AI Learns One City’s Air to Forecast Another’s: Transfer Learning Sharpens AQI Prediction in China

Air quality forecasting has long been a stubbornly local problem. Models trained on the pollution rhythms of one city often stumble when asked to describe another, where traffic patterns, industry, weather, and topography combine in different proportions. Now a team of Chinese researchers has built a prediction framework designed to break that dependence, using transfer learning to carry knowledge about atmospheric behavior from one city to another while a suite of signal-processing and optimization techniques keeps the noise and complexity of real-world monitoring data from overwhelming the model. The work, published in the journal Air Quality, Atmosphere & Health, was tested on daily data from four Chinese cities and reports strong performance across a range of cross-city prediction tasks.

The study, led by Yuanhao Du and Xiuli Geng of the University of Shanghai for Science and Technology, together with Sheng Cheng and Hongliu Zhang, addresses two weaknesses the authors identify in the existing literature. First, most air quality index (AQI) prediction models do not adequately account for the noisy character of AQI data, which blends pollutant concentrations, meteorological influences, and measurement artifacts into a single fluctuating number. Second, previous models have largely focused on a single region, leaving the cross-regional prediction problem—arguably the more useful one for public health planning—largely unsolved. The new framework is an explicit attempt to handle both challenges within one pipeline.

The first stage of the pipeline is data preparation, and it begins with a technique called complementary ensemble empirical mode decomposition with adaptive noise, or CEEMDAN. Rather than filtering the raw AQI signal with fixed mathematical filters, CEEMDAN adaptively decomposes it into a set of simpler components at different scales, adding controlled white noise in complementary pairs so that the residual noise in the decomposition largely cancels itself out. The result, according to the researchers, is a significant reduction in the noise effects that plague raw monitoring data. Once the data has been denoised in this way, the team applies phase space reconstruction, a method drawn from nonlinear dynamics that rebuilds the hidden geometric structure of the time series by embedding past values into a higher-dimensional space. This step is intended to expose the dynamic characteristics of the AQI series—the delayed dependencies and oscillations that a predictive model must capture in order to look even one day into the future.

With the data conditioned, the framework turns to its predictive engine: a deep echo state network modified by what the authors call a dynamic activation mechanism, abbreviated DCM-DESN. Echo state networks belong to the reservoir computing family, in which a large, sparsely connected recurrent layer—the reservoir—transforms input signals into a rich set of internal states, while only the readout weights connecting the reservoir to the output are trained. This makes them fast to train and well suited to time-series problems, and stacking reservoirs into a deep architecture extends their memory and representational capacity. The catch is that deep reservoirs typically demand a large number of neurons to perform well, which inflates computational cost. The dynamic activation mechanism adjusts the nonlinear connection mode of the deep echo state network, and by doing so it reduces the model’s dependence on the number of reservoir neurons—allowing the network to reach strong performance with a leaner internal structure.

Training the readout weights of such a network is itself an optimization problem, and this is where the second custom ingredient enters. The researchers propose an improved version of atom search optimization, a nature-inspired algorithm that models the movement of atoms under interaction forces to search a solution space. Their improved atomic search optimization, or IASO, is used to tune the network weights of the DCM-DESN, replacing gradient-based tuning of the reservoir-to-output connections with a global search that is less likely to be trapped in poor local solutions. The combination—CEEMDAN and phase space reconstruction on the front end, a dynamically activated deep reservoir in the middle, and IASO on the training side—forms what the authors describe as an intelligent AQI monitoring framework built on multi-source data.

The transfer learning component is what elevates the framework from a single-city forecaster to a cross-regional one. Transfer learning, a strategy now central to modern machine learning, allows a model trained on one task or domain to be adapted to another with far less data than training from scratch would require. In this study, the researchers constructed multiple cross-city transfer-learning tasks using daily data from Xi’an, Wuhan, Changsha, and Shenzhen covering the period from January 1, 2021 to March 24, 2024. For each task, the IASO-optimized DCM-DESN is first pretrained on data from a source city. The pretrained reservoir-based model is then transferred to the target city and frozen—its internal dynamics held fixed—while only the output weights are fine-tuned using a limited target-domain adaptation set drawn from the destination city.

