Stochastic Environmental Research and Risk Assessment最新文献

{"title":"Using complex systems theory to comprehend the coordinated control effects of PM2.5 and O3 in Yangtze River Delta industrial base in China","authors":"Ruhui Cao, Yaxi Xiao, Yangbin Dong, Fuwang Zhang, Kai Shi, Zhanyong Wang","doi":"10.1007/s00477-024-02791-3","DOIUrl":"https://doi.org/10.1007/s00477-024-02791-3","url":null,"abstract":"<p>Regional air pollution represents a multifaceted and dynamic system, rendering linear statistical approaches insufficient for capturing its inherent variability, particularly the intricate fluctuations of multiple pollution indicators. Therefore, this study investigates the synergistic evolution mechanisms of PM_2.5 and O₃ in four cities within China’s Yangtze River Delta industrial base from 2013 to 2022, employing complex systems theory. Initially, the presence of multifractality and long-term persistence between PM_2.5 and O₃ is confirmed in each city using the multifractal detrended cross-correlation analysis. Quantitative indicators are then established to evaluate the synergistic control effects of PM_2.5 and O₃. Furthermore, factors influencing coordinated control are analyzed using the ensemble empirical mode decomposition. Finally, the self-organized criticality (SOC) theory is introduced to elucidate dynamic pollution patterns. The results indicate the following: (1) Multifractality and long-term persistence exist between PM_2.5 and O₃ in the four cities, with persistence strengthening alongside the implementation of atmospheric pollution prevention and control policies. The application of complex systems theory facilitates the explanation and quantification of the synergistic control effectiveness of PM_2.5 and O₃. (2) Since 2013, with the exception of Nanjing, the coordinated control effects of PM_2.5 and O₃ in Shanghai, Hangzhou, and Suzhou have been unsatisfactory and have shown little improvement. (3) Compared to short-term pollution emissions from human activities, annual atmospheric control measures, periodic meteorological variations, and long-range transport of regional pollutants exert a greater influence on the synergistic regulation effects of PM_2.5 and O₃. (4) SOC may serve as the primary mechanism influencing the effectiveness of the synergistic regulation of PM_2.5 and O₃. Sudden events, such as epidemic control measures, can disrupt the existing balance between PM_2.5 and O₃, thereby diminishing the coordinated control effects.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"12 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141969768","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":"Improving deep learning-based streamflow forecasting under trend varying conditions through evaluation of new wavelet preprocessing technique","authors":"Mohammad Reza M. Behbahani, Maryam Mazarei, Amvrossios C. Bagtzoglou","doi":"10.1007/s00477-024-02788-y","DOIUrl":"https://doi.org/10.1007/s00477-024-02788-y","url":null,"abstract":"<p>Accurate machine learning streamflow prediction often requires coupling data-driven models with preprocessing techniques. This study aims to improve the performance of deep learning (DL) models, including long short-term memory, recurrent neural network (RNN), and gated recurrent unit (GRU) by incorporating maximal overlap discrete wavelet entropy transform (MODWET) techniques for streamflow forecasting. The merit of MODWET over maximal overlap discrete wavelet transform (MODWT) is that MODWET utilizes Entropy to determine the optimal decomposition level and suitable wavelet function, which was an unaddressed problem in wavelet-based decomposition models. Suitable decomposition level prevents providing unnecessary information or missing essential information. In this study we show that a unique decomposition level and wavelet filter is not suitable for any dataset. The research focuses on monthly streamflow data from three case studies in the CAMEL dataset in the USA. The accuracy of the models is evaluated using statistical measures such as Nash–Sutcliffe efficiency (NSE), root-mean-squared error, percent bias, and correlation coefficient (r). To determine the optimal model, a Taylor diagram is utilized. The results demonstrate the effectiveness of coupling MODWET with DL models in flood forecasting. Furthermore, genetic programming (GP) and partial correlation index (PCI) are employed for predictor selection. Hybrid models, namely MODWET-GP-GRU (NSE of 0.83), MODWET-GP-RNN (NSE of 0.95), and MODWET-PCI-GRU (NSE of 0.95), outperform simple DL models in terms of NSE and Taylor diagram evaluation. This study emphasizes the potential of hybrid models that combine DL algorithms with the recently proposed MODWET technique for streamflow prediction.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"38 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141948713","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":"Optimal decision rules for marked point process models","authors":"M. N. M. van Lieshout","doi":"10.1007/s00477-024-02769-1","DOIUrl":"https://doi.org/10.1007/s00477-024-02769-1","url":null,"abstract":"<p>We study a Markov decision problem in which the state space is the set of finite marked point patterns in the plane, the actions represent thinnings, the reward is proportional to the mark sum which is discounted over time, and the transitions are governed by a birth-death-growth process. We show that thinning points with large marks maximises the discounted total expected reward when births follow a Poisson process and marks grow logistically. Explicit values for the thinning threshold and the discounted total expected reward over finite and infinite horizons are also provided. When the points are required to respect a hard core distance, upper and lower bounds on the discounted total expected reward are derived.