1
|
Fei G, Li H, Yang S, Wang H, Ge Y, Wang Z, Zhang X, Wei P, Li L. Burden of lung cancer attributed to particulate matter pollution in China: an epidemiological study from 1990 to 2019. Public Health 2024; 227:141-147. [PMID: 38232561 DOI: 10.1016/j.puhe.2023.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 11/22/2023] [Accepted: 12/05/2023] [Indexed: 01/19/2024]
Abstract
OBJECTIVES The aim of this study was to examine the disease burden of lung cancer attributable to particulate matter (PM2.5) pollution in China from 1990 to 2019. STUDY DESIGN Data from the Global Burden of Disease Study 2019 were used to estimate the disease burden of tracheal, bronchus and lung cancer attributed to PM2.5 over time in China. METHODS Joinpoint regression models were applied to disability-adjusted life years (DALYs) to assess the time trends and estimate the impact of PM2.5 on the overall disease burden of lung cancer. Furthermore, age-period-cohort models were conducted to assess the relationships between lung cancer DALYs attributed to PM2.5 exposure and age, calendar period and birth cohort trends in China from 1990 to 2019. RESULTS Lung cancer DALYs attributable to household air pollution from solid fuels decreased with an average annual percent change (AAPC) of 2.9 % per 100,000 population, while those attributable to ambient particular matter pollution (APE) increased (AAPC: -4.7 % per 100,000 population) over the past 30 years. The burden of lung cancer in terms of DALYs in males was higher than in females, and it demonstrated an age-dependent increase. The period and cohort effects also had significant impacts on the DALYs rates of lung cancer attributable to APE, indicating an overall increase in lung cancer DALYs for all age groups in each year. CONCLUSIONS This study highlights the need for effective strategies to reduce PM2.5 exposure in China, particularly from outdoor sources. Gender differences and age, period and cohort effects observed in the study provide valuable insights into long-term trends of lung cancer burden attributed to PM2.5.
Collapse
Affiliation(s)
- G Fei
- Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing, Jiangsu Province, China; University College London Great Ormond Street Institute of Child Health, Population, Policy & Practice Research and Teaching Department, London, UK; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu Province, China
| | - H Li
- Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing, Jiangsu Province, China
| | - S Yang
- School of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing, Jiangsu Province, China
| | - H Wang
- Lianyungang Meteorological Bureau, Lianyungang, Jiangsu Province, China
| | - Y Ge
- Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing, Jiangsu Province, China
| | - Z Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing, Jiangsu Province, China
| | - X Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing, Jiangsu Province, China; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu Province, China
| | - P Wei
- Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing, Jiangsu Province, China; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu Province, China.
| | - L Li
- University College London Great Ormond Street Institute of Child Health, Population, Policy & Practice Research and Teaching Department, London, UK
| |
Collapse
|
2
|
Tariq S, Mariam A, Ul-Haq Z, Mehmood U. Assessment of variability in PM 2.5 and its impact on human health in a West African country. CHEMOSPHERE 2023; 344:140357. [PMID: 37802479 DOI: 10.1016/j.chemosphere.2023.140357] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 09/26/2023] [Accepted: 10/02/2023] [Indexed: 10/10/2023]
Abstract
PM2.5 has become a global challenge threatening human health, climate, and the environment. PM2.5 is ranked as the most common cause of premature mortality and morbidity. Therefore, the current study endeavors to probe the spatiodynamic characteristics of PM2.5 in the Republic of Niger and its impacts on human health from 1998 to 2019. Based on remotely sensed satellite datasets, the study found that the concentration of PM2.5 continued to rise in Niger from 68.85 μg/m3 in 1998 to 70.47 μg/m3 in 2019. During the study period, the annual average PM2.5 concentration is far above the WHO guidelines and the interim target-1 (35 μg/m3). The overall annual growth rate of PM2.5 concentration in Niger is 0.02 μg/m3/year. The health risk (HR) due to PM2.5 exposure is also escalated in Niger, particularly, in Southern Niger. The extent of the extremely high-risk areas corresponding to 1 × 104-9.4 × 105 μg.persons/m3 is increased from 0.9% (2000) to 2.8% (2019). Niamey, southern Dakoro, Mayahi, Tessaoua, Mirriah, Magaria, Matameye, Aguié, Madarounfa, Groumdji, Madaoua, Bouza, Keita, eastern Tahoua, eastern Illéla, Bkomnni, southern Dogon-Doutchi, Gaya, eastern Boboye, central Kollo, and western Tillabéry are experienced high HR due to long-term exposure to PM2.5. These findings indicate that PM2.5 causes a serious health risk across Niger. There is an immediate need to carry out its regional control. Therefore, policymakers and the Nigerien government should make conscious efforts to identify the priority target areas with radically innovative appropriate mitigation interventions.
