Source Identification Model Research Articles

Research on mine water source classifications based on BO-CatBoost.

Mine water surge is one of the main safety risks in coal mines. This research offers a novel mine water source identification model (BO-CatBoost) to successfully avoid and control mine sudden water catastrophes by properly identifying the sources of mine water. First, the classification model is trained and built using the Categorical Boosting (CatBoost) algorithm. The Gaussian process Bayesian optimization (BO) algorithm is used to optimize parameters, and the optimal parameter combination is integrated into the CatBoost algorithm to build the BO-CatBoost mine water source identification model, which further improves the accuracy of mine water source identification. The model was also applied to the Pingdingshan mine to verify the practicality of the model. Then, 29 groups of unknown water sources in Pingdingshan were selected as validation samples for the model and compared with the conventional CatBoost, Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (Xgboost) models. The comparison results demonstrate that the accuracy of LightGBM, Xgboost, CatBoost, and BO-CatBoost models can reach 69%, 79.3%, 79.3%, and 100% respectively, and the RMSE is 0.947, 0.643, 0.719, and 0.0 respectively. The comprehensive analysis shows that, when it comes to mine water source detection, the BO-CatBoost model performs noticeably better than other models in terms of discriminative accuracy and generalization capacity. Lastly, the multi-output prediction and decision-making process of the BO-CatBoost water source identification model is visualized by the interpretability analysis performed with the SHAP approach. The research demonstrates that the BO-CatBoost model can more precisely and impartially identify mine water sources, offering fresh concepts for mine water source detection.

Mine water inrush source discrimination model based on KPCA-ISSA-KELM.

In order to ensure the safety of coal mine production, a mine water source identification model is proposed to improve the accuracy of mine water inrush source identification and effectively prevent water inrush accidents based on kernel principal component analysis (KPCA) and improved sparrow search algorithm (ISSA) optimized kernel extreme learning machine (KELM). Taking Zhaogezhuang mine as the research object, firstly, Na+, Ca2+, Mg2+, Cl-, SO2- 4 and HCO- 3 were selected as evaluation indexes, and their correlation was analyzed by SPSS27 software, with reducing the dimension of the original data by KPCA. Secondly, the Sine Chaotic Mapping, dynamic adaptive weights, and Cauchy Variation and Reverse Learning were introduced to improve the Sparrow Search Algorithm (SSA) to strengthen global search ability and stability. Meanwhile, the ISSA was used to optimize the kernel parameters and regularization coefficients in the KELM to establish a mine water inrush source discrimination model based on the KPCA-ISSA-KELM. Then, the mine water source data are input into the model for discrimination in compared with discrimination results of KPCA-SSA-KELM, KPCA-KELM, ISSA-KELM, SSA-KELM and KELM models. The results of the study show as follows: The discrimination results of the KPCA-ISSA-KELM model are in agreement with the actual results. Compared with the other models, the accuracy of the KPCA-ISSA-KELM model is improved by 8.33%, 12.5%, 4.17%, 21.83%, and 25%, respectively. Finally, when these models were applied to discriminate water sources in a coal mine in Shanxi, and the misjudgment rates of each model were 28.57%, 19.05%, 14.29%, 23.81%, 9.52% and 4.76%, respectively. From this, the KPCA-ISSA-KLEM model is the most accurate about discrimination and significantly better than other models in other evaluation indicators, verifying the universality and stability of the model. It can be effectively applied to the discrimination of inrush water sources in mines, providing important guarantees for mine safety production.

An outbreak of Legionnaires' disease linked to a municipal and industrial wastewater treatment plant, The Netherlands, September-October 2022.

Wastewater treatment plants (WWTPs) are increasingly identified as Legionnaires' disease (LD) sources. An outbreak investigation was initiated following five LD cases reported in September 2022 in Houten, the Netherlands. Case identification was based on the European LD case definition, with symptom onset from 1 September 2022, residence in or within 5 km of Houten, or visit to Houten within the incubation period, without other likely sources. We sampled potential sources and genotyped environmental and clinical isolates. We identified 15 LD cases with onset between 13 September and 23 October 2022. A spatial source identification and wind direction model suggested an industrial (iWWTP) and a municipal WWTP (mWWTP) as potential sources, with the first discharging water into the latter. Both tested positive for Legionella pneumophila serogroups 1 and 6 with multiple sequence types (ST). We detected L. pneumophila sg1 ST42 in the mWWTP, matching with one of three available clinical isolates. Following control measures at the WWTPs, no further cases were observed. This outbreak underlines that municipal and industrial WWTPs can play an important role in community LD cases and outbreaks, especially those with favourable conditions for Legionella growth and dissemination, or even non-favourable conditions for growth but with the influx of contaminated water.

