particulate air pollution and mortality in china: a time- series … › sites › default › files...

Confidential: For Review O

nly

Particulate Air Pollution and Mortality in China: A Time-

Series Analysis in the Largest 38 Chinese Cities

Journal: BMJ

Manuscript ID BMJ.2016.033574.R1

Article Type: Research

BMJ Journal: BMJ

Date Submitted by the Author: 20-Oct-2016

Complete List of Authors: Yin, Peng; China CDC He, Guojun; The Hong Kong University of Science and Technology Chiu, Kowk Yan ; HKUST Fan, Maorong; Xiyuan Hospital, China Academy of Chinese Medical Sciences

Liu, Tong; HKUST Mu, Quan; The NatureConservancy Fan, Maoyong; Ball State University, Zhou, Maigeng; CDC,

Keywords: Mortality, Particulate air pollution, Generalized linear model, Heterogeneous effects of air pollution

https://mc.manuscriptcentral.com/bmj

BMJ


nly

i

Particulate Air Pollution and Mortality in China: A Time-Series

Analysis in the Largest 38 Chinese Cities

Authors:

Peng Yina,*

, Guojun Heb,*

, Maoyong Fanc,*

, Kowk Yan Chiud, Maorong Fan

e, Chang

Liuf, An Xue

g, Tong Liu

d, Yuhang Pan

d, Quan Mu

h, Maigeng Zhou

a,§

Affiliations: a National Center for Chronic and Non-communicable Disease Control and

Prevention, Chinese Center for Disease Control and Prevention, Beijing, China b Division of Social Science, Division of Environment, and Economics Department,

The Hong Kong University of Science and Technology, HK c Department of Economics, Ball State University, Muncie, IN, USA

d The Hong Kong University of Science and Technology, HK

e Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China

f Scheller College of Business, Georgia Institute of Technology, GA, USA

g Department of Environmental Engineering, Beijing University, Beijing, China

h The Nature Conservancy, Beijing, China

* These authors contribute equally to this manuscript.

§ Corresponding author: Maigeng Zhou National Center for Chronic and Non-

communicable Disease Control and Prevention, Chinese Center for Disease Control

and Prevention, Nanwei Road, Xicheng District, Beijing, 100050, China; Email:

[email protected].

“The Corresponding Authors have the right to grant on behalf of all authors and do

grant on behalf of all authors, a worldwide licence to the Publishers and its licensees

in perpetuity, in all forms, formats and media (whether known now or created in the

future), to i) publish, reproduce, distribute, display and store the Contribution, ii)

translate the Contribution into other languages, create adaptations, reprints, include

within collections and create summaries, extracts and/or, abstracts of the

Contribution, iii) create any other derivative work(s) based on the Contribution, iv) to

exploit all subsidiary rights in the Contribution, v) the inclusion of electronic links

from the Contribution to third party material where-ever it may be located; and, vi)

licence any third party to do any or all of the above.”

Page 1 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

ii

ABSTRACT

Objectives

To estimate the overall effect of particulate air pollution (particulate matter with

aerodynamic diameter <10 µm, or PM10) on mortality and explore the heterogeneity

of air pollution effects in major cities in China.

Design

Generalized linear models with different lag structures using time series data.

Setting

Thirty-eight large cities in 27 provinces of China. These cities have a combined

population of more than 200 million people.

Participants

350,638 deaths (200,912male, 149,726 female) recorded in 38 city districts by the

Disease Surveillance Point System (DSPS) of the Chinese Center for Disease Control

and Prevention (CDC) from January 1st, 2010 through June 29

th, 2013.

Main outcome measure

Daily numbers of deaths from all causes, cardiovascular and respiratory (CVR)

diseases, and non-CVR diseases and among different demographic groups were used

to estimate the associations between particulate air pollutantion and mortality.

Results

A 10-��/�� change in concurrent day PM10 concentrations was associated with 0.44

percent (95 percent CI: 0.30 to 0.58) change in the daily number of deaths. Previous

day and two-day lagged PM10 levels were also statistically significantly associated

with increased mortality at lower magnitudes. The estimate for the effect of PM10 on

CVR deaths was 0.62 percent (95 percent CI: 0.43 to 0.81) per 10 ��/��, compared

to 0.26 percent (95 percent CI: 0.09 to 0.42) for other-cause mortality. Exposure to

PM10 had similar impacts on both males and females. The elderly (≥ 60 years) were

more vulnerable to particulate air pollution than young people at high levels of air

pollution. The PM10 effect varies across cities and decreases as pollution levels rise.

Conclusion

Air pollution has a greater impact on CVR mortality than it does on other-cause

mortality. Older people have a higher risk of death from air pollution than younger

people. The magnitude of the effect varies across cities and decreases as PM10 levels

increase.

Page 2 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

iii

For Print Publication

ABSTRACT

Study question

To estimate the overall effect of particulate air pollution (particulate matter with

aerodynamic diameter <10 µm, or PM10) on mortality and explore the heterogeneity

of air pollution effects in major cities in China.

Methods

Generalized linear models with different lag structures were estimated using time

series data from thirty-eight large cities in 27 provinces of China. There were in total

350,638 deaths (200,912male, 149,726 female) in 38 city districts recorded by the

Disease Surveillance Point System (DSPS) of China CDC from January 1st, 2010

through June 29th

, 2013. Outcome measures are daily numbers of deaths from all

causes, cardiovascular and respiratory (CVR) diseases, and non-CVR diseases. A 10-

��/�� change in concurrent day PM10 concentration was associated with 0.44

percent (95 percent CI: 0.30 to 0.58) change in the daily number of deaths. Previous

day and two-day lagged PM10 levels were also statistically significantly associated

with increased mortality at lower magnitudes. The effects were greater for CVR

mortality than non-CVR mortality. The elderly (≥ 60 years) were more vulnerable to

air pollution than young people. The PM10 effect decreases as pollution levels rise.

Study answer and limitations

The air pollution effects cover a wide range and vary across cities, causes of diseases

and age groups. Other pollutants such as NO2, SO2 and O3 and indoor air pollution

were unexamined due to data limitation.

What this study adds

Acute air pollution effects are city-specific, affected by many local factors, and cannot

be generalized to all cities.

Funding, competing interests, data sharing

The study was funded by the SBI Research Grant from the HKUST and China

National Science and Technology Pillar Program 2013. The authors have no conflicts

of interests to declare. The mortality data can be applied through:

http://www.phsciencedata.cn/Share/edtShare.jsp. Other data are available upon

request.

Page 3 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

iv

Summary Box

What is already known on this topic

Many studies have shown a positive association between daily mortality and

particulate air pollution.

The air pollution effects in developed countries cannot be directly transferred to

China because of differences in air pollution levels, particle compositions, and

population characteristics.

Despite immense interest in the effect of air pollution on a national scale in China,

multi-city analysis is very limited.

What this study adds

Acute air pollution effects are city-specific, affected by many local factors, and

cannot be generalized to all cities.

The air pollution effects are smaller in more polluted cities and are more

homogenous in northern cities than in southern cities in China.

The effect of air pollution is only weakly associated with GDP per capita (the

association is positive but not statistically significant).

Page 4 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

1

Introduction

Air pollution and its negative health consequences are a major public concern

in China nowadays.1-3

According to the Global Burden of Disease Study, a loss of 25

million healthy years and more than 1.2 million premature deaths in China can be

attributed to the outdoor air pollution in 2010.4 OECD (2012) estimated that, world-

wide, up to 3.6 million people annually could die prematurely from air pollution each

year by 2050, with most of the deaths in China and India.5

Many time-series studies conducted in Chinese cities have consistently found

that temporarily elevated air pollution levels were associated with increased

mortality.6 7

A limiting factor in these studies is that the data were from a single city,

or a few cities. Additionally, many of most previous studies focused on heavily

polluted cities where pollution levels were several orders of magnitude greater than

those in cleaner cities. Therefore, these studies might be inadequate for setting

optimal environmental and public health policies at the national level. If air pollution

effects are greater in the most polluted cities and the government policy sets national

pollution control policies based on health risks estimated from heavily polluted cities,

then it might over-regulate pollution which would hinder local economic growth in

cleaner cities. Alternatively, if air pollution effects are smaller in more polluted cities,

then the government may under-assess the health risks and under regulate.

Much of the multi-city analysis has been focused on developed countries

where both health and pollution data are readily available.8-10

Estimates from these

studies have had profound implications for designing environmental regulations to

protect public health in the western world, however, they cannot be directly

transferred to China because of differences in air pollution levels, particle

compositions, and population characteristics. For example, the peak daily PM10

Page 5 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

2

(particulate matter with aerodynamic diameter < 10µm) concentrations in China often

goes beyond 600 ��/�� whereas the typical range of daily PM10 concentrations in

Europe and the United States is from 20 to 80 ��/��.

Due to the lack of multi-city studies in China, researchers have resorted to

meta-analysis to combine estimates from different studies.6 7 11-13

It is noteworthy that

meta-analyses based on published papers often suffer from publication bias and its

validity has been questioned due to incomparability across different studies. First,

“positive” results are more likely to be published than “negative” results, leading to

the censoring of studies with non-significant results.14

Second, heterogeneity in study

samples (e.g. the difference in age structure, ICD codes, and study periods) leads to

different interpretations in the coefficients of differing studies. Third, the differences

in study designs and statistical methods would affect the estimated sizes of air

pollution effects, which make comparisons across different studies difficult or even

impossible.

In this study, we assembled the most up-to-date and most comprehensive daily

mortality and particulate matter pollution data for China. With these data, we then

estimated the associations between PM10 and mortality in the Chinese population.

Daily mortality data and PM10 in China’s largest 38 cities are from Jan 1st, 2010 to

June 29th

, 2013. We used flexible modeling strategies to estimate the relationship

between PM10 and mortality and controlled for potential confounding factors, such as

temperature, dew point, day of week, holiday, and year effects. We estimated a

generalized linear model using daily time-series data for each city. Under this

research design, we were able to: (1) provide city-specific estimates of air pollution

effect and compare the estimates across cities with different levels of PM10

concentrations; and (2) estimate the national air pollution effects using the random

Page 6 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

3

effects meta-analysis and explore the heterogeneity of air pollution effects.

Methods

Study Area

We collected daily data on particulate matter air pollution, mortality, and

weather condition for the largest 38 cities most populated in China. Figure 1 shows

the geographical locations of these cities. They cover 27 provinces and encompass

much of China’s geography. Their combined population totals more than 200 million

people.

Measurement of Air Pollution

Air quality data were collected from the Ministry of Environmental Protection

(MEP). The MEP reported the daily air pollution index (API) and the primary

pollutant in all the major Chinese cities during the study period. The API is based on

the concentrations of three major air pollutants (PM10, SO2, and NO2) and provides an

overall measure of ambient air quality for each city. The higher the API, the worse is

air quality. The method used by the MEP to construct the API allows us to calculate

the daily concentration of PM10. The methods employed for estimating concentrations

of PM10 are available in the Supplementary Material 1 in the Appendix.

