Environmetrics最新文献_第8页

A Bayesian change point modeling approach to identify local temperature changes related to urbanization 一种识别与城市化相关的局部温度变化的贝叶斯变点建模方法

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2023-02-11 DOI: 10.1002/env.2794

C. Berrett, B. Gurney, D. Arthur, T. Moon, G. P. Williams

{"title":"A Bayesian change point modeling approach to identify local temperature changes related to urbanization","authors":"C. Berrett, B. Gurney, D. Arthur, T. Moon, G. P. Williams","doi":"10.1002/env.2794","DOIUrl":"https://doi.org/10.1002/env.2794","url":null,"abstract":"Changes to the environment surrounding a temperature measuring station can cause local changes to the recorded temperature that deviate from regional temperature behavior. This phenomenon—often caused by construction or urbanization—occurs at a local level. If these local changes are assumed to represent regional or global processes it can have significant impacts on historical data analyses. These changes or deviations are generally gradual, but can be abrupt, and arise as construction or other environmental changes occur near a recording station. We propose a methodology to examine if changes in temperature behavior at a point in time exist at a local level at various locations in a region assuming that regional or global processes are correlated among nearby stations. Specifically, we propose a Bayesian change point model for spatio-temporally dependent data where we select the number of change points at each location using a “forward” selection process using deviance information criterion. We then fit the selected version of the model and examine the linear slopes across time to quantify the local changes in long-term temperature behavior. We show the utility of this model and method using both synthetic data and observed temperature measurements from eight stations in Utah consisting of daily temperature data for 60 years.","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"34 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50149034","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: 1

CO2 emissions and growth: A bivariate bidimensional mean-variance random effects model 二氧化碳排放与增长：一个双变量二维均方差随机效应模型

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2023-02-11 DOI: 10.1002/env.2793

Antonello Maruotti, Pierfrancesco Alaimo Di Loro

{"title":"CO2 emissions and growth: A bivariate bidimensional mean-variance random effects model","authors":"Antonello Maruotti, Pierfrancesco Alaimo Di Loro","doi":"10.1002/env.2793","DOIUrl":"https://doi.org/10.1002/env.2793","url":null,"abstract":"We introduce a bivariate bidimensional mixed-effects regression model, motivated by the analysis of <math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mtext>CO</mtext>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>2</mn>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {mathrm{CO}}_2 $$</annotation>\u0000 </semantics></math> emission levels and growth on OECD countries from 1990 to 2018. The model is able to capture heterogeneity across countries and allows for a full association structure among outcomes, assuming a discrete distribution for the random terms with a possibly different number of support points in each univariate profile. We test the behavior of the proposed approach via a simulation study, considering several factors such as the number of observed units, times, and levels of heterogeneity in the data. Empirically, we define an extended version of the STIRPAT model where all model parameters, and not only the mean, vary according to a regression model. Our empirical findings provide evidence of heterogeneous behaviors across countries and suggest the need of a flexible approach to properly reflect the heterogeneity in both the emission levels and the growth processes.","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"34 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2793","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50128681","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: 1

Nonlinear prediction of functional time series 函数时间序列的非线性预测

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2023-02-05 DOI: 10.1002/env.2792

Haixu Wang, Jiguo Cao

{"title":"Nonlinear prediction of functional time series","authors":"Haixu Wang, Jiguo Cao","doi":"10.1002/env.2792","DOIUrl":"https://doi.org/10.1002/env.2792","url":null,"abstract":"We propose a nonlinear prediction (NOP) method for functional time series. Conventional methods for functional time series are mainly based on functional principal component analysis or functional regression models. These approaches rely on the stationary or linear assumption of the functional time series. However, real data sets are often nonstationary, and the temporal dependence between trajectories cannot be captured by linear models. Conventional methods are also hard to analyze multivariate functional time series. To tackle these challenges, the NOP method employs a nonlinear mapping for functional data that can be directly applied to multivariate functions without any preprocessing step. The NOP method constructs feature space with forecast information, hence it provides a better ground for predicting future trajectories. The NOP method avoids calculating covariance functions and enables online estimation and prediction. We examine the finite sample performance of the NOP method with simulation studies that consider linear, nonlinear and nonstationary functional time series. The NOP method shows superior prediction performances in comparison with the conventional methods. Three real applications demonstrate the advantages of the NOP method model in predicting air quality, electricity price and mortality rate.","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"34 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2792","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50121496","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: 3

Front Cover Image, Volume 34, Number 1, February 2023 封面图片，第34卷，第1期，2023年2月

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2023-01-29 DOI: 10.1002/env.2791

Sameh Abdulah, Yuxiao Li, Jian Cao, Hatem Ltaief, David E. Keyes, Marc G. Genton, Ying Sun

引用次数: 0

Environmental data science: Part 1 环境数据科学：第1部分

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2023-01-29 DOI: 10.1002/env.2787

