Environmetrics最新文献_第9页

Coherent Disaggregation and Uncertainty Quantification for Spatially Misaligned Data 空间失调数据的相干分解与不确定性量化

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2026-02-13 DOI: 10.1002/env.70078

Man Ho Suen, Mark Naylor, Finn Lindgren

{"title":"Coherent Disaggregation and Uncertainty Quantification for Spatially Misaligned Data","authors":"Man Ho Suen, Mark Naylor, Finn Lindgren","doi":"10.1002/env.70078","DOIUrl":"10.1002/env.70078","url":null,"abstract":"Spatial misalignment arises when datasets are aggregated or collected at different spatial scales, leading to information loss. We develop a Bayesian disaggregation framework that links misaligned data to a continuous-domain model through an iteratively linearised integration scheme implemented with the Integrated Nested Laplace Approximation (INLA). The framework accommodates different ways of handling observations depending on the application, resulting in four variants: (i) Raster at Full Resolution, (ii) Raster Aggregation, (iii) Polygon Aggregation (PolyAgg), and (iv) Point Values (PointVal). The first three represent increasing levels of spatial averaging, while the last two address situations with incomplete covariate information. For PolyAgg and PointVal, we reconstruct the covariate field using three strategies—Value Plugin, Joint Uncertainty, and Uncertainty Plugin—with the latter two propagating uncertainty. We illustrate the framework with an example motivated by landslide modelling, focusing on methodology rather than interpreting landslide processes. Simulations show that uncertainty-propagating approaches outperform Value Plugin method and remain robust under model misspecification. Point-pattern observations and full-resolution covariates are therefore preferable, and when covariate fields are incomplete, uncertainty-aware methods are most reliable. The framework is well suited to landslide susceptibility modelling and other spatial mapping tasks, and integrates seamlessly with INLA-based tools.","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70078","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146216931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling Spatio-Temporal Transport: From Rigid Advection to Realistic Dynamics 模拟时空运输：从刚性平流到现实动力学

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2026-02-06 DOI: 10.1002/env.70079

Maria Laura Battagliola, Sofia C. Olhede

{"title":"Modeling Spatio-Temporal Transport: From Rigid Advection to Realistic Dynamics","authors":"Maria Laura Battagliola, Sofia C. Olhede","doi":"10.1002/env.70079","DOIUrl":"10.1002/env.70079","url":null,"abstract":"<div>\u0000 \u0000 Stochastic models for spatio-temporal transport face a critical trade-off between physical realism and interpretability. The advection model with a single constant velocity is interpretable but physically limited by its perfect correlation over time. This work aims to bridge the gap between this simple framework and its physically realistic extensions. Our guiding principle is to introduce a spatial correlation structure that vanishes over time. To achieve this, we present two distinct approaches. The first constructs complex velocity structures, either through superpositions of advection components or by allowing the velocity to vary locally. The second is a spectral technique that replaces the singular spectrum of rigid advection with a more flexible form, introducing temporal decorrelation controlled by parameters. We accompany these models with efficient simulation algorithms and demonstrate their success in replicating complex dynamics, such as tropical cyclones and the solutions of partial differential equations. Finally, we illustrate the practical utility of the proposed framework by comparing its simulations to real-world precipitation data from Hurricane Florence.\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146216751","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}

引用次数: 0

A New Unit-Lindley Mixed-Effects Model With an Application to Electricity Access Data 一种新的单元-林德利混合效应模型及其在电力接入数据中的应用

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2026-02-02 DOI: 10.1002/env.70077

Nirajan Bam, Laxmi Prasad Sapkota, Josmar Mazucheli

{"title":"A New Unit-Lindley Mixed-Effects Model With an Application to Electricity Access Data","authors":"Nirajan Bam, Laxmi Prasad Sapkota, Josmar Mazucheli","doi":"10.1002/env.70077","DOIUrl":"10.1002/env.70077","url":null,"abstract":"This paper introduces a novel unit-Lindley mixed-effects model (NULMM) within the generalized linear mixed model (GLMM) framework, designed for analyzing correlated response variables bounded within the unit interval. Parameter estimation was conducted via maximum likelihood, using Laplace approximation and adaptive Gaussian- Hermite quadrature (AGHQ). Simulation studies revealed that the Laplace approximation yielded biased estimates, while AGHQ with 5 or 11 quadrature points produced unbiased results. The proposed model was applied to rural electricity access data from South Asian countries, with covariates including time, log(GDP), log(Rural Population), and income level. Results show that time and log(GDP) are positively associated with rural electricity access, whereas log(Rural Population) has a negative association but is not statistically significant. Additionally, significant disparities were observed between low-income and upper-middle-income countries. Model comparisons demonstrated that NULMM provides a better fit to the data than the beta mixed model and the unit-Lindley (UL) mixed model.","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70077","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Accounting for Missing Data When Modelling Block Maxima 建模块极值时对缺失数据的考虑

