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Article(id=1323941271206756838, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1323941237702668764, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202506145, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1749657600000, receivedDateStr=2025-06-12, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793581796694, onlineDateStr=2026-11-02, pubDate=1765296000000, pubDateStr=2025-12-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793581796694, onlineIssueDateStr=2026-11-02, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793581796694, creator=13701087609, updateTime=1793581796694, updator=13701087609, issue=Issue{id=1323941237702668764, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='23', pageStart='4225', pageEnd='4416', issueExtLink='null', onlineDate='null', pubDate='1765296000000', pubDateStr='2025-12-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1793581788707, creator='13701087609', updateTime=1793581887795, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1323941653374959780, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1323941237702668764, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1323941653374959781, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1323941237702668764, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=4283, endPage=4290, ext={EN=ArticleExt(id=1323941271395500519, articleId=1323941271206756838, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=The effects of meteorological factors and air pollutants on respiratory diseases and their interaction, columnId=1228016570660745413, journalTitle=Modern Preventive Medicine, columnName=Environmental and Occupational Health, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the independent and interactive effects of meteorological factors and air pollutants on the outpatient visits of respiratory diseases in Xining City, and to provide a scientific basis for urban public health protection in plateau areas.

Methods

A time-stratified case-crossover design was used. Outpatient visit data for respiratory diseases from three hospitals in Xining during 2016—2021 were included, and pollutant data were obtained from the China Air Quality Online Monitoring Platform. A generalized nonlinear model was applied to assess the lagged effects of CO, O3, SO2, NO2, PM10,PM2.5 and temperature. Interactions were quantified using a bivariate response surface model, relative excess risk due to interaction (RERI), and attributable proportion (AP).

Results

The single pollutant model analysis showed that the risk of outpatient visits increased with higher concentrations of CO and NO2. SO2, PM10 and PM2.5 showed a ‘first increase and then decrease’ curve, and the peak RRs were 1.69, 1.55 and 1.80, respectively. O3 showed a ‘U’ shaped curve, and the trough RR was 0.76. The lag effect analysis indicated that same-day effects were strongest for SO2, NO2 and PM10. The peak RR of SO2 reached (RR=1.54, 95% CI: 1.26-1.88) at lag 7 days, and the cumulative lag effect of NO2 reached (RR=1.20, 95% CI: 1.11-1.31) at lag 3 days. The interaction analysis showed that SO2, NO2, PM10 and PM2.5 had a significant synergistic effect at low temperatures, but had an antagonistic effect with O3. Under high temperature conditions, it only had a synergistic effect with O3.

Conclusion

The risk of respiratory diseases in Xining is affected by the concentration of pollutants, temperature and their interaction. Low temperature aggravates the health hazards of gaseous pollutants and particulate matter, and high temperature enhances the hazard effect of O3.

, authors=Gen-shan LI1, Shu-zhen HE2, 1, Chun-guang MA2, Jing WU2, Yu-hua MA3, Zhong-hua HU4, Zhen-xu NING1, Jian-xiu ZHANG1, Xiao-juan HUA1, authorsList=Gen-shan LI, Shu-zhen HE, Chun-guang MA, Jing WU, Yu-hua MA, Zhong-hua HU, Zhen-xu NING, Jian-xiu ZHANG, Xiao-juan HUA, authorCompany=null, correspAuthors=Shu-zhen HE, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1323941274599948788, articleId=1323941271206756838, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=气象因素与空气污染物对呼吸系统疾病的影响及其交互作用, columnId=1228016571273113811, journalTitle=现代预防医学, columnName=环境与职业卫生, runingTitle=null, highlight=null, articleAbstract=
目的

探讨西宁市气象因素与空气污染物对呼吸系统疾病门诊就诊量的独立和交互作用，为高原地区城市公共健康防护提供科学依据。

方法

采用时间分层病例交叉设计，纳入2016—2021年西宁市三家医院呼吸系统门诊数据,污染物数据来源于中国空气质量在线监测平台。通过广义非线性模型分析CO、O3、SO2、NO2、PM10、PM2.5及温度的滞后效应，并利用双变量响应面模型和相对超额风险、归因比例量化交互作用。

结果

单污染物模型分析显示：CO、NO2随浓度升高就诊风险增大；SO2、PM10和PM2.5呈现出“先升后降”的曲线，峰值RR分别为1.69、1.55和1.80；O3呈“U”形曲线，低谷RR为0.76。滞后效应分析表明，SO2、NO2、PM10的当日效应最强， SO2在滞后第7天达峰值（RR=1.54 ,95% CI：1.26～1.88），NO2在第三天累积滞后效应达到峰值（RR=1.20 ,95% CI：1.11～1.31）。交互作用分析显示，在低温条件下SO2、NO2、PM10、PM2.5存在显著协同效应，而与O3呈拮抗效应；在高温条件下仅与O3呈协同效应。

结论

西宁市呼吸系统疾病风险受污染物浓度、温度及其交互作用共同影响，低温加剧气态污染物和颗粒物对健康的危害，高温增强O3的危害效应。

, authors=李根山1, 何淑珍2, 1, 马春光2, 吴晶2, 马玉花3, 胡钟桦4, 宁振旭1, 张建秀1, 华晓娟1, authorsList=李根山, 何淑珍, 马春光, 吴晶, 马玉花, 胡钟桦, 宁振旭, 张建秀, 华晓娟, authorCompany=null, correspAuthors=何淑珍, authorNote=

