Individual Stations Research Articles

Major Artifacts in ERA5 2‐m Air Temperature Trends Over Antarctica Prior to and During the Modern Satellite Era

AbstractGlobal reanalyzes are widely used for investigations of Antarctic climate variability and change. The European Centre for Medium‐Range Weather Forecasts 5th generation reanalysis (ERA5) is well regarded and spans 1940 to today. We investigate whether ERA5 reliably represents the 2‐m air temperature trends across the 1940–2022 (83 years) period at seasonal and annual time scales. We compare ERA5 temperatures with an observation‐based temperature reconstruction for Antarctica (RECON) that has monthly resolution for 1958–2022, the period of reliable observational availability. Results for individual stations are also examined. ERA5 anomalously warms Antarctica in relation RECON especially for the period prior to 1979 when satellite observations over the Southern Ocean were sparse. Trend hotspots that are shown to be artifacts are found at three locations and are present until today. The results demonstrate that ERA5 temperature trends can be questionable even today, but variability is well captured after 1979.

Statistical Analysis of Atmospheric Delay Gradient and Rainfall Prediction in a Tropical Region

In recent years, precipitable water vapor has emerged as a key atmospheric parameter in weather prediction research. However, relying on only one parameter does not always accurately predict rainfall, as other atmospheric parameters also contribute to initiating rain events. In our previous study, we explored a methodology for rainfall forecasting based on the atmospheric delay gradient at an individual station in the tropical region. Commonly referred to as the atmospheric gradient, it accounts for the delay in GNSS signals due to changes in horizontal refractivity while propagating through the troposphere to the ground station. This paper discusses the spatial and temporal correlation of the gradient with precipitation occurring over a region. We have investigated different features, such as gradient magnitude, gradient convergence, and the flux of the atmospheric gradient, to propose potential nowcasting criteria for precipitation in the tropical climatic zone. The atmospheric gradient in the surrounding region orients toward the area of precipitation as the rain event approaches. This gradient gradually alters its orientation and converges toward the region of rainfall at the time of precipitation, introducing the concept of gradient convergence. This phenomenon results in an inward gradient flux at the time of the downpour, which presents a potential parameter for rainfall prediction over an area. We also proposed investigating area sizes of 4° × 4° or 8° × 8°, depending on the gradient feature, to establish a forecasting methodology for rainfall. The weather front in the tropical region begins to initiate 12 h before the actual time of occurrence and advances toward the area where it will have an impact from a more distant location. This article presents a detailed investigation of various features of the atmospheric gradient and its potential to nowcast rainfall for a region, as well as proposes the lead time for long-term weather predictions. Furthermore, a deep neural network has been employed to predict rainfall events for the next 6 h over an area in the tropical region.

Natural frequency tree- versus conditional probability formula-based training for medical students’ estimation of screening test predictive values: a randomized controlled trial

BackgroundMedical students and professionals often struggle to understand medical test results, which can lead to poor medical decisions. Natural frequency tree-based training (NF-TT) has been suggested to help people correctly estimate the predictive value of medical tests. We aimed to compare the effectiveness of NF-TT with conventional conditional probability formula-based training (CP-FT) and investigate student variables that may influence NF-TT’s effectiveness.MethodsWe conducted a parallel group randomized controlled trial of NF-TT vs. CP-FT in two medical schools in South Korea (a 1:1 allocation ratio). Participants were randomly assigned to watch either NF-TT or CP-FT video at individual computer stations. NF-TT video showed how to translate relevant probabilistic information into natural frequencies using a tree structure to estimate the predictive values of screening tests. CP-FT video showed how to plug the same information into a mathematical formula to calculate predictive values. Both videos were 15 min long. The primary outcome was the accuracy in estimating the predictive value of screening tests assessed using multiple-choice questions at baseline, post-intervention (i.e., immediately after training), and one-month follow-up. The secondary outcome was the accuracy of conditional probabilistic reasoning in non-medical contexts, also assessed using multiple-choice questions, but only at follow-up as a measure of transfer of learning. 231 medical students completed their participation.ResultsOverall, NF-TT was not more effective than CP-FT in improving the predictive value estimation accuracy at post-intervention (NF-TT: 87.13%, CP-FT: 86.03%, p = .86) and follow-up (NF-TT: 72.39%, CP-FT: 68.10%, p = .40) and facilitating transfer of training (NF-TT: 75.54%, CP-FT: 71.43%, p = .41). However, for participants without relevant prior training, NF-TT was more effective than CP-FT in improving estimation accuracy at follow-up (NF-TT: 74.86%, CP-FT: 58.71%, p = .02) and facilitating transfer of learning (NF-TT: 82.86%, CP-FT: 66.13%, p = .04).ConclusionsIntroducing NF-TT early in the medical school curriculum, before students are exposed to a pervasive conditional probability formula-based approach, would offer the greatest benefit.Trial registrationKorea Disease Control and Prevention Agency Clinical Research Information Service KCT0004246 (the date of first trial registration: 27/08/2019). The full trial protocol can be accessed at https://cris.nih.go.kr/cris/search/detailSearch.do?seq=15616&search_page=L.

