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Karim N, Hod R, Wahab MIA, Ahmad N. Projecting non-communicable diseases attributable to air pollution in the climate change era: a systematic review. BMJ Open 2024; 14:e079826. [PMID: 38719294 PMCID: PMC11086555 DOI: 10.1136/bmjopen-2023-079826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 04/16/2024] [Indexed: 05/12/2024] Open
Abstract
OBJECTIVES Climate change is a major global issue with significant consequences, including effects on air quality and human well-being. This review investigated the projection of non-communicable diseases (NCDs) attributable to air pollution under different climate change scenarios. DESIGN This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 flow checklist. A population-exposure-outcome framework was established. Population referred to the general global population of all ages, the exposure of interest was air pollution and its projection, and the outcome was the occurrence of NCDs attributable to air pollution and burden of disease (BoD) based on the health indices of mortality, morbidity, disability-adjusted life years, years of life lost and years lived with disability. DATA SOURCES The Web of Science, Ovid MEDLINE and EBSCOhost databases were searched for articles published from 2005 to 2023. ELIGIBILITY CRITERIA FOR SELECTING STUDIES The eligible articles were evaluated using the modified scale of a checklist for assessing the quality of ecological studies. DATA EXTRACTION AND SYNTHESIS Two reviewers searched, screened and selected the included studies independently using standardised methods. The risk of bias was assessed using the modified scale of a checklist for ecological studies. The results were summarised based on the projection of the BoD of NCDs attributable to air pollution. RESULTS This review included 11 studies from various countries. Most studies specifically investigated various air pollutants, specifically particulate matter <2.5 µm (PM2.5), nitrogen oxides and ozone. The studies used coupled-air quality and climate modelling approaches, and mainly projected health effects using the concentration-response function model. The NCDs attributable to air pollution included cardiovascular disease (CVD), respiratory disease, stroke, ischaemic heart disease, coronary heart disease and lower respiratory infections. Notably, the BoD of NCDs attributable to air pollution was projected to decrease in a scenario that promotes reduced air pollution, carbon emissions and land use and sustainable socioeconomics. Contrastingly, the BoD of NCDs was projected to increase in a scenario involving increasing population numbers, social deprivation and an ageing population. CONCLUSION The included studies widely reported increased premature mortality, CVD and respiratory disease attributable to PM2.5. Future NCD projection studies should consider emission and population changes in projecting the BoD of NCDs attributable to air pollution in the climate change era. PROSPERO REGISTRATION NUMBER CRD42023435288.
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Affiliation(s)
- Norhafizah Karim
- Department of Public Health Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Bandar Tun Razak, Kuala lumpur, Malaysia
| | - Rozita Hod
- Department of Public Health Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Bandar Tun Razak, Kuala lumpur, Malaysia
| | - Muhammad Ikram A Wahab
- Center of Toxicology and Health Risk Studies (CORE), Universiti Kebangsaan Malaysia Fakulti Sains Kesihatan, Kuala Lumpur, Wilayah Persekutuan, Malaysia
| | - Norfazilah Ahmad
- Department of Public Health Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Bandar Tun Razak, Kuala lumpur, Malaysia
