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Romano D, Novielli P, Cilli R, Amoroso N, Monaco A, Bellotti R, Tangaro S. Air pollution and mortality for cancer of the respiratory system in Italy: an explainable artificial intelligence approach. Front Public Health 2024; 12:1344865. [PMID: 38774048 PMCID: PMC11106463 DOI: 10.3389/fpubh.2024.1344865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 04/08/2024] [Indexed: 05/24/2024] Open
Abstract
Respiratory system cancer, encompassing lung, trachea and bronchus cancer, constitute a substantial and evolving public health challenge. Since pollution plays a prominent cause in the development of this disease, identifying which substances are most harmful is fundamental for implementing policies aimed at reducing exposure to these substances. We propose an approach based on explainable artificial intelligence (XAI) based on remote sensing data to identify the factors that most influence the prediction of the standard mortality ratio (SMR) for respiratory system cancer in the Italian provinces using environment and socio-economic data. First of all, we identified 10 clusters of provinces through the study of the SMR variogram. Then, a Random Forest regressor is used for learning a compact representation of data. Finally, we used XAI to identify which features were most important in predicting SMR values. Our machine learning analysis shows that NO, income and O3 are the first three relevant features for the mortality of this type of cancer, and provides a guideline on intervention priorities in reducing risk factors.
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Affiliation(s)
- Donato Romano
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti Universita' degli Studi di Bari Aldo Moro, Bari, Italy
- Istituto Nazionale di Fisica Nucleare Sezione di Bari, Bari, Italy
| | - Pierfrancesco Novielli
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti Universita' degli Studi di Bari Aldo Moro, Bari, Italy
- Istituto Nazionale di Fisica Nucleare Sezione di Bari, Bari, Italy
| | - Roberto Cilli
- Istituto Nazionale di Fisica Nucleare Sezione di Bari, Bari, Italy
- Dipartimento Interateneo di Fisica, “M. Merlin” Universita' degli Studi di Bari Aldo Moro, Bari, Italy
| | - Nicola Amoroso
- Istituto Nazionale di Fisica Nucleare Sezione di Bari, Bari, Italy
- Dipartimento di Farmacia Scienze, del Farmaco Universita' degli Studi di Bari Aldo Moro, Bari, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare Sezione di Bari, Bari, Italy
- Dipartimento Interateneo di Fisica, “M. Merlin” Universita' degli Studi di Bari Aldo Moro, Bari, Italy
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare Sezione di Bari, Bari, Italy
- Dipartimento Interateneo di Fisica, “M. Merlin” Universita' degli Studi di Bari Aldo Moro, Bari, Italy
| | - Sabina Tangaro
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti Universita' degli Studi di Bari Aldo Moro, Bari, Italy
- Istituto Nazionale di Fisica Nucleare Sezione di Bari, Bari, Italy
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Romano D, Novielli P, Diacono D, Cilli R, Pantaleo E, Amoroso N, Bellantuono L, Monaco A, Bellotti R, Tangaro S. Insights from Explainable Artificial Intelligence of Pollution and Socioeconomic Influences for Respiratory Cancer Mortality in Italy. J Pers Med 2024; 14:430. [PMID: 38673057 PMCID: PMC11051343 DOI: 10.3390/jpm14040430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 04/10/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024] Open
Abstract
Respiratory malignancies, encompassing cancers affecting the lungs, the trachea, and the bronchi, pose a significant and dynamic public health challenge. Given that air pollution stands as a significant contributor to the onset of these ailments, discerning the most detrimental agents becomes imperative for crafting policies aimed at mitigating exposure. This study advocates for the utilization of explainable artificial intelligence (XAI) methodologies, leveraging remote sensing data, to ascertain the primary influencers on the prediction of standard mortality rates (SMRs) attributable to respiratory cancer across Italian provinces, utilizing both environmental and socioeconomic data. By scrutinizing thirteen distinct machine learning algorithms, we endeavor to pinpoint the most accurate model for categorizing Italian provinces as either above or below the national average SMR value for respiratory cancer. Furthermore, employing XAI techniques, we delineate the salient factors crucial in predicting the two classes of SMR. Through our machine learning scrutiny, we illuminate the environmental and socioeconomic factors pertinent to mortality in this disease category, thereby offering a roadmap for prioritizing interventions aimed at mitigating risk factors.