Daily Mortality

Daily mortality data are provided by the Disease Surveillance Point System

(DSPS) of the Chinese Center for Disease Control and Prevention (CDC). The DSPS

collects mortality data from certain city districts in each city it covers. Mortality data

include basic demographic characteristics (e.g. sex and age) of the decedent and the

Page 7 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

4

cause of death. The causes of death are coded according to the International

Classification of Diseases 10 (ICD-10). Mortality data are classified by causes of

death: cardiovascular (I00-99) and respiratory (J00-99) diseases, and all other

diseases. For this study we had daily numbers of deaths by age group, gender, and

cause of death for all the DSPS districts in the 38 cities. The data are from January 1st,

2010 through June 29th

, 2013. We discuss the details of the DSPS in the

Supplementary Material 2 in the Appendix.

Measurement of Weather Conditions

We obtained daily weather information from local weather stations from Jan 1,

2010 through June 29th

, 2013, for all 38 cities in the study.

Model

We estimated the associations between PM10 and daily mortality using a set of

generalized linear models (GLMs). For each city, we estimated the following equation

using daily time-series data:

��~��

log�� = � + �� + ∑ �� , "#$��%�&� + '()� + *+�",�� + -.,/� + #�0�

(1)

where �� is the number of deaths on day 0; �� is assumed to originate from a Poisson

distribution with 12��3 = �� and canonical log-link in the regression. �� is the

PM10 concentration on day 0 − +, and + is a day lag. � represents the log-relative rate

of mortality associated with air pollution. �� is a set of meteorological factors that are

correlated with air pollution levels, which include temperature and dew point. Also

included are several sets of dummy variables representing different time effects. They

Page 8 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

5

are dummy variables for day of the week ('()�), holidays (*+�",��), and year

(-.,/�). Dummy variables for day of the week capture weekly mortality patters.

Holiday dummy variables were used to capture the effect of holiday related conditions

on mortality such as unusually heavy traffics. Year dummy variables control for

potential discontinuous change in mortality levels in different years due to yearly

changes in policies. We also included a cubic function of time #�0� to control for the

long-term trends.

Meteorological factors � were modeled in the regressions through a set of

natural spline functions ��. The spline functions allow very flexible relationships

between meteorological factors and the outcomes. We chose the degrees of freedom

for each meteorological factor ("#$�) based on its best prediction for air pollution

levels. Using degrees of freedom that best predict air pollution levels is advantageous

because they produce unbiased or asymptotically unbiased estimates of the pollution

log-relative risk.15

The optimal degree of freedom for each natural spline was

obtained via generalized cross-validation method that best predicts PM10

concentrations.16

After controlling for these potential confounding factors, the high

frequency PM10 concentration data should provide a plausible source of exogenous

variation.

Since air pollution may affect mortality in a lagged fashion, we therefore

examined the air pollution effects separately for different lag structures (+ =

0, 1, 2, …). We also explored heterogeneous air pollution effects by examining

different age groups and different genders.

To estimate national air pollution effects, we conducted a heterogeneity test

and used random-effects meta-analysis to synthesize city-specific air pollution effect

estimates. Compared with the fixed effects model, the random-effects approach

Page 9 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

6

allows the true air pollution effects �9 to vary between studies. Such heterogeneity in

treatment effects may be caused by differences in study populations, local

socioeconomic conditions, and baseline population health. In the random-effects

meta-analysis, the air pollution effect is assumed to have a normal distribution around

a mean effect.

Finally, we investigated the patterns of heterogeneous air pollution effects

across cities using a set of linear regressions. The explanatory variables are mean

PM10 concentrations, a geographic (north) indicator, GDP per capita, and share of

workers in construction industry.

Patient involvement

No patients were involved in setting the research question or the outcome

measures, nor were they involved in developing plans for recruitment, design, or

implementation of the study. No patients were asked to advise on interpretation or

writing up of results. There are no plans to disseminate the results of the research

directly to study participants or any specific patient community.

Results

Descriptive Statistics

Table 1 summarizes the descriptive statistics for each city in our sample for

daily mean PM10 concentrations, daily number of all-cause deaths, and cardiovascular

and respiratory (CVR) deaths. Over the sample period (Jan 1st, 2010 to June 29

th,

2013), the daily mean of PM10 concentrations across all locations was 92.9 ��/��,

with a standard deviation of 46.3. The most polluted city in our sample was Urumqi in

Xinjiang Province with an average daily mean PM10 concentrations of 136 ��/��.

The least polluted city was Qinhuangdao in Hebei Province with an average daily

mean PM10 concentrations of 66.9 ��/��. The lowest daily PM10 concentration

Page 10 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

7

observed in our sample was 11 ��/��, while the highest was above 600��/��. On

average, there were 8.6 deaths per day in the sampled city districts, including 4.4 from

CVR diseases. The daily mean temperature and dew point across all cities were

56.3°F and 42.9°F, respectively. More information about these DSPS cities is

provided in Appendix Table SM2.

Mortality and PM10 Associations

We estimated the air pollution effects of concurrent day and lagged (up to 6

days) PM10 concentrations on all-cause deaths by fitting model (1) to the time series

data for each city independently. Figure 2 plots city-specific estimates and their 95

percent confidence intervals (CI) for concurrent day PM10. The estimates were

positive and statistically significant at the 5-percent level for 11 cities. None of the

estimates in the 38 cities was negative and statistically significant.

Although the majority of estimates were positive, the heterogeneity across the

cities was obvious. We performed a chi-squared test for heterogeneity on all 38 cities.

The chi-squared statistics was 91.6 (p-value=0) indicating considerable between-city

heterogeneity in the effect of PM10. The I-squared statistics showed that 59 percent of

between-city heterogeneity was attributable to variability in the true treatment effect,

rather than sampling variation. Since we used the same model and data period for all

cities, heterogeneity was smaller than that in meta-analysis using different studies.

When we rejected the hypothesis of homogeneity, we took into account the identified

between-cities variation and fitted a DerSimonian-Laird random effects model.17

The

combined estimate of the PM10 effects is at the bottom of figure 2. Overall, we found

that a 10-��/�� increase in PM10 concentration was associated with a 0.44 percent

(95 percent CI: 0.30 to 0.58) increase in total mortality.

Page 11 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

8

For lagged PM10, the estimates for individual cities and combined effects

forest plots are presented in supplementary figures SM1-SM6. The estimates for

individual cities became smaller in magnitude and the heterogeneity across cities was

decreasing as the lag time became longer. Similarly, the combined effects converged

to zero as the lag time became longer. For example, for lag 1 day PM10 pollution, the

combined random effects estimate was 0.26 percent excess mortality (95 percent CI:

0.15 to 0.37) per 10 ��/�� increase in PM10. The overall effect decreased to 0.13

percent (95 percent CI: 0.03 to 0.23) per 10 ��/�� increase in lag 2 days PM10

pollution. The combined effects became statistically insignificant and close to zero for

PM10 lagged more than 2 days. Given the aforementioned results, we also estimated

the air pollution effect using the 3-day moving average (lags 0, 1 and 2).

Supplementary figure SM7 in the Appendix plots the estimates and their 95 percent

confidence intervals. The combined random effects estimate was 0.45 percent excess

mortality (95 percent CI: 0.28 to 0.62) per 10 ��/�� increase in PM10. These

estimates are similar to those using concurrent day PM10.

Heterogeneity by Cause of Death, Gender, and Age Group

We plots the PM10 effects for CVR mortality in figure 3 and non-CVR

mortality in figure 4. We used concurrent day PM10 concentrations for disease

specific analysis and subgroup analysis because the analysis for overall mortality

showed that the estimates for concurrent day pollution were similar to those for 3-day

moving average. For both CVR and non-CVR diseases, the pollution effect showed

significant heterogeneity across cities. For most cities, the PM10 effect for CVR

mortality was more likely to be positive and greater than that for non-CVR mortality.

The combined effect for CVR mortality was more than double that for non-CVR

Page 12 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

9

mortality. For CVR mortality, the combined effect for all cities was: a 10-��/��

change in concurrent day PM10 concentration was associated with 0.62 percent (95

percent CI: 0.43 to 0.81) change in daily number of deaths. The combined estimate on

the effects of PM10 on non-CVR deaths was less than half (0.26 percent per 10

��/�� PM10; 95 percent CI: 0.09 to 0.42).

We then explored the heterogeneous effects of PM10 by examining subgroups

by gender and age. Supplementary figure SM9 plots city-specific estimates and 95

percent confidence intervals for males and females separately. The majority of all

estimates were positive for both males and females. The combined effects for males

were 0.39 percent (95 percent CI: 0.23 to 0.54) and for females 0.51 percent (95

percent CI: 0.34 to 0.68) per 10 ��/��. Air pollution effects were slightly greater for

females than for males, however the difference was not statistically significant as the

95 percent CIs for both estimates substantially overlapped.

Supplementary figure SM10 plots city-specific estimates and 95 percent

confidence intervals for subgroups by age. We divided all deaths into two age groups.

For younger people (< 60 years of age), there were positive and statistically

significant association between PM10 concentrations and mortality only in two cities.

The combined effect for younger people was 0.19 percent per 10 ��/�� and

statistically insignificant (95 percent CI: -0.02 to 0.40). For older people (≥ 60 years

of age), the estimates were positive and statistically significant in thirteen cities. The

combined effect for older people was 0.50 percent per 10 ��/�� and statistically

significant at the 5-percent level (95 percent CI: 0.34 to 0.66). This indicates that

older people are more vulnerable to air pollution than younger people.

Air Pollution Effects and City-specific Characteristics

Page 13 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

10

We explored the relationship between the estimated air pollution effects and

some city-specific characteristics. First, we examined the relationship between

estimated air pollution effects and mean PM10 concentrations. Supplementary figure

SM11 plots this relationship; it shows that air pollution effects were smaller in more

polluted cities. We estimated this correlation using a linear function with the

dependent variable being the estimated air pollution effect in each city. These results

are presented in column 1 of Table 3. The coefficient of PM10 was -0.01 (p-

value=0.07) in the linear regression.

Northern China burns a lot of coal during the winter months to provide central

heating, and its weather patterns are significantly different from Southern China, so

we checked whether air pollution effects differed between the North and the South.

Our division of the North and South followed the Huai River line (typically the

demarcation used in the literature).18

We examine the regional effect-concentration

relationship by regressing pollution effects on mean PM10 concentrations for northern

and southern cities separately. Supplementary figure SM12 compares the relationship

for northern cities with that for southern cities. It shows that the marginal effects of

PM10 was almost constant at different pollution levels for northern cities. The

coefficient of mean PM10 concentrations was 0 (p-value=0.97). In sharp comparison,

the marginal effect of PM10 decreased rapidly as pollution levels increased in southern

cities. The coefficient of mean PM10 concentrations was -0.02 (p-value=0.08).