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2026-01-29 DOI: 10.1002/env.70075

Emma S. Simpson, Paul J. Northrop

{"title":"Accounting for Missing Data When Modelling Block Maxima","authors":"Emma S. Simpson, Paul J. Northrop","doi":"10.1002/env.70075","DOIUrl":"10.1002/env.70075","url":null,"abstract":"Modeling block maxima using the generalized extreme value (GEV) distribution is a classical and widely used method for studying univariate extremes. It allows for theoretically motivated estimation of return levels, including extrapolation beyond the range of observed data. A frequently overlooked challenge in applying this methodology comes from handling datasets containing missing values. In this case, one cannot be sure whether the true maximum has been recorded in each block, and simply ignoring the issue can lead to biased parameter estimators and, crucially, underestimated return levels. We propose an extension of the standard block maxima approach to overcome such missing data issues. This is achieved by explicitly accounting for the proportion of missing values in each block within the GEV model. Inference is carried out using likelihood-based techniques, and we propose an update to commonly used diagnostic plots to assess model fit. We assess the performance of our method via a simulation study, with results that are competitive with the “ideal” case of having no missing values. The practical use of our methodology is demonstrated on sea surge data from Brest, France, and air pollution data from Plymouth, U.K.","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70075","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146155236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Bayesian Spatiotemporal Functional Model for Data With Block Structure and Repeated Measures 块结构重复测度数据的贝叶斯时空函数模型

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2026-01-26 DOI: 10.1002/env.70071

David H. da Matta, Mariana R. Motta, Nancy L. Garcia, Alexandre B. Heinemann

{"title":"A Bayesian Spatiotemporal Functional Model for Data With Block Structure and Repeated Measures","authors":"David H. da Matta, Mariana R. Motta, Nancy L. Garcia, Alexandre B. Heinemann","doi":"10.1002/env.70071","DOIUrl":"10.1002/env.70071","url":null,"abstract":"The analysis of spatiotemporal data is fundamental across multiple scientific disciplines, particularly in assessing the behavior of climate effects over space and time. A key challenge in this area is effectively capturing recurring climate phenomena, such as El Niño/La Niña (ENSO) phases, which induce prolonged periods of similar weather patterns across affected regions. To address this, our study introduces a novel spatiotemporal regression model that explicitly incorporates block structures representing these recurring climate effects. These blocks accommodate ENSO phases and manage the within-block correlations and shared characteristics, enhancing the model's ability to capture the influence of such phenomena on precipitation variability. The model further integrates functional predictors of both fixed and random nature, along with spatial covariance modeled via the Matérn class, to accommodate complex spatial, temporal, and block-related structures. Motivated by a monthly precipitation dataset from meteorological stations in Goiás State, Brazil, spanning 21 years (1980–2001), our approach assigns spatial effects to individual stations, temporal effects to months, blocks to ENSO phases, and repeated measures to years within those blocks. The results from simulation studies demonstrate the model's robustness and effectiveness, providing deeper insight into how recurring climate effects like ENSO impact rainfall patterns. This framework represents a significant methodological advancement in spatiotemporal modeling, highlighting the importance of explicitly modeling and estimating the effects of recurrent climate phenomena through block structures.","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70071","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146058120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bayesian Inference for Spatially-Temporally Misaligned Data Using Predictive Stacking 基于预测叠加的时空错位数据贝叶斯推断

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2026-01-25 DOI: 10.1002/env.70072

