Stochastic Environmental Research and Risk Assessment最新文献

{"title":"Risk assessment of river water quality using long-memory processes subject to divergence or Wasserstein uncertainty","authors":"Hidekazu Yoshioka, Yumi Yoshioka","doi":"10.1007/s00477-024-02726-y","DOIUrl":"https://doi.org/10.1007/s00477-024-02726-y","url":null,"abstract":"<p>River water quality often follows a long-memory stochastic process with power-type autocorrelation decay, which can only be reproduced using appropriate mathematical models. The selection of a stochastic process model, particularly its memory structure, is often subject to misspecifications owing to low data quality and quantity. Therefore, environmental risk assessment should account for model misspecification through mathematically rigorous and efficiently implementable approaches; however, such approaches have been still rare. We address this issue by first modeling water quality dynamics through the superposition of an affine diffusion process that is stationary and has a long memory. Second, the worst-case upper deviation of the water quality value from a prescribed threshold value under model misspecifications is evaluated using either the divergence risk or Wasserstein risk measure. The divergence risk measure can consistently deal with the misspecification of the memory structure to the worst-case upper deviation. The Wasserstein risk measure is more flexible but fails in this regard, as it does not directly consider the memory structure information. We theoretically compare both approaches to demonstrate that their assumed uncertainties differed substantially. From the application to the 30-year water quality data of a river in Japan, we categorized the water quality indices to be those with truly long memory (Total nitrogen, NO₃-N, NH₄-N, and <span>({{text{SO}}}_{4}^{2-})</span>), those with moderate power-type memory (NO₂-N, PO₄-P, and Total Organic Carbon), and those with almost exponential memory (Total phosphorus and Chemical Oxygen demand). The risk measures are successfully computed numerically considering the seasonal variations of the water quality indices.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"25 2 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141061824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of eco-environmental quality and analysis of driving forces in the yellow river delta based on improved remote sensing ecological indices","authors":"Dongling Ma, Qingji Huang, Qian Zhang, Qian Wang, Hailong Xu, Yingwei Yan","doi":"10.1007/s00477-024-02740-0","DOIUrl":"https://doi.org/10.1007/s00477-024-02740-0","url":null,"abstract":"<p>The ecological environment of the Yellow River Delta is undergoing serious degradation due to the pressures of economic development and population growth. To improve and protect the ecological environment, it is crucial to accurately assess and monitor its eco-environmental quality. With consideration of the characteristics of terrestrial salinization in the region and the need for long-term ecological monitoring, we first utilized Google Earth Engine (GEE) to construct the Improved Remote Sensing Ecological Index (IRSEI). The IRSEI is based on the Remote Sensing Ecological Index (RSEI), which consists of the Normalized Difference Vegetation Index (NDVI), WET, Land Surface Temperature (LST), and Normalized Difference Built-Up and Soil Index (NDBSI), as well as the Net Primary Productivity (NPP) index. The entropy weighting method was employed to construct the IRSEI for assessing the eco-environmental quality of the Yellow River Delta. The validity of the index was verified through image entropy and contrast assessment. We then employed the Hurst exponent, Sen's slope estimation, and Coefficient of Variation (CV) to calculate the range of variation of the IRSEI in the Yellow River Delta over a 20-year period to analyze the spatio-temporal evolution of the ecological quality and its distribution pattern. Furthermore, we conducted a comprehensive analysis combining the Geographically and Temporally Weighted Regression (GTWR) model and Geodetector to understand the influence of drivers such as topography, soil, and climate on the IRSEI, considering both the temporal and spatial dimensions. The results indicate that: (1) The proposed IRSEI demonstrates higher reliability, adaptability, and sensitivity compared to RSEI in monitoring the eco-environmental quality of the Yellow River Delta. (2) From 2000 to 2020, the eco-environmental quality of the Yellow River Delta remained generally stable, with a spatial distribution resembling a \"Y\" shape, showing significant improvement, particularly in Lijin County and its surrounding areas. However, the middle and eastern estuary exhibited a declining trend in eco-environmental quality. (3) The impact of driving factors on the eco-environmental quality varied across the four subordinate regions of the Yellow River Delta, indicating spatial heterogeneity. Factors such as FVC, Soil, LST, JS, and Srad significantly influenced and explained the spatial differentiation of eco-environmental quality in the region. The proposed IRSEI demonstrates better monitoring capabilities in the Yellow River Delta compared to RSEI, providing a scientific basis for land use planning and ecological protection in the area.