Finally, we examined the relationship between the size of estimated air

pollution effects and two socio-economic factors: GDP per capita and the share of

workers in the construction industry. Supplementary figure SM13 plots the estimated

effects against those two characteristics. The linear fits showed a weak negative

relationship between pollution effect and GDP per capita and a positive relationship

Page 14 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

11

between pollution effects and the share of workers in the construction industry.

Columns 2-4 of Table 3 presents the corresponding regression results for

supplementary figures SM12 and SM13. The last column of Table 3 shows that even

if all variables were included in the regression at the same time, the observed

relationships in supplementary figures SM12 and SM13 remained; this indicates that

the relationships are unlikely to be spurious.

Discussion

Acquiring internally coherent multi-city estimates of air pollution effects in

fast developing countries like China is valuable to cost-benefit analysis of pollution

abatement strategies and setting optimal air quality standards. Here we estimated the

associations between PM10 concentrations and daily mortality from all causes, CVR

diseases, and non-CVR diseases for the largest 38 cities in China. We employed a set

of flexible generalized linear models to obtain the estimates of air pollution effects.

The specification of the statistical model required a series of analytic choices

including: (1) the specification of lag structure of the air pollution variable, and (2)

how to adjust flexibly for weather conditions.

Principal Findings

The results showed positive associations between daily mortality and PM10

exposure in most sampled cities. Compared with one or more days lagged PM10

pollution, concurrent day PM10 pollution had the largest impact on mortality. For

example, at the national level a 10-��/�� increase in concurrent day PM10, one-day

lagged PM10, and two-day lagged PM10, was respectively associated with 0.44, 0.26,

and 0.13 percent increase in daily number of deaths. We failed to find similar positive

Page 15 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

12

and statistically significant effects for air pollution lagged longer than 2 days. This

suggests that particulate pollution has relatively short lagging acute effect on

mortality.

The results showed that PM10-mortality associations were substantially

heterogeneous across cities. Based on our analysis of the largest 38 cities, we found

that PM10 concentrations were not statistically significantly associated with mortality

for more than half of all cities. Moreover, the effect estimates covered a wide range

and included both negative and positive domains. For example, the estimates of

concurrent day pollution ranged from high of 1.80 percent (95 percent CI: 0.6 to 3)

per 10 ��/�� in Yuxi to low of -0.98 percent (95 percent CI: -2.45 to 0.48) per 10

��/�� PM10 in Panzhihua. These findings show the limitation of past studies that

focused on a particular or a few large cities and were often biased towards positive

results.14

The findings of our study suggest that acute air pollution effects are city-

specific, affected by many local factors, and cannot be generalized to all cities.

In a closer examination of heterogeneity in the PM10 effects on mortality, we

conducted subgroup analyses. We found that air pollution had a much greater impact

on CVR mortality than it did on non-CVR mortality, with the difference being

statistically significant at the 5-percent level. This is consistent with the literature:

people with CVR diseases are more sensitive to short-term air pollution deterioration

than those with non-CVR diseases. Also found was that PM10 concentrations had a

much greater impact on the older people than the younger people. For young people,

we found that the overall effect was not statistically indifferent from zero. In sharp

contrast, the overall effect for older people was 0.50 percent (95 percent CI: 0.34 to

0.66). Positive and statically significant associations between PM10 concentrations

and mortality were found for one third of the cities. This indicates that older people

Page 16 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

13

are more vulnerable to particulate air pollution than younger people at high levels of

air pollution. Our gender subgroup analysis shows that exposure to PM10 had a

slightly greater impact on females than on males but the difference was not

statistically significant.

Interestingly, overall magnitudes of PM10 effects found in our study are

comparable to those found in several large meta-analyses. For example, summarizing

thirty-three time-series and case-crossover studies, Shang and coauthors reported that

a 10 ��/�� increase in PM10 was associated with a 0.32 percent (95 percent CI: 0.28

to 0.35) increase in non-accidental mortality, a 0.44 percent (95 percent CI: 0.33 to

0.54) increase in mortality due to cardiovascular diseases, and a 0.32 percent (95

percent CI: 0.23 to 0.40) increase in mortality due to respiratory diseases.7

Explanation of Heterogeneity

The pattern of the associations between air pollution effects and baseline PM10

level and city-specific characteristics is enlightening. First, air pollution effects were

smaller in more polluted cities. This could be because of the “saturation” effect, in

which underlying biochemical and cellular processes became saturated when

exposed to a very high level of a toxic component.19 20

It is also possible that in

more polluted cities, people adopted more defensive measures, such as reducing

outdoor activities, wearing face masks or installing air filters. As a result,

despite of living in more polluted areas, people’s actual exposure may have

been mitigated by avoidance behaviors. Second, we found that the air pollution

effects were more homogenous in northern cities than in southern cities. This

result may also be related to avoidance behaviors. A recent study showed that

people living in the north are more likely to buy air filters than those in the

Page 17 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

14

south.21

Third, there was only weak evidence that the marginal PM10 effect decreases

as GDP per capita increases (the association was positive but not statistically

significant), indicating that cities with better economic conditions might have better

medical services, and therefore lower marginal effects. The fourth and final

interesting finding was that if a city had a larger share of workers hired in the

construction industry, then the air pollution effects were greater. Possible explanation

for these results may be that construction workers were more likely to be exposed to

air pollution, or, alternatively, that the construction industry generated more

particulates. Much of the above discussion is conjectural because the sample size is

limited in this analysis (38 data points). Future research is warranted on these issues.

Limitations of Study

There are several limitations in this study. First, we were unable to

examine the pollution effects of other pollutants, such as NO2, SO2 and O3 due

to data limitation. Quantification of the health effect of other air pollutants is

also important for setting appropriate air quality standards. Second, we only

focused on urban cities so the estimates of air pollution effects cannot be

generalized to rural areas. Air pollution, including in-door air pollution, might

have a greater impact on rural residents;3 more research focusing on rural areas

is warranted.

Page 18 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

15

Figure 1. Geographical Distribution of 38 cities in the sample

See separate file titled “Figure 1 38 cities”

Note: The figure plots the locations of the 38 cities in our sample. They cover 27 of

31 provinces in China.

Page 19 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

16

Figure 2. Percentage Change in Daily Number of All-Cause Deaths per 10-

��/�� Increase in Concurrent Day PM10 in 38 Chinese Cities

See separate file titled “Figure 2”

PM10 Effect (10 ��/��, Lag=0)

Note: Maximum likelihood estimates and 95 percent confidence intervals of the city-

specific mean PM10 effects on total mortality, the largest 38 cities in China. The

dependent variable is the percentage change in the number of daily all-cause deaths.

X-axis is percentage change. Each solid square represents an effect size. Horizontal

lines indicate 95 percent CIs. ES=Effect Size.

Page 20 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

17

Figure 3. Percentage Change in Daily Number of Deaths from Cardiovascular

and Respiratory Diseases per 10-��/�� Increase in Concurrent Day PM10 in 38

Chinese Cities


PM10 Effect (10 ��/��, Lag=0)


specific mean lag PM10 effects on total mortality for cardiorespiratory and non-

cardiorespiratory deaths separately, the largest 38 cities in China. The dependent

variable is the percentage change in the number of daily deaths for cardiorespiratory

and non-cardiorespiratory diseases respectively.

Page 21 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

18

Figure 4. Percentage Change in Daily Number of Deaths from Non-

Cardiovascular and Respiratory Diseases per 10-��/�� Increase in Concurrent

Day PM10 in 38 Chinese Cities


PM10 Effect (10 ��/��, Lag=0)


specific mean lag PM10 effects on total mortality for cardiorespiratory and non-

cardiorespiratory deaths separately, the largest 38 cities in China. The dependent

variable is the percentage change in the number of daily deaths for cardiorespiratory

and non-cardiorespiratory diseases respectively.

Page 22 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

19

Table 1. Summary Statistics

PM10 (��/��) Daily All-Cause Deaths Daily CVR Deaths

City Mean Std. Dev. IQR Mean Std. Dev. Mean Std. Dev.

Urumqi 136 74.2 64 5.2 2.4 2.6 1.7

Beijing 113.4 71.5 84 21.8 5.4 12 4

Chengdou 109.5 57.2 70 7.1 2.9 3.4 2

Zaozhuang 108.9 53 74 2.4 1.6 1.3 1.2

Zhengzhou 108.5 51.1 58 6.3 3.5 3.2 2.3

Xining 108.3 55.8 66 10 4.8 4 2.7

Nanjing 104.4 53.4 64 9.1 3.3 5 2.6

Anshan 102.7 45.9 54 11.9 3.9 7.3 2.9

Wuhan 101.8 54.3 72 12.2 4.1 6.4 3

Tianjin 101.4 53.9 58 3.7 2.6 2.1 1.7

Tongchuan 100.9 44.6 50 3 1.9 1.6 1.4

Shenyang 100.7 49.6 58 19.4 5.4 10.3 3.6

Harbin 100.2 47.7 58 6.5 2.8 3.7 2

Yinchuan 98.3 49.6 50 4.8 2.6 2.8 2

Panzhihua 97.8 31.1 42 5.5 2.5 2.7 1.8

Maanshan 97.3 37.9 50 3.8 2 1.7 1.4

Xuzhou 97 49.7 52 21.8 5.4 12 4

Chongqing 96.5 49.4 62 3.3 2 1.8 1.4

Hangzhou 92.5 47.1 60 6.5 3 2.9 1.9

Yichang 92.3 42.3 48 6 2.8 2.5 1.7

Taiyuan 92.2 53.4 68 6.5 3.3 3.2 2.1

Changde 92.1 43.7 56 2.5 1.6 1.3 1.1

Changchun 89.9 45.6 48 7.6 2.8 4.2 2.1

Qingdao 89.8 49.3 58 21.8 5.4 12 4

Nanchang 89.8 42 56 3.4 2.2 2.1 1.8

Tangshan 88.7 51.9 52 10.6 3.6 5.1 2.7

Changsha 86 44.4 56 6 2.6 3.1 1.9

Suzhou 84.9 45.7 50 7.3 3.3 3.5 2.2

Zunyi 83.6 30.4 40 7.9 3.3 4.5 2.4

Hohhot 82.2 44.9 59 4 2.7 2.1 1.7

Liuzhou 80 35 42 5 2.5 2 1.5

Qiqihar 75.9 37.2 42 21.8 5.4 12 4

Shanghai 75.1 47.6 52 2.8 1.8 1.7 1.4

Guilin 72.4 38.7 51 1.8 1.4 0.8 0.9

Yantai 71.4 38.9 44 9.5 3.2 4.6 2.2

Yuxi 70.4 24.7 32 7.1 3.1 3.7 2.1

Guangzhou 69.3 31.9 43 19.2 5.6 9.1 3.7

Qinhuangdao 66.9 35.2 32 5.7 2.6 2.7 1.6

All Cities 92.9 46.3 58 8.6 6.9 4.4 4.1

Page 23 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

20

Note: PM10 concentrations are calculated using API reported by the Ministry of

Environmental Protection in China. Mortality Data come from the Chinese Center for Disease

Control and Prevention. See details in the Supplementary Material 1 in the Appendix.

