# Conditional Generative Adversarial Networks to Model Urban Outdoor Air Pollution

arXiv: https://arxiv.org/abs/2010.02244 · HTML: https://mlsanity.com/paper/2010.02244/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [Leveraging Generative AI for Urban Digital Twins: A Scoping Review on the Autonomous Generation of Urban Data, Scenarios, Designs, and 3D City Models for Smart City Advancement](https://mlsanity.com/paper/2405.19464/) (2024)
- [Evolutionary latent space search for driving human portrait generation](https://mlsanity.com/paper/2204.11887/) (2022)
- [Synthetic Data Augmentation for Enhancing Harmful Algal Bloom Detection with Machine Learning](https://mlsanity.com/paper/2503.03794/) (2025)

## Abstract (arXiv)

This is a relevant problem because the design of most cities prioritizes the use of motorized vehicles, which has degraded air quality in recent years, having a negative effect on urban health. Modeling, predicting, and forecasting ambient air pollution is an important way to deal with this issue because it would be helpful for decision-makers and urban city planners to understand the phenomena and to take solutions. In general, data-driven methods for modeling, predicting, and forecasting outdoor pollution requires an important amount of data, which may limit their accuracy. In order to deal with such a lack of data, we propose to train models able to generate synthetic nitrogen dioxide daily time series according to a given classification that will allow an unlimited generation of realistic data. The main experimental results indicate that the proposed approach is able to generate accurate and diverse pollution daily time series, while requiring reduced computational time.