Groundwater chemical characteristic analysis and water source identification model study in Gubei coal mine, Northern Anhui Province, China

This study aims to accurately identify mine water sources and reduce the hazards caused by water inrush accidents in coal mines. Taking the Gubei coal mine as an example, the water quality results of the water samples from the Cenozoic unconsolidated aquifer, Permian sandstone fracture aquifer, and Carboniferous Taiyuan Formation limestone karst fracture aquifer in the mine area were tested, and K++Na+, Ca2+, Mg2+, Cl−, SO42−, HCO3−, TDS (Total Dissolved Solids), and pH were selected as the main indicators to study the water chemistry characteristics of the aquifer through water chemistry component analysis, major ion content analysis, Piper trilinear analysis, and correlation analysis. Thirty-five groups of water samples were randomly selected and imported into SPSS software for factor analysis (FA) and downsized to three main factors as the input variables of the artificial neural network model. The particle swarm optimization (PSO) code was written based on the MATLAB platform to improve the self-adjustment weights and acceleration factors for optimizing the initial weights and thresholds of the Back-Propagation (BP) neural network. The training and prediction samples were learned in the ratio of 8:2, and the recognition results were compared with the traditional BP neural network model. Results showed that the groundwater of the Gubei coal mine demonstrated a water quality vertical zoning pattern, and the chemical composition was dominated by cation K++Na+ and anion Cl−. The FA-PSO-BP neural network model has a higher accuracy of water source discrimination compared with the cluster analysis and the FA-BP neural network model. The FA-PSO-BP neural network model is worthy of further application in the problem of water source identification in mine water inrush.

The Potential of Using SDGSAT-1 TIS Data to Identify Industrial Heat Sources in the Beijing–Tianjin–Hebei Region

It is crucial to detect and classify industrial heat sources for sustainable industrial development. Sustainable Development Science Satellite 1 (SDGSAT-1) thermal infrared spectrometer (TIS) data were first introduced for detecting industrial heat source production areas to address the difficulty in identifying factories with low combustion temperatures and small scales. In this study, a new industrial heat source identification and classification model using SDGSAT-1 TIS and Landsat 8/9 Operational Land Imager (OLI) data was proposed to improve the accuracy and granularity of industrial heat source recognition. First, multiple features (thermal and optical features) were extracted using SDGSAT-1 TIS and Landsat 8/9 OLI data. Second, an industrial heat source identification model based on a support vector machine (SVM) and multiple features was constructed. Then, industrial heat sources were generated and verified based on the topological correlation between the identification results of the production areas and Google Earth images. Finally, the industrial heat sources were classified into six categories based on point-of-interest (POI) data. The new model was applied to the Beijing–Tianjin–Hebei (BTH) region of China. The results showed the following: (1) Multiple features enhance the differentiation and identification accuracy between industrial heat source production areas and the background. (2) Compared to active-fire-point (ACF) data (375 m) and Landsat 8/9 thermal infrared sensor (TIRS) data (100 m), nighttime SDGSAT-1 TIS data (30 m) facilitate the more accurate detection of industrial heat source production areas. (3) Greater than 2~6 times more industrial heat sources were detected in the BTH region using our model than were reported by Ma and Liu. Some industrial heat sources with low heat emissions and small areas (53 thermal power plants) were detected for the first time using TIS data. (4) The production areas of cement plants exhibited the highest brightness temperatures, reaching 301.78 K, while thermal power plants exhibited the lowest brightness temperatures, averaging 277.31 K. The production areas and operational statuses of factories could be more accurately identified and monitored with the proposed approach than with previous methods. A new way to estimate the thermal and air pollution emissions of industrial enterprises is presented.

