Abstract
This paper presents an artificial intelligence (AI)-driven atmospheric emission monitoring framework for sustainability-oriented project management that integrates predictive analytics, blockchain-based data verification, and digital-twin technologies. Reliable environmental monitoring and short-term forecasting are increasingly required to support regulatory compliance, environmental risk assessment, and evidence-based project decision-making. The objective of this study is to develop and evaluate an integrated framework combining real-time industrial monitoring, neural-network forecasting, and decision-support mechanisms. The empirical forecasting component uses 39,803 synchronized time-stamped observations obtained from an operating industrial monitoring system in Kazakhstan with a 20-minute temporal resolution. The monitored variables include NO, NO₂, SO₂, CO, particulate matter, oxygen concentration, temperature, humidity, pressure, and gas-flow indicators. After quality control, synchronization, outlier removal, and Min-Max normalization, historical sequences of 72 consecutive observations (24 hours) were generated. A Long Short-Term Memory (LSTM) neural network with recurrent layers of 32 and 16 hidden units, a dropout rate of 0.2, and a dense output layer was trained for one-step-ahead forecasting corresponding to the next 20-minute observation. The dataset was divided chronologically into 64% training, 16% validation, and 20% testing subsets to preserve temporal dependencies. The proposed model achieved an overall MSE of 0.87 and R² of 0.86, while reducing pollutant-specific RMSE by 33.1–46.8% compared with the ARIMA baseline. The monitoring platform was additionally piloted at Promanalit LLP, where telemetry acquisition, preprocessing, visualization, reporting, and blockchain-based microblock verification were successfully validated. The study explicitly distinguishes empirical forecasting and platform validation from conceptual digital-twin and ESG scenarios, which are included only to demonstrate project decision-support capabilities. The proposed framework provides a practical basis for intelligent environmental monitoring, short-term emission forecasting, and sustainability-oriented project management in industrial applications.