李根山（2000—），男，硕士在读，研究方向：环境与健康

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何淑珍，E-mail：
, copyrightStatement=本刊刊出的所有文章不代表中华预防医学会和本刊编委会的观点，除非特别声明。, copyrightOwner=中华预防医学会和四川大学华西公共卫生学院, extLink=null, articleAbsUrl=null, sourceXml=hzO1tmyc2EsEXjXwxK5ZgA==, magXml=ItCfhROXesMMN8J3vc6qeA==, pdfUrl=null, pdf=LwngmIH7DJi5kjjokAw3yQ==, pdfFileSize=1051487, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=53/yacn4yQkooaSpt0pZDg==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=kYG/6fPJMydlyityeyRq5w==, mapNumber=null, fund=null)}, authors=[Author(id=1323941275405255167, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941271206756838, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=null, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1323941275531084289, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941271206756838, authorId=1323941275405255167, language=EN, stringName=Gen-shan LI, firstName=Gen-shan, middleName=null, lastName=LI, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, address=Department of Public Health, Faculty of Medicine, Qinghai University, Xining, Qinghai 810000, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1323941275627553282, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941271206756838, authorId=1323941275405255167, language=CN, stringName=李根山, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, address=1.青海大学医学部公共卫生系，青海　西宁　810000, bio={"content":"

李根山（2000—），男，硕士在读，研究方向：环境与健康

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(In Chinese), articleTitle=Risk factors of severe and critical patients with COVID-19 in Hubei, China, refAbstract=null), Reference(id=1323941292954223197, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941271206756838, doi=null, pmid=null, pmcid=null, year=2025, volume=25, issue=4, pageStart=537, pageEnd=544, url=null, language=null, rfNumber=[18], rfOrder=22, authorNames=王俊安, 王森路, 刘年强, journalName=热带医学杂志, refType=null, unstructuredReference=王俊安，王森路，刘年强, 等. 2015-2023年和田地区大气污染物和气象因素对肺结核发病的影响 [J]. 热带医学杂志, 2025, 25 (4): 537-544., articleTitle=2015-2023年和田地区大气污染物和气象因素对肺结核发病的影响, refAbstract=null), Reference(id=1323941293235241566, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941271206756838, doi=null, pmid=null, pmcid=null, year=2025, volume=25, issue=4, pageStart=537, pageEnd=544, url=null, language=null, rfNumber=[18], rfOrder=23, authorNames=Wang JA, Wang SL, Liu NQ, journalName=Journal of Tropical Medicine, refType=null, unstructuredReference=Wang JA, Wang SL, Liu NQ, et al. The impact of atmospheric pollutants and meteorological factors on the incidence of tuberculosis in the Hotan region from 2015 to 2023 [J]. Journal of Tropical Medicine, 2025, 25 (4): 537-544. (In Chinese), articleTitle=The impact of atmospheric pollutants and meteorological factors on the incidence of tuberculosis in the Hotan region from 2015 to 2023, refAbstract=null), Reference(id=1323941293633700447, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941271206756838, doi=null, pmid=null, pmcid=null, year=2014, volume=36, issue=3, pageStart=608, pageEnd=613, url=null, language=null, rfNumber=[19], rfOrder=24, authorNames=何永晴, 赵玉成, 冯蜀青, journalName=冰川冻土, refType=null, unstructuredReference=何永晴，赵玉成，冯蜀青. 西宁市逆温特征及其与空气污染物浓度的相关分析 [J]. 冰川冻土, 2014, 36 (3): 608-613., articleTitle=西宁市逆温特征及其与空气污染物浓度的相关分析, refAbstract=null), Reference(id=1323941293746946656, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941271206756838, doi=null, pmid=null, pmcid=null, year=2014, volume=36, issue=3, pageStart=608, pageEnd=613, url=null, language=null, rfNumber=[19], rfOrder=25, authorNames=He YQ, Zhao YC, Feng SQ, journalName=Journal of Glaciology and Geocryology, refType=null, unstructuredReference=He YQ, Zhao YC, Feng SQ. Temperature inversion in Xining City:Characteristics and correlation with air pollutant concentrations [J]. Journal of Glaciology and Geocryology, 2014, 36 (3): 608-613. (In Chinese), articleTitle=Temperature inversion in Xining City:Characteristics and correlation with air pollutant concentrations, refAbstract=null), Reference(id=1323941293843415649, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941271206756838, doi=null, pmid=null, pmcid=null, year=2019, volume=39, issue=12, pageStart=5312, pageEnd=5318, url=null, language=null, rfNumber=[20], rfOrder=26, authorNames=付桂琴, 田亚芹, 张成伟, journalName=中国环境科学, refType=null, unstructuredReference=付桂琴，田亚芹，张成伟. 气温与臭氧协同作用对COPD住院人数的影响 [J]. 中国环境科学, 2019, 39 (12): 5312-5318., articleTitle=气温与臭氧协同作用对COPD住院人数的影响, refAbstract=null), Reference(id=1323941294061519458, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941271206756838, doi=null, pmid=null, pmcid=null, year=2019, volume=39, issue=12, pageStart=5312, pageEnd=5318, url=null, language=null, rfNumber=[20], rfOrder=27, authorNames=Fu GQ, Tian YQ, Zhang CW, journalName=China Environmental Science, refType=null, unstructuredReference=Fu GQ, Tian YQ, Zhang CW. 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(In Chinese), articleTitle=Synergistic effect of temperature and O3 on the numbers of COPD hospitalizations, refAbstract=null), Reference(id=1323941295693103715, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941271206756838, doi=null, pmid=null, pmcid=null, year=2021, volume=41, issue=12, pageStart=5904, pageEnd=5911, url=null, language=null, rfNumber=[21], rfOrder=28, authorNames=周骥, 付世华, 彭丽, journalName=中国环境科学, refType=null, unstructuredReference=周骥，付世华，彭丽, 等. 臭氧和PM2.5对慢阻肺死亡影响及气温修饰效应 [J]. 中国环境科学, 2021, 41 (12): 5904-5911., articleTitle=臭氧和PM2.5对慢阻肺死亡影响及气温修饰效应, refAbstract=null), Reference(id=1323941295907013220, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941271206756838, doi=null, pmid=null, pmcid=null, year=2021, volume=41, issue=12, pageStart=5904, pageEnd=5911, url=null, language=null, rfNumber=[21], rfOrder=29, authorNames=Zhou J, Fu SH, Peng L, journalName=China Environmental Science, refType=null, unstructuredReference=Zhou J, Fu SH, Peng L, et al. 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(In Chinese), articleTitle=Effect of ozone and PM2.5 on chronic obstructive pulmonary disease deaths and the effect modification by the air temperature, refAbstract=null)], funds=[Fund(id=1323941287384187461, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941271206756838, awardId=中疾控环协合同气候发[2024]1-2号, language=CN, fundingSource=中国疾病预防控制中心——环境卫生与健康防护宣教项目(中疾控环协合同气候发[2024]1-2号), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1323941274985824757, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941271206756838, xref=1., ext=[AuthorCompanyExt(id=1323941275002601974, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941271206756838, companyId=1323941274985824757, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Department of Public Health, Faculty of Medicine, Qinghai University, Xining, Qinghai 810000, China), AuthorCompanyExt(id=1323941275010990583, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941271206756838, companyId=1323941274985824757, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.青海大学医学部公共卫生系，青海　西宁　810000)]), AuthorCompany(id=1323941275082293752, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941271206756838, xref=2., ext=[AuthorCompanyExt(id=1323941275094876665, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941271206756838, companyId=1323941275082293752, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.西宁市疾病预防控制中心职业卫生科，青海　西宁　810000)]), AuthorCompany(id=1323941275182957050, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941271206756838, xref=3., ext=[AuthorCompanyExt(id=1323941275195539963, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941271206756838, companyId=1323941275182957050, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3.西宁市第三人民医院)]), AuthorCompany(id=1323941275279426044, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941271206756838, xref=4., ext=[AuthorCompanyExt(id=1323941275292008957, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941271206756838, companyId=1323941275279426044, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=4.西宁市卫生健康委员会)])], figs=[ArticleFig(id=1323941283496067641, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941271206756838, language=EN, label=Figure 1, caption=Time series diagram of residential pollutants, meteorological factors and outpatient number of respiratory diseases in Xining City from 2016 to 2021, figureFileSmall=eU4PALqDBACuWiKvb08TBw==, figureFileBig=53/yacn4yQkooaSpt0pZDg==, tableContent=null), ArticleFig(id=1323941283563176506, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941271206756838, language=CN, label=图1, caption=2016—2021年西宁市污染物、气象因素和呼吸系统疾病门诊数的时间序列图

