# A machine learning-based framework for high resolution mapping of PM2.5 in Tehran, Iran, using MAIAC AOD data

arXiv: https://arxiv.org/abs/2204.02093 · HTML: https://mlsanity.com/paper/2204.02093/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 76 references that are in the map.
- First posted on arXiv 5 Apr 2022.

## Builds on (most cited first, 2 of 2)

- [Gradient Boosted Feature Selection](https://mlsanity.com/paper/1901.04055/) (2019)
- [Fusion of TanDEM-X and Cartosat-1 Elevation Data Supported by NeuralNetwork-Predicted Weight Maps](https://mlsanity.com/paper/1810.11415/) (2018)

## Built on by (most cited first, 4 of 4)

- [Data level and decision level fusion of satellite multi-sensor AOD retrievals for improving PM2.5 estimations, a study on Tehran](https://mlsanity.com/paper/2302.10278/) (2023)
- [Using Deep Ensemble Forest for High Resolution Mapping of PM2.5 from MODIS MAIAC AOD in Tehran, Iran](https://mlsanity.com/paper/2402.02139/) (2024)
- [Optimizing Urban Critical Green Space Development Using Machine Learning](https://mlsanity.com/paper/2505.09175/) (2025)
- [Structured Adaptive Tensor Prediction for Streaming Data](https://mlsanity.com/paper/2606.10085/) (2026)

## Abstract (arXiv)

This paper investigates the possibility of high resolution mapping of PM2.5 concentration over Tehran city using high resolution satellite AOD (MAIAC) retrievals. For this purpose, a framework including three main stages, data preprocessing; regression modeling; and model deployment was proposed. The output of the framework was a machine learning model trained to predict PM2.5 from MAIAC AOD retrievals and meteorological data. The results of model testing revealed the efficiency and capability of the developed framework for high resolution mapping of PM2.5, which was not realized in former investigations performed over the city. Thus, this study, for the first time, realized daily, 1 km resolution mapping of PM2.5 in Tehran with R2 around 0.74 and RMSE better than 9.0 mg/m3. Keywords: MAIAC; MODIS; AOD; Machine learning; Deep learning; PM2.5; Regression