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

Does the introduction explain the objective of the research presented in the preprint?

Yes

The authors mention in the introduction several existing studies that describe various modeling frameworks for predicting air pollution concentrations, while highlighting the lack of practical application of such forecasts by a wide range of stakeholders. As the goal of their research, the authors refer to the possibility of practical application of their proposed iterative approach. The authors offer a source code package in the Python programming language that will allow a wide range of stakeholders to apply the forecasting results in practice, namely, to download, analyze, and visualize data on air pollution concentrations.

Are the methods well-suited for this research?

Highly appropriate

The research follows best practices by providing access to air pollution data, historical data retrieval, and utilizing machine learning models for predictive analytics. Also, the authors fills a gap in the current offerings of air pollution software by providing a single, easy-to-use Python package for accessing, visualizing, and predicting air pollution concentration data.

Are the conclusions supported by the data?

Highly supported

The authors have presented thorough explanations of the software's features, encompassing data access, visualization, predictive modeling, and intervention planning tools. They provide a comprehensive examination of air pollution data and its effects on different sectors such as human health, ecosystems, economies, and urban planning. The study outlines functionalities for accessing detailed air pollution data, establishing a direct connection between the tool's capabilities and the desired results for different stakeholders (Sections 4 and 4.1).

Are the data presentations, including visualizations, well-suited to represent the data?

Highly appropriate and clear

By integrating dynamic visualizations and tools into the Environmental Insights open-source Python package, the research effectively communicates the results and key patterns in the data, facilitating comprehension and interpretation by various stakeholders. By presenting data in a clear and accessible manner, the study ensures that the conclusions drawn from the data are well-supported and easily understandable by a wide range of stakeholders.

How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research?

Very clearly

The authors explain how the "Environmental Insights" package fills a crucial void in accessing and analyzing air pollution data. They emphasize the package's capacity to democratize air quality data, making it accessible and comprehensible for a wide audience for urban planning, policy development, and community engagement, stressing the diverse stakeholders utilizing air pollution concentration data.

Is the preprint likely to advance academic knowledge?

Highly likely

By democratizing access to high-resolution air pollution concentration predictions, the preprint addresses a critical gap in environmental research. It enables a broader range of stakeholders, including those without extensive technical expertise, to engage with air pollution data. This approach is likely to spur new research inquiries and methodologies in environmental science.

Would it benefit from language editing?

No

Would you recommend this preprint to others?

Yes, it’s of high quality

The preprint presents a significant contribution to the domain of environmental research by providing a tool that democratizes access to air pollution data and predictive analytics.

Is it ready for attention from an editor, publisher or broader audience?

Yes, as it is

Competing interests

The author declares that they have no competing interests.