This study aims to estimate \(P{M}_{2.5}\) concentrations in Delhi, addressing the escalating menace of air pollution that poses a critical challenge to environmental sustainability. The analysis utilizes time-series data of \(P{M}_{2.5}\) concentrations, encompassing historical air quality measurements in Delhi over the year 2019 to 2023. Leveraging deep learning methodologies, we employ a multifaceted evaluation framework that includes R-squared (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE) to assess model performance. Our novel hybrid approach amalgamates Bidirectional Long-Short-Term Memory (Bi-LSTM) and Gated Recurrent Unit (GRU) architectures to enhance estimation accuracy. The proposed Bi-LSTM-GRU model demonstrates superior performance in \(P{M}_{2.5}\) estimation, yielding an RMSE of 0.37, R2 of 0.75, MAE of 8.22, MAPE of 34.69, and MSE of 0.13. Statistical analyses, including the Friedman test, affirm the pre-eminence of our proposed model. The model's enhanced performance supports more effective air quality management strategies, contributing to healthier urban environments and providing a robust tool for policymakers to devise targeted interventions and regulations.
