Multiple Stations Research Articles

Geomagnetic Disturbances and Pulse Amplitude Anomalies Preceding M > 6 Earthquakes from 2021 to 2022 in Sichuan-Yunnan, China.

Compelling evidence has shown that geomagnetic disturbances in vertical intensity polarization before great earthquakes are promising precursors across diverse rupture conditions. However, the geomagnetic vertical intensity polarization method uses the spectrum of smooth signals, and the anomalous waveforms of seismic electromagnetic radiation, which are basically nonstationary, have not been adequately considered. By combining pulse amplitude analysis and an experimental study of the cumulative frequency of anomalies, we found that the pulse amplitudes before the 2022 Luding M6.8 earthquake show characteristics of multiple synchronous anomalies, with the highest (or higher) values occurring during the analyzed period. Similar synchronous anomalies were observed before the 2021 Yangbi M6.4 earthquake, the 2022 Lushan M6.1 earthquake and the 2022 Malcolm M6.0 earthquake, and these anomalies indicate migration from the periphery toward the epicenters over time. The synchronous changes are in line with the recognition of previous geomagnetic anomalies with characteristics of high values before an earthquake and gradual recovery after the earthquake. Our study suggests that the pulse amplitude is effective for extracting anomalies in geomagnetic vertical intensity polarization, especially in the presence of nonstationary signals when utilizing observations from multiple station arrays. Our findings highlight the importance of incorporating pulse amplitude analysis into earthquake prediction research on geomagnetic disturbances.

The 18 May 2024 Iberian superbolide from a sunskirting orbit: USG space sensors and ground-based independent observations

ABSTRACT On 18 May 2024, a superbolide traversed the western part of the Iberian Peninsula, culminating its flight over the Atlantic Ocean and generating significant media attention. This event was caused by a weak carbonaceous meteoroid of 1 m, entering the atmosphere at 40.4 km s$^{-1}$ with an average slope of 8.5$^\circ$. The luminous phase started at 133 km and ended at an altitude of 54 km. The meteoroid’s heliocentric orbit had an inclination of 16.4$^\circ$, a high eccentricity of 0.952, a semimajor axis of 2.4 au, and a short perihelion distance of 0.12 au. The superbolide was recorded by multiple ground-based stations of the Spanish Fireball and Meteorite Network and the European Space Agency, as well as by the U.S. Government sensors from space. Due to the absence of observable deceleration, we successfully reconciled satellite radiometric data with a purely dynamic atmospheric flight model, constraining the meteoroid’s mass and coherently fitting its velocity profile. Our analysis shows a good agreement with the radiant and velocity data reported by the Center for Near-Earth Object Studies, with a deviation of 0.56$^\circ$ and 0.1 km s$^{-1}$, respectively. The presence of detached fragments in the lower part of the luminous trajectory suggests that the meteoroid was a polymict carbonaceous chondrite, containing higher-strength macroscopic particles in its interior due to collisional gardening, or a thermally processed C-type asteroid. The orbital elements indicate that the most likely source is the Jupiter-Family Comet region, aligning with the Solar and Heliospheric Observatory comet family, as its sunskirting orbit is decoupled from Jupiter. This event provides important information to characterize the disruption mechanism of near-Sun objects.

Multi-step ahead forecasting of wind vector for multiple wind turbines based on new deep learning model

Obtaining wind speed and direction predictions with high accuracy is of vital significance for the utilization of wind energy. This research focuses on three problems: how to realize multiple station prediction, multi-step ahead prediction and wind vector prediction. Firstly, a 4-D time series variable scene is proposed for multi-step ahead forecasting of multiple station wind vector. Then, a wind vector prediction model (CM-G) based on Convolutional Minimum Gate Memory Neural Network and Graph Convolution Neural Network is constructed to improve the prediction accuracy. Further, three multi-step ahead forecasting modes are compared and their advantages, disadvantages and application scenarios are analyzed. Finally, the model and method proposed in this study are applied to two datasets in Tibet, China. The experimental results and conclusions are as follows: (1) The prediction accuracy of CM-G proposed in this study is higher than that of the existing state-of-the-art model in 90 % of the metrics. (2) From single-step to multi-step ahead forecasting, the prediction accuracy of wind vector gradually decreases. (3) In wind vector prediction, the prediction accuracy of wind speed is higher than that of wind direction. (4) The direction of accuracy improvement for multiple stations is relatively consistent with the average wind direction.

