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Shams SR, Choi Y, Singh D, Ghahremanloo M, Momeni M, Park J. Innovative approaches for accurate ozone prediction and health risk analysis in South Korea: The combined effectiveness of deep learning and AirQ. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 946:174158. [PMID: 38909816 DOI: 10.1016/j.scitotenv.2024.174158] [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: 02/03/2024] [Revised: 05/28/2024] [Accepted: 06/18/2024] [Indexed: 06/25/2024]
Abstract
Short-term exposure to ground-level ozone (O3) poses significant health risks, particularly respiratory and cardiovascular diseases, and mortality. This study addresses the pressing need for accurate O3 forecasting to mitigate these risks, focusing on South Korea. We introduce Deep Bias Correction (Deep-BC), a novel framework leveraging Convolutional Neural Networks (CNNs), to refine hourly O3 forecasts from the Community Multiscale Air Quality (CMAQ) model. Our approach involves training Deep-BC using data from 2016 to 2019, including CMAQ's 72-hour O3 forecasts, 31 meteorological variables from the Weather Research and Forecasting (WRF) model, and previous days' station measurements of 6 air pollutants. Deep-BC significantly outperforms CMAQ in 2021, reducing biases in O3 forecasts. Furthermore, we utilize Deep-BC's daily maximum 8-hour average O3 (MDA8 O3) forecasts as input for the AirQ+ model to assess O3's potential impact on mortality across seven major provinces of South Korea: Seoul, Busan, Daegu, Incheon, Daejeon, Ulsan, and Sejong. Short-term O3 exposure is associated with 0.40 % to 0.48 % of natural cause and respiratory deaths and 0.67 % to 0.81 % of cardiovascular deaths. Gender-specific analysis reveals higher mortality rates among men, particularly from respiratory causes. Our findings underscore the critical need for region-specific interventions to address air pollution's detrimental effects on public health in South Korea. By providing improved O3 predictions and quantifying its impact on mortality, this research offers valuable insights for formulating targeted strategies to mitigate air pollution's adverse effects. Moreover, we highlight the urgency of proactive measures in health policies, emphasizing the significance of accurate forecasting and effective interventions to safeguard public health from the deleterious effects of air pollution.
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Affiliation(s)
- Seyedeh Reyhaneh Shams
- Department of Earth and Atmospheric Sciences, University of Houston, Houston, TX 77004, USA
| | - Yunsoo Choi
- Department of Earth and Atmospheric Sciences, University of Houston, Houston, TX 77004, USA.
| | - Deveshwar Singh
- Department of Earth and Atmospheric Sciences, University of Houston, Houston, TX 77004, USA
| | - Masoud Ghahremanloo
- Department of Earth and Atmospheric Sciences, University of Houston, Houston, TX 77004, USA
| | - Mahmoudreza Momeni
- Department of Earth and Atmospheric Sciences, University of Houston, Houston, TX 77004, USA
| | - Jincheol Park
- Department of Earth and Atmospheric Sciences, University of Houston, Houston, TX 77004, USA
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Gaffney A, Himmelstein DU, Woolhandler S. Population-level trends in asthma and chronic obstructive pulmonary disease emergency department visits and hospitalizations before and during the coronavirus disease 2019 pandemic in the United States. Ann Allergy Asthma Immunol 2023; 131:737-744.e8. [PMID: 37619778 DOI: 10.1016/j.anai.2023.08.016] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 08/09/2023] [Accepted: 08/09/2023] [Indexed: 08/26/2023]
Abstract
BACKGROUND Previous studies have identified reductions in exacerbations of chronic lung disease in many locales after onset of the coronavirus disease 2019 (COVID-19) pandemic. OBJECTIVE To evaluate the population-level impacts of COVID-19 on asthma and chronic obstructive pulmonary disease (COPD) exacerbations-with a focus on disadvantaged communities-in the United States. METHODS We analyzed 2016 to 2020 county-level data on asthma and COPD acute care use, with myocardial infarction hospitalizations as a comparator condition. We linked this with county-level lower respiratory disease mortality data. We calculated rates of emergency department (ED) visits, hospitalizations, and deaths and evaluated changes using linear regressions adjusted for year and county-fixed effects. For a supplementary analysis, we calculated ED visit rates nationwide for asthma, COPD, or any diagnosis using the 2016 to 2020 National Hospital Ambulatory Medical Care Survey. RESULTS Our county-level data included 685 counties in 13 states. Rates of each outcome fell in 2020. In adjusted analyses, we found large reductions in asthma and COPD ED visit rates (eg, a 21.5 per 10,000-person reduction in COPD ED visits; 95% confidence interval, -23.8 to -19.1), with smaller reductions in hospitalizations and chronic lower respiratory mortality. Disadvantaged communities had mostly higher baseline rates of respiratory morbidity and larger absolute reductions in some outcomes. Among 90,808 ED visits in the National Hospital Ambulatory Medical Care Survey, asthma ED visits/y fell 33% during the pandemic and COPD visits by 51%; overall ED visits fell by only 7%. CONCLUSION Onset of the COVID-19 pandemic coincided with reductions in acute care utilization for asthma and COPD. Understanding the mechanism of this reduction might inform future efforts to prevent exacerbations.