Detection of Hidden Low-Frequency Earthquakes in Southern Vancouver Island with Deep Learning

Low-frequency earthquakes (LFEs) are small-magnitude earthquakes that are depleted in high-frequency content relative to traditional earthquakes of the same magnitude. These events occur in conjunction with slow slip events (SSEs) and can be used to infer the space and time evolution of SSEs. However, because LFEs have weak signals, and the methods used to identify them are computationally expensive, LFEs are not routinely cataloged in most places. Here, we develop a deep-learning model that learns from an existing LFE catalog to detect LFEs in 14 years of continuous waveform data in southern Vancouver Island. The result shows significant increases in detection rates at individual stations. We associate the detections and locate them using a grid search approach in a 3D regional velocity model, resulting in over 1 million LFEs during the performing period. Our resulting catalog is consistent with a widely used tremor catalog during periods of large-magnitude SSEs. However, there are time periods where it registers far more LFEs than the tremor catalog. We highlight a 16-day period in May 2010, when our model detects nearly 3,000 LFEs, whereas the tremor catalog contains only one tremor detection in the same region. This suggests the possibility of hidden small-magnitude SSEs that are undetected by current approaches. Our approach improves the temporal and spatial resolution of the LFE activities and provides new opportunities to understand deep subduction zone processes in this region.

Resilience of socio-technical transportation systems: A demand-driven community detection in human mobility structures

Existing scholarship on transportation resilience analysis has primarily focused on engineering resilience, often overlooking the intricate socio-technical dimensions. This oversight underscores the necessity for a more comprehensive understanding of the dynamic interplay between social, including travel behaviors, and technical infrastructure components within transportation systems. This article delves into the impact of “social shocks” on transportation systems, which are defined as disturbances affecting the social subsystem without yet affecting the technical subsystem. Drawing inspiration from C.S. Holling’s ecological resilience, which signifies a system’s ability to cope with change by adapting its structure and functionality, we propose a multi-level resilience assessment framework. It encompasses four mobility-related indicators: entropy (measuring network-level complexity), stationarity (assessing community compositional changes at the cluster level), and two node-level metrics — within-module degree and weighted participation coefficient — capturing location connectivity. These indicators proxy for evaluating the mobility structure and node functionality within the social subsystem. In a case study, we analyze historical smart card data to examine the mobility pattern’s structural changes within Hong Kong, a rail-oriented metropolis, during a prolonged and city-wide protest. The framework and associated indicators provide an alternative perspective for transit planners and operators, allowing them to assess both the overall system and individual stations, moving beyond traditional assessments of service supply and patronage changes.

Domain knowledge-enhanced multi-spatial multi-temporal [formula omitted] forecasting with integrated monitoring and reanalysis data

