Article
Meteorology & Atmospheric Sciences
Weiyang Hu, Tianliang Zhao, Yongqing Bai, Shaofei Kong, Lijuan Shen, Jie Xiong, Yue Zhou, Yao Gu, Junnan Shi, Huang Zheng, Xiaoyun Sun, Kai Meng
Summary: This study reveals the significant regulation of large-scale synoptic circulation on regional PM2.5 transport for heavy air pollution in central China. The observations and simulations identify the transport pathways and circulation patterns governing the transport of PM2.5 from upwind areas to the receptor region.
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
(2022)
Article
Environmental Sciences
Lifeng Guo, Baozhang Chen, Huifang Zhang, Jingchun Fang
Summary: The study developed the LPD model to forecast and determine sources of PM2.5 pollution, showing significant improvement in accuracy after optimization, with industrial and residential emissions identified as the main pollution sources.
Article
Environmental Sciences
Guangqiang Zhou, Zhongqi Yu, Yuanhao Qu
Summary: The study found that local emissions have a significant impact on PM2.5 concentration in the Yangtze River Delta region, while mutual inner-regional transport among provinces also plays a role. Controlling local emissions and reducing inter-provincial transport can effectively reduce the rate of PM2.5 pollution.
ATMOSPHERIC ENVIRONMENT
(2021)
Article
Environmental Sciences
Haopeng Zhang, Hongquan Song, Xiaowei Wang, Yaobin Wang, Ruiqi Min, Minghui Qi, Xutong Ru, Tianqi Bai, Hua Xue
Summary: Agricultural soil wind erosion is a major source of atmospheric particulate matter (PM) in dryland areas, but is often not considered in current air quality models, leading to uncertainties in PM simulations. In this study, the impact of agricultural PM2.5 emissions on air pollution in Kaifeng, China was estimated using the Wind Erosion Prediction System (WEPS) and the Multi-resolution Emission Inventory for China (MEIC). The results showed that considering agricultural dust emissions significantly improved the accuracy of PM2.5 simulations in the Weather Research and Forecasting model coupled with chemistry (WRF-Chem). The agricultural soil wind erosion contributed around 37.79% of the PM2.5 in the Kaifeng municipal district during the pollution episode.
Article
Environmental Sciences
Idrees Ahmad, Osama Bin Muhammad, Rizwan Ahmed, Shakeel Ahmad
Summary: The selection of power plant sites, especially nuclear power plants, involves considerations of radiation doses and dispersion of radioactive particles. In this study, a combination of weather forecasting and particle dispersion codes were used to simulate an accidental release from a nuclear power plant, showing better accuracy than previous investigations. The study found that WRF predictions of wind speeds and directions had a significant impact on tracking particle trajectories and spatial dose distribution.
JOURNAL OF RADIOLOGICAL PROTECTION
(2021)
Article
Engineering, Chemical
Dinesh Krishnamoorthy, Francis J. Doyle
Summary: Conventional real-time optimization (RTO) requires detailed process models, which may be challenging or expensive to obtain. Model-free RTO methods based on estimating the cost gradient are slow for processes with long settling times. To avoid gradient estimation, this article proposes a model-free Bayesian optimization framework that guarantees feasible setpoints with high probability. The method is demonstrated on a Williams-Otto reactor example.
Article
Chemistry, Multidisciplinary
Medhavi Gupta, Diljit Kumar Nayak, Sri Harsha Kota
Summary: India's air quality is severely affected by high levels of particulate matter (PM), leading to the implementation of the National Clean Air Programme (NCAP) to reduce PM concentrations. However, studies show that PM-centric action plans may negatively impact ozone air quality. This study utilizes the WRF-Chem model to analyze the effect of the NCAP on PM and ozone levels in India, finding that the NCAP can improve PM levels in most nonattainment cities (NACs) but may exacerbate ozone issues in some regions. The reduction in ozone is attributed to decreased nitrogen oxides and VOC emissions, as well as an increase in forest cover.
ACS EARTH AND SPACE CHEMISTRY
(2023)
Article
Multidisciplinary Sciences
Xinyu Dou, Jinpyo Hong, Philippe Ciais, Frederic Chevallier, Feifan Yan, Ying Yu, Yifan Hu, Da Huo, Yun Sun, Yilong Wang, Steven. J. J. Davis, Monica Crippa, Greet Janssens-Maenhout, Diego Guizzardi, Efisio Solazzo, Xiaojuan Lin, Xuanren Song, Biqing Zhu, Duo Cui, Piyu Ke, Hengqi Wang, Wenwen Zhou, Xia Huang, Zhu Deng, Zhu Liu
Summary: This study presents a near-real-time global gridded daily CO2 emissions dataset (GRACED) for 2021. GRACED provides detailed information on CO2 emissions at a high spatial and temporal resolution from various sectors such as industry, power, transportation, and aviation. The dataset is based on reliable national emissions estimates, spatial activity data, and satellite observations, allowing policymakers to monitor and adjust energy and climate policies effectively.