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Affiliation(s)
- Adam Gaffney
- Department of Medicine, Cambridge Health Alliance, Cambridge, Massachusetts; Harvard Medical School, Boston, Massachusetts.
| | - David U Himmelstein
- Department of Medicine, Cambridge Health Alliance, Cambridge, Massachusetts; Harvard Medical School, Boston, Massachusetts; Hunter College, City University of New York, New York, New York; Public Citizen Health Research Group, Washington, District of Columbia
| | - Steffie Woolhandler
- Department of Medicine, Cambridge Health Alliance, Cambridge, Massachusetts; Harvard Medical School, Boston, Massachusetts; Hunter College, City University of New York, New York, New York; Public Citizen Health Research Group, Washington, District of Columbia
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Nelson D, Choi Y, Sadeghi B, Yeganeh AK, Ghahremanloo M, Park J. A comprehensive approach combining positive matrix factorization modeling, meteorology, and machine learning for source apportionment of surface ozone precursors: Underlying factors contributing to ozone formation in Houston, Texas. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 334:122223. [PMID: 37481031 DOI: 10.1016/j.envpol.2023.122223] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 07/13/2023] [Accepted: 07/17/2023] [Indexed: 07/24/2023]
Abstract
Ozone concentrations in Houston, Texas, are among the highest in the United States, posing significant risks to human health. This study aimed to evaluate the impact of various emissions sources and meteorological factors on ozone formation in Houston from 2017 to 2021 using a comprehensive PMF-SHAP approach. First, we distinguished the unique sources of VOCs in each area and identified differences in the local chemistry that affect ozone production. At the urban station, the primary sources were n_decane, biogenic/industrial/fuel evaporation, oil and gas flaring/production, industrial emissions/evaporation, and ethylene/propylene/aromatics. At the industrial site, the main sources were industrial emissions/evaporation, fuel evaporation, vehicle-related sources, oil and gas flaring/production, biogenic, aromatic, and ethylene and propylene. And then, we performed SHAP analysis to determine the importance and impact of each emissions factor and meteorological variables. Shortwave radiation (SHAP values are ∼5.74 and ∼6.3 for Milby Park and Lynchburg, respectively) and humidity (∼4.87 and ∼4.71, respectively) were the most important variables for both sites. For the urban station, the most important emissions sources were n_decane (∼2.96), industrial emissions/evaporation (∼1.89), and ethylene/propylene/aromatics (∼1.57), while for the industrial site, they were oil and gas flaring/production (∼1.38), ethylene/propylene (∼1.26), and industrial emissions/evaporation (∼0.95). NOx had a negative impact on ozone production at the urban station due to the NOx-rich chemical regime, whereas NOx had positive impacts at the industrial site. The study's findings suggest that the PMF-SHAP approach is efficient, inexpensive, and can be applied to other similar applications to identify factors contributing to ozone-exceedance events. The study's results can be used to develop more effective air quality management strategies for Houston and other cities with high levels of ozone.