Page 24 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

21

Table 2. Weather Conditions

Variable Mean Std. Dev. Min Max

Temperature (°F) 56.3 21.3 -21.7 96.9

Dew Point (°F) 42.9 23.0 -29.3 83.4

Note: Weather data are from Global Historical Climatology Network. Link:

https://www.ncdc.noaa.gov/data-access/land-based-station-data/land-based-

datasets/global-historical-climatology-network-ghcn.

Page 25 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

Confidential: For Review Only22

Table 3. Relationships between Air Pollution Effect and City-specific Factors

(1) (2) (3) (4) (5) (6)

Mean PM10 -0.010*

-0.009*

(0.071) (0.100)

Mean PM10×North -0.000

(0.971)

Mean PM10×South -0.022*

(0.079)

GDP Per Capita -0.034 -0.048

(0.542) (0.308)

Share of Workers in Construction Industry

6.247** 7.491***

(0.025) (0.002)

North Indicator

-0.371**

(0.025)

Constant 1.394** 2.654** 0.671*** 0.542** 0.108 1.213**

(0.018) (0.025) (0.000) (0.036) (0.547) (0.028)

Observations 38 18 38 38 38 38

R-squared 0.075 0.158 0.158 0.008 0.119 0.342

Note: We regress the estimated air pollution effects in Figure 2 on mean PM10 concentrations, a north indicator, GDP per capita (in 10,000

Yuan), and share of workers in construction industry. GDP per capita is collected from city statistical yearbooks. Share of workers in

construction industry is calculated using 2005 micro census data. P-values are in parenthesis. *, **, and *** indicate statistical significance at

10%, 5%, and 1% respectively.

Page 26 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

23

We thank Philip Coelho, Ball State University, for providing comments on the

manuscript.

Contributors: MYF, GH, PY and MZ designed the study. PY and MZ collected and

cleaned the mortality data. MYF, KYC, and GH conducted the analyses. MRF, AX,

and CL reviewed the literature, conducted GIS matching, and contributed to

interpretation of the results. TL, YP, and QM cleaned the pollution data, collected

socioeconomic data, and summarized the results. MYF and GH finished the first draft.

All authors commented on this draft and contributed to the final version. All authors

had full access to all of the data (including statistical reports and tables) in the study

and can take responsibility for the integrity of the data and the accuracy of the data

analysis. MYF, GH, PY and MZ are study guarantors.

Funding: The study was financially supported by the SBI Research Grant

(SBI15HS06) from the Hong Kong University of Science and Technology and China

National Science and Technology Pillar Program 2013 (2013BAI04B02). The funders

were not involved in the research and preparation of the article, including study

design; collection, analysis, and interpretation of data; writing of the article; nor in the

decision to submit it for publication.

Competing interests: All authors have completed the ICMJE uniform disclosure form

at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding

author) and declare: the study was financially supported by the SBI Research Grant

from the Hong Kong University of Science and Technology and China National

Science and Technology Pillar Program 2013; no financial relationships with any

organizations that might have an interest in the submitted work; no other relationships

or activities that could appear to have influenced the submitted work.

Ethical approval: Not required.

Data sharing: The pollution data and weather data are all available from Maoyong Fan

at [email protected]. The mortality data can only be applied through a government data

sharing portal: http://www.phsciencedata.cn/Share/edtShare.jsp.

Page 27 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

24

References

1. Ouyang Y. China wakes up to the crisis of air pollution. The Lancet Respiratory

Medicine 2013;1(1):12.

2. Chan CK, Yao X. Air pollution in mega cities in China. Atmos Environ

2008;42(1):1-42. doi: http://dx.doi.org/10.1016/j.atmosenv.2007.09.003

3. Zhou M, He G, Fan M, et al. Smog episodes, fine particulate pollution and

mortality in China. Environ Res 2015;136(0):396-404. doi:

http://dx.doi.org/10.1016/j.envres.2014.09.038

4. Kassebaum NJ, Bertozzi-Villa A, Coggeshall MS, et al. Global, regional, and

national levels and causes of maternal mortality during 1990–2013: a

systematic analysis for the Global Burden of Disease Study 2013. The

Lancet 2014;384(9947):980-1004.

5. Marchal V, Dellink R, Van Vuuren D, et al. OECD environmental outlook to

2050: Organization for Economic Co-operation and Development, 2011.

6. Lu F, Xu D, Cheng Y, et al. Systematic review and meta-analysis of the adverse

health effects of ambient PM2.5 and PM10 pollution in the Chinese

population. Environ Res 2015;136:196-204. doi:

http://dx.doi.org/10.1016/j.envres.2014.06.029

7. Shang Y, Sun Z, Cao J, et al. Systematic review of Chinese studies of short-term

exposure to air pollution and daily mortality. Environment International

2013;54:100-11. doi: http://dx.doi.org/10.1016/j.envint.2013.01.010

8. Samet JM, Dominici F, Curriero FC, et al. Fine Particulate Air Pollution and

Mortality in 20 U.S. Cities, 1987–1994. N Engl J Med 2000;343(24):1742-

49. doi: doi:10.1056/NEJM200012143432401

9. Dominici F, Samet JM, Zeger SL. Combining evidence on air pollution and daily

mortality from the 20 largest US cities: a hierarchical modelling strategy.

Journal of the Royal Statistical Society: Series A (Statistics in Society)

2000;163(3):263-302. doi: 10.1111/1467-985X.00170

10. Katsouyanni K, Touloumi G, Samoli E, et al. Confounding and effect

modification in the short-term effects of ambient particles on total

mortality: results from 29 European cities within the APHEA2 project.

Epidemiology 2001;12(5):521-31.

11. Aunan K, Pan X-C. Exposure-response functions for health effects of ambient

air pollution applicable for China – a meta-analysis. Sci Total Environ

2004;329(1–3):3-16. doi:

http://dx.doi.org/10.1016/j.scitotenv.2004.03.008

12. Kan H, Chen B, Chen C, et al. Establishment of exposure-response functions of

air particulate matter and adverse health outcomes in China and

worldwide. Biomed Environ Sci 2005;18(3):159-63.

13. Lai H-K, Tsang H, Wong C-M. Meta-analysis of adverse health effects due to

air pollution in Chinese populations. BMC Public Health 2013;13(360) doi:

10.1186/1471-2458-13-360

14. Walker E, Hernandez AV, Kattan MW. Meta-analysis: Its strengths and

limitations. Cleve Clin J Med 2008;75(6):431-39.

15. Dominici F, McDermott A, Hastie TJ. Improved semiparametric time series

models of air pollution and mortality. J Amer Statistical Assoc

2004;99(468):938-48.

16. Gu C. Smoothing spline ANOVA models: Springer 2013.

Page 28 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

25

17. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials

1986;7(3):177-88. doi: http://dx.doi.org/10.1016/0197-2456(86)90046-

2

18. Chen Y, Ebenstein A, Greenstone M, et al. Evidence on the impact of sustained

exposure to air pollution on life expectancy from China’s Huai River

policy. Proceedings of the National Academy of Sciences

2013;110(32):12936-41. doi: 10.1073/pnas.1300018110

19. Ambrose JA, Barua RS. The pathophysiology of cigarette smoking and

cardiovascular diseaseAn update. J Am Coll Cardiol 2004;43(10):1731-37.

doi: 10.1016/j.jacc.2003.12.047

20. Pope CA, Burnett RT, Krewski D, et al. Cardiovascular Mortality and Exposure

to Airborne Fine Particulate Matter and Cigarette Smoke: Shape of the

Exposure-Response Relationship. Circulation 2009;120(11):941-48. doi:

10.1161/circulationaha.109.857888

21. Ito K, Zhang S. Willingness to Pay for Clean Air: Evidence from Air Purifier

Markets in China. NBER Working Paper No. 22367, 2016.

Page 29 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

Confidential: For Review Only

Page 30 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


NOTE: Weights are from random effects analysis

Overall (I-squared = 59.1%, p = 0.000)

Nanchang

Shanghai

Beijing

Wuhan

QiqiharYinchuan

City

Changchun

Hohhot

TianjinChangsha

Shenyang

Tangshan

Changde

Xuzhou

Hangzhou

Yichang

Yuxi

Anshan

Zaozhuang

UrumqiXining

Taiyuan

Harbin

Chengdu

Tongchuan

Nanjing

Qinhuangdao

Suzhou

Guangzhou

Qingdao

Panzhihua

Zunyi

Zhengzhou

Yantai

Guilin

Maanshan

Chongqing

Liuzhou

0.44 (0.30 to 0.58)

0.22 (-0.50 to 0.94)

0.31 (0.00 to 0.61)

0.29 (0.10 to 0.48)

0.77 (0.40 to 1.15)

-0.24 (-1.21 to 0.73)-0.43 (-1.07 to 0.21)

ES (95% CI)

0.64 (0.14 to 1.14)

0.17 (-0.72 to 1.05)

0.54 (0.19 to 0.90)0.54 (-0.05 to 1.13)

0.58 (0.03 to 1.13)

-0.16 (-1.15 to 0.83)

0.71 (0.10 to 1.32)

-0.64 (-1.49 to 0.21)

0.50 (-0.09 to 1.09)

0.47 (-0.60 to 1.54)

1.80 (0.60 to 3.00)

0.12 (-0.53 to 0.77)

1.09 (0.36 to 1.82)

0.03 (-0.43 to 0.49)0.02 (-0.88 to 0.93)

0.76 (0.24 to 1.29)

0.10 (-0.28 to 0.48)

0.29 (-0.20 to 0.79)

0.20 (-1.07 to 1.46)

0.59 (0.16 to 1.02)

0.37 (-0.49 to 1.22)

0.44 (-0.00 to 0.89)

1.65 (1.17 to 2.13)

0.20 (-0.11 to 0.50)

-0.98 (-2.45 to 0.48)

0.45 (-0.46 to 1.37)

0.21 (-0.26 to 0.67)

0.25 (-0.30 to 0.81)

0.42 (-0.76 to 1.60)

1.72 (0.72 to 2.72)

0.48 (0.19 to 0.78)

1.69 (0.88 to 2.50)

100.00

2.23

4.29

4.90

3.90

1.502.53

Weight

3.21

1.71

3.982.76

2.93

1.46

2.68

1.81

2.76

1.30

1.09

2.49

2.20

3.431.66

3.09

3.87

3.22

1.00

3.58

1.79

3.48

3.28

4.30

0.78

1.63

3.39

2.92

1.12

1.43

4.37

1.92

%

0.44 (0.30 to 0.58)

