# Deep Air Learning: Interpolation, Prediction, and Feature Analysis of Fine-grained Air Quality

arXiv: https://arxiv.org/abs/1711.00939 · HTML: https://mlsanity.com/paper/1711.00939/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2017.

## Built on by (most cited first, 16 of 16)

- [Group-Aware Graph Neural Network for Nationwide City Air Quality Forecasting](https://mlsanity.com/paper/2108.12238/) (2021)
- [Machine Learning for Urban Air Quality Analytics: A Survey](https://mlsanity.com/paper/2310.09620/) (2023)
- [Building Autocorrelation-Aware Representations for Fine-Scale Spatiotemporal Prediction](https://mlsanity.com/paper/2112.05313/) (2021)
- [Deep-AIR: A Hybrid CNN-LSTM Framework for Air Quality Modeling in Metropolitan Cities](https://mlsanity.com/paper/2103.14587/) (2021)
- [AQuaMoHo: Localized Low-Cost Outdoor Air Quality Sensing over a Thermo-Hygrometer](https://mlsanity.com/paper/2204.11484/) (2022)
- [Spatiotemporal Predictions of Toxic Urban Plumes Using Deep Learning](https://mlsanity.com/paper/2406.02582/) (2024)
- [AirDDE: Multifactor Neural Delay Differential Equations for Air Quality Forecasting](https://mlsanity.com/paper/2603.17529/) (2026)
- [MAQ-CaF: A Modular Air Quality Calibration and Forecasting method for cross-sensitive pollutants](https://mlsanity.com/paper/2104.12594/) (2021)
- [PlumeCityNet: Multi-Resolution Air Quality Forecasting](https://mlsanity.com/paper/2110.02661/) (2021)
- [ISTRBoost: Importance Sampling Transfer Regression using Boosting](https://mlsanity.com/paper/2204.12044/) (2022)
- [From Limited Annotated Raw Material Data to Quality Production Data: A Case Study in the Milk Industry (Technical Report)](https://mlsanity.com/paper/2204.12302/) (2022)
- [Air Quality Prediction with A Meteorology-Guided Modality-Decoupled Spatio-Temporal Network](https://mlsanity.com/paper/2504.10014/) (2025)
- [STeP-Diff: Spatio-Temporal Physics-Informed Diffusion Models for Mobile Fine-Grained Pollution Forecasting](https://mlsanity.com/paper/2512.04385/) (2025)
- [Deep classifier kriging for probabilistic spatial prediction of air quality index](https://mlsanity.com/paper/2512.23474/) (2025)
- [NEXUS: A compact neural architecture for high-resolution spatiotemporal air quality forecasting in Delhi National Capital Region](https://mlsanity.com/paper/2602.19654/) (2026)
- [MVG-KAN: Multi-View Geo-Wind Guided KAN for PM2.5 Forecasting](https://mlsanity.com/paper/2606.24347/) (2026)

## Abstract (arXiv)

The interpolation, prediction, and feature analysis of fine-gained air quality are three important topics in the area of urban air computing. The solutions to these topics can provide extremely useful information to support air pollution control, and consequently generate great societal and technical impacts. Most of the existing work solves the three problems separately by different models. In this paper, we propose a general and effective approach to solve the three problems in one model called the Deep Air Learning (DAL). The main idea of DAL lies in embedding feature selection and semi-supervised learning in different layers of the deep learning network. The proposed approach utilizes the information pertaining to the unlabeled spatio-temporal data to improve the performance of the interpolation and the prediction, and performs feature selection and association analysis to reveal the main relevant features to the variation of the air quality. We evaluate our approach with extensive experiments based on real data sources obtained in Beijing, China. Experiments show that DAL is superior to the peer models from the recent literature when solving the topics of interpolation, prediction, and feature analysis of fine-gained air quality.