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Cho IS, Lim J, Chang MS, Lee JH. Impact of the coronavirus disease 2019 pandemic on hospital admissions for idiopathic pulmonary fibrosis: a nationwide population-based study. BMC Pulm Med 2024; 24:430. [PMID: 39217306 PMCID: PMC11365253 DOI: 10.1186/s12890-024-03230-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Accepted: 08/19/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND Previous studies have consistently reported a decrease in hospital admissions for respiratory diseases during the coronavirus disease 2019 (COVID-19) pandemic. However, the impact of the pandemic on idiopathic pulmonary fibrosis (IPF) admissions remains unknown. METHODS This study used data from the Korean National Health Insurance Service database. IPF was defined based on the International Classification of Diseases 10th Revision (ICD-10) and rare intractable disease (RID) codes. The rate of IPF admissions was calculated by dividing the number of IPF admissions by the prevalence of IPF. The rate of IPF admissions during the COVID-19 pandemic (2020-2021) was compared with the mean rate of admissions during the prepandemic period (2017-2019) and presented as the rate ratio (RR). A sensitivity analysis was conducted on patients treated with systemic corticosteroids during IPF admission. RESULTS In patients with IPF defined based on the ICD-10 (analysis 1), the RRs significantly decreased from March in 2020 to December 2021, except for June and September in 2020. Similarly, in patients with IPF defined based on the ICD-10 and RID (analysis 2), the RRs significantly decreased from March 2020 to December 2021, except for June and September 2020. In the sensitivity analysis of analysis 1, the RR significantly decreased in 2020 (0.93; 95%CI: 0.88-0.99; P = 0.029), whereas the RR in 2021 was not significantly different. The RRs in the sensitivity analysis of analysis 2 significantly decreased to 0.85 (0.79-0.92; P < 0.001) in 2020 and 0.82 (0.76-0.88; P < 0.001) in 2021. In the subgroup analysis, the rates of IPF admissions significantly decreased in 2020 and 2021 across both sexes, patients aged ≥ 60 years, and all household income groups. CONCLUSIONS The rate of IPF admissions significantly decreased during the COVID-19 pandemic. This result indicates that preventive measures against COVID-19 may effectively mitigate IPF exacerbation. Therefore, it is assumed that there is a close relationship between respiratory viral infections and IPF exacerbations.
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Affiliation(s)
- In-So Cho
- Department of Internal Medicine, Yonsei University Wonju College of Medicine, 20 Ilsan-ro, Wonju, 26426, Korea
| | - Jihye Lim
- Department of Medical Informatics and Biostatistics, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Min-Seok Chang
- Department of Internal Medicine, Yonsei University Wonju College of Medicine, 20 Ilsan-ro, Wonju, 26426, Korea
| | - Ji-Ho Lee
- Department of Internal Medicine, Yonsei University Wonju College of Medicine, 20 Ilsan-ro, Wonju, 26426, Korea.
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De Ridder D, Ladoy A, Choi Y, Jacot D, Vuilleumier S, Guessous I, Joost S, Greub G. Environmental and geographical factors influencing the spread of SARS-CoV-2 over 2 years: a fine-scale spatiotemporal analysis. Front Public Health 2024; 12:1298177. [PMID: 38957202 PMCID: PMC11217542 DOI: 10.3389/fpubh.2024.1298177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 06/03/2024] [Indexed: 07/04/2024] Open
Abstract
Introduction Since its emergence in late 2019, the SARS-CoV-2 virus has led to a global health crisis, affecting millions and reshaping societies and economies worldwide. Investigating the determinants of SARS-CoV-2 diffusion and their spatiotemporal dynamics at high spatial resolution is critical for public health and policymaking. Methods This study analyses 194,682 georeferenced SARS-CoV-2 RT-PCR tests from March 2020 and April 2022 in the canton of Vaud, Switzerland. We characterized five distinct pandemic periods using metrics of spatial and temporal clustering like inverse Shannon entropy, the Hoover index, Lloyd's index of mean crowding, and the modified space-time DBSCAN algorithm. We assessed the demographic, socioeconomic, and environmental factors contributing to cluster persistence during each period using eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP), to consider non-linear and spatial effects. Results Our findings reveal important variations in the spatial and temporal clustering of cases. Notably, areas with flatter epidemics had higher total attack rate. Air pollution emerged as a factor showing a consistent positive association with higher cluster persistence, substantiated by both immission models and, to a lesser extent, tropospheric NO2 estimations. Factors including population density, testing rates, and geographical coordinates, also showed important positive associations with higher cluster persistence. The socioeconomic index showed no significant contribution to cluster persistence, suggesting its limited role in the observed dynamics, which warrants further research. Discussion Overall, the determinants of cluster persistence remained across the study periods. These findings highlight the need for effective air quality management strategies to mitigate air pollution's adverse impacts on public health, particularly in the context of respiratory viral diseases like COVID-19.
