Construction of AQHI based on the exposure relationship between air pollution and YLL in northern China

Science of the Total Environment 710 (2020) 136264

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Construction of AQHI based on the exposure relationship between air pollution and YLL in northern China Qiang Zeng a,1, Lin Fan b,1, Yang Ni a, Guoxing Li c, Qing Gu a,⁎ a b c

Tianjin Centers for Disease Control and Prevention, Tianjin 300011, PR China Institute of Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing 100050, PR China Department of Occupational and Environmental Health, School of Public Health, Peking University, Beijing 100191, PR China

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• We used a new indicator (AQHI) to assess the air quality of Tianjin. • Principal component analysis, a new method, was used to construct AQHI. • AQHI has considered the exposureresponse relationship of six air pollutants on health. • YLL was used to construct the exposureresponse relationship between air pollution and health. • AQHI could provide a scientific basis for Tianjin's air quality health risk forecast.

a r t i c l e

i n f o

Article history: Received 9 October 2019 Received in revised form 18 December 2019 Accepted 20 December 2019 Available online xxxx Editor: Lidia Morawska Keywords: Air pollution Years of life lost Air quality health index Principal component analysis K-fold cross validation

a b s t r a c t The current air quality index (AQI) has been argued for failing to respond to the combined health effects of multiple air pollutants. Thus, it is a challenge to construct a new indicator, air quality health index (AQHI) to comprehensively assess and predict air quality and the health effects caused by air pollution. Here, we have comprehensively considered the relationship between six air pollutants and the total mortality. And we constructed AQHI using the principal component analysis (FCA) by taking into account of the associations between six main air pollutants and YLL in Tianjin, China from 2014 to 2017. Then, we used the K-fold cross-validation method and the method of comparing AQHI with AQI to assess the validity of AQHI, respectively. Two principal components (F1 and F2) were used to construct AQHI; the cumulative contribution rate of variance for them was N70% (53.6% and 16.4%, respectively). With each unit increase of F1, the total daily YLL increased by 18.420 person-years. With each unit increase of F2, the total daily YLL increased by 22.409 person-years. The correlation between the predicted and actual values of total mortality and total YLL of AQHI was 0.742 (P b 0.001) and 0.700 (P b 0.001), respectively. The difference between AQI and AQHI was statistically significant (χ2 = 103.15, P b 0.001). There was a correlation between AQHI and AQI (r = 0.807, P b 0.01), and the grading was also correlated (rs = 0.580, P b 0.01). With an increase of interquartile range (IQR) for AQHI, the daily YLL increased by 32.797 (95% CI: 14.559, 51.034), while for the AQI, the daily YLL increased by 22.367 (95% CI: 6.619, 38.116),

⁎ Corresponding author at: Tianjin Centers for Disease Control and Prevention, No. 6 Huayue Road, Hedong District, Tianjin 300011, China. E-mail address: [email protected] (Q. Gu). 1 These authors contributed equally to this work.

https://doi.org/10.1016/j.scitotenv.2019.136264 0048-9697/© 2019 Published by Elsevier B.V.

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which was less than AQHI. These results imply that AQHI can comprehensively consider the impact of various pollutants on disease mortality and YLL, and can comprehensively reflect air quality, which has an important practical significance. © 2019 Published by Elsevier B.V.

