重点路段流量调配,抑制冲突。

不同季节影响下复合道面力学响应分析

为了研究复合道面在不同季节时的力学响应,以中原地区某机场改造项目为项目背景,借助三维有限元软件,建立了道面有限元模型,分析了复合道面在温度影响下的力学响应,并与机场监测数据进行对比,验证了模型的准确性。研究发现:大气温度所能影响的道面深度范围在0.56 m以内,和大气温度相比,道面面层温度峰值远大于气温峰值;道面内部不同结构层之间温度变化趋势相同,随着道面深度的加深,温度峰值逐渐递减,并且出现时间具有延迟现象;在不同季节,道面在相同机型作用下所产生的变形不同,说明温度对于道面的受力是有影响的,其中混凝土层在冬季时所产生的应变值比夏季所产生的减少约74%,变化比较明显。

基于时间窗的机场地面保障车辆动态调度

机场各类地面资源的优化配置是机场场面运行优化的核心问题,而机场地面保障任务的调度是其中的关键一环。针对机场地面保障车辆的调度问题,考虑航班延误、提前等情况,构建了双阶段机场地面保障车辆调度模型,并设计双阶段启发式算法进行求解;基于中国某大型机场的实际运行数据,以清水车和食品车调度为例分别进行仿真实验。结果表明:对比先到先服务策略,清水车行驶总距离减少55.31%,食品车行驶总距离减少47.38%;对比传统遗传算法,清水车行驶总距离减少19.31%,食品车行驶总距离减少22.93%;动态调整后,清水车新增总行驶距离1.2%,食品车总行驶距离新增3.2%,均在可接受范围之内。可见,双阶段机场地面保障车辆调度模型能提高大型机场场面运行效率,为机场航班实际地面保障任务调度提供理论依据和决策支持。

基于改进YOLOX的机场场面飞机目标检测

机场场面飞机实时监控是远程塔台系统的基础。为实现对机场场面飞机目标快速而准确的检测,提出一种基于YOLOX融合注意力机制的机场场面飞机目标检测方法。在加强特征提取网络中引入卷积块注意力模块,增大对飞机目标空间位置和特征的关注度,同时利用CIoU方法计算目标框回归损失函数,并基于Tensorflow深度学习框架对YOLOX及改进模型开展对比实验。结果表明,YOLOX模型具有较高的检测精度与速度,提出的YOLOX-CT与YOLOX-CS模型的mAP0.5分别达到97.34%及97.28%,FPS值达到46及35。基于YOLOX的改进模型对飞机目标具有较高的检测效率,可保障机场运行安全、提升运行效率。