Article(id=1241036247997665338, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241036242561855785, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202504298, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1744646400000, receivedDateStr=2025-04-15, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773815698861, onlineDateStr=2026-03-18, pubDate=1756051200000, pubDateStr=2025-08-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773815698861, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773815698861, creator=13701087609, updateTime=1773815698861, updator=13701087609, issue=Issue{id=1241036242561855785, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='16', pageStart='2881', pageEnd='3072', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773815697565, creator=13701087609, updateTime=1773840190562, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241138973712634304, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241036242561855785, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241138973712634305, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241036242561855785, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=2932, endPage=2937, ext={EN=ArticleExt(id=1241036250652659792, articleId=1241036247997665338, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Time-series study on the impact of particulate matter components with different size fractions on circulatory system disease mortality, columnId=1228016570660745413, journalTitle=Modern Preventive Medicine, columnName=Environmental and Occupational Health, runingTitle=null, highlight=null, articleAbstract=
Objective

To investigate the impact of chemical components in multi-size particulate matter on mortality risk from circulatory system diseases, and to provide evidence for establishing refined air pollution control strategies.

Methods

Based on mortality data for circulatory system diseases in Tianjin from 2019 to 2022, integrated with multi-size particulate matter component profiles and meteorological parameters, a generalized linear regression model based on quasi-Poisson distribution was constructed to quantitatively assess the differential contributions of particulate matter components across size ranges to mortality risk.

Results

During the study period, 37 416 circulatory system disease deaths were recorded, with a weekly average mortality of 393.85±93.70 cases. The health effects of particulate matter were most pronounced in the 1.1- 2.1 μm size range, where a 1 interquartile range (IQR) increase was associated with a 6.83% (95%CI: 1.25%-12.72%) elevation in circulatory mortality risk. Component-specific analyses identified chloride ions, potassium, chromium, titanium, and cadmium as significantly correlated with circulatory mortality. Smaller particle sizes exhibited greater toxicity, with heightened sensitivity observed in females and individuals over 65 years old.

Conclusion

Particulate matter health effects demonstrate significant size-dependent characteristics, with diminishing particle sizes correlating to increased toxic complexity. Targeted control strategies are recommended, prioritizing metal components and secondary inorganic ions in particles below 2.5μm.

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目的

探讨多粒径颗粒物不同组分对循环系统疾病死亡的影响,为建立精细化空气污染防控策略提供依据。

方法

基于天津市2019-2022年循环系统疾病死亡数据,整合大气污染物多粒径组分谱及气象参数,构建基于准泊松分布的广义线性模型,定量评估不同粒径段颗粒物各组分对死亡风险的贡献差异。

结果

研究期间共37 416例循环系统疾病死亡病例,周均死亡人数为393.85±93.70例;结果显示,颗粒物在1.1~2.1 μm粒径段中的健康效应最为显著,每升高1个四分位距(IQR)可导致循环系统疾病死亡风险增加6.83%(95%CI:1.25%~12.72%);组分效应结果表明,氯离子、钾、铬、钛、镉5种组分与循环系统疾病死亡显著相关,粒径越小,毒性效应更大,且女性、65岁以上人群更为敏感。

结论

颗粒物健康效应呈现显著粒径依赖性,粒径越小,毒性越复杂。建议实施精细化管控,重点监管2.5 μm以下粒径中的金属及二次无机离子组分。

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韩丹丹,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=5d5dMt1De5FgKfMd4KYyWw==, magXml=NE4QTVEFygqpachk7ecSbQ==, pdfUrl=null, pdf=/VPS0W7kSn5IBK7j2zRxkQ==, pdfFileSize=1355591, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=Z2xDT1K4F0Kwv2UETXfGgg==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=Uuh+q6doe6TsjStm2ME05Q==, mapNumber=null, authorCompany=null, fund=null, authors=

龙朝东(1997—),男,硕士在读,研究方向:环境与健康

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龙朝东(1997—),男,硕士在读,研究方向:环境与健康

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龙朝东(1997—),男,硕士在读,研究方向:环境与健康