0.22 (-0.50 to 0.94)

0.31 (0.00 to 0.61)

0.29 (0.10 to 0.48)

0.77 (0.40 to 1.15)

-0.24 (-1.21 to 0.73)-0.43 (-1.07 to 0.21)

ES (95% CI)

0.64 (0.14 to 1.14)

0.17 (-0.72 to 1.05)

0.54 (0.19 to 0.90)0.54 (-0.05 to 1.13)

0.58 (0.03 to 1.13)

-0.16 (-1.15 to 0.83)

0.71 (0.10 to 1.32)

-0.64 (-1.49 to 0.21)

0.50 (-0.09 to 1.09)

0.47 (-0.60 to 1.54)

1.80 (0.60 to 3.00)

0.12 (-0.53 to 0.77)

1.09 (0.36 to 1.82)

0.03 (-0.43 to 0.49)0.02 (-0.88 to 0.93)

0.76 (0.24 to 1.29)

0.10 (-0.28 to 0.48)

0.29 (-0.20 to 0.79)

0.20 (-1.07 to 1.46)

0.59 (0.16 to 1.02)

0.37 (-0.49 to 1.22)

0.44 (-0.00 to 0.89)

1.65 (1.17 to 2.13)

0.20 (-0.11 to 0.50)

-0.98 (-2.45 to 0.48)

0.45 (-0.46 to 1.37)

0.21 (-0.26 to 0.67)

0.25 (-0.30 to 0.81)

0.42 (-0.76 to 1.60)

1.72 (0.72 to 2.72)

0.48 (0.19 to 0.78)

1.69 (0.88 to 2.50)

100.00

2.23

4.29

4.90

3.90

1.502.53

Weight

3.21

1.71

3.982.76

2.93

1.46

2.68

1.81

2.76

1.30

1.09

2.49

2.20

3.431.66

3.09

3.87

3.22

1.00

3.58

1.79

3.48

3.28

4.30

0.78

1.63

3.39

2.92

1.12

1.43

4.37

1.92

%

0-4 -2 0 2 4

Page 31 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960




ChengduZunyi

BeijingTianjin

Hangzhou

Liuzhou

Yinchuan

Shanghai

Changde

Shenyang

Wuhan

Zaozhuang

Urumqi

TangshanXuzhouPanzhihua

Guangzhou

Maanshan

Yantai

Suzhou

Xining

Yichang

Nanchang

Qingdao

Qinhuangdao

Nanjing

Yuxi

City

Tongchuan

Hohhot

Chongqing

Guilin

Changsha

Zhengzhou

Qiqihar

Changchun

Harbin

Taiyuan

Anshan

0.62 (0.43 to 0.81)

0.38 (-0.31 to 1.07)0.35 (-0.83 to 1.53)

0.54 (0.28 to 0.80)0.53 (0.09 to 0.97)

0.50 (-0.38 to 1.39)

2.35 (1.13 to 3.57)

-0.23 (-1.11 to 0.66)

0.28 (-0.14 to 0.69)

0.70 (-0.18 to 1.58)

0.68 (-0.01 to 1.38)

1.09 (0.58 to 1.60)

1.52 (0.59 to 2.45)

0.02 (-0.60 to 0.65)

-0.34 (-1.56 to 0.87)-1.09 (-2.28 to 0.10)-1.65 (-3.61 to 0.31)

2.07 (1.39 to 2.75)

2.38 (0.85 to 3.91)

0.90 (0.12 to 1.69)

1.02 (0.37 to 1.67)

0.06 (-1.20 to 1.32)

1.08 (-0.35 to 2.51)

0.51 (-0.47 to 1.49)

0.21 (-0.21 to 0.63)

0.19 (-0.97 to 1.36)

1.10 (0.53 to 1.67)

0.93 (-0.71 to 2.57)

ES (95% CI)

1.52 (-0.15 to 3.19)

-0.01 (-1.14 to 1.13)

0.62 (0.23 to 1.02)

1.98 (0.32 to 3.64)

1.38 (0.58 to 2.18)

0.70 (0.04 to 1.35)

-0.13 (-1.37 to 1.11)

0.20 (-0.49 to 0.90)

0.02 (-0.48 to 0.52)

1.05 (0.30 to 1.80)

0.31 (-0.57 to 1.19)

100.00

3.171.77

4.884.17

2.52

1.70

2.53

4.28

2.53

3.15

3.89

2.39

3.42

1.721.760.82

3.20

1.22

2.83

3.31

1.63

1.35

2.24

4.29

1.81

3.65

1.09

Weight

1.06

1.86

4.37

1.07

2.77

3.31

1.66

3.15

3.94

2.97

2.53

%

0.62 (0.43 to 0.81)

0.38 (-0.31 to 1.07)0.35 (-0.83 to 1.53)

0.54 (0.28 to 0.80)0.53 (0.09 to 0.97)

0.50 (-0.38 to 1.39)

2.35 (1.13 to 3.57)

-0.23 (-1.11 to 0.66)

0.28 (-0.14 to 0.69)

0.70 (-0.18 to 1.58)

0.68 (-0.01 to 1.38)

1.09 (0.58 to 1.60)

1.52 (0.59 to 2.45)

0.02 (-0.60 to 0.65)

-0.34 (-1.56 to 0.87)-1.09 (-2.28 to 0.10)-1.65 (-3.61 to 0.31)

2.07 (1.39 to 2.75)

2.38 (0.85 to 3.91)

0.90 (0.12 to 1.69)

1.02 (0.37 to 1.67)

0.06 (-1.20 to 1.32)

1.08 (-0.35 to 2.51)

0.51 (-0.47 to 1.49)

0.21 (-0.21 to 0.63)

0.19 (-0.97 to 1.36)

1.10 (0.53 to 1.67)

0.93 (-0.71 to 2.57)

ES (95% CI)

1.52 (-0.15 to 3.19)

-0.01 (-1.14 to 1.13)

0.62 (0.23 to 1.02)

1.98 (0.32 to 3.64)

1.38 (0.58 to 2.18)

0.70 (0.04 to 1.35)

-0.13 (-1.37 to 1.11)

0.20 (-0.49 to 0.90)

0.02 (-0.48 to 0.52)

1.05 (0.30 to 1.80)

0.31 (-0.57 to 1.19)

100.00

3.171.77

4.884.17

2.52

1.70

2.53

4.28

2.53

3.15

3.89

2.39

3.42

1.721.760.82

3.20

1.22

2.83

3.31

1.63

1.35

2.24

4.29

1.81

3.65

1.09

Weight

1.06

1.86

4.37

1.07

2.77

3.31

1.66

3.15

3.94

2.97

2.53

%

0-4 -2 0 2 4

Page 32 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960




City

Tongchuan

Zunyi

Changchun

Urumqi

Yantai

Tangshan

Hangzhou

TaiyuanYichang

Shenyang

Wuhan

Xuzhou

Qinhuangdao

Qiqihar

Anshan

Chongqing

Hohhot

Suzhou

Liuzhou

Yinchuan

Chengdu

Harbin

Tianjin

Changsha

Yuxi

Nanchang

Guilin

Nanjing

Panzhihua

Qingdao

Zhengzhou

Xining

Shanghai

Beijing

Guangzhou

Changde

Maanshan

Zaozhuang

0.26 (0.09 to 0.42)

ES (95% CI)

-1.48 (-3.31 to 0.35)

0.58 (-0.77 to 1.94)

1.16 (0.43 to 1.89)

0.04 (-0.58 to 0.66)

-0.40 (-1.18 to 0.37)

0.21 (-1.34 to 1.75)

0.50 (-0.24 to 1.24)

0.48 (-0.21 to 1.18)-0.20 (-1.77 to 1.37)

0.43 (-0.44 to 1.31)

0.40 (-0.13 to 0.94)

-0.06 (-1.28 to 1.16)

0.53 (-0.64 to 1.70)

-0.41 (-1.92 to 1.10)

-0.11 (-1.10 to 0.87)

0.32 (-0.12 to 0.75)

0.36 (-0.86 to 1.58)

-0.12 (-0.73 to 0.50)

1.20 (0.12 to 2.28)

-0.64 (-1.58 to 0.31)

0.23 (-0.32 to 0.77)

0.19 (-0.36 to 0.73)

0.57 (0.03 to 1.11)

-0.39 (-1.25 to 0.48)

2.72 (1.09 to 4.35)

-0.07 (-1.05 to 0.91)

-1.03 (-2.64 to 0.58)

-0.06 (-0.68 to 0.57)

-0.25 (-2.43 to 1.93)

0.19 (-0.25 to 0.63)

-0.27 (-0.92 to 0.39)

-0.02 (-1.37 to 1.33)

0.35 (-0.10 to 0.79)

-0.03 (-0.31 to 0.25)

1.26 (0.62 to 1.90)

0.72 (-0.06 to 1.49)

1.17 (-0.16 to 2.50)

0.64 (-0.30 to 1.59)

100.00

%Weight

0.70

1.20

3.04

3.65

2.82

0.95

2.98

3.210.93

2.38

4.24

1.43

1.53

0.99

2.00

5.12

1.42

3.73

1.74

2.13

4.20

4.17

4.20

2.43

0.86

2.01

0.89

3.64

0.51

5.07

3.44

1.21

5.00

6.50

3.51

2.82

1.23

2.13

0.26 (0.09 to 0.42)

ES (95% CI)

-1.48 (-3.31 to 0.35)

0.58 (-0.77 to 1.94)

1.16 (0.43 to 1.89)

0.04 (-0.58 to 0.66)

-0.40 (-1.18 to 0.37)

0.21 (-1.34 to 1.75)

0.50 (-0.24 to 1.24)

0.48 (-0.21 to 1.18)-0.20 (-1.77 to 1.37)

0.43 (-0.44 to 1.31)

0.40 (-0.13 to 0.94)

-0.06 (-1.28 to 1.16)

0.53 (-0.64 to 1.70)

-0.41 (-1.92 to 1.10)

-0.11 (-1.10 to 0.87)

0.32 (-0.12 to 0.75)

0.36 (-0.86 to 1.58)

-0.12 (-0.73 to 0.50)

1.20 (0.12 to 2.28)

-0.64 (-1.58 to 0.31)

0.23 (-0.32 to 0.77)

0.19 (-0.36 to 0.73)

0.57 (0.03 to 1.11)

-0.39 (-1.25 to 0.48)

2.72 (1.09 to 4.35)

-0.07 (-1.05 to 0.91)

-1.03 (-2.64 to 0.58)

-0.06 (-0.68 to 0.57)

-0.25 (-2.43 to 1.93)

0.19 (-0.25 to 0.63)

-0.27 (-0.92 to 0.39)

-0.02 (-1.37 to 1.33)

0.35 (-0.10 to 0.79)

-0.03 (-0.31 to 0.25)