A supervised learning tool for heatwave predictions using daily high summer temperatures

AbstractGlobal temperature is increasing at an alarming rate, which increases the number of heatwaves. Heatwaves have significant impacts, both directly and indirectly, on human and natural systems and can create considerable risk to public health. Predicting the occurrence of a heatwave can save lives, increase the production of crops, improve water quality, and reduce transportation restrictions. Because of its geographical location, Bangladesh is particularly vulnerable to cyclones, droughts, earthquakes, floods, and heatwaves. The Bangladesh Meteorological Department collects temperature data at multiple weather stations, and we use data from 10 weather stations in this research. Data show that most heatwaves occur in the summer months, namely, April, May, and June. In this research, we develop Classification and Regression Tree (CART) models that use daily temperature data for the months of March, April, May, and June to predict the likelihood of a heatwave within the next 7 days, the next 28 days, and on any particular day based on daily high temperatures from the previous 14 days. We also use different model parameters to evaluate the accuracy of the models. Finally, we develop treed Stepwise Logistic Regression models to predict the probability of heatwaves occurring. Even though this research uses data from Bangladesh Meteorological Department, the developed modeling approach can be used in other geographic regions.

FEATURES OF COMBINING THE RESULTS OF 3D LASER SCANNING FROM DIFFERENT STATIONS USING MAGNETIC SPHERES

3D laser scanning is becoming an increasingly important technology in the modern world, opening up endless possibilities for obtaining accurate geometric parameters of various objects. So, due to its efficiency and accuracy, this technology is becoming indispensable in such industries as surveying, construction support, executive surveying, geodetic monitoring, and others. However, despite the many advantages of laser scanning, it has a limitation that can significantly affect the results, namely the algorithm for stitching individual scans into a single-point cloud. Most scanners do not have built-in capabilities to stitch together scan results from multiple stations automatically. Therefore, obtaining a single-point cloud requires using alternative methods. Modern laser scanner companies offer a variety of models with unique technical characteristics. The right choice of scanner functionality is crucial when performing various technological tasks throughout the entire process, from scanning to obtaining the result. The technical parameters of laser scanners, such as measurement accuracy, maximum scanning range, and scanning speed, vary depending on the type of objects scanned and the necessary results. The use of magnetic spheres becomes an essential aspect of ensuring scanning accuracy. They facilitate stitching data from different stations and allow automated search for stitching points, simplifying data processing. An algorithm built into Trimble RealWorks software automatically detects magnetic spheres in the point cloud. It uses an appropriate transformation to join two adjacent scans from different stations if more than three identical spheres are present. The article aims to study the peculiarities of combining the results of 3D laser scanning from several stations using magnetic spheres. It discusses the analysis of the technical parameters of the Trimble TX6 laser scanner, variations in the use of magnetic spheres to ensure the accuracy of combining scanning results, and the advantages and disadvantages of this approach. Keywords: 3D laser scanning, magnetic spheres, stitching algorithm, point cloud, Trimble RealWorks.