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Affiliation(s)
- Delaney Nelson
- Department of Earth and Atmospheric Science, University of Houston, Texas, USA
| | - Yunsoo Choi
- Department of Earth and Atmospheric Science, University of Houston, Texas, USA.
| | - Bavand Sadeghi
- Air Resources Laboratory, National Oceanic and Atmospheric Administration, College Park, MD, 20740, USA; Cooperative Institute for Satellite Earth System Studies, University of Maryland, College Park, MD, 20740, USA
| | | | - Masoud Ghahremanloo
- Department of Earth and Atmospheric Science, University of Houston, Texas, USA
| | - Jincheol Park
- Department of Earth and Atmospheric Science, University of Houston, Texas, USA
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Yang J, Wang G, Zhang C. Forecast of Fine Particles in Chengdu under Autumn-Winter Synoptic Conditions. TOXICS 2023; 11:777. [PMID: 37755787 PMCID: PMC10535754 DOI: 10.3390/toxics11090777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 08/31/2023] [Accepted: 09/09/2023] [Indexed: 09/28/2023]
Abstract
We conducted an evaluation of the impact of meteorological factor forecasts on the prediction of fine particles in Chengdu, China, during autumn and winter, utilizing the European Cooperation in Science and Technology (COST)733 objective weather classification software and the Community Multiscale Air Quality model. This analysis was performed under four prevailing weather patterns. Fine particle pollution tended to occur under high-pressure rear, homogeneous-pressure, and low-pressure conditions; by contrast, fine particle concentrations were lower under high-pressure bottom conditions. The forecasts of fine particle concentrations were more accurate under high-pressure bottom conditions than under high-pressure rear and homogeneous-pressure conditions. Moreover, under all conditions, the 24 h forecast of fine particle concentrations were more accurate than the 48 and 72 h forecasts. Regarding meteorological factors, forecasts of 2 m relative humidity and 10 m wind speed were more accurate under high-pressure bottom conditions than high-pressure rear and homogeneous-pressure conditions. Moreover, 2 m relative humidity and 10 m wind speed were important factors for forecasting fine particles, whereas 2 m air temperature was not. Finally, the 24 h forecasts of meteorological factors were more accurate than the 48 and 72 h forecasts, consistent with the forecasting of fine particles.
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Affiliation(s)
- Jingchao Yang
- Institute of Plateau Meteorology, China Meteorological Administration, Chengdu 610072, China;
- Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province, Chengdu 610072, China
| | - Ge Wang
- Institute of Plateau Meteorology, China Meteorological Administration, Chengdu 610072, China;
- Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province, Chengdu 610072, China
| | - Chao Zhang
- Department of Atmospheric Science, School of Environmental Studies, China University of Geosciences, Wuhan 430074, China
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Ghahremanloo M, Choi Y, Lops Y. Deep learning mapping of surface MDA8 ozone: The impact of predictor variables on ozone levels over the contiguous United States. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 326:121508. [PMID: 36967006 DOI: 10.1016/j.envpol.2023.121508] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 03/19/2023] [Accepted: 03/22/2023] [Indexed: 06/18/2023]
Abstract
The limited number of ozone monitoring stations imposes uncertainty in various applications, calling for accurate approaches to capturing ozone values in all regions, particularly those with no in-situ measurements. This study uses deep learning (DL) to accurately estimate daily maximum 8-hr average (MDA8) ozone and examines the spatial contribution of several factors on ozone levels over the contiguous U.S. (CONUS) in 2019. A comparison between in-situ observations and DL-estimated MDA8 ozone values shows a correlation coefficient (R) of 0.95, an index of agreement (IOA) of 0.97, and a mean absolute bias (MAB) of 2.79 ppb, highlighting the promising performance of the deep convolutional neural network (Deep-CNN) at estimating surface MDA8 ozone. Spatial cross-validation also confirms the high spatial accuracy of the model, which obtains an R of 0.91, and IOA of 0.96 and an MAB of 3.46 ppb when it is trained and tested on separate stations. To interpret the black-box nature of our DL model, we use Shapley additive explanations (SHAP) to generate a spatial feature contribution map (SFCM), the results of which confirm an advanced ability of Deep-CNN to capture the interactions between most predictor variables and ozone. For instance, the model shows that solar radiation (SRad) SFCM, with higher values, enhances the formation of ozone, particularly in the south and southwestern CONUS. As SRad triggers ozone precursors to produce ozone via photochemical reactions, it increases ozone concentrations. The model also shows that humidity, with its low values, increases ozone concentrations in the western mountainous regions. The negative correlation between humidity and ozone levels can be attributed to factors such as higher ozone decomposition resulting from increased levels of humidity and OH radicals. This study is the first to introduce the SFCM to investigate the spatial role of predictor variables on changes in estimated MDA8 ozone levels.
