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Responsible urban innovation with local government artificial intelligence (Ai): A conceptual framework and research agenda

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Journal of Open Innovation: technology, Market, and Complexity 2021年第1期7卷 1-16页

作者： Yigitcanlar, Tan Corchado, Juan M. Mehmood, Rashid Li, Rita Yi Man Mossberger, Karen Desouza, Kevin School of Architecture and Built Environment Queensland University of Technology 2 George Street Brisbane 4000 QLD Australia School of Technology Federal University of Santa Catarina Campus Universitario Trindade Florianópolis SC 88040-900 Brazil Bisite Research Group University of Salamanca Salamanca 37007 Spain Air Institute IoT Digital Innovation Hub Salamanca 37188 Spain Department of Electronics Information and Communication Faculty of Engineering Osaka Institute of Technology Osaka 535-8585 Japan High Performance Computing Center King Abdulaziz University Al Ehtifalat St Jeddah 21589 Saudi Arabia Department of Economics and Finance Hong Kong Shue Yan University 10 Wai Tsui Cres North Point Hong Kong School of Public Affairs Arizona State University 411 North Central Avenue Phoenix 85004 AZ United States School of Management Queensland University of Technology 2 George Street Brisbane 4000 QLD Australia

The urbanization problems we face may be alleviated using innovative digital technology. However, employing these technologies entails the risk of creating new urban problems and/or intensifying the old ones instead of alleviating them. Hence, in a world with immense technological opportunities and at the same time enormous urbanization challenges, it is critical to adopt the principles of responsible urban innovation. These principles assure the delivery of the desired urban outcomes and futures. We contribute to the existing responsible urban innovation discourse by focusing on local government artificial intelligence (AI) systems, providing a literature and practice overview, and a conceptual framework. In this perspective paper, we advocate for the need for balancing the costs, benefits, risks and impacts of developing, adopting, deploying and managing local government AI systems in order to achieve responsible urban innovation. The statements made in this perspective paper are based on a thorough review of the literature, research, developments, trends and applications carefully selected and analyzed by an expert team of investigators. This study provides new insights, develops a conceptual framework and identifies prospective research questions by placing local government AI systems under the microscope through the lens of responsible urban innovation. The presented overview and framework, along with the identified issues and research agenda, offer scholars prospective lines of research and development;where the outcomes of these future studies will help urban policymakers, managers and planners to better understand the crucial role played by local government AI systems in ensuring the achievement of responsible outcomes. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： AI revolution Artificial intelligence (AI) Community engagement Local government AI Responsible AI Responsible innovation Responsible urban innovation Smart city Urban policy Urban technology

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Evaluation of OpenAI o1: Opportunities and Challenges of AGI

arXiv

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arXiv 2024年

作者： Zhong, Tianyang Liu, Zhengliang Pan, Yi Zhang, Yutong Zhou, Yifan Liang, Shizhe Wu, Zihao Lyu, Yanjun Shu, Peng Yu, Xiaowei Cao, Chao Jiang, Hanqi Chen, Hanxu Li, Yiwei Chen, Junhao Hu, Huawen Liu, Yihen Zhao, Huaqin Xu, Shaochen Dai, Haixing Zhao, Lin Zhang, Ruidong Zhao, Wei Yang, Zhenyuan Chen, Jingyuan Wang, Peilong Ruan, Wei Wang, Hui Zhao, Huan Zhang, Jing Ren, Yiming Qin, Shihuan Chen, Tong Li, Jiaxi Zidan, Arif Hassan Jahin, Afrar Chen, Minheng Xia, Sichen Holmes, Jason Zhuang, Yan Wang, Jiaqi Xu, Bochen Xia, Weiran Yu, Jichao Tang, Kaibo Yang, Yaxuan Sun, Bolun Yang, Tao Lu, Guoyu Wang, Xianqiao Chai, Lilong Li, He Lu, Jin Sun, Lichao Zhang, Xin Ge, Bao Hu, Xintao Zhang, Lian Zhou, Hua Zhang, Lu Zhang, Shu Liu, Ninghao Jiang, Bei Kong, Linglong Xiang, Zhen Ren, Yudan Liu, Jun Jiang, Xi Bao, Yu Zhang, Wei Li, Xiang Li, Gang Liu, Wei Shen, Dinggang Sikora, Andrea Zhai, Xiaoming Zhu, Dajiang Zhang, Tuo Liu, Tianming Department of Mathematical and Statistical Sciences University of Alberta Edmonton Canada School of Computing University of Georgia GA United States Institute of Medical Research Northwestern Polytechnical University Xi'An China College of Arts and Sciences University of Georgia Athens United States Institute of Plant Breeding Genetics & Genomics University of Georgia AthensGA United States Department of Computer Science and Engineering University of Texas ArlingtonTX United States The Lamar Dodd School of Art University of Georgia GA United States School of Computer Science Northwestern Polytechnical University Xi'An China School of Automation Northwestern Polytechnical University Xi'An China University of California Los AngelesCA United States Department of Radiology The Second Xiangya Hospital Central South University Changsha China Clinical Research Center for Medical Imaging in Hunan Province Changsha China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Guanghua School of Management Peking University Beijing China Department of Radiation Oncology Mayo Clinic PhoenixAZ United States Second Language Acquisition and Teaching University of Arizona TucsonAZ United States School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China The First Hospital of Hebei Medical University Hebei Shijiazhuang China School of Computer and Cyber Sciences Augusta University AugustaGA United States School of Physics and Information Technology Shaanxi Normal University Xi'An China School of Future Technology South China University of Technology Guangzhou China Department of Radiology and BRIC University of North Carolina Chapel HillNC United States Department of Educational Psychology University of Georgia AthensGA United States Johns Hopkins University BaltimoreMD United States School of Architecture Tsinghua Universit