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Affiliation(s)
- Donato Romano
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, 70126 Bari, Italy; (D.R.); (P.N.)
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70126 Bari, Italy; (D.D.); (R.C.); (E.P.); (N.A.); (L.B.); (A.M.); (R.B.)
| | - Pierfrancesco Novielli
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, 70126 Bari, Italy; (D.R.); (P.N.)
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70126 Bari, Italy; (D.D.); (R.C.); (E.P.); (N.A.); (L.B.); (A.M.); (R.B.)
| | - Domenico Diacono
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70126 Bari, Italy; (D.D.); (R.C.); (E.P.); (N.A.); (L.B.); (A.M.); (R.B.)
| | - Roberto Cilli
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70126 Bari, Italy; (D.D.); (R.C.); (E.P.); (N.A.); (L.B.); (A.M.); (R.B.)
- Dipartimento Interateneo di Fisica “M. Merlin”, Università degli Studi di Bari Aldo Moro, 70126 Bari, Italy
| | - Ester Pantaleo
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70126 Bari, Italy; (D.D.); (R.C.); (E.P.); (N.A.); (L.B.); (A.M.); (R.B.)
- Dipartimento Interateneo di Fisica “M. Merlin”, Università degli Studi di Bari Aldo Moro, 70126 Bari, Italy
| | - Nicola Amoroso
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70126 Bari, Italy; (D.D.); (R.C.); (E.P.); (N.A.); (L.B.); (A.M.); (R.B.)
- Dipartimento di Farmacia Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70126 Bari, Italy
| | - Loredana Bellantuono
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70126 Bari, Italy; (D.D.); (R.C.); (E.P.); (N.A.); (L.B.); (A.M.); (R.B.)
- Dipartimento di Biomedicina Traslazionale e Neuroscienze, Università degli Studi di Bari Aldo Moro, 70126 Bari, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70126 Bari, Italy; (D.D.); (R.C.); (E.P.); (N.A.); (L.B.); (A.M.); (R.B.)
- Dipartimento Interateneo di Fisica “M. Merlin”, Università degli Studi di Bari Aldo Moro, 70126 Bari, Italy
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70126 Bari, Italy; (D.D.); (R.C.); (E.P.); (N.A.); (L.B.); (A.M.); (R.B.)
- Dipartimento Interateneo di Fisica “M. Merlin”, Università degli Studi di Bari Aldo Moro, 70126 Bari, Italy
| | - Sabina Tangaro
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, 70126 Bari, Italy; (D.R.); (P.N.)
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70126 Bari, Italy; (D.D.); (R.C.); (E.P.); (N.A.); (L.B.); (A.M.); (R.B.)
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Fania A, Monaco A, Amoroso N, Bellantuono L, Cazzolla Gatti R, Firza N, Lacalamita A, Pantaleo E, Tangaro S, Velichevskaya A, Bellotti R. Machine learning and XAI approaches highlight the strong connection between O 3 and N O 2 pollutants and Alzheimer's disease. Sci Rep 2024; 14:5385. [PMID: 38443419 DOI: 10.1038/s41598-024-55439-1] [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: 09/06/2023] [Accepted: 02/23/2024] [Indexed: 03/07/2024] Open
Abstract
Alzheimer's disease (AD) is the most common type of dementia with millions of affected patients worldwide. Currently, there is still no cure and AD is often diagnosed long time after onset because there is no clear diagnosis. Thus, it is essential to study the physiology and pathogenesis of AD, investigating the risk factors that could be strongly connected to the disease onset. Despite AD, like other complex diseases, is the result of the combination of several factors, there is emerging agreement that environmental pollution should play a pivotal role in the causes of disease. In this work, we implemented an Artificial Intelligence model to predict AD mortality, expressed as Standardized Mortality Ratio, at Italian provincial level over 5 years. We employed a set of publicly available variables concerning pollution, health, society and economy to feed a Random Forest algorithm. Using methods based on eXplainable Artificial Intelligence (XAI) we found that air pollution (mainly O 3 and N O 2 ) contribute the most to AD mortality prediction. These results could help to shed light on the etiology of Alzheimer's disease and to confirm the urgent need to further investigate the relationship between the environment and the disease.