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Characterization and source apportionment of PM2.5 based on the online observation in Tianjin[J]. Environmental Science, 2020, 41(10): 4355-4363.(In Chinese), articleTitle=Characterization and source apportionment of PM2.5 based on the online observation in Tianjin, refAbstract=null), Reference(id=1241057512892453177, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036247997665338, doi=null, pmid=null, pmcid=null, year=2023, volume=218, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[36], rfOrder=42, authorNames=Liu X, Tian Y, Xue Q, journalName=Environmental Research, refType=null, unstructuredReference=Liu X, Tian Y, Xue Q, et al. Contributors to reductions of PM2.5-bound heavy metal concentrations and health risks in a Chinese megacity during 2013, 2016 and 2019: anadvanced method to quantify source-specific risks from various directions[J]. Environmental Research. 2023, 218: 114989., articleTitle=Contributors to reductions of PM2.5-bound heavy metal concentrations and health risks in a Chinese megacity during 2013, 2016 and 2019: anadvanced method to quantify source-specific risks from various directions, refAbstract=null), Reference(id=1241057513039253823, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036247997665338, doi=null, pmid=null, pmcid=null, year=2023, volume=30, issue=8, pageStart=908, pageEnd=912, url=null, language=null, rfNumber=[37], rfOrder=43, authorNames=张文军, 雷立健, journalName=实用预防医学, refType=null, unstructuredReference=张文军,雷立健.2019—2020年阳泉市大气PM2.5污染与居民心血管疾病死亡效应的时间序列分析[J].实用预防医学2023,30(8):908-912., articleTitle=2019—2020年阳泉市大气PM2.5污染与居民心血管疾病死亡效应的时间序列分析, refAbstract=null), Reference(id=1241057513257357638, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036247997665338, doi=null, pmid=null, pmcid=null, year=2023, volume=30, issue=8, pageStart=908, pageEnd=912, url=null, language=null, rfNumber=[37], rfOrder=44, authorNames=Zhang WJ, Lei LJ, journalName=Practical Preventive Medicine, refType=null, unstructuredReference=Zhang WJ, Lei LJ. Time series analysis on the effect between atmospheric PM2.5 pollution and deaths from cardiovascular diseases among residents in Yangquan City, 2019-2020[J]. Practical Preventive Medicine, 2023, 30(8): 908-912.(In Chinese), articleTitle=Time series analysis on the effect between atmospheric PM2.5 pollution and deaths from cardiovascular diseases among residents in Yangquan City, 2019-2020, refAbstract=null), Reference(id=1241057513370603853, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036247997665338, doi=null, pmid=null, pmcid=null, year=2024, volume=364, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[38], rfOrder=45, authorNames=Li YX, Lu B, Wei J, journalName=Chemosphere, refType=null, unstructuredReference=Li YX, Lu B, Wei J, et al. Short-term exposure to ambient fine particulate matter constituents and myocardial infarction mortality[J]. Chemosphere, 2024, 364: 143101., articleTitle=Short-term exposure to ambient fine particulate matter constituents and myocardial infarction mortality, refAbstract=null), Reference(id=1241057513479655762, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036247997665338, doi=null, pmid=null, pmcid=null, year=2023, volume=294, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[39], rfOrder=46, authorNames=Ma X, Duan H, Zhang, H, journalName=Atmospheric Environment, refType=null, unstructuredReference=Ma X, Duan H, Zhang, H, et al. Short-term effects of PM1, PM2.5, and PM2.5 constituents on myocardial infarction mortality in Qingdao, China: a time-stratified case-crossover analysis[J]. Atmospheric Environment, 2023, 294: 119478., articleTitle=Short-term effects of PM1, PM2.5, and PM2.5 constituents on myocardial infarction mortality in Qingdao, China: a time-stratified case-crossover analysis, refAbstract=null)], funds=[Fund(id=1241057506978485214, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036247997665338, awardId=NKPMF202408, language=CN, fundingSource=生态环境部城市空气颗粒物污染防治重点实验室2025年度开发基金资助项目(NKPMF202408), fundOrder=null, country=null), Fund(id=1241057507108508650, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036247997665338, awardId=IT2023B11, language=CN, fundingSource=河北大学公共卫生安全科研创新团队项目(IT2023B11), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241057503279108777, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036247997665338, xref=1., ext=[AuthorCompanyExt(id=1241057503287497385, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036247997665338, companyId=1241057503279108777, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Hebei Provincial Key Laboratory of Public Health Security, School of Public Health, Hebei University, Baoding, Hebei 071000, China), AuthorCompanyExt(id=1241057503295885994, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036247997665338, companyId=1241057503279108777, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.河北大学公共卫生学院,河北省公共卫生安全重点实验室,河北 保定 071000)]), AuthorCompany(id=1241057503413326516, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036247997665338, xref=2., ext=[AuthorCompanyExt(id=1241057503417520822, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036247997665338, companyId=1241057503413326516, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.天津市疾病预防控制中心)])], figs=[ArticleFig(id=1241057505791497090, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036247997665338, language=EN, label=Fig.1, caption=Descriptive analysis of component concentrations in particulate matter with different particle sizes in Tianjin, 2019-2022, figureFileSmall=X/ABB7g605bx6dLZD4uWkw==, figureFileBig=bONhEyo1wVNoqxfjvTWSMA==, tableContent=null), ArticleFig(id=1241057505904743309, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036247997665338, language=CN, label=图1, caption=2019-2022年天津市不同粒径颗粒物各组分浓度描述性分析, figureFileSmall=X/ABB7g605bx6dLZD4uWkw==, figureFileBig=bONhEyo1wVNoqxfjvTWSMA==, tableContent=null), ArticleFig(id=1241057506043155354, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036247997665338, language=EN, label=Fig.2, caption=Association of component concentrations in particulate matter with different particle sizes and circulatory system disease mortality, figureFileSmall=ecVQfg49+KDS1VTUkS69nQ==, figureFileBig=0tK5upBW1o8y5T0SFLlJ4A==, tableContent=null), ArticleFig(id=1241057506127041445, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036247997665338, language=CN, label=图2, caption=不同粒径段颗粒物各组分对循环系统疾病死亡的影响, figureFileSmall=ecVQfg49+KDS1VTUkS69nQ==, figureFileBig=0tK5upBW1o8y5T0SFLlJ4A==, tableContent=null), ArticleFig(id=1241057506223510443, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036247997665338, language=EN, label=Table 1, caption=