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Affiliation(s)
- Masoud Ghahremanloo
- Department of Earth and Atmospheric Sciences, University of Houston, Houston, TX, 77004, USA.
| | - Yunsoo Choi
- Department of Earth and Atmospheric Sciences, University of Houston, Houston, TX, 77004, USA.
| | - Yannic Lops
- Department of Earth and Atmospheric Sciences, University of Houston, Houston, TX, 77004, USA.
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Tuluri F, Remata R, Walters WL, Tchounwou PB. Impact of Regional Mobility on Air Quality during COVID-19 Lockdown in Mississippi, USA Using Machine Learning. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:6022. [PMID: 37297626 PMCID: PMC10252722 DOI: 10.3390/ijerph20116022] [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: 04/19/2023] [Revised: 05/17/2023] [Accepted: 05/19/2023] [Indexed: 06/12/2023]
Abstract
Social distancing measures and shelter-in-place orders to limit mobility and transportation were among the strategic measures taken to control the rapid spreading of COVID-19. In major metropolitan areas, there was an estimated decrease of 50 to 90 percent in transit use. The secondary effect of the COVID-19 lockdown was expected to improve air quality, leading to a decrease in respiratory diseases. The present study examines the impact of mobility on air quality during the COVID-19 lockdown in the state of Mississippi (MS), USA. The study region is selected because of its non-metropolitan and non-industrial settings. Concentrations of air pollutants-particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), ozone (O3), nitrogen oxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO)-were collected from the Environmental Protection Agency, USA from 2011 to 2020. Because of limitations in the data availability, the air quality data of Jackson, MS were assumed to be representative of the entire region of the state. Weather data (temperature, humidity, pressure, precipitation, wind speed, and wind direction) were collected from the National Oceanic and Atmospheric Administration, USA. Traffic-related data (transit) were taken from Google for the year 2020. The statistical and machine learning tools of R Studio were used on the data to study the changes in air quality, if any, during the lockdown period. Weather-normalized machine learning modeling simulating business-as-scenario (BAU) predicted a significant difference in the means of the observed and predicted values for NO2, O3, and CO (p < 0.05). Due to the lockdown, the mean concentrations decreased for NO2 and CO by -4.1 ppb and -0.088 ppm, respectively, while it increased for O3 by 0.002 ppm. The observed and predicted air quality results agree with the observed decrease in transit by -50.5% as a percentage change of the baseline, and the observed decrease in the prevalence rate of asthma in MS during the lockdown. This study demonstrates the validity and use of simple, easy, and versatile analytical tools to assist policymakers with estimating changes in air quality in situations of a pandemic or natural hazards, and to take measures for mitigating if the deterioration of air quality is detected.
