scholarly journals Driving factors and spatio-temporal features underlying industrial SO2 emissions in “2+26” in North China and extended cities

2021 ◽  
Author(s):  
Zhuang Miao ◽  
Sicen Liu ◽  
Xiaodong Chen
Keyword(s):  
North China ◽  
Driving Factors ◽  
So2 Emissions ◽  
Temporal Features ◽  
Spatio Temporal

scholarly journals Characteristics of the Spatio-Temporal Trends and Driving Factors of Industrial Development and Industrial SO2 Emissions Based on Niche Theory: Taking Henan Province as an Example

2020 ◽  
Vol 12 (4) ◽  
pp. 1389 ◽  
Author(s):  
Pengyan Zhang ◽  
Yu Zhang ◽  
Jay Lee ◽  
Yanyan Li ◽  
Jiaxin Yang ◽  
...  
Keyword(s):  
Industrial Development ◽  
Industrial Structure ◽  
Temporal Trends ◽  
Driving Factors ◽  
Spatial Data Analysis ◽  
Energy Structure ◽  
Niche Theory ◽  
Industrial Output ◽  
So2 Emissions ◽  
Spatio Temporal

Industrial development is critical in improving a nation’s economy and in how it consumes energy resources. However, such development often causes environmental problems. Among others, the haze caused by industrial SO2 emissions is particularly prominent. Based on Niche theory and combined with Exploratory Spatial Data Analysis (ESDA, a decoupling index model, and a Logarithmic Mean Divisia Index (LMDI) factor decomposition model, this paper reports a study on the spatio-temporal distribution and the driving factors of industrial development and industrial SO2 emissions of cities in Henan, China between 2005 and 2014. The results showed that over the studied period in Henan: (1) SO2 emissions reduced by 4.56 × 105 tons and showed a slowly decreasing trend, which gradually transitioned to a “green health” industrial structure in Henan cities; (2) studied cities with high and low industrial niche values (0.038–0.139) showed an absolute decoupling relationship between industrial development and industrial SO2 emissions; (3) except for Zhengzhou city and Hebi city, other studied cities showed a trend of gradually increasing industrial output; (4) along with increases in the values of industrial output, studied cities showed increased levels of SO2 emissions but with energy intensity and energy structure showing a fluctuating trend of increases and decreases in their contributions to SO2 emissions; and (5) the energy consumption intensity and environmental technology were critical factors that were conducive to industrial SO2 emissions and the evolving industrial structure. These findings are important for the control of industrial SO2 emissions, though the levels of their influences are different in different cities. The scale of industrial production and the composition of energy structure in a region could lead to further deterioration of industrial SO2 emissions in the future.

Fire Detection Based on Fractal Analysis and Spatio-Temporal Features

2021 ◽  
Author(s):  
Monir Torabian ◽  
Hossein Pourghassem ◽  
Homayoun Mahdavi-Nasab
Keyword(s):  
Fractal Analysis ◽  
Fire Detection ◽  
Temporal Features ◽  
Spatio Temporal

scholarly journals Spatio-temporal variation and driving factors analysis of PM2.5 health risks in Chinese cities

2021 ◽  
Vol 129 ◽  
pp. 107937
Author(s):  
Qian Liu ◽  
Zheyu Zhang ◽  
Chaofeng Shao ◽  
Run Zhao ◽  
Yang Guan ◽  
...  
Keyword(s):  
Temporal Variation ◽  
Health Risks ◽  
Driving Factors ◽  
Chinese Cities ◽  
Factors Analysis ◽  
Spatio Temporal

The temporal change of driving factors during the course of land desertification in arid region of North China: the case of Minqin County

2006 ◽  
Vol 51 (6) ◽  
pp. 999-1008 ◽  
Author(s):  
Yonghuan Ma ◽  
Shengyue Fan ◽  
Lihua Zhou ◽  
Zhaoyang Dong ◽  
Kecun Zhang ◽  
...  
Keyword(s):  
Arid Region ◽  
North China ◽  
Temporal Change ◽  
Driving Factors ◽  
Land Desertification

Kinematic Adaptive Frequency Sampling Combined Spatio Temporal Features for Snow Monitoring in Aerospace Applications

2021 ◽  
pp. 115472
Author(s):  
Parameshwaran Ramalingam ◽  
Lakshminarayanan Gopalakrishnan ◽  
Manikandan Ramachandran ◽  
Rizwan Patan
Keyword(s):  
Aerospace Applications ◽  
Temporal Features ◽  
Frequency Sampling ◽  
Spatio Temporal

P4-225: Analysis of Spatio-Temporal Features of Histopathology in Murine Alzheimer's Disease Models and Human Alzheimer's Disease Samples

2016 ◽  
Vol 12 ◽  
pp. P1115-P1115
Author(s):  
Vera Niederkofler ◽  
Christina Hoeller ◽  
Joerg Neddens ◽  
Ewald Auer ◽  
Heinrich Roemer ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Models ◽  
Temporal Features ◽  
Spatio Temporal

Predicting Human Mobility with Reinforcement-Learning-Based Long-Term Periodicity Modeling

2021 ◽  
Vol 12 (6) ◽  
pp. 1-23
Author(s):  
Shuo Tao ◽  
Jingang Jiang ◽  
Defu Lian ◽  
Kai Zheng ◽  
Enhong Chen
Keyword(s):  
Reinforcement Learning ◽  
Human Mobility ◽  
Recurrent Network ◽  
Mobility Prediction ◽  
Learning Framework ◽  
Temporal Features ◽  
Wide Range ◽  
Spatio Temporal ◽  
Historical Trajectory

Mobility prediction plays an important role in a wide range of location-based applications and services. However, there are three problems in the existing literature: (1) explicit high-order interactions of spatio-temporal features are not systemically modeled; (2) most existing algorithms place attention mechanisms on top of recurrent network, so they can not allow for full parallelism and are inferior to self-attention for capturing long-range dependence; (3) most literature does not make good use of long-term historical information and do not effectively model the long-term periodicity of users. To this end, we propose MoveNet and RLMoveNet. MoveNet is a self-attention-based sequential model, predicting each user’s next destination based on her most recent visits and historical trajectory. MoveNet first introduces a cross-based learning framework for modeling feature interactions. With self-attention on both the most recent visits and historical trajectory, MoveNet can use an attention mechanism to capture the user’s long-term regularity in a more efficient way. Based on MoveNet, to model long-term periodicity more effectively, we add the reinforcement learning layer and named RLMoveNet. RLMoveNet regards the human mobility prediction as a reinforcement learning problem, using the reinforcement learning layer as the regularization part to drive the model to pay attention to the behavior with periodic actions, which can help us make the algorithm more effective. We evaluate both of them with three real-world mobility datasets. MoveNet outperforms the state-of-the-art mobility predictor by around 10% in terms of accuracy, and simultaneously achieves faster convergence and over 4x training speedup. Moreover, RLMoveNet achieves higher prediction accuracy than MoveNet, which proves that modeling periodicity explicitly from the perspective of reinforcement learning is more effective.

Sign in / Sign up

Export Citation Format

Share Document