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Affiliation(s)
- Alessandro Fania
- Dipartimento Interateneo di Fisica M. Merlin, Universitá degli Studi di Bari Aldo Moro, 70125, Bari, Italy
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, 70125, Bari, Italy
| | - Alfonso Monaco
- Dipartimento Interateneo di Fisica M. Merlin, Universitá degli Studi di Bari Aldo Moro, 70125, Bari, Italy.
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, 70125, Bari, Italy.
| | - Nicola Amoroso
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, 70125, Bari, Italy
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125, Bari, Italy
| | - Loredana Bellantuono
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, 70125, Bari, Italy
- Dipartimento di Biomedicina Traslazionale e Neuroscienze (DiBraiN), Università degli Studi di Bari Aldo Moro, 70124, Bari, Italy
| | - Roberto Cazzolla Gatti
- Department of Biological Sciences, Geological and Environmental (BiGeA), Alma Mater Studiorum - University of Bologna, 40126, Bologna, Italy
| | - Najada Firza
- Dipartimento di Economia e Finanza, Università degli Studi di Bari Aldo Moro, 70124, Bari, Italy
- Catholic University Our Lady of Good Counsel, 1031, Tirana, Albania
| | - Antonio Lacalamita
- Dipartimento Interateneo di Fisica M. Merlin, Universitá degli Studi di Bari Aldo Moro, 70125, Bari, Italy
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, 70125, Bari, Italy
| | - Ester Pantaleo
- Dipartimento Interateneo di Fisica M. Merlin, Universitá degli Studi di Bari Aldo Moro, 70125, Bari, Italy
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, 70125, Bari, Italy
| | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, 70125, Bari, Italy
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, 70126, Bari, Italy
| | | | - Roberto Bellotti
- Dipartimento Interateneo di Fisica M. Merlin, Universitá degli Studi di Bari Aldo Moro, 70125, Bari, Italy
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, 70125, Bari, Italy
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Mu G, Xu L, Zhang J. Study of the utilization of main crop straw resources in Southern China and its potential as a replacement for chemical fertilizers. FRONTIERS IN PLANT SCIENCE 2024; 14:1172689. [PMID: 38250439 PMCID: PMC10796737 DOI: 10.3389/fpls.2023.1172689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 10/26/2023] [Indexed: 01/23/2024]
Abstract
Although straw returning to the field (SRTTF) is conducive to promoting sustainable agricultural production and protecting the environment, straw resources are still wasted due to the lack of suitable straw-returning technology in southern China. Based on the statistical yearbook and a large number of studies, different methods were used to calculate the total straw resources and SRTTF potential, and differences in these methods were compared. The results indicate that the total amount of straw resources in southern China in 2021 was 3.35×108 t. The nutrient content of K2O in the straw accounted for the highest proportion of total nutrient resources (63.66%), followed by N (26.88%) and P2O5 (9.46%). In theory, total SRTTF could replace almost all K2O and part of N and P2O5, indicating that the nutrient substitution potential of SRTTF was high. It is suggested that the SRTTF method be adopted in the middle and lower reaches of the Yangtze River, which mainly uses direct returning (DR) supplemented by indirect returning (IDR). In southeast China, straw returning is carried out by the combination of IDR and IR. In southwest China, straw returning is mainly carried out by IR and supplemented by MDR. This study will provide theoretical support for the government to formulate straw-returning policy.