Andrew Zammit-Mangion, Nathaniel K. Newlands, Wesley S. Burr

引用次数: 1

2022 Editorial Collaborators 2022年编辑合作者

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2023-01-29 DOI: 10.1002/env.2790

引用次数: 0

The role of data science in environmental digital twins: In praise of the arrows 数据科学在环境数字双胞胎中的作用：赞美箭头

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2023-01-26 DOI: 10.1002/env.2789

Gordon S. Blair, Peter A. Henrys

引用次数: 2

Families of complex-valued covariance models through integration 通过积分的复值协方差模型族

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2023-01-13 DOI: 10.1002/env.2779

Sandra De Iaco

引用次数: 2

A Bayesian time series model for reconstructing hydroclimate from multiple proxies 从多个代理重建水文气候的贝叶斯时间序列模型

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2023-01-06 DOI: 10.1002/env.2786

Niamh Cahill, Jacky Croke, Micheline Campbell, Kate Hughes, John Vitkovsky, Jack Eaton Kilgallen, Andrew Parnell

{"title":"A Bayesian time series model for reconstructing hydroclimate from multiple\u0000 proxies","authors":"Niamh Cahill, Jacky Croke, Micheline Campbell, Kate Hughes, John Vitkovsky, Jack Eaton Kilgallen, Andrew Parnell","doi":"10.1002/env.2786","DOIUrl":"https://doi.org/10.1002/env.2786","url":null,"abstract":"We propose a Bayesian model which produces probabilistic reconstructions of hydroclimatic variability in Queensland Australia. The model provides a standardized approach to hydroclimate reconstruction using multiple palaeoclimate proxy records derived from natural archives such as speleothems, ice cores and tree rings. The method combines time-series modeling with inverse prediction to quantify the relationships between a given hydroclimate index and relevant proxies over an instrumental period and subsequently reconstruct the hydroclimate back through time. We present case studies for Brisbane and Fitzroy catchments focusing on two hydroclimate indices, the Rainfall Index (RFI) and the Standardized Precipitation-Evapotranspiration Index (SPEI). The probabilistic nature of the reconstructions allows us to estimate the probability that a hydroclimate index in any reconstruction year was lower (higher) than the minimum (maximum) value observed over the instrumental period. In Brisbane, the RFI is unlikely (probabilities < 5%) to have exhibited extremes beyond the minimum/maximum values observed between 1889 and 2019. However, in Fitzroy there are several years during the reconstruction period where the RFI is likely (>50% probability) to have exhibited behavior beyond the minimum/maximum of what has been observed, during the instrumental period. For SPEI, the probability of observing such extremes prior to the beginning of the instrumental period in 1889 doesn't exceed 30% in any reconstruction year in Brisbane, but exceeds 50% in multiple years in Fitzroy.","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"34 4","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2786","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50133412","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

Multivariate receptor modeling with widely dispersed Lichens as bioindicators of air quality 以广泛分布的地衣作为空气质量生物指标的多变量受体建模

IF 1.7 3区环境科学与生态学

Environmetrics Pub Date : 2022-12-25 DOI: 10.1002/env.2785

Matthew Heiner, Taylor Grimm, Hayden Smith, Steven D. Leavitt, William F. Christensen, Gregory T. Carling, Larry L. St. Clair

{"title":"Multivariate receptor modeling with widely dispersed Lichens as bioindicators of air quality","authors":"Matthew Heiner, Taylor Grimm, Hayden Smith, Steven D. Leavitt, William F. Christensen, Gregory T. Carling, Larry L. St. Clair","doi":"10.1002/env.2785","DOIUrl":"https://doi.org/10.1002/env.2785","url":null,"abstract":"Biomonitoring studies evaluating air quality via airborne element accumulation patterns in lichens typically control variability by focusing on narrow geographic regions and short time windows. Using samples of the widespread “rock-posy” lichen sampled across the Intermountain Region of the United States, we investigate whether accumulation patterns of generic pollution sources are detectable on broad geographic and temporal scales. We develop a novel Bayesian multivariate receptor modeling (BMRM) approach that sharpens detection and discrimination of candidate pollution sources through (i) regularization of source contributions to each sample and (ii) incorporating estimated lichen secondary chemistry as a factor. Through a simulation study, we demonstrate a distinct advantage in shrinking contributions when they are truly sparse, as would be expected with heterogeneous samples from dispersed collection sites. We contrast analyses employing both standard and sparse BMRMs, and positive matrix factorization (PMF). The sparse model better maintains source identity, as specified though informative prior distributions on elemental profiles. We advocate quantitative profile matching, which reveals that PMF primarily captures variations of the baseline profile for lichen secondary chemistry. Both PMF and BMRM results suggest that the most detectable signatures relate to aeolian dust deposition, while spatial patterns hint at sporadic anthropogenic influence.","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"34 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2022-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50143191","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