Soumyakanti Pan, Sudipto Banerjee

{"title":"Bayesian Inference for Spatially-Temporally Misaligned Data Using Predictive Stacking","authors":"Soumyakanti Pan, Sudipto Banerjee","doi":"10.1002/env.70072","DOIUrl":"10.1002/env.70072","url":null,"abstract":"Air pollution remains a major environmental risk factor that is often associated with adverse health outcomes. However, quantifying and evaluating its effects on human health is challenging due to the complex nature of exposure data. Recent technological advances have led to the collection of various indicators of air pollution at increasingly high spatial-temporal resolutions (e.g., daily averages of pollutant levels at spatial locations referenced by latitude-longitude). However, health outcomes are typically aggregated over several spatial-temporal coordinates (e.g., annual prevalence for a county) to comply with survey regulations. This article develops a Bayesian hierarchical model to analyze such spatially-temporally misaligned exposure and health outcome data. We develop Bayesian predictive stacking for spatially and temporally misaligned data to optimally combine inference from multiple predictive spatial-temporal models. Stacking allows us to avoid iterative estimation algorithms such as Markov chain Monte Carlo that struggle due to convergence issues inflicted by the presence of weakly identified parameters. We apply our proposed method to study the effects of ozone on asthma in the state of California.","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70072","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146091415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing the Accuracy of Spatio-Temporal Models for Wind Speed Prediction by Incorporating Bias-Corrected Crowdsourced Data 利用众包数据修正偏置提高风速时空预报模型的精度

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2026-01-22 DOI: 10.1002/env.70069

Eamonn Organ, Maeve Upton, Denis Allard, Lionel Benoit, James Sweeney

{"title":"Enhancing the Accuracy of Spatio-Temporal Models for Wind Speed Prediction by Incorporating Bias-Corrected Crowdsourced Data","authors":"Eamonn Organ, Maeve Upton, Denis Allard, Lionel Benoit, James Sweeney","doi":"10.1002/env.70069","DOIUrl":"https://doi.org/10.1002/env.70069","url":null,"abstract":"<div>\u0000 \u0000 Accurate high-resolution spatial and temporal wind speed data is critical for estimating the wind energy potential of a location. For real-time wind speed prediction, statistical models typically depend on high-quality (near) real-time data from official meteorological stations to improve forecasting accuracy. Personal weather stations (PWS) offer an additional source of real-time data and broader spatial coverage than official stations. However, they are not subject to rigorous quality control and may exhibit bias or measurement errors. This article presents a framework for incorporating PWS data into statistical models for validated official meteorological station data via a two-stage approach. First, bias correction is performed on PWS wind speed data using reanalysis data. Second, we implement a Bayesian hierarchical spatiotemporal model that accounts for varying measurement error in the PWS data. This enables wind speed prediction across a target area, and is particularly beneficial for improving predictions in regions sparse in official monitoring stations. Our results show that including bias-corrected PWS data improves prediction accuracy compared with using meteorological station data alone, with a 5% reduction in prediction error on average across all sites. The results are comparable with popular reanalysis products, but unlike these numerical weather models our approach is available in real-time and offers improved uncertainty quantification.\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146099333","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}

引用次数: 0

Enhancing the Accuracy of Spatio-Temporal Models for Wind Speed Prediction by Incorporating Bias-Corrected Crowdsourced Data 利用众包数据修正偏置提高风速时空预报模型的精度

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2026-01-22 DOI: 10.1002/env.70069

Eamonn Organ, Maeve Upton, Denis Allard, Lionel Benoit, James Sweeney

{"title":"Enhancing the Accuracy of Spatio-Temporal Models for Wind Speed Prediction by Incorporating Bias-Corrected Crowdsourced Data","authors":"Eamonn Organ, Maeve Upton, Denis Allard, Lionel Benoit, James Sweeney","doi":"10.1002/env.70069","DOIUrl":"https://doi.org/10.1002/env.70069","url":null,"abstract":"<div>\u0000 \u0000 Accurate high-resolution spatial and temporal wind speed data is critical for estimating the wind energy potential of a location. For real-time wind speed prediction, statistical models typically depend on high-quality (near) real-time data from official meteorological stations to improve forecasting accuracy. Personal weather stations (PWS) offer an additional source of real-time data and broader spatial coverage than official stations. However, they are not subject to rigorous quality control and may exhibit bias or measurement errors. This article presents a framework for incorporating PWS data into statistical models for validated official meteorological station data via a two-stage approach. First, bias correction is performed on PWS wind speed data using reanalysis data. Second, we implement a Bayesian hierarchical spatiotemporal model that accounts for varying measurement error in the PWS data. This enables wind speed prediction across a target area, and is particularly beneficial for improving predictions in regions sparse in official monitoring stations. Our results show that including bias-corrected PWS data improves prediction accuracy compared with using meteorological station data alone, with a 5% reduction in prediction error on average across all sites. The results are comparable with popular reanalysis products, but unlike these numerical weather models our approach is available in real-time and offers improved uncertainty quantification.\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146099335","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}