Explicit K-Means Clustering Assisted Direct Model for Identification of Groundwater Pollution Sources

A computationally inexpensive and data parsimonious direct model for identification of groundwater pollution sources that captures their location, spatial extent and flux intensities at a chosen reference time is presented in this study. The model is based upon instantaneous micro-level mass balance that is depicted by a pseudo steady state solute transport equation. Clusters of polluting locations were identified by finite differencing the solute transport equation and applying K-means clustering algorithm. The model is validated by the problem taken from Singh, Datta, and Jain (2004) and is also illustrated by applying it to an area lying between river Krishni and Hindon. Validation results showed that the model captured closely the pollution source locations; their corresponding spatial extents and flux intensities. Model captured the sources even when the concentration plumes are far away from the source locations due to advection dominated contaminant transport. Illustration results showed that the model identified the leachate flux intensity from irrigation in the form of a regional source. Further, it established a few local sources arising possibly out of localized industrial and/or municipal waste water disposal.

An integrated modelling framework for multiple pollution source identification in surface water

Pollution source identification is vital in water safety management. An integrated simulation-optimization modelling framework comprising a process-based hydrodynamic water quality model, artificial neural network surrogate model and particle swarm optimization (PSO) was proposed to achieve rapid, accurate and reliable pollution source identification. In this study, the hydrodynamics and water quality processes in a straight lab-based flume were simulated to test pollution source identification under steady flow conditions. Additionally, the pollution source identification in the unsteady flow conditions was examined using a real-life estuary, specifically the Yangtze River estuary. First, we developed two process-based models to simulate hydrodynamics and water quality in the flume and estuary. Then, the data generated from the process-based models were used to develop surrogate models. Three typical artificial neural networks (ANNs) algorithms: backpropagation (BP), radial basis function (RBF) and general regression neural networks (GRNN) were selected to develop surrogates for process-based models (PBMs), and they were coupled with PSO algorithm to achieve the hybrid modelling framework for pollution source identification. Our results showed that hybrid PBM-ANNs-PSO models could be applied to identify the pollution source and quantify release intensity in spatial distribution when the discharge type was assumed as the point source with a continuous release. Multiple-performance criteria metrics, in terms of the coefficient of determination, root-mean-square error, mean absolute error, evaluated the model performance as “Excellent prediction”. The BP-PSO models consistently appear to be the top-performing source identification model within the developed models, with most cases of relative error (RE) values lower than 5%. The new insights from the hybrid modelling framework would provide useful information for the local government agency to make reasonable decisions regarding pollution source identification issues.

A risk source identification model for network security of power CPS system (CPS) based on fuzzy artificial neural network

In order to improve the effect and accuracy of risk source identification, this paper studied the network security risk source identification model of power CPS system based on fuzzy artificial neural network. The network security risk source index system of power CPS system was constructed, and the dimension of index data was reduced by principal component analysis. Fuzzy theory is used to process the index data after dimension reduction, and the comprehensive membership vector of each index is obtained. The dynamic clustering algorithm is used to determine the number of hidden layer units of radial basis function neural network, and the network security risk source identification model is established. Finally, the quantitative value of risk source identification is output. The experimental results show that the model can effectively reduce the dimension of the network security risk source index data of the power CPS system. The optimal distance threshold of the hidden layer is 4.2, and the optimal number of units is 6. In the final identification results, four severe risk sources and five moderate risk sources were obtained, and the quantitative values of risk source identification of each index were 63, 70, 71, 77, 65, 89 and 96, respectively, indicating that the model can effectively identify network security risk sources of power CPS systems. With the increase of the proportion of communication nodes removed, when there are various types of security vulnerability information, the mean square error value of the model is always lower than the set threshold, indicating that the model has high recognition accuracy.

Integrative assessment of urban dust polycyclic aromatic hydrocarbons using ground and satellite data in Iran.