This comprehensive study evaluates the performance of OpenAI's o1-preview large language model across a diverse array of complex reasoning tasks, spanning multiple domains, including computer science, mathematics, natural sciences, medicine, linguistics, and social sciences. Through rigorous testing, o1-preview demonstrated remarkable capabilities, often achieving human-level or superior performance in areas ranging from coding challenges to scientific reasoning and from language processing to creative problem-solving. Key findings include: • 83.3% success rate in solving complex competitive programming problems, surpassing many human experts. • Superior ability in generating coherent and accurate radiology reports, outperforming other evaluated models. • 100% accuracy in high school-level mathematical reasoning tasks, providing detailed step-by-step solutions. • Advanced natural language inference capabilities across general and specialized domains like medicine. • Impressive performance in chip design tasks, outperforming specialized models in areas such as EDA script generation and bug analysis. • Remarkable proficiency in anthropology and geology, demonstrating deep understanding and reasoning in these specialized fields. • Strong capabilities in quantitative investing. O1 has comprehensive financial knowledge and statistical modeling skills. • Effective performance in social media analysis, including sentiment analysis and emotion recognition. The model excelled particularly in tasks requiring intricate reasoning and knowledge integration across various fields. While some limitations were observed, including occasional errors on simpler problems and challenges with certain highly specialized concepts, the overall results indicate significant progress towards artificial general intelligence. This evaluation not only highlights o1-preview's current strengths and limitations but also identifies crucial areas for future development, including multi-modal integratio

关键词： Emotion Recognition

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Hypercomplex Low Rank Reconstruction for Nmr Spectroscopy with Cloud Computing ⋆

SSRN

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SSRN 2022年

作者： Guo, Yi Zhan, Jiaying Tu, Zhangren Zhou, Yirong Wu, Jianfan Hong, Qing Orekhov, Vladislav Qu, Xiaobo Guo, Di School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361005 China China Mobile Group Xiamen361005 China Swedish NMR Center Department of Chemistry and Molecular Biology University of Gothenburg Gothenburg41390 Sweden

Nuclear magnetic resonance (NMR) spectroscopy serves as an important tool to analyze chemicals and proteins in bioengineering. Multi-dimensional NMR offers a major improvement in resolution with multi-dimensional spectrum, but significantly increases data acquisition time and produces hypercomplex data that is difficult to be handled. To reduce this time, non-uniformly sampling can be applied to obtain undersampled data and using a reconstruction approach, such as the state-of-the-art low-rank method, to remove the spectral artifacts introduced by undersampling. However, only complex format of signal, including the real and imaginary parts, is considered in previous low-rank approach, which is less efficient when dealing with hypercomplex data that has multiple components. To deal with this problem, a hypercomplex low-rank model is proposed by introducing an adjoint matrix operation and then solved with a fast matrix factorization algorithm. This method efficiently uses redundant information among all the components of hypercomplex signal. Using simulated data and real protein data, we demonstrate that the proposed method provides a fast and high-fidelity reconstruction of hypercomplex NMR spectroscopy. Furthermore, an open-access and easy-to-use cloud-computing platform is developed for this method. © 2022, The Authors. All rights reserved.