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Affiliation(s)
- David De Ridder
- Geographic Information Research and Analysis in Population Health (GIRAPH) Lab, Faculty of Medicine, University of Geneva (UNIGE), Geneva, Switzerland
- Geospatial Molecular Epidemiology Group (GEOME), Laboratory for Biological Geochemistry (LGB), School of Architecture, Civil and Environmental Engineering (ENAC), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Division and Department of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Anaïs Ladoy
- Geographic Information Research and Analysis in Population Health (GIRAPH) Lab, Faculty of Medicine, University of Geneva (UNIGE), Geneva, Switzerland
- Geospatial Molecular Epidemiology Group (GEOME), Laboratory for Biological Geochemistry (LGB), School of Architecture, Civil and Environmental Engineering (ENAC), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Yangji Choi
- Institute of Microbiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Damien Jacot
- Institute of Microbiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Séverine Vuilleumier
- La Source School of Nursing, University of Applied Sciences and Arts Western Switzerland (HES-SO), Lausanne, Switzerland
| | - Idris Guessous
- Geographic Information Research and Analysis in Population Health (GIRAPH) Lab, Faculty of Medicine, University of Geneva (UNIGE), Geneva, Switzerland
- Division and Department of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Stéphane Joost
- Geographic Information Research and Analysis in Population Health (GIRAPH) Lab, Faculty of Medicine, University of Geneva (UNIGE), Geneva, Switzerland
- Geospatial Molecular Epidemiology Group (GEOME), Laboratory for Biological Geochemistry (LGB), School of Architecture, Civil and Environmental Engineering (ENAC), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Division and Department of Primary Care Medicine, Geneva University Hospitals, Geneva, Switzerland
- La Source School of Nursing, University of Applied Sciences and Arts Western Switzerland (HES-SO), Lausanne, Switzerland
| | - Gilbert Greub
- Institute of Microbiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Infectious Diseases Service, Lausanne University Hospital, Lausanne, Switzerland
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Yilmaz S, Menteş Y, Angin SN, Qaid A. Impact of the COVID-19 outbreak on urban air, Land surface temperature and air pollution in cold climate zones. ENVIRONMENTAL RESEARCH 2023; 237:116887. [PMID: 37611782 DOI: 10.1016/j.envres.2023.116887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 07/16/2023] [Accepted: 08/12/2023] [Indexed: 08/25/2023]
Abstract
The objective of this study was to analyze air pollution and thermal environment in Turkey's cold region before, during, and after COVID-19 in 2019, 2020 and 2021. The CO, NO2, O3, PM10 and SO2 data from the state air quality stations, as well as ground air temperature data from six weather stations, and land satellite images from the USGS website using ArcGIS 10.4.1 software were collected in January, March, April and August of 2019, 2020 an 2021. In order to evaluate the impact of COVID-19 measures and restrictions on cold region cities, air pollution indicators, land surface temperature and air temperature as well as statistical data were analyzed. The results indicated that the CO, NO2, PM10 and SO2 emissions decreased by 14.9%, 14.3%, 47.1% and 28.5%, but O3 increased by 16.9%, respectively, during the COVID-19 lockdown in 2020 as compared to these of the pre-COVID-19 levels in 2019. A positive correlation between air temperature and O3 in 2019 (r2 = 0.80), and in 2020 and 2021 (r2 = 0.64) was obtained. Air temperature in 2020 and 2021 decreased due to lockdowns and quarantine measures that led to lower O3 emissions as compared to 2019. Negative correlations were also found between air temperature and NO2 (r2 = 0.60) and SO2 (r2 = 0.5). There was no correlation between air temperature and PM10. During the COVID-19 lockdown and intense restrictions in April 2020, the average LST and air temperature values dropped by 14.7 °C and 1.6 °C respectively, compared to April 2019. These findings may be beneficial for future urban planning, particularly in cold regions.
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Affiliation(s)
- Sevgi Yilmaz
- Atatürk University, Faculty of Architecture and Design, Department of Landscape Architecture, 25240 Erzurum, Turkey.
| | - Yaşar Menteş
- Ministry of Agriculture and Forestry, Elazığ Provincial Directorate of Agriculture and Forestry, Elazığ - PhD Candidate, Atatürk University, Faculty of Architecture and Design Department of Landscape Architecture Affiliation, Erzurum, Turkey
| | - Sena Nur Angin
- , Atatürk University, Faculty of Architecture and Design, Department of Landscape Architecture, 25240 Erzurum, Turkey
| | - Adeb Qaid
- Department of Architecture Engineering, Kingdom University, Riffa, Bahrain.