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Affiliation(s)
- Guiting Mu
- Guizhou Institute of Biology, Guizhou Academy of Sciences, Guiyang, China
| | - Lifu Xu
- Guizhou Provincial Forest Resources and Environment Research Center, Guizhou University, Guiyang, China
| | - Jiachun Zhang
- Guizhou Botanical Garden, Guizhou Academy of Sciences, Guiyang, China
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Qian J, Zhang T, Sun X, Chai Y. The coordination of collective and individual solutions in risk-resistant scenarios. THE EUROPEAN PHYSICAL JOURNAL. B 2023; 96:21. [PMID: 36852005 PMCID: PMC9947898 DOI: 10.1140/epjb/s10051-023-00487-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Accepted: 01/27/2023] [Indexed: 06/18/2023]
Abstract
ABSTRACT Human societies are constantly coping with global risks. In the face of these risks, people typically have two options, that is, to respond together as a whole (collective solution) or to respond independently (individual solution). Based on these two solutions, individuals have a variety of behavioral strategies. On the other hand, various regulatory bodies supported by the population limit people's choices and punish individuals who do not contribute to collective solutions. So with different risks, how do the two solutions, the various individual strategies, and the constraints from regulators affect the group's response to risk? This paper proposes an extended public goods game model involving opportunists and the regulator to explore the effectiveness of collective and individual solutions against risks. The results show that requiring individuals to invest more in the collective solution reduces the group' s success in resisting risk. To improve the group's ability to resist risk, investment in individual solution should be at least no less than that in collective solution. The establishment fund and punishment intensity of the regulatory agency have no significant effect on the success of collective and individual solutions. This inspires us to contemplate the role and measures of various types of authorities in coping with global risks.
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Affiliation(s)
- Jun Qian
- Department of Automation, Tsinghua University, 100084 Beijing, China
| | - Tongda Zhang
- Department of Mechanical and Energy Engineering, Southern University of Science and Technology, 518055 Shenzhen, China
| | - Xiao Sun
- Department of Automation, Tsinghua University, 100084 Beijing, China
| | - Yueting Chai
- Department of Automation, Tsinghua University, 100084 Beijing, China
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Detecting the socio-economic drivers of confidence in government with eXplainable Artificial Intelligence. Sci Rep 2023; 13:839. [PMID: 36646810 PMCID: PMC9841965 DOI: 10.1038/s41598-023-28020-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 01/10/2023] [Indexed: 01/18/2023] Open
Abstract
The European Quality of Government Index (EQI) measures the perceived level of government quality by European Union citizens, combining surveys on corruption, impartiality and quality of provided services. It is, thus, an index based on individual subjective evaluations. Understanding the most relevant objective factors affecting the EQI outcomes is important for both evaluators and policy makers, especially in view of the fact that perception of government integrity contributes to determine the level of civic engagement. In our research, we employ methods of Artificial Intelligence and complex systems physics to measure the impact on the perceived government quality of multifaceted variables, describing territorial development and citizen well-being, from an economic, social and environmental viewpoint. Our study, focused on a set of regions in European Union at a subnational scale, leads to identifying the territorial and demographic drivers of citizens' confidence in government institutions. In particular, we find that the 2021 EQI values are significantly related to two indicators: the first one is the difference between female and male labour participation rates, and the second one is a proxy of wealth and welfare such as the average number of rooms per inhabitant. This result corroborates the idea of a central role played by labour gender equity and housing policies in government confidence building. In particular, the relevance of the former indicator in EQI prediction results from a combination of positive conditions such as equal job opportunities, vital labour market, welfare and availability of income sources, while the role of the latter is possibly amplified by the lockdown policies related to the COVID-19 pandemics. The analysis is based on combining regression, to predict EQI from a set of publicly available indicators, with the eXplainable Artificial Intelligence approach, that quantifies the impact of each indicator on the prediction. Such a procedure does not require any ad-hoc hypotheses on the functional dependence of EQI on the indicators used to predict it. Finally, using network science methods concerning community detection, we investigate how the impact of relevant indicators on EQI prediction changes throughout European regions. Thus, the proposed approach enables to identify the objective factors at the basis of government quality perception by citizens in different territorial contexts, providing the methodological basis for the development of a quantitative tool for policy design.