关键词： Nuclear magnetic resonance spectroscopy

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Predicting mass transfer activation energy and physicochemical properties of dried onion using numerical modeling and artificial intelligence

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Scientific Reports 2025年第1期15卷 1-24页

作者： Liu, Fangfang El-Mesery, Hany S. ElMesiry, Ahmed H. Hu, Zicheng Salem, Ali School of Mechanical and Electrical Engineering Suqian University Suqian 223800 China School of Energy and Power Engineering Jiangsu University Zhenjiang 212013 China Agricultural Engineering Research Institute Agricultural Research Center Dokki Giza 12611 Egypt Faculty of Computer Science and Engineering New Mansoura University Mansoura 35742 Egypt Civil Engineering Department Faculty of Engineering Minia University Minia Egypt Structural Diagnostics and Analysis Research Group Faculty of Engineering and Information Technology University of Pécs Pécs 7622 Hungary

The quality of the onion slices was statistically evaluated based on the variables of drying conditions, considering the following characteristics: drying time, color, shrinkage, water activity, and rehydration ratio, critical parameters in evaluating food processing and preservation methods. The onion slices were subjected to drying under the ensuing settings: infrared powers of 1500, 3500, and 5500 W/m²;airflow rates of 0.3, 0.7, and 1.0 m/s;and air temperatures of 40, 50, and 60 °C. The findings indicated that the drying time increased with air velocity to 0.7 and 1.0 m/s at a constant infrared intensity of 5500 W/m², resulting in a significant increase of 26 and 37%. The water activity values for the sample ranged from 0.40 to 0.49. The maximum vitamin C content was 24 mg/g, followed by an air temperature of 40 °C and 0.3 m/s air velocity under 1500 W/m². Conditions of 1.5 m/s, 40 °C, and 1500 W/m² indicate the lowest rehydration ratio. The ANN model proved to be a robust tool for predicting and optimizing drying parameters, including drying duration, energy consumption, and quality. Additionally, it highlights the potential of advanced heating technologies to optimize food drying approaches, providing important insights for the food sector in its quest for increased efficiency.

关键词： Artificial intelligence Computational simulation Onion Quality Thin-layer equations

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Correction: MMD-MII Model: A Multilayered Analysis and Multimodal Integration Interaction Approach Revolutionizing Music Emotion Classification

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International Journal of Computational Intelligence Systems 2024年第1期17卷 1-1页

作者： Wang, Jingyi Sharifi, Alireza Gadekallu, Thippa Reddy Shankar, Achyut School of Music Jiangxi Normal University Nanchang China Department of Surveying Engineering Faculty of Civil Engineering Shahid Rajaee Teacher Training University Tehran Iran Zhongda Group Haiyan County Jiaxing China Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon School of Information Technology and Engineering Vellore Institute of Technology Vellore India WMG University of Warwick Coventry UK Center of Research Impact and Outcome Chitkara University Rajpura India School of Computer Science Engineering Lovely Professional University Phagwara India

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QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge

arXiv

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arXiv 2024年

作者： Li, Hongwei Bran Navarro, Fernando Ezhov, Ivan Bayat, Amirhossein Das, Dhritiman Kofler, Florian Shit, Suprosanna Waldmannstetter, Diana Paetzold, Johannes C. Hu, Xiaobin Wiestler, Benedikt Zimmer, Lucas Amiranashvili, Tamaz Prabhakar, Chinmay Berger, Christoph Weidner, Jonas Alonso-Basanta, Michelle Rashid, Arif Baid, Ujjwal Adel, Wesam Alis, Deniz Baheti, Bhakti Bai, Yingbin Bhat, Ishaan Cetindag, Sabri Can Chen, Wenting Cheng, Li Dutande, Prasad Dular, Lara Elattar, Mustafa A. Feng, Ming Gao, Shengbo Huisman, Henkjan Hu, Weifeng Innani, Shubham Ji, Wei Karimi, Davood Kuijf, Hugo J. Kwak, Jin Tae Le, Hoang Long Li, Xiang Lin, Huiyan Liu, Tongliang Ma, Jun Ma, Kai Ma, Ting Oksuz, Ilkay Holland, Robbie Oliveira, Arlindo L. Pal, Jimut Bahan Pei, Xuan Qiao, Maoying Saha, Anindo Selvan, Raghavendra Shen, Linlin Silva, Joao Lourenco Spiclin, Ziga Talbar, Sanjay Wang, Dadong Wang, Wei Wang, Xiong Wang, Yin Xi, Ruiling Xu, Kele Yang, Yanwu Yergin, Mert Yu, Shuang Zeng, Lingxi Zhang, YingLin Zhao, Jiachen Zheng, Yefeng Zukovec, Martin Do, Richard Becker, Anton Simpson, Amber Konukoglu, Ender Jakab, Andras Bakas, Spyridon Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School United States Department of Quantitative Biomedicine University of Zurich Switzerland University Children’s Hospital Zurich University of Zurich Switzerland Department of Radioncology and Radiation Theraphy Klinikum rechts der Isar Technical University of Munich Germany Department of Information Technology and Electrical Engineering ETH-Zurich Switzerland Department of Radiology Memorial Sloan Kettering Cancer Center New York City United States Department of Biomedical and Molecular Sciences Queen’s University Canada TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany McGovern Institute Massachusetts Institute of Technology United States Institute for Diagnostic and Interventional Radiology Unveristy Zurich Hospital Switzerland BioMedIA Imperial College London United Kingdom Department of Radiation Oncology University of Pennsylvania PA United States University of Pennsylvania PA United States Department of Radiation Oncology Winship Cancer Institute of Emory University Georgia United States Nile University Cairo Egypt Department of Medical Sciences Acibadem University Istanbul Turkey Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Trustworthy Machine Learning Lab University of Sydney Australia Image Sciences Institute University Medical Center Utrecht Netherlands Computer Engineering Department Istanbul Technical University Istanbul Turkey School of Computer Science Shenzhen University Shenzhen China University of Alberta United States University of Ljubljana Faculty of Electrical Engineering Ljubljana Slovenia Tongji University Shanghai China OPPO Research Institute Shanghai China School of Biological and Medical Engineering Beihang University Beijing China Harvard Medical School Boston

Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a significant challenge in achieving consistent and reliable image segmentation. This variability not only reflects the inherent complexity and subjective nature of medical image interpretation but also directly impacts the development and evaluation of automated segmentation algorithms. Accurately modeling and quantifying this variability is essential for enhancing the robustness and clinical applicability of these algorithms. We report the set-up and summarize the benchmark results of the Quantification of Uncertainties in Biomedical Image Quantification Challenge (QUBIQ), which was organized in conjunction with International Conferences on Medical Image Computing and computer-Assisted Intervention (MICCAI) 2020 and 2021. The challenge focuses on the uncertainty quantification of medical image segmentation which considers the omnipresence of inter-rater variability in imaging datasets. The large collection of images with multi-rater annotations features various modalities such as MRI and CT;various organs such as the brain, prostate, kidney, and pancreas;and different image dimensions 2D-vs-3D. A total of 24 teams submitted different solutions to the problem, combining various baseline models, Bayesian neural networks, and ensemble model techniques. The obtained results indicate the importance of the ensemble models, as well as the need for further research to develop efficient 3D methods for uncertainty quantification methods in 3D segmentation tasks. © 2024, CC BY-NC-ND.

关键词： Image segmentation

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Shaping the Metaverse into Reality: A Holistic Multidisciplinary Understanding of Opportunities, Challenges, and Avenues for Future Investigation

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Data Processor for Better Business Education 2023年第3期63卷 735-765页