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Lushington GH, Linde A, Melgarejo T. Bacterial Proteases as Potentially Exploitable Modulators of SARS-CoV-2 Infection: Logic from the Literature, Informatics, and Inspiration from the Dog. BIOTECH 2023; 12:61. [PMID: 37987478 PMCID: PMC10660736 DOI: 10.3390/biotech12040061] [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: 07/11/2023] [Revised: 08/19/2023] [Accepted: 10/18/2023] [Indexed: 11/22/2023] Open
Abstract
(1) Background: The COVID-19 pandemic left many intriguing mysteries. Retrospective vulnerability trends tie as strongly to odd demographics as to exposure profiles, genetics, health, or prior medical history. This article documents the importance of nasal microbiome profiles in distinguishing infection rate trends among differentially affected subgroups. (2) Hypothesis: From a detailed literature survey, microbiome profiling experiments, bioinformatics, and molecular simulations, we propose that specific commensal bacterial species in the Pseudomonadales genus confer protection against SARS-CoV-2 infections by expressing proteases that may interfere with the proteolytic priming of the Spike protein. (3) Evidence: Various reports have found elevated Moraxella fractions in the nasal microbiomes of subpopulations with higher resistance to COVID-19 (e.g., adolescents, COVID-19-resistant children, people with strong dietary diversity, and omnivorous canines) and less abundant ones in vulnerable subsets (the elderly, people with narrower diets, carnivorous cats and foxes), along with bioinformatic evidence that Moraxella bacteria express proteases with notable homology to human TMPRSS2. Simulations suggest that these proteases may proteolyze the SARS-CoV-2 spike protein in a manner that interferes with TMPRSS2 priming.
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Affiliation(s)
| | - Annika Linde
- College of Veterinary Medicine, Western University of Health Sciences, Pomona, CA 91766, USA;
| | - Tonatiuh Melgarejo
- College of Veterinary Medicine, Western University of Health Sciences, Pomona, CA 91766, USA;
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5
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Mihăilă D, Lazurca LG, Bistricean IP, Horodnic VD, Mihăilă EV, Emandi EM, Prisacariu A, Nistor A, Nistor B, Roșu C. Air quality changes in NE Romania during the first Covid 19 pandemic wave. Heliyon 2023; 9:e18918. [PMID: 37636459 PMCID: PMC10447937 DOI: 10.1016/j.heliyon.2023.e18918] [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: 10/29/2022] [Revised: 08/01/2023] [Accepted: 08/02/2023] [Indexed: 08/29/2023] Open
Abstract
This study analyzes for the first time uniformly and causally the level of pollution and air quality for the NE-Romania Region, one of the poorest region in the European Union. Knowing the level of pollution and air quality in this region, which can be taken as a benchmark due to its positional and economic-geographical attributes, responds to current scientific and practical needs. The study uses an hourly database (for five pollutants and five climate elements), from 2009 to 2020, from 19 air quality monitoring stations in northeastern Romania. Pollutant levels were statistically and graphically/cartographically modeled for the entire 2009-2020 interval on the distributive-spatial and regime, temporal component. Inter-station differences and similarities were analyzed causally. Taking advantage of the emergency measures between March 16 and May 14, 2020, we observed the impact of the event on the regional air quality in northeastern Romania. During the emergency period, the metropolitan area of Suceava (with over 100,000 inhabitants) was quarantined, which allowed us to analyze the impact of the quarantine period on the local air quality. We found that, in this region, air quality falls into class I (for NO2, SO2 and CO), II for O3 and III for PM10. During the lockdown periods NO2 and SO2 decreased for the entire region by 8.6 and 14.3%, respectively, and in Suceava by 13.9 and 40.1%, respectively. The causes of the reduction were anthropogenic in nature.
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Affiliation(s)
- Dumitru Mihăilă
- Department of Geography, Stefan Cel Mare University, Suceava, Romania
- Applied Geography Research Center - GEA, Department of Geography, Stefan Cel Mare University, Suceava, Romania
| | - Liliana Gina Lazurca
- Department of Geography, Stefan Cel Mare University, Suceava, Romania
- Applied Geography Research Center - GEA, Department of Geography, Stefan Cel Mare University, Suceava, Romania
| | - Ionel-Petruț Bistricean
- Department of Geography, Stefan Cel Mare University, Suceava, Romania
- Applied Geography Research Center - GEA, Department of Geography, Stefan Cel Mare University, Suceava, Romania
| | - Vasilică-Dănuț Horodnic
- Department of Geography, Stefan Cel Mare University, Suceava, Romania
- Applied Geography Research Center - GEA, Department of Geography, Stefan Cel Mare University, Suceava, Romania
| | | | - Elena-Maria Emandi
- Department of Geography, Stefan Cel Mare University, Suceava, Romania
- Applied Geography Research Center - GEA, Department of Geography, Stefan Cel Mare University, Suceava, Romania