Baseline characteristics of circulatory system disease mortality, meteorological parameters, and pollutant profiles in Tianjin, 2019-2022

, figureFileSmall=null, figureFileBig=null, tableContent=
变量 $\bar{x} \pm s$最小值P25P50P75最大值
每周死亡人数
总人群393.85±93.70175.00325.00370.00455.50630.00
192.25±28.16106.00173.00191.00208.50263.00
201.60±79.3969.00147.00166.00251.00433.00
≤65岁57.83±12.7925.0049.0056.0065.5096.00
>65岁336.02±84.79150.00275.00315.00396.00552.00
气象指标
湿度(%)47.80±12.7222.0438.5446.3956.4479.50
温度(℃)13.26±10.14-5.844.2213.8022.0530.46
污染物浓度(μg/m3
O354.55±32.8011.0624.8648.0981.44132.84
NO241.99±14.2717.5329.1742.9953.4274.36
各粒径段PM浓度(μg/m3
PM0~0.4312.57±5.244.628.5011.3616.5829.17
PM0.43~0.6515.34±6.993.6610.4414.3220.3735.96
PM0.65~1.118.25±9.874.389.8416.3823.8147.46
PM1.1~2.117.69±10.774.3810.6815.2021.8567.73
PM2.1~3.314.06±6.434.309.7013.6417.4235.86
PM3.3~4.716.05±6.943.5611.4615.1819.2938.21
PM4.7~5.815.52±7.314.0610.2114.6718.6434.60
PM5.8~9.019.80±8.984.5813.8318.3125.0046.51
PM9.0~1014.37±7.992.439.1612.9619.5253.21
), ArticleFig(id=1241057506328368050, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036247997665338, language=CN, label=表1, caption=

2019—2022年天津市循环系统疾病死亡人数、气象及污染物基本情况

, figureFileSmall=null, figureFileBig=null, tableContent=
变量 $\bar{x} \pm s$最小值P25P50P75最大值
每周死亡人数
总人群393.85±93.70175.00325.00370.00455.50630.00
192.25±28.16106.00173.00191.00208.50263.00
201.60±79.3969.00147.00166.00251.00433.00
≤65岁57.83±12.7925.0049.0056.0065.5096.00
>65岁336.02±84.79150.00275.00315.00396.00552.00
气象指标
湿度(%)47.80±12.7222.0438.5446.3956.4479.50
温度(℃)13.26±10.14-5.844.2213.8022.0530.46
污染物浓度(μg/m3
O354.55±32.8011.0624.8648.0981.44132.84
NO241.99±14.2717.5329.1742.9953.4274.36
各粒径段PM浓度(μg/m3
PM0~0.4312.57±5.244.628.5011.3616.5829.17
PM0.43~0.6515.34±6.993.6610.4414.3220.3735.96
PM0.65~1.118.25±9.874.389.8416.3823.8147.46
PM1.1~2.117.69±10.774.3810.6815.2021.8567.73
PM2.1~3.314.06±6.434.309.7013.6417.4235.86
PM3.3~4.716.05±6.943.5611.4615.1819.2938.21
PM4.7~5.815.52±7.314.0610.2114.6718.6434.60
PM5.8~9.019.80±8.984.5813.8318.3125.0046.51
PM9.0~1014.37±7.992.439.1612.9619.5253.21
), ArticleFig(id=1241057506458391486, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036247997665338, language=EN, label=Table 2, caption=

Association of mass concentrations of particulate matter with different particle sizes and circulatory system disease mortality (%)

