杜娟, 唐应萍, 何平, 蒋励. 贵阳市空气污染物对呼吸系统疾病门诊量的短期效应[J]. 环境与职业医学, 2024, 41(1): 62-69. DOI: 10.11836/JEOM23277
引用本文: 杜娟, 唐应萍, 何平, 蒋励. 贵阳市空气污染物对呼吸系统疾病门诊量的短期效应[J]. 环境与职业医学, 2024, 41(1): 62-69. DOI: 10.11836/JEOM23277
DU Juan, TANG Yingping, HE Ping, JIANG Li. Short-term effects of air pollutants on outpatient volume of respiratory diseases in Guiyang[J]. Journal of Environmental and Occupational Medicine, 2024, 41(1): 62-69. DOI: 10.11836/JEOM23277
Citation: DU Juan, TANG Yingping, HE Ping, JIANG Li. Short-term effects of air pollutants on outpatient volume of respiratory diseases in Guiyang[J]. Journal of Environmental and Occupational Medicine, 2024, 41(1): 62-69. DOI: 10.11836/JEOM23277

贵阳市空气污染物对呼吸系统疾病门诊量的短期效应

Short-term effects of air pollutants on outpatient volume of respiratory diseases in Guiyang

  • 摘要:
    背景 受空气污染物浓度、成分及人群耐受性影响,空气污染物与人群健康的关系具有地域性差异,目前贵阳市尚有研究空白。
    目的 探讨低污染区空气污染物浓度对呼吸系统疾病门诊量影响的短期效应。
    方法 采用斯皮尔曼相关分析2013年1月1日—2020年12月31日贵阳市空气污染物、气象因素与呼吸系统门诊量的相关性,进一步采用泊松分布建立单污染物分布滞后非线性模型及多污染物交互模型,绘制空气污染物与呼吸系统门诊量的暴露滞后风险关系三维图,定量分析贵阳市空气污染物浓度与呼吸系统疾病门诊量的归因风险及滞后效应。
    结果 单污染物模型结果显示,细颗粒物(PM2.5)、二氧化氮(NO2)、一氧化碳(CO)和二氧化硫(SO2)均为呼吸系统疾病门诊量的风险因素,随污染物浓度升高,呼吸系统门诊量均呈上升趋势。PM2.5、NO2、CO和SO2相对危险度(RR)峰值分别出现在第2、0、5和6天,RR(95%CI)峰值分别为1.019(1.015~1.023)、1.146(1.122~1.171)、1.129(1.116~1.143)和1.046(1.040~1.052)。PM2.5、NO2、CO和SO2浓度每升高一个四分位间距,呼吸系统门诊量分别增加0.943%(0.111%~1.782%)、4.050%(3.573%~4.529%)、0.595%(0.317%~0.874%)和0.667%(0.235%~1.100%);臭氧(O3)的RR峰值出现在当日,RR(95%CI)峰值为1.015(1.007~1.023)。多污染物模型结果显示,PM2.5、NO2、CO、SO2和O3对呼吸系统疾病门诊量均具有风险效应,RR峰值分别出现在第14、0、5、7和0天,RR(95%CI)峰值分别为1.027(1.021~1.034)、1.213(1.179~1.248)、1.059(1.043~1.074)、1.016(1.005~1.026)和1.024(1.015~1.033),与单污染物模型相比PM2.5、NO2和O3对呼吸系统疾病门诊量的RR均呈上升趋势,CO和SO2的RR均呈下降趋势。
    结论 低浓度的PM2.5、NO2、CO和SO2对人群健康的影响不容忽视。

     

    Abstract:
    Background Affected by concentration, composition, and population tolerance of air pollutants, the relationship between air pollutants and population health has regional differences. There is still a research gap in Guiyang.
    Objective To explore the short-term effects of air pollutant concentrations in low-pollution areas on the outpatient volume of respiratory diseases.
    Methods Spearman correlation analysis was used to evaluate the correlation between air pollutants, meteorological factors, and respiratory outpatient volume from January 1, 2013 to December 31, 2020 in Guiyang City. A single pollutant distribution lag nonlinear model and a multi-pollutant interaction model were established based on Poisson distribution. A three-dimensional diagram was drawn to display the relationship between air pollutants and respiratory outpatient volume. Quantitative analysis was conducted on the attribution risk and lag effect of air pollutant concentration on outpatient volume of respiratory diseases in Guiyang City.
    Results The results of the single pollutant model showed that fine particulate matter (PM2.5), nitrogen dioxide (NO2), carbon monoxide (CO), and sulfur dioxide (SO2) elevated the outpatient volume of respiratory diseases. The maximum relative risk (RR) and 95%CI values of PM2.5, NO2,CO, and SO2 appeared on Day 2, 0, 5, and 6, respectively, which were 1.019 (1.015, 1.023), 1.146 (1.122, 1.171), 1.129 (1.116, 1.143), and 1.046(1.040, 1.052), respectively. For every quartile concentration increment of PM2.5, NO2, CO, or SO2, the outpatient volume of respiratory diseases increased by 0.943% (0.111%, 1.782%), 4.050% (3.573%, 4.529%), 0.595% (0.317%, 0.874%), or 0.667% (0.235%, 1.100%), respectively. The maximum RR (95%CI) of O3 was 1.015 (1.007, 1.023) and appeared on Day 0. The results of multi-pollutant model showed that PM2.5, NO2, CO, SO2, and O3 all elevated the outpatient volume of respiratory diseases. The maximum RR values of PM2.5, NO2, CO, SO2 and O3 appeared on Day 14, 0, 5, 7 and 0, respectively, which were 1.027 (1.021, 1.034), 1.213 (1.179, 1.248), 1.059 (1.043, 1.074), 1.016 (1.005, 1.026), and 1.024 (1.015, 1.033), respectively. Compared with the single pollutant model, the RR values of PM2.5, NO2, and O3 on the outpatient volume of respiratory diseases in the multi-pollutant model showed an upward trend, while the RR values of CO and SO2 in the multi-pollutant model showed a downward trend.
    Conclusion The impact of low concentrations of PM2.5, NO2, CO, and SO2 on human health cannot be ignored.

     

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