Water Flow Prediction Based on Improved Spatiotemporal Attention Mechanism of Long Short-Term Memory Network

The prediction of water plant flow should establish relationships between upstream and downstream hydrological stations, which is crucial for the early detection of flow anomalies. Long Short-Term Memory Networks (LSTMs) have been widely applied in hydrological time series forecasting. However, due to the highly nonlinear and dynamic nature of hydrological time series, as well as the intertwined coupling of data between multiple hydrological stations, the original LSTM models fail to simultaneously consider the spatiotemporal correlations among input sequences for flow prediction. To address this issue, we propose a novel flow prediction method based on the Spatiotemporal Attention LSTM (STA-LSTM) model. This model, based on an encoder–decoder architecture, integrates spatial attention mechanisms in the encoder to adaptively capture hydrological variables relevant to prediction. The decoder combines temporal attention mechanisms to better propagate gradient information and dynamically discover key encoder hidden states from all time steps within a window. Additionally, we construct an extended dataset, which preprocesses meteorological data with forward filling and rainfall encoding, and combines hydrological data from multiple neighboring pumping stations with external meteorological data to enhance the modeling capability of spatiotemporal relationships. In this paper, the actual production data of pumping stations and water plants along the East-to-West Water Diversion Project are taken as examples to verify the effectiveness of the model. Experimental results demonstrate that our STA-LSTM model can better capture spatiotemporal relationships, yielding improved prediction performance with a mean absolute error (MAE) of 3.57, a root mean square error (RMSE) of 4.61, and a mean absolute percentage error (MAPE) of 0.001. Additionally, our model achieved a 3.96% increase in R2 compared to the baseline model.

Time-Series Embeddings from Language Models: A Tool for Wind Direction Nowcasting

Wind direction nowcasting is crucial in various sectors, particularly for ensuring aviation operations and safety. In this context, the TELMo (Time-series Embeddings from Language Models) model, a sophisticated deep learning architecture, has been introduced in this work for enhanced wind-direction nowcasting. Developed by using three years of data from multiple stations in the complex terrain of an international airport, TELMo incorporates the horizontal u (east–west) and v (north–south) wind components to significantly reduce forecasting errors. On a day with high wind direction variability, TELMo achieved mean absolute error values of 5.66 for 2-min, 10.59 for 10-min, and 14.79 for 20-min forecasts, processed within a swift 9-ms/step timeframe. Standard degree-based analysis, in comparison, yielded lower performance, emphasizing the effectiveness of the u and v components. In contrast, a Vanilla neural network, representing a shallow-learning approach, underperformed in all analyses, highlighting the superiority of deep learning methodologies in wind direction nowcasting. TELMo is an efficient model, capable of accurately forecasting wind direction for air traffic operations, with an error less than 20° in 97.49% of the predictions, aligning with recommended international thresholds. This model design enables its applicability across various geographical locations, making it a versatile tool in global aviation meteorology.

Optimal Design of Data Acquisition System for Fault Diagnosis of Hydraulic Machinery

Abstract Fault diagnosis of hydraulic machinery has high requirements on the synchronization of monitoring data and the anti-interference of monitoring equipment. The performance and data quality of the existing operation monitoring equipment of the unit cannot meet the requirements. At the same time, due to the variety of sensors and the different wiring methods of different types of sensors, the preparation work such as wiring of data acquisition equipment in the test is complicated. This paper analyzes the application requirements of fault diagnosis data and the problems existing in the conventional data collection process. A data collection solution specified for diagnosis of hydraulic machinery, which better solves the problem of data synchronization, is designed. The new designed system has high sampling accuracy and anti-interference characteristics, as well as the advantages of strong capability and simple equipment deployment. The proposed system has been applied in multiple power stations, pumping stations and pump test platforms. The relevant functions have been effectively verified.

Smart Slot Assignment for Urban EV Charging Using Folium API Integration

Abstract: Efficient management of electric vehicle (EV) charging distribution in urban areas is crucial amid increasing demand and varying traffic conditions. This study proposes a novel web-based solution that integrates Folium Map API for realtime location tracking and Python Flask backend with MySQL database storage to allocate charging demands. By leveraging real-time traffic information from the Folium Map API, the proposed model optimizes charging resource allocation across multiple stations and road networks. The web interface, built using HTML, CSS, and JavaScript, allows users to interact with the system seamlessly. Charging allocation is dynamically adjusted based on real-time data, prioritizing maxi- mum returns. Additionally, an emergency charge counter facility is introduced to cater to unforeseen charging needs, enhancing user flexibility. The system offers two interfaces and a dashboard for both station administrators and users, en- abling pre-booking services for travelers during their journey, facilitating access to nearby charging stations, and providing a facility for prebooking. These features contribute to improved station revenue and pile utilization, highlighting the efficacy of the proposed web-based approach. By leveraging advanced web technologies and APIs, this comprehensive strategy enhances charging infrastructure efficiency, fosters the widespread adoption of electric vehicles, and contributes to sustainable urban mobility management.