Collapse
Affiliation(s)
- Salman Tariq
- Department of Space Science, University of the Punjab, Lahore, Pakistan; Remote Sensing, GIS and Climatic Research Lab (National Center of GIS and Space Applications), Centre for Remote Sensing, University of the Punjab, Lahore, Pakistan.
| | - Ayesha Mariam
- Remote Sensing, GIS and Climatic Research Lab (National Center of GIS and Space Applications), Centre for Remote Sensing, University of the Punjab, Lahore, Pakistan
| | - Zia Ul-Haq
- Department of Space Science, University of the Punjab, Lahore, Pakistan; Remote Sensing, GIS and Climatic Research Lab (National Center of GIS and Space Applications), Centre for Remote Sensing, University of the Punjab, Lahore, Pakistan
| | - Usman Mehmood
- Remote Sensing, GIS and Climatic Research Lab (National Center of GIS and Space Applications), Centre for Remote Sensing, University of the Punjab, Lahore, Pakistan; Department of Political Science, University of Management and Technology, Lahore, Pakistan
| |
Collapse
|
3
|
Zheng Y, Dong J, Yang X, Shuai P, Li Y, Li H, Dong S, Gong Y, Liu M, Zeng Q. Benign-malignant classification of pulmonary nodules by low-dose spiral computerized tomography and clinical data with machine learning in opportunistic screening. Cancer Med 2023. [PMID: 37248730 DOI: 10.1002/cam4.5886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 03/14/2023] [Accepted: 03/19/2023] [Indexed: 05/31/2023] Open
Abstract
BACKGROUND Many people were found with pulmonary nodules during physical examinations. It is of great practical significance to discriminate benign and malignant nodules by using data mining technology. METHODS The subjects' demographic data, baseline examination results, and annual follow-up low-dose spiral computerized tomography (LDCT) results were recorded. The findings from annual physical examinations of positive nodules, including highly suspicious nodules and clinically tentative benign nodules, was analyzed. The extreme gradient boosting (XGBoost) model was constructed and the Grid Search CV method was used to select the super parameters. External unit data were used as an external validation set to evaluate the generalization performance of the model. RESULTS A total of 135,503 physical examinees were enrolled. Baseline testing found that 27,636 (20.40%) participants had clinically tentative benign nodules and 611 (0.45%) participants had highly suspicious nodules. The proportion of highly suspicious nodules in participants with negative baseline was about 0.12%-0.46%, which was lower than the baseline level except the follow-up of >5 years. In the 27,636 participants with clinically tentative benign nodules, only in the first year of LDCT re-examination was the proportion of highly suspicious nodules (1.40%) significantly greater than that of baseline screening (0.45%) (p < 0.001), and the proportion of highly suspicious nodules was not different between the baseline screening and other follow-up years (p > 0.05). Furthermore, 322 cases with benign nodules and 196 patients with malignant nodules confirmed by surgery and pathology were compared. A model and the top 15 most important clinical variables were determined by XGBoost algorithm. The area under the curve (AUC) of the model was 0.76 [95% CI: 0.67-0.84], and the accuracy was 0.75. The sensitivity and specificity of the model under this threshold were 0.78 and 0.73, respectively. In the validation of model using external data, the AUC was 0.87 and the accuracy was 0.80. The sensitivity and specificity were 0.83 and 0.77, respectively. CONCLUSIONS It is important that pulmonary nodules could be more accurately identified at the first LDCT examination. A model with 15 variables which are routinely measured in the clinic could be helpful to distinguish benign and malignant nodules. It could help the radiological team issue a more accurate report; and it may guide the clinical team regarding LDCT follow-up.
