SENTINEL:用于处理和分析围栏传感器数据的闪亮应用程序

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
MacDonald M.K., Champion W.M., Thoma E.D.
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引用次数: 0

摘要

SENTINEL(传感器网络智能排放定位器)是在R Shiny开发的一款应用程序,用于支持低成本围栏传感器的新兴用户群体,例如监测工业设施内部和附近的挥发性有机化合物或甲烷浓度或用于应急响应应用。在部署过程中,传感器采集了大量高频污染物浓度数据、时序气象信息和传感器性能指标。这些传感器可以收集大量的数据,如果没有指定的软件,用户无法处理和理解。SENTINEL应用程序为用户提供了一致的框架,用于处理、分析和可视化围栏传感器数据。SENTINEL暂时收集数据进行综合分析和解释。质量保证筛选自动去除异常数据点,基线校正算法减少污染物浓度数据的背景漂移。SENTINEL通过用户友好的图形用户界面提供简化的传感器数据分析,支持源发射数据和传感器触发的现场样品的解释。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

SENTINEL: A Shiny App for Processing and Analysis of Fenceline Sensor Data

SENTINEL: A Shiny App for Processing and Analysis of Fenceline Sensor Data
SENTINEL (SEnsor NeTwork INtelligent Emissions Locator) is an application developed in R Shiny to support emerging user groups of lower cost fenceline sensors, such as those monitoring volatile organic compound or methane concentrations inside and near industrial facilities or for emergency response applications. During deployment, sensors collect a large quantity of high-frequency pollutant concentration data, time-aligned meteorological information, and sensor performance indicators. These sensors can collect a quantity of data that is overwhelming for users to process and understand without designated software. The SENTINEL application provides users with a consistent framework for processing, analyzing, and visualizing fenceline sensor data. SENTINEL temporally aggregates data for synthesized analysis and interpretation. Quality assurance screening automatically removes anomalous datapoints and a baseline correction algorithm reduces background drift in pollutant concentration data. SENTINEL offers streamlined sensor data analysis through a user-friendly graphical user interface that supports interpretation of source emission data and sensor-triggered field samples.
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来源期刊
Environmental Modelling & Software
Environmental Modelling & Software 工程技术-工程:环境
CiteScore
9.30
自引率
8.20%
发文量
241
审稿时长
60 days
期刊介绍: Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.
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