Environmental Insights: Democratizing access to ambient air pollution data and predictive analytics with an open-source Python package

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Environmental Modelling & Software Pub Date : 2024-07-02 DOI:10.1016/j.envsoft.2024.106131
Liam J. Berrisford , Ronaldo Menezes
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Abstract

Ambient air pollution is a pervasive issue with wide-ranging effects on human health, ecosystem vitality, and economic structures. Utilizing data on ambient air pollution concentrations, researchers can perform comprehensive analyses to uncover the multifaceted impacts of air pollution across society. To this end, we introduce Environmental Insights, an open-source Python package designed to democratize access to air pollution concentration data. This tool enables users to easily retrieve historical air pollution data and employ a Machine Learning model for forecasting potential future conditions. Moreover, Environmental Insights includes a suite of tools aimed at facilitating the dissemination of analytical findings and enhancing user engagement through dynamic visualizations. This comprehensive approach ensures that the package caters to the diverse needs of individuals looking to explore and understand air pollution trends and their implications.

Code repository clickable link

Environmental Insights Github Home Page.

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环境洞察:通过开源 Python 软件包实现环境空气污染数据和预测分析的民主化访问
环境空气污染是一个普遍存在的问题,对人类健康、生态系统活力和经济结构有着广泛的影响。利用环境空气污染浓度数据,研究人员可以进行综合分析,揭示空气污染对整个社会的多方面影响。为此,我们推出了开源 Python 软件包 Environmental Insights,旨在实现空气污染浓度数据访问的民主化。该工具使用户能够轻松检索历史空气污染数据,并利用机器学习模型预测未来的潜在状况。此外,"环境洞察 "还包括一套工具,旨在促进分析结果的传播,并通过动态可视化提高用户参与度。这种综合方法可确保该软件包满足个人探索和了解空气污染趋势及其影响的不同需求。代码库点击链接环境洞察 Github 主页。
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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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