Environmetrics最新文献

{"title":"A Functional Principal Component Analysis Framework for Vibration-Based Structural Condition Assessment of Highway Bridge Spans","authors":"Gianna Agrò,&nbsp;Mariantonietta Ruggieri","doi":"10.1002/env.70124","DOIUrl":"https://doi.org/10.1002/env.70124","url":null,"abstract":"<p>The management of lifespan and safety performance, aimed at ensuring the structural integrity and durability of highway infrastructure, has become an increasingly important area of research. In civil engineering, traditional methods for evaluating dynamic behavior, such as Operational Modal Analysis and the Peak Picking technique, often fall short in detecting subtle signs of damage. This study proposes a novel approach based on Functional Principal Component Analysis (FPCA) to enhance the sensitivity of vibration-based structural condition and anomaly assessment. A preliminary analysis based on classical PCA applied to the same dataset was previously presented in Agrò et al. (2025a). That study highlighted the capability of PCA to capture dominant variance patterns in the vibration signals. However, PCA operates on discretized time series and does not explicitly account for their functional nature. In contrast, the FPCA framework proposed in this study models the signals as continuous functions, enabling a more refined characterization of temporal variability and improving sensitivity to subtle structural differences. By comparing the optimal functional subspaces derived from FPCA for both intact and potentially compromised bridge spans, we quantify structural differences through the cosine of principal angles. The approach is applied to a real case involving a viaduct on the Palermo–Catania highway, demonstrating its potential to reveal anomalies not evident through conventional modal analysis.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70124","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616415","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}

{"title":"Hierarchical Spatio-Temporal Model Under \u0000 \u0000 \u0000 t\u0000 \u0000 $$ t $$\u0000 -Process With Application","authors":"Chunzheng Cao,&nbsp;Wenzhu Chen,&nbsp;Xiaoxin Zhu","doi":"10.1002/env.70123","DOIUrl":"https://doi.org/10.1002/env.70123","url":null,"abstract":"<div>\u0000 \u0000 <p>Gaussian process has been widely used in spatio-temporal models to analyze and reveal the underlying correlation of spatio-temporal data. Nevertheless, inferences drawn from Gaussian process-based models can be highly sensitive to outliers or distributional misspecifications. To enhance robustness, we propose a robust hierarchical spatio-temporal model based on Student-<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>t</mi>\u0000 </mrow>\u0000 <annotation>$$ t $$</annotation>\u0000 </semantics></math> process framework to enable reliable inferences under data contamination. The proposed model employs two independent <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>t</mi>\u0000 </mrow>\u0000 <annotation>$$ t $$</annotation>\u0000 </semantics></math>-processes to respectively describe the latent spatial random effects and random errors, thereby ensuring robust inferences against various types of outliers. A variational Expectation-Maximization algorithm is developed for efficient parameter estimation and prediction. Simulation studies demonstrate that the proposed model delivers reliable and accurate results even when the data are contaminated by substantial outliers. The practical usefulness of the model is further illustrated through an application to <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mtext>PM</mtext>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>2</mn>\u0000 <mo>.</mo>\u0000 <mn>5</mn>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {mathrm{PM}}_{2.5} $$</annotation>\u0000 </semantics></math> concentration data, highlighting its effectiveness in robust spatio-temporal inference.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148615855","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":"Multi-Step Forecasting of Wind Speed and Solar Irradiance Using Lag-Optimized Stochastic Stacked LSTM Networks","authors":"Brahim Taoussi,&nbsp;Chahtou Amina,&nbsp;Fares Sofiane Mazouni","doi":"10.1002/env.70122","DOIUrl":"https://doi.org/10.1002/env.70122","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;p&gt;The rapid expansion of solar PV and wind generation intensifies the challenge of day-ahead grid planning under stochastic meteorological conditions. While the literature is dominated by sub-day forecasting studies, day-ahead predictions remain critical for unit commitment and grid scheduling. This study proposes a stochastic stacked Long Short-Term Memory (LSTM) framework, tuned via Bayesian optimization, for simultaneous day-ahead forecasting (96 step-ahead) of wind speed and solar irradiation using high-resolution 15-min measurements from the Adrar 20 MW solar PV facility in Algeria. Four input strategies were rigorously evaluated: full autoregressive lags (S-LSTM), Random Forest importance-based lag selection (RF-LSTM), engineered auxiliary features (AUX-RF-LSTM), and exogenous meteorological variables (EXG-RF-LSTM), benchmarked against Holt-Winters and naïve persistence baselines. The parsimonious RF-LSTM architecture demonstrated superior performance across all evaluation frameworks. In deterministic point forecasting, it achieved the highest accuracy for both variables (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msup&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;R&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;2&lt;/mn&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 &lt;mn&gt;85&lt;/mn&gt;\u0000 &lt;mo&gt;.