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Affiliation(s)
- Francis Tuluri
- Department of Industrial Systems & Technology, Jackson State University, Jackson, MS 39217, USA
| | - Reddy Remata
- Department of Atmospheric Sciences, Jackson State University, Jackson, MS 39217, USA;
| | - Wilbur L. Walters
- College of Sciences, Engineering & Technology, Jackson State University, Jackson, MS 39217, USA;
| | - Paul B. Tchounwou
- RCMI Center for Health Disparities Research, Jackson State University, Jackson, MS 39217, USA
- RCMI Center for Urban Health Disparities Research and Innovation, Morgan State University, Baltimore, MD 21251, USA
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Galiwango R, Bainomugisha E, Kivunike F, Kateete DP, Jjingo D. Air pollution and mobility patterns in two Ugandan cities during COVID-19 mobility restrictions suggest the validity of air quality data as a measure for human mobility. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:34856-34871. [PMID: 36520281 PMCID: PMC9751517 DOI: 10.1007/s11356-022-24605-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 12/01/2022] [Indexed: 06/17/2023]
Abstract
We explored the viability of using air quality as an alternative to aggregated location data from mobile phones in the two most populated cities in Uganda. We accessed air quality and Google mobility data collected from 15th February 2020 to 10th June 2021 and augmented them with mobility restrictions implemented during the COVID-19 lockdown. We determined whether air quality data depicted similar patterns to mobility data before, during, and after the lockdown and determined associations between air quality and mobility by computing Pearson correlation coefficients ([Formula: see text]), conducting multivariable regression with associated confidence intervals (CIs), and visualized the relationships using scatter plots. Residential mobility increased with the stringency of restrictions while both non-residential mobility and air pollution decreased with the stringency of restrictions. In Kampala, PM2.5 was positively correlated with non-residential mobility and negatively correlated with residential mobility. Only correlations between PM2.5 and movement in work and residential places were statistically significant in Wakiso. After controlling for stringency in restrictions, air quality in Kampala was independently correlated with movement in retail and recreation (- 0.55; 95% CI = - 1.01- - 0.10), parks (0.29; 95% CI = 0.03-0.54), transit stations (0.29; 95% CI = 0.16-0.42), work (- 0.25; 95% CI = - 0.43- - 0.08), and residential places (- 1.02; 95% CI = - 1.4- - 0.64). For Wakiso, only the correlation between air quality and residential mobility was statistically significant (- 0.99; 95% CI = - 1.34- - 0.65). These findings suggest that air quality is linked to mobility and thus could be used by public health programs in monitoring movement patterns and the spread of infectious diseases without compromising on individuals' privacy.
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Affiliation(s)
- Ronald Galiwango
- The African Center of Excellence in Bioinformatics and Data Intensive Sciences, The Infectious Diseases Institute, Makerere University, Kampala, Uganda.
- Center for Computational Biology, Uganda Christian University, Mukono, Uganda.
| | - Engineer Bainomugisha
- Department of Computer Science, College of Computing and Information Sciences, Makerere University, Kampala, Uganda
| | - Florence Kivunike
- Department of Computer Science, College of Computing and Information Sciences, Makerere University, Kampala, Uganda
| | - David Patrick Kateete
- Department of Immunology and Molecular Biology, College of Health Sciences, Makerere University, Kampala, Uganda
- Department of Medical Microbiology, College of Health Sciences, Makerere University, Kampala, Uganda
| | - Daudi Jjingo
- The African Center of Excellence in Bioinformatics and Data Intensive Sciences, The Infectious Diseases Institute, Makerere University, Kampala, Uganda
- Department of Computer Science, College of Computing and Information Sciences, Makerere University, Kampala, Uganda
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Casallas A, Castillo-Camacho MP, Sanchez ER, González Y, Celis N, Mendez-Espinosa JF, Belalcazar LC, Ferro C. Surface, satellite ozone variations in Northern South America during low anthropogenic emission conditions: a machine learning approach. AIR QUALITY, ATMOSPHERE, & HEALTH 2023; 16:745-764. [PMID: 36687138 PMCID: PMC9839215 DOI: 10.1007/s11869-023-01303-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 01/04/2023] [Indexed: 06/17/2023]
Abstract
UNLABELLED 2020 presented the ideal conditions for studying the air quality response to several emission reductions due to the COVID-19 lockdowns. Numerous studies found that the tropospheric ozone increased even in lockdown conditions, but its reasons are not entirely understood. This research aims to better understand the ozone variations in Northern South America. Satellite and reanalysis data were used to analyze regional ozone variations. An analysis of two of the most polluted Colombian cities was performed by quantifying the changes of ozone and its precursors and by doing a machine learning decomposition to disentangle the contributions that precursors and meteorology made to form O3. The results indicated that regional ozone increased in most areas, especially where wildfires are present. Meteorology is associated with favorable conditions to promote wildfires in Colombia and Venezuela. Regarding the local analysis, the machine learning ensemble shows that the decreased titration process associated with the NO plummeting owing to mobility reduction is the main contributor to the O3 increase (≈50%). These tools lead to conclude that (i) the increase in O3 produced by the reduction of the titration process that would be associated with an improvement in mobile sources technology has to be considered in the new air quality policies, (ii) a boost in international cooperation is essential to control wildfires since an event that occurs in one country can affect others and (iii) a machine learning decomposition approach coupled with sensitivity experiments can help us explain and understand the physicochemical mechanism that drives ozone formation. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s11869-023-01303-6.