Accurate air quality forecasting is crucial for public health, environmental monitoring and protection, and urban planning. However, existing methods fail to effectively utilize multi-scale information, both spatially and temporally. There is a lack of integration between individual monitoring stations and city-wide scales. Temporally, the periodic nature of air quality variations is often overlooked or inadequately considered. To overcome these limitations, we conduct a thorough analysis of the data and tasks, integrating spatio-temporal multi-scale domain knowledge. We present a novel Multi-spatial Multi-temporal air quality forecasting method based on Graph Convolutional Networks and Gated Recurrent Units (M2G2), bridging the gap in air quality forecasting across spatial and temporal scales. The proposed framework consists of two modules: Multi-scale Spatial GCN (MS-GCN) for spatial information fusion and Multi-scale Temporal GRU (MT-GRU) for temporal information integration. In the spatial dimension, the MS-GCN module employs a bidirectional learnable structure and a residual structure, enabling comprehensive information exchange between individual monitoring stations and the city-scale graph. Regarding the temporal dimension, the MT-GRU module adaptively combines information from different temporal scales through parallel hidden states. Leveraging meteorological indicators and four air quality indicators, we present comprehensive comparative analyses and ablation experiments, showcasing the higher accuracy of M2G2 in comparison to nine currently available advanced approaches across all aspects. The improvements of M2G2 over the second-best method on RMSE of 72-h future predictions are as follows: PM2.5: 6%∼10%; PM10: 5%∼7%; NO2: 5%∼16%; O3: 6%∼9%. Furthermore, we demonstrate the effectiveness of each module of M2G2 by ablation study. We conduct a sensitivity analysis of air quality and meteorological data, finding that the introduction of O3 adversely impacts the prediction accuracy of PM2.5.

Machine learning-based estimation of evapotranspiration under adaptation conditions: a case study in Heilongjiang Province, China.

The prediction of evapotranspiration (ET0) is crucial for agricultural ecosystems, irrigation management, and environmental climate regulation. Traditional methods for predicting ET0 require a variety of meteorological parameters. However, obtaining data for these multiple parameters can be challenging, leading to inaccuracies or inability to predict ET0 using traditional methods. This affects decision-making in critical applications such as agricultural irrigation scheduling and water management, consequently impacting the development of agricultural ecosystems. This issue is particularly pronounced in economically underdeveloped regions. Therefore, this paper proposes a machine learning-based evapotranspiration estimation method adapted to evapotranspiration conditions. Compared to traditional methods, our approach relies less on the variety of meteorological parameters and yields higher prediction accuracy. Additionally, we introduce a 'region of evapotranspiration adaptability' division method, which takes into account geographical differences in ET0 prediction. This effectively mitigates the negative impact of anomalies or missing data from individual meteorological stations, making our method more suitable for practical agricultural irrigation and ecosystem water resource management. We validated our approach using meteorological data from 25 stations in Heilongjiang, China. Our results indicate that non-adjacent geographical areas, despite different climatic conditions, can have similar impacts on ET0 prediction. In summary, our method facilitates accurate ET0 prediction, offering new insights for the development of agricultural irrigation and ecosystems, and further contributes to agricultural food supply.

Multi-Source Data-Driven Local-Global Dynamic Multi-Graph Convolutional Network for Bike-Sharing Demands Prediction

The prediction of bike-sharing demand plays a pivotal role in the optimization of intelligent transportation systems, particularly amidst the COVID-19 pandemic, which has significantly altered travel behaviors and demand dynamics. In this study, we examine various spatiotemporal influencing factors associated with bike-sharing and propose the Local-Global Dynamic Multi-Graph Convolutional Network (LGDMGCN) model, driven by multi-source data, for multi-step prediction of station-level bike-sharing demand. In the temporal dimension, we dynamically model temporal dependencies by incorporating multiple sources of time semantic features such as confirmed COVID-19 cases, weather conditions, and holidays. Additionally, we integrate a time attention mechanism to better capture variations over time. In the spatial dimension, we consider factors related to the addition or removal of stations and utilize spatial semantic features, such as urban points of interest and station locations, to construct dynamic multi-graphs. The model utilizes a local-global structure to capture spatial dependencies among individual bike-sharing stations and all stations collectively. Experimental results, obtained through comparisons with baseline models on the same dataset and conducting ablation studies, demonstrate the feasibility and effectiveness of the proposed model in predicting bike-sharing demand.

Generation of global 1 km all-weather instantaneous and daily mean land surface temperatures from MODIS data