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"20 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141870249","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":"Identifying regional hotspots of heatwaves, droughts, floods, and their co-occurrences","authors":"Marlon Vieira Passos, Jung-Ching Kan, Georgia Destouni, Karina Barquet, Zahra Kalantari","doi":"10.1007/s00477-024-02783-3","DOIUrl":"https://doi.org/10.1007/s00477-024-02783-3","url":null,"abstract":"<p>In this paper we present a framework to aid in the selection of optimal environmental indicators for detecting and mapping extreme events and analyzing trends in heatwaves, meteorological and hydrological droughts, floods, and their compound occurrence. The framework uses temperature, precipitation, river discharge, and derived climate indices to characterize the spatial distribution of hazard intensity, frequency, duration, co-occurrence, and dependence. The relevant climate indices applied are Standardized Precipitation Index, Standardized Precipitation and Evapotranspiration Index (SPEI), Standardized Streamflow Index, heatwave indices based on fixed (HWI<span>(_textrm{S})</span>) and anomalous temperatures (HWI<span>(_textrm{E})</span>), and Daily Flood Index (DFI). We selected suitable environmental indicators and corresponding thresholds for each hazard based on estimated extreme event detection performance using receiver operating characteristics (ROC), area under curve (AUC), and accuracy, which is defined as the proportion of correct detections. We assessed compound hazard dependence using a Likelihood Multiplication Factor (LMF). We tested the framework for the case of Sweden, using daily data for the period 1922–2021. The ROC results showed that HWI<span>(_textrm{S})</span>, SPEI12 and DFI are suitable indices for representing heatwaves, droughts, and floods, respectively (AUC &gt; 0.83). Application of these indices revealed increasing heatwave and flood occurrence in large areas of Sweden, but no significant change trend for droughts. Hotspots with LMF &gt; 1, mostly concentrated in Northern Sweden from June to August, indicated that compound drought-heatwave and drought-flood events are positively correlated in those areas, which can exacerbate their impacts. The novel framework presented here adds to existing hydroclimatic hazard research by (1) using local data and historical records of extremes to validate indicator-based hazard hotspots, (2) evaluating compound hazards at regional scale, (3) being transferable and streamlined, (4) attaining satisfactory performance for indicator-based hazard detection as demonstrated by the ROC method, and (5) being generalizable to various hazard types.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"213 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141870250","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":"Evaluating changes in flood frequency due to climate change in the Western Cape, South Africa","authors":"Kamleshan Pillay, Mulala Danny Simatele","doi":"10.1007/s00477-024-02786-0","DOIUrl":"https://doi.org/10.1007/s00477-024-02786-0","url":null,"abstract":"<p>This study assesses the impact of climate change on flood frequency across seven sites in the Western Cape province of South Africa. The calibrated Water Resources Simulation Model (WRSM)/Pitman hydrological model was run using precipitation inputs from two representative concentration pathways (RCP) scenarios (RCP 4.5 and 8.5) using a combination of eight global circulatory models (GCM) for the two periods (2030–2060 and 2070–2100). GCMs were statistically downscaled using the delta change (DC), linear scaling (LS) and quantile delta mapping (QDM) approaches. Average daily discharge was estimated from each downscaled daily precipitation dataset using the Pitman/WRSM model with the Fuller and Sangal estimation methods used to calculate daily instantaneous peak flows. Flood frequency curves (FFC) were generated using the annual maximum series (AMS) for the GCM ensemble mean and individual GCMs for the return periods between 2 and 100 years. FFCs generated based on LS and QDM downscaling methods were aligned for the GCM ensemble mean in terms of the direction of FFCs. Further analysis was conducted using outputs based on the QDM approach, given its suitability in projecting peak flows. Under this method, both Fuller and Sangal FFCs exhibited a decreasing trend across the Jonkershoek and Little Berg River sites; however, estimated quantiles for low-probability events were higher under the Fuller method. This study noted the variation in FFCs from individual GCMs compared to the FFC representing the GCM ensemble mean. Further research on climate change flood frequency analysis (FFA) in South Africa should incorporate other advanced downscaling and instantaneous peak flow estimation (IPF) methods.