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Jiang S, Tang L, Lou Z, Wang H, Huang L, Zhao W, Wang Q, Li R, Ding Z. The changing health effects of air pollution exposure for respiratory diseases: a multicity study during 2017-2022. Environ Health 2024; 23:36. [PMID: 38609898 PMCID: PMC11015632 DOI: 10.1186/s12940-024-01083-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Accepted: 04/10/2024] [Indexed: 04/14/2024]
Abstract
BACKGROUND Multifaceted SARS-CoV-2 interventions have modified exposure to air pollution and dynamics of respiratory diseases. Identifying the most vulnerable individuals requires effort to build a complete picture of the dynamic health effects of air pollution exposure, accounting for disparities across population subgroups. METHODS We use generalized additive model to assess the likely changes in the hospitalisation and mortality rate as a result of exposure to PM2.5 and O3 over the course of COVID-19 pandemic. We further disaggregate the population into detailed age categories and illustrate a shifting age profile of high-risk population groups. Additionally, we apply multivariable logistic regression to integrate demographic, socioeconomic and climatic characteristics with the pollution-related excess risk. RESULTS Overall, a total of 1,051,893 hospital admissions and 34,954 mortality for respiratory disease are recorded. The findings demonstrate a transition in the association between air pollutants and hospitalisation rates over time. For every 10 µg/m3 increase of PM2.5, the rate of hospital admission increased by 0.2% (95% CI: 0.1-0.7%) and 1.4% (1.0-1.7%) in the pre-pandemic and dynamic zero-COVID stage, respectively. Conversely, O3-related hospitalization rate would be increased by 0.7% (0.5-0.9%) in the pre-pandemic stage but lowered to 1.7% (1.5-1.9%) in the dynamic zero-COVID stage. Further assessment indicates a shift of high-risk people from children and young adolescents to the old, primarily the elevated hospitalization rates among the old people in Lianyungang (RR: 1.53, 95%CI: 1.46, 1.60) and Nantong (RR: 1.65, 95%CI: 1.57, 1.72) relative to those for children and young adolescents. Over the course of our study period, people with underlying diseases would have 26.5% (22.8-30.3%) and 12.7% (10.8-14.6%) higher odds of having longer hospitalisation and over 6 times higher odds of deaths after hospitalisation. CONCLUSIONS Our estimates provide the first comprehensive evidence on the dynamic pollution-health associations throughout the pandemic. The results suggest that age and underlying diseases collectively determines the disparities of pollution-related health effect across population subgroups, underscoring the urgency to identifying the most vulnerable individuals to air pollution.
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Affiliation(s)
- Siyu Jiang
- School of Public Health, Nanjing Medical University, 101 Longmian AV, Nanjing, 211166, Jiangsu, China
| | - Longjuan Tang
- School of Public Health, Nanjing Medical University, 101 Longmian AV, Nanjing, 211166, Jiangsu, China
| | - Zhe Lou
- School of Public Health, Nanjing Medical University, 101 Longmian AV, Nanjing, 211166, Jiangsu, China
| | - Haowei Wang
- School of Public Health, Imperial College London, London, UK
- MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Ling Huang
- College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Wei Zhao
- School of Public Health, Nanjing Medical University, 101 Longmian AV, Nanjing, 211166, Jiangsu, China
| | - Qingqing Wang
- Jiangsu Provincial Center for Disease Prevention and Control, 172 Jiangsu Rd, Nanjing, 210009, Jiangsu, China
| | - Ruiyun Li
- School of Public Health, Nanjing Medical University, 101 Longmian AV, Nanjing, 211166, Jiangsu, China.
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China.
| | - Zhen Ding
- Jiangsu Provincial Center for Disease Prevention and Control, 172 Jiangsu Rd, Nanjing, 210009, Jiangsu, China.