Abstract. Land surface temperature (LST) serves as a crucial variable in characterizing climatological, agricultural, ecological, and hydrological processes. Thermal infrared (TIR) remote sensing provides high temporal and spatial resolutions for obtaining LST information. Nevertheless, TIR-based satellite LST products frequently exhibit missing values due to cloud interference. Prior research on estimating all-weather instantaneous LST has predominantly concentrated on regional or continental scales. This study involved generating a global all-weather instantaneous and daily mean LST product spanning from 2000 to 2020 using XGBoost. Multisource data, including Moderate-Resolution Imaging Spectroradiometer (MODIS) top-of-atmosphere (TOA) observations, surface radiation products, and reanalysis data, were employed. Validation using an independent dataset of 77 individual stations demonstrated the high accuracy of our products, yielding root mean squared errors (RMSEs) of 2.787 K (instantaneous) and 2.175 K (daily). The RMSE for clear-sky conditions was 2.614 K for the instantaneous product, which is slightly lower than the cloudy-sky RMSE of 2.931 K. Our instantaneous and daily mean LST products exhibit higher accuracy compared to the MODIS official LST product (instantaneous RMSE = 3.583 K; daily 3.105 K) and the land component of the fifth generation of the European ReAnalysis (ERA5-Land) LST product (instantaneous RMSE = 4.048 K; daily 2.988 K). Significant improvements are observed in our LST product, notably at high latitudes, compared to the official MODIS LST product. The LST dataset from 2000 to 2020 at the monthly scale, the daily mean LST on the first day of 2010 can be freely downloaded from https://doi.org/10.5281/zenodo.4292068 (Li et al., 2024), and the complete product will be available at https://glass-product.bnu.edu.cn/ (last access: 22 August 2024).

Research on Machine Learning-Based Method for Predicting Industrial Park Electric Vehicle Charging Load

To achieve global sustainability goals and meet the urgent demands of carbon neutrality, China is continuously transforming its energy structure. In this process, electric vehicles (EVs) are playing an increasingly important role in energy transition and have become one of the primary user groups in the electricity market. Traditional load prediction algorithms have difficulty in constructing mathematical models for predicting the charging load of electric vehicles, which is characterized by high randomness, high volatility, and high spatial heterogeneity. Moreover, the predicted results often exhibit a certain degree of lag. Therefore, this study approaches the analysis from two perspectives: the overall industrial park and individual charging stations. By analyzing specific load data, the overall framework for the training dataset was established. Additionally, based on the evaluation system proposed in this study and utilizing both Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) algorithms, a framework for machine learning-based load prediction methods was constructed to forecast electric vehicle charging loads in industrial parks. Through a case analysis, it was found that the proposed solution for the short-term prediction of the charging load in industrial park electric vehicles can achieve accurate and stable forecasting results. Specifically, in terms of data prediction for normal working days and statutory holidays, the Long Short-Term Memory (LSTM) algorithm demonstrated high accuracy, with R2 coefficients of 0.9283 and 0.9154, respectively, indicating the good interpretability of the model. In terms of weekend holiday data prediction, the Multilayer Perceptron (MLP) algorithm achieved an R2 coefficient of as high as 0.9586, significantly surpassing the LSTM algorithm’s value of 0.9415, demonstrating superior performance.

Drivers of long-term changes in summer compound hot extremes in China: Climate change, urbanization, and vegetation greening

Induced by numerous environmental and anthropogenic factors, the frequency of compound hot extremes (CoHot) is increasing globally. However, there is a lack of a comprehensive understanding of the relative importance of different factors in the change of CoHot, as most studies focused solely on individual factors. We systematically attribute the long-term trends of summertime CoHot in China to 24 factors including climate background, climate change, urbanization (including impervious area expansion and three-dimensional urban morphology), and vegetation change, using partial least square regression (PLSR) and variable importance projections for analysis. Cross-evaluation results based on 2259 meteorological stations indicate that the PLSR model accurately simulates the trends of CoHot frequency, duration, and intensity, with a relative error of 4–20% from national to individual station scales. A warm and humid climate background, rising average summertime temperatures, impervious area expansions, high building volume densities, low building heights, and vegetation wilting are the main factors that favoring the increasing and intensifying trends of CoHot. Although summertime warming contributes the most, the significance of urbanization and vegetation greening has been growing in recent years. Urbanization's contribution increases from 9.3% on a national scale to over 40% in urban centers, mainly due to increased building volume density rather than expansions of impervious area. In northern China, vegetation greening has reduced the local increasing trends in frequency/duration of CoHot by 20% to 40%, and such mitigation effects are primarily induced by increased vegetation cover. Our results suggest that, reducing building volume density and promoting vegetation greening can mitigate the increasing risk of CoHot under global warming.