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"15 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141870252","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":"Displacement prediction for landslide with step-like behavior based on stacking ensemble learning strategy","authors":"Min Ren, Feng Dai, Longqiang Han, Chao Wang, Xinpeng Xu, Qin Meng","doi":"10.1007/s00477-024-02784-2","DOIUrl":"https://doi.org/10.1007/s00477-024-02784-2","url":null,"abstract":"<p>Predicting landslide displacement is crucial for the prevention and mitigation of landslide disasters. This study proposes a method based on a stacking ensemble learning strategy to predict landslide displacement, incorporating distinct yet effective individual models: the Voight model, the GM(1,1) grey model, and the backpropagation neural network (BPNN). These models are respectively emblematic of empirical, statistical, and nonlinear approaches to modeling. The stacking ensemble learning method marries creep theory, grey system theory, and nonlinear theory to accurately capture the statistical trends and step-like behavior characteristic of landslide displacement. A key feature of this approach is the tailored use of non-cross-validation, partial cross-validation, and 5-fold cross-validation for the Voight, GM(1,1), and BPNN models, respectively. This ensures that the conditions for model applicability are satisfied while fully leveraging their strengths, allowing the ensemble method to enhance prediction performance. The method is demonstrated through its application to the Xintan landslide in Zigui County, Hubei, China. Comparative analysis of the Voight, GM(1,1), BPNN, and the proposed stacking ensemble learning model reveals that the ensemble model achieves superior accuracy, underscoring its effectiveness in predicting landslide displacement. This promising method can effectively capture the landslide evolution process and be promoted to predict displacement in other landslide scenarios.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"69 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141870251","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":"Dynamic disaster risk assessment of urban waterlogging on pedestrian flow by intelligent simulation of hydrodynamics coupled with agent-based models in Chao-yang river basin of Nanning, China","authors":"Huangkang Lan, Yunchuan Yang, Hao Fu, Haixiang Liao, Liping Liao, Shanqi Huang, Xungui Li","doi":"10.1007/s00477-024-02782-4","DOIUrl":"https://doi.org/10.1007/s00477-024-02782-4","url":null,"abstract":"<p>Global climate change leads to an increase in the frequency and intensity of extreme rainstorms. At present, in China, which is experiencing rapid urbanization, urban flooding triggered by heavy rainstorms has emerged as a prominent issue, exerting far-reaching influences on socio-economic development, ecology, and people’s livelihoods. In response to this, China has put forward the concept of sponge cities and has shifted from pilot projects to comprehensive and systematic citywide implementation, with the aim of strengthening urban resilience in rainstorm management. This article takes the Chaoyang River area in Nanning City, South China as an example and proposes a dynamic risk assessment framework integrating hydrodynamic models and ABM to reflect flooding and pedestrian response to rainstorms. The research results show that under the design rainstorm scenarios with return periods of 5 years and 30 years, the rainstorm-induced flooding process in the study area presents a dynamic evolution pattern. It develops rapidly to the extreme or severe hazard grade within 1–2 h, and then declines slowly and persists until 8 h. The exposure and sensitivity of pedestrian mobility to flooding disasters extend across most areas of the study area within 1–5 h during the rainstorm-induced flooding process, with medium, high, or extreme risk levels observed during the 2–3 h period.Among the affected sensitive pedestrians, the gender ratio is roughly equal, and the proportion of the elderly and child populations is as high as 46.5%. The overall disaster resilience capacity of the study area is significantly insufficient, leading to a predominance of medium, high, or extreme risk levels for pedestrian mobility during the 2–3 h period. In conclusion, the study area should accelerate the construction of various sponge facilities on the underlying surface and comprehensively enhance diverse emergency management measures for excessive flooding disasters. The dynamic disaster risk simulation and assessment techniques proposed in this article can serve as essential scientific support for the construction of a digital twin system in China’s sponge cities, reflecting both virtual and real scenarios and facilitating comprehensive resilience capabilities such as forecasting, warning, simulation, and contingency planning for intelligent rainstorm management.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"57 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141774449","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":"Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in Çoruh River","authors":"Okan Mert Katipoğlu, Veysi Kartal, Chaitanya Baliram Pande","doi":"10.1007/s00477-024-02785-1","DOIUrl":"https://doi.org/10.1007/s00477-024-02785-1","url":null,"abstract":"<p>The service life of downstream dams, river hydraulics, waterworks construction, and reservoir management is significantly affected by the amount of sediment load (SL). This study combined models such as the artificial neural network (ANN) algorithm with the Firefly algorithm (FA) and Artificial Bee Colony (ABC) optimization techniques for the estimation of monthly SL values in the Çoruh River in Northeastern Turkey. The estimation of SL values was achieved using inputs of previous SL and streamflow values provided to the models. Various statistical metrics were used to evaluate the accuracy of the established hybrid