1. Introduction Air pollution has remained a major issue of concern over the years. According to the data of World Health Organization (WHO) in 2016, 4.2 million people worldwide died from environmental air pollution every year, and 91% of the population lived in places where the levels of the air pollution exceed WHO's air quality standards (Babatola, 2018). Judging from this situation, the air pollution situation is very serious, so it is necessary to construct an index that can comprehensively assess air quality and the impact of air pollution on health. Air quality assessment index has gone from threshold-based to health-risk-based. In 1976, the Environmental Protection Agency (EPA) used the method of the index evaluation to construct the air quality index (AQI), and established national air quality standards to protect public health. In 2012, China began to monitor real-time concentrations of air pollutants nationwide, the Ministry of Environmental Protection of China approved the technical regulation on ambient air quality index and used AQI to indicate air quality. However, the AQI value which uses a specific atmospheric pollutant to indicate the air quality does not adequately account for the influence of other coexisting pollutants, and it is difficult to directly reflect the linear non-threshold relationship between the air pollution and the health effects. The Air Quality Health Index (AQHI) is a new type of assessment and measurement of air quality and health risks, which combines the apparent threshold-free concentration response relationship between air pollution and acute health risks. Therefore, AQHI may be more comprehensive in assessing and predicting the impact of air pollution on health than AQI (Stieb et al., 2008). In 2008, AQHI which was based on Canadian epidemiological studies was first proposed by the Canadian Ministry of the Environment and Health (Stieb et al., 2008). South Africa also used a similar approach to construct a comprehensive risk index to describe the health effects associated with the coexistence of several different air pollutants in Cape Town, South Africa Cairncross et al., 2007. After that, Europe developed a composite index that considers the effects of PM2.5, PM10, NO2, O3 and SO2 to health outcomes (Sicard et al., 2011). Sweden conducted research on the characteristics of local atmospheric pollutants and constructed AQHI in 2019 (Olstrup et al., 2019). In China, AQHI was only used in Hong Kong. On December 30, 2013, the Hong Kong EPA launched AQHI basing on the exposure response relationship of air pollution and outpatient visits of hospitals in Hong Kong citizens (Thach et al., 2018). AQHI comprehensively considered the exposure response relationship of various air pollutants and the health effects, so it can accurately reflect the short-term health effects of daily pollutants. However, the current health outcomes indicators used in the construction of AQHI were mainly based on population mortality, hospitalization rate or consultation rate, the disease burden indicators such as years of life lost (YLL) which not only considers the number of mortality, but also refers to the age of mortality and life expectancy, were fewer used in this kind of studies. Therefore, the ignorance of the information such as the age of mortality may lead to bias in air quality prediction and health hazards reflected by AQHI, especially for sensitive population (Balakrishnan et al., 2014; Hanninen et al., 2014; Zeng et al., 2017). Several previous studies have established the AQHI, however, only the pollutants that had a significant impact on air quality have been selected to represent the city's air quality, so that these studies have failed to fully consider the combined effects of various pollutants (Stieb et al., 2008; Chen et al., 2013; Li et al., 2017). In this study, the principal components of different moving average concentrations were selected to weight the

pollutants, so the lagging and comprehensive effects of six air pollutants on health were considered. In China, the Beijing-Tianjin-Hebei region is a representative area of northern China, which owns a good industrial base and a highly developed commodity economy. According to the national data, the current atmospheric pollution in the Beijing-Tianjin-Hebei region is the most serious. Tianjin is a municipality directly under the Central Government and the most important industrial city in the Beijing-Tianjin-Hebei region, where the background level of air pollution is high. The construction of AQHI in Tianjin not only has an important theoretical significance, but also provides a quantitative scientific basis for the Chinese government to formulate environmental protection policies. There is an urgent need to build AQHI that meets regional characteristics in order to more effectively guide the forecast of public health risks of air pollution. 2. Material and methods 2.1. Study area We used a multi-district four-year (from January 1st, 2014 to December 31th, 2017) time-series study design to investigate the potential effects of air pollutants exposures on mortality counts and YLL in Tianjin, China. Six urban districts were selected in Tianjin as study area, including Hedong district, Hebei district, Hexi district, Nankai district, Heping district and Hongqiao district. 2.2. Data sources In this study, the daily average concentrations of air pollutants (PM2.5, PM10, SO2, NO2, CO and O3) and AQI throughout Tianjin from January 1st, 2014 to December 31th, 2017 were obtained from Tianjin Environmental Monitoring Center. There are total seven city central air-monitoring stations in Tianjin, and each district has at least one monitoring station. We obtained the meteorological data (daily mean temperature and relative humidity) for the same study period from the Tianjin Meteorological Administration. We ignored the spatial changes of different air pollutants in each district. We did not consider the temporal and spatial variations regarding the different air pollutants in each district, and used the average concentrations of various atmospheric pollutants at seven city central air-monitoring stations as the concentrations of air pollutants for the day. Daily mortality data of Tianjin were collected from Tianjin Centers for Disease Control and Prevention (TJCDC), including the date of birth, sex and date of mortality. The mortality was further stratified by the date of mortality, age and sex. 2.3. The calculation of YLL YLL is the life year loss caused by early mortality. YLL data was estimated using the formula based on the WHO's standard life table for YLL. YLL for mortality was calculated by matching age to the life table (WHO, 2018) (Supplementary material, Table A1). The formula is as follows: YLL ¼ N  L where N is the number of mortality of each sex in each age group; L is the Standard Expected Years of Life Lost (SEYLL) for each age group.