Article
Automation & Control Systems
Jongchan Baek, Hayoung Jun, Jonghyuk Park, Hakjun Lee, Soohee Han
Summary: The proposed SVDPG algorithm integrates Bayesian pruning with policy update in reinforcement learning to achieve efficient policy network compression and competitive performance in continuous control benchmark tasks. Additionally, SVDPG demonstrates superiority in low-computing power devices and reliability in real-world scenarios.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Energy & Fuels
Jinbo Song, Jianlong Wang, Bingqing Li, Linlin Gan, Feifei Zhang, Xueying Wang, Qiong Wu
Summary: By using a Bayesian optimization algorithm, the drilling parameters can be optimized in real time to improve drilling efficiency. The algorithm maintains timeliness while effectively assisting drillers in decision-making and avoiding low-efficiency rock-breaking areas.
Article
Green & Sustainable Science & Technology
Lionel P. Joseph, Ravinesh C. Deo, Ramendra Prasad, Sancho Salcedo-Sanz, Nawin Raj, Jeffrey Soar
Summary: This research proposes a novel hybrid bidirectional LSTM model for near real-time wind speed forecasting. The model utilizes wind speed and selected climate indices to predict wind speed, and applies a 3-stage feature selection to extract significant input features. The proposed hybrid BiLSTM algorithm outperforms other tested algorithms in wind speed prediction.
Article
Energy & Fuels
Daohua Zhang, Xinxin Jin, Piao Shi, XinYing Chew
Summary: A smart grid is a modern power system that utilizes advanced technology to monitor and optimize power system operations in real-time. This paper proposes a smart grid real-time prediction model based on CNN, BiLSTM, and Bayesian optimization, which outperforms traditional prediction models such as ARMA and decision trees in accuracy and efficiency. The model can accurately predict real-time power system load, providing valuable guidance for power system dispatch and optimization.
FRONTIERS IN ENERGY RESEARCH
(2023)
Article
Engineering, Biomedical
Guang Ouyang, Joseph Dien, Romy Lorenz
Summary: This study presents a cost-efficient method for online artifact handling that can extract and preserve ERP effects, providing better support for ERP-based neuroadaptive research.
JOURNAL OF NEURAL ENGINEERING
(2022)
Article
Environmental Sciences
Ting Zhou, Hui Hu, Jiaxin Chen, Ruoqiao Bai, Feifei Wang, Yuxuan Wang, Jinjie Zhang, Xiaoyong Liu, Nan Chen, Ke Xu
Summary: This study utilized WRF-CALPUFF coupling models and the second pollution source survey data to analyze the contribution rates of point and area source emissions to the concentrations of SO2, NO2, and PM2.5 in Wuhan, as well as the source emission sharing rate of each region's AEC. The results revealed significant seasonal and spatial changes in AEC and different characteristics of the impact of urban point and area sources on pollutant concentrations.
ATMOSPHERIC POLLUTION RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Jiaxu Cui, Qi Tan, Chunxu Zhang, Bo Yang
Summary: This study focuses on utilizing Bayesian optimization to adjust network structure and proposes a flexible framework called graph Bayesian optimization (GBO) to handle arbitrary graph inputs. By combining with graph kernels, it can leverage implicit graph structural features and identify important features during the optimization process.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Oceanography
Kong Yawen, Zhang Xiuzhi, Sheng Lifang, Chen Baozhang
ACTA OCEANOLOGICA SINICA
(2016)
Article
Oceanography
Chu Yingjia, Sheng Lifang, Liu Qian, Zhao Dongliang, Jia Nan, Kong Yawen
JOURNAL OF OCEAN UNIVERSITY OF CHINA
(2016)
Article
Environmental Sciences
Xiaofeng Lin, Baozhang Chen, Huifang Zhang, Fei Wang, Jing Chen, Lifeng Guo, Yawen Kong
Article
Environmental Sciences
Yawen Kong, Baozhang Chen, Simon Measho
Article
Meteorology & Atmospheric Sciences
Yawen Kong, Lifang Sheng, Yanpeng Li, Weihang Zhang, Yang Zhou, Wencai Wang, Yuanhong Zhao
Summary: The study found that the 4D-LETKF-PM2.5 data assimilation system significantly improved the accuracy of PM2.5 forecasts during severe haze episodes, particularly in polluted regions. After assimilation, the root-mean-square error of PM2.5 concentrations decreased, and the correlation coefficient of the analysis increased.
ATMOSPHERIC RESEARCH
(2021)
Article
Agronomy
Xiaofeng Lin, Baozhang Chen, Jing Chen, Huifang Zhang, Shaobo Sun, Guang Xu, Lifeng Guo, Mengyu Ge, Junfeng Qu, Lijuan Li, Yawen Kong
AGRICULTURAL AND FOREST METEOROLOGY
(2017)