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"20 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140941164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discharge coefficient estimation of modified semi-cylindrical weirs using machine learning approaches","authors":"Reza Fatahi-Alkouhi, Ehsan Afaridegan, Nosratollah Amanian","doi":"10.1007/s00477-024-02739-7","DOIUrl":"https://doi.org/10.1007/s00477-024-02739-7","url":null,"abstract":"<p>Based on the principles design of hydrofoil weirs, Modified Semi-Cylindrical Weirs (MSCWs) incorporate an innovative tangential ramp along the downstream crest contour, thereby significantly enhancing their performance compared to conventional semi-cylindrical weirs. A pivotal parameter in the calculation of flow discharge over the weir is the discharge coefficient (<i>C</i>_<i>d</i>). This study involves a comprehensive comparative analysis of various <i>C</i>_<i>d</i> estimation methodologies for MSCWs, employing a range of machine learning-based models, notably including Artificial Neural Network (ANN), Multivariate Adaptive Regression Splines (MARS), M5 tree, Locally Weighted Polynomial Regression (LWPR), and Support Vector Machine (SVM) models. To begin, a feature selection analysis utilizing the Gamma Test (GT) method was conducted to identify the optimal input configuration for modeling the discharge of MSCWs. The results of the feature selection revealed that the <i>C</i>_<i>d</i> of the MSCWs is primarily influenced by the ratio of upstream flow depth (<i>y</i>_<i>up</i>) to crest radius (<i>R</i>), while showing negligible sensitivity to the slope of the downstream ramp (<i>θ</i>). The dataset was partitioned into two segments: 70% were assigned to the training stage, while the remaining 30% were allocated to the testing stage. The precision of <i>C</i>_<i>d</i> predictions is evaluated through four key statistical metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), Correlation Coefficient (<i>R</i>²), and Nash –Sutcliff Efficiency (NSE). The outcomes reveal that, for the training and testing phases, the <i>R</i>² values for the ANN, MARS, M5 tree, LWPR and SVM models are respectively 0.967, 0.931, 0.974, 0.937, and 0.933, and 0.925, 0.953, 0.953, 0.980, and 0.954. Notably, the LWPR model outperforms the ANN, MARS, M5 tree, and SVM models, boasting MAE, MSE, RMSE, and NSE values of 0.0167, 0.0005, 0.0217, and 0.942 during training, and 0.0107, 0.0002, 0.0136, and 0.949 during testing. As a result, the LWPR model clearly emerges as the superior model, followed by the M5 model tree.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"12 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140940965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Groundwater salinization risk in coastal regions triggered by earthquake-induced saltwater intrusion","authors":"Alban Kuriqi, Ismail Abd-Elaty","doi":"10.1007/s00477-024-02734-y","DOIUrl":"https://doi.org/10.1007/s00477-024-02734-y","url":null,"abstract":"<p>Anthropogenic factors such as over-pumping and natural events such as earthquakes impact coastal aquifers by reducing freshwater recharge, aquifer water budgets, and increasing saltwater intrusion (SWI). This study investigates the impact of hydrodynamic forces induced by earthquakes on SWI in one hypothetical case, namely, the Henry problem, and a real case of the Biscayne aquifer located in Florida, USA. The analysis was carried out using the analytical solution of estimating the earthquake’s induced hydrodynamic pressure and applying the SEAWAT code to investigate the SWI for the base case and three scenarios, namely for the horizontal acceleration (α_<i>h</i>) by 0.10 g, 0.20 g, and 0.30 g. The results show that earthquakes might considerably increase the SWI in coastal aquifers. Moreover, the rise in salinity across expansive land areas significantly threatens agricultural productivity and jeopardizes food security. Namely, in the case of Biscayne aquifer, salinity was increased by 12.10%, 21.90%, and 45.70% for the horizontal seismic acceleration of 0.1 g, 0.20 g, and 0.30 g, respectively. Hence, the conclusions drawn from this study underscore the need for carefull consideration of earthquake impacts in future planning and water management strategies for coastal regions. This proactive approach is crucial to preemptively address and mitigate the groundwater salinization hazard associated with SWI fluctuations due to earthquakes.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"405 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140941032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Insights into airborne particulate matter: artificial intelligence-driven PM2.5 modelling in Hyderabad district, India","authors":"Nandan A K, Aneesh Mathew","doi":"10.1007/s00477-024-02728-w","DOIUrl":"https://doi.org/10.1007/s00477-024-02728-w","url":null,"abstract":"<p>Air pollution is one of the grave concerns of the modern era, claiming millions of lives and adversely impacting the economy. The primary objective of this study was to develop advanced forecast models for PM_2.5 levels in the Hyderabad district of India using artificial intelligence techniques. This study presents a novel approach to PM_2.5 modelling, leveraging the power of artificial intelligence (AI) and data-driven insights for Hyderabad District. Factor analysis was performed to check for correlations of PM_2.5 and aerosol optical depth (AOD) with various meteorological and pollutant variables, based on which it was observed