Collapse
Affiliation(s)
- Yansong Zheng
- Department of Health Medicine, Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Jing Dong
- Research of Medical Big Data Center & National Engineering Laboratory for Medical Big Data Application Technology, Chinese PLA General Hospital, Beijing, China
| | - Xue Yang
- Department of Health Medicine, Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Ping Shuai
- Health Management Center, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Yongli Li
- Department of Health Management/ Henan Provincial People's Hospital of Zhengzhou University, Henan Key Laboratory of Chronic Disease Management, Zhengzhou, China
| | - Hailin Li
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, China
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, China
| | - Shengyong Dong
- Department of Health Medicine, Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Yan Gong
- Department of Health Medicine, Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Miao Liu
- Graduate School, Chinese PLA general hospital, Beijing, China
| | - Qiang Zeng
- Department of Health Medicine, Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese People's Liberation Army General Hospital, Beijing, China
| |
Collapse
|
4
|
Jin H, Zhong R, Liu M, Ye C, Chen X. Spatiotemporal distribution characteristics of PM2.5 concentration in China from 2000 to 2018 and its impact on population. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 323:116273. [PMID: 36261986 DOI: 10.1016/j.jenvman.2022.116273] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/29/2022] [Accepted: 09/11/2022] [Indexed: 06/16/2023]
Abstract
PM2.5 is an important indicator reflecting changes in air quality. In recent years, affected by climate change and human activities, the problem of environmental pollution has become more and more prominent. In this study, the PM2.5 data from 2000 to 2018 obtained by satellite remote sensing inversion algorithm were selected to analyze the temporal and spatial distribution of PM2.5 in China. The results show that the areas with higher PM2.5 concentrations were mainly in the North China, the Sichuan Basin, and the Tarim Basin. The areas with a significant increase in PM2.5 were mainly in the Northeast China, while the areas with a significant decrease were mainly in the Sichuan Basin and southeastern Gansu. The change of PM2.5 in southern China was not significantly correlated with the change of population and economy, while PM2.5 in Northeast China increases with the increase of population and economy. In 2000, 2005, 2010, and 2015, the proportion of the population polluted by PM2.5 was 8.65%, 7.2%, 22.99%, and 9.75%, respectively. The year with the highest percentage (37.63%) of population when air quality reached EXCELLENT was 2015. When the PM2.5 spatial cluster number was six, it can better reflect the PM2.5 spatial distribution state. The places with large changes in PM2.5 spatial clustering were mainly in the Northeast China, Sichuan Basin, and Tarim Basin, which were also areas with large changes in PM2.5. This study provides an important reference for atmospheric environmental monitoring and protection.
Collapse
Affiliation(s)
- Haoyu Jin
- School of Civil Engineering, Sun Yat-sen University, Guangzhou, 510275, China; Guangdong Engineering Technology Research Center of Water Security Regulation and Control for Southern China, Sun Yat-sen University, Guangzhou, 510275, China; Key Laboratory of Water Cycle and Water Security in Southern China of Guangdong High Education Institute, Sun Yat-sen University, Guangzhou, 510275, China
| | - Ruida Zhong
- School of Civil Engineering, Sun Yat-sen University, Guangzhou, 510275, China; Guangdong Engineering Technology Research Center of Water Security Regulation and Control for Southern China, Sun Yat-sen University, Guangzhou, 510275, China; Key Laboratory of Water Cycle and Water Security in Southern China of Guangdong High Education Institute, Sun Yat-sen University, Guangzhou, 510275, China
| | - Moyang Liu
- The Fenner School of Environment and Society, The Australian National University (ANU), Canberra, ACT, 0200, Australia
| | - Changxin Ye
- School of Civil Engineering, Sun Yat-sen University, Guangzhou, 510275, China; Guangdong Engineering Technology Research Center of Water Security Regulation and Control for Southern China, Sun Yat-sen University, Guangzhou, 510275, China; Key Laboratory of Water Cycle and Water Security in Southern China of Guangdong High Education Institute, Sun Yat-sen University, Guangzhou, 510275, China
| | - Xiaohong Chen
- School of Civil Engineering, Sun Yat-sen University, Guangzhou, 510275, China; Guangdong Engineering Technology Research Center of Water Security Regulation and Control for Southern China, Sun Yat-sen University, Guangzhou, 510275, China; Key Laboratory of Water Cycle and Water Security in Southern China of Guangdong High Education Institute, Sun Yat-sen University, Guangzhou, 510275, China.
