Double-Layer Distributed and Integrated Fault Detection Strategy for Non-Gaussian Dynamic Industrial Systems.

Currently, with the increasing scale of industrial systems, multisensor monitoring data exhibit large-scale dynamic Gaussian and non-Gaussian concurrent complex characteristics. However, the traditional principal component analysis method is based on Gaussian distribution and uncorrelated assumptions, which are greatly limited in practice. Therefore, developing a new fault detection method for large-scale Gaussian and non-Gaussian concurrent dynamic systems is one of the urgent challenges to be addressed.

Random Fourier feature kernel recursive maximum mixture correntropy algorithm for online time series prediction.

In the paper, a novel kernel recursive least-squares (KRLS) algorithm named random Fourier feature kernel recursive maximum mixture correntropy (RFF-RMMC) algorithm is proposed, which improves the prediction efficiency and robustness of the KRLS algorithm. Random Fourier feature (RFF) method as well as maximum mixture correntropy criterion (MMCC) are combined and applied into KRLS algorithm afterwards. Using RFF to approximate the kernel function in KRLS with a fixed cost can greatly reduce the computational complexity and simultaneously improve the prediction efficiency.

Estimation of ground-level PM2.5 concentration using MODIS AOD and corrected regression model over Beijing, China.

To establish a new model for estimating ground-level PM2.5 concentration over Beijing, China, the relationship between aerosol optical depth (AOD) and ground-level PM2.5 concentration was derived and analysed firstly.

Air quality co-benefits from climate mitigation for human health in South Korea.

Climate change mitigation efforts to reduce greenhouse gas (GHG) emissions have associated costs, but there are also potential benefits from improved air quality, such as public health improvements and the associated cost savings. A multidisciplinary modeling approach can better assess the co-benefits from climate mitigation for human health and provide a justifiable basis for establishment of adequate climate change mitigation policies and public health actions. An integrated research framework was adopted by combining a computable general equilibrium model, an air quality model, and a health impact assessment model, to explore the long-term economic impacts of climate change mitigation in South Korea through 2050.

Multitask Air-Quality Prediction Based on LSTM-Autoencoder Model.

With the development of the data-driven modeling techniques, using the neural network to simulate the transport process of atmospheric pollutants and constructing PM time-series prediction model have become a hot topic. The existing data-driven approaches often ignore the dynamical relationships among multiple sites in urban areas, which results in nonideal prediction accuracy. In response to this problem, this article proposes a long short-term memory (LSTM) autoencoder multitask learning model to predict PM time series in multiple locations city wide.

Co-benefits of climate mitigation on air quality and human health in Asian countries.

Climate change mitigation involves reducing fossil fuel consumption and greenhouse gas emissions, which is expensive, particularly under stringent mitigation targets. The co-benefits of reducing air pollutants and improving human health are often ignored, but can play significant roles in decision-making. In this study, we quantified the co-benefits of climate change mitigation on ambient air quality and human health in both physical and monetary terms with a particular focus on Asia, where air quality will likely be degraded in the next few decades if mitigation measures are not undertaken.