注：RSON为呼吸系统疾病门诊人数。

, figureFileSmall=eU4PALqDBACuWiKvb08TBw==, figureFileBig=53/yacn4yQkooaSpt0pZDg==, tableContent=null), ArticleFig(id=1323941283768697403, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941271206756838, language=EN, label=Figure 2, caption=Relationship between different pollutants and outpatient risk of respiratory diseases in Xining from 2016 to 2021, figureFileSmall=mo2bLkKAzv6MYGilCoTxLw==, figureFileBig=GCKcwUuHhSx3QZVrKvPx7w==, tableContent=null), ArticleFig(id=1323941283869360700, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941271206756838, language=CN, label=图2, caption=2016—2021年西宁市不同污染物对呼吸系统疾病门诊风险的关系

注：图A、B、C、D、E、F分别为CO、O3、SO2、NO2、PM10、PM2.5。

, figureFileSmall=mo2bLkKAzv6MYGilCoTxLw==, figureFileBig=GCKcwUuHhSx3QZVrKvPx7w==, tableContent=null), ArticleFig(id=1323941283949052477, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941271206756838, language=EN, label=Figure 3, caption=Effect of interaction between temperature and pollutants on outpatient visits of respiratory diseases in Xining City from 2016 to 2021, figureFileSmall=iaY55JF/vjJSDl+wC1ZTZQ==, figureFileBig=4e6WrQga1L016dN/4d9ULw==, tableContent=null), ArticleFig(id=1323941284066492990, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941271206756838, language=CN, label=图3, caption=2016—2021年西宁市温度与污染物交互作用对呼吸系统疾病门诊量的影响

注：图A、B、C、D、E、F分别为O3、SO2、NO2、CO、PM2.5、PM10与温度交互作用结果。

, figureFileSmall=iaY55JF/vjJSDl+wC1ZTZQ==, figureFileBig=4e6WrQga1L016dN/4d9ULw==, tableContent=null), ArticleFig(id=1323941284456563263, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941271206756838, language=EN, label=Table 1, caption=

Descriptive analysis of pollutants, meteorological factors and outpatient number of respiratory diseases in Xining City from 2016 to 2021

, figureFileSmall=null, figureFileBig=null, tableContent=
变量最小值 P25P50P75最大值
O3/(μg/m3)11.0093.31±33.4668.0092.00115.00281.00
CO/(mg·m3)0.201.36±0.780.811.131.646.10
SO2/(μg/m3)1.0020.03±13.1211.0016.0025.00133.00
NO2/(μg/m3)1.0039.31±16.1128.0036.0049.00110.00
PM10/(μg/m3)6.0087.87±76.0247.0071.00106.00978.00
PM2.5/(μg/m3)4.0040.17±27.322.0033.0051.00290.00
相对湿度（%）15.0056.67±16.2345.0057.0069.0094.00
日均温度（℃）-16.206.46±9.15-1.807.2514.4025.60
日均门诊量9.00163.71±77.51108.00155.50204.00493.00
), ArticleFig(id=1323941284594975296, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941271206756838, language=CN, label=表1, caption=