作者： Alex Koohang Jeretta Horn Nord Keng-Boon Ooi Garry Wei-Han Tan Mostafa Al-Emran Eugene Cheng-Xi Aw Abdullah Mohammed Baabdullah Dimitrios Buhalis Tat-Huei Cham Charles Dennis Vincent Dutot Yogesh K. Dwivedi Laurie Hughes Emmanuel Mogaji Neeraj Pandey Ian Phau Ramakrishnan Raman Anshuman Sharma Marianna Sigala Akiko Ueno Lai-Wan Wong a School of Computing Middle Georgia State University Macon GA USA b Department of Management Science and Information Systems Oklahoma State University Stillwater OK USA c UCSI Graduate Business School UCSI University Kuala Lumpur Malaysiad School of Finance and Economics Nanchang Institute of Technology Nanchang People’s Republic of Chinau Faculty of Business Design and Arts Swinburne University of Technology Sarawak Campus Malaysia c UCSI Graduate Business School UCSI University Kuala Lumpur Malaysiad School of Finance and Economics Nanchang Institute of Technology Nanchang People’s Republic of Chinae School of Economics and Management Yunnan Normal University Kunming People’s Republic of Chinau Faculty of Business Design and Arts Swinburne University of Technology Sarawak Campus Malaysia f Faculty of Engineering & IT The British University in Dubai Dubai United Arab Emiratesg Department of Computer Techniques Engineering Dijlah University College Baghdad Iraq c UCSI Graduate Business School UCSI University Kuala Lumpur Malaysia h Department of Management Information Systems Faculty of Economics and Administration King Abdulaziz University Jeddah Saudi Arabia i Department of Tourism and Hospitality Bournemouth University Poole UK c UCSI Graduate Business School UCSI University Kuala Lumpur Malaysiaj Tashkent State University of Economics Tashkent Uzbekistan k Business School Middlesex University London UK l EM Normandie Business School Métis Lab Clichy France m Digital Futures for Sustainable Business & Society Research Group School of Management Swansea University Swansea UKn Department of Management Symbiosis Institute of Business Management Pune & Symbiosis International (Deemed University) Pune India m Digital Futures for Sustainable Business & Society Research Group School of Management Swansea University Swansea UK o School of Management & Marketing University of Greenwich London UK p National Institute of Industrial Engineering Mum

ABSTRACTABSTRACTThe term metaverse is described as the next iteration of the Internet. Metaverse is a virtual platform that uses extended reality technologies, i.e. augmented reality, virtual reality, mixed reality, 3D graphics, and other emerging technologies to allow real-time interactions and experiences in ways that are not possible in the physical world. Companies have begun to notice the impact of the metaverse and how it may help maximize profits. The purpose of this paper is to offer perspectives on several important areas, i.e. marketing, tourism, manufacturing, operations management, education, the retailing industry, banking services, healthcare, and human resource management that are likely to be impacted by the adoption and use of a metaverse. Each includes an overview, opportunities, challenges, and a potential research agenda.

关键词： Metaverse marketing tourism hospitality manufacturing operations management education the retailing industry banking services healthcare human resource management

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Factor Graph Weight Particles aided Distributed Underwater Cooperative Positioning Algorithm

Research Square

引用

research Square 2021年

作者： Zhang, Lingling Yu, Baoguo Tang, Chengkai Zhang, Yi Song, Houbing School of Marine Science and Technology Northwestern Polytechnical University Xi’an710072 China The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang China School of Electronics and Information Northwestern Polytechnical University Xi’an710072 China Department of Electrical Computer Software and Systems Engineering Embry-Riddle Aeronautical University FL32114 United States

The growing scale of marine exploration requires high-resolution underwater localization, which necessitates cooperation among underwater network nodes, considering the channel complexity and power efficiency. In this paper, we proposed factor graph weight particles aided distributed underwater nodes cooperative positioning algorithm (WP-DUCP). It capitalized on the factor graph and sum-product algorithm to decompose the global optimization to the product of several local optimization functions. Combined with the Gaussian parameters to construct the weighted particles and to realize the belief transfer, it shows low complexity and high efficiency, suitable to the energy-restricted and communication distance-limited underwater networks. In terms of convergence, localization resolution, and computation complexity, we conducted the simulation and real-test with comparison to the existing co-localization methods. The results verified a higher resolution of the proposed method with no extra computation burden. © 2021, CC BY.

关键词： Global optimization

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A Service-Environment-Oriented Shape Reconstruction Method of Vehicle-borne Active Phased Array Antenna

A Service-Environment-Oriented Shape Reconstruction Method o...

