张江华, 许慧慧, 东春阳, 贾晓东. 2018年上海市PM2.5的时空变异特征[J]. 环境与职业医学, 2020, 37(4): 314-320. DOI: 10.13213/j.cnki.jeom.2020.19569
引用本文: 张江华, 许慧慧, 东春阳, 贾晓东. 2018年上海市PM2.5的时空变异特征[J]. 环境与职业医学, 2020, 37(4): 314-320. DOI: 10.13213/j.cnki.jeom.2020.19569
ZHANG Jiang-hua, XU Hui-hui, DONG Chun-yang, JIA Xiao-dong. Spatiotemporal variation characteristics of PM2.5 across Shanghai in 2018[J]. Journal of Environmental and Occupational Medicine, 2020, 37(4): 314-320. DOI: 10.13213/j.cnki.jeom.2020.19569
Citation: ZHANG Jiang-hua, XU Hui-hui, DONG Chun-yang, JIA Xiao-dong. Spatiotemporal variation characteristics of PM2.5 across Shanghai in 2018[J]. Journal of Environmental and Occupational Medicine, 2020, 37(4): 314-320. DOI: 10.13213/j.cnki.jeom.2020.19569

2018年上海市PM2.5的时空变异特征

Spatiotemporal variation characteristics of PM2.5 across Shanghai in 2018

  • 摘要: 背景

    PM2.5持续暴露对人类健康构成严重威胁。空气污染成因复杂,大型城市内部空气污染存在变异性,研究若使用单一测量值作为暴露水平,可能导致暴露错分。

    目的

    了解上海地区空气污染物PM2.5的污染水平及时空变异来源,分析城市PM2.5空间分布特征。

    方法

    2018年在上海全市范围内设置区域背景点、城市背景点和交通点等20个固定监测点,于冬季、夏季、秋季分别开展为期2周的PM2.5监测,并在距离国控自动监测站2 km范围内设置一个校准点进行全年监测,对全年及3个季节的监测数据分别进行时间校准。观察不同采样时段PM2.5的浓度时空变化情况,使用方差组分分析法分析变异来源,利用普通克里金插值法对不同采样季节的PM2.5浓度进行空间插值,采用留一交叉验证(LOOCV)法进行插值精度检验。

    结果

    全市校准年均浓度值为39.87 μg·m-3,秋冬季校准PM2.5平均浓度较高,夏季明显降低。全年监测显示各个点位类型之间差异均无统计学意义(均P>0.05)。全年总体浓度变异的83.84%归因于点位之间的变异,16.16%归因于季节变异;不同季节PM2.5校准浓度贡献率主要来自点位间变异。普通克里金插值法PM2.5的决定系数R2波动范围为0.33~0.85,LOOCV法均方根误差波动范围为4.88~8.41μg·m-3。PM2.5浓度趋势面显示浓度污染高值区位于西部地区。

    结论

    上海市PM2.5浓度呈现秋冬季高、夏季低的变化趋势,空间上呈现西部高、东部低的格局。可以利用克里金插值法提供PM2.5暴露浓度空间分析。

     

    Abstract: Background

    Long-term exposure to PM2.5 poses a serious threat to human health. The spatial variability in air pollution within a large city is resulted from complex causes of air pollution. It will lead to exposure misclassification if a single measurement is used to evaluate exposure in epidemiological studies.

    Objective

    This study aims to understand the pollution levels of PM2.5 and potential causes of spatiotemporal variability and analyze the spatial distribution characteristics of PM2.5 in Shanghai.

    Methods

    Twenty fixed monitoring sites were selected in Shanghai in 2018, including regional background, urban background, and street-level sites. Three two-week PM2.5 samples were measured during winter, summer, and fall per site, and the average concentrations in the year and the three seasons for each site were calculated using continuous measurements at one routine background site 2km away from the national automatic monitoring station as a reference. Spatiotemporal variation analysis and variance apportionment analysis were conducted. PM2.5 concentrations in different seasons were estimated by ordinary Kriging interpolation method, and their precisions were evaluated by leave one out cross validation (LOOCV).

    Results

    The annual average calibrated concentration of PM2.5 was 39.87 μg·m-3, and the average calibrated concentration was higher in autumn and winter than in summer. There were no significant differences between different site types across the year (Ps>0.05). In addition, 83.84% of the overall PM2.5 variances were attributable to the variability among sites and 16.16% to the variability of seasons. The PM2.5 spatial variability in different seasons was mostly determined by differences among sites. The coefficient of determination (R2) by Kriging interpolation model ranged from 0.33 to 0.85, and the LOOCV root mean squared error ranged from 4.88 to 8.41 μg·m-3. PM2.5 concentration trend surface showed that the area with high-concentration pollution was located in the western region of Shanghai.

    Conclusion

    The PM2.5 concentration exhibits a distinct spatiotemporal variation trend that the level is higher in winter and autumn than in summer, and higher in the western area than in the eastern area of Shanghai. Kriging interpolation can provide a spatial analysis on PM2.5 exposure.

     

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