2016—2021年西宁市污染物、气象因素和呼吸系统疾病门诊数的描述分析

, figureFileSmall=null, figureFileBig=null, tableContent=
变量最小值 P25P50P75最大值
O3/(μg/m3)11.0093.31±33.4668.0092.00115.00281.00
CO/(mg·m3)0.201.36±0.780.811.131.646.10
SO2/(μg/m3)1.0020.03±13.1211.0016.0025.00133.00
NO2/(μg/m3)1.0039.31±16.1128.0036.0049.00110.00
PM10/(μg/m3)6.0087.87±76.0247.0071.00106.00978.00
PM2.5/(μg/m3)4.0040.17±27.322.0033.0051.00290.00
相对湿度（%）15.0056.67±16.2345.0057.0069.0094.00
日均温度（℃）-16.206.46±9.15-1.807.2514.4025.60
日均门诊量9.00163.71±77.51108.00155.50204.00493.00
), ArticleFig(id=1323941284863410753, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941271206756838, language=EN, label=Table 2, caption=

Correlation coefficient between meteorological factors and pollutants in Xining City from 2016 to 2021 (r)

, figureFileSmall=null, figureFileBig=null, tableContent=
变量COO3RHSO2NO2PM10PM2.5Temp
CO1
O3-0.42*1
RH-0.33*-0.01*1
SO20.66*-0.27*-0.44*1
NO20.67*-0.5*-0.29*0.62*1
PM100.59*-0.34*-0.46*0.54*0.55*1
PM2.50.68*-0.48*-0.34*0.55*0.59*0.83*1
Temp-0.53*0.72*0.22*-0.38*-0.47*-0.48*-0.61*1
), ArticleFig(id=1323941285266063938, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941271206756838, language=CN, label=表2, caption=

2016—2021年西宁市气象因素与污染物的相关系数（r）

, figureFileSmall=null, figureFileBig=null, tableContent=
变量COO3RHSO2NO2PM10PM2.5Temp
CO1
O3-0.42*1
RH-0.33*-0.01*1
SO20.66*-0.27*-0.44*1
NO20.67*-0.5*-0.29*0.62*1
PM100.59*-0.34*-0.46*0.54*0.55*1
PM2.50.68*-0.48*-0.34*0.55*0.59*0.83*1
Temp-0.53*0.72*0.22*-0.38*-0.47*-0.48*-0.61*1
), ArticleFig(id=1323941286931202627, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941271206756838, language=EN, label=Table 3, caption=

Interaction between extreme temperature and pollutants on outpatient risk of respiratory diseases in Xining City from 2016 to 2021

, figureFileSmall=null, figureFileBig=null, tableContent=
污染物气温暴露因素RRRERIAPS
CO低温RR011.02 (0.98～1.06)0.03 (-0.02～0.07)0.02 (0.02～0.02)1.02 (0.97～1.06)
RR101.03 (1.02～1.04)
RR111.07 (1.05～1.09)
高温RR011.06 (1.03～1.08)0.03 (-0.02～0.07)0.03 (0.03～0.03)1.03 (1.00～1.05)
RR101.03 (1.02～1.04)
RR111.11 (1.07～1.15)
SO2低温RR010.98 (0.94～1.02)0.07 (0.03～0.10)0.07 (0.06～0.07)1.07 (1.03～1.12)
RR101.01 (1.00～1.02)
RR111.06 (1.04～1.08)
高温RR011.06 (1.03～1.09)0.01 (-0.03～0.04)0.01 (0.01～0.01)1.01 (1.00～1.04)
RR101.02 (1.01～1.03)
RR111.08 (1.05～1.11)
NO2低温RR010.95 (0.91～0.98)0.11 (0.06～0.15)0.10 (0.09～0.10)1.11 (1.06～1.15)
RR101.05 (1.04～1.06)
RR111.10 (1.08～1.13)
高温RR011.08 (1.05～1.10)-0.03(-0.08～0.02)-0.02 (-0.03～-0.02)0.98 (0.95～1.00)
RR101.06 (1.05～1.07)
RR111.11 (1.06～1.16)
O3低温RR011.04 (1.02～1.06)-0.17 (-0.24～-0.10)-0.20 (-0.20～-0.19)0.84 (0.82～0.86)
RR100.99 (0.98～1.00)
RR110.87 (0.80～0.94)
高温RR010.92 (0.83～1.02)0.15 (0.05～0.25)0.14(0.13～0.15)1.16 (1.03～1.30)
RR100.98 (0.97～0.99)
RR111.05 (1.03～1.08)
PM10低温RR010.99 (0.96～1.03)0.06 (0.02～0.10)0.05 (0.05～0.06)1.06 (1.02～1.10)
RR101.05 (1.04～1.06)
RR111.10 (1.08～1.12)
高温RR011.06 (1.04～1.09)0.03 (-0.02～0.08)0.03 (0.02～0.03)1.03 (1.00～1.05)
RR101.05 (1.04～1.06)
RR111.14 (1.10～1.19)
PM2.5低温RR010.93 (0.89～0.98)0.12 (0.07～0.18)0.11 (0.10～0.11)1.12 (1.06～1.19)
RR101.03 (1.02～1.04)
RR111.08 (1.06～1.10)
高温RR011.07 (1.05～1.09)-0.04 (-0.10～0.01)-0.04 (-0.04～-0.04)0.96 (0.94～0.98)
RR101.04 (1.02～1.05)
RR111.06 (1.01～1.11)
), ArticleFig(id=1323941287036060228, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1323941271206756838, language=CN, label=表3, caption=