| | - Alin Prisacariu
- Department of Geography, Stefan Cel Mare University, Suceava, Romania
- Applied Geography Research Center - GEA, Department of Geography, Stefan Cel Mare University, Suceava, Romania
| | - Alina Nistor
- Department of Geography, Stefan Cel Mare University, Suceava, Romania
- Applied Geography Research Center - GEA, Department of Geography, Stefan Cel Mare University, Suceava, Romania
| | | | - Constantin Roșu
- Department of Geography, Stefan Cel Mare University, Suceava, Romania
- Applied Geography Research Center - GEA, Department of Geography, Stefan Cel Mare University, Suceava, Romania
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Li Y, Li R. A hybrid model for daily air quality index prediction and its performance in the face of impact effect of COVID-19 lockdown. PROCESS SAFETY AND ENVIRONMENTAL PROTECTION : TRANSACTIONS OF THE INSTITUTION OF CHEMICAL ENGINEERS, PART B 2023; 176:673-684. [PMID: 37350802 PMCID: PMC10264166 DOI: 10.1016/j.psep.2023.06.021] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 05/22/2023] [Accepted: 06/06/2023] [Indexed: 06/24/2023]
Abstract
Accurate and dependable air quality forecasting is critical to environmental and human health. However, most methods usually aim to improve overall prediction accuracy but neglect the accuracy for unexpected incidents. In this study, a hybrid model was developed for air quality index (AQI) forecasting, and its performance during COVID-19 lockdown was analyzed. Specifically, the variational mode decomposition (VMD) was employed to decompose the original AQI sequence into some subsequences with the parameters optimized by the Whale optimization algorithm (WOA), and the residual sequence was further decomposed by the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). On this basis, a deep learning method bidirectional long short-term memory coupled with added time filter layer and attention mechanism (TFA-BiLSTM) was employed to explore the latent dynamic characteristics of each subsequence. This WOA-VMD-CEEMDAN-TFA-BiLSTM hybrid model was used to forecast AQI values for four cities in China, and results verified that the accuracy of the hybrid model outperformed other proposed models, achieving R2 values of 0.96-0.97. In addition, the improvement in MAE (34.71-49.65%) and RMSE (32.82-48.07%) were observed over single decomposition-based model. Notably, during the epidemic lockdown period, the hybrid model had significant superiority over other proposed models for AQI prediction.
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Affiliation(s)
- Yuting Li
- School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, PR China
| | - Ruying Li
- School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, PR China
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7
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Das CP, Goswami S, Swain BK, Panda BP, Das M. Air mapping during COVID-19 and association between air pollutants and physiochemical parameters of the plants using structural equal modeling: a case study. ENVIRONMENTAL MONITORING AND ASSESSMENT 2023; 195:997. [PMID: 37493963 DOI: 10.1007/s10661-023-11614-x] [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: 12/05/2022] [Accepted: 07/17/2023] [Indexed: 07/27/2023]
Abstract
In urban areas around the world, air pollution introduced by vehicular movement is a key concern. However, restricting vehicular traffic during the COVID-19 shutdown improved air quality to some extent. This study was conducted out in the smart city of Bhubaneswar, which is also the state capital of Odisha, India. The study has tried to map Bhubaneswar by collecting the air quality data before, during, and after the COVID lockdown of six air quality monitoring stations present in Bhubaneswar established under "National Ambient Air Monitoring Program" (NAMP). Furthermore, plants, which are the most vulnerable to air pollution, can show a variety of visible changes depending on their level of sensitivity. Moreover, leaves of Mangifera indica, Monoon longifolium, Azadirachta indica, Millettia pinnata, Aegle marmelos were collected from nearby of six air monitoring stations to assess the "Air Pollution Tolerance Index." M. indica was found to be intermediately tolerant, and all of the other species were found to be sensitive. The structural equation modeling results also revealed a significant relationship between total chlorophyll content, relative water content, ascorbic acid content, leaf extract pH, APTI with species, air quality index, and PM10.
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Affiliation(s)
- Chidananda Prasad Das
- Environmental Science Program, Department of Chemistry, ITER, S 'O' A Deemed to be University, Bhubaneswar, Odisha, India
| | - Shreerup Goswami
- Department of Geology, Utkal University, Vanivihar, Odisha, India
| | | | - Bibhu Prasad Panda
- Environmental Science Program, Department of Chemistry, ITER, S 'O' A Deemed to be University, Bhubaneswar, Odisha, India
| | - Mira Das
- Environmental Science Program, Department of Chemistry, ITER, S 'O' A Deemed to be University, Bhubaneswar, Odisha, India.