&lt;/mo&gt;\u0000 &lt;mn&gt;12&lt;/mn&gt;\u0000 &lt;mo&gt;%&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ {R}^2=85.12% $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; for wind speed and &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mn&gt;97&lt;/mn&gt;\u0000 &lt;mo&gt;.&lt;/mo&gt;\u0000 &lt;mn&gt;15&lt;/mn&gt;\u0000 &lt;mo&gt;%&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ 97.15% $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; for solar irradiation) while substantially reducing residual serial dependence relative to the Holt-Winters benchmark. These gains were confirmed as statistically significant via the Harvey-Leybourne-Newbold corrected Diebold-Mariano test across all pairwise comparisons. In probabilistic forecasting via Monte Carlo Dropout, the RF-LSTM achieved near-perfect calibration for solar irradiation (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;P&lt;/mi&gt;\u0000 &lt;mi&gt;I&lt;/mi&gt;\u0000 &lt;mi&gt;C&lt;/mi&gt;\u0000 &lt;mi&gt;P&lt;/mi&gt;\u0000 &lt;mo&gt;=&lt;/mo&gt;\u0000 &lt;mn&gt;95&lt;/mn&gt;\u0000 &lt;mo&gt;.&lt;/mo&gt;\u0000 &lt;mn&gt;10&lt;/mn&gt;\u0000 &lt;mo&gt;%&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$$ PICP=95.10% $$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;) and acceptable risk-aware uncertainty boundaries for wind profiles. Interestingly, expanding the input space with engineered or exogenous features systematically degrad","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148534039","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":"Secondary Fuzzy Clustering of Multi-Dimensional 2D Time Series Arrays (SFC-2DTSA) Using DTW Metric: An Application to Drought Data","authors":"Abdul Wahid,&nbsp;Asad Ellahi,&nbsp;Waqar Hassan,&nbsp;Abdul Qudair Baig","doi":"10.1002/env.70109","DOIUrl":"https://doi.org/10.1002/env.70109","url":null,"abstract":"<div>\u0000 \u0000 <p>In many industrial applications, such as healthcare, economics, and environmental science, unsupervised learning tasks are often associated with multivariate time series in multi-dimensional datasets. This type of data has unique challenges and needs robust machine learning models. However, the existing literature has shown that the Euclidean distance-based clustering techniques are ineffective. To this end, we propose two hybrid unsupervised learning models for secondary cluster analysis of multi-dimensional multivariate time series datasets using multiple dendrograms of data matrices using fuzzy learning, traditional Dynamic Time Warping distance metric and its robust variant. A fuzzy least squares version is minimized with respect to ultrametric distance matrices and fuzzy membership degrees to obtain an optimal secondary fuzzy partition of the set of primary dendrograms. Furthermore, we also modify the previously proposed evaluation metric to assess new methods on real-world data. Because the ground truth labels for the real-world data are not available, particularly for the secondary partition, and the existing evaluation metric can only be used to measure similarity between exactly two dendrograms. The experimental results demonstrated the best performance of the new approaches compared to the existing Euclidean distance-based approach on simulated and real-world data.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148615568","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":"Counterfactual Evaluation of Traffic Restrictions on Air Quality in Milan's Congestion Charge Zone Using Matrix Completion","authors":"Roxana Adam,&nbsp;Francesco Biancalani,&nbsp;Rodolfo Metulini","doi":"10.1002/env.70111","DOIUrl":"https://doi.org/10.1002/env.70111","url":null,"abstract":"<div>\u0000 \u0000 <p>Urban traffic is widely recognized as a major contributor to air pollution in densely populated areas. To address this issue, many cities have implemented traffic restriction zones to reduce pollutant concentrations. <i>Area C</i>, a congestion charge and traffic restriction zone in central Milan, introduced in January 2012 and still in operation today, is a prominent example of such an initiative. This study investigates the causal impact of <i>Area C</i> on air quality by applying advanced statistical learning techniques, specifically Matrix Completion. These methods enable robust counterfactual analysis while relaxing traditional econometric assumptions, such as parallel trends. Using monthly pollution data from 2008 to 2019 in Lombardy and incorporating meteorological variables to control for confounding influences, we find a statistically significant reduction in <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mtext>PM</mtext>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>10</mn>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {mathrm{PM}}_{10} $$</annotation>\u0000 </semantics></math> concentrations within <i>Area C</i> following the policy's implementation. However, no consistent effect is observed for nitrogen oxides (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mtext>NO</mtext>\u0000 </mrow>\u0000 <mrow>\u0000 <mi>X</mi>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {mathrm{NO}}_{mathrm{X}} $$</annotation>\u0000 </semantics></math>), suggesting that additional or alternative interventions may be required to address gaseous pollutants. Our findings underscore the effectiveness of targeted traffic restrictions in reducing particulate pollution and highlight the value of statistical learning methods for the evaluation of environmental policy.