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Affiliation(s)
- Alejandro Casallas
- Earth System Physics, Abdus Salam International Centre for Theoretical Physics, Trieste, Italy
- Escuela de Ciencias Exactas e Ingeniería, Universidad Sergio Arboleda, Bogotá, Colombia
| | | | - Edwin Ricardo Sanchez
- Departamento de Ingeniería Química y Ambiental, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Yuri González
- Departamento de Ingeniería Química y Ambiental, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Nathalia Celis
- Università degli Studi di Padova. Dipartimento di Ingegneria Civile, Edile e Ambientale, Padua, Italy
| | - Juan Felipe Mendez-Espinosa
- Ingeniería Ambiental, Escuela de Ciencias Agrícolas, Pecuarias y del Medio Ambiente – ECAPMA, Universidad Nacional Abierta y a Distancia – UNAD, Bogotá, Colombia
| | - Luis Carlos Belalcazar
- Departamento de Ingeniería Química y Ambiental, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Camilo Ferro
- Escuela de Ciencias Exactas e Ingeniería, Universidad Sergio Arboleda, Bogotá, Colombia
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Luo X, Yang Q, Zheng D, Tian H, Chen L, Wu J, Ji Z, Chen Y, Li Z. A bibliometric and visualization analysis on the association between chronic exposure to fine particulate matter and cancer risk. Front Public Health 2022; 10:1039078. [PMID: 36544791 PMCID: PMC9762493 DOI: 10.3389/fpubh.2022.1039078] [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: 09/07/2022] [Accepted: 11/14/2022] [Indexed: 12/12/2022] Open
Abstract
INTRODUCTION As one of the major pollutants in ambient air pollution, fine particulate matter (PM2.5) has attracted public attention. A large body of laboratory and epidemiological research has shown that PM2.5 exposure is harmful to human health. METHODS To investigate its association with the commonly observed PM-related cancer, a bibliometric study was performed on related publications from 2012 to 2021 from a macroscopic perspective with the help of the Web of Science database and scientometric software VOSviewer, CiteSpace V, HistCite, and Biblioshiny. RESULTS The results indicated that of the 1,948 enrolled documents, scientific productions increased steadily and peaked in 2020 with 348 publications. The most prolific authors, journals, organizations, and countries were Raaschou-Nielsen O, Science of the Total Environment, the Chinese Academy of Sciences, and China, respectively. The top five keywords in frequency order were "air pollution," "particulate matter," "lung cancer," "exposure," and "mortality." DISCUSSION The toxic mechanism of carcinogenicity was explained and is worthy of further investigation. China and the US collaborated most closely, and it is hoped the two countries can strengthen their collaboration to combat air pollution. There is also a need to identify the components of PM2.5 and refine the models to assess the global burden of disease attributed to PM2.5 exposure.
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Affiliation(s)
- Xuman Luo
- Department of Thyroid, Breast, and Hernia Surgery, General Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China
| | - Qiuping Yang
- Department of Thyroid, Breast, and Hernia Surgery, General Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China
| | - Daitian Zheng
- Department of Thyroid, Breast, and Hernia Surgery, General Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China
| | - Huiting Tian
- Department of Thyroid, Breast, and Hernia Surgery, General Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China
| | - Lingzhi Chen
- Department of Thyroid, Breast, and Hernia Surgery, General Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China
| | - Jinyao Wu
- Department of Thyroid, Breast, and Hernia Surgery, General Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China
| | - Zeqi Ji
- Department of Thyroid, Breast, and Hernia Surgery, General Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China
| | - Yexi Chen
- Department of Thyroid, Breast, and Hernia Surgery, General Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China
| | - Zhiyang Li
- Department of Thyroid, Breast, and Hernia Surgery, General Surgery, The Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China
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Casallas A, Jiménez-Saenz C, Torres V, Quirama-Aguilar M, Lizcano A, Lopez-Barrera EA, Ferro C, Celis N, Arenas R. Design of a Forest Fire Early Alert System through a Deep 3D-CNN Structure and a WRF-CNN Bias Correction. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22228790. [PMID: 36433386 PMCID: PMC9693021 DOI: 10.3390/s22228790] [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: 10/20/2022] [Revised: 11/08/2022] [Accepted: 11/09/2022] [Indexed: 05/04/2023]
Abstract
Throughout the years, wildfires have negatively impacted ecological systems and urban areas. Hence, reinforcing territorial risk management strategies against wildfires is essential. In this study, we built an early alert system (EAS) with two different Machine Learning (ML) techniques to calculate the meteorological conditions of two Colombian areas: (i) A 3D convolutional neural net capable of learning from satellite data and (ii) a convolutional network to bias-correct the Weather Research and Forecasting (WRF) model output. The results were used to quantify the daily Fire Weather Index and were coupled with the outcomes from a land cover analysis conducted through a Naïve-Bayes classifier to estimate the probability of wildfire occurrence. These results, combined with an assessment of global vulnerability in both locations, allow the construction of daily risk maps in both areas. On the other hand, a set of short-term preventive and corrective measures were suggested to public authorities to implement, after an early alert prediction of a possible future wildfire. Finally, Soil Management Practices are proposed to tackle the medium- and long-term causes of wildfire development, with the aim of reducing vulnerability and promoting soil protection. In conclusion, this paper creates an EAS for wildfires, based on novel ML techniques and risk maps.
