A Multi-Output Deep Learning Framework for Simultaneous Forecasting of PM10 and Air Quality Index in High-Altitude Basins: A Case Study of Igdir, Türkiye

Air pollution forecasting is particularly challenging in basins with frequent winter seasons and temperature inversions. In this study, we developed and rigorously evaluated deep learning models to forecast PM10 and the Air Quality Index (AQI) in Igdır, Türkiye, using a five-year, hourly dataset (2020–2024) from the Igdır/Central station (PM10, NO2, O3, SO2; meteorology: pressure, temperature, wind speed, relative humidity, precipitation, cloud cover). Using linear interpolation and Z-score normalization, sine/cosine features (hour, month) were used to encode temporal periodicity, and a 72-h lookback → 24-h look-ahead design was employed. LSTM, GRU, BiLSTM, and CNN-LSTM models were compared under a three-stage ablation (meteorology only; +cyclic encoders; +lagged targets), and their hyperparameters were tuned via Bayesian optimization. The deep learning results were further contextualized against a Multiple Linear Regression (MLR) baseline serving as a snapshot persistence model to evaluate the specific advantage of LSTM’s temporal memory in short-horizon forecasting. Multi-output forecasting is central to the proposed design, featuring a multi-task learning (MTL) framework based on a single shared temporal encoder with two task-specific regression heads that simultaneously predict PM10 and AQI. Compared with separate single-task models, the multi-output setup exploits cross-target covariance (AQI’s dependence on pollutant loads under meteorology), improves data efficiency and generalization through shared representations, and promotes coherent, horizon-stable forecasts across targets, which is particularly valuable when winter stagnation regimes couple PM10 and AQI dynamics. Moreover, this study introduces a structured ablation design to explicitly evaluate the added value of multi-output forecasting under inversion-dominated basin conditions. The results show stepwise gains from cyclic encoders and, most strongly, from lagged target histories. Under the optimized 24-h setting, LSTM performs best (R2_{PM10} = 0.7989, RMSE = 48.74 µg/m3; R2_{AQI} = 0.6626, RMSE = 37.81), marginally surpassing GRU and clearly outperforming BiLSTM and CNN-LSTM. Horizon sensitivity confirms the benefit of nowcasting: when retrained for shorter horizons, LSTM attains R2 = 0.9991 for PM10 (MAE = 2.44; RMSE = 3.30 µg/m3) and 0.9535 for AQI (MAE = 4.87; RMSE = 14.03) at 1 h, and R2 = 0.9792 (PM10; MAE = 9.70; RMSE = 15.67) and 0.8849 (AQI; MAE = 11.19; RMSE = 22.08) at 6 h. Residual diagnostics reveal heteroskedastic, regime-dependent errors peaking near 0 °C and low winds, as well as a conservative bias that underpredicts extremes. Collectively, the findings show that multi-output, temporally aware deep models enable accurate operational forecasting in Igdır. The proposed framework provides real-time air quality alerts and daily planning, providing decision support for sustainable air quality management, public health protection, and evidence-based urban policy and is transferable to similar continental basin environments.