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148534210","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":"The \u0000 \u0000 \u0000 Q\u0000 \u0000 $$ Q $$\u0000 -Score: A Magnitude-Weighted Goodness-of-Fit Score for Earthquake Forecasting","authors":"Julia Jansson Valter,&nbsp;Alejandra Arjon,&nbsp;Francesco Serafini,&nbsp;Frederic Schoenberg","doi":"10.1002/env.70113","DOIUrl":"https://doi.org/10.1002/env.70113","url":null,"abstract":"<p>Accurate forecasting of large earthquakes is of great importance, yet most current earthquake forecast evaluation metrics, such as the log-likelihood score, do not give additional weight to large-magnitude events. To this end, magnitude-weighted goodness-of-fit scores for earthquake forecasting have recently been introduced, such as potency-weighted log-likelihood and the <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>Q</mi>\u0000 </mrow>\u0000 <annotation>$$ Q $$</annotation>\u0000 </semantics></math>-score. In this article, we investigate properties of the <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>Q</mi>\u0000 </mrow>\u0000 <annotation>$$ Q $$</annotation>\u0000 </semantics></math>-score, which is a quotient emphasizing model fit for the largest 5% of earthquakes. We explore the theoretical properties of the <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>Q</mi>\u0000 </mrow>\u0000 <annotation>$$ Q $$</annotation>\u0000 </semantics></math>-score, demonstrating that under certain null conditions, the expectations of the numerator and denominator are equal and thus the expectation of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>Q</mi>\u0000 </mrow>\u0000 <annotation>$$ Q $$</annotation>\u0000 </semantics></math> is 1 in a ratio sense. Additionally, the score satisfies a law of large numbers. We evaluated the <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>Q</mi>\u0000 </mrow>\u0000 <annotation>$$ Q $$</annotation>\u0000 </semantics></math>-score of 21 next-day gridded earthquake forecasts for California, provided by the Collaborative for the Study of Earthquake Predictability (CSEP) for the years 2012, 2014, and 2017. We also calculate the log-likelihood scores of the forecasts to obtain a more comprehensive evaluation of how well different models perform, both for predicting the largest events and also in terms of overall model fit.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70113","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148467352","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}

{"title":"The \u0000 \u0000 \u0000 Q\u0000 \u0000 $$ Q $$\u0000 -Score: A Magnitude-Weighted Goodness-of-Fit Score for Earthquake Forecasting","authors":"Julia Jansson Valter,&nbsp;Alejandra Arjon,&nbsp;Francesco Serafini,&nbsp;Frederic Schoenberg","doi":"10.1002/env.70113","DOIUrl":"https://doi.org/10.1002/env.70113","url":null,"abstract":"<p>Accurate forecasting of large earthquakes is of great importance, yet most current earthquake forecast evaluation metrics, such as the log-likelihood score, do not give additional weight to large-magnitude events. To this end, magnitude-weighted goodness-of-fit scores for earthquake forecasting have recently been introduced, such as potency-weighted log-likelihood and the <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>Q</mi>\u0000 </mrow>\u0000 <annotation>$$ Q $$</annotation>\u0000 </semantics></math>-score. In this article, we investigate properties of the <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>Q</mi>\u0000 </mrow>\u0000 <annotation>$$ Q $$</annotation>\u0000 </semantics></math>-score, which is a quotient emphasizing model fit for the largest 5% of earthquakes. We explore the theoretical properties of the <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>Q</mi>\u0000 </mrow>\u0000 <annotation>$$ Q $$</annotation>\u0000 </semantics></math>-score, demonstrating that under certain null conditions, the expectations of the numerator and denominator are equal and thus the expectation of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>Q</mi>\u0000 </mrow>\u0000 <annotation>$$ Q $$</annotation>\u0000 </semantics></math> is 1 in a ratio sense. Additionally, the score satisfies a law of large numbers. We evaluated the <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>Q</mi>\u0000 </mrow>\u0000 <annotation>$$ Q $$</annotation>\u0000 </semantics></math>-score of 21 next-day gridded earthquake forecasts for California, provided by the Collaborative for the Study of Earthquake Predictability (CSEP) for the years 2012, 2014, and 2017. We also calculate the log-likelihood scores of the forecasts to obtain a more comprehensive evaluation of how well different models perform, both for predicting the largest events and also in terms of overall model fit.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70113","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148467490","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}

