Environments 2026, 13(5), 273; https://doi.org/10.3390/environments13050273 (registering DOI) - 14 May 2026
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This study uses data from Taichung Fengyuan Station in Taiwan’s Civil IoT to conduct short-term forecasting of the Air Quality Index (AQI). We compile multiple pollutant and meteorological features and develop three models—Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Autoregressive Integrated
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This study uses data from Taichung Fengyuan Station in Taiwan’s Civil IoT to conduct short-term forecasting of the Air Quality Index (AQI). We compile multiple pollutant and meteorological features and develop three models—Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Autoregressive Integrated Moving Average with exogenous variables (ARIMAX)—together with a persistence baseline for comparison. The purpose of this study is to clarify whether deep sequence models and a classical statistical model can provide reliable one-hour-ahead AQI forecasts at the site level and to examine the practical value of such forecasts for early warning and air-quality management. Results show that GRU achieves the lowest overall prediction errors, followed by LSTM. The persistence baseline outperforms ARIMAX but remains clearly inferior to both recurrent models. In sum, the study shows that site-level AQI forecasting can benefit from recurrent deep-learning models not only in terms of numerical accuracy, but also in terms of capturing short-term temporal structure beyond a naive carry-forward baseline. These findings provide a benchmark-oriented and application-oriented reference for short-horizon AQI warning scenarios.
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