, figureFileSmall=null, figureFileBig=null, tableContent=
粒径总人群男性女性≤65岁>65岁
PM0~0.431.67(-5.44~9.31)-2.09(-7.47~3.61)5.04(-5.27~16.47)-1.82(-8.95~5.88)2.24(-5.34~10.43)
PM0.43~0.651.06(-5.42~7.97)-2.96(-7.79~2.13)4.68(-4.80~15.10)0.69(-5.97~7.83)4.68(-4.80~15.10)
PM0.65~1.14.65(-2.37~12.16)-1.10(-6.33~4.41)9.93(-0.41~21.35)4.21(-2.99~11.94)4.70(-2.77~12.75)
PM1.1~2.16.83(1.25~12.72)a0.85(-3.33,5.22)12.62(4.27~21.63)a-1.82(-8.95~5.88)7.16(1.18~13.48)a
PM2.1~3.32.08(-3.57~8.07)-0.62(-4.89~3.85)4.50(-3.76~13.46)0.68(-5.08~6.78)2.32(-3.72~8.72)
PM3.3~4.7-0.78(-5.76~4.48)-1.60(-5.46~2.42)-0.24(-7.37~7.43)-1.15(-6.27~4.26)-0.73(-6.03~4.88)
PM4.7~5.8-1.68(-6.69~3.60)-1.96(-5.89~2.13)-1.41(-8.52~6.25)-2.14(-7.30~3.31)-1.61(-6.94~4.03)
PM5.8~9.0-3.00(-8.57~2.91)-2.75(-7.14~1.85)-3.18(-11.04~5.37)-1.65(-7.51~4.57)-3.23(-9.14~3.06)
PM9.0~10-1.77(-7.38~4.18)-0.30(-4.74~4.34)-3.12(-10.99~5.44)1.25(-4.60~7.44)-2.30(-8.25~4.03)
), ArticleFig(id=1241057506559054785, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036247997665338, language=CN, label=表2, caption=

不同粒径段颗粒物对循环系统疾病死亡的影响(%)

, figureFileSmall=null, figureFileBig=null, tableContent=
粒径总人群男性女性≤65岁>65岁
PM0~0.431.67(-5.44~9.31)-2.09(-7.47~3.61)5.04(-5.27~16.47)-1.82(-8.95~5.88)2.24(-5.34~10.43)
PM0.43~0.651.06(-5.42~7.97)-2.96(-7.79~2.13)4.68(-4.80~15.10)0.69(-5.97~7.83)4.68(-4.80~15.10)
PM0.65~1.14.65(-2.37~12.16)-1.10(-6.33~4.41)9.93(-0.41~21.35)4.21(-2.99~11.94)4.70(-2.77~12.75)
PM1.1~2.16.83(1.25~12.72)a0.85(-3.33,5.22)12.62(4.27~21.63)a-1.82(-8.95~5.88)7.16(1.18~13.48)a
PM2.1~3.32.08(-3.57~8.07)-0.62(-4.89~3.85)4.50(-3.76~13.46)0.68(-5.08~6.78)2.32(-3.72~8.72)
PM3.3~4.7-0.78(-5.76~4.48)-1.60(-5.46~2.42)-0.24(-7.37~7.43)-1.15(-6.27~4.26)-0.73(-6.03~4.88)
PM4.7~5.8-1.68(-6.69~3.60)-1.96(-5.89~2.13)-1.41(-8.52~6.25)-2.14(-7.30~3.31)-1.61(-6.94~4.03)
PM5.8~9.0-3.00(-8.57~2.91)-2.75(-7.14~1.85)-3.18(-11.04~5.37)-1.65(-7.51~4.57)-3.23(-9.14~3.06)
PM9.0~10-1.77(-7.38~4.18)-0.30(-4.74~4.34)-3.12(-10.99~5.44)1.25(-4.60~7.44)-2.30(-8.25~4.03)
), ArticleFig(id=1241057506621969350, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036247997665338, language=EN, label=Table 3, caption=

Association of mass concentrations of particulate matter with different particle sizes and circulatory system disease mortality.(Multi-pollutant model)(%)