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IEEE International Conference on Radar

作者： Pengying Xu Xiaoxian Xu Yan Wang Yucheng Teng Congsi Wang Dongming Ge Mingkui Kang Shuai Yuan Xuezi Wang Key Laboratory of Electronic Equipment Structure Design Ministry of Education Xidian University Xi'an China School of Information and Control Engineering Xi'an University of Architecture and Technology Xi'an China Beijing Institute of Spacecraft System Engineering Beijing China No.771 Institute of China Aerospace Science and Technology Corporation Xi'an China China Electronics Technology Group Corporation No.38 Research Institute Hefei China

ISBN: (纸本)9781665468893

Modern electronic equipment has been moving towards large scale and increased complexity. Affected by such factors as complex working environments, the performance of a large number of structures in engineering is degraded or even destroyed. Therefore, structure shape estimation is of great significance. The complexity of the structure, however, makes it impossible for a single deformation evaluation algorithm to evaluate the structure comprehensively and accurately. In this paper, a modal-approximate curve (M-AC) method, using a small amount of strain data, is proposed for block shape estimation of structures. Three typical wind load experiments are carried out on the finite element model (FEM) of the vehicle-borne active phased array antenna, and the effectiveness of the algorithm is verified. Under the wind speed of 28 (m/s), the deformation of antenna front is 104.70 mm and the estimation accuracy of the M-AC method is 87.43%, which is 16.47% higher than that of the modal approach. The results indicate that the estimated displacement is consistent well with the simulation results of the finite element method.

关键词： Phased arrays Shape Estimation Radar Radar antennas Finite element analysis Loaded antennas

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Assessment of PM2.5-associated mortality burden among the elderly: Insights into demographic, socio-economic, and geographic factors

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Air Quality, Atmosphere & Health 2024年第7期17卷 1535-1545页

作者： Sharma, Ayushi Hsiao, Hsi-yu Liu, Jia-Yi Lung, Shih-Chun Candice Su, Huey-Jen Shen, Ching-Fen Chen, Nai-Tzu Wu, Pei-Chih Lin, Cheng-Yu Liang, Sheng-Fu Cheng, Tain-Junn Chan, Ta-Chien Tsay, Yaw-Shyan Chung, Hsin-Ying Wang, Yu-Chun Department of Environmental Engineering College of Engineering Chung Yuan Christian University Taoyuan Taiwan Department of Civil Engineering College of Engineering Chung Yuan Christian University Taoyuan Taiwan Research Center for Environmental Changes Academia Sinica Taipei City Taiwan Department of Environmental and Occupational Health National Cheng Kung University Tainan City Taiwan National Cheng Kung University Hospital Pediatrics Office Tainan City Taiwan Department of Green Energy and Environmental Resources Chang Jung Christian University Tainan City Taiwan Sleep Medicine Center College of Medicine National Cheng Kung University Hospital National Cheng Kung University Tainan Taiwan Department of Computer Science and Information Engineering & Institute of Medical Informatics National Cheng Kung University Tainan City Taiwan Department of Neurology and Occupational Medicine Chi Mei Medical Center Tainan City Taiwan School of Medicine College of Medicine National Sun Yat-Sen University Kaohsiung City Taiwan Research Center for Humanities and Social Sciences Academia Sinica Taipei City Taiwan Department of Architecture National Cheng Kung University Tainan City Taiwan Department of Plant Industry National Pingtung University of Science and Technology Tainan City Taiwan

Since long-term exposure to PM2.5 is a growing concern in Taiwan, therefore, our study aimed to investigate the association between different PM2.5 concentrations and cause-specific mortalities, as well as explore the influence of demographic, socio-economic, and geographic determinants on mortality. Utilizing a Distributed Lag Non-Linear Model (DLNM), we estimated the PM2.5-attributable mortality burden among the elderly population (above 65 years) in Taiwan from 2005 to 2018. Our analysis considered various thresholds defined by the World Health Organization (WHO) to assess the impact of PM2.5 concentrations, ranging from standard Air Quality Guidelines (AQG) levels (0–15 μg/m3) to higher interim targets (up to 80 μg/m3). Our study reveals that achieving AQG levels (< 15 μg/m3) in Taiwan could potentially prevent 5571 elderly deaths, including 889 deaths from CVDs, 934 deaths from RTIs, and 680 deaths from chronic diseases. We observed a rapid increase in attributable deaths at lower PM2.5 concentrations, with a subsequent marginal increase at higher levels of exposure. We also identified several factors that modify the effects of PM2.5 exposures among elderly. For example, we found that higher elderly population ratios and high-income households were associated with an elevated risk of PM2.5-related deaths. In contrast, higher social welfare expenditure, better availability of medical practitioners per square kilometer, and higher forest-to-land ratios were associated with positive effects, indicating their potential in reducing mortality risk. This study fills a knowledge gap by examining the health consequences of varying PM2.5 levels and explored the complex interplay between demographic, socio-economic, and geographic factors in relation to PM2.5-related mortality.

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