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.

环境遗产是一个开源的 Python 软件包，用于获取历史空气污染数据、使用机器学习模型预测未来情况，并通过动态可视化工具促进分析结果的传播和用户参与。

环境洞察：通过开源 Python 软件包向大众提供环境空气污染数据和预测分析

Environmental Insights: Democratizing Access to Ambient Air Pollution  Data and Predictive Analytics with an Open-Source Python Package

Global ambient air pollution, a transboundary challenge, is typically
addressed through interventions relying on data from spatially sparse and
heterogeneously placed monitoring stations. These stations often encounter
temporal data gaps due to issues such as power outages. In response, we have
developed a scalable, data-driven, supervised machine learning framework. This
model is designed to impute missing temporal and spatial measurements, thereby
generating a comprehensive dataset for pollutants including NO$_2$, O$_3$,
PM$_{10}$, PM$_{2.5}$, and SO$_2$. The dataset, with a fine granularity of
0.25$^{\circ}$ at hourly intervals and accompanied by prediction intervals for
each estimate, caters to a wide range of stakeholders relying on outdoor air
pollution data for downstream assessments. This enables more detailed studies.
Additionally, the model's performance across various geographical locations is
examined, providing insights and recommendations for strategic placement of
future monitoring stations to further enhance the model's accuracy.

通过数据驱动的监督机器学习框架填补空缺的时间和空间测量来生成全面的污染物数据集，为依赖室外空气污染数据进行下游评估的各方提供了具有 0.25 度细粒度和小时间隔的数据，同时为每个估计值提供了预测区间，从而支持更详细的研究。还通过检查模型在不同地理位置上的表现，为将来监测站的战略放置提供了见解和建议，以进一步提高模型的准确性。

基于数据驱动的监督式机器学习方法估计全球环境空气污染浓度及其相关预测区间

A Data-Driven Supervised Machine Learning Approach to Estimating Global  Ambient Air Pollution Concentrations With Associated Prediction Intervals

Ambient air pollution remains a critical issue in the United Kingdom, where
data on air pollution concentrations form the foundation for interventions
aimed at improving air quality. However, the current air pollution monitoring
station network in the UK is characterized by spatial sparsity, heterogeneous
placement, and frequent temporal data gaps, often due to issues such as power
outages. We introduce a scalable data-driven supervised machine learning model
framework designed to address temporal and spatial data gaps by filling missing
measurements. This approach provides a comprehensive dataset for England
throughout 2018 at a 1kmx1km hourly resolution. Leveraging machine learning
techniques and real-world data from the sparsely distributed monitoring
stations, we generate 355,827 synthetic monitoring stations across the study
area, yielding data valued at approximately \pounds70 billion. Validation was
conducted to assess the model's performance in forecasting, estimating missing
locations, and capturing peak concentrations. The resulting dataset is of
particular interest to a diverse range of stakeholders engaged in downstream
assessments supported by outdoor air pollution concentration data for NO2, O3,
PM10, PM2.5, and SO2. This resource empowers stakeholders to conduct studies at
a higher resolution than was previously possible.

通过机器学习填补缺失的空气污染浓度数据，我们提供了英国 2018 年每小时 1kmx1km 的全面数据集，对空气污染的浓度进行预测、估计和捕捉，使相关利益相关方能以更高分辨率进行研究。