摘要:

背景

累積風(fēng)險指數(shù)（CRI）可綜合考慮多種大氣污染物對健康的聯(lián)合作用，但目前罕見基于大氣污染CRI構(gòu)建空氣質(zhì)量健康指數(shù)（AQHI）的研究。

目的

基于大氣污染物CRI構(gòu)建天津市AQHI并對其有效性進(jìn)行驗(yàn)證。

方法

收集天津市2015—2019年每日大氣污染、氣象因素、非意外死亡數(shù)據(jù)，并建立時間序列數(shù)據(jù)庫，采用描述性統(tǒng)計(jì)分析方法對數(shù)據(jù)的基本分布特征進(jìn)行分析。采用廣義相加模型的單污染物模型和雙污染物模型建立大氣污染物與非意外死亡的暴露反應(yīng)關(guān)系，篩選最佳滯后天數(shù)和指示污染物，然后采用多污染物模型計(jì)算大氣污染的CRI值，并依據(jù)CRI值構(gòu)建AQHI并分級。最后采用廣義相加模型分別建立AQHI和空氣質(zhì)量指數(shù)（AQI）與非意外死亡的暴露反應(yīng)關(guān)系，并對暴露反應(yīng)關(guān)系系數(shù)進(jìn)行比較，以廣義交叉驗(yàn)證（GCV）值和模型的R2對AQHI的有效性進(jìn)行驗(yàn)證。

結(jié)果

本研究選擇lag1為最佳滯后天數(shù)，在綜合考慮超標(biāo)情況和統(tǒng)計(jì)學(xué)模型結(jié)果的基礎(chǔ)上，選擇PM2.5、SO2、NO2和O3作為最終的指示污染物。4種指示污染物的濃度每升高1 μg·m?3對非意外死亡影響的效應(yīng)值b，分別為?0.00002、0.00079、0.00015和0.00042。基于上述系數(shù)計(jì)算CRI，并構(gòu)建AQHI。按照分級標(biāo)準(zhǔn)提示，63%的時間是處于低風(fēng)險級別，有34%的時間處于中風(fēng)險級別。分別建立了AQHI和AQI與全人群、女性人群和男性人群非意外死亡的暴露反應(yīng)關(guān)系，結(jié)果顯示，AQHI每增加一個四分位數(shù)間距（IQR），全人群、女性人群和男性人群非意外死亡的超額風(fēng)險均高于AQI的相應(yīng)指標(biāo)值；且AQHI模型的GCV值（分別為2.694、1.819、1.938）均低于AQI模型的GCV值（分別為2.747、1.850、1.961），AQHI模型的R2值（分別為0.849、0.780、0.820）均高于AQI模型的R2值（分別為0.846、0.776、0.817）。

結(jié)論

與AQI相比，基于大氣污染物的CRI構(gòu)建的AQHI能夠較好地預(yù)測天津市空氣污染對人群的健康風(fēng)險。

Abstract:

Background

Cumulative risk index (CRI), as a commonly used approach to estimate the joint effects of multiple air pollutants on health, has been used by few studies to construct an air quality health index (AQHI).

Objective

To construct an AQHI based on the CRI of air pollution in Tianjin and evaluate the validity of the AQHI.

Methods

Daily data on air pollutants, meteorological factors, and non-accidental deaths during 2015–2019 in Tianjin were collected to create a time-series object. Descriptive statistical analyses were used to describe the characteristics of the data. To determine the best lag day and indicative pollutant, single-pollutant and two-pollutant generalized additive models were fitted to construct the exposure-response relationships between air pollutants and non-accidental deaths. After that we evaluated a CRI of air pollution using multi-pollutant models and constructed an AQHI and its classifications based on the CRI. Finally, we compared the exposure-response associations and coefficients of the AQHI and the conventional air quality index (AQI) with non-accidental deaths, and evaluated the health risk communication validity of the AQHI using generalized cross validation (GCV) values and R2 values.

