应圣洁, 顾怡勤, 汪曦, 何丹丹, 宫志敏, 蒋丽花, 徐莺, 张颖, 张雄伟, 甄玲燕. 大气污染与上海市闵行区学生因呼吸道疾病缺课关系的时间序列研究[J]. 环境与职业医学, 2018, 35(5): 394-399. DOI: 10.13213/j.cnki.jeom.2018.17683
引用本文: 应圣洁, 顾怡勤, 汪曦, 何丹丹, 宫志敏, 蒋丽花, 徐莺, 张颖, 张雄伟, 甄玲燕. 大气污染与上海市闵行区学生因呼吸道疾病缺课关系的时间序列研究[J]. 环境与职业医学, 2018, 35(5): 394-399. DOI: 10.13213/j.cnki.jeom.2018.17683
YING Sheng-jie, GU Yi-qin, WANG Xi, HE Dan-dan, GONG Zhi-min, JIANG Li-hua, XU Ying, ZHANG Ying, ZHANG Xiong-wei, ZHEN Ling-yan. Time-series analysis on association between air pollution and student absence caused by respiratory disorders in Minhang District of Shanghai[J]. Journal of Environmental and Occupational Medicine, 2018, 35(5): 394-399. DOI: 10.13213/j.cnki.jeom.2018.17683
Citation: YING Sheng-jie, GU Yi-qin, WANG Xi, HE Dan-dan, GONG Zhi-min, JIANG Li-hua, XU Ying, ZHANG Ying, ZHANG Xiong-wei, ZHEN Ling-yan. Time-series analysis on association between air pollution and student absence caused by respiratory disorders in Minhang District of Shanghai[J]. Journal of Environmental and Occupational Medicine, 2018, 35(5): 394-399. DOI: 10.13213/j.cnki.jeom.2018.17683

大气污染与上海市闵行区学生因呼吸道疾病缺课关系的时间序列研究

Time-series analysis on association between air pollution and student absence caused by respiratory disorders in Minhang District of Shanghai

  • 摘要: 目的 探讨大气污染对上海市闵行区学生因呼吸道疾病缺课的短期影响。

    方法 收集2013年9月1日—2016年6月30日闵行区学生因呼吸道疾病缺课人数和同期闵行区大气污染及气象监测资料,采用时间序列的广义相加模型,在控制了长期趋势、星期几效应、假期效应及气象因素等混杂因素的基础上,分析当日至前5 d单日滞后(lag0~lag5)和当日至前1、3、5 d累积滞后(lag01、lag03、lag05)的大气污染物浓度与学生因呼吸道疾病缺课人数的关系。

    结果 研究期间,NO2、PM2.5、PM10和O3的超标率分别为8.51%、20.79%、5.84%和8.12%,SO2、CO未超出限值。单污染模型中,大气AQI、PM2.5、PM10、SO2和NO2与学生呼吸道疾病的新发缺课人数及总缺课人数均呈正相关(P < 0.05)。对总缺课人数,NO2、PM2.5和PM10在lag1效应最为明显RR及95%CI分别为3.53(2.15~4.90)、11.80(8.85~14.75)、4.04(2.48~5.60),SO2在lag5效应最为明显(RR=18.20;95%CI:13.95~22.45);对于新发缺课人数,NO2在lag0效应最为明显(RR=11.65,95%CI:8.59~14.71),SO2、PM2.5和PM10在lag1效应最为明显RR及95%CI分别为3.39(1.91~4.88)、17.90(12.96~22.84)、3.89(2.20~5.58)。累积效应各污染物均在lag05时对学生新发及总缺课的效应最强。多污染模型中,PM2.5和PM10对学生呼吸道疾病缺课的影响在调整了其他主要空气颗粒物(PM10、PM2.5)和气态污染物(S02、NO2)后均无统计学意义。

    结论 大气污染物PM2.5、PM10、SO2和NO2浓度与学生因呼吸道疾病缺课存在正相关。

     

    Abstract: Objective To evaluate the short-term effects of air pollution on student absence caused by respiratory disorders in Minhang District of Shanghai.

    Methods Daily data on student absence caused by respiratory disorders, meteorological data, and air pollution data of Minhang District from September 1, 2013 to June 30, 2016 were collected. A time-series analysis by generalized additive model was conducted to examine the relationship between air pollutant concentrations on single lag days from the current day to previous 5 days (lag0-lag5) and on cumulative lag days from the current day to previous 1, 3, and 5 days (lag01, lag03, and lag05) and student absence caused by respiratory disorders after controlling for time trend, day-of-week effect, holiday effect, and weather conditions.

    Results During the study period, the unqualified rates of NO2, PM2.5, PM10, and O3 were 8.51%, 20.79%, 5.84%, and 8.12%, respectively, while SO2 and CO were within the national limits. In the single-pollutant models, AQI, PM2.5, PM10, SO2, and NO2 were positively correlated with both emerging and total student absence caused by respiratory disorders (P < 0.05). The NO2, PM2.5, and PM10 concentrations on lag1RR and 95%CI were 3.53 (2.15-4.90), 11.80 (8.85-14.75), and 4.04 (2.48-5.60), respectively and the SO2 concentration on lag5 (RR=18.20; 95%CI:13.95-22.45) showed the most significant effects on total student absence caused by respiratory disorders. The NO2 concentration on lag0 (RR=11.65; 95%CI:8.59-14.71) and the SO2, PM2.5, and PM10 concentrations on lag1RR and 95%CI were 3.39 (1.91-4.88), 17.90 (12.96-22.84), and 3.89 (2.20-5.58), respectively showed the most significant effects on emerging student absence caused by respiratory disorders. Cumulative effects of all pollutants on lag05 were most significant for both emerging and total student absences. In the multiple-pollutant models, the effects of PM2.5 and PM10 on student absence caused by respiratory disorders were not statistically significant after adjusting for main air particulate matters (PM10 or PM2.5) and gaseous pollutants (SO2 and NO2).

    Conclusion The ambient air pollutant concentrations of PM2.5, PM10, SO2, and NO2 are positively associated with student absence caused by respiratory disorders.

     

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