Advancements in daily precipitation forecasting: A deep dive into daily precipitation forecasting hybrid methods in the Tropical Climate of Thailand

Climate change and increasing water demands underscore the importance of water resource management. Precise precipitation forecasting is critical to effective management. This study introduced a Daily Precipitation Forecasting Hybrid (DPFH) technique for central Thailand, which uses three different input-based models to improve prediction accuracy.•The proposed methods precisely combine the biorthogonal wavelet transformation (BWT) function through BWT-RBFNN (Radial Basis Function Neural Networks) and (BWT-LSTM-RNN)Long Short-Term Memory Recurrent Neural Networks. Comparative analyses reveal that hybrid models perform better than conventional deep LSTM-RNN and Multilayer Perceptron Artificial Neural Networks (MLP-ANN). Although MLP-ANN showed moderate effectiveness, LSTM-RNN displayed notable enhancements, particularly evidenced by an impressive R2 (0.96) in Model M-2.•The combination of BWT-LSTM-RNN yielded substantial enhancements, constantly surpassing standalone models. Specifically, DPFH-3 exhibited superior performance across multiple observation stations.•The findings emphasize the efficiency of the BWT-LSTM-RNN models in capturing varied precipitation patterns, highlighting their potential to significantly improve the accuracy of precipitation forecasts, particularly in the context of water resource management in central Thailand.

Teasing out the Effects of Natural Stressors at Chemically Contaminated Sites.

Aquatic ecosystems are often impacted by a multitude of stressors, many of which are introduced by a combination of anthropogenic activities such as agricultural development, urbanization, damming, and industrial discharge. Determining the primary stressors responsible for ecological impairments at a site can be complex and challenging; however, it is crucial for making informed management decisions. Improper diagnosis of an impaired system can lead to misguided attempts at remediation, which can be both time consuming and costly. We focused on the development, implementation, and evaluation of methodologies that, in combination, allowed us to identify the primary stressors. These included a four-phase, weight-of-evidence (WOE) assessment including in situ Toxicity Identification and Evaluation (iTIE) testing, physicochemical and macrobenthos characterization, reciprocal sediment transplants, and laboratory and in situ toxicity testing. The contaminants of concern (COCs) at the site were elevated levels of ammonia, chloride, pH, and total dissolved solids in groundwater upwellings into a high-quality waterway. Reciprocal transplants of site sediments and nearby reference sediments and traditional benthic sampling showed impaired benthic indices and multiple stations around a contaminated industrial settling basin. Impaired stations had elevated COCs in groundwaters but exhibited a steep vertical concentration gradient, with concentrations decreasing near the sediment-surface water interface. We describe Phase 4 of the study, which focused on teasing out the role of dissolved oxygen sags in benthic macroinvertebrate responses. Extensive submerged and emergent macrophytes, algae, and cyanobacteria co-occurred at the impaired sites and increased throughout the summer. Laboratory testing suggested that ammonia and pH were possibly toxic at the sites, based on groundwater concentrations. The in situ toxicity testing, however, showed toxicity occurring even at stations with low levels of COCs concurrently with large diurnal fluxes in dissolved oxygen (DO). A final phase using a type of iTIE approach utilized limnocorrals with and without aeration and with in situ toxicity measures using Hyalella azteca. The Phase 4 assessment revealed that low DO levels were primarily responsible for impaired benthic communities, and COC upwellings were diluted at the sediment-water interface to nontoxic levels. These findings will allow for improved management decisions for more efficient and effective restoration activities. Environ Toxicol Chem 2024;43:1524-1536. © 2024 SETAC.