Results

We selected lag1 as the best lag day and PM2.5, SO2, NO2 and O3 as the appropriate pollutants according to the unqualified rates of pollutants and significant statistical results. One μg·m?3 increase of PM2.5, SO2, NO2, and O3 was associated with ?0.00002, 0.00079, 0.00015, and 0.00042 increase in effect size b of the non-accidental mortality, respectively. Based on these coefficients, we calculated the CRI and AQHI. According to a pre-determined classification scheme of the AQHI, the air quality of 63% study days was low risks and that of 34% study days was median risks. The associations of AQHI and AQI with non-accidental deaths in different populations were evaluated. The results showed that the excess risks of non-accidental deaths in total, female, and male populations for per interquartile range (IQR) increase in AQHI were higher than the corresponding values of AQI. The GCV values of the AQHI model (2.694, 1.819, and 1.938, respectively) were lower than those of the AQI model (2.747, 1.850, and 1.961, respectively), and the R2 values of the AQHI model (0.849, 0.780, and 0.820, respectively) were higher than those of the AQI model (0.846, 0.776, and 0.817, respectively).

Conclusion

Compared with AQI, the CRI-based AQHI may communicate the air pollution-related health risk to the public more effectively in Tianjin.

圖  1   天津市2015—2019年各大氣污染物不同滯后日的日均濃度每升高10 μg·m?3對非意外死亡影響的單污染物模型效應(yīng)圖

Figure  1.   Percent changes of excess risk in non-accidental deaths for a 10 μg·m?3 increase in daily average air pollutant concentrations at different lag days in single-pollutant models in Tianjin, China, 2015–2019

圖  2   天津市2015—2019年各大氣污染物在lag1天的日均濃度每升高10 μg·m?3對非意外死亡影響的雙污染物模型效應(yīng)圖

Figure  2.   Percent changes of excess risk in non-accidental deaths for a 10 μg·m?3 increase in daily average air pollutant concentrations at lag 1 in two-pollutant models in Tianjin, China, 2015–2019

圖  3   AQHI和AQI的各風(fēng)險等級比例分布圖

Figure  3.   The distribution of health risk percentages of AQHI and AQI

表  1   天津市2015—2019年日非意外死亡數(shù)、日均溫度和相對濕度以及大氣污染物日均濃度的分布特征

Table  1   Distribution characteristics of daily averages of non-accidental death counts, temperature, relative humidity, and air pollutant concentrations in Tianjin, China, 2015–2019

變量
(Variable)均數(shù)±標(biāo)準(zhǔn)差(Mean±SD)最小值(Minimum)中位數(shù)(Median)最大值(Maximum)四分位數(shù)
間距(IQR) 每日非意外死亡人數(shù)(Daily non-accidental death counts) 　合計(jì)(Total) 154.0 ± 52.1 2.0 132.0 312.0 85.0 　男性(Male) 85.3± 30.1 1.0 74.0 180.0 49.0 　女性(Female) 68.6± 23.6 1.0 62.0 151.0 36.0 日均溫度/℃
(Daily average temperature/℃) 13.9 ± 11.3 ?16.0 15.7 34.0 21.0 日均相對濕度/%
(Daily average relative humidity/%) 55.2 ± 19.1 12.0 55.0 100.0 30.0 PM2.5/(μg·m?3) 61.5 ± 48.3 6.0 47.9 367.5 47.1 SO2/(μg·m?3) 18.3 ± 19.2 2.0 12.6 216.4 13.8 NO2/(μg·m?3) 44.6 ± 21.8 8.1 39.5 167.6 28.4 O3/(μg·m?3) 114.5 ± 65.9 2.7 102.1 348.7 96.4 AQI 93.8 ± 55.7 17.0 80.0 437.0 54.0

表  2   天津市2015—2019年各大氣污染物之間的相關(guān)關(guān)系（rs）

Table  2   Spearman correlation coefficients for included air pollutants in Tianjin, China, 2015–2019 (rs)

變量(Variable)PM2.5SO2NO2O3 PM2.51.0000SO20.5632*1.0000NO20.5835*0.7030*1.0000O3?0.0680*?0.3526*?0.4482*1.0000 [注(Note)] *：P<0.01。

表  3   天津市2015—2019年大氣污染物的CRI和AQHI的分布特征

Table  3   Distribution characteristics of CRI and AQHI for included air pollutants in Tianjin, China, 2015–2019

變量名稱
(Variable)均數(shù)±標(biāo)準(zhǔn)差
(Mean±SD)最小值
(Minimum)中位數(shù)
(Median)最大值
(Maximum)四分位數(shù)間距
(IQR) CRI1.07±0.031.021.071.220.04AQHI3.20±1.290.793.0410.001.86

表  4   AQHI和AQI對天津市全人群、女性人群和男性人群非意外死亡超額風(fēng)險的預(yù)測比較

Table  4   Comparisons of excess risk in non-accidental deaths among total, female, and male populations for AQHI and AQI

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