2016—2021年西宁市极端温度与污染物对居民呼吸系统疾病门诊风险的交互作用

, figureFileSmall=null, figureFileBig=null, tableContent=
污染物气温暴露因素RRRERIAPS
CO低温RR011.02 (0.98～1.06)0.03 (-0.02～0.07)0.02 (0.02～0.02)1.02 (0.97～1.06)
RR101.03 (1.02～1.04)
RR111.07 (1.05～1.09)
高温RR011.06 (1.03～1.08)0.03 (-0.02～0.07)0.03 (0.03～0.03)1.03 (1.00～1.05)
RR101.03 (1.02～1.04)
RR111.11 (1.07～1.15)
SO2低温RR010.98 (0.94～1.02)0.07 (0.03～0.10)0.07 (0.06～0.07)1.07 (1.03～1.12)
RR101.01 (1.00～1.02)
RR111.06 (1.04～1.08)
高温RR011.06 (1.03～1.09)0.01 (-0.03～0.04)0.01 (0.01～0.01)1.01 (1.00～1.04)
RR101.02 (1.01～1.03)
RR111.08 (1.05～1.11)
NO2低温RR010.95 (0.91～0.98)0.11 (0.06～0.15)0.10 (0.09～0.10)1.11 (1.06～1.15)
RR101.05 (1.04～1.06)
RR111.10 (1.08～1.13)
高温RR011.08 (1.05～1.10)-0.03(-0.08～0.02)-0.02 (-0.03～-0.02)0.98 (0.95～1.00)
RR101.06 (1.05～1.07)
RR111.11 (1.06～1.16)
O3低温RR011.04 (1.02～1.06)-0.17 (-0.24～-0.10)-0.20 (-0.20～-0.19)0.84 (0.82～0.86)
RR100.99 (0.98～1.00)
RR110.87 (0.80～0.94)
高温RR010.92 (0.83～1.02)0.15 (0.05～0.25)0.14(0.13～0.15)1.16 (1.03～1.30)
RR100.98 (0.97～0.99)
RR111.05 (1.03～1.08)
PM10低温RR010.99 (0.96～1.03)0.06 (0.02～0.10)0.05 (0.05～0.06)1.06 (1.02～1.10)
RR101.05 (1.04～1.06)
RR111.10 (1.08～1.12)
高温RR011.06 (1.04～1.09)0.03 (-0.02～0.08)0.03 (0.02～0.03)1.03 (1.00～1.05)
RR101.05 (1.04～1.06)
RR111.14 (1.10～1.19)
PM2.5低温RR010.93 (0.89～0.98)0.12 (0.07～0.18)0.11 (0.10～0.11)1.12 (1.06～1.19)
RR101.03 (1.02～1.04)
RR111.08 (1.06～1.10)
高温RR011.07 (1.05～1.09)-0.04 (-0.10～0.01)-0.04 (-0.04～-0.04)0.96 (0.94～0.98)
RR101.04 (1.02～1.05)
RR111.06 (1.01～1.11)
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气象因素与空气污染物对呼吸系统疾病的影响及其交互作用
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李根山 1 , 何淑珍 2, 1 , 马春光 2 , 吴晶 2 , 马玉花 3 , 胡钟桦 4 , 宁振旭 1 , 张建秀 1 , 华晓娟 1
现代预防医学 | 环境与职业卫生 2025,52(23): 4283-4290
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现代预防医学 |环境与职业卫生 2025 , 52 (23) : 4283 -4290
气象因素与空气污染物对呼吸系统疾病的影响及其交互作用
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李根山（2000—），男，硕士在读，研究方向：环境与健康

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李根山（2000—），男，硕士在读，研究方向：环境与健康

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李根山1, 何淑珍2, 1 , 马春光2, 吴晶2, 马玉花3, 胡钟桦4, 宁振旭1, 张建秀1, 华晓娟1
作者信息
  • 1.青海大学医学部公共卫生系，青海　西宁　810000
  • 2.西宁市疾病预防控制中心职业卫生科，青海　西宁　810000
  • 3.西宁市第三人民医院
  • 4.西宁市卫生健康委员会
通讯作者:
何淑珍，E-mail：
作者简介:

李根山（2000—），男，硕士在读，研究方向：环境与健康

The effects of meteorological factors and air pollutants on respiratory diseases and their interaction
Gen-shan LI1, Shu-zhen HE2, 1 , Chun-guang MA2, Jing WU2, Yu-hua MA3, Zhong-hua HU4, Zhen-xu NING1, Jian-xiu ZHANG1, Xiao-juan HUA1
Affiliations
  • Department of Public Health, Faculty of Medicine, Qinghai University, Xining, Qinghai 810000, China
出版时间: 2025-12-10 doi: 10.20043/j.cnki.MPM.202506145
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摘要
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目的

探讨西宁市气象因素与空气污染物对呼吸系统疾病门诊就诊量的独立和交互作用，为高原地区城市公共健康防护提供科学依据。

方法

采用时间分层病例交叉设计，纳入2016—2021年西宁市三家医院呼吸系统门诊数据,污染物数据来源于中国空气质量在线监测平台。通过广义非线性模型分析CO、O3、SO2、NO2、PM10、PM2.5及温度的滞后效应，并利用双变量响应面模型和相对超额风险、归因比例量化交互作用。

结果

单污染物模型分析显示：CO、NO2随浓度升高就诊风险增大；SO2、PM10和PM2.5呈现出“先升后降”的曲线，峰值RR分别为1.69、1.55和1.80；O3呈“U”形曲线，低谷RR为0.76。滞后效应分析表明，SO2、NO2、PM10的当日效应最强， SO2在滞后第7天达峰值（RR=1.54 ,95% CI：1.26～1.88），NO2在第三天累积滞后效应达到峰值（RR=1.20 ,95% CI：1.11～1.31）。交互作用分析显示，在低温条件下SO2、NO2、PM10、PM2.5存在显著协同效应，而与O3呈拮抗效应；在高温条件下仅与O3呈协同效应。

结论

西宁市呼吸系统疾病风险受污染物浓度、温度及其交互作用共同影响，低温加剧气态污染物和颗粒物对健康的危害，高温增强O3的危害效应。

污染物  /  交互作用  /  呼吸系统疾病  /  滞后效应
Objective

To explore the independent and interactive effects of meteorological factors and air pollutants on the outpatient visits of respiratory diseases in Xining City, and to provide a scientific basis for urban public health protection in plateau areas.