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Li JX, Li L, Zhong X, Fan SJ, Cen T, Wang J, He C, Zhang Z, Luo YN, Liu XX, Hu LX, Zhang YD, Qiu HL, Dong GH, Zou XG, Yang BY. Machine learning identifies prominent factors associated with cardiovascular disease: findings from two million adults in the Kashgar Prospective Cohort Study (KPCS). Glob Health Res Policy 2022; 7:48. [PMID: 36474302 PMCID: PMC9724436 DOI: 10.1186/s41256-022-00282-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 11/18/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Identifying factors associated with cardiovascular disease (CVD) is critical for its prevention, but this topic is scarcely investigated in Kashgar prefecture, Xinjiang, northwestern China. We thus explored the CVD epidemiology and identified prominent factors associated with CVD in this region. METHODS A total of 1,887,710 adults at baseline (in 2017) of the Kashgar Prospective Cohort Study were included in the analysis. Sixteen candidate factors, including seven demographic factors, 4 lifestyle factors, and 5 clinical factors, were collected from a questionnaire and health examination records. CVD was defined according to International Clinical Diagnosis (ICD-10) codes. We first used logistic regression models to investigate the association between each of the candidate factors and CVD. Then, we employed 3 machine learning methods-Random Forest, Random Ferns, and Extreme Gradient Boosting-to rank and identify prominent factors associated with CVD. Stratification analyses by sex, ethnicity, education level, economic status, and residential setting were also performed to test the consistency of the ranking. RESULTS The prevalence of CVD in Kashgar prefecture was 8.1%. All the 16 candidate factors were confirmed to be significantly associated with CVD (odds ratios ranged from 1.03 to 2.99, all p values < 0.05) in logistic regression models. Further machine learning-based analysis suggested that age, occupation, hypertension, exercise frequency, and dietary pattern were the five most prominent factors associated with CVD. The ranking of relative importance for prominent factors in stratification analyses showed that the factor importance generally followed the same pattern as that in the overall sample. CONCLUSIONS CVD is a major public health concern in Kashgar prefecture. Age, occupation, hypertension, exercise frequency, and dietary pattern might be the prominent factors associated with CVD in this region.In the future, these factors should be given priority in preventing CVD in future.
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Affiliation(s)
- Jia-Xin Li
- grid.12981.330000 0001 2360 039XGuangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080 China
| | - Li Li
- grid.12981.330000 0001 2360 039XDepartment of Respiratory and Critical Care Medicine, The First People’s Hospital of Kashi (The Affiliated Kashi Hospital of Sun Yat-Sen University), No.66, Yingbin Avenue, Kashgar City, 844000 China
| | - Xuemei Zhong
- grid.12981.330000 0001 2360 039XDepartment of Respiratory and Critical Care Medicine, The First People’s Hospital of Kashi (The Affiliated Kashi Hospital of Sun Yat-Sen University), No.66, Yingbin Avenue, Kashgar City, 844000 China
| | - Shu-Jun Fan
- grid.508371.80000 0004 1774 3337Guangzhou Center for Disease Control and Prevention, Guangzhou, 510440 China
| | - Tao Cen
- grid.284723.80000 0000 8877 7471Department of Research and Development, Nanfang Hospital, Southern Medical University, Guangzhou, 510515 China
| | - Jianquan Wang
- grid.12981.330000 0001 2360 039XDepartment of Respiratory and Critical Care Medicine, The First People’s Hospital of Kashi (The Affiliated Kashi Hospital of Sun Yat-Sen University), No.66, Yingbin Avenue, Kashgar City, 844000 China
| | - Chuanjiang He
- grid.12981.330000 0001 2360 039XDepartment of Respiratory and Critical Care Medicine, The First People’s Hospital of Kashi (The Affiliated Kashi Hospital of Sun Yat-Sen University), No.66, Yingbin Avenue, Kashgar City, 844000 China
| | - Zhoubin Zhang
- grid.508371.80000 0004 1774 3337Guangzhou Center for Disease Control and Prevention, Guangzhou, 510440 China
| | - Ya-Na Luo
- grid.12981.330000 0001 2360 039XGuangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080 China
| | - Xiao-Xuan Liu
- grid.12981.330000 0001 2360 039XGuangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080 China
| | - Li-Xin Hu
- grid.12981.330000 0001 2360 039XGuangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080 China
| | - Yi-Dan Zhang
- grid.12981.330000 0001 2360 039XGuangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080 China
| | - Hui-Ling Qiu
- grid.12981.330000 0001 2360 039XGuangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080 China
| | - Guang-Hui Dong
- grid.12981.330000 0001 2360 039XGuangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080 China
| | - Xiao-Guang Zou
- grid.12981.330000 0001 2360 039XDepartment of Respiratory and Critical Care Medicine, The First People’s Hospital of Kashi (The Affiliated Kashi Hospital of Sun Yat-Sen University), No.66, Yingbin Avenue, Kashgar City, 844000 China
| | - Bo-Yi Yang
- grid.12981.330000 0001 2360 039XGuangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080 China
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