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Statistical Advances in Environmental Sciences
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Statistical Advances in Environmental Sciences

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Environmental Science and Engineering".

Deadline for manuscript submissions: closed (20 September 2022) | Viewed by 8345

Special Issue Editors

Prof. Dr. E.Marian Scott

E-Mail Website
Guest Editor
School of Mathematics and Statistics, University of Glasgow, Glasgow G12 8SQ, UK
Interests: application of statistical methods to environmental sciences; water and air quality; design of monitoring networks
Dr. Craig Anderson

E-Mail Website
Guest Editor
School of Mathematics and Statistics, University of Glasgow, Glasgow G12 8QQ, UK
Interests: developing statistical methodology for health data; spatial statistics; disease mapping
Prof. Dr. Claire A. Miller

E-Mail Website
Guest Editor
School of Mathematics and Statistics, University of Glasgow, Glasgow G12 8QW, UK
Interests: environmental statistics; nonparametric, varying-coefficient and multivariate models
Dr. Ruth A. O'Donnell

E-Mail Website
Guest Editor
School of Mathematics and Statistics, University of Glasgow, Glasgow G12 8QQ, UK
Interests: statistics; data analytics

Special Issue Information

Dear Colleagues,

The digital environment is a phrase that has been coined describing the vision of distributed networks of sensors, earth observation, citizen science and traditional monitoring schemes to provide a rich and dynamic representation of our environment and the changes occurring within it. Such diverse data streams present statistical challenges including, but not limited to, high dimensionality, differing spatial and temporal support, data fusion and assimilation. Effective environmental decision making relies on fusing different data streams; combining data from different sources has the potential to significantly reduce the uncertainty in model parameter estimates when making predictions. Data communication, quality assurance and anomaly detection, predictive analytics and visualisation, quantifying uncertainty and dealing with large volumes and flow of data to develop decision support systems are all elements essential to the digital environment.

 

This Special Issue seeks papers on statistical and analytical solutions to these and other challenges, as well as illustrative case studies.

Prof. Dr. Ethel Marian Scott
Dr. Craig Anderson
Prof. Dr. Claire A. Miller
Dr. Ruth A. O'Donnell
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • digital environment
  • spatio-temporal
  • machine learning
  • networks
  • data fusion