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2.4. Estimating the associations of air pollutants with mortality counts or YLL In order to select the representative time points at various exposure levels of air pollutants to health effects. Generalized additive model (GAM) was used to estimate the exposure response relationship of air pollutants on daily total mortality counts and YLL in six districts of Tianjin. To estimate potentially delayed and cumulative exposure associations, we fitted the models with lag structures from lag 0 to lag 7 day and moving average from 0 to 7 day (The calculation method is shown in Supplementary material). 2.5. The calculation of AQHI Principal component analysis (PCA) was used to analyze the concentration of six atmospheric pollutants in different moving average periods, mv01 represents the moving average concentration on the day to the previous one day before, mv02 represents the moving average concentration on the day to the previous two days before, mv03 represents the moving average concentration on the day to the previous three days before. And we constructed new principal components variables which can represent air quality comprehensively. Furthermore, we used GAM to estimate the association between the new principal component pollutants and the daily total YLL. Because the dependent variable of daily YLL was normally distributed, we used a generalized linear regression model (Zeng et al., 2017). The smoothing function was used to control the confounding factors including time, temperature and humidity. Heating season, holiday, and week variables were controlled as dummy variables (Dominici and Peng, 2008). Then we built the AQHI according to this exposure response relationship, the model is as follows: ERkt ¼ 100 




 eβpkt −1

AQHI ¼ 10  ð∑k¼1⋯n ERkt Þ= maxk¼1⋯n ERkt þ 5 where ERkt is the excess disease risk of different main component pollutants; β is the coefficient of the indicator variable estimated by the GAM, which is the value of YLL associated with a unit increase in principal component pollutants; pkt is the value of different principal component pollutants. After that, we graded the established AQHI which was reported on a scale of 1 to 10 and 10+, then AQHI grouped into four health risk categories. The grading standard referred to the Canadian grading standard (Table 1) (Berry et al., 2011). 2.6. Evaluating the validity of AQHI The health risk communicating validity of AQHI was evaluated in following two ways. First, the K-fold cross-validation method was used to verify the relationship between AQHI and health outcomes (including total mortality

and total YLL). The time series data set was divided into four equal parts according to the time series year (twelve months as a set), three parts were used for training, and one part was used for testing. The AQHI and health outcomes obtained from the training set using GAM to obtain the training β, then calculated the AQHI of the test set by using GAM based on the obtained training β and health outcomes in testing. In this way, the process was repeated four times, and four test results were generated. Then, the Pearson correlation analysis was performed on the test set and the real set of AQHI, and the parameters including the root mean square error (RMSE), mean absolute error (MAE) (including maximum, minimum and mean) and correlation coefficient. These results can accurately measure the performance of the model. Second, we compared AQHI with AQI. The Chi-square test was performed to compare the excellent air quality rate of AQI and AQHI. For AQHI, the rate of excellence was the ratio of the health risk low level days to the total days. For AQI, the rate of excellence was the ratio of the number of days in good and moderate air quality to the total days. Then, the Pearson correlation analysis was performed on the AQHI and AQI values, and the Spearman correlation analysis was performed on the grading. The difference between the two indexes was compared by the above method. Moreover, the daily values of AQHI and AQI in study period were included in GAM respectively. For total mortality, Quasi-Poisson regression model combined with GAM was used. And for total YLL, Gaussian regression model combined with GAM was used. Because the dimension in AQHI (0–10+) and AQI (0–300+) were different, we used interquartile range (IQR) with increase of AQHI and AQI to measure the ability of indexes to predict health hazards. Principal component analysis, Chi-square test, correlation analysis, generalized additive analysis and K-fold cross-validation method were conducted by using R software (Version 3.5.2). In order to construct the main component of various air pollutants at different moving average periods in Tianjin, we used PCA. To construct the AQHI, GAM was mainly used to analyze the exposure response relationship between the main components of atmospheric pollutants and the daily YLL. To validate verify the relationship between AQHI and health outcomes, the K-fold cross-validation method was used. And in order to compare the excellent air quality rate of AQI and AQHI, the Chi-square test was performed. The Pearson correlation analysis was performed on the AQHI and AQI values, and the Spearman correlation analysis was performed on the grading. Statistical analyses were done using the built-in function or calling package of R software. Function of Princomp() was used for principal component analysis. The function of chisq.test() was used for Chi-square test on the excellent air quality rates of AQI and AQHI. The function of rcorr() in Hmisc package was used to Pearson correlation and Spearman correlation. The function of gam () in the mgcv package was used to establish the generalized additive model. Results were presented as changes in excess risk (ER, %) with 95% confidence intervals (CI) in daily mortality and changes with 95% CI in YLL. P b 0.05 (2-tailed) was considered statistically significant.