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Affiliation(s)
- Alejandro Casallas
- Escuela de Ciencias Exactas e Ingeniería, Universidad Sergio Arboleda, Bogotá 11011, Colombia
- Earth System Physics, Abdus Salam International Centre for Theoretical Physics, 34151 Trieste, Italy
- Correspondence:
| | - Camila Jiménez-Saenz
- Facultad de Estudios Ambientales y Rurales, Pontificia Universidad Javeriana, Bogotá 11011, Colombia
| | - Victor Torres
- Escuela de Ciencias Exactas e Ingeniería, Universidad Sergio Arboleda, Bogotá 11011, Colombia
| | - Miguel Quirama-Aguilar
- Escuela de Ciencias Exactas e Ingeniería, Universidad Sergio Arboleda, Bogotá 11011, Colombia
| | - Augusto Lizcano
- Escuela de Ciencias Exactas e Ingeniería, Universidad Sergio Arboleda, Bogotá 11011, Colombia
| | - Ellie Anne Lopez-Barrera
- Instituto de Estudios y Servicios Ambientales-IDEASA, Universidad Sergio Arboleda, Bogotá 11011, Colombia
| | - Camilo Ferro
- Departamento de Ingeniería, Aqualogs SAS, Bogotá 11011, Colombia
| | - Nathalia Celis
- Dipartimento di Ingegneria Civile, Edile e Ambientale, Università degli Studi di Padova, 35122 Padova, Italy
- Departamento de Medio Ambiente y Sostenibilidad, Universidad Andina Simón Bolivar, Sucre 703030, Bolivia
| | - Ricardo Arenas
- Centro de Investigación de Filosofía y Derecho, Universidad Externado de Colombia, Bogotá 11011, Colombia
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11
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Sadeghi B, Ghahremanloo M, Mousavinezhad S, Lops Y, Pouyaei A, Choi Y. Contributions of meteorology to ozone variations: Application of deep learning and the Kolmogorov-Zurbenko filter. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 310:119863. [PMID: 35963387 DOI: 10.1016/j.envpol.2022.119863] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 07/07/2022] [Accepted: 07/23/2022] [Indexed: 06/15/2023]
Abstract
From hourly ozone observations obtained from three regions⸻Houston, Dallas, and West Texas⸻we investigated the contributions of meteorology to changes in surface daily maximum 8-h average (MDA8) ozone from 2000 to 2019. We applied a deep convolutional neural network and Shapely additive explanation (SHAP) to examine the complex underlying nonlinearity between variations of surface ozone and meteorological factors. Results of the models showed that between 2000 and 2019, specific humidity (38% and 27%) and temperature (28% and 37%) contributed the most to ozone formation over the Houston and Dallas metropolitan areas, respectively. On the other hand, the results show that solar radiation (50%) strongly impacted ozone variation over West Texas during this time. Using a combination of the Kolmogorov-Zurbenko (KZ) filter and multiple linear regression, we also evaluated the influence of meteorology on ozone and quantified the contributions of meteorological parameters to trends in surface ozone formation. Our findings showed that in Houston and Dallas, meteorology influenced ozone variations to a large extent. The impacts of meteorology on West Texas, however, showed meteorological factors had fewer influences on ozone variabilities from 2000 to 2019. This study showed that SHAP analysis and the KZ approach can investigate the contributions of the meteorological factors on ozone concentrations and help policymakers enact effective ozone mitigation policies.