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8
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Ravindra K, Bahadur SS, Katoch V, Bhardwaj S, Kaur-Sidhu M, Gupta M, Mor S. Application of machine learning approaches to predict the impact of ambient air pollution on outpatient visits for acute respiratory infections. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 858:159509. [PMID: 36257414 DOI: 10.1016/j.scitotenv.2022.159509] [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: 02/09/2022] [Revised: 09/13/2022] [Accepted: 10/13/2022] [Indexed: 06/16/2023]
Abstract
With a remarkable increase in industrialization among fast-developing countries, air pollution is rising at an alarming rate and has become a public health concern. The study aims to examine the effect of air pollution on patient's hospital visits for respiratory diseases, particularly Acute Respiratory Infections (ARI). Outpatient hospital visits, air pollution and meteorological parameters were collected from March 2018 to October 2021. Eight machine learning algorithms (Random Forest model, K-Nearest Neighbors regression model, Linear regression model, LASSO regression model, Decision Tree Regressor, Support Vector Regression, X.G. Boost and Deep Neural Network with 5-layers) were applied for the analysis of daily air pollutants and outpatient visits for ARI. The evaluation was done by using 5-cross-fold confirmations. The data was randomly divided into test and training data sets at a scale of 1:2, respectively. Results show that among the studied eight machine learning models, the Random Forest model has given the best performance with R2 = 0.606, 0.608 without lag and 1-day lag respectively on ARI patients and R2 = 0.872, 0.871 without lag and 1-day lag respectively on total patients. All eight models did not perform well with the lag effect on the ARI patient dataset but performed better on the total patient dataset. Thus, the study did not find any significant association between ARI patients and ambient air pollution due to the intermittent availability of data during the COVID-19 period. This study gives insight into developing machine learning programs for risk prediction that can be used to predict analytics for several other diseases apart from ARI, such as heart disease and other respiratory diseases.
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Affiliation(s)
- Khaiwal Ravindra
- Department of Community Medicine & School of Public Health, PGIMER, Chandigarh 160012, India.
| | - Samsher Singh Bahadur
- Department of Community Medicine & School of Public Health, PGIMER, Chandigarh 160012, India
| | - Varun Katoch
- Department of Community Medicine & School of Public Health, PGIMER, Chandigarh 160012, India; Department of Environment Studies, Panjab University, Chandigarh 160014, India
| | - Sanjeev Bhardwaj
- Department of Community Medicine & School of Public Health, PGIMER, Chandigarh 160012, India
| | - Maninder Kaur-Sidhu
- Department of Community Medicine & School of Public Health, PGIMER, Chandigarh 160012, India
| | - Madhu Gupta
- Department of Community Medicine & School of Public Health, PGIMER, Chandigarh 160012, India
| | - Suman Mor
- Department of Environment Studies, Panjab University, Chandigarh 160014, India
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Mohammadi A, Pishgar E, Fatima M, Lotfata A, Fanni Z, Bergquist R, Kiani B. The COVID-19 Mortality Rate Is Associated with Illiteracy, Age, and Air Pollution in Urban Neighborhoods: A Spatiotemporal Cross-Sectional Analysis. Trop Med Infect Dis 2023; 8:85. [PMID: 36828501 PMCID: PMC9962969 DOI: 10.3390/tropicalmed8020085] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 01/23/2023] [Accepted: 01/24/2023] [Indexed: 01/28/2023] Open
Abstract
There are different area-based factors affecting the COVID-19 mortality rate in urban areas. This research aims to examine COVID-19 mortality rates and their geographical association with various socioeconomic and ecological determinants in 350 of Tehran's neighborhoods as a big city. All deaths related to COVID-19 are included from December 2019 to July 2021. Spatial techniques, such as Kulldorff's SatScan, geographically weighted regression (GWR), and multi-scale GWR (MGWR), were used to investigate the spatially varying correlations between COVID-19 mortality rates and predictors, including air pollutant factors, socioeconomic status, built environment factors, and public transportation infrastructure. The city's downtown and northern areas were found to be significantly clustered in terms of spatial and temporal high-risk areas for COVID-19 mortality. The MGWR regression model outperformed the OLS and GWR regression models with an adjusted R2 of 0.67. Furthermore, the mortality rate was found to be associated with air quality (e.g., NO2, PM10, and O3); as air pollution increased, so did mortality. Additionally, the aging and illiteracy rates of urban neighborhoods were positively associated with COVID-19 mortality rates. Our approach in this study could be implemented to study potential associations of area-based factors with other emerging infectious diseases worldwide.
