• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

纽约州九个县的新型冠状病毒病例与空气污染和气象要素的关系。

Association between the New COVID-19 Cases and Air Pollution with Meteorological Elements in Nine Counties of New York State.

机构信息

Department of Probability and Statistics, IIMAS, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico.

Department of Mathematics and IMAC, Universitat Jaume I, Castellón, 12006 Castellón, Spain.

出版信息

Int J Environ Res Public Health. 2020 Dec 4;17(23):9055. doi: 10.3390/ijerph17239055.

DOI:10.3390/ijerph17239055
PMID:33291673
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7729491/
Abstract

The principal objective of this article is to assess the possible association between the number of COVID-19 infected cases and the concentrations of fine particulate matter (PM) and ozone (O), atmospheric pollutants related to people's mobility in urban areas, taking also into account the effect of meteorological conditions. We fit a generalized linear mixed model which includes spatial and temporal terms in order to detect the effect of the meteorological elements and COVID-19 infected cases on the pollutant concentrations. We consider nine counties of the state of New York which registered the highest number of COVID-19 infected cases. We implemented a Bayesian method using integrated nested Laplace approximation (INLA) with a stochastic partial differential equation (SPDE). The results emphasize that all the components used in designing the model contribute to improving the predicted values and can be included in designing similar real-world data (RWD) models. We found only a weak association between PM and ozone concentrations with COVID-19 infected cases. Records of COVID-19 infected cases and other covariates data from March to May 2020 were collected from electronic health records (EHRs) and standard RWD sources.

摘要

本文的主要目的是评估与城市地区人们流动性相关的大气污染物（细颗粒物 (PM) 和臭氧 (O)）的浓度与 COVID-19 感染病例数量之间的可能关联，同时考虑到气象条件的影响。我们拟合了一个广义线性混合模型，其中包括时空项，以检测气象要素和 COVID-19 感染病例对污染物浓度的影响。我们考虑了纽约州的九个县，这些县的 COVID-19 感染病例数量最高。我们使用带有随机偏微分方程 (SPDE) 的集成嵌套拉普拉斯逼近 (INLA) 实现了贝叶斯方法。结果强调，模型设计中使用的所有组件都有助于提高预测值，并可用于设计类似的真实世界数据 (RWD) 模型。我们仅发现 COVID-19 感染病例与 PM 和臭氧浓度之间存在微弱关联。从电子健康记录 (EHR) 和标准 RWD 来源收集了 2020 年 3 月至 5 月的 COVID-19 感染病例记录和其他协变量数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604a/7729491/e16791ac7796/ijerph-17-09055-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604a/7729491/6bfb72c64f71/ijerph-17-09055-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604a/7729491/5382f50e6171/ijerph-17-09055-g0A2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604a/7729491/c5cea4e341f0/ijerph-17-09055-g0A3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604a/7729491/47165a26c3c5/ijerph-17-09055-g0A4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604a/7729491/81798e195cc6/ijerph-17-09055-g0A5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604a/7729491/d59e83507835/ijerph-17-09055-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604a/7729491/b2cae50566f4/ijerph-17-09055-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604a/7729491/19b5dc899e29/ijerph-17-09055-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604a/7729491/25d8c0486841/ijerph-17-09055-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604a/7729491/b53759528f89/ijerph-17-09055-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604a/7729491/dba44b32d31a/ijerph-17-09055-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604a/7729491/e8d78f0b2f6e/ijerph-17-09055-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604a/7729491/f9e320dc57ae/ijerph-17-09055-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604a/7729491/e3dd6be150e2/ijerph-17-09055-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604a/7729491/e16791ac7796/ijerph-17-09055-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604a/7729491/6bfb72c64f71/ijerph-17-09055-g0A1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604a/7729491/5382f50e6171/ijerph-17-09055-g0A2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604a/7729491/c5cea4e341f0/ijerph-17-09055-g0A3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604a/7729491/47165a26c3c5/ijerph-17-09055-g0A4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604a/7729491/81798e195cc6/ijerph-17-09055-g0A5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604a/7729491/d59e83507835/ijerph-17-09055-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604a/7729491/b2cae50566f4/ijerph-17-09055-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604a/7729491/19b5dc899e29/ijerph-17-09055-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604a/7729491/25d8c0486841/ijerph-17-09055-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604a/7729491/b53759528f89/ijerph-17-09055-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604a/7729491/dba44b32d31a/ijerph-17-09055-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604a/7729491/e8d78f0b2f6e/ijerph-17-09055-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604a/7729491/f9e320dc57ae/ijerph-17-09055-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604a/7729491/e3dd6be150e2/ijerph-17-09055-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604a/7729491/e16791ac7796/ijerph-17-09055-g010.jpg