Two-station Lg wave attenuation tomography in Eastern Asia

SUMMARY Utilizing over 31 000 Lg waveforms from 136 crustal earthquakes recorded at 346 regional stations, we conduct detailed tomographic mappings of the Lg Q structure across Eastern Asia in a frequency range from 0.5 to 4.0 Hz. By improving the standard two-station (TS) method, we effectively correct non-unity site response ratios using site responses estimated at individual stations. This innovative approach combines the flexible recording geometry of the TS method with the precision of reversed two-station (RTS) and reversed two-event (RTE) methods, producing a comprehensive data set devoid of source and site effects for Q tomography. To address unsolvable 3-D structural effects in the Lg spectral amplitude modelling, we justify these as modelling errors with a Gaussian distribution. This approach supports our SVD-based tomographic method, allowing for effective inversion of attenuation parameters and quantitative assessment of model resolution and errors. Our results reveal a complex relationship between Lg Q and the tectonic characteristics of Eastern Asia. In well-resolved regions, low Qo (1-Hz Q) values correspond to areas with high heat flow, partial melt, thick sediment and recent tectonic-thermal activities, in contrast to high Qo values in stable, ancient crusts lacking recent tectonic activity. Rift basins are characterized by low Lg Qo, whereas flexural basins generally have high Qo basements. We also note that post-formation factors, such as sedimentation and crustal flow intrusion, significantly impact Qo values. Furthermore, Lg Q shows a complex frequency relationship, though the power-law approximation with positive power η remains useful. The frequency dependence power η is inversely related to Qo: the regions with low Qo typically have high η and vice versa. This study provides reliable attenuation tomographic and relative site response models for Lg waves in Eastern Asia, pertinent for relative geophysical studies.

Energy management algorithm development for smart car parks including charging stations, storage, and renewable energy sources

In this study, a photovoltaic system and stationary energy storage unit integrated vehicle charging station energy management algorithm were developed using a long-short term based prediction model (LSTM). The aim of the proposed system is to develop intelligent car parks with controlled charging stations aggregated by centralized charging stations instead of individual or dispersed uncontrolled charging stations to eliminate the imbalance in the demands of charging. The system has four energy sources: grid, vehicle batteries, PV system, and the stationary battery group. By calculating the power demand for vehicles in the car park, a dynamic energy management algorithm has been developed that provides efficient use of energy resources by considering the power demand density. Moreover, the forecasting model has been created by LSTM) not only to adjust charging timing and effectively use energy sources. The model was performed by forecasting not to take energy from the grid at critical times (overloaded times). The grid load is analyzed by the energy management algorithm and run for different times of the year, creating a load profile for 16 vehicles. The results of this study show that energy demand during the critical time interval can be reduced by 8–20 % solely through load shifting before and after the critical time interval without any additional resource support. Moreover, while the integration of only the PV system supports the grid by 15–20 %, the optimal utilization of other energy sources during the relevant time frame supports the grid by 65–75 %. In addition, by collecting charging stations at a central point, energy storage capacity is increased and effective energy management is achieved. In conclusion, it is proposed that the infrastructure of such a parking system should be set up before the use of electric vehicles increases significantly.

Anthropogenic impacts on polycyclic aromatic hydrocarbons in surface water: Evidence from the COVID-19 lockdown

The lockdown restrictions against coronavirus disease 2019 (COVID-19) have led to unprecedented reductions in global anthropogenic activities. Polycyclic aromatic hydrocarbons (PAHs) are carcinogenic combustion-induced pollutants, but the influence of anthropogenic responses to COVID-19 on PAH contamination remains largely unknown. Here we quantified the impacts of lockdown restrictions on 16PAH pollution based on the data in concentrations dissolved in the water phase and absorbed on the suspended particulate matter (SPM) in the Elbe River from 2015 to 2021 and determined the changes in source contributions classified by individual years and stations. Results show that the annual average PAH concentrations in water and SPM were determined as 0.055 μg·L-1 and 3.77 mg·kg-1 from 2015 to 2021, respectively. Pronounced declines in PAH on SPM (up to -18 %) were observed during the three lockdowns in Germany from 2020 to 2021. However, dramatic rebounds of anthropogenic activities during the removal of the lockdown led to increases (up to 29 %) in ∑16PAH concentrations compared to the same period in previous years. Through the source apportionment method, vehicle and coal emissions were the two most predominant sources of PAHs in the river. Vehicle contribution decreased during the lockdown, while coal emissions increased by 5 %. Health risks for three age groups were assessed as potential low risk and decreased by 18 % from 1.54 × 10–4 in 2015 to 1.27 × 10–4 in 2019, and rebounded to 1.40 × 10–4 in 2020–2021. The findings of this study highlight the strong consistency between PAH concentrations and anthropogenic intensity, implying that source control from improved cleaner production is an effective pathway for mitigating PAH contamination in the aquatic environment.