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Affiliation(s)
- Alireza Mohammadi
- Department of Geography and Urban Planning, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran
| | - Elahe Pishgar
- Department of Human Geography, Faculty of Earth Sciences, Shahid Beheshti University, Tehran 19839-69411, Iran
| | - Munazza Fatima
- Department of Geography, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
- Department of Geography, University of Zurich, CH-8006 Zurich, Switzerland
| | - Aynaz Lotfata
- Geography Department, Chicago State University, Chicago, IL 60628-1598, USA
| | - Zohreh Fanni
- Department of Human Geography, Faculty of Earth Sciences, Shahid Beheshti University, Tehran 19839-69411, Iran
| | | | - Behzad Kiani
- Centre de Recherche en Santé Publique, Université de Montréal, 7101, Avenue du Parc, Montreal, QC H3N 1X9, Canada
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Xu S, Cheng B, Huang Z, Liu T, Li Y, Jiang L, Guo W, Xiong J. Impact of the COVID-19 on electricity consumption of open university campus buildings - The case of Twente University in the Netherlands. ENERGY AND BUILDINGS 2023; 279:112723. [PMID: 36536944 PMCID: PMC9753509 DOI: 10.1016/j.enbuild.2022.112723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 11/27/2022] [Accepted: 12/11/2022] [Indexed: 06/17/2023]
Abstract
Since the COVID-19 outbreak, the restrictive policies enacted by countries in response to the epidemic have led to changes in the movement of people in public places, which has had a direct impact on the use and energy consumption of various public buildings. This study was based on electricity consumption data for 25 on-campus public buildings at 1-hour intervals between January 2020 and June 2022 at Tewnte University in the Netherlands, and after the data were climate-corrected by multiple regression analysis, the changes in EU and EUI for various types of buildings were compared for different restriction periods using ANOVA, LSD and t-tests. And additionally, further analyzed the changes and reasons for the electricity consumption of various public buildings on campus and customers' electricity consumption behavior in a period of time after the lifting of the epidemic restriction policy. The results of ANOVA analysis show that the restriction policy has a significant effect on teaching, sports, and cultural buildings, and the electricity intensity of the three types of buildings is reduced by 0.28, 0.09, and 0.07 kwh/m2/day respectively under the strict restriction policy; The t-test results show that during the restriction period, all building types, except for living and academic buildings, show a significant decreasing trend, with the teaching buildings having the greatest energy saving potential, with an average daily EU reduction of 1088kwh/day and an EUI reduction of 0.075kwh/ m2/day. The above findings provide a case study of a complete cycle of energy consumption changes in university buildings under similar epidemic restriction policies before and after the epidemic restriction, and inform the electricity allocation policies of university and government energy management authorities.
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Affiliation(s)
- Sheng Xu
- School of Civil Engineering and Architecture, Southwest University of Science and Technology, Mianyang 621010, China
| | - Bin Cheng
- School of Civil Engineering and Architecture, Southwest University of Science and Technology, Mianyang 621010, China
| | - Zefeng Huang
- School of Urban Design, Wuhan University, Wuhan 430072, China
| | - Tao Liu
- School of Earth Sciences, Tsinghua University, Beijing 100084, China
| | - Yuan Li
- School of Architecture and Civil Engineering, Xiamen University, 361005, China
| | - Lin Jiang
- School of Civil Engineering and Architecture, Southwest University of Science and Technology, Mianyang 621010, China
| | - Wei Guo
- Department of Architecture, Deyang Installation Technician College, Deyang 618099, China
| | - Jie Xiong
- School of Civil Engineering and Architecture, Southwest University of Science and Technology, Mianyang 621010, China
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Russo MA, Rafael S, Lopes D, Quinteiro P, Monteiro A. An integrated analysis of COVID-19 impacts on energy and environment: Lessons learnt. ATMOSPHERIC POLLUTION RESEARCH 2023; 14:101637. [PMID: 36540303 PMCID: PMC9754326 DOI: 10.1016/j.apr.2022.101637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 12/14/2022] [Accepted: 12/15/2022] [Indexed: 06/17/2023]
Abstract
Carbon neutrality, sustainable development and reducing our impact on the environment is the top priority in future measures. The COVID-19 pandemic brought challenges to every sector at a global scale but can provide valuable insight to reach these goals. The main objective of this work is to provide an integrated analysis of the impact of the COVID-19 pandemic, focused on energy and its related aspects, i.e., environment and costs. Mainland Portugal was used as a case study and two years were analysed, one pre pandemic (2019) and another post pandemic (2020). In 2020, the majority of sectors - Transport, Services, Industry and Agriculture & Fisheries - show a reduction of energy consumption, atmospheric emissions, carbon footprint and related monetary and social costs. In contrast, the Domestic sector presents an overall increase, with maximums of 25.4% in electricity consumption (during Spring), 0.72% in the PM10 (particulate matter) and NOx (nitrogen dioxides) emissions (in Summer), and 2.9% in carbon footprint (in Spring). The integrated analysis proposed in this work was crucial to identify the paths to a post pandemic world focused on the different aspects of sustainability - new concepts of mobility and workplace, as well as increased investment in energy performance and renewable energy sources. This study showed that changing our energy consumption patterns could significantly affect future greenhouse gas emissions, and contribute to the sustainable growth of the economy, while maintaining good progress towards climate-neutral goals.