相似文献

1
Association between the New COVID-19 Cases and Air Pollution with Meteorological Elements in Nine Counties of New York State.纽约州九个县的新型冠状病毒病例与空气污染和气象要素的关系。
Int J Environ Res Public Health. 2020 Dec 4;17(23):9055. doi: 10.3390/ijerph17239055.
2
Spatiotemporal joint analysis of PM and Ozone in California with INLA approach.加州利用 INLA 方法进行 PM 和臭氧的时空联合分析。
J Environ Manage. 2024 Jul;363:121294. doi: 10.1016/j.jenvman.2024.121294. Epub 2024 Jun 8.
3
Mortality-Air Pollution Associations in Low Exposure Environments (MAPLE): Phase 2.低暴露环境下死亡率与空气污染关联研究（MAPLE）：第二阶段。
Res Rep Health Eff Inst. 2022 Jul;2022(212):1-91.
4
Variability of PM and O concentrations and their driving forces over Chinese megacities during 2018-2020.2018-2020 年中国特大城市 PM 和 O 浓度及其驱动因素的变化。
J Environ Sci (China). 2023 Feb;124:1-10. doi: 10.1016/j.jes.2021.10.014. Epub 2022 Feb 2.
5
A global observational analysis to understand changes in air quality during exceptionally low anthropogenic emission conditions.一项旨在了解人为排放异常低的情况下空气质量变化的全球观测分析。
Environ Int. 2021 Dec;157:106818. doi: 10.1016/j.envint.2021.106818. Epub 2021 Aug 20.
6
Short-Term Effects of Ambient Ozone, PM and Meteorological Factors on COVID-19 Confirmed Cases and Deaths in Queens, New York.纽约皇后区大气臭氧、PM 及气象因素对新冠确诊病例和死亡的短期影响。
Int J Environ Res Public Health. 2020 Jun 5;17(11):4047. doi: 10.3390/ijerph17114047.
7
Effect of Environmental Pollutants PM2.5, CO, NO, and O on the Incidence and Mortality of SARS-CoV-2 Infection in Five Regions of the USA.美国五个地区环境污染物 PM2.5、CO、NO 和 O 对 SARS-CoV-2 感染发病率和死亡率的影响。
Int J Environ Res Public Health. 2021 Jul 23;18(15):7810. doi: 10.3390/ijerph18157810.
8
Sandstorm and its effect on particulate matter PM 2.5, carbon monoxide, nitrogen dioxide, ozone pollutants and SARS-CoV-2 cases and deaths.沙尘暴及其对颗粒物 PM 2.5、一氧化碳、二氧化氮、臭氧污染物以及 SARS-CoV-2 病例和死亡的影响。
Sci Total Environ. 2021 Nov 15;795:148764. doi: 10.1016/j.scitotenv.2021.148764. Epub 2021 Jun 30.
9
Association between air quality, meteorological factors and COVID-19 infection case numbers.空气质量、气象因素与 COVID-19 感染病例数之间的关联。
Environ Res. 2021 Jun;197:111024. doi: 10.1016/j.envres.2021.111024. Epub 2021 Mar 17.
10
Assessment of Meteorological Variables and Air Pollution Affecting COVID-19 Cases in Urban Agglomerations: Evidence from China.评估气象变量和空气污染对城市群中 COVID-19 病例的影响：来自中国的证据。
Int J Environ Res Public Health. 2022 Jan 4;19(1):531. doi: 10.3390/ijerph19010531.