1.26 (0.62 to 1.90)

0.72 (-0.06 to 1.49)

1.17 (-0.16 to 2.50)

0.64 (-0.30 to 1.59)

100.00

%Weight

0.70

1.20

3.04

3.65

2.82

0.95

2.98

3.210.93

2.38

4.24

1.43

1.53

0.99

2.00

5.12

1.42

3.73

1.74

2.13

4.20

4.17

4.20

2.43

0.86

2.01

0.89

3.64

0.51

5.07

3.44

1.21

5.00

6.50

3.51

2.82

1.23

2.13

0-4 -2 0 2 4

Page 33 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

1

Supplementary Materials to

Particulate Air Pollution and Mortality in China: A Time-Series Analysis in the Largest 38 Chinese Cities

Authors: Peng Yina,*, Guojun Heb,*, Maoyong Fanc,*, Kowk Yan Chiud, Maorong Fane, Chang Liuf, An Xueg, Tong Liud, Yuhang Pand, Quan Muh, Maigeng Zhoua,§

Affiliations: a National Center for Chronic and Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China b Division of Social Science, Division of Environment, and Economics Department, The Hong Kong University of Science and Technology, HK c Department of Economics, Ball State University, Muncie, IN, USA d The Hong Kong University of Science and Technology, HK e Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China f Scheller College of Business, Georgia Institute of Technology, GA, USA g Department of Environmental Engineering, Beijing University, Beijing, China h The Nature Conservancy, Beijing, China * These authors contribute equally to this manuscript. § Corresponding author: Maigeng Zhou National Center for Chronic and Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Nanwei Road, Xicheng District, Beijing, 100050, China; Email: [email protected].

Page 34 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

mailto:[email protected]


nly

2

Supplementary Material 1: PM10 calculation

The API is constructed based on the concentrations of 3 atmospheric pollutants, namely sulfur dioxide (𝑆𝑆𝑂𝑂2), nitrogen dioxide (𝑁𝑁𝑂𝑂2), and suspended particulates of 10 micrometers or less (𝑃𝑃𝑀𝑀10) measured at the monitoring stations throughout each city. It is a proxy measure of the ambient air quality. The API indicates the maximum concentration of the three pollutants. Table SM1 shows the relationship between the API and the concentration of the three air pollutants.

Table SM1. The Relationship between the API and Air Pollutant Concentrations

API 𝑆𝑆𝑂𝑂2 𝑁𝑁𝑂𝑂2 𝑃𝑃𝑀𝑀10 Air Quality Levels 0-50 0-0.05 0-0.08 0-0.05 Excellent 50-100 0.05-0.15 0.08-0.12 0.05-0.15 Good 100-200 0.15-0.8 0.12-0.28 0.15-0.35 Slightly Polluted 200-300 0.8-1.6 0.28-0.565 0.35-0.42 Moderately Polluted 300-400 1.6-2.1 0.565-0.75 0.42-0.5 Severely Polluted 400-500 2.1-2.62 0.75-0.94 0.5-0.6 Severely Polluted Note: Pollutant concentration is measured by 𝑚𝑚𝑚𝑚/𝑚𝑚3. The last column is the official air quality description based on the API

The construction of the API takes four steps. First, measure the daily average concentration of each pollutant. Second, for each pollutant, find out its corresponding concentration interval in Table SM1. Third, calculate the pollution index (PI) of each pollutant linearly. Finally, take the maximum of all pollution indices and define it as the API. For example, assume the concentrations of the three pollutants are: 𝐶𝐶𝑆𝑆𝑂𝑂2 =0.07𝑚𝑚𝑚𝑚/𝑚𝑚3, 𝐶𝐶𝑁𝑁𝑂𝑂2 = 0.10𝑚𝑚𝑚𝑚/𝑚𝑚3, and 𝐶𝐶𝑃𝑃𝑀𝑀10 = 0.30𝑚𝑚𝑚𝑚/𝑚𝑚3, then use Table SM1 we find that the concentrations of 𝑆𝑆𝑂𝑂2, and 𝑁𝑁𝑂𝑂2 are in the interval [50,100] while the 𝑃𝑃𝑀𝑀10 concentration falls into the interval [100,200]. Within each interval we can calculate pollution index of each pollutant linearly:

𝑃𝑃𝐼𝐼𝑆𝑆𝑂𝑂2 =100 − 50

0.15 − 0.05∗ (0.07 − 0.05) + 50 = 60

𝑃𝑃𝐼𝐼𝑁𝑁𝑂𝑂2 =100 − 50

0.12 − 0.08∗ (0.10 − 0.08) + 50 = 75

𝑃𝑃𝐼𝐼𝑃𝑃𝑀𝑀10 =200 − 1000.35 − 0.15

∗ (0.30 − 0.15) + 100 = 175 Then the 𝐴𝐴𝑃𝑃𝐼𝐼 = max[𝑃𝑃𝐼𝐼𝑆𝑆𝑂𝑂2 ,𝑃𝑃𝐼𝐼𝑁𝑁𝑂𝑂2 ,𝑃𝑃𝐼𝐼𝑃𝑃𝑀𝑀10} = 175 and PM10 is called the

primary pollutant. Reverse this process, we can recover the concentrations of the primary pollutant. We use daily API to recover daily 𝑃𝑃𝑀𝑀10 concentrations because the Chinese government did not provide daily individual pollution concentrations to the public. In our daily API data, PM10 is the primary pollutant for more than 90% of the days. So the reverse calculation can provide us accurate PM10 concentration data for 90% of the time. To deal with missing values in time series data, we use two different strategies to interpolate PM10 concentrations for the rest less than 10% sample: (1) treat those days as if PM10 is the primary pollutant, and (2) use linear interpolate for the missing values. We tried both methods and it turned out that both methods generated quantitatively similar empirical results. We also tried dropping the days

Page 35 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

3

with missing PM10 and re-estimated all the equations, and the results remained the same. The main results reported in the paper used PM10 concentrations from method (1), and the results using alterative PM10 measures are available upon request.

Page 36 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

4

Supplementary Material 2: Disease Surveillance Point System

Daily mortality data come from the Disease Surveillance Point System (DSPS) of the Chinese Center for Disease Control and Prevention (CDC). The DSPS was established by the Chinese government to provide timely information on the causes and number of deaths in 1978. To represent national population and mortality trends, the DSPS adopted a multi-stage cluster population probability sampling method. The main objectives of the DSPS are to: (1) identify the number of deaths related to each disease category and provide basic mortality information about the deceased for public health officials; and (2) provide feedback to evaluate the impacts of the public health interventions. The DSPS initially covered 71 counties and city-districts in 29 provinces; this was expanded to 145 counties and city-districts in 31 provinces in 1990. The DSPS was overhauled following the Severe Acute Respiratory Syndrome (SARS) outbreak in 2003; 161 city districts and counties were in the system from 2004 to the present. Currently 81.5 million people, or roughly 6 percent of the Chinese population live in those DSPS city districts and counties.

In the event of a death, the doctor or decedent’s family is required to fill out a death certificate and submit it to the DSPS. The mortality data include basic demographic characteristics of the decedent and the cause of death. The causes of death are coded in the International Classification of Diseases 10 (ICD-10). Total mortality is classified by causes of death: cardiovascular (I00-99) and respiratory (J00-99) diseases, and all other diseases. For this study, we had daily numbers of deaths by age group, gender, and cause of death for all the DSPS districts in the largest 38 cities. The data period is from Jan 1st, 2010 to June 29th, 2013. Table SM2 presents more details about the cities covered by the DSPS.

Page 37 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


5

Table SM2. Characteristics of DSPS Cities

City DSPS Districts City Area

(KM2) City Pop.

(1000) District Pop.

Share of Female

Population

Share of the Elderly

(>60)

GDP Per Capita

(10,000)

Share of Construction

Workers Anshan Qianshan 9254 3512 211410 0.49 0.15 6.6 0.08 Beijing Dongcheng,Tongzhou 16411 20166 1206066 0.49 0.12 7.1 0.08 Changchun Nanguan 20604 7592 550888 0.5 0.12 7.0 0.04 Changde Wuling 18670 5908 530319 0.5 0.12 5.4 0.06 Changsha Tianxin 11816 6920 472816 0.5 0.13 10.4 0.07 Chengdu Qingyang 12125 13247 823355 0.5 0.15 4.2 0.05 Chongqing Dazu 82677 29162 704640 0.49 0.18 2.6 0.02 Guangzhou Yuexiu 7323 12767 1163069 0.5 0.15 14.6 0.04 Guilin Xiufeng 27809 5145 155925 0.5 0.13 4.6 0.01 Hangzhou Xiacheng 16588 8748 506795 0.49 0.16 11.1 0.02 Harbin Nangang 53068 9929 1313002 0.49 0.13 5.2 0.03 Hohhot Huimin 17292 2719 394146 0.5 0.11 10.9 0.05 Liuzhou Liubei 18617 3677 425676 0.49 0.14 8.8 0.03 Maanshan Yushan 3259 1921 295972 0.48 0.15 7.9 0.18 Nanchang Donghu 7402 5081 575977 0.5 0.14 7.2 0.02 Nanjing Pukou 6587 7531 719366 0.48 0.13 9.1 0.04 Panzhihua Renhe 7427 1188 261717 0.48 0.15 6.1 0.01 Qingdao Shibei 11079 8056 512573 0.5 0.12 10.3 0.04 Qinhuangdao Haigang 7709 2970 610139 0.5 0.13 6.0 0.04 Qiqihar Meilisi 42469 5648 165790 0.49 0.13 3.3 0.01 Shanghai Luwan 6340 23435 1553413 0.48 0.09 11.9 0.06 Shenyang Shenbei 12980 7224 347655 0.49 0.15 8.0 0.04

Page 38 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


6

Suzhou Wuzhong 8488 9148 1165065 0.49 0.11 14.5 0.11 Taiyuan Xinghualingqu 6972 4049 650546 0.5 0.14 5.8 0.04 Tangshan Kaiping 13472 7549 270456 0.48 0.15 7.1 0.05 Tianjin Hongqiao 11865 13557 550101 0.48 0.19 11.2 0.12 Tongchuan WangYi 3882 838 200762 0.5 0.17 2.5 0.10 Urumqi Tianshan 13788 2501 711443 0.5 0.12 6.3 0.02 Wuhan Jiang'an 8494 9502 902296 0.5 0.15 9.5 0.05 Xining Chengzhong 7662 2218 298421 0.5 0.13 4.8 0.05 Xuzhou Yunlong 11259 8955 320533 0.5 0.14 6.1 0.04 Yantai Zhifu 13746 6671 828652 0.5 0.13 10.2 0.04 Yichang Wujiagang 21084 4047 213884 0.49 0.14 7.2 0.05 Yinchuan Xingqing 8975 2026 679976 0.47 0.06 5.4 0.04 Yuxi Hongta 15285 2318 494672 0.49 0.13 10.7 0.06 Zaozhuang Xuecheng 4563 3875 391613 0.48 0.14 3.1 0.06 Zhengzhou Zhongyuan 7446 8849 620825 0.49 0.11 4.1 0.05 Zunyi Qianshan 30762 6117 627764 0.49 0.11 3.5 0.03

Note: Data are collected from city statistical yearbooks and 2005 small census.