Table 1 Grading standard for AQHI. AQHI Health risk level

Warning color

0–3 4–6

Low Green Moderate Yellow

7–10

High

Red

N10

Serious

Black

3

Sensitive people

General public

No response action is required. People who feel uncomfortable are advised to consider reducing outdoor physical exertion. Older, children and people who feel uncomfortable are advised to consider reducing outdoor physical exertion. Elderly, children and people who feel unconfortable are advised to avoid outdoor physical exertion.

No response action is required. No response action is required. If people have symptoms such as cough, throat irritation, etc., they are advised to consider reducing outdoor physical exertion. The general public is advised to reduce outdoor physical exertion.

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Table 2 Correlation coefficient of the moving average concentration of air pollutants Tianjin, China, during 2014–2017. Moving average concentration of air pollutants

PM2.5mv01

PM10mv01

SO2mv01

NO2mv01

COmv03

O3mv02

PM2.5mv01 PM10mv01 SO2mv01 NO2mv01 COmv03 O3mv02

– 0.881⁎⁎ 0.557⁎⁎ 0.745⁎⁎ 0.291⁎⁎

– – 0.528⁎⁎ 0.654⁎⁎ 0.256⁎⁎ −0.154⁎⁎

– – – 0.639⁎⁎ 0.352⁎⁎ −0.357⁎⁎

– – – – 0.236⁎⁎ −0.267⁎⁎

– – – – – −0.081⁎⁎

– – – – – –

−0.214⁎⁎

PM2.5mv01: the moving average concentration of PM2.5 on the day to the previous one day before. PM10mv01: the moving average concentration of PM10 on the day to the previous one day before. SO2mv01: the moving average concentration of SO2 on the day to the previous one day before. NO2mv01: the moving average concentration of NO2 on the day to the previous one day before. COmv03: the moving average concentration of CO on the day to the previous three days before. O3mv02: the moving average concentration of O3 on the day to the previous two days before. ⁎⁎ P b 0.001.

3. Results 3.1. Principal component analysis of the moving average concentrations of air pollutants As results showed that the exposure response relationship between the concentrations of different air pollutants and the daily total mortality or YLL in Tianjin were different (Detailed was shown in Supplementary material Table A2-A5). Therefore, we fitted the models with different air pollutants to investigate the potential delayed and cumulative effects. According to the exposure response relationship between different atmospheric pollutants and health outcomes in Tianjin from 2014 to 2017, we choose the moving average days of air pollutants depend on, the minimum P value of the exposure response relationship, and the lagging days when the risk of mortality from air pollutants causes the maximum of ER value. PM10, PM2.5, SO2, and NO2 were selected as the moving average concentrations of the day to the previous one day before (mv01). CO was selected as the moving average concentration of the day to the previous three days before (mv03). O3 was selected as the moving average concentration of the day to the previous two days before (mv02). There was a significant correlation among these moving average concentrations (P b 0.001) (Table 2). In the result of principal component analysis, two principal components were used, the cumulative contribution rate of variance for the two principal components was N70% (53.6% and 16.4%, respectively) (Cattell, 1996) (Table 3). Two main factors from the factor loading matrix can represent air quality conditions, comprehensively. PM2.5, PM10, SO2 and NO2 account for a larger proportion in F1; O3 and CO account for a large proportion in F2 (Table 4). The first principal component function was as follows: F 1 ¼ 0:503  Z PM 2:5 mv01 þ 0:479  Z PM10 mv01 þ 0:442  Z SO2 mv01 þ 0:484  ZNO2 mv01 þ 0:199  Z COmv03–0:219  Z O3 mv02