引用本文的文献

1
A Bayesian spatio-temporal model of COVID-19 spread in England.贝叶斯时空模型在英格兰新冠病毒传播中的应用
Sci Rep. 2024 May 6;14(1):10335. doi: 10.1038/s41598-024-60964-0.
2
The Effects of Pandemic Restrictions on Public Health-Improvements in Urban Air Quality.大流行限制措施对公共卫生的影响——城市空气质量的改善。
Int J Environ Res Public Health. 2022 Jul 25;19(15):9022. doi: 10.3390/ijerph19159022.

本文引用的文献

1
Four-Month Changes in Air Quality during and after the COVID-19 Lockdown in Six Megacities in China.中国六个特大城市在新冠疫情封锁期间及之后空气质量的四个月变化
Environ Sci Technol Lett. 2020 Sep 9;7(11):802-808. doi: 10.1021/acs.estlett.0c00605. eCollection 2020 Nov 10.
2
Abnormally Shallow Boundary Layer Associated With Severe Air Pollution During the COVID-19 Lockdown in China.中国新冠疫情封锁期间与严重空气污染相关的异常浅薄边界层
Geophys Res Lett. 2020 Oct 28;47(20):e2020GL090041. doi: 10.1029/2020GL090041. Epub 2020 Oct 19.
3
Human mobility and COVID-19 initial dynamics.
人类流动性与新冠疫情初期动态
Nonlinear Dyn. 2020;101(3):1901-1919. doi: 10.1007/s11071-020-05854-6. Epub 2020 Sep 2.
4
Disentangling the Impact of the COVID-19 Lockdowns on Urban NO From Natural Variability.厘清新冠疫情封锁对城市一氧化氮自然变化的影响。
Geophys Res Lett. 2020 Sep 16;47(17):e2020GL089269. doi: 10.1029/2020GL089269. Epub 2020 Sep 5.
5
Diurnal and temporal changes in air pollution during COVID-19 strict lockdown over different regions of India.新冠疫情严格封控期间印度不同地区空气污染的日变化和时变化。
Environ Pollut. 2020 Nov;266(Pt 3):115368. doi: 10.1016/j.envpol.2020.115368. Epub 2020 Aug 13.
6
Harnessing Real-World Data for Regulatory Use and Applying Innovative Applications.利用真实世界数据用于监管用途并应用创新型应用程序。
J Multidiscip Healthc. 2020 Jul 22;13:671-679. doi: 10.2147/JMDH.S262776. eCollection 2020.
7
SARS-CoV-2 pandemic lockdown: Effects on air quality in the industrialized Gujarat state of India.SARS-CoV-2 大流行封锁：对印度工业化古吉拉特邦空气质量的影响。
Sci Total Environ. 2020 Oct 1;737:140391. doi: 10.1016/j.scitotenv.2020.140391. Epub 2020 Jun 20.
8
Air quality changes in New York City during the COVID-19 pandemic.纽约市在 COVID-19 大流行期间的空气质量变化。
Sci Total Environ. 2020 Nov 10;742:140496. doi: 10.1016/j.scitotenv.2020.140496. Epub 2020 Jun 25.
9
Air pollution in Ontario, Canada during the COVID-19 State of Emergency.加拿大安大略省在 COVID-19 紧急状态下的空气污染。
Sci Total Environ. 2020 Nov 10;742:140516. doi: 10.1016/j.scitotenv.2020.140516. Epub 2020 Jun 28.
10
How mobility habits influenced the spread of the COVID-19 pandemic: Results from the Italian case study.流动性习惯如何影响 COVID-19 大流行的传播：来自意大利案例研究的结果。
Sci Total Environ. 2020 Nov 1;741:140489. doi: 10.1016/j.scitotenv.2020.140489. Epub 2020 Jun 24.