Recently, for quick urbanization and industrialization, pollutants, especially urban dust, have posed many threats to the human environment. Polycyclic aromatic hydrocarbons (PAHs) are regarded as the main dangerous pollutants that are widespread, persistent, and carcinogenic. The present work aimed to investigate the contamination and sources of PAHs, as well as to assess the risk of cancer for 16 priority PAHs, in urban dust samples in Ahvaz, Isfahan, and Shiraz cities in Iran. We measured PAH concentrations by gas chromatography-mass spectrometry (GC-MS). The average concentrations of the 16 PAHs in Ahvaz, Isfahan, and Shiraz were 6215.11, 7611.03, and 7810.37 μg kg-1, respectively. The domination of low-molecular-weight (LMW) PAHs was observed in Ahvaz, while maximum contribution was observed for high-molecular-weight (HMW) PAHs in Esfahan and Shiraz. For PAHs' source identification, diagnostic ratio, correlation analysis, clustering, and positive matrix factorization (PMF) model were used. PAHs had a combustion (coal and wood, oil, fossil fuels) and gasoline/diesel engine emissions in all cities. Comparative studies suggest that the PAH compounds' level is higher in the research area than in other countries, except for China and India. Also, the pollution of urban dust PAHs has increased over time compared to previous studies in the same cities. The cancer risk from exposure to dust contaminated with PAHs was assessed using the Incremental Lifetime Cancer Risk (ILCR) model. According to the findings, a high risk of exposure to cancer was observed in Ahvaz, Isfahan, and Shiraz. However, compared to adults, children are at higher risk of cancer in their daily lives via dermal contact and unconscious ingestion. Based on the ILCR values, the risk of cancer is in the order of Shiraz > Isfahan > Ahvaz. To assess air pollutants and their effects on urban dust, TROPOMI onboard the Sentinel-5P data were used in the studied cities during 2018-2021. The results show that Ahvaz has different high levels of CO compared to the other 2 cities. Also, Isfahan has different high levels of NO2 compared to the other 2 cities, but Shiraz has different low levels of O3. According to satellite time series data, the trend of the Aerosol Absorbing Index (AAI) has been increasing, while there was a decreasing trend in AAI from the beginning of the COVID-19 pandemic until 12 months later. Therefore, the natural and anthropogenic sources of urban dust PAHs have been increasing in all studied cities. Our findings show that PAH compounds in urban dust pose a significant threat to human health. Therefore, strategic management and planning are vital in reducing urban dust pollution.

Using Data-Driven Methods and Aging Information to Quantitatively Identify Microplastic Environmental Sources and Establish a Comprehensive Discrimination Index.

The global distribution of microplastics (MPs) across various environmental compartments has garnered significant attention. However, the differences in the characteristics of MPs in different environments remain unclear, and there is still a lack of quantitative analysis of their environmental sources. In addition, the inclusion of aging in source apportionment is a novel approach that has not been widely explored. In this study, we conducted a meta-analysis of the literature from the past 10 years and extracted conventional and aging characteristic data of MPs from 321 sampling points across 7 environmental compartments worldwide. We established a data-driven analysis framework using these data sets to identify different MP communities across environmental compartments, screen key MP features, and develop an environmental source analysis model for MPs. Our results indicate significant differences in the characteristics of MP communities across environments. The key features of differentiation were identified using the LEfSe method and include the carbonyl index, hydroxyl index, fouling index, proportions of polypropylene, white, black/gray, and film/sheet. These features were screened for each environmental compartment. An environmental source identification model was established based on these features with an accuracy of 75.1%. In order to accurately represent the single/multisource case in a more probabilistic manner, we proposed the MP environmental source index (MESI) to provide a probability estimation of the sample having multiple sources. Our findings contribute to a better understanding of MP migration trends and fluxes in the plastic cycle and inform effective prevention and control strategies for MP pollution.

Groundwater Source Identification and Flow Model of the Dareh-Zar Copper Mine in Central Iran by Chemo-isotopic Techniques

Groundwater Source Identification and Flow Model of the Dareh-Zar Copper Mine in Central Iran by Chemo-isotopic Techniques

Identification of mixing water source and response mechanism of radium and radon under mining in limestone of coal seam floor

With the gradual increase of the coal mining depth, the mixing of multiple water sources intensifies and the activity of radium and radon in groundwater increases. Identifying the source of mine water inrush by using radium and radon isotopes is a new choice. In this paper, the mathematical statistics method, radioactive isotope decay theory, the mass conservation principle, and the numerical simulation method are used to analyze the influence of total dissolved solids (TDS), pH, and the hydrochemical ion content in groundwater on the isotope activity of radium, radon, uranium, thorium, and lead. The activity of thorium and lead is lower than the detection limit of the instrument, and the influence of coal mining activities on it is small. The simulation of the radium–radon mass balance in groundwater shows that the greater the adsorption coefficient (k) of solid particles in groundwater is, the more obvious the adsorption effect and the greater the influence on the radium–radon activity balance are. The radium–radon dating method is used to calculate the groundwater age. Results show that the groundwater age in the closed pit coal mining area is generally older than that in the mining coal mining area. Combined with the 222Rn, 226Ra, and 234U radioactive isotopes and temperature, a mixing water source identification model of limestone in the coal seam floor is constructed. The model shows that the radium activity and temperature of the groundwater are inversely proportional to the mixing ratio of the Permian sandstone water. From the closed pit coal mining area to the mining coal mining area, the radium radon activity of the groundwater increases gradually, the groundwater age decreases significantly, the water cycle is accelerated, the mixing ratio of the Permian sandstone water decreases gradually, the mixing ratio of the Ordovician limestone water increases gradually, and the risk of coal mine water inrush increases. The research results prove the feasibility of the new method for accurately discriminating the mixing water sources in coal mine areas, which is of great significance to the improvement of the theory of coal mine water disaster prevention and control.