PM10mv01 is the normalized value of moving average concentration of the PM10 concentration on the day to the day before; Z SO2mv01 is the normalized value of moving average concentration of the SO2 concentration on the day to the day before; Z NO2mv01 is the normalized value of moving average concentration of the NO2 concentration on the day to the day before; Z COmv03 is the normalized value of moving average concentration of the CO concentration on the day to the previous three days before; Z O3mv02 is the normalized value of moving average concentration of the O3 concentration on the day to the previous two days before. 3.2. Exposure response relationship of the main component variables and the total YLL in Tianjin The descriptive statistics for two main principal components were shown in Table 5. During the study period, the daily average F1 in Tianjin were ranged from −2.847 to 8.415, the daily average F2 in Tianjin were ranged from −8.210 to 6.171. We used GAM to estimate the burden of two main variables of air pollutions on daily total YLL in Tianjin, China, during 2014–2017. For YLL, one unit increasing of F1 and F2 corresponded to 18.420 (95% CI: 7.762, 29.078) and 22.409 (95% CI: 3.053, 41.766) person years in daily total YLL, respectively. 3.3. Tianjin AQHI According to the exposure response relationships of the main components variables and the total YLL, the maximum daily life loss was 293.286 person-years with a unit increase of F1 and F2. The time series distribution of Tianjin AQHI rom January 1st, 2014 to December 31th, 2017 was shown in Fig. 1, which presents a significant annual trend. The distribution of Tianjin AQHI was similar to that of AQHI in Canada (Fig. A1), both AQHIs produced frequency distributions were rightTable 4 Loadings matrix of principal components.

The second principal component function was as follows:

Principal component F 2 ¼ 0:286  Z PM 2:5 mv01 þ 0:346  Z PM10 mv01–0:232  Z SO2 mv01–0:487 Z COmv0 3 þ 0:706  Z O3 mv02 PM2.5mv01

where Z PM2.5 mv01 is the normalized value of moving average concentration of the PM2.5 concentration on the day to the day before; Z Table 3 Variance contribution of each principal components. Principal component

1

2

3

4

5

6

Standard deviation Proportion of variance Cumulative proportion

1.794 0.536 0.536

0.991 0.164 0.700

0.957 0.153 0.852

0.685 0.078 0.931

0.559 0.052 0.983

0.324 0.017 1.000

PM10mv01 SO2mv01 NO2mv01 COmv03 O3mv02

1

2

3

4

5

6

0.503 0.479 0.442 0.484 0.199 −0.219

0.286 0.346 −0.232 – −0.487 0.706

– – – – −0.821 −0.567

0.306 0.356 −0.736 −0.250 0.214 −0.360

– 0.374 0.453 −0.806 – –

0.754 −0.619 – −0.206 – –

PM2.5mv01: the moving average concentration of PM2.5 on the day to the day before. PM10mv01: the moving average concentration of PM10 on the day to the day before. SO2mv01: the moving average concentration of SO2on the day to the day before. NO2mv01: the moving average concentration of NO2 on the day to the day before. COmv03: the moving average concentration of CO on the day to the previous three days before. O3mv02: the moving average concentration of O3 on the day to the previous two days before.

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5

Main principal components

Minimum

P25

P50

P75

Maximum

associated with a 1.149% (95%CI: 0.331%, 1.974%). For YLL, with IQR increasing of the AQHI, the daily YLL increased by 32.797 (95% CI: 14.559, 51.035) person years. And IQR increasing of AQI was significantly related with 22.367 (95% CI: 6.619, 38.116) person years for total YLL.

F1 F2

−2.847 −8.210

−1.237 −0.582

−0.505 −0.1463

0.762 0.501

8.415 6.171

4. Discussion

Table 5 Descriptive statistics of two main principal components in Tianjin, China, during 2014–2017.

