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"Distribution" Keyword-tagged Publications:

Title Authors PubMed ID
1 Neural topic modeling on hyperspheres: Spherical representation learning with von Mises-Fisher mixtures Guo D; Luo Z; Bouguila N; Fan W; 41791177
ENCS
2 Spatio-temporal distribution of AOD and its response to regional energy consumption and air pollution factors in China Su Y; Chen X; Guo J; Yang A; 41308902
ENCS
3 Disentangled representation learning for multi-view clustering via von Mises-Fisher hyperspherical embedding Li Z; Luo Z; Bouguila N; Su W; Fan W; 40664160
ENCS
4 No species left behind: borrowing strength to map data-deficient species Sharma S; Winner K; Pollock LJ; Thorson JT; Mäkinen J; Merow C; Pedersen EJ; Chefira KF; Portmann JM; Iannarilli F; Beery S; de Lutio R; Jetz W; 40571432
BIOLOGY
5 Strategies to Reduce Uncertainties from the Best Available Physicochemical Parameters Used for Modeling Novel Organophosphate Esters across Multimedia Environments Xing C; Ge J; Chen R; Li S; Wang C; Zhang X; Geng Y; Jones KC; Zhu Y; 40105294
CHEMBIOCHEM
6 Exon junction complexes regulate osteoclast-induced bone resorption by influencing the NFATc1 m6A distribution through the "shield effect" Sun B; Yang JG; Wang Z; Wang Z; Feng W; Li X; Liu SN; Li J; Zhu YQ; Zhang P; Wang W; 40051055
ENCS
7 Spatial Variations of Atmospheric Alkylated Polycyclic Aromatic Hydrocarbons across the Western Pacific to the Southern Ocean: Unexpected Increasing Deposition Zhu FJ; Lu XM; Jia JW; Zhang X; Xing DF; Cai MH; Kallenborn R; Li YF; Muir DCG; Zhang ZF; Zhang X; 40025703
CHEMBIOCHEM
8 In Shift and In Variance: Assessing the Robustness of HAR Deep Learning Models Against Variability Khaked AA; Oishi N; Roggen D; Lago P; 39860799
ENCS
9 Asymmetric autocatalytic reactions and their stationary distribution Gallinger C; Popovic L; 39679357
MATHSTATS
10 Brain tumor detection based on a novel and high-quality prediction of the tumor pixel distributions Sun Y; Wang C; 38493601
ENCS
11 The infimum values of two probability functions for the Gamma distribution Sun P; Hu ZC; Sun W; 38261930
MATHSTATS
12 Unsupervised Mixture Models on the Edge for Smart Energy Consumption Segmentation with Feature Saliency Al-Bazzaz H; Azam M; Amayri M; Bouguila N; 37837127
ENCS
13 The evolution of plasticity at geographic range edges Usui T; Lerner D; Eckert I; Angert AL; Garroway CJ; Hargreaves A; Lancaster LT; Lessard JP; Riva F; Schmidt C; van der Burg K; Marshall KE; 37183152
BIOLOGY
14 Tide-induced infiltration and resuspension of microplastics in shorelines: Insights from tidal tank experiments Feng Q; Chen Z; An C; Yang X; Wang Z; 37084574
ENCS
15 Identifying climate change refugia for South American biodiversity Sales LP; Pires MM; 36919472
BIOLOGY
16 Human Activity Recognition with an HMM-Based Generative Model Manouchehri N; Bouguila N; 36772428
ENCS
17 Species compositions mediate biomass conservation: the case of lake fish communities Arranz I; Fournier B; Lester NP; Shuter BJ; Peres-Neto PR; 34905222
BIOLOGY
18 Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images Bourouis S; Alharbi A; Bouguila N; 34460578
ENCS
19 Formation of oil-particle aggregates: Impacts of mixing energy and duration Ji W; Boufadel M; Zhao L; Robinson B; King T; An C; Zhang BH; Lee K; 34252767
ENCS
20 Grape seed extract supplementation along with a restricted-calorie diet improves cardiovascular risk factors in obese or overweight adult individuals: A randomized, placebo-controlled trial. Yousefi R, Parandoosh M, Khorsandi H, Hosseinzadeh N, Madani Tonekaboni M, Saidpour A, Babaei H, Ghorbani A 33044768
HKAP
21 The Odonata of Quebec: Specimen data from seven collections. Favret C, Moisan-De Serres J, Larrivée M, Lessard JP 32174757
CONCORDIA
22 Diversity, evolution, and classification of virophages uncovered through global metagenomics. Paez-Espino D, Zhou J, Roux S, Nayfach S, Pavlopoulos GA, Schulz F, McMahon KD, Walsh D, Woyke T, Ivanova NN, Eloe-Fadrosh EA, Tringe SG, Kyrpides NC 31823797
BIOLOGY
23 Aegilops tauschii Genome Sequence: A Framework for Meta-analysis of Wheat QTLs. Xu J, Dai X, Ramasamy RK, Wang L, Zhu T, McGuire PE, Jorgensen CM, Dehghani H, Gulick PJ, Luo MC, Müller HG, Dvorak J 30670607
BIOLOGY

 

Title:Spatio-temporal distribution of AOD and its response to regional energy consumption and air pollution factors in China
Authors:Su YChen XGuo JYang A
Link:https://pubmed.ncbi.nlm.nih.gov/41308902/
DOI:10.1016/j.envres.2025.123393
Publication:Environmental research
Keywords:Aerosol optical depthAir pollutionEnergy consumptionGeographical detectorSpatio-temporal distribution
PMID:41308902 Category: Date Added:2025-12-01
Dept Affiliation: ENCS
1 Laboratory for Environmental Biotechnology of Higher Education of Fujian Province, Xiamen University of Technology, Xiamen, 361024, China; Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, H3G 1M8, Canada.
2 Department of Civil Engineering, University of Texas at Arlington, Arlington, TX, 76019, United States. Electronic address: xiujuan.chen@uta.edu.
3 Laboratory for Environmental Biotechnology of Higher Education of Fujian Province, Xiamen University of Technology, Xiamen, 361024, China.
4 Laboratory for Environmental Biotechnology of Higher Education of Fujian Province, Xiamen University of Technology, Xiamen, 361024, China. Electronic address: yangaililoy@gmail.com.

Description:

Aerosols serve as crucial indicators of air pollution assessment, and exert significant effects on climate change, ecological environment, and human health. However, the relationship between energy consumption, a significant contributor to air pollution, and aerosols in different economic development zones in China remains understudied. This paper employs satellite-derived aerosol optical depth (AOD) data to investigate the spatio-temporal variation of aerosols over China in the last two decades, applying Pearson correlation and geographical detector methods to evaluate the impacts of energy consumption and air pollution factors, and to examine their response to regional economic development and emission reduction policies. Results indicated that elevated AOD values are primarily concentrated within the six major urban agglomerations. The spatio-temporal variations of AOD differ significantly among various economic development regions. AOD values in the central and eastern regions have consistently decreased but still exhibit higher compared to the regions in the west and northeast in the past decade. Natural gas and coal are the main energy sources affecting AOD, and the influences of crude oil, petrol, and kerosene on AOD can be ignored. Over the years, nitrogen dioxide is the main air pollution emissions in various regions. Notably, the influence of ozone is significant in the eastern region, with an increase in the northeast and western in recent years. This study enhances the understanding of how energy consumption affects aerosol pollution across different economic regions, and provides a theoretical basis for targeted air pollution prevention and control.





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