Methods

A time-stratified case-crossover design was used. Outpatient visit data for respiratory diseases from three hospitals in Xining during 2016—2021 were included, and pollutant data were obtained from the China Air Quality Online Monitoring Platform. A generalized nonlinear model was applied to assess the lagged effects of CO, O3, SO2, NO2, PM10,PM2.5 and temperature. Interactions were quantified using a bivariate response surface model, relative excess risk due to interaction (RERI), and attributable proportion (AP).

Results

The single pollutant model analysis showed that the risk of outpatient visits increased with higher concentrations of CO and NO2. SO2, PM10 and PM2.5 showed a ‘first increase and then decrease’ curve, and the peak RRs were 1.69, 1.55 and 1.80, respectively. O3 showed a ‘U’ shaped curve, and the trough RR was 0.76. The lag effect analysis indicated that same-day effects were strongest for SO2, NO2 and PM10. The peak RR of SO2 reached (RR=1.54, 95% CI: 1.26-1.88) at lag 7 days, and the cumulative lag effect of NO2 reached (RR=1.20, 95% CI: 1.11-1.31) at lag 3 days. The interaction analysis showed that SO2, NO2, PM10 and PM2.5 had a significant synergistic effect at low temperatures, but had an antagonistic effect with O3. Under high temperature conditions, it only had a synergistic effect with O3.

Conclusion

The risk of respiratory diseases in Xining is affected by the concentration of pollutants, temperature and their interaction. Low temperature aggravates the health hazards of gaseous pollutants and particulate matter, and high temperature enhances the hazard effect of O3.