Analysis and Prevention of Road Accidents for Gautam Buddha Nagar District (U.P)

Road accidents pose a significant threat to public safety, necessitating proactive measures for analysis and prevention. This abstract delves into the analysis and prevention of road accidents in the Kasna to Surajpur region over a seven-year period from 2015 to 2021. Through data gathered from multiple police stations, the study aims to identify accident causes, locations, and frequencies to pinpoint blackspots for future road safety audits. Additionally, a novel solution leveraging Pascal's law is proposed to minimize accidents and we can determine the Number of vehicles and Types of vehicles. The system, triggered by vehicle movement, alerts drivers on the opposite side of the road to reduce speed, thus mitigating collision risks. This innovative approach offers promise in enhancing road safety and reducing accidents

Multinetwork constrained planning of hydrogen-power supplying facilities under double-layer coupling mechanisms

The proliferation of hydrogen fuel cell vehicles calls for effective planning of hydrogen supply facilities. This paper focuses on renewable energy-based distributed hydrogen supply networks (DHSNs), with on-site production and vehicle-based transport. To explore the energy synergism effect, we propose multinetwork constrained planning of hydrogen-power supplying facilities. Considering the double-layer coupling mechanism (DCM), multiple DHSN equipment planning is coordinated with the generation planning of the power distributed networks (PDN). Firstly, the DCM is proposed considering structured multi-component equipment, inter-layer hydrogen couplings, and cross-network power couplings. Secondly, to formulate the DCM, we develop a structured deployment model for DHSN equipment to enhance system integrality and non-redundancy. Specially, to release more flexibilities, inter-layer transport is modeled by proposing a spatiotemporal extended vehicle routing problem model incorporating multiple loading stations with trip coordination. Thirdly, a planning method is proposed to coordinate the planning of hydrogen-power supply facilities in coupled networks, incorporating the double-layer coupling constraints, station operation constraints, and PDN constraints. Test systems are employed to verify the effectiveness of the proposed method.

Risk overbounding for ionospheric gradient monitor using geometry-free double differenced carrier phase measurements

Ionosphere anomaly can cause large spatial gradients in the double-differenced carrier phase (DDCP) measurements. Taking advantage of this characteristic, the ionospheric gradient monitor (IGM) is designed with multiple ground reference stations to detect threatening ionospheric gradients for safety of life applications. An optimal IGM should be most sensitive to ionospheric gradients and least sensitive to other errors. However, current IGM suffers from the influence of tropospheric error, which can degrade monitor performance under extreme weather conditions by causing risks of false alarms and missed detections. To address this issue, an alternative IGM is proposed using the geometry-free combination of DDCP as the test statistic. Ambiguity resolution procedure is designed for estimating the ambiguity term in the test statistic. The risk induced by the wrong ambiguity fix and tropospheric error are analyzed and bounded with required averaging period and minimum baseline length. The results show that the proposed theoretical IGM is capable of achieving probability of false alarm of 10–8 and probability of missed detection of 10–6 with a filtering period of 612 s and baseline length of 371.7 m or a filtering period of 544 s and baseline length of 384.4 m. The experimental results using data collected from Hong Kong Satellite Positioning Reference Station Network demonstrate that the performance of the proposed theoretical IGM is comparable to that of the existing detection algorithm.

Staged SWAT calibration with bias-corrected precipitation product for enhancing flow data continuity in tributaries of the Mekong River