| |
Collapse
|
5
|
Tsai DR, Jhuang JR, Su SY, Chiang CJ, Yang YW, Lee WC. A stabilized spatiotemporal kriging method for disease mapping and application to male oral cancer and female breast cancer in Taiwan. BMC Med Res Methodol 2022; 22:270. [PMID: 36229788 PMCID: PMC9563856 DOI: 10.1186/s12874-022-01749-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 10/06/2022] [Indexed: 11/29/2022] Open
Abstract
Mapping spacetime disease rates can provide a more in-depth understanding of their distribution and trends. Traditional spatiotemporal kriging methods can break the constraints of geopolitical boundaries and time intervals. Still, disease rates in densely and sparsely populated areas are stabilized to the same degree, resulting in a map that is oversmoothed in some places but undersmoothed in others. The stabilized spatiotemporal kriging method proposed in this study overcomes this problem by allowing for nonconstant variances over space and time. A spatiotemporal map of the standardized incidence ratio for oral cancer in men in Taiwan between 1997 and 2017 reveals that the high-risk areas for oral cancer are in the midwestern and southeastern regions of Taiwan, spreading toward the center and north, with persistent cold spots in the northern and southwestern urban regions. However, the corresponding map for breast cancer in women in Taiwan reveals that the high-risk areas for breast cancer are concentrated in densely populated urban regions in the west. Spatiotemporal maps facilitate our understanding of disease risk dynamics. We recommend using the proposed stabilized spatiotemporal kriging method for mapping disease rates across space and time.
Collapse
Affiliation(s)
- Dai-Rong Tsai
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Rm. 536, No. 17, Xuzhou Rd., Taipei, 100, Taiwan
| | - Jing-Rong Jhuang
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Rm. 536, No. 17, Xuzhou Rd., Taipei, 100, Taiwan.,Taiwan Cancer Registry, Taipei, Taiwan
| | - Shih-Yung Su
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Rm. 536, No. 17, Xuzhou Rd., Taipei, 100, Taiwan
| | - Chun-Ju Chiang
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Rm. 536, No. 17, Xuzhou Rd., Taipei, 100, Taiwan.,Taiwan Cancer Registry, Taipei, Taiwan
| | | | - Wen-Chung Lee
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Rm. 536, No. 17, Xuzhou Rd., Taipei, 100, Taiwan. .,Taiwan Cancer Registry, Taipei, Taiwan.
| |
Collapse
|
6
|
Xu H, Pan W, Xin M, Pan W, Hu C, Wanqiang D, Huang G. Study of the Economic, Environmental, and Social Factors Affecting Chinese Residents' Health Based on Machine Learning. Front Public Health 2022; 10:896635. [PMID: 35774578 PMCID: PMC9237364 DOI: 10.3389/fpubh.2022.896635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 05/09/2022] [Indexed: 11/14/2022] Open
Abstract
The Healthy China Strategy puts realistic demands for residents' health levels, but the reality is that various factors can affect health. In order to clarify which factors have a great impact on residents' health, based on China's provincial panel data from 2011 to 2018, this paper selects 17 characteristic variables from the three levels of economy, environment, and society and uses the XG boost algorithm and Random forest algorithm based on recursive feature elimination to determine the influencing variables. The results show that at the economic level, the number of industrial enterprises above designated size, industrial added value, population density, and per capita GDP have a greater impact on the health of residents. At the environmental level, coal consumption, energy consumption, total wastewater discharge, and solid waste discharge have a greater impact on the health level of residents. Therefore, the Chinese government should formulate targeted measures at both economic and environmental levels, which is of great significance to realizing the Healthy China strategy.