, figureFileSmall=null, figureFileBig=null, tableContent=
粒径PM+O3PM+NO2PM+O3+NO2
PM0~0.434.54(-2.61~12.22)1.84(-4.65~8.78)3.12(-3.67~10.39)
PM0.43~0.653.61(-2.89~10.56)0.20(-5.69~6.46)1.40(-4.4~8.05)
PM0.65~1.17.74(0.75~15.21)a2.49(-3.88~9.28)4.48(-2.43~11.87)
PM1.1~2.18.84(3.48~14.47)a4.56(-0.57~9.96)6.38(0.94~12.12)a
PM2.1~3.34.25(-1.35~10.17)1.82(-3.33~7.25)2.88(-2.49~8.54)
PM3.3~4.70.84(-4.11~6.06)-0.40(-4.99~4.41)0.20(-4.52~5.16)
PM4.7~5.80.03(-4.99~5.32)-0.92(-5.56~3.94)-0.35(-5.12~4.67
PM5.8~9.0-1.10(-6.74~4.87)-2.24(-7.41~3.33)-1.64(-6.99~4.02)
PM9.0~100.26(-5.40~6.26)-1.31(-6.50~4.17)-0.59(-5.98~5.12)
), ArticleFig(id=1241057506743604179, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036247997665338, language=CN, label=表3, caption=

不同粒径段颗粒物对循环系统疾病死亡的影响(多污染物模型)(%)

, figureFileSmall=null, figureFileBig=null, tableContent=
粒径PM+O3PM+NO2PM+O3+NO2
PM0~0.434.54(-2.61~12.22)1.84(-4.65~8.78)3.12(-3.67~10.39)
PM0.43~0.653.61(-2.89~10.56)0.20(-5.69~6.46)1.40(-4.4~8.05)
PM0.65~1.17.74(0.75~15.21)a2.49(-3.88~9.28)4.48(-2.43~11.87)
PM1.1~2.18.84(3.48~14.47)a4.56(-0.57~9.96)6.38(0.94~12.12)a
PM2.1~3.34.25(-1.35~10.17)1.82(-3.33~7.25)2.88(-2.49~8.54)
PM3.3~4.70.84(-4.11~6.06)-0.40(-4.99~4.41)0.20(-4.52~5.16)
PM4.7~5.80.03(-4.99~5.32)-0.92(-5.56~3.94)-0.35(-5.12~4.67
PM5.8~9.0-1.10(-6.74~4.87)-2.24(-7.41~3.33)-1.64(-6.99~4.02)
PM9.0~100.26(-5.40~6.26)-1.31(-6.50~4.17)-0.59(-5.98~5.12)
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不同粒径颗粒物组分对循环系统疾病死亡影响的时间序列研究
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龙朝东 1 , 叶萌 1 , 张经纬 2 , 韩丹丹 1
现代预防医学 | 环境与职业卫生 2025,52(16): 2932-2937
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现代预防医学 | 环境与职业卫生 2025, 52(16): 2932-2937
不同粒径颗粒物组分对循环系统疾病死亡影响的时间序列研究
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龙朝东1, 叶萌1, 张经纬2, 韩丹丹1
作者信息
  • 1.河北大学公共卫生学院,河北省公共卫生安全重点实验室,河北 保定 071000
  • 2.天津市疾病预防控制中心
  • 龙朝东(1997—),男,硕士在读,研究方向:环境与健康

通讯作者:

韩丹丹,E-mail:
Time-series study on the impact of particulate matter components with different size fractions on circulatory system disease mortality
Chao-dong LONG1, Meng YE1, Jing-wei ZHANG2, Dan-dan HAN1
Affiliations
  • Hebei Provincial Key Laboratory of Public Health Security, School of Public Health, Hebei University, Baoding, Hebei 071000, China
出版时间: 2025-08-25 doi: 10.20043/j.cnki.MPM.202504298
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目的

探讨多粒径颗粒物不同组分对循环系统疾病死亡的影响,为建立精细化空气污染防控策略提供依据。

方法

基于天津市2019-2022年循环系统疾病死亡数据,整合大气污染物多粒径组分谱及气象参数,构建基于准泊松分布的广义线性模型,定量评估不同粒径段颗粒物各组分对死亡风险的贡献差异。

结果

研究期间共37 416例循环系统疾病死亡病例,周均死亡人数为393.85±93.70例;结果显示,颗粒物在1.1~2.1 μm粒径段中的健康效应最为显著,每升高1个四分位距(IQR)可导致循环系统疾病死亡风险增加6.83%(95%CI:1.25%~12.72%);组分效应结果表明,氯离子、钾、铬、钛、镉5种组分与循环系统疾病死亡显著相关,粒径越小,毒性效应更大,且女性、65岁以上人群更为敏感。

结论

颗粒物健康效应呈现显著粒径依赖性,粒径越小,毒性越复杂。建议实施精细化管控,重点监管2.5 μm以下粒径中的金属及二次无机离子组分。

颗粒物  /  组分  /  循环系统疾病  /  死亡风险
Objective

To investigate the impact of chemical components in multi-size particulate matter on mortality risk from circulatory system diseases, and to provide evidence for establishing refined air pollution control strategies.