Accurate and continuous flow data are crucial for effective water management in large river basins worldwide. However, these catchments often face challenges regarding data continuity in the mainstream and their tributaries. This study proposes a methodological framework for enhancing flow data continuity that uses a staged calibration scheme of Soil and Water Assessment Tool (SWAT) modeling with an appropriate satellite precipitation product (SPP) for each watershed. This framework is successfully applied in the Mun-Chi River Basin, the largest tributary of the Lower Mekong River, over a 20-year period at 34 stations. The staged calibration involves partitioning the catchment into smaller sub-catchments, guided by the flow monitoring stations and flow discontinuity structures such as dams. The sequential calibration from upstream to downstream partitions the overall model calibration challenge into smaller problems and contributes to a more efficient and accurate calibration and validation. Alternative SPPs were considered to overcome monitoring gaps. Their bias was corrected using quantile mapping, and their performance was evaluated with flow simulations using the SWAT model. The assessment indicates that the CMORPH-CRT product, with a spatial resolution of 0.25°, demonstrates good suitability for hydrological modeling of the Mun-Chi River Basin. The proposed methodological framework provides a continuous time series of flow discharge at multiple stations within the watershed, offering valuable insights for sustainable water resource management strategies in river systems under changing climate and land use conditions, and supporting future studies on environmental issues.

Modelling impacts of climate change on snow drought, groundwater drought, and their feedback mechanism in a snow-dominated watershed in western Canada

Snow accumulation and its melt are key hydrological processes in cold watersheds, which can affect groundwater (GW). With climate change projected to alter snow processes in these regions, understanding their impacts on the development of droughts is vital. A deficit in snow precipitation or accelerated snowmelt due to warming can trigger snow drought, potentially leading to GW drought. To investigate this relationship at a watershed scale, we coupled the Soil and Water Assessment Tool (SWAT) and MODFLOW to simulate various surface and groundwater processes under historical (1980–2013) and future (2040–2073) warming scenarios in western Canada. We calibrated, validated, and verified our models using streamflow, GW heads, and snow depth data from multiple hydrometric stations, observation wells, and a grid product. Using simulated data, we analyzed snow and GW drought characteristics, their dominant physical processes, and GW response time across eco-hydro(geo)logical regions like Mountains, Foothills, and Plains. Historical data show that Mountains are experiencing more snow droughts while Plains are facing greater GW droughts. However, future scenarios suggest increased snow droughts in all regions and a shift towards more severe GW droughts in Plains. Historical response time of GW to changes in snow processes spans 4–6 months from Mountains to Plains, with projected reductions in Mountains and Foothills, and a slight increase in Plains in the future. The dominant physical processes controlling GW response across all regions are soil moisture and percolation, with curve number displaying more significance in Mountains, and water yield exerts more control in Foothills and Plains. During cold seasons, SWE and snowmelt had minimal impact on GW response in Plains, while they presented a major role in Mountains. This study lays the basis for further research on snow-groundwater interactions in cold watersheds, aiding water resource management in mid-to-high latitude regions and providing a unified framework for analyzing snow and GW drought relationships.

New Decomposition Models for Hourly Direct Normal Irradiance Estimations for Southern Africa

This research develops and validates new decomposition models for hourly direct Normal Irradiance (DNI) estimations for Southern African data. Localised models were developed using data collected from the Southern African Universities Radiometric Network (SAURAN). Clustered areas within Southern Africa were identified, and the developed cluster decomposition models highlighted the potential advantages of grouping data based on shared geographical and climatic attributes. This clustering approach could enhance decomposition model performance, particularly when local data are limited or when data are available from multiple nearby stations. Further, a regional Southern African decomposition model, which encompasses a wide spectrum of climatic regions and geographic locations, exhibited notable improvements over the baseline models despite occasional overestimation or underestimation. The results demonstrated improved DNI estimation accuracy compared to the baseline models across all testing and validation datasets. These outcomes suggest that utilising a localised model can significantly enhance DNI estimations for Southern Africa and potentially for developing similar models in diverse geographic regions worldwide. The overall metrics affirm the substantial advancement achieved with the regional model as an accurate decomposition model representing Southern Africa. Two stations were used as a validation study, as an application example where no localised model was available, and the cluster and regional models both outperformed the comparative decomposition models. This study focused on validating the model for hourly DNI in Southern Africa within a range of Kt-intervals from 0.175 to 0.875, and the range could be expanded and validated for future studies. Implementing accurate decomposition models in developing countries can accelerate the adoption of renewable energy sources, diminishing reliance on coal and fossil fuels.