Pollutants  /  Interaction  /  Respiratory diseases  /  Lag effect
李根山, 何淑珍, 马春光, 吴晶, 马玉花, 胡钟桦, 宁振旭, 张建秀, 华晓娟. 气象因素与空气污染物对呼吸系统疾病的影响及其交互作用. 现代预防医学, 2025 , 52 (23) : 4283 -4290 . DOI: 10.20043/j.cnki.MPM.202506145
Gen-shan LI, Shu-zhen HE, Chun-guang MA, Jing WU, Yu-hua MA, Zhong-hua HU, Zhen-xu NING, Jian-xiu ZHANG, Xiao-juan HUA. The effects of meteorological factors and air pollutants on respiratory diseases and their interaction[J]. Modern Preventive Medicine, 2025 , 52 (23) : 4283 -4290 . DOI: 10.20043/j.cnki.MPM.202506145
正文
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呼吸系统疾病是全球公共卫生负担的重要组成部分，其发生与加重过程与多种环境暴露因素密切相关。世界卫生组织数据显示，空气污染每年导致约700万人过早死亡[1]，空气污染也会导致变应性鼻炎和哮喘等呼吸系统疾病的发病率增加[2]。与此同时，气候变化所引发的极端温度事件正不断加剧人群的健康风险。已有研究表明，温度不仅会通过改变污染物的扩散行为，还可能影响人体的免疫反应，从而与空气污染物共同作用，增强对健康的不利影响[3-5]。
在高海拔地区，独特的气候条件可显著改变环境因素对健康结局的影响模式。西宁市作为中国典型的高原城市，其地理与气候特征尤为特殊。该市位于湟水河谷地带，地形封闭，加之降水稀少，不利于空气污染物的扩散与稀释。同时，该地区强烈的太阳辐射为光化学反应提供了充足能量，促进二次污染物的形成，进一步加重了对居民呼吸系统健康的威胁。为全面了解污染物与气象因素对西宁市呼吸系统疾病门诊就诊量的影响，本研究利用2016—2021年西宁市居民就诊数据，结合气象和污染物监测数据，构建分布滞后非线性模型，分析污染物和气象因素对呼吸系统疾病门诊风险的影响及其滞后效应，并探讨其交互作用。
1　材料与方法
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1.1　资料来源
本研究收集西宁市中心城区三家三甲医院2016—2021年的就诊数据，包括就诊者性别、年龄、就诊时间、国际疾病分类(International Classification of Diseases 10,ICD-10)编码等信息。将ICD-10编码为J00～J99呼吸系统疾病的个案剔除重复数据后全部纳入此次研究。气象数据来自于西宁市气象局监测站同期逐日气象数据，包括平均气温、平均相对湿度。空气污染物数据来源于中国空气质量在线监测平台，主要包括SO2（μg/m3）、NO2（μg/m3）、O3-8h（μg/m3）、CO（mg/m3）、PM2.5（μg/m3）、PM10（μg/m3）6项数据。本研究将四个国控监测点污染物逐日浓度的平均值作为西宁市居民的每日平均污染物暴露浓度。
1.2　描述性分析
采用R软件统计分析研究期间各个变量的均值和标准差、最大值、最小值、中位数、四分位数等。采用Spearman相关分析描述污染物及气象因素间的相关性。
1.3　统计学分析
本研究采用时间分层病例交叉设计，把与病例事件发生日同一个月份中同一个星期日的其他病例最为对照日，来控制长期趋势和星期效应等时间混杂因素[6-7]。并且采用广义非线性模型，以捕捉空气污染物对呼吸系统疾病的影响。在模型构建中引入了交叉基函数，探索污染物浓度的非线性关系及其滞后效应。模型如下；
其中β0、β1、β2分别为模型的截距项和变量的系数；Yt为第t日的呼吸系统门诊人次数；f (Xt，l)为交叉基函数；ns (RHt,v=3)[8]是相对湿度在t日的自然样条函数，自由度为3；α为时间分层系数；stratum是基于年月与星期几的时间分层变量，用于控制潜在时间混杂。
为评估空气污染物与温度对呼吸系统疾病门诊就诊量的交互作用，首先采用双变量响应面模型，通过绘制三维空间图对交互作用进行定性分析，直观地展示两者交互作用对居民门诊量的影响。其次将空气污染物按照中位数分为低浓度和高浓度两组，并将日平均温度高于第95百分位数定义为高温暴露，低于第5百分位数定义为低温暴露[9]。将污染物与温度组合形成等级变量纳入模型，计算污染物与温度的交互作用，交互作用通过相对超额风险（relative excess risk due to interaction,RERI）、归因比例（attributable proportion,AP）以及交互作用指数（synergy index,S）进行量化[10-11]，所有指标的95%置信区间(confidence interval，CI)采用delta法计算。公式如下：
本研究采用R 4.4.2软件的“dlnm”和“gnm”软件包进行模型构建和统计分析。检验水准α=0.05。
2　结果
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2.1　基本情况
污染物O3、SO2、NO2、PM2.5、PM10、CO的均值分别为93.31、20.03、39.31、87.87和40.17 μg/m3以及1.36 mg/m3。PM2.5和PM10最大值分别达到290.00和978.00 μg/m3，体现出研究地区的独有特征。气象参数也显示出研究区域的独特气候特征，在研究期间，日均温度跨度达41.8℃，在-16.20℃～25.60℃之间波动，平均为6.46℃，而相对湿度在15%～94%之间，均值为56.67%。本研究共纳入了358 851例研究对象，日均门诊量为163.71人次，最小值为9人次，最大值为493人次，呈现一定程度的日常波动。见图1，表1。
2.2　相关性分析
为探究污染物与气象因素之间的关系，采用Spearman相关系数进行分析，表2结果显示，多数污染物之间存在中等至强的正相关性，CO与SO2（r=0.66）、NO2（r=0.67）、PM10（r=0.59）、PM2.5（r=0.68）呈现正相关关系，表明这些污染物在一定程度上具有相似的来源或排放机制，可能受到共同的人类活动或环境因素影响。同时，CO与O3（r=-0.42）、相对湿度（RH ,r=-0.33）、日均温度（Temp ,r=-0.53）呈负相关。此外，PM10与PM2.5之间的相关性亦较强（r=0.83），符合其颗粒组成和生成机制的重叠特性。
气象因素中，气温与O3呈较强正相关（r=0.72），与PM2.5（r=-0.61）、CO（r=-0.53）呈负相关，这表明高温条件下臭氧生成增强，但其他污染物（如交通和燃烧相关）浓度可能因大气扩散条件改善而降低。相对湿度与多个污染物呈负相关，其中PM10（r=-0.46）和SO2（r=-0.44）相关性较强，可能与湿度条件下颗粒物的吸湿增长和洗脱作用相关。
2.3　污染物对呼吸系统疾病门诊风险的影响
图2为污染物与呼吸系统疾病就诊风险的总体暴露-反应关系、污染物在中位数时的单日滞后以及累积滞后。由图可见污染物CO与NO2随着浓度的升高就诊风险逐渐上升；SO2、PM10和PM2.5呈现出先上升后下降，在36.00、503.00和152.30 μg/m3达到最大值，RR值分别为1.69（95% CI：1.42～2.01）、1.55（95% CI：1.28～1.88）、1.80（95% CI：1.49～2.20）；而O3呈现出“U”形曲线，当O3浓度处于第 87% 至第 97% 分位数时（浓度值为130.00～170.00 μg/m3），结果具有统计学意义，当浓度在159.40 μg/m3时风险最小，此时RR=0.76（95% CI：0.59～0.98），臭氧降低了呼吸系统疾病门诊就诊量。