Collapse
Affiliation(s)
- Hui Xu
- Dong Fureng Institute of Economic and Social Development, Wuhan University, Wuhan, China
| | - Wei Pan
- School of Applied Economics, Renmin University of China, Beijing, China
| | - Meng Xin
- School of Economics and Management, North China Electric Power University, Beijing, China
| | - Wulin Pan
- School of Economic and Management, Wuhan University, Wuhan, China
| | - Cheng Hu
- School of Economic and Management, Wuhan University, Wuhan, China
| | - Dai Wanqiang
- School of Economic and Management, Wuhan University, Wuhan, China
| | - Ge Huang
- School of Economic and Management, Wuhan University, Wuhan, China
| |
Collapse
|
7
|
Impact of Residential Concentration of PM2.5 Analyzed as Time-Varying Covariate on the Survival Rate of Lung Cancer Patients: A 15-Year Hospital-Based Study in Upper Northern Thailand. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19084521. [PMID: 35457386 PMCID: PMC9026284 DOI: 10.3390/ijerph19084521] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 04/01/2022] [Accepted: 04/06/2022] [Indexed: 02/01/2023]
Abstract
Air pollutants, especially particulate matter (PM) ≤ 2.5 µm (PM2.5) and PM ≤ 10 µm (PM10), are a major concern in upper northern Thailand. Data from a retrospective cohort comprising 9820 lung cancer patients diagnosed from 2003 to 2018 were obtained from the Chiang Mai Cancer Registry, and used to evaluate mortality and survival rates. Cox proportional hazard models were used to identify the association between the risk of death and risk factors including gender, age, cancer stage, smoking history, alcohol-use history, calendar year of enrollment, and time-updated PM2.5, PM10, NO2 and O3 concentrations. The mortality rate was 68.2 per 100 persons per year of follow-up. In a multivariate analysis, gender, age, cancer stage, calendar year of enrollment, and time-varying residential concentration of PM2.5 were independently associated with the risk of death. The lower the annually averaged PM2.5 and PM10 concentrations, the higher the survival probability of the patient. As PM2.5 and PM10 were factors associated with a higher risk of death, lung cancer patients who are inhabitant in the area should reduce their exposure to high concentrations of PM2.5 and PM10 to increase survival rates.
Collapse
|
8
|
Wen J, Chuai X, Gao R, Pang B. Regional interaction of lung cancer incidence influenced by PM 2.5 in China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 803:149979. [PMID: 34487906 DOI: 10.1016/j.scitotenv.2021.149979] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 08/05/2021] [Accepted: 08/24/2021] [Indexed: 05/16/2023]
Abstract
PM2.5 is the key pollutant threatening human health and can even cause lung cancer. Pollution is the most serious problem in China with its fast industrialisation, urbanisation and high population density. This pollutant is conveyed through the atmosphere, trade and the embodied emission flow amongst regions. Scientific evaluation of the responsibility for regional lung cancer by considering both internal and external influences seems to be meaningful in addressing regional inequity. This study develops a relatively convenient and practical method to evaluate the regional inequity reflected by lung cancer associated with PM2.5 pollution in China. Results show that PM2.5 emissions and concentrations have similar distribution patterns: high values were predominant in the east and south where has high population density, while the west had low values. The cancer incidence rate showed high values mainly in eastern and central China. At a provincial scale, the lung cancer incidence rate was significantly correlated with PM2.5 concentration levels, and a high correlation was also found between PM2.5 concentration and emissions, indicating that emission reduction is the key to lung cancer prevention. Due to domestic trade, some developed regions more pulled lung cancer in less developed regions, and some less developed regions also have an obvious influence on external regions. Spatially, provinces in northern and central China are always more influenced by external regions. Lung cancer inequity analysis shows that coastline regions are more advantaged, while the reverse applies to inland China. The central government needs to further strengthen regional coordinated development measures, such as economic compensation for medical care and adjustments to industry structure. It should optimise spatial allocation and comprehensively consider regional inequity and character.
Collapse
Affiliation(s)
- Jiqun Wen
- School of Public Administration, Guangdong University of Finance and Economics, Guangzhou 510320, Guangdong Province, China
| | - Xiaowei Chuai
- School of Geography & Ocean Science, Nanjing University, Nanjing 210023, Jiangsu Province, China.
| | - Runyi Gao
- School of Geography & Ocean Science, Nanjing University, Nanjing 210023, Jiangsu Province, China
| | - Baoxin Pang
- Department of Philosophy, Nanjing University, Nanjing 210023, Jiangsu Province, China; School of Geography & Ocean Science, Nanjing University, Nanjing 210023, Jiangsu Province, China
| |
Collapse
|