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

21 pages, 9219 KiB  
Article
Multivariate Statistical Analysis for the Detection of Air Pollution Episodes in Chemical Industry Parks
by Xiangyu Zhao, Kuang Cheng, Wang Zhou, Yi Cao and Shuang-Hua Yang
Int. J. Environ. Res. Public Health 2022, 19(12), 7201; https://doi.org/10.3390/ijerph19127201 - 12 Jun 2022
Cited by 1 | Viewed by 2006
Abstract
Air pollution episodes (APEs) caused by excessive emissions from chemical industry parks (CIPs) have resulted in severe environmental damage in recent years. Therefore, it is of great importance to detect APEs timely and effectively using contaminant measurements from the air quality monitoring network [...] Read more.
Air pollution episodes (APEs) caused by excessive emissions from chemical industry parks (CIPs) have resulted in severe environmental damage in recent years. Therefore, it is of great importance to detect APEs timely and effectively using contaminant measurements from the air quality monitoring network (AQMN) in the CIP. Traditionally, APE can be detected by determining whether the contaminant concentration at any ambient monitoring station exceeds the national environmental standard. However, the environmental standards used are unified in various ambient monitoring stations, which ignores the source–receptor relationship in the CIP and challenges the effective detection of excessive emissions in some scenarios. In this paper, an approach based on a multivariate statistical analysis (MSA) method is proposed to detect the APEs caused by excessive emissions from CIPs. Using principal component analysis (PCA), the spatial relationships hidden among the historical environmental monitoring data are extracted, and the high-dimensional data are projected into only two subspaces. Then, two monitoring indices, T2 and Q, which represent the variability in these subspaces, are utilized to monitor the pollution status and detect the potential APEs in the CIP. In addition, the concept of APE detectability is also defined, and the condition for APE detectability is derived, which explains when the APEs can be detectable. A simulated case for a CIP in Zhejiang province of China is studied to evaluate the performance of this approach. The study indicates that the method can have an almost 100% APE detection rate. The real-world measurements of Total Volatile Organic Compounds (TVOC) at a 10-min time interval from 3 December 2020∼12 December 2020 are also analyzed, and 64 APEs caused by excessive TVOC emissions are detected in a total of 1440 time points. Full article
(This article belongs to the Special Issue Statistical Advances in Environmental Sciences)
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31 pages, 7954 KiB  
Article
Forecasting Multiple Groundwater Time Series with Local and Global Deep Learning Networks
by Stephanie R. Clark, Dan Pagendam and Louise Ryan
Int. J. Environ. Res. Public Health 2022, 19(9), 5091; https://doi.org/10.3390/ijerph19095091 - 22 Apr 2022
Cited by 12 | Viewed by 2570
Abstract
Time series data from environmental monitoring stations are often analysed with machine learning methods on an individual basis, however recent advances in the machine learning field point to the advantages of incorporating multiple related time series from the same monitoring network within a [...] Read more.
Time series data from environmental monitoring stations are often analysed with machine learning methods on an individual basis, however recent advances in the machine learning field point to the advantages of incorporating multiple related time series from the same monitoring network within a ‘global’ model. This approach provides the opportunity for larger training data sets, allows information to be shared across the network, leading to greater generalisability, and can overcome issues encountered in the individual time series, such as small datasets or missing data. We present a case study involving the analysis of 165 time series from groundwater monitoring wells in the Namoi region of Australia. Analyses of the multiple time series using a variety of different aggregations are compared and contrasted (with single time series, subsets, and all of the time series together), using variations of the multilayer perceptron (MLP), self-organizing map (SOM), long short-term memory (LSTM), and a recently developed LSTM extension (DeepAR) that incorporates autoregressive terms and handles multiple time series. The benefits, in terms of prediction performance, of these various approaches are investigated, and challenges such as differing measurement frequencies and variations in temporal patterns between the time series are discussed. We conclude with some discussion regarding recommendations and opportunities associated with using networks of environmental data to help inform future resource-related decision making. Full article
(This article belongs to the Special Issue Statistical Advances in Environmental Sciences)
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14 pages, 2211 KiB  
Article
Reconstructing Missing and Anomalous Data Collected from High-Frequency In-Situ Sensors in Fresh Waters
by Claire Kermorvant, Benoit Liquet, Guy Litt, Jeremy B. Jones, Kerrie Mengersen, Erin E. Peterson, Rob J. Hyndman and Catherine Leigh
Int. J. Environ. Res. Public Health 2021, 18(23), 12803; https://doi.org/10.3390/ijerph182312803 - 4 Dec 2021
Cited by 6 | Viewed by 2720
Abstract
In situ sensors that collect high-frequency data are used increasingly to monitor aquatic environments. These sensors are prone to technical errors, resulting in unrecorded observations and/or anomalous values that are subsequently removed and create gaps in time series data. We present a framework [...] Read more.
In situ sensors that collect high-frequency data are used increasingly to monitor aquatic environments. These sensors are prone to technical errors, resulting in unrecorded observations and/or anomalous values that are subsequently removed and create gaps in time series data. We present a framework based on generalized additive and auto-regressive models to recover these missing data. To mimic sporadically missing (i) single observations and (ii) periods of contiguous observations, we randomly removed (i) point data and (ii) day- and week-long sequences of data from a two-year time series of nitrate concentration data collected from Arikaree River, USA, where synoptically collected water temperature, turbidity, conductance, elevation, and dissolved oxygen data were available. In 72% of cases with missing point data, predicted values were within the sensor precision interval of the original value, although predictive ability declined when sequences of missing data occurred. Precision also depended on the availability of other water quality covariates. When covariates were available, even a sudden, event-based peak in nitrate concentration was reconstructed well. By providing a promising method for accurate prediction of missing data, the utility and confidence in summary statistics and statistical trends will increase, thereby assisting the effective monitoring and management of fresh waters and other at-risk ecosystems. Full article
(This article belongs to the Special Issue Statistical Advances in Environmental Sciences)
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