The design choice to freeze the reservoir and retrain only the readout is both computationally elegant and practically significant. Because reservoir computing trains only its output layer in the first place, adaptation to a new city reduces to a lightweight re-fitting of a small set of weights, using just a limited sample of local data. In operational terms, that means a city with a short monitoring history could borrow the learned atmospheric dynamics of a data-rich neighbor and still produce accurate one-day-ahead AQI forecasts. The inputs to the model are historical pollutant concentrations and related environmental variables available up to the current day, which are used to directly predict the AQI one day ahead—a horizon short enough to be actionable for public health advisories yet long enough to require genuine predictive skill.

To evaluate the framework, the team measured performance across the various transfer tasks using three standard regression metrics: mean absolute error (MAE), root mean square error (RMSE), and the coefficient of determination (R²). Together these metrics capture both the typical size of forecast errors and the proportion of variance in the observed AQI that the model explains. The authors report that the proposed model demonstrated excellent performance across the different tasks, validating the architecture’s ability to generalize across cities with distinct pollution profiles. The four cities themselves span a meaningful range of conditions: Xi’an in the northwest, Wuhan and Changsha in central China, and Shenzhen in the subtropical south, each with its own mix of emissions sources and climatic drivers.

The study also connects to a broader body of work on decomposition-based air quality forecasting. Previous research has combined CEEMDAN with recurrent architectures such as independent recurrent neural networks and long short-term memory networks, and with attention-augmented convolutional models, to squeeze better forecasts out of noisy AQI series. Spatiotemporal graph convolutional networks have been applied to exploit correlations among monitoring stations, and transfer learning has been explored for predicting different pollutants and for assessing events such as COVID-19 lockdown effects on air quality. What distinguishes the new framework is the integration of these strands—adaptive decomposition, phase space reconstruction, deep reservoir computing with a dynamic activation mechanism, metaheuristic weight optimization, and cross-city transfer—into a single end-to-end pipeline aimed squarely at the one-day-ahead AQI problem.

The implications reach beyond the four cities studied. Accurate real-time understanding of AQI evolution is described by the authors as essential for atmospheric pollution prevention and urban public health management, and a framework that can be adapted to a new city with limited local data could lower the barrier for municipalities that lack long monitoring records. The work was supported by the National Natural Science Foundation of China under grant number 72271164, and the authors state they have no competing financial or non-financial interests to disclose. As cities worldwide grapple with the health burden of air pollution—exposure that the World Health Organization continues to flag as a major environmental health risk—tools that make accurate forecasting portable from one urban environment to another may prove among the most consequential applications of machine learning in atmospheric science. The study’s data will be made available on request, and the full article is published in Air Quality, Atmosphere & Health, volume 19, article number 218.

Subject of Research: Cross-city air quality index prediction using transfer learning and an optimized deep echo state network

Article Title: An intelligent AQI prediction method using transfer learning and an IASO-optimized DCM-DESN under multi-source data: A case study of four Chinese cities

Article References: Du, Y., Geng, X., Cheng, S., & Zhang, H. (2026). An intelligent AQI prediction method using transfer learning and an IASO-optimized DCM-DESN under multi-source data: A case study of four Chinese cities. Air Quality, Atmosphere & Health, 19(10), Article 218. https://doi.org/10.1007/s11869-026-02109-y

Image Credits: AI Generated

DOI: 10.1007/s11869-026-02109-y

Keywords: air quality index, transfer learning, deep echo state network, CEEMDAN, phase space reconstruction, atom search optimization, reservoir computing, cross-city prediction, machine learning, air pollution forecasting, China, public health