PyCHAM: CHemistry with Aerosol Microphysics in Python box model for Windows, Linux and Mac
Indoor Air Quality Dataset with Activities of Daily Living in Low to Middle-income Communities
A collection of CircuitPython helpers used for the calculation of PM2.5 and CO2 air quality levels.
Indoor air quality data acquisition node using ESPHome
An EnergyPlus version 8 based Ventilation Controls Virtual Test Bed for accelerated modelling, testing and comparing of single and mixed-mode ventilation systems and their control strategies
HE1040 Electronics design, project course. KTH Royal Institute of Technology.
R package to process and store data for the IEA EBC - Annex 86 - Energy Efficient Indoor Air Quality Management in Residential Buildings project. For details visit https://annex86.iea-ebc.org/. Documentation and examples available on https://iea-ebc-annex86.github.io/annex/
An app to show indoor co2 levels and air quality
Repository for code related to the Total Reactive Nitrogen (tNr) Instrument built by the VandenBoer research group
Details on the whole-house air quality monitoring system used during the Indoor Air Quality & Climate Change study (2015-2018)
The application classifies the minutes in which the user has cooked. The dataset contains one record per minute for approximately one month.
ML prediction of HVAC runtime status using a set of features including temperature, relative humidity, and their first and second derivatives
Low-cost wireless sensor node for environmental data (CO2, T, RH, P) used during the Indoor Air Quality & Climate Change study (2015-2018)
Collection of scripts to analyze the Indoor Air Quality (IAQ) dataset produced in project IEA EBC Annex 86 (ST2)
Functions to help with indoor air analysis in R
Official documentation and code examples for integrating with the LSI Lastem CUBE cloud platform via REST APIs. Access environmental monitoring data from ENVIRO-CUBE and INDOOR-CUBE applications using secure, header-based authentication. Ideal for developers building custom data pipelines or dashboards.
Building a classification to assess neighbourhood indoor air quality vulnerabilities
Sharing all the data pipelines and processing codes, statistical modellings, descriptive statistics, plot visualizations, and machine learning from Mahdavi & Siegel (2021) (Indoor Air) Project Miestone: 2017 - 2020 Full-length article: https://onlinelibrary.wiley.com/doi/abs/10.1111/ina.12782
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