Multi-Task Learning for Urban Air Quality Assessment with Meteorological Data

Anad, Maan Y

doi:10.69513/jncs.v3.i1.a3

	Multi-Task Learning for Urban Air Quality Assessment with Meteorological Data
Al-Noor Journal for Information Technology and Cybersecurity
Article 3, Volume 3, Issue 1, June 2026, Pages 18-26 PDF (470.79 K)
Document Type: Original Article
DOI: 10.69513/jncs.v3.i1.a3
Author
Maan Y Anad^*
Directorate of Education in Nineveh
Abstract
Urban air quality assessment consists of both categorical interpretation of pollution severity and continuous estimation of pollutant concentrations. While the Air Quality Index (AQI) categories are effective at communicating with the public, particulate matter concentrations, such as PM2.5 and PM10, are also essential to the quantitative assessment of air quality. Although there is a high degree of correspondence between the two types of assessments, they are often modeled separately. This paper proposes to use a multi-task learning (MTL) approach that can be used to assess urban air quality using data from meteorological and air-quality related tabular datasets. Specifically, under the MTL approach, the proposed model will jointly conduct AQI category classification and PM2.5/PM10 regression using a shared neural network backbone with outputs specifically designed for each task. The performance of the MTL framework will be tested on the TRAQID dataset and compared with that of single-task models using the same data-preprocessing and data-partitioning techniques. Results indicate that the MTL approach creates an integrated modeling framework that provides stable performance across both tasks, achieving an AQI classification accuracy of 93.72%, and offering insight into the trade-off between task specialization and joint representation learning.
Keywords
multi-task learning; air quality assessment; PM2.5; PM10; AQI

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