Methods

Based on mortality data for circulatory system diseases in Tianjin from 2019 to 2022, integrated with multi-size particulate matter component profiles and meteorological parameters, a generalized linear regression model based on quasi-Poisson distribution was constructed to quantitatively assess the differential contributions of particulate matter components across size ranges to mortality risk.

Results

During the study period, 37 416 circulatory system disease deaths were recorded, with a weekly average mortality of 393.85±93.70 cases. The health effects of particulate matter were most pronounced in the 1.1- 2.1 μm size range, where a 1 interquartile range (IQR) increase was associated with a 6.83% (95%CI: 1.25%-12.72%) elevation in circulatory mortality risk. Component-specific analyses identified chloride ions, potassium, chromium, titanium, and cadmium as significantly correlated with circulatory mortality. Smaller particle sizes exhibited greater toxicity, with heightened sensitivity observed in females and individuals over 65 years old.

Conclusion

Particulate matter health effects demonstrate significant size-dependent characteristics, with diminishing particle sizes correlating to increased toxic complexity. Targeted control strategies are recommended, prioritizing metal components and secondary inorganic ions in particles below 2.5μm.

Particulate matter  /  Components  /  Circulatory system diseases  /  Mortality risk
龙朝东, 叶萌, 张经纬, 韩丹丹. 不同粒径颗粒物组分对循环系统疾病死亡影响的时间序列研究. 现代预防医学, 2025 , 52 (16) : 2932 -2937 . DOI: 10.20043/j.cnki.MPM.202504298
Chao-dong LONG, Meng YE, Jing-wei ZHANG, Dan-dan HAN. Time-series study on the impact of particulate matter components with different size fractions on circulatory system disease mortality[J]. Modern Preventive Medicine, 2025 , 52 (16) : 2932 -2937 . DOI: 10.20043/j.cnki.MPM.202504298
我国大气颗粒物(Particulate matter,PM)污染已成为工业化与城市化进程中的重大环境健康挑战。2019年监测数据显示,全国PM2.5和PM10年均浓度达标城市比例分别仅为52.9%和67.9%,PM2.5浓度较世界卫生组织2021年指导值存在显著差距[1]。以天津市为例,2019—2023年间PM2.5、PM10年均浓度达到37 ~ 51 μg/m3和65 ~ 76 μg/m3,已经超出国家二级标准限值[2],区域大气污染治理形势严峻。
循环系统疾病多年位列我国居民主要死因首位,其疾病负担逐年加重[3]。研究证实,长期暴露于颗粒物污染会显著增加心肌梗死、脑卒中等循环系统疾病的死亡风险[4-5]。PM2.5的疾病归因风险已超越传统危险因素,成为仅次于高血压的第二位致病因子[6],2019年我国因PM2.5暴露引发的循环系统疾病超额死亡人数约占总死亡病例的20%[7],大气颗粒物污染已成为严重的公共卫生问题。