P25: quartile, the 25th percentile. P50: median, the 50th percentile. P75: the 75th percentile.

skewed rather than normal, with values b10% of values at 7 or above on the 10-point scale (Stieb et al., 2008). Therefore, the study divides AQHI into four levels by using banding standards of Canadian AQHI. The results were shown in Fig. 2. 3.4. Evaluating the validity of AQHI 3.4.1. Rationality of Tianjin AQHI The K-fold cross-validation method was used to verify the rationality of Tianjin AQHI, the results were shown in Table 6. For the total mortality, the minimum and maximum of the difference between the test set and the actual set were 0.341 and 1.849, respectively. For the total YLL, the minimum and maximum values of the difference between the test set and the actual value were 1.110 and 2.651, respectively. The correlation between the test set and the actual set values about total mortality and YLL were all significant, and the correlation coefficients were 0.742 and 0.700, respectively. The RMSE values were 1.381 and 1.800 for total mortality and total YLL, respectively (Table 6). 3.4.2. Comparison of AQHI and AQI The comparison results of AQI and AQHI in Tianjin during 2014 to 2017 were shown in Table 7. The median (P50) of AQI and AQHI were 92.0 and 4.8, respectively, the IQR were 64.5 and 1.5, respectively. The difference in the rate of excellent air quality rates between AQI and AQHI was significant (χ2 = 103.15, P b 0.001). There was a correlation between AQHI and AQI (r = 0.807, P b 0.01), and the grading value was also correlated (rs = 0.580, P b 0.01). The effects of AQHI and AQI on total mortality and YLL by age and sex were shown in Table 8. The associations between AQHI and total mortality and YLL among were all stronger than those among the AQI. For mortality, we found that IQR increasing of the AQHI was significantly associated with a 1.692% (95% CI: 0.746%, 2.648%) increasing in excess risk for total mortality, and IQR increasing of the AQI was significantly

Previous studies have demonstrated that AQHI can predict the health risks of residents with air quality (Kyrkilis et al., 2007; To et al., 2013; Szyszkowicz and Kousha, 2014; Kousha and Castner, 2016; Szyszkowicz, 2019). AQHI has been constructed and applied in many areas to evaluate the effect of air quality to health effects. The Health Canada's initial pollutant considered the health effect of five types of air pollutants including PM2.5, O3, NO2, SO2 and CO. It is difficult to assess the health effects of SO2 and CO, so they were not included in the Canadian AQHI(Stieb et al., 2008). After that, Szyszkowicz constructed Health Air Study Index (HASI) based on the exposure response relationship between six air pollutants(CO, NO2, SO2, O3, and two measures of particulate matter) and the frequency of emergency department (ED) visits due to colitis among young patients, this index could comprehensively assess the health risks of air pollutants (Szyszkowicz, 2015). South Africa constructed a comprehensive risk index to describe the health effects associated with the coexistence of several different air pollutants including PM2.5, PM10, SO2, NO2, O3 and CO in Cape Town, South Africa (Cairncross et al., 2007). Greece also took into account the combined exposure response of PM2.5, PM10, NO2, O3 and SO2 to health effects and constructed AQHI(Kyrkilis et al., 2007). Sweden has carried out related research to construct the local AQHI, which is based on the exposure response relationship between the emergency treatment of asthma and the concentration of NOx, O3, PM10 and birch pollen in Stockholm during 2001–2005 (Olstrup et al., 2019). These AQHIs are becoming more comprehensive in estimating the health effect of multiple pollutants than previous air quality index, and including the increasing number of pollutants. In China, Hong Kong first used AQHI to assess air quality. On December 30, 2013, the Hong Kong EPA launched AQHI based on the outpatient visits of hospitals of Hong Kong citizens and air pollution data. After that, Guangzhou conducted related research to construct AQHI which considered the collinearity and health effects of PM10 and PM2.5(Li et al., 2017). However, these studies did not take the age of mortality into consideration in the construction of AQHI, only used the mortality and the number of outpatient visits as a health outcome caused by air pollution. In our study, YLL has been considered in the research of AQHI. Firstly, our research mainly

Fig. 1. The time series distribution of Tianjin AQHI from 2014 to 2017.