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Affiliation(s)
- Bavand Sadeghi
- Department of Earth and Atmospheric Science, University of Houston, Texas, USA
| | - Masoud Ghahremanloo
- Department of Earth and Atmospheric Science, University of Houston, Texas, USA
| | | | - Yannic Lops
- Department of Earth and Atmospheric Science, University of Houston, Texas, USA
| | - Arman Pouyaei
- Department of Earth and Atmospheric Science, University of Houston, Texas, USA
| | - Yunsoo Choi
- Department of Earth and Atmospheric Science, University of Houston, Texas, USA.
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12
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Casey JA, Kioumourtzoglou MA, Ogburn EL, Melamed A, Shaman J, Kandula S, Neophytou A, Darwin KC, Sheffield JS, Gyamfi-Bannerman C. Long-Term Fine Particulate Matter Concentrations and Prevalence of Severe Acute Respiratory Syndrome Coronavirus 2: Differential Relationships by Socioeconomic Status Among Pregnant Individuals in New York City. Am J Epidemiol 2022; 191:1897-1905. [PMID: 35916364 PMCID: PMC9384549 DOI: 10.1093/aje/kwac139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 06/22/2022] [Accepted: 07/27/2022] [Indexed: 02/01/2023] Open
Abstract
We aimed to determine whether long-term ambient concentrations of fine particulate matter (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5)) were associated with increased risk of testing positive for coronavirus disease 2019 (COVID-19) among pregnant individuals who were universally screened at delivery and whether socioeconomic status (SES) modified this relationship. We used obstetrical data collected from New-York Presbyterian Hospital/Columbia University Irving Medical Center in New York, New York, between March and December 2020, including data on Medicaid use (a proxy for low SES) and COVID-19 test results. We linked estimated 2018-2019 PM2.5 concentrations (300-m resolution) with census-tract-level population density, household size, income, and mobility (as measured by mobile-device use) on the basis of residential address. Analyses included 3,318 individuals; 5% tested positive for COVID-19 at delivery, 8% tested positive during pregnancy, and 48% used Medicaid. Average long-term PM2.5 concentrations were 7.4 (standard deviation, 0.8) μg/m3. In adjusted multilevel logistic regression models, we saw no association between PM2.5 and ever testing positive for COVID-19; however, odds were elevated among those using Medicaid (per 1-μg/m3 increase, odds ratio = 1.6, 95% confidence interval: 1.0, 2.5). Further, while only 22% of those testing positive showed symptoms, 69% of symptomatic individuals used Medicaid. SES, including unmeasured occupational exposures or increased susceptibility to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) due to concurrent social and environmental exposures, may explain the increased odds of testing positive for COVID-19 being confined to vulnerable pregnant individuals using Medicaid.
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Affiliation(s)
- Joan A Casey
- Correspondence Address: Correspondence to Joan A. Casey, Department of Environmental Health Sciences, Columbia Mailman School of Public Health, 722 W 168th St, Rm 1206 New York, NY 10032-3727 ()
| | - Marianthi-Anna Kioumourtzoglou
- Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, New York, United States
| | - Elizabeth L Ogburn
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States
| | - Alexander Melamed
- Department of Obstetrics and Gynecology, Columbia University College of Physicians and Surgeons, New York, New York, United States
| | - Jeffrey Shaman
- Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, New York, United States
| | - Sasikiran Kandula
- Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, New York, United States
| | - Andreas Neophytou
- Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, Colorado, United States
| | - Kristin C Darwin
- Department of Gynecology and Obstetrics, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Jeanne S Sheffield
- Department of Gynecology and Obstetrics, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Cynthia Gyamfi-Bannerman
- Department of Obstetrics and Gynecology, Columbia University College of Physicians and Surgeons, New York, New York, United States,Department of Obstetrics, Gynecology & Reproductive Sciences, University of California San Diego School of Medicine and UC San Diego Health
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