that except temperature and solar radiation, all the variables showed considerable correlations with aerosols. The hybrid deep learning-based CNN – LSTM model was identified as the best-fit model for predicting PM_2.5 with an R² = 0.88, MSE = 68.93 (µg/m³)², RMSE = 8.30 µg/m³, and MAE = 6.45 µg/m³ as against the MLP – ARIMA and MLP models. A study on feature importance showed that AOD is a significant contributor to PM_2.5 prediction with a factor importance of 6.8%, ranking second only to meteorological factors. Wind direction and relative humidity exhibited factor importance values of 10.94% and 8.69%, respectively. The AI-driven PM_2.5 modelling approach offers a more comprehensive understanding of pollution patterns and their relationship with meteorological conditions and geographical characteristics. These results highlight the strong predictive power of the CNN – LSTM model and the significant influence of AOD and meteorological factors on PM_2.5 levels. These insights can inform policymakers, urban planners, and environmental agencies in formulating effective pollution control strategies and mitigation measures, leading to improved air quality and public health in the Hyderabad district and beyond.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"27 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140941016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Frequency curves of high and low flows in intermittent river basins for hydrological analysis and hydraulic design","authors":"Gokhan Sarigil, Yonca Cavus, Hafzullah Aksoy, Ebru Eris","doi":"10.1007/s00477-024-02732-0","DOIUrl":"https://doi.org/10.1007/s00477-024-02732-0","url":null,"abstract":"<p>Upper and lower percentiles of Flow Duration Curves (FDCs) of daily streamflow data were investigated to develop frequency curves. Upper percentiles with exceedance probability of 1, 5 and 10% (Q₁, Q₅, Q₁₀) were used for high flows, and lower percentiles with non-exceedance probability of 10, 5 and 1% (Q₉₀, Q₉₅, Q₉₉) for low flows. Median value (Q₅₀) was covered to represent the average conditions of streamflow. A mixed frequency analysis based on the total probability theorem taking zero values into account was applied for the lower percentiles of FDC. Case studies were performed for three intermittent Streamflow Gauging Stations (SGSs) from Kucuk Menderes River Basin in western Turkey. An overall assessment of results shows that the best-fit probability distribution function does not change from one SGS to another considerably for low flows while each SGS has its own probability distribution function for high flows. Upper and lower percentiles, and median value were calculated at various return periods by using the identified probability distribution functions. The calculated values were plotted in the form of frequency curves of high flow percentiles and low flow percentiles. The frequency curves have a practically significant potential use in hydrological analysis, water resources management and hydraulic design under high and low flow conditions. They are yet open to further development for regionalization and their applicability can be extended to ungauged sites in river basins.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"18 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140941017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of radiation and temperature-based empirical models for accurate daily reference evapotranspiration estimation in Iraq","authors":"Alaa A. Jasim Al-Hasani, Shamsuddin Shahid","doi":"10.1007/s00477-024-02736-w","DOIUrl":"https://doi.org/10.1007/s00477-024-02736-w","url":null,"abstract":"<p>Reliable estimation of reference evapotranspiration (ET_o), an essential component of optimal irrigation management, is challenging in many regions due to its complex dependence on meteorological factors. Alternative empirical models, often used to estimate ET_o considering data limitations, provide highly unreliable estimates for Iraq. This study aimed to formulate simpler empirical models for accurate ET_o estimation with fewer variables in different climate regions of Iraq. The metaheuristic Whale Optimization Algorithm (WOA) was used to finetune the coefficients of the nonlinear least square fitting regression (NLLSF) model during development. Two simpler models were developed based on (1) only mean air temperature (T) (NLLSF-T) and (2) solar radiation and T (NLLSF-R) as inputs. The performance of the models was validated using historical ground observations (2012–2021), and the ET_o was estimated using the Penman–Monteith method from the reanalyzed (ERA5) datasets (1959–2021). The models' spatial, seasonal, and temporal performance in estimating daily ET_o was rigorously evaluated using multiple statistical metrics and visual presentations. The Kling-Gupta Efficiency (KGE) and normalized root mean square error (NRMSE) of the NLLSF-T model were 0.95 and 0.30, respectively, compared to 0.75 and 0.40 for Kharrufa, the best-performing temperature-based models in Iraq. Similarly, NLLSF-R improved the KGE from 0.78 to 0.97 in KGE and NRMSE from 0.44 to 0.22 compared to Caprio, the best-performing radiation-based model in Iraq. The spatial assessment revealed both the models' excellent performance over most of Iraq, except in the far north, indicating their suitability in estimating ET_o in arid and semi-arid regions.