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The prediction of tap water quality serves as a pivotal component in enhancing water resource management. The intricate nonlinearity and inherent instability in water quality data make this task challenging. In this paper, we present a Tap-Water Quality Temporal Prediction Network (TWQ-TPN) to accurately predict tap-water quality by focusing on the impact of temporal nonlinear patterns and long-term seasonal fluctuations. To achieve this, we design two modules, namely the Temporal Feature Extraction Module (TFEM) and the Feature Transformation and Prediction Module (FTPM). The TFEM learns complex dynamic nonlinear features in the temporal domain. The FTPM is to realize feature transformation in the high-dimensional features for long-term seasonal fluctuations. Thus, our TWQ-TPN can accurately predict tap water quality trends to help improve water management. We validate TWQ-TPN's superiority using 5 years' data from 33 major water facilities in South Korea, demonstrating excellence in pH, turbidity, and residual chlorine. Ablation experiments support TWQ-TPN's rationale. [ABSTRACT FROM AUTHOR]

Accurate and stable prediction of atmospheric PM2.5 concentrations is crucial for air pollution prevention and control. Existing studies usually rely on a single model or use a single evaluation criterion in multi-model ensemble weighted forecasts, neglecting the dual needs for accuracy and stability in PM2.5 forecast. In this study, a novel ensemble forecast model is proposed that overcomes these drawbacks by simultaneously taking into account both forecast accuracy and stability. Specifically, four advanced deep learning models—Long Short-Term Memory Network (LSTM), Graph Convolutional Network (GCN), Transformer, and Graph Sample and Aggregation Network (GraphSAGE)—are firstly introduced. And then, two combined models are constructed as predictors, namely LSTM–GCN and Transformer–GraphSAGE. Finally, a combined weighting strategy is adopted to assign weights to these two combined models using a multi-objective optimization algorithm (MOO), so as to carry out more accurate and stable predictions. The experiments are conducted on the dataset from 36 air quality monitoring stations in Beijing, and results show that the proposed model achieves more accurate and stable predictions than other benchmark models. It is hoped that this proposed ensemble forecast model will provide effective support for PM2.5 pollution forecast and early warning in the future. [ABSTRACT FROM AUTHOR]

PM2.5 pollution used to be severe in China and the government has been devoted to PM2.5 control in recent years. Based on the latest multisource data and advanced algorithms, systematic environmental impact estimation of PM2.5 is achieved to demonstrate its trend and impacts under control policy. Policy validity is proved by a significant decrease in PM2.5 concentration (28%), energy intensity of GDP (13%), particulate matter emission (29%), and PM2.5 exceedance probability (51%) from the nation level. However, it still does not meet the requirement of WHO, and industrial and domestic sources are dominant for PM emission. The disaster risk index of air pollution is proposed to quantify and compare PM2.5 threat in 31 provinces, and risk gradually declines in 24 provinces except steady trend in Xinjiang, Shaanxi, Tianjin, Ningxia, Sichuan, Liaoning, and Tibet. Henan, Shanxi, Xinjiang, Hebei, Shaanxi, Tianjin, Ningxia, Jiangxi, Heilongjiang, and Anhui are identified as 10 high-risk regions with distinct driving factors. Current disease burdens attributable to PM2.5 exposure of provincial capitals in high-risk regions indicate huge health risks and economic losses. The heaviest health burden and economic burden are separately in Tianjin with 604,101 (95% CI: 302,796, 874,058) cases and 5.45% (95% CI: 2.73%, 7.89%) of population, and in Xi'an with 3122.24 (95% CI: 1398.55, 4274.53) million dollars and 2.31% (95% CI: 1.04%, 3.16%) of GDP. Results can provide references for worldwide PM2.5 control: scientific policy, treatment by the whole people and targeted control are crucial; it is urgent to arouse broad consensus on PM2.5 pollution and convert disease burden to health benefit. [ABSTRACT FROM AUTHOR]