The Study on Anomalies of the Geomagnetic Topology Network Associated with the 2022 Ms6.8 Luding Earthquake

On 5 September 2022, the Ms 6.8 Luding earthquake occurred at 29.59°N and 102.08°E in China. To investigate the variations in geomagnetic signals before the earthquake, this study analyzes the geomagnetic data from nine stations around the epicenter. First, we apply the Multi-channel Singular Spectrum Analysis to reconstruct the periodic components of the geomagnetic data from multiple stations. Second, we employ K-means clustering to rule out the possibility of occasional anomalies caused by a single station. Subsequently, we construct a geomagnetic topology network considering the remaining stations. Network centrality is defined as a measure of overall network connectivity, where the higher the correlation between multiple stations, the greater the network centrality. Finally, we examine the network centrality 45 days before and 15 days after the Luding earthquake. The results show that several anomalies in network centrality are extracted about one week before the earthquake. We further validate the significance of the anomalies in terms of time as well as space and verify the utility of the centrality anomalies through the SEA technique. The anomalies are found to have a statistical correlation with the earthquake event. We consider that this study provides a new way and a novel observational perspective for earthquake precursor analysis of ground-based magnetic data.

Sources of wintertime PM2.5 at a major city in an Alpine Valley: The role of atmospheric dispersion and inversion dynamics

Urban areas in mountain environments are generally located on valley floors surrounded by slopes, where mountain orography drives peculiar meteorology and atmospheric circulation. Also, persistent inversion dynamics may occur strongly affecting air pollution. This study characterizes the PM2.5 pollution in a major city located in an Alpine valley (Belluno, Northeastern Italy) during the cold season (Autumn-Winter). Major particulate species (elemental and organic carbon, major inorganic ions) and minor/trace elements conventionally used as tracers for source apportionment were analyzed, including oxalate and specific PM2.5-bound tracers for biomass burning (K+, levoglucosan, mannosan, galactosan) and for primary biogenic organic aerosol (arabitol, mannitol, glucose). PM2.5 sources are identified through positive matrix factorization and a series of post-processing tools. Results indicate that biomass burning, mostly emitted by residential wood combustion for domestic heating, is the major source (52% PM2.5 mass concentration), followed by secondary aerosol (21%), biogenic aerosol (20%), traffic (4%), and dust resuspension (3%). The source contributions are assessed by accounting for the local meteorology. Insights into the dispersion or buildup of PM2.5 sources were then investigated by dispersion normalization. In addition, the possible effects of persistent thermal inversion events were evaluated by assessing the inversion strength from temperature profiles measured at multiple weather stations at different elevations with respect to the source contributions. Beyond the identification of those emission sources requiring further mitigation actions, this study also analyses the potential effects of local meteorology on PM2.5 pollution. The methodologies applied in this study can be easily adopted to other mountain environments for successful management of air quality.

PV Power Forecasting in the Hexi Region of Gansu Province Based on AP Clustering and LSTNet

Accurate PV power forecasting is becoming a mandatory task to integrate the PV system into the power grid, schedule it, and ensure the safety of the power grid. In this paper, a novel model for PV power prediction using AP-LSTNet has been proposed. It consists of a combination of affinity propagation clustering and long-term and short-term time series network models. First, the affinity propagation algorithm is used to divide the regionally distributed photovoltaic station clusters into different seasons. The Pearson correlation coefficient is used to determine the strong correlation between meteorological factors of photovoltaic power, and the bilinear interpolation method is used to encrypt the meteorological data of the corresponding photovoltaic station cluster. Furthermore, LSTNet is used to mine the long-term and short-term temporal and spatial dependence of photovoltaic power, and meteorological factor series and linear components of auto-regression are superimposed to realize the simultaneous prediction of multiple photovoltaic stations in the group. Finally, PV power plants in five cities, Wuwei, Jinchang, Zhangye, Jiuquan, and Jiayuguan in the Hexi region of Gansu Province, China, will be selected to test the proposed model. The experimental comparison shows that the prediction model achieves high prediction accuracy and robustness.