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Fig. 2. The banding of Tianjin AQHI: 0–3 means air pollution has low risk on health, and 4–6 means air pollution has moderate risk, 7–10 means air pollution has high risk on health, and N 10 means air pollution has serious risk on health.

Table 6 Rationality of Tianjin AQHI by using K-fold cross-validation method. Disease burden

Total death Total YLL

MAE Minimum

Mean

Maximum

0.341 1.110

1.262 1.710

1.849 2.651

RMSE

r

P

1.381 1.800

0.742 0.700

b0.001 b0.001

RMSE: root mean square error. MAE: mean absolute error.

focuses on the exposure-response relationship of air pollution to health effects, which is a kind of acute health effect of air pollution. And YLL is more sensitive to time than disability adjusted life year (DALY), so it is relatively appropriate to choose YLL rather than DALY. Secondly, compared with the outcome indicator of mortality, YLL not only considers mortality but also considers the age of mortality, and can more comprehensively evaluate health effects than mortality. Additionally, the lagging effects and interactions of all pollutants on health effects have been also considered in our study. Basing on the results, the proposed AQHI of Tianjin combines the health effects of six air pollutants including PM2.5, PM10, SO2, NO2, O3 and CO. In other regions, they only simply superimposed the risk of representing pollutants when AQHI is established (Cairncross et al., 2007; Kyrkilis et al., 2007; Stieb et al., 2008; Li et al., 2017). Compared with the construction methods of previous AQHIs, PCA was used in our study to build the main components of air pollutants including F1 and F2. The average moving concentration of all atmospheric pollutants was weighted, so we could take into account the effects of atmospheric pollutants on health more properly. To a certain extent, it can solve the

problem that PM2.5 is a part of PM10 (Ho et al., 2003), and all of them included in the model may cause overestimation of the disease burden. Besides, if simply superimpose the risk associated with each pollutant, we found that the maximum daily YLL was 1317.017 person-years (Supplementary material Table A6), based on the exposure response relationship of air pollutants in different moving average periods and the daily YLL. It was much more than the maximum daily YLL calculated for the principal component pollutants. Therefore, we speculated that the health effects of various air pollutants may be co-linear, that is, there is a certain antagonism. Previous AQHIs were often based on indicative atmospheric pollutants, neglected the linear non-threshold relationship between health effects and atmospheric pollutants. Tianjin AQHI ranging from 0 to 10 integrated the exposure response relationship among six air pollutants and YLL. So Tianjin AQHI may provide more convenient and accurate health risk prediction for the potential delayed and cumulative effects of air pollution. The health risk evaluating validity of Tianjin AQHI was assessed in the present study. We observed the predictability and rationality of the AQHI model by using K-fold cross-validation. And found that the difference between the predicted and actual values of AQHI was small, and there was a strong correlation between the two values. Moreover, we found that AQHI would be a better predictor than AQI in evaluating total daily mortality and YLL caused by air pollution. At the same time, there were some differences in the prediction for health outcomes of different sexes and ages. Therefore, we need to analyze the exposure response relationship of different sexes and ages to establish the AQHI for different people. Previous studies also found that AQHI was a better predictor of health outcomes than AQI (Chen et al., 2013; Li et al., 2017). This may be partly explained by the fact that AQI only considered the

Table 7 Comparison of AQHI and AQI in Tianjin, China, during 2014–2017. Index

Minimum

Maximum

P25

P50

P75

IQR

Days of excellent air quality (day)

Rates of excellent air quality (%)

χ2

P

AQI AQHI

25.0 1.0

500.0 13.0

65.5 4.1

92.0 4.8

130.0 5.6

64.5 1.5

134 13

9.17% 0.89%

103.15

b0.001

P25: quartile, the 25th percentile. P50: median, the 50th percentile. P75: the 75th percentile. IQR: interquartile range.

Table 8 Combined estimated changes with 95% confidence intervals in excess risk and YLL of total deaths associated with IQR increasing in AQHI and AQI of different people, Tianjin, China, during 2014–2017. Index

Total Male Female Age N 65 years Age b 65 years ⁎ P b 0.05.