尽管颗粒物与循环系统疾病的健康效应研究已取得进展,但组分毒性解析仍面临挑战。污染类型转变使颗粒物组分因时空异质性呈现健康效应差异[8-11],其独毒理机制涉及氧化应激、系统性炎症等多途径分异[12]。但现有研究多局限于单一粒径组分分析或总质量浓度评估[13-16],导致多粒径组分效应量化不足,制约精准防控策略制定。
本研究创新性整合天津市2019—2022年高分辨率颗粒物数据与循环系统疾病死亡病例数据,构建多粒径、多组分协同暴露分析框架,定量解析不同粒径颗粒物及其组分死亡风险贡献差异。研究成果将为京津冀地区颗粒物管控提供依据,助力“减污降碳协同增效”战略目标实现。
本研究死亡数据来源于天津市疾病预防控制中心环境与健康监测平台,系统采集2019年1月1日至2022年12月31日期间天津市全人群循环系统疾病逐日死亡病例。所有病例根本死因均依据国际疾病分类第十版(ICD-10)进行标准化编码。基于以下标准进行数据清洗:(1)排除居住地信息缺失病例;(2)剔除诊断依据不明确病例。
不同粒径段颗粒物组分谱由南开大学大气环境超级观测站提供。颗粒物组分数据包括有机碳(Organic Carbon,OC)、水溶性无机离子硝酸盐(Nitrate,NO3-)、金属元素钙(Calcium,Ca)等23种成分。此外,同期大气污染物(O3、NO2)浓度数据来源于天津市环境空气质量监测站点。地面气象观测数据来源于天津市气象局,包括日均气温(℃)和相对湿度(%)。
对研究期间天津市大气污染物和气象资料采用四分位数[MP25P75)]、最大、最小值以及均值和标准差进行描述性统计。由于每周循环系统疾病死亡人数服从半泊松回归分布,为方便表达,也采用上述指标进行描述。
由于不同粒径颗粒物各组分的逐日质量浓度较低,我们采用每周的均值作为暴露浓度,同时匹配每周的平均死亡人数,构建基于准泊松分布的广义线性模型来评估不同粒径细颗粒物组分暴露浓度与每周循环系统疾病死亡人数的关联。同时,采用自然样条函数控制温度、湿度和时间等混杂因素的影响。具体模型如下:
式(1)中:Yi为第i周的死亡人数,EYi)为第i周的预期死亡人数;Zi为第i周的不同粒径颗粒物各组分的浓度;ns为自然样条平滑函数;df为自由度;由于本研究是基于每周构成的时间序列,所以时间自由度确定为3,温度和湿度的自由度均选择3。先验研究表明,通过引入双污染物模型进行敏感性分析,可控制其他污染物导致的共线性和混杂效应,以此检验模型的稳定性[17-20]。因此,本研究在分析不同粒径颗粒物对循环系统疾病死亡的影响中,向模型中引入NO2和O3同期暴露浓度构建双污染物模型以估计潜在的修饰效应。
暴露效应估计值以颗粒物及其组分浓度每增加1个四分位距(Interquartile Range,IQR)对应的循环系统疾病超额死亡风险(Excess risk,ER)变化百分比及其95%置信区间(95%CI)表示。所有统计分析均使用R统计软件4.3.3版进行。采用双侧检验,当P值<0.05时考虑统计显著性。
研究期间,天津市共有37 416名患者诊断为因患循环系统疾病而死亡,其中男、女各占比48.81%、51.19%,65岁以上人群占比较大,占比85.32%;每周平均死亡人数393.85±93.70人;每周平均温湿度分别为13.26±10.14℃和47.80±12.72%,每周O3平均浓度为54.55±32.80 μg/m3,每周NO2平均浓度为41.99±14.27 μg/m3,不同粒径段颗粒物中,5.8~9.0 μm粒径段浓度最高,每周平均浓度为19.80±8.98 μg/m3。见表1
本研究通过系统分析不同粒径颗粒物各成分分布特征,揭示了显著的粒径分异规律。颗粒物总质量浓度呈现多模态分布特征,浓度中位数(Median)范围介于11.36~18.31 μg/m3,其中5.8~9.0 μm粒径段出现浓度峰值(Median+IQR:18.31+11.16 μg/m3)。对于地壳元素铁、铝、钙、钛呈现典型的粒径依赖性特征,其浓度均随粒径增大显著升高,其中铁和钙在5.8~9.0 μm粒径段出现浓度峰值。值得注意的是,二次无机离子组分(铵根、硝酸根、硫酸根)的粒径分布呈现差异化特征:硫酸根、硝酸根均在0.65~1.1 μm粒径段中显著富集,其浓度峰值分别为1.18+2.72 μg/m3、2.05+2.12 μg/m3。各粒径段中的碳质组分浓度富集特征不明显,有机碳和元素碳浓度在0.65~1.1 μm粒径段最高。微量重金属元素(砷、镉、铅等)整体浓度水平较低,其浓度中位数均小于0.01 μg/m3。见图1
粒径特异性分析显示,风险方向存在粒径依赖性,3.3 μm以下粒径范围的颗粒物多呈现正向风险(ER>0),而3.3 μm以上粒径范围的颗粒物则普遍显示负向或中性效应(ER≤0),但大多数未表现出统计学显著性;其中,1.1~2.1 μm粒径段对全人群循环系统疾病死亡具有显著影响,颗粒物浓度每增加一个四分位距(IQR),死亡风险增加6.83%(95%CI:1.25%~12.72%)(P<0.05)。分层分析表明,女性以及65岁以上人群对细颗粒物暴露更为敏感,且均在1.1~2.1 μm粒径段的超额死亡风险均达到最大,分别为12.62%(95%CI:4.27%~21.63%)和7.16%(95%CI:11.18%~13.48%),其他粒径段的超额死亡风险未达统计学显著性。见表2