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Pari P, Abbasi T, Abbasi SA. AI-based prediction of the improvement in air quality induced by emergency measures. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 351:119716. [PMID: 38064985 DOI: 10.1016/j.jenvman.2023.119716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 11/16/2023] [Accepted: 11/23/2023] [Indexed: 01/14/2024]
Abstract
Several cities in the developing world, of which the capital city of India, New Delhi, is an example, often experience air quality in which pollutant levels go way above the levels considered hazardous for human health. To bring down the air quality to within permissible limits quickly, the measures typically taken involve shutting down certain high-polluting activities for some time to enable the air quality to recover temporarily. This paper presents a first-ever model based on artificial neural networks to forecast the extent of reduction in air quality parameters that can be achieved and the time period within which a change can be experienced when the source of the emissions is cut off temporarily. The model is based on the extensive data on the extent of reduction in air quality parameters that occurred during the lockdown that was imposed during the COVID-19 pandemic. The non-linear autoregressive exogenous network-based model chosen for the purpose employs the hour since stopping of emissions, relative humidity, wind speed, wind direction, and ambient temperature as input parameters to predict the rate of change of PM2.5 with respect to the concentration at the start of the stopping of the emissions. Air quality data from a key monitoring station in New Delhi was used to develop the model. The model predicted the rate of drop in PM2.5 with an R and MSE of 0.0044 and 0.9736, respectively, while training and 0.0095 and 0.9583 while testing. The model was then tested with data from 19 other stations in New Delhi, and accuracy of the model was found to be exceptionally accurate, with the correlation between the measured and the predicted PM2.5 levels ranging from 0.74 to 0.94 and the MSE ranging from 0.0110 to 1.0746. Thus, the model can be employed to determine the number of hours of temporary stoppage of emissions required for the PM2.5 concentration to reach safe levels. The methodology of development of the model can be extrapolated to construct models tailored for use in other parts of the world as well.
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Affiliation(s)
- Pavithra Pari
- Centre for Pollution Control and Environmental Engineering, Pondicherry University, Pondicherry, 605014, India
| | - Tasneem Abbasi
- Centre for Pollution Control and Environmental Engineering, Pondicherry University, Pondicherry, 605014, India.
| | - S A Abbasi
- Centre for Pollution Control and Environmental Engineering, Pondicherry University, Pondicherry, 605014, India
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Bui LT, Nguyen NHT, Nguyen PH. Chronic and acute health effects of PM 2.5 exposure and the basis of pollution control targets. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:79937-79959. [PMID: 37291347 DOI: 10.1007/s11356-023-27936-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 05/22/2023] [Indexed: 06/10/2023]
Abstract
Ho Chi Minh City (HCMC) is changing and expanding quickly, leading to environmental consequences that seriously threaten human health. PM2.5 pollution is one of the main causes of premature death. In this context, studies have evaluated strategies to control and reduce air pollution; such pollution-control measures need to be economically justified. The objective of this study was to assess the socio-economic damage caused by exposure to the current pollution scenario, taking 2019 as the base year. A methodology for calculating and evaluating the economic and environmental benefits of air pollution reduction was implemented. This study aimed to simultaneously evaluate the impacts of both short-term (acute) and long-term (chronic) PM2.5 pollution exposure on human health, providing a comprehensive overview of economic losses attributable to such pollution. Spatial partitioning (inner-city and suburban) on health risks of PM2.5 and detailed construction of health impact maps by age group and sex on a spatial resolution grid (3.0 km × 3.0 km) was performed. The calculation results show that the economic loss from premature deaths due to short-term exposure (approximately 38.86 trillion VND) is higher than that from long-term exposure (approximately 14.89 trillion VND). As the government of HCMC has been developing control and mitigation solutions for the Air Quality Action Plan towards short- and medium-term goals in 2030, focusing mainly on PM2.5, the results of this study will help policymakers develop a roadmap to reduce the impact of PM2.5 during 2025-2030.
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Affiliation(s)
- Long Ta Bui
- Laboratory for Environmental Modelling, Faculty of Environment and Natural Resources, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam.
- Vietnam National University Ho Chi Minh City (VNU-HCM), Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam.