引用次数: 0

Modeling Bounded Count Environmental Data Using a Contaminated Beta-Binomial Regression Model 使用污染β -二项回归模型建模有界计数环境数据

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2026-01-20 DOI: 10.1002/env.70067

Arnoldus F. Otto, Antonio Punzo, Johannes T. Ferreira, Andriëtte Bekker, Salvatore D. Tomarchio, Cristina Tortora

{"title":"Modeling Bounded Count Environmental Data Using a Contaminated Beta-Binomial Regression Model","authors":"Arnoldus F. Otto, Antonio Punzo, Johannes T. Ferreira, Andriëtte Bekker, Salvatore D. Tomarchio, Cristina Tortora","doi":"10.1002/env.70067","DOIUrl":"https://doi.org/10.1002/env.70067","url":null,"abstract":"Bounded count data are commonly encountered in environmental studies. This paper examines two environmental applications illustrating their relevance. The first investigates the effect of winter malnutrition on mule deer (Odocoileus hemionus) fawn mortality. The second application analyzes public perceptions of environmental issues using data from the Eurobarometer 95.1 survey (March–April 2021), which includes a question rating the perceived severity of climate change on a scale from 1 to 10. Together, these studies demonstrate the need for flexible bounded count models in environmental research. In this context, the binomial and beta-binomial (BB) models are widely used for bounded count data, with the BB model offering the advantage of accounting for overdispersion. However, atypical observations in real-world applications may hinder the performance of the BB model and lead to biased or misleading inferences. To address this limitation, we propose the contaminated beta-binomial (cBB) distribution (cBB-D), which introduces an additional BB component to accommodate atypical observations while preserving the mean and variance structure of the BB model. The cBB-D thus captures both overdispersion and contamination effects in bounded count data. To incorporate explanatory variables, we further develop the contaminated BB regression model (cBB-RM), in which none, some, or all cBB parameters may depend on covariates. The proposed models are applied to two environmental datasets, complemented by a sensitivity analysis on simulated data to assess the influence of atypical observations on parameter estimation. The methodology is implemented in the open-source cBB package for R, available at \u0000https://github.com/arnootto/cBB.","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70067","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146083320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Demonstrating the Power and Flexibility of Variational Assumptions for Amortized Neural Posterior Estimation in Environmental Applications 证明变分假设在环境应用中平摊神经后验估计的能力和灵活性

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2026-01-19 DOI: 10.1002/env.70074

Elliot Maceda, Emily C. Hector, Amanda Lenzi, Brian J. Reich

{"title":"Demonstrating the Power and Flexibility of Variational Assumptions for Amortized Neural Posterior Estimation in Environmental Applications","authors":"Elliot Maceda, Emily C. Hector, Amanda Lenzi, Brian J. Reich","doi":"10.1002/env.70074","DOIUrl":"https://doi.org/10.1002/env.70074","url":null,"abstract":"Classic Bayesian methods with complex environmental models are frequently infeasible due to an intractable likelihood. Simulation-based inference methods, such as neural posterior estimation, calculate posteriors without accessing a likelihood function by leveraging the fact that data can be quickly simulated from the model, but converge slowly and/or poorly in high-dimensional settings. In this paper, we suggest that imposing strict variational assumptions on the form of the posterior can often combat these computational issues. Posterior distributions of model parameters are efficiently obtained by assuming a parametric form for the posterior, parametrized by the machine learning model, which is trained with the simulated data as inputs and the associated parameters as outputs. We show theoretically that if the parametric family of the variational posterior is correct, our posteriors converge to the true posteriors in Kullback–Leibler divergence. We also provide tools to help us identify if our parametric assumption is close to the true posterior, and modeling options if that is not the case. Comprehensive simulation studies using environmental models highlight our method's robustness and versatility. An analysis of the Zika virus in Brazil provides a thorough case study.","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146091232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0