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Affiliation(s)
- M A Russo
- CESAM, Department of Environment and Planning, University of Aveiro, 3810-193, Aveiro, Portugal
| | - S Rafael
- CESAM, Department of Environment and Planning, University of Aveiro, 3810-193, Aveiro, Portugal
| | - D Lopes
- CESAM, Department of Environment and Planning, University of Aveiro, 3810-193, Aveiro, Portugal
| | - P Quinteiro
- CESAM, Department of Environment and Planning, University of Aveiro, 3810-193, Aveiro, Portugal
| | - A Monteiro
- CESAM, Department of Environment and Planning, University of Aveiro, 3810-193, Aveiro, Portugal
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12
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Stephan T, Al-Turjman F, Ravishankar M, Stephan P. Machine learning analysis on the impacts of COVID-19 on India's renewable energy transitions and air quality. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:79443-79465. [PMID: 35715677 PMCID: PMC9205654 DOI: 10.1007/s11356-022-20997-2] [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: 02/23/2022] [Accepted: 05/17/2022] [Indexed: 05/12/2023]
Abstract
India is severely affected by the COVID-19 pandemic and is facing an unprecedented public health emergency. While the country's immediate measures focus on combating the coronavirus spread, it is important to investigate the impacts of the current crisis on India's renewable energy transition and air quality. India's economic slowdown is mainly compounded by the collapse of global oil prices and the erosion of global energy demand. A clean energy transition is a key step in enabling the integration of energy and climate. Millions in India are affected owing to fossil fuel pollution and the increasing climate heating that has led to inconceivable health impacts. This paper attempts to study the impact of COVID-19 on India's climate and renewable energy transitions through machine learning algorithms. India is observing a massive collapse in energy demand during the lockdown as its coal generation is suffering the worst part of the ongoing pandemic. During this current COVID-19 crisis, the renewable energy sector benefits from its competitive cost and the Indian government's must-run status to run generators based on renewable energy sources. In contrast to fossil fuel-based power plants, renewable energy sources are not exposed to the same supply chain disruptions in this current pandemic situation. India has the definite potential to surprise the global community and contribute to cost-effective decarbonization. Moreover, the country has a good chance of building more flexibility into the renewable energy sector to avoid an unstable future.
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Affiliation(s)
- Thompson Stephan
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, M. S. Ramaiah University of Applied Sciences, Bangalore, Karnataka India 560054
| | - Fadi Al-Turjman
- Artificial Intelligence Engineering Dept., AI and Robotics Institute, Near East University, Mersin 10, Turkey
| | - Monica Ravishankar
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, M. S. Ramaiah University of Applied Sciences, Bangalore, Karnataka India 560054
| | - Punitha Stephan
- Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu India 641114
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13
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O'Piela DR, Durisek GR, Escobar YNH, Mackos AR, Wold LE. Particulate matter and Alzheimer's disease: an intimate connection. Trends Mol Med 2022; 28:770-780. [PMID: 35840480 PMCID: PMC9420776 DOI: 10.1016/j.molmed.2022.06.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 05/09/2022] [Accepted: 06/10/2022] [Indexed: 10/17/2022]
Abstract
The environmental role in disease progression has been appreciated for decades; however, understanding how airborne toxicant exposure can affect organs beyond the lungs is an underappreciated area of scientific inquiry. Particulate matter (PM) includes various gases, liquids, and particles in suspension and is produced by industrial activities such as fossil fuel combustion and natural events including wildfires and volcanic eruptions. Although agencies have attempted to reduce acceptable airborne particulate levels, with urbanization and population growth, these policies have been only moderately effective in mitigating disease progression. A growing area of research is focused on the role of PM exposure in the progression of Alzheimer's disease (AD). This review will summarize the knowns and unknowns of this expanding field.
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Affiliation(s)
- Devin R O'Piela
- Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University College of Medicine and Wexner Medical Center, Columbus, OH, USA; College of Nursing, The Ohio State University, Columbus, OH, USA
| | - George R Durisek
- Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University College of Medicine and Wexner Medical Center, Columbus, OH, USA; College of Nursing, The Ohio State University, Columbus, OH, USA
| | - Yael-Natalie H Escobar
- Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University College of Medicine and Wexner Medical Center, Columbus, OH, USA; College of Nursing, The Ohio State University, Columbus, OH, USA
| | - Amy R Mackos
- Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University College of Medicine and Wexner Medical Center, Columbus, OH, USA; College of Nursing, The Ohio State University, Columbus, OH, USA
| | - Loren E Wold
- Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University College of Medicine and Wexner Medical Center, Columbus, OH, USA; College of Nursing, The Ohio State University, Columbus, OH, USA; Department of Physiology and Cell Biology, The Ohio State University College of Medicine and Wexner Medical Center, Columbus, OH, USA.
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14
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Burns CJ, LaKind JS, Naiman J, Boon D, Clougherty JE, Rule AM, Zidek A. Research on COVID-19 and air pollution: A path towards advancing exposure science. ENVIRONMENTAL RESEARCH 2022; 212:113240. [PMID: 35390303 PMCID: PMC8979614 DOI: 10.1016/j.envres.2022.113240] [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: 02/28/2022] [Revised: 03/30/2022] [Accepted: 03/31/2022] [Indexed: 05/26/2023]
Abstract
The COVID-19 pandemic has resulted in an extraordinary incidence of morbidity and mortality, with almost 6 million deaths worldwide at the time of this writing (https://covid19.who.int/). There has been a pressing need for research that would shed light on factors - especially modifiable factors - that could reduce risks to human health. At least several hundred studies addressing the complex relationships among transmission of SARS-CoV-2, air pollution, and human health have been published. However, these investigations are limited by available and consistent data. The project goal was to seek input into opportunities to improve and fund exposure research on the confluence of air pollution and infectious agents such as SARS-CoV-2. Thirty-two scientists with expertise in exposure science, epidemiology, risk assessment, infectious diseases, and/or air pollution responded to the outreach for information. Most of the respondents expressed value in developing a set of common definitions regarding the extent and type of public health lockdown. Traffic and smoking ranked high as important sources of air pollution warranting source-specific research (in contrast with assessing overall ambient level exposures). Numerous important socioeconomic factors were also identified. Participants offered a wide array of inputs on what they considered to be essential studies to improve our understanding of exposures. These ranged from detailed mechanistic studies to improved air quality monitoring studies and prospective cohort studies. Overall, many respondents indicated that these issues require more research and better study design. As an exercise to solicit opinions, important concepts were brought forth that provide opportunities for scientific collaboration and for consideration for funding prioritization. Further conversations on these concepts are needed to advance our thinking on how to design research that moves us past the documented limitations in the current body of research and prepares us for the next pandemic.