研究发现,不同粒径段的主要毒性组分及其效应强度存在明显差异。氯离子、钾、铬、钛、镉对循环系统疾病死亡的影响最为显著,且多在3.3 μm以下粒径段出现统计学关联,随着粒径增大,各组分效应强度逐渐降低且出现非统计学意义的保护效应。其中,氯离子和铬表现出跨粒径段的广泛影响,在0 ~ 0.43 μm粒径段中表现出最强的风险效应,每变化一个IQR,可导致循环系统疾病死亡风险增加16%(95%CI:7.87% ~ 24.76%),铬在5.8 ~ 9.0 μm粒径段中效应值达到最大(ER=6.79%,95%CI:2.82% ~ 10.92%)。地壳元素(铝、钙、铁、镁、钛)的关联性较弱且方向不一致,仅钛在1.1 μm以下粒径段中表现出显著的死亡效应,金属元素(如铜、锌、砷)在细颗粒物中风险微弱且不显著,且在部分粒径段中出现了保护效应。此外,化学成分(有机碳、元素碳、硫酸根、硝酸根、铵根)的毒性效应较为复杂,元素碳在3.3 μm以下粒径段中表现出显著的风险效应。见图2
在单污染模型中,分别引入O3、NO2、O3+NO2,比较不同污染物模型的差异。结果显示,与单污染模型即主模型相比,PM2+O3模型中,各粒径段颗粒物对总循环系统疾病死亡的效应值增大;而PM+NO2以及PM+O3+NO2模型中,各粒径段颗粒物对总循环系统疾病死亡的效应略有减小。见表3
研究显示,不同粒径颗粒物组分对循环系统疾病死亡风险存在粒径依赖性效应,其毒性效应随粒径减小而增大,毒性成分越复杂,这可能与细颗粒物比表面积增大、吸附重金属(如铬、镉)及活性化学物质能力提升有关,这些组分通过肺泡入血诱发氧化应激与系统性炎症,加剧心脑血管损伤[21-26]。结果显示,3.3 μm以下粒径段颗粒物质量与循环系统疾病死亡存在正向关联,但仅在1.1~2.1 μm粒径段存在显著的统计学关联。此外,研究揭示了暴露反应显著的异质性。女性对细颗粒物暴露更具敏感性,这可能与雌激素增强氧化应激反应、女性肺泡表面积更大或生活方式差异相关[27-28]。65岁以上人群因脑血管系统功能衰退,更易患有高血压、高血脂等基础疾病,因此对颗粒物暴露更为敏感,这与先前的研究结论相似[29-30]。与既往研究对比,PM2.5暴露浓度引发的循环系统死亡风险存在差异[31-32],这可能源于研究区域的环境特征不同,不同地区的细颗粒物来源、成分和浓度分布存在较大差异[33-36]。此外,研究方法和样本量的不同也可能导致结果的差异,本研究在分析过程中采用了特定的统计模型和样本选择标准,这些因素可能影响了统计学意义的判定。
多污染物模型分析发现,颗粒物对循环系统疾病死亡的影响呈现特异性调整效应:单独引入O3时,颗粒物效应值显著增大,而引入NO2或同时引入NO2+O3时,PM效应值降低,这一结果与现有研究存在差异性[37-39]。这表明,各污染物之间存在共线性,未来研究需进一步探究颗粒物与其他污染物共存时对循环系统疾病死亡的影响。
此外,由于数据来源和研究设计,本研究存在无法避免的局限性。首先,本研究来自单中心观测数据,不可避免的存在暴露测量误差,导致空间代表性不足,限制结论外推性。其次,本研究基于时间序列设计,采用每周颗粒物暴露浓度和死亡人数计数,未考虑个体实际暴露水平和其他社会经济因素的混杂作用,这种生态学谬误会混淆统计关联。尽管存在空间尺度局限,但是来自于超级观测站的高精度数据仍为解析污染-健康机制提供了独特视角。未来研究中,需要获取更加全面准确的暴露数据,同时通过队列研究证实各暴露组分的健康效应。
综上所述,本研究首次系统构建了多粒径-多组分协同暴露的健康效应模型,突破了传统单一粒径分析的局限,强调颗粒物健康风险评估需结合粒径分级与化学组分解析,为解析颗粒物毒性机制及制定精准化治理策略提供了科学依据。同时,研究明确了超细颗粒物中氯离子和铬的优先管控需求,建议将PM2.5管控标准细化至PM1.1~2.1等亚粒径范围,并加强对工业及交通源超细颗粒物的排放监管。
  • 生态环境部城市空气颗粒物污染防治重点实验室2025年度开发基金资助项目(NKPMF202408)
  • 河北大学公共卫生安全科研创新团队项目(IT2023B11)
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2025年第52卷第16期
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doi: 10.20043/j.cnki.MPM.202504298
  • 接收时间:2025-04-15
  • 首发时间:2026-03-18
  • 出版时间:2025-08-25
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  • 收稿日期:2025-04-15
基金
生态环境部城市空气颗粒物污染防治重点实验室2025年度开发基金资助项目(NKPMF202408)
河北大学公共卫生安全科研创新团队项目(IT2023B11)
作者信息
    1.河北大学公共卫生学院,河北省公共卫生安全重点实验室,河北 保定 071000
    2.天津市疾病预防控制中心

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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
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