Investigation of sniffer technique on remote measurement of ship emissions: A case study in Shanghai, China

Shipping emissions have aroused wide concern in the world. In order to promote the implementation of emission regulations, this study develop a ship based sniffing technique to perform remote measurement of the SO2, NOx and CO2 from ships entering and leaving Shanghai port at the open sea. The ship emission prediction model, Smoke diffusion model and source identification model were developed to automatically analyze the emission data and screen the object ship source based on Automatic Identification System (AIS) system. The fueling documents of the detected ship were obtained from maritime sector and the results precision of the sniffer technique was evaluated by comparing the measured Fuel sulfur content (FSC) with actual value deduced from fueling documents. The influences of wind speed and direction, object ship parameters and monitoring distance on the identification of object ship and accuracy of the calculated FSC were thoroughly investigated and the corresponding correction factors under different conditions were deduced. The modified emission factor ratio of CO2 to NOx were proposed in order to improve the accuracy. It is demonstrated that with wind speed higher than 2 m/s and test distance shorter than 400m, the sniffer technique exhibit high efficiency and accuracy for the remote emissions measurement of ship upwind with detection rate higher than 90% and test error of FSC below 15%. To reduce the influence of the wind direction, at least two sniffer systems were required to guarantee that at least one station is in the downwind of the ship lane. Based on the results and discussion, a novel sniffer monitoring system with two buoy based sniffing stations placed close to each side of the ship lane far off shore was proposed to realize the remote monitoring of ship emissions.

The estimation of distance and power of multiple sound sources by combining three-dimensional sound intensity and beamforming

The problem of identifying multiple sound sources occurs in various fields. This study proposes a method for multi-source localization and sound power estimation based on three-dimensional sound intensity (3DSI) measurement combined with high-precision beamforming. First, according to the propagation law of the point source in the free field and 3DSI measurement method, a mathematical model of the sound intensity vector, sound power, and source location in 3D space for multiple sources is derived. The 3DSI components can be measured by 3DSI probes. To reduce the computational cost required to solve the mathematical model, the angles of the sources are estimated via compressive sensing beamforming orthogonal matching pursuit for the method. Moreover, the sound intensity components and angles are used as inputs in the multiple sound source identification model, and the dimensions of the model are significantly reduced. The model was solved to obtain sound source distance and sound power. Two tetrahedral intensity probes and a 16-channel microphone array were used for experiments in a semi-anechoic chamber. The results demonstrate that the method can accurately identify the locations of multiple sound sources and determine the sound power. Considering three sound sources, the maximum angular error is 1.41°, while the maximum distance error is 0.08 m. This method can use a small number of probes to obtain the sound sources spatial position and sound source power, and it improve the resolution and calculation efficiency.

Study on key technology of identification of mine water inrush source by PSO-LightGBM

Abstract Mine water inrush is a major type of disaster in coal mine production in China. It causes heavy casualties and serious economic losses and threatens coal mine safety. To quickly and accurately identify mine water inrush source, according to the hydrochemical characteristics of different aquifers in the Donghuantuo mining area, this paper systematically analyzes the hydraulic connection of the aquifers in main coal mining areas before and after mining activities. Four types of hydrochemical data were collected: No. 5 coal seam roof water, No. 8 coal seam roof water, No. 122 coal seam floor water, and No. 1214 coal seam aquifer water in the Donghuantuo mining area. In addition, based on the hydrochemical data, the parameter selection of LightGBM was optimized by Particle Swarm Optimization (PSO) and constructed the PSO-LightGBM water inrush source identification model. The recognition accuracy of PSO-LightGBM model was compared with LightGBM model, classification regression tree (CART) model, and random forest (RF) model. The results showed that coal mining activities would have a significant impact on the water quality characteristics of the roof sandstone fissure water of No. 5 coal mine. Mining activities had a certain impact on the accuracy of the identification model. In addition, compared with the four recognition models, PSO-LightGBM model had the highest recognition accuracy of 97.22%. It showed that the model had high accuracy, stability, generalization ability, and important reference value for the identification of mine water inrush source.