and stand-alone models. The hybrid model is a novel approach for estimating sediment load based on various input variables. The results of the analysis determined that the ABC-ANN hybrid approach outperformed others in SL estimation. In this study, two combinations, M1 and M2, with different input variables, were used to assess the model's accuracy, and the best-performing model for monthly SL estimation was identified. Two scenarios, Q(t) and Q(t − 1), were coupled with the ABC-ANN algorithm, resulting in a highly effective hybrid approach with the best accuracy results (R² = 0.90, RMSE = 1406.730, MAE = 769.545, MAPE = 5.861, MBE = − 251.090, Bias Factor = − 4.457, and KGE = 0.737) compared to other models. Furthermore, the utilization of FA and ABC optimization techniques facilitated the optimization of the ANN model parameters. The significant results demonstrated that the optimization and hybrid techniques provided the most effective outcomes in forecasting SL for both combination scenarios. As a result, the prediction outputs achieved higher accuracy than those of a stand-alone ANN model. The findings of this study can provide essential resources to various managers and policymakers for the management of water resources.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"67 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141774447","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":"Flood prediction through hydrological modeling of rainfall using Conv1D-SBiGRU algorithm and RDI estimation: A hybrid approach","authors":"G. Selva Jeba, P. Chitra","doi":"10.1007/s00477-024-02768-2","DOIUrl":"https://doi.org/10.1007/s00477-024-02768-2","url":null,"abstract":"<p>Time series prediction of natural calamities is effectively solved with deep neural networks due to their ability to automatically assimilate the temporal linkages in time series data. This research develops a hybrid stacked deep learning with one-dimensional Convolution–Stacked Bidirectional Gated Recurrent Unit (Conv1D-SBiGRU) algorithm, unifying the predictive advantages of one-dimensional Convolution (Conv1D) and Bidirectional Gated Recurrent Unit (BiGRU) using hydro-meteorological and atmospheric data to build and evaluate a flood prediction model in forecasting the phenomenon of forthcoming flood events. The one-dimensional Convolution model effectively obtains valuable information and learns the time series cognitive representation. The stacked BiGRU model efficiently identifies and models the data sequence with temporal dependencies due to their ability to learn from past and future moments. The developed predictive model uses statistically significant predicted rainfall value to estimate the daily Relative Departure Index (RDI) which is used to predict floods. The proposed work was trained and evaluated for predicting floods on the real-world data of Alappuzha district, Kerala, India. The findings demonstrate the preeminence of the Conv1D-SBiGRU-based flood model with around 33% reduced MAE and RMSE and 9% improved R² over the benchmark and some hybrid techniques. The outcomes showed the efficiency of Conv1D-SBiGRU in precisely forecasting floods during extreme weather events with an accuracy of 98.6%.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"41 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141737845","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":"Guidance on the construction and selection of relatively simple to complex data-driven models for multi-task streamflow forecasting","authors":"Trung Duc Tran, Jongho Kim","doi":"10.1007/s00477-024-02776-2","DOIUrl":"https://doi.org/10.1007/s00477-024-02776-2","url":null,"abstract":"<p>With the goal of forecasting streamflow time series with sufficient lead time, we evaluate the efficiency and accuracy of data-based models ranging from relatively simple to complex. Based on this, we systematically explain the model construction and selection process according to lead time, type and amount of data, and optimization method. This analysis involved optimizing the inputs and hyperparameters of four unique data-driven models: Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and Transformer (TRANS), which were applied to the Soyang watershed, South Korea. The type and amount of model inputs are determined through a fine-tuning process that samples based on a correlation threshold, correlation to predictand, and autocorrelation to historical data and evaluates the simulated objective function. Hyperparameters are simultaneously optimized using three conventional optimization methods: Bayesian optimization (BO), particle swarm optimization (PSO), and gray wolf optimization (GWO). The experimental results provide insight into the role of input predictors, data preparations (e.g., wavelet transform), hyperparameter optimization, and model structures. From this, we can provide guidelines for model selection. Relatively simple models can be used when the dataset is small or there are few input variables, when only the near future is predicted, or when the selection of optimization methods is limited. However, a more complex model should be selected if the type and amount of data are sufficient, various optimization methods can be applied, or it is necessary to secure more lead time. More parameters, more complex model structures, and more training materials make this possible.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"307 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141720027","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}