Page 39 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

7

Figure SM1. Percentage Change in Daily Number of All-Cause Deaths per 10- 𝜇𝜇𝑚𝑚/𝑚𝑚3 Increase in PM10 in 38 Chinese Cities (Lag 1 Day)

Note: Maximum likelihood estimates and 95 percent confidence intervals of the city-specific mean PM10 effects on total mortality, the largest 38 cities in China. The dependent variable is the percentage change in the number of daily all-cause deaths. X-axis is percentage change. Each solid square represents an effect size. Horizontal lines indicate 95 percent CIs. ES=Effect Size.



Zaozhuang

Tangshan

Taiyuan

Liuzhou

Urumqi

Chengdu

Changde

Beijing

Guangzhou

Anshan

Maanshan

Xuzhou

Wuhan

Hangzhou

Qinhuangdao

Qiqihar

Tongchuan

Xining

Shenyang

Nanchang

City

Tianjin

Qingdao

Guilin

Suzhou

Harbin

Nanjing

Zhengzhou

Chongqing

Shanghai

Changsha

Yantai

Zunyi

Changchun

Yinchuan

Panzhihua

Yuxi

Yichang

Hohhot

0.26 (0.15 to 0.37)

0.72 (-0.01 to 1.45)

-0.79 (-1.74 to 0.16)

0.21 (-0.31 to 0.73)

1.46 (0.66 to 2.26)

-0.05 (-0.51 to 0.41)

-0.19 (-0.69 to 0.31)

0.23 (-0.38 to 0.84)

0.14 (-0.05 to 0.33)

1.07 (0.60 to 1.54)

0.20 (-0.45 to 0.84)

1.08 (0.08 to 2.08)

-0.16 (-0.97 to 0.65)

0.50 (0.14 to 0.86)

0.17 (-0.41 to 0.76)

0.00 (-0.85 to 0.86)

-0.21 (-1.20 to 0.77)

0.20 (-1.04 to 1.43)

-0.36 (-1.26 to 0.54)

0.58 (0.05 to 1.12)

0.14 (-0.55 to 0.83)

ES (95% CI)

0.36 (0.01 to 0.72)

0.06 (-0.24 to 0.36)

0.49 (-0.68 to 1.65)

0.33 (-0.10 to 0.76)

0.20 (-0.17 to 0.57)

0.47 (0.05 to 0.90)

0.11 (-0.35 to 0.58)

0.33 (0.03 to 0.62)

0.19 (-0.10 to 0.49)

0.63 (0.06 to 1.20)

0.19 (-0.36 to 0.74)

0.61 (-0.30 to 1.52)

0.06 (-0.46 to 0.57)

-0.36 (-0.99 to 0.28)

-0.37 (-1.84 to 1.10)

1.32 (0.14 to 2.50)

0.48 (-0.61 to 1.57)

0.11 (-0.76 to 0.99)

100.00

1.79

1.16

2.94

1.54

3.45

%

3.12

2.36

7.05

3.36

2.15

1.05

1.51

4.51

2.49

1.38

1.08

0.72

1.27

2.84

1.97

Weight

4.59

5.35

0.80

3.73

4.37

3.83

3.43

5.43

5.38

2.60

2.74

1.25

3.00

2.22

0.52

0.79

0.90

1.33

0.26 (0.15 to 0.37)

0.72 (-0.01 to 1.45)

-0.79 (-1.74 to 0.16)

0.21 (-0.31 to 0.73)

1.46 (0.66 to 2.26)

-0.05 (-0.51 to 0.41)

-0.19 (-0.69 to 0.31)

0.23 (-0.38 to 0.84)

0.14 (-0.05 to 0.33)

1.07 (0.60 to 1.54)

0.20 (-0.45 to 0.84)

1.08 (0.08 to 2.08)

-0.16 (-0.97 to 0.65)

0.50 (0.14 to 0.86)

0.17 (-0.41 to 0.76)

0.00 (-0.85 to 0.86)

-0.21 (-1.20 to 0.77)

0.20 (-1.04 to 1.43)

-0.36 (-1.26 to 0.54)

0.58 (0.05 to 1.12)

0.14 (-0.55 to 0.83)

ES (95% CI)

0.36 (0.01 to 0.72)

0.06 (-0.24 to 0.36)

0.49 (-0.68 to 1.65)

0.33 (-0.10 to 0.76)

0.20 (-0.17 to 0.57)

0.47 (0.05 to 0.90)

0.11 (-0.35 to 0.58)

0.33 (0.03 to 0.62)

0.19 (-0.10 to 0.49)

0.63 (0.06 to 1.20)

0.19 (-0.36 to 0.74)

0.61 (-0.30 to 1.52)

0.06 (-0.46 to 0.57)

-0.36 (-0.99 to 0.28)

-0.37 (-1.84 to 1.10)

1.32 (0.14 to 2.50)

0.48 (-0.61 to 1.57)

0.11 (-0.76 to 0.99)

100.00

1.79

1.16

2.94

1.54

3.45

%

3.12

2.36

7.05

3.36

2.15

1.05

1.51

4.51

2.49

1.38

1.08

0.72

1.27

2.84

1.97

Weight

4.59

5.35

0.80

3.73

4.37

3.83

3.43

5.43

5.38

2.60

2.74

1.25

3.00

2.22

0.52

0.79

0.90

1.33

0-4 -2 0 2 4

PM10 Effect (10μg/m3, Lag=1)

Page 40 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

8

Figure SM2. Percentage Change in Daily Number of All-Cause Deaths per 10- 𝜇𝜇𝑚𝑚/𝑚𝑚3 Increase in PM10 in 38 Chinese Cities (Lag 2 Days)




Yuxi

Taiyuan

QiqiharQinhuangdao

Shenyang

Wuhan

Changsha

Zunyi

ChongqingYantai

Changchun

Panzhihua

Beijing

Hangzhou

Harbin

Nanchang

Changde

GuangzhouLiuzhou

Qingdao

Nanjing

Suzhou

City

HohhotZhengzhou

YichangZaozhuang

Urumqi

Xining

Xuzhou

Tongchuan

ShanghaiGuilin

Tianjin

Anshan

Yinchuan

Maanshan

TangshanChengdu

0.13 (0.03 to 0.23)

1.26 (0.11 to 2.41)

0.27 (-0.26 to 0.79)

-0.43 (-1.42 to 0.55)-0.48 (-1.36 to 0.39)

0.31 (-0.24 to 0.87)

-0.04 (-0.40 to 0.32)

0.15 (-0.42 to 0.73)

0.64 (-0.27 to 1.54)

0.18 (-0.11 to 0.48)0.17 (-0.38 to 0.72)

0.24 (-0.27 to 0.74)

-1.13 (-2.60 to 0.34)

-0.03 (-0.22 to 0.16)

0.50 (-0.08 to 1.07)

0.07 (-0.31 to 0.44)

0.20 (-0.47 to 0.88)

0.06 (-0.55 to 0.67)

0.68 (0.22 to 1.14)1.22 (0.42 to 2.02)

0.20 (-0.10 to 0.50)

0.32 (-0.11 to 0.74)

0.30 (-0.13 to 0.73)

ES (95% CI)

-0.08 (-0.96 to 0.79)-0.12 (-0.59 to 0.35)

0.64 (-0.46 to 1.74)0.54 (-0.21 to 1.29)

-0.17 (-0.64 to 0.29)

0.34 (-0.53 to 1.20)

0.06 (-0.74 to 0.87)

-0.00 (-1.25 to 1.24)

0.10 (-0.19 to 0.38)0.07 (-1.09 to 1.24)

-0.02 (-0.38 to 0.34)

0.25 (-0.40 to 0.89)

-0.19 (-0.82 to 0.43)

0.50 (-0.52 to 1.52)

-0.55 (-1.50 to 0.40)-0.50 (-1.00 to 0.00)

100.00

0.67

2.73

0.901.13

2.50

4.80

2.36

1.05

6.172.54

2.90

0.42

9.27

2.36

4.53

1.78

2.15

3.361.33

6.08

3.83

3.76

Weight

1.133.27

0.731.49

3.33

1.14

1.31

0.58

6.260.66

4.73

1.93

2.07

0.85

0.962.96

%

0.13 (0.03 to 0.23)

1.26 (0.11 to 2.41)

0.27 (-0.26 to 0.79)

-0.43 (-1.42 to 0.55)-0.48 (-1.36 to 0.39)

0.31 (-0.24 to 0.87)

-0.04 (-0.40 to 0.32)

0.15 (-0.42 to 0.73)

0.64 (-0.27 to 1.54)

0.18 (-0.11 to 0.48)0.17 (-0.38 to 0.72)

0.24 (-0.27 to 0.74)

-1.13 (-2.60 to 0.34)

-0.03 (-0.22 to 0.16)

0.50 (-0.08 to 1.07)

0.07 (-0.31 to 0.44)

0.20 (-0.47 to 0.88)

0.06 (-0.55 to 0.67)

0.68 (0.22 to 1.14)1.22 (0.42 to 2.02)

0.20 (-0.10 to 0.50)

0.32 (-0.11 to 0.74)

0.30 (-0.13 to 0.73)

ES (95% CI)

-0.08 (-0.96 to 0.79)-0.12 (-0.59 to 0.35)

0.64 (-0.46 to 1.74)0.54 (-0.21 to 1.29)

-0.17 (-0.64 to 0.29)

0.34 (-0.53 to 1.20)

0.06 (-0.74 to 0.87)

-0.00 (-1.25 to 1.24)

0.10 (-0.19 to 0.38)0.07 (-1.09 to 1.24)

-0.02 (-0.38 to 0.34)

0.25 (-0.40 to 0.89)

-0.19 (-0.82 to 0.43)

0.50 (-0.52 to 1.52)

-0.55 (-1.50 to 0.40)-0.50 (-1.00 to 0.00)

100.00

0.67

2.73

0.901.13

2.50

4.80

2.36

1.05

6.172.54

2.90

0.42

9.27

2.36

4.53

1.78

2.15

3.361.33

6.08

3.83

3.76

Weight

1.133.27

0.731.49

3.33

1.14

1.31

0.58

6.260.66

4.73

1.93

2.07

0.85

0.962.96

%

0-4 -2 0 2 4


Page 41 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

9





Beijing

Qinhuangdao

Nanchang

Zaozhuang

Yinchuan

Changde

Xuzhou

Qingdao

Yuxi

Taiyuan

Guilin

Shanghai

Zunyi

SuzhouAnshan

Changsha

Wuhan

Urumqi

Hohhot

Tongchuan

Shenyang

Tangshan

Chongqing

Nanjing

Panzhihua

Qiqihar

Yichang

Liuzhou

Chengdu

Xining

Maanshan

Zhengzhou

Changchun

Harbin

City

Hangzhou

TianjinYantai

Guangzhou

0.09 (-0.01 to 0.19)