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"61 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140881540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Case-based risk analysis model for rainstorm inundation in metro systems based on a bayesian network","authors":"Chao Zhang, Tingxin Qin, Wan Wang, Fengjiao Xu, Qian Zhou","doi":"10.1007/s00477-024-02737-9","DOIUrl":"https://doi.org/10.1007/s00477-024-02737-9","url":null,"abstract":"<p>The intensities and frequencies of extreme rainstorms are increasing, which may result in severe inundation of urban metro systems. Although there is some risk assessment research on regional metro systems based on spatiotemporal data, the characteristics of specific metro stations and shortcomings in the emergency response process need more consideration. In this paper, a risk analysis model for rainstorm inundation in metro systems based on a Bayesian network and a practical case study are proposed. First, the risk factors are obtained by integrating general mechanism analysis and the case study. Second, an event evolution diagram is established to represent the comprehensive evolution process of a potential event. Third, the risk analysis model is established using a Bayesian network model considering the quantitative causal relationships between risk factors. This model is used to analyze the risk of supporting emergency management, including emergency preparation based on critical risk factor sensitivity identification, prewarning response strategy development based on risk analysis as rainstorms occur, and rescue strategy development based on risk analysis as rainstorm water flows into metro tunnels. Furthermore, this model can be flexibly improved as natural hazards and metro systems change and as new problems are exposed in practical cases.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"161 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140881503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying the sampling error on burn counts in Monte-Carlo wildfire simulations using Poisson and Gamma distributions","authors":"Valentin Waeselynck, Gary Johnson, David Schmidt, Max A. Moritz, David Saah","doi":"10.1007/s00477-024-02724-0","DOIUrl":"https://doi.org/10.1007/s00477-024-02724-0","url":null,"abstract":"<p>This article provides a precise, quantitative description of the sampling error on burn counts in Monte-Carlo wildfire simulations - that is, the prediction variability introduced by the fact that the set of simulated fires is random and finite. We show that the marginal burn counts are (very nearly) Poisson-distributed in typical settings and infer through Bayesian updating that Gamma distributions are suitable summaries of the remaining uncertainty. In particular, the coefficient of variation of the burn count is equal to the inverse square root of its expected value, and this expected value is proportional to the number of simulated fires multiplied by the asymptotic burn probability. From these results, we derive practical guidelines for choosing the number of simulated fires and estimating the sampling error. Notably, the required number of simulated years is expressed as a power law. Such findings promise to relieve fire modelers of resource-consuming iterative experiments for sizing simulations and assessing their convergence: statistical theory provides better answers, faster.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"41 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140889756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting duration characteristics of near fault pulse-like ground motions using machine learning algorithms","authors":"Faisal Mehraj Wani, Jayaprakash Vemuri, K. S. K. Karthik Reddy, Chenna Rajaram","doi":"10.1007/s00477-024-02729-9","DOIUrl":"https://doi.org/10.1007/s00477-024-02729-9","url":null,"abstract":"<p>The duration characteristics of near-fault earthquake ground motions play a significant role in the dynamic response of a structure. Linear regression-based models are extensively used to forecast ground motions and duration parameters. However, such an approach fails to account for the complexity arising from the non-linear patterns in the data set. Nevertheless, implementing machine learning algorithms has the ability to uncover these unexplored patterns as well as the unique characteristics of ground motions comprised in the datasets. In this study, statistical relationships between several duration metrics and intensity measures of near-fault ground motions are evaluated using machine learning algorithms. Four different machine learning algorithms, namely Regression, Decision Tree, Support Vector machines, and Gaussian Process regression model are trained to determine the optimum model. All these machine learning models were examined using the selected database of 200 near-fault pulse-like ground motions, which was split into two parts, with 75% of data used for training and the remaining 25% for testing. The results indicate that the fine tree model for bracketed duration, stepwise linear regression model for uniform duration, and the exponential and rational gaussian process regression model for significant and effective duration, showed more accurate and reliable results as compared to other models.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"91 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140881507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}