SO2在暴露当天（lag0）风险最大（RR=1.10 ,95% CI：1.03～1.18），随着滞后天数的增加，风险呈现出先降低再增加。对于累积滞后效应，则随着滞后天数的累积风险逐渐增大，在第七天（lag07）达到最大（RR=1.54 ,95% CI：1.26～1.88）。NO2同样在暴露当天（lag0）风险最大（RR=1.20 ,95% CI：1.11～1.31），随着滞后天数的增加，风险逐渐降低，在第三天（lag3）之后效应不再具有显著性。在累积滞后效应中，在第三天（lag03）达到最大（RR=1.49 ,95% CI：1.25～1.77），之后几天依然维持较高风险。PM10在暴露当天（lag0）风险最大（RR=1.08 ,95% CI：1.02～1.15），随着滞后天数的增加，风险逐渐降低，在第一天（lag1）之后效应不再具有显著性。对于累积滞后效应，则随着滞后天数的累积风险逐渐增大，在第一天（lag01）达到最大（RR=1.12,95% CI：1.02～1.23）。污染物CO、O3和PM2.5在各自中位数浓度时的单日滞后效应和累积滞后效应不具有显著性（P>0.05）。
2.4　气象因素与污染物对呼吸系统疾病门诊量的交互作用分析
表3显示，低温条件下，SO2、NO2、PM10和PM2.5表现出较强的协同效应，而O3呈现拮抗效应；在高温暴露时，O3则转为协同效应，其他污染物与高温的交互作用不具有显著性。
具体而言，SO2与低温暴露存在强正向交互作用，交互作用导致的超额危险占比高而且对风险修饰明显。NO2在低温条件下RERI=0.11（95% CI：0.06～0.15）、AP=0.10（95% CI：0.09～0.10）、S=1.11（95% CI：1.06～1.15）,说明低温与NO2暴露的联合作用显著增加健康风险。O3的交互模式呈现温度依赖性特征：低温时RR=0.87（95% CI：0.80～0.94）、RERI=-0.17（95% CI：-0.24～-0.10）、AP=-0.20（95% CI：-0.20～-0.19）、S=0.84（95% CI：0.82～0.86），表明低温与O3暴露存在显著负向交互，即联合暴露风险低于单独暴露风险之和；在高温场景下RR=1.05（95% CI：1.03～1.08）、RERI=0.15（95% CI：0.05～0.25）、AP=0.14（95% CI：0.13～0.15）、S=1.16（95% CI：1.03～1.30），提示高温与O3暴露存在正向交互作用。颗粒物PM10和PM2.5在低温条件下的交互作用均表现为协同作用，PM10RERI=0.06（95% CI：0.02～0.10）、AP=0.05（95% CI：0.05～0.06）、S=1.06 ,95% CI：1.02～1.10）， PM2.5在低温下RERI=0.12（95% CI：0.07～0.18）、AP=0.11（95% CI：0.10～0.11）、S=1.12 （95% CI：1.06～1.19），提示低温下颗粒物对呼吸系统的影响较为显著,表现出较强的协同效应。见图3。
3　讨论
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本研究使用时间分层病例交叉研究分析空气污染物和气象因素对西宁市呼吸系统疾病门诊就诊的交互影响。研究揭示了西宁市空气污染物、气象因素与呼吸系统疾病门诊量之间的复杂关联。单污染模型结果表明，污染物CO、O3、SO2、NO2、PM10和PM2.5与呼吸系统疾病门诊就诊显著相关。同时使用二元响应面模型和分层分析揭示了气温与空气污染之间的相互作用。
在暴露反应关系中，研究结果符合环境健康研究的普遍规律。污染物CO与NO2与国内多数研究结果一致[12-13]，均表现出随着污染物的浓度升高，直接加剧就诊风险，这可能归因于二者的毒性作用，尤其是 NO2对呼吸道上皮细胞的直接损伤[14-15]；污染物SO2、PM10和PM2.5呈现先上升后下降的倒“U”形曲线，表明在西宁市存在阈值效应，当污染物浓度超过一定水平后，可能触发居民防护行为或医疗资源的提前介入，从而降低后续风险。而O3则表现出复杂性，其浓度与呼吸系统疾病就诊量之间呈现出非线性的“U”形曲线，对呼吸系统疾病就诊量的影响先下降后上升，浓度在第 87% 至第 97% 分位数呈现出保护因素，这一发现与O3是一种有害空气污染物的认知相悖。而在京津冀[16]、湖北[17]与和田[18]的研究中均发现O3作为一种保护性因素降低了呼吸系统疾病的发生与加重。另一方面，是由于强烈的季节混杂引起的，在研究期间高浓度O3主要集中在夏季，而西宁市呼吸系统疾病如病毒性流感和慢阻肺的就诊高峰集中在秋冬季，而此时的O3浓度处于全年最低水平。虽然已经对模型进行了时间混杂的调整，但残余的混杂可能依然存在。因此在未来的研究中需要使用更加精细化的暴露评估和模型来分离这种复杂的季节效应。
在交互作用研究结果显示，与温度负相关的污染物SO2、NO2和颗粒物在低温条件下协同效应显著增强，这与西宁市冬季气温低、逆温现象频繁[19]、使污染物更易滞留和富集，同时冬季采暖需求上升导致污染源排放增加，在双重作用背景下使居民暴露在更高浓度的环境中[20]。此外低温会削弱呼吸道的黏膜屏障功能，使污染物更易穿透呼吸道深层，触发炎症反应。在这些因素的共同作用下，导致低温与污染物复合暴露对就诊风险的提升更为显著。而在高温条件下仅O3表现协同效应，主要是O3的生成依赖高温与强紫外线，在高温期间，太阳辐射强，促进光化学反应，使O3浓度增高，高O3暴露直接刺激呼吸道上皮细胞，引发氧化应激和炎症反应，增加就诊风险[21]。
综上所述，暴露于CO、O3、SO2、NO2、PM10和PM2.5会增加西宁市居民呼吸系统疾病就诊风险并存在滞后效应。在交互作用中，SO2、NO2、PM10和PM2.5与低温之间存在协同作用。O3与高温存在协同作用，反之在低温时拮抗。综上所述，本研究为西宁市制定基于温度与污染交互作用的呼吸系统疾病防控策略提供了科学依据，未来需加强多部门协同，优化能源结构，完善空气污染预警体系，并针对不同季节的环境特征开展精准干预，以降低居民呼吸系统疾病负担。
基金
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  • 中国疾病预防控制中心——环境卫生与健康防护宣教项目(中疾控环协合同气候发[2024]1-2号)
文献
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2025年第52卷第23期
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文章信息
doi: 10.20043/j.cnki.MPM.202506145
  • 接收时间：2025-06-12
  • 首发时间：2026-11-02
  • 出版时间：2025-12-10
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出版历史
  • 收稿日期：2025-06-12
基金
中国疾病预防控制中心——环境卫生与健康防护宣教项目(中疾控环协合同气候发[2024]1-2号)
作者信息
    1.青海大学医学部公共卫生系，青海　西宁　810000
    2.西宁市疾病预防控制中心职业卫生科，青海　西宁　810000
    3.西宁市第三人民医院
    4.西宁市卫生健康委员会

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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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