| | - Nhi Hoang Tuyet Nguyen
- Laboratory for Environmental Modelling, Faculty of Environment and Natural Resources, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam
- Vietnam National University Ho Chi Minh City (VNU-HCM), Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam
| | - Phong Hoang Nguyen
- Laboratory for Environmental Modelling, Faculty of Environment and Natural Resources, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam
- Vietnam National University Ho Chi Minh City (VNU-HCM), Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam
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Li Y, Li B, Liao H, Zhou BB, Wei J, Wang Y, Zang Y, Yang Y, Liu R, Wang X. Changes in PM 2.5-related health burden in China's poverty and non-poverty areas during 2000-2020: A health inequality perspective. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 861:160517. [PMID: 36464040 DOI: 10.1016/j.scitotenv.2022.160517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/30/2022] [Accepted: 11/22/2022] [Indexed: 06/17/2023]
Abstract
China suffers from severe PM2.5 pollution that has resulted in a huge health burden. Such PM2.5-related health burden has long been suspected to differ between China's poverty-stricken areas (PAs) and non-poverty-stricken areas (NPAs). Yet, evidence-based examination of this long-held belief, which is critical as a barrier of environmental injustice to advancing China's sustainability, is still missing. Here our study shows that the PM2.5 pollution is more serious in China's NPAs than PAs-with their annual averages being respectively 54.83 μg/m3 and 43.63 μg/m3-causing higher premature mortality in the NPAs. Compared to economic inequality, China's total PM2.5-related premature mortality was relatively evenly distributed during 2000-2015 across regions of varying levels of gross domestic product (GDP) per capita but increased slightly in 2015-2020 owing to the dramatic change in age structure. The elderly population increased by 31 %. PM2.5-related premature deaths were more severe for populations of low socioeconomic status, and such environmental health inequalities could be amplified by population aging. Additionally, population migration from China's PAs to developed cities contributed to 638, 779, 303, 954, and 896 premature deaths in 2000, 2005, 2010, 2015, and 2020, respectively. Changes in the age structure (53 %) and PM2.5 concentration (28 %) had the greatest impact on premature deaths, followed by changes in population (12 %) and baseline mortality (8 %). The contribution rate of changes in the age structure and PM2.5 concentration was higher in PAs than in NPAs. Our findings provide insight into PM2.5-related premature death and environmental inequality, and may inform more equitable clean air policies to achieve China's sustainable development goals.
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Affiliation(s)
- Yan Li
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Baojie Li
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China.
| | - Hong Liao
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Bing-Bing Zhou
- School of International Affairs and Public Administration, Ocean University of China, Qingdao 266100, China
| | - Jing Wei
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, United States
| | - Yuxia Wang
- School of Geographic Sciences, East China Normal University, Shanghai 200241, China
| | - Yuzhu Zang
- School of Public Administration, China University of Geosciences, Wuhan 430074, China
| | - Yang Yang
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Rui Liu
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Xiaorui Wang
- Jiangsu Provincial Land Development and Consolidation Center, Nanjing 210017, China
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Chung CY, Yang J, Yang X, He J. Mathematical modeling in the health risk assessment of air pollution-related disease burden in China: A review. Front Public Health 2022; 10:1060153. [PMID: 36504933 PMCID: PMC9727382 DOI: 10.3389/fpubh.2022.1060153] [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: 10/03/2022] [Accepted: 11/08/2022] [Indexed: 11/24/2022] Open
Abstract
This review paper covers an overview of air pollution-related disease burden in China and a literature review on the previous studies which have recently adopted a mathematical modeling approach to demonstrate the relative risk (RR) of air pollution-related disease burden. The associations between air pollution and disease burden have been explored in the previous studies. Therefore, it is necessary to quantify the impact of long-term exposure to ambient air pollution by using a suitable mathematical model. The most common way of estimating the health risk attributable to air pollution exposure in a population is by employing a concentration-response function, which is often based on the estimation of a RR model. As most of the regions in China are experiencing rapid urbanization and industrialization, the resulting high ambient air pollution is influencing more residents, which also increases the disease burden in the population. The existing RR models, including the integrated exposure-response (IER) model and the global exposure mortality model (GEMM), are critically reviewed to provide an understanding of the current status of mathematical modeling in the air pollution-related health risk assessment. The performances of different RR models in the mortality estimation of disease are also studied and compared in this paper. Furthermore, the limitations of the existing RR models are pointed out and discussed. Consequently, there is a need to develop a more suitable RR model to accurately estimate the disease burden attributable to air pollution in China, which contributes to one of the key steps in the health risk assessment. By using an updated RR model in the health risk assessment, the estimated mortality risk due to the impacts of environment such as air pollution and seasonal temperature variation could provide a more realistic and reliable information regarding the mortality data of the region, which would help the regional and national policymakers for intensifying their efforts on the improvement of air quality and the management of air pollution-related disease burden.