Effect of multiscale metro network-wide attributes on peak-hour station passenger and flow balancing

Analyzing the balance of station passenger and passenger flow is essential for understanding jobs-housing balance and built environment in station areas and network-wide range as well as for enhancing the efficiency of urban rail transit operations. Taking the Shanghai rail transit network as a case study, this paper defines the Multiscale Subnetwork (MSSN) based on a specific spatial scope. By extracting the network features and built-environment elements of the stations and the MSSN, this study analyzes the factors affecting the peak-hour station passenger and the imbalance of regional network passenger flow. The research suggests that the small MSSN analysis, within 6-8 km from a station, can provide valuable results from a network-wide perspective, rather than solely focusing on individual station areas or the entire network. The regional attributes of jobs-housing balance and the transportation conditions in the MSSN range have great impact on both station passengers and flow imbalance. This research provides theoretical insights for urban planners and policymakers to formulate effective strategies for urban rail transit networks.

Standard setting OSCE: A comparison of arbitrary and Hofstee methods in a low stake OSCE

Objectives: To compare the cut scores and pass/fail rates achieved by arbitrary 60% method and Hofstee method in an undergraduate year 4 end semester objective structured clinical examination (OSCE) and check the possibility of using Hofstee method of standard setting in future exams. Method: 102 medical students of year 4 underwent a 10 station OSCE exam conducted in a state of art simulation lab in 3 cycles. The cut scores were calculated using arbitrary method aiming at 60% of task achievement and by Hofstee method. The student’s obtained scores were compared for cut scores and pass rates for individual stations and the entire exam. Results: The arbitrary and Hofstee methods of standard setting leads to different cut scores. For the individual stations it was 60% vs 65-70% and for the overall score it was 60% vs 70%. The percentage of students failing the exam is 13.7% based on arbitrary scores and is 29.4% when Hofstee cut score is applied. Conclusions: The two methods lead to different cut scores and students’ failure rates. Overall, Hofstee method is more appropriate for assessing competencies in an OSCE exam in medical schools as it leads to calculation of cut scores based on the difficulty level of the station/exam and the examiners expected level of performance by the students. Keywords: Objective Structured Clinical Examination, Standard Setting, Hofstee Method, Arbitrary Method

Calibration of CAMS PM2.5 data over Hungary: a machine learning approach

Air pollution is a major environmental problem, and reliable monitoring of particulate matter (PM) concentrations is critical for assessing its impact on human health and the environment. The Copernicus Atmosphere Monitoring Service (CAMS) offers vital data on PM2.5 concentrations by applying a worldwide modelling system. This study compares in situ PM2.5 measurements and raw CAMS data at 0.1° × 0.1° resolutions for 2019 and 2020 in Hungary. It proposes a calibration method to improve the accuracy of CAMS PM2.5 data at the scale of air monitoring stations. In the study, the accuracy of the raw CAMS PM2.5 data is assessed based on the chosen air quality stations. Then, to improve the precision, we employed machine learning algorithms (LightGBM, Random Forest (RF), and Multiple Linear Regression (MLR)) for calibration. Initial assessment of the raw CAMS PM2.5 data showed positive hourly Spearman correlation coefficient values (SR between 0.64 and 0.87 for the 14 air quality stations used), indicating a positive relationship between the datasets but a systemic underestimation. Our findings highlight LightGBM as the most effective method, consistently demonstrating elevated correlation SR and coefficient of determination R2 values reaching up to 0.95 and 0.93, respectively, and very good RSR (Root mean square error ratio) and NSE (Nash-Sutcliffe Efficiency) values (lower than 0.5 and higher than 0.75 for RSR and NSE, respectively). In contrast, RF yields mixed results, and MLR exhibits variable performance. By correcting underestimation and lowering modelling biases, the calibrated PM2.5 data better matches ground-based observations, which can be promising for using the obtained model for accurate estimation at individual air monitoring stations.