Potential hydraulic connectivity of coal mine aquifers based on statistical analysis of hydrogeochemistry

Mining activities interfere with the natural groundwater chemical environment, which may lead to hydrogeochemical changes of aquifers and mine water inrush disasters. This study analyzed the hydrochemical compositions of 80 water samples in three aquifers and developed a water source identification model to explore the control factors and potential hydraulic connection of groundwater chemistry in a coal mine. The results showed that the hydrochemical types of the three aquifers were different. The main hydrochemical compositions of the loose-layer, coal-bearing, and limestone aquifers were HCO3·Cl–Na, SO4·HCO3–Na, and SO4–Na·Ca, respectively. The correlation, Unmix, and factor analyses showed that the hydrochemical composition of groundwater was controlled by the dissolution of soluble minerals (such as calcite, dolomite, gypsum, and halite) and the weathering of silicate minerals. The factor score plot combined with Q-mode cluster analysis demonstrated no remarkable hydraulic connection among the three aquifers in the study area. The water source identification model effectively identified the source of inrush water. Moreover, the mixing ratio model rationally quantified the contributions of the three aquifers to inrush water.

Study on Characteristic Raman Shift Screening Method Based on MPA for Raman Spectrum of Mine Water Inrush Source

In the process of mine water inrush disaster prevention, accurate and rapid identification of water inrush source type is of great significance to coal mine safety production. However, traditional hydrochemical methods have shortcomings such as time-consuming and complex detection. Therefore, a new idea of identifying mine water inrush source by Raman spectroscopy is proposed. Goaf water, roof sandstone fissure water, Ordovician limestone water, Taiyuan limestone water, and surface water as well as their mixed water samples are selected as research objects, and Raman spectral data of different water samples are collected by the Raman spectroscopy system. To eliminate the influence of laser power fluctuation and spectrometer system noise in Raman spectrum acquisition, detrend correction (DC), multiplicative scatter correction (MSC), standard normal variate transformation (SNV), first derivative (FD), and mean centering (MC) were used to preprocess the raw Raman spectra. Due to the large dimension and long analysis time of Raman spectrum data, the marine predator algorithm (MPA) is used to screen the characteristic Raman shifts of the Raman spectrum of water samples, and the characteristic Raman shift information that can best characterize the mine water samples is obtained. Finally, to verify the feasibility of MPA screening the characteristic Raman shifts of Raman spectrum of mine water inrush source, the selected characteristic Raman displacement information is used as input to construct BP neural network (BP), k-nearest neighbor algorithm (KNN), support vector machine (SVM), and decision tree (DT) classification models, respectively. Experiments show that SNV has the best preprocessing effect on the raw Raman spectrum, which can effectively eliminate part of the noise in the Raman spectrum data and improve the accuracy of Raman spectrum identification. Using MPA, 226 characteristic Raman shifts can be screened from 2048 Raman data points, reducing the number of Raman shifts to 11.04%, and the modeling accuracy of characteristic Raman shift information screened by MPA is higher than that of full Raman data. In addition, the average analysis speed of BP, KNN, SVM, and DT water source identification models is 7.61 times faster than that of all Raman data. The results show that MPA is adopted to screen the characteristic Raman displacement of mine water source Raman spectrum, which can effectively reduce the redundancy of Raman spectral data and greatly improve the speed of Raman spectral analysis, which is of great significance to ensure the real-time detection of the mine water source.

