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Atmospheric chemical transport models (CTMs) have been widely used to simulate spatiotemporally resolved PM 2.5 concentrations. However, CTM results are usually prone to bias and errors. In this study, we improved the accuracy of PM 2.5 predictions by developing an ensemble deep learning framework to fuse model simulations with ground-level observations. The framework encompasses four machine-learning models, i.e., general linear model, fully connected neural network, random forest, and gradient boosting machine, and combines them by stacking approach. This framework is applied to PM 2.5 concentrations simulated by the Community Multiscale Air Quality (CMAQ) model for China from 2014 to 2017, which has complete spatial coverage over the entirety of China at a 12-km resolution, with no sampling biases. The fused PM 2.5 concentration fields were evaluated by comparing with an independent network of observations. The R 2 values increased from 0.39 to 0.64, and the RMSE values decreased from 33.7 μg/m 3 to 24.8 μg/m 3 . According to the fused data, the percentage of Chinese population residing under the level II National Ambient Air Quality Standards of 35 μg/m 3 for PM 2.5 has increased from 46.5% in 2014 to 61.7% in 2017. The method is readily adapted to utilize near-real-time observations for operational analyses and forecasting of pollutant concentrations and can be extended to provide source apportionment forecasts as well.

The increasing scale of the network and the demand for data privacy‐preserving have brought several challenges for existing intrusion detection schemes, which presents three issues: large computational overhead, long training period, and different feature distribution which leads low model performance. The emergence of transfer learning has solved the above problems. However, the existing transfer learning‐based schemes can only operate in plaintext when different domains and clouds are untrusted entities, the privacy during data processing cannot be preserved. Therefore, this paper designs a privacy‐preserving multi‐source transfer learning intrusion detection system (IDS). Firstly, we used the Paillier homomorphic to encrypt models which trained from different source domains and uploaded to the cloud. Then, based on privacy‐preserving scheme, we first proposed a multisource transfer learning IDS based on encrypted XGBoost (E‐XGBoost). The experimental results show that the proposed scheme can successfully transfer the encryption models from multiple source domains to the target domain, and the accuracy rate can reach 93.01% in ciphertext, with no significant decrease in detection performance compared with works in plaintext. The training time of the model is significantly reduced from the traditional hour‐level to the minute‐level.

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