Work

Urban Air Quality Shifts in China: Application of Additive Model and Transfer Learning to Major Cities

Year: 2025

Type: article

Abstract: The impact of reduced human activity on air quality in seven major Chinese cities was investigated by utilizing datasets of air pollutants and meteorological conditions from 2016 to 2021. A Generalized Additive Model (GAM) was developed to predict air quality during reduced-activity periods and rigorously validated against ground station measurements, achieving an R2 of 0.85–0.93. Predictions were compared to the observed pollutant reductions (e.g., NO2 declined by 34% in 2020 vs. 2019), confirming model reliability. Transfer learning further refined the accuracy, reducing RMSE by 32–44% across pollutants when benchmarked against real-world data. Notable NO2 declines were observed in Beijing (42%), Changchun (38%), and Wuhan (36%), primarily due to decreased vehicular traffic and industrial activity. Despite occasional anomalies caused by localized events such as fireworks (Beijing, February 2020) and agricultural burning (Changchun, April 2020), our findings highlight the strong influence of human activity reductions on urban air quality. These results offer valuable insights for designing long-term pollution mitigation strategies and urban air quality policies.

Source: Toxics

DOI: 10.3390/toxics13050334

Authors: Yuchen Ji, Xiaonan Zhang, Yueqian Cao

Institutions: Nantong University, Beijing Normal University

Language: English

Topic: COVID-19 impact on air quality

Subfield: Global and Planetary Change

Field: Environmental Science

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Climate action, Good health and well-being

Open Access status: gold

Estimated APC paid: $3,137