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Affiliation(s)
- Carol J Burns
- Burns Epidemiology Consulting, LLC, 255 W Sunset Ct., Sanford, MI, 48657, USA.
| | - Judy S LaKind
- LaKind Associates, LLC, 106 Oakdale Avenue, Catonsville, MD, 21228, USA; Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, 21201, USA.
| | - Josh Naiman
- Naiman Consulting, LLC, 504 S 44th St, Apt 2, Phila, PA, 19104, USA.
| | - Denali Boon
- Corteva Agriscience, 9330 Zionsville Rd, Indianapolis, IN, 46268, USA.
| | - Jane E Clougherty
- Department of Environmental and Occupational Health, 3215 Market St, Dornsife School of Public Health, Drexel University, Philadelphia, PA, 19104, USA.
| | - Ana M Rule
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, The Johns Hopkins University, 615 N Wolfe St, Baltimore, MD, 21205, USA.
| | - Angelika Zidek
- Existing Substances Risk Assessment Bureau, 269 Laurier Ave, West, Health Canada, Ottawa, Ontario, Canada.
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Lara R, Megido L, Negral L, Suárez-Peña B, Castrillón L. Impact of COVID-19 restrictions on the dry deposition fraction of settleable particulate matter at three industrial urban/suburban locations in northern Spain. ATMOSPHERIC ENVIRONMENT (OXFORD, ENGLAND : 1994) 2022; 284:119216. [PMID: 36373064 PMCID: PMC9637955 DOI: 10.1016/j.atmosenv.2022.119216] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 05/28/2022] [Accepted: 05/29/2022] [Indexed: 05/09/2023]
Abstract
Ninety 24-h samples of the dry deposition fraction of settleable particulate matter (DSPM) were collected at one suburban industrial site ('EMA') and two urban industrial sites ('Lauredal' and 'Laboratory') in the western area of Gijón (North of Spain) from December 2019 to June 2020. The levels registered point to an environmental issue that should receive close attention from environmental authorities. Before lockdown restrictions due to COVID-19 were established, all samples collected at the EMA site exceeded 300 mg·m-2·d-1 (the Spanish limit value until 2002). Large amounts of DSPM were also registered at the Lauredal and Laboratory sites, maximum levels reaching 1039.2 and 672.7 mg·m-2·d-1, respectively. Seven metals were analysed in DSPM samples: Al, Ca, Fe, K, Mg, Mn and Na. Fe reached the highest values: 2473.4, 463.4 and 293.3 mg·m-2·d-1 (EMA, Lauredal and Laboratory sites, respectively). This study quantifies the reductions in the DSPM levels registered (on average, 97.2, 73.5 and 90.5% at the EMA, Lauredal and Laboratory sites, respectively) during the lockdown, which involved the restriction of population mobility and industrial activity. The influence of wind speed and its direction were also assessed to better understand the role of these restrictions in the observed reductions. The concentrations of all the metals in the DSPM were reduced by more than 75%, on average, except for K at the Laboratory and Lauredal sites. These decreases were much higher than those found by other authors for smaller fractions of the atmospheric particulate matter (PM10, PM2.5). The findings of the present study highlight the importance of DSPM in highly industrialized urban/suburban locations and indicate the direction that legal measures might take, given the influence of anthropogenic emissions.
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Affiliation(s)
- Rosa Lara
- Department of Chemical and Environmental Engineering, Polytechnic School of Engineering, Gijón Campus, University of Oviedo, 33203, Gijón, Spain
| | - Laura Megido
- Department of Chemical and Environmental Engineering, Polytechnic School of Engineering, Gijón Campus, University of Oviedo, 33203, Gijón, Spain
| | - Luis Negral
- Department of Chemical and Environmental Engineering, Technical University of Cartagena, Cartagena, Spain
| | - Beatriz Suárez-Peña
- Department of Materials Science and Metallurgical Engineering, Polytechnic School of Engineering, Gijón Campus, University of Oviedo, 33203, Gijón, Spain
| | - Leonor Castrillón
- Department of Chemical and Environmental Engineering, Polytechnic School of Engineering, Gijón Campus, University of Oviedo, 33203, Gijón, Spain
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