Optimizing Structural Integrity of Fighter Aircraft Wing Stations: a Finite Element Analysis Approach

Modern fighter aircraft are equipped with multiple stations on the fuselage and under the wings to accommodate various external stores, both jettisonable and non-jettisonable. Each configuration undergoes airworthiness certification, including structural analysis of individual stations within the carriage flight envelope. This study focuses on the structural analysis of a fighter aircraft wing station within this specified envelope. To perform this analysis, the wing station is extracted from the comprehensive global wing model, creating a sub-model with equivalent stiffness properties. Utilizing ANSYS Workbench®, Finite Element Analysis (FEA) is conducted for critical load cases to determine the Factor of Safety (FoS). The initial analysis reveals that the wing station has an FoS of 1.2 under the maximum design load. Prestressed modal and buckling analyses indicate a 10% increase in stiffness due to stress-stiffening effects. To further enhance load-carrying capacity, parametric design changes are introduced. Increasing the bolt diameter from 8 mm to 10 mm raises the FoS to 1.33, resulting in an 8% increase in the maximum load-carrying capacity of the wing station. This comprehensive approach, employing FEA, ensures the wing’s structural integrity under static load conditions within the carriage envelope. The study's findings support the wing station's enhanced performance and contribute to safer and more efficient aircraft operations.

Predicting air quality using a multi-scale spatiotemporal graph attention network

As urbanization accelerates, air quality has become a pressing concern. Accurate air quality prediction is essential for informed governmental decision-making and for protecting public health. Variations in air quality are influenced by complex multi-scale spatiotemporal processes. Existing research primarily relies on capturing single spatiotemporal features of air quality to predict changes. Meanwhile, when constructing spatiotemporal dynamic graphs, the inherent characteristics of the input data and the comprehensive effects of both global and local influences are not fully considered. To address these problems, we propose a graph-attention-based approach, named Multi-scale Spatiotemporal Graph Attention Network (MSTGAN). MSTGAN addresses the intricate spatiotemporal patterns of air quality across various scales through three key components: (1) a multistation transformer to model the temporal patterns of air quality at individual monitoring stations; (2) a bilinear spatiotemporal attention mechanism to capture the spatiotemporal dynamic global dependencies among all stations in a region; and (3) a set of spatiotemporal dependence graph-coupled Chebyshev graph convolution gate recurrent units to extract and aggregate the local spatiotemporal features of interrelated stations. Experiments conducted on three real-world datasets demonstrated that MSTGAN achieved significant improvements of 4.2%, 3.9%, and 7.8% in the mean absolute error, root mean square error, and R2 evaluation metrics, respectively, compared to seven state-of-the-art time-series forecasting methods. This code is publicly available at https://github.com/HPSCIL/MSTGAN-airquality-prediction.

Improvement in the Forecasting of Low Visibility over Guizhou, China, Based on a Multi-Variable Deep Learning Model

High-quality visibility forecasting benefits traffic transportation safety, public services, and tourism. For a more accurate forecast of the visibility in the Guizhou region of China, we constructed several visibility forecasting models via progressive refinements in different compositions of input observational variables and the adoption of the Unet architecture to perform hourly visibility forecasts with lead times ranging from 0 to 72 h over Guizhou, China. Three Unet-based visibility forecasting models were constructed according to different inputs of meteorological variables. The model training via multiple observational variables and visibility forecasts of a high-spatiotemporal-resolution numerical weather prediction model (China Meteorological Administration, Guangdong, CMA-GD) produced a higher threat score (TS), which led to substantial improvements for different thresholds of visibility compared to CMA-GD. However, the Unet-based models had a larger bias score (BS) than the CMA-GD model. By introducing the U2net architecture, there was a further improvement in the TS of the model by approximately a factor of two compared to the Unet model, along with a significant reduction in the BS, which enhanced the stability of the model forecast. In particular, the U2net-based model performed the best in terms of the TS below the visibility threshold of 200 m, with a more than eightfold increase over the CMA-GD model. Furthermore, the U2net-based model had some improvements in the TS, BS, and RMSE (root-mean-square error) compared to the LSTM_Attention model. The spatial distribution of the TS showed that the U2net-based model performed better at the model grid scale of 3 km than at the scale of individual weather stations. In summary, the visibility forecasting model based on the U2net algorithm, multiple observational variables, and visibility data from the CMA-GD model performed the best. The compositions of input observational variables were the key factor in improving the deep learning model’s forecasting capability, and these improvements could improve the value of forecasts and support the socioeconomic needs of sectors reliant on visibility forecasting.