{"title":"Bayesian Nonparametric Clustering for Spatiotemporal Data, With an Application to Air Pollution","authors":"Luca Aiello,&nbsp;Raffaele Argiento,&nbsp;Sirio Legramanti,&nbsp;Lucia Paci","doi":"10.1002/env.70118","DOIUrl":"https://doi.org/10.1002/env.70118","url":null,"abstract":"<div>\u0000 \u0000 <p>Air pollution is a major global health hazard, with fine particulate matter (PM10) linked to severe respiratory and cardiovascular diseases. Hence, analyzing and clustering spatiotemporal air quality data is crucial for understanding pollution dynamics and guiding policy interventions. This work illustrates Bayesian nonparametric clustering methods, with a particular focus on their application to spatiotemporal data, which are ubiquitous in environmental sciences. We first introduce key modeling approaches for point-referenced spatiotemporal data, highlighting their flexibility in capturing complex spatial and temporal dependencies. We then provide an overview of recent advancements in Bayesian nonparametric clustering, specifically focusing on spatial product partition models, which incorporate spatial structure into the partition. We illustrate the proposed methods on PM10 data from Northern Italy, demonstrating their ability to identify meaningful pollution patterns by clustering together monitoring stations with similar PM10 dynamics. This paper highlights the potential of Bayesian nonparametric methods for environmental data and offers insights into future research directions in the clustering of spatially referenced time-series data in environmental sciences.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466879","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":"Hierarchical Bayesian Modeling of Total Column Ozone: Unraveling Equatorial Variability Over Ethiopia Using Satellite Data and Multisource Covariates","authors":"Yassin Tesfaw Abebe,&nbsp;Abdu Mohammed Seid,&nbsp;Lassi Roininen,&nbsp;U. Jaya Parakash Raju,&nbsp;Abebaw Bizuneh Alemu","doi":"10.1002/env.70115","DOIUrl":"https://doi.org/10.1002/env.70115","url":null,"abstract":"<p>Understanding the spatiotemporal dynamics of total column ozone (TCO) is critical for monitoring ultraviolet (UV) exposure and ozone trends, particularly in equatorial regions where variability remains underexplored. This study investigates monthly TCO over Ethiopia (2012–2022) using a Bayesian hierarchical model implemented via Integrated Nested Laplace Approximation (INLA). The model incorporates nine environmental covariates, capturing meteorological, stratospheric, and topographic influences alongside spatiotemporal random effects. Spatial dependence is modeled using the stochastic partial differential equation (SPDE) approach, while temporal autocorrelation is handled through an autoregressive structure. The model shows strong predictive accuracy, with correlation coefficients of 0.94 (training) and 0.91 (validation), and RMSE values of 3.91 DU and 4.45 DU, respectively. Solar radiation, stratospheric temperature, and the quasi-biennial oscillation are positively associated with TCO, whereas surface temperature, precipitation, humidity, water vapor, and altitude exhibit negative associations. Random effects highlight persistent regional clusters and seasonal peaks during summer. These findings provide new insights into regional ozone behavior over complex equatorial terrains, contributing to the understanding of the equatorial ozone paradox. The approach demonstrates the utility of combining satellite observations with environmental data in data-scarce regions, supporting improved UV risk monitoring and climate-informed policy planning.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.70115","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466878","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}

{"title":"Bayesian Nonparametric Clustering for Spatiotemporal Data, With an Application to Air Pollution","authors":"Luca Aiello,&nbsp;Raffaele Argiento,&nbsp;Sirio Legramanti,&nbsp;Lucia Paci","doi":"10.1002/env.70118","DOIUrl":"https://doi.org/10.1002/env.70118","url":null,"abstract":"<div>\u0000 \u0000 <p>Air pollution is a major global health hazard, with fine particulate matter (PM10) linked to severe respiratory and cardiovascular diseases. Hence, analyzing and clustering spatiotemporal air quality data is crucial for understanding pollution dynamics and guiding policy interventions. This work illustrates Bayesian nonparametric clustering methods, with a particular focus on their application to spatiotemporal data, which are ubiquitous in environmental sciences. We first introduce key modeling approaches for point-referenced spatiotemporal data, highlighting their flexibility in capturing complex spatial and temporal dependencies. We then provide an overview of recent advancements in Bayesian nonparametric clustering, specifically focusing on spatial product partition models, which incorporate spatial structure into the partition. We illustrate the proposed methods on PM10 data from Northern Italy, demonstrating their ability to identify meaningful pollution patterns by clustering together monitoring stations with similar PM10 dynamics. This paper highlights the potential of Bayesian nonparametric methods for environmental data and offers insights into future research directions in the clustering of spatially referenced time-series data in environmental sciences.</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"37 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466880","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}