AQHI AQI AQHI AQI AQHI AQI AQHI AQI AQHI AQI

Death(%) ER

95% CI

1.692 1.149 1.501 0.012 1.292 0.025 1.206 0.019 0.910 0.015

(0.746, 2.648)⁎ (0.331, 1.974)⁎ (0.324, 2.692)⁎ (−0.004, 0.028) (0.445, 2.147)⁎ (0.008, 0.042)⁎ (0.511, 1.907)⁎ (0.005, 0.033)⁎ (−0.241, 2.075) (−0.007, 0.037)

YLL(person years) R

2

0.305 0.302 0.196 0.193 0.227 0.226 0.306 0.303 0.064 0.063

RR

95% CI

R2

32.797 22.367 16.350 8.475 16.695 14.441 18.720 13.415 11.999 7.4542

(14.559, 51.035)⁎ (6.619, 38.116)⁎ (2.336, 30.364)⁎

0.232 0.229 0.142 0.140 0.150 0.150 0.282 0.280 0.061 0.292

(−3.638, 20.587) (6.514, 26.875)⁎ (5.626, 23.256)⁎ (7.685, 29.754)⁎ (3.910, 22.920)⁎ (−2.262, 26.260) (−2.305, 17.214)

Q. Zeng et al. / Science of the Total Environment 710 (2020) 136264

concentration of the one pollutant with the highest sub-index. In contrast, Tianjin AQHI can represent the lagging and interaction effects of various pollutants to population health risks by using a variety of statistical methods. And Tianjin AQHI took YLL as a health outcome indicator which could assess disease burden more accurately than mortality. From these perspectives, Tianjin AQHI will better represent the overall health risks of air pollutants. There are several strengths of our study. A key strength of our study is that we used YLL as a health effect indicator to construct an exposure response relationship and it may be more informative to reflect the acute health effects than mortality (Gao et al., 2015). An additional strength of our study is the construction of main components of atmospheric pollutants by using PCA which can comprehensively evaluate the hazards of various air pollutants. Further, the study used two methods to comprehensively assess the predictive power of AQHI for health. Specifically, compared with other studies which only compares AQHI with AQI (Chen et al., 2013; Li et al., 2017), this study also used K-fold cross-validation method to examine the predictive ability of AQHI, our results demonstrated AQHI was suitable for providing a scientific basis for the forecast of air quality health risks in Tianjin. Overall, the results of this study might contribute to providing scientific reference for the revision and presentation of environmental air quality standards and policies, and have a good application value. However, this study has several limitations. First, Tianjin AQHI in this study is only basing on the exposure response relationship of air pollutants with the total YLL, but not considering the relationship with other health outcomes such as the number of emergency and hospitalizations of various diseases. However, previous study showed that these health outcomes play an important role when assessing the adverse health effects of air pollutants on human health (To et al., 2013; Szyszkowicz and Kousha, 2014; Kousha and Castner, 2016). Second, the time series method used in this study belongs to ecological study, and ecological fallacy is one of the shortcomings of this type of research. So it may have some unavoidable ecological fallacies or exposure measurement errors, and not consider the possible differences in different regions. In the following studies, the generalized additive mixed effect model can be used or the model can be used with ArcGIS to try to accurately assess the exposure relationship between air pollutants and the health outcomes. There remains great interest, therefore, in considering different susceptible population, and more health outcomes, and future studies should seek accurate model of exposure response relationship. As noted above, it has been hypothesized that the establishment of Tianjin AQHI might provide a scientific basis for Tianjin's air quality health risk forecast, and may effectively inform the public about the health risks of air quality and improve China's current air quality daily report system. 5. Conclusion We established Tianjin AQHI basing on the short-term associations of main component of atmospheric pollutants which taking into account of the lagging and the interaction among different pollutants (PM2.5, PM10, SO2, NO2, and O3) on total YLL in Tianjin, China. The Kfold cross-validation of the Tianjin AQHI confirmed that the AQHI has a good and reliable fit. And compared with the existing AQI, the AQHI has a higher predictive ability for disease burden than AQI, and has a good applicability and application prospects to the public. Declaration of competing interest The authors declare no conflicts of interest. Acknowledgements We would like to acknowledge to Tianjin Centers for Disease Control and Prevention for supporting daily mortality data of Tianjin population.

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