A Catalog of LAMOST Variable Sources Based on Time-domain Photometry of ZTF

Abstract The identification and analysis of different variable sources is a hot topic in astrophysical research. The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) spectroscopic survey has accumulated a mass of spectral data but contains no information about variable sources. Although a few related studies present variable source catalogs for the LAMOST, the studies still have a few deficiencies regarding the type and number of variable sources identified. In this study, we present a statistical modeling approach to identify variable source candidates. We first cross-match the Kepler, Sloan Digital Sky Survey, and Zwicky Transient Facility catalogs to obtain light-curve data of variable and nonvariable sources. The data are then modeled statistically using commonly used variability parameters. Then, an optimal variable source identification model is determined using the Receiver Operating Characteristic curve and four credible evaluation indices such as precision, accuracy, recall, and F1-score. Based on this identification model, a catalog of LAMOST variable sources (including 631,769 variable source candidates with a probability greater than 95%, and so on) is obtained. To validate the correctness of the catalog, we perform a two-by-two cross-comparison with the Gaia catalog and other published variable source catalogs. We achieve the correct rate ranging from 50% to 100%. Among the 123,756 sources cross-matched, our variable source catalog identifies 85,669 with a correct rate of 69%, which indicates that the variable source catalog presented in this study is credible.

Understanding China's resumption of work and production during the critical period of COVlD‐19 based on multi‐source data

AbstractMonitoring the resumption of work and production (RWP) is of great significance to the restoration of social order and the economy after the coronavirus disease 2019 (COVID‐19) epidemic. In this study, the most critical period of epidemic prevention and control and economic recovery was divided into four phases according to the changes in emergency response levels during the epidemic in China, and based on VIIRS I‐Band 375‐m active fire data (VNP14IMG), NO2 tropospheric vertical column density (TVCD) data, and intracity travel intensity data, the density‐based spatial clustering of applications with the noise logistic regression industrial heat source identification model, NO2 difference model, and urban relative recovery index were constructed to analyze work resumption at different phases. The results indicate that: (1) the production intensity of China's heat source enterprises decreased first and then exceeded the epidemic period compared with the same period in history. From phases 1 to 3, the heat source enterprise production intensity was lower than the same period in 2019 (the production intensities were 19,562.79, 22,462.57, and 38,466.89, respectively), and the reduction rates of production intensity were 27.5, 26.1, and 22.7%, respectively. In phase 4, enterprise production intensity achieved an increase of 7%. (2) The process of the RWP in China is closely related to NO2 TVCD. In the first three phases, significantly decreased emission sources of NO2 reduced the total value of NO2 by varying degrees in most provinces of the country. Correspondingly, in phase 4, the NO2 TVCD in some provinces recovered to the value of the same period in 2019, including typical industrial bases (Shandong, Hebei, Shanxi, etc.) and economically developed coastal areas (Jiangsu, Guangdong, Zhejiang, etc.). (3) In phase 3, the intracity travel intensity in all cities improved, and the city relative recovery index (RRI) rose across the board. In particular, the western cities were better than the eastern cities. Among them, the RRIs of most cities in southwest China (Sichuan, Yunnan) and northwest China (Gansu) were more than 90%. Notably, the recovery situation exhibited an obvious “siege phenomenon,” and the recovery of core cities required a longer period. Overall, the sudden COVID‐19 epidemic seriously affected the production of Chinese enterprises. However, benefitting from rapid and effective epidemic prevention and control measures, the resumption of work and production has been a success. Our study provides guidance for economic recovery strategies during post‐pandemic stages.

A Bayesian-SWMM coupled stochastic model developed to reconstruct the complete profile of an unknown discharging incidence in sewer networks

Unknown illicit discharges from manufactories often contain toxic chemical matters that are detrimental to the receiving waterbody by deteriorating the performance of wastewater treatment plants. Numerical models that identify these sources and reconstruct the discharging profiles are highly desired for environment management purpose. In this study, a stochastic source identification model that couples Bayesian inference with SWMM is developed to reconstruct the profile of an instantaneous dumpling incidence in sewer networks. The unknown source parameters include location, dumping rate and time of the dumping incidence. Key factors that impact the convergence and performance of the model including walking step size, numbers of unknown source parameters and numbers of monitoring sites are investigated. Results show that the Bayesian-SWMM coupled model is effective and accurate in identifying the unknown sources parameters in an instantaneous dumping event. It is also found that walking step size is crucial for the results to converge to true solutions. Furthermore, it shows that the identified results are highly dependent on the numbers of unknown source parameters. More unknowns result to unsatisfying results. However, the study shows that this limitation could be significantly reduced by using more monitoring site data. One contribution of the study is that errors from measurements and numerical simulation are considered in the identification while results are presented in probabilities with all possible values revealed. This feature is highly practical and efficient when it comes to assist further field screening efforts to pinpoint the true sources.