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Affiliation(s)
- Chee Yap Chung
- Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, Ningbo, Zhejiang Province, China,*Correspondence: Chee Yap Chung
| | - Jie Yang
- Department of Mathematics, University of Hull, Hull, United Kingdom
| | - Xiaogang Yang
- Department of Mechanical, Materials and Manufacturing Engineering, University of Nottingham Ningbo China, Ningbo, Zhejiang Province, China,Xiaogang Yang
| | - Jun He
- Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, Ningbo, Zhejiang Province, China
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Aix ML, Petit P, Bicout DJ. Air pollution and health impacts during the COVID-19 lockdowns in Grenoble, France. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 303:119134. [PMID: 35283200 PMCID: PMC8908221 DOI: 10.1016/j.envpol.2022.119134] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 03/05/2022] [Accepted: 03/09/2022] [Indexed: 06/14/2023]
Abstract
It is undeniable that exposure to outdoor air pollution impacts the health of populations and therefore constitutes a public health problem. Any actions or events causing variations in air quality have repercussions on populations' health. Faced with the worldwide COVID-19 health crisis that began at the end of 2019, the governments of several countries were forced, in the beginning of 2020, to put in place very strict containment measures that could have led to changes in air quality. While many works in the literature have studied the issue of changes in the levels of air pollutants during the confinements in different countries, very few have focused on the impact of these changes on health risks. In this work, we compare the 2020 period, which includes two lockdowns (March 16 - May 10 and a partial shutdown Oct. 30 - Dec. 15) to a reference period 2015-2019 to determine how these government-mandated lockdowns affected concentrations of NO2, O3, PM2.5, and PM10, and how that affected human health factors, including low birth weight, lung cancer, mortality, asthma, non-accidental mortality, respiratory, and cardiovascular illnesses. To this end, we structured 2020 into four periods, alternating phases of freedom and lockdowns characterized by a stringency index. For each period, we calculated (1) the differences in pollutant levels between 2020 and a reference period (2015-2019) at both background and traffic stations; and (2) the resulting variations in the epidemiological based relative risks of health outcomes. As a result, we found that relative changes in pollutant levels during the 2020 restriction period were as follows: NO2 (-32%), PM2.5 (-22%), PM10 (-15%), and O3 (+10.6%). The pollutants associated with the highest health risk reductions in 2020 were PM2.5 and NO2, while PM10 and O3 changes had almost no effect on health outcomes. Reductions in short-term risks were related to reductions in PM2.5 (-3.2% in child emergency room visits for asthma during the second lockdown) and NO2 (-1.5% in hospitalizations for respiratory causes). Long-term risk reductions related to PM2.5 were low birth weight (-8%), mortality (-3.3%), and lung cancer (-2%), and to NO2 for mortality (-0.96%). Overall, our findings indicate that the confinement period in 2020 resulted in a substantial improvement in air quality in the Grenoble area.
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Affiliation(s)
- Marie-Laure Aix
- Univ. Grenoble Alpes, CNRS, UMR 5525, VetAgro Sup, Grenoble INP, TIMC, 38000, Grenoble, France
| | - Pascal Petit
- Univ. Grenoble Alpes, CNRS, UMR 5525, VetAgro Sup, Grenoble INP, TIMC, 38000, Grenoble, France
| | - Dominique J Bicout
- Univ. Grenoble Alpes, CNRS, UMR 5525, VetAgro Sup, Grenoble INP, TIMC, 38000, Grenoble, France.
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