-0.12 (-0.30 to 0.07)

0.05 (-0.84 to 0.93)

-0.24 (-0.91 to 0.43)

0.44 (-0.31 to 1.18)

0.08 (-0.53 to 0.69)

0.02 (-0.59 to 0.63)

-0.19 (-1.00 to 0.62)

0.25 (-0.05 to 0.55)

1.13 (-0.02 to 2.28)

0.33 (-0.20 to 0.86)

-0.84 (-2.02 to 0.34)

0.13 (-0.16 to 0.42)

0.40 (-0.50 to 1.31)

0.31 (-0.12 to 0.73)0.27 (-0.38 to 0.91)

-0.04 (-0.62 to 0.54)

0.05 (-0.31 to 0.41)

0.22 (-0.24 to 0.68)

-0.49 (-1.37 to 0.39)

-0.04 (-1.28 to 1.21)

0.07 (-0.49 to 0.63)

0.58 (-0.37 to 1.53)

0.10 (-0.19 to 0.39)

0.37 (-0.04 to 0.79)

-0.22 (-1.67 to 1.24)

-0.57 (-1.57 to 0.42)

-0.12 (-1.24 to 0.99)

0.65 (-0.15 to 1.45)

-0.72 (-1.22 to -0.21)

0.25 (-0.62 to 1.12)

-0.11 (-1.14 to 0.91)

-0.18 (-0.65 to 0.29)

0.25 (-0.26 to 0.75)

-0.06 (-0.44 to 0.32)

ES (95% CI)

1.00 (0.44 to 1.56)

-0.17 (-0.53 to 0.20)-0.15 (-0.70 to 0.40)

0.44 (-0.03 to 0.90)

100.00

7.96

1.22

1.93

1.62

2.24

2.26

1.41

5.69

0.75

2.83

0.72

5.87

1.16

3.822.07

2.46

4.64

3.42

1.23

0.65

2.56

1.06

5.72

3.92

0.48

0.98

0.80

1.44

3.02

1.26

0.93

3.34

2.97

4.39

Weight

2.58

4.582.63

3.40

%

0.09 (-0.01 to 0.19)

-0.12 (-0.30 to 0.07)

0.05 (-0.84 to 0.93)

-0.24 (-0.91 to 0.43)

0.44 (-0.31 to 1.18)

0.08 (-0.53 to 0.69)

0.02 (-0.59 to 0.63)

-0.19 (-1.00 to 0.62)

0.25 (-0.05 to 0.55)

1.13 (-0.02 to 2.28)

0.33 (-0.20 to 0.86)

-0.84 (-2.02 to 0.34)

0.13 (-0.16 to 0.42)

0.40 (-0.50 to 1.31)

0.31 (-0.12 to 0.73)0.27 (-0.38 to 0.91)

-0.04 (-0.62 to 0.54)

0.05 (-0.31 to 0.41)

0.22 (-0.24 to 0.68)

-0.49 (-1.37 to 0.39)

-0.04 (-1.28 to 1.21)

0.07 (-0.49 to 0.63)

0.58 (-0.37 to 1.53)

0.10 (-0.19 to 0.39)

0.37 (-0.04 to 0.79)

-0.22 (-1.67 to 1.24)

-0.57 (-1.57 to 0.42)

-0.12 (-1.24 to 0.99)

0.65 (-0.15 to 1.45)

-0.72 (-1.22 to -0.21)

0.25 (-0.62 to 1.12)

-0.11 (-1.14 to 0.91)

-0.18 (-0.65 to 0.29)

0.25 (-0.26 to 0.75)

-0.06 (-0.44 to 0.32)

ES (95% CI)

1.00 (0.44 to 1.56)

-0.17 (-0.53 to 0.20)-0.15 (-0.70 to 0.40)

0.44 (-0.03 to 0.90)

100.00

7.96

1.22

1.93

1.62

2.24

2.26

1.41

5.69

0.75

2.83

0.72

5.87

1.16

3.822.07

2.46

4.64

3.42

1.23

0.65

2.56

1.06

5.72

3.92

0.48

0.98

0.80

1.44

3.02

1.26

0.93

3.34

2.97

4.39

Weight

2.58

4.582.63

3.40

%

0-4 -2 0 2 4


Page 42 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

10





Taiyuan

Urumqi

Changde

Zunyi

Qingdao

Xining

Changchun

Qinhuangdao

Zaozhuang

Qiqihar

Shanghai

Guilin

Maanshan

Chengdu

Zhengzhou

Suzhou

Beijing

City

Shenyang

Guangzhou

Hohhot

Xuzhou

Tongchuan

Panzhihua

Tangshan

Wuhan

Chongqing

AnshanYantai

Hangzhou

Yinchuan

Liuzhou

Nanchang

Nanjing

Yichang

Changsha

Tianjin

Harbin

Yuxi

0.08 (-0.00 to 0.16)

-0.28 (-0.82 to 0.26)

0.13 (-0.33 to 0.59)

0.08 (-0.53 to 0.69)

0.16 (-0.74 to 1.06)

0.20 (-0.09 to 0.50)

-0.74 (-1.63 to 0.15)

0.04 (-0.48 to 0.56)

-0.16 (-1.08 to 0.77)

0.90 (0.15 to 1.64)

-0.26 (-1.25 to 0.73)

0.25 (-0.03 to 0.53)

-1.28 (-2.47 to -0.09)

0.52 (-0.49 to 1.53)

-0.10 (-0.60 to 0.40)

0.04 (-0.43 to 0.50)

0.13 (-0.29 to 0.56)

-0.12 (-0.31 to 0.07)

ES (95% CI)

0.10 (-0.47 to 0.67)

0.30 (-0.16 to 0.76)

0.02 (-0.85 to 0.90)

0.09 (-0.71 to 0.89)

0.30 (-0.96 to 1.56)

0.14 (-1.31 to 1.59)

1.04 (0.07 to 2.01)

0.05 (-0.31 to 0.42)

0.18 (-0.11 to 0.48)

-0.00 (-0.65 to 0.64)0.02 (-0.53 to 0.57)

0.48 (-0.09 to 1.05)

0.21 (-0.40 to 0.82)

0.66 (-0.14 to 1.47)

-0.22 (-0.90 to 0.46)

0.16 (-0.26 to 0.57)

-0.30 (-1.43 to 0.83)

-0.06 (-0.64 to 0.52)

-0.19 (-0.56 to 0.18)

0.25 (-0.13 to 0.62)

0.48 (-0.67 to 1.63)

100.00

2.13

2.93

1.68

0.78

7.09

0.80

2.32

0.74

1.14

0.64

7.79

0.45

0.62

2.51

2.88

3.46

17.18

Weight

1.93

2.93

0.82

0.98

0.40

0.30

0.67

4.75

7.00

1.512.07

1.93

1.68

0.97

1.36

3.62

0.50

1.88

4.60

4.50

0.48

%

0.08 (-0.00 to 0.16)

-0.28 (-0.82 to 0.26)

0.13 (-0.33 to 0.59)

0.08 (-0.53 to 0.69)

0.16 (-0.74 to 1.06)

0.20 (-0.09 to 0.50)

-0.74 (-1.63 to 0.15)

0.04 (-0.48 to 0.56)

-0.16 (-1.08 to 0.77)

0.90 (0.15 to 1.64)

-0.26 (-1.25 to 0.73)

0.25 (-0.03 to 0.53)

-1.28 (-2.47 to -0.09)

0.52 (-0.49 to 1.53)

-0.10 (-0.60 to 0.40)

0.04 (-0.43 to 0.50)

0.13 (-0.29 to 0.56)

-0.12 (-0.31 to 0.07)

ES (95% CI)

0.10 (-0.47 to 0.67)

0.30 (-0.16 to 0.76)

0.02 (-0.85 to 0.90)

0.09 (-0.71 to 0.89)

0.30 (-0.96 to 1.56)

0.14 (-1.31 to 1.59)

1.04 (0.07 to 2.01)

0.05 (-0.31 to 0.42)

0.18 (-0.11 to 0.48)

-0.00 (-0.65 to 0.64)0.02 (-0.53 to 0.57)

0.48 (-0.09 to 1.05)

0.21 (-0.40 to 0.82)

0.66 (-0.14 to 1.47)

-0.22 (-0.90 to 0.46)

0.16 (-0.26 to 0.57)

-0.30 (-1.43 to 0.83)

-0.06 (-0.64 to 0.52)

-0.19 (-0.56 to 0.18)

0.25 (-0.13 to 0.62)

0.48 (-0.67 to 1.63)

100.00

2.13

2.93

1.68

0.78

7.09

0.80

2.32

0.74

1.14

0.64

7.79

0.45

0.62

2.51

2.88

3.46

17.18

Weight

1.93

2.93

0.82

0.98

0.40

0.30

0.67

4.75

7.00

1.512.07

1.93

1.68

0.97

1.36

3.62

0.50

1.88

4.60

4.50

0.48

%

0-4 -2 0 2 4


Page 43 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

11





Suzhou

Changsha

Wuhan

Chongqing

Taiyuan

Urumqi

Nanchang

Hangzhou

Zaozhuang

Changde

Zhengzhou

Yuxi

Changchun

Qingdao

Liuzhou

Yichang

Hohhot

Yantai

Beijing

Guangzhou

Shenyang

Xining

Zunyi

Qiqihar

Tongchuan

Nanjing

Guilin

Tianjin

Maanshan

City

Harbin

Xuzhou

Chengdu

Anshan

Qinhuangdao

Shanghai

Yinchuan

PanzhihuaTangshan

0.07 (-0.01 to 0.15)

0.22 (-0.20 to 0.65)

-0.17 (-0.76 to 0.41)

0.02 (-0.35 to 0.38)

0.19 (-0.11 to 0.49)

-0.44 (-0.98 to 0.10)

0.27 (-0.20 to 0.74)

-0.31 (-0.99 to 0.37)

0.10 (-0.47 to 0.67)

0.50 (-0.25 to 1.24)

-0.27 (-0.88 to 0.34)

0.16 (-0.31 to 0.62)

-0.12 (-1.27 to 1.03)

-0.38 (-0.90 to 0.15)

0.13 (-0.17 to 0.43)

0.34 (-0.47 to 1.15)

-0.10 (-1.23 to 1.02)

0.16 (-0.71 to 1.02)

-0.10 (-0.65 to 0.46)

-0.02 (-0.21 to 0.17)

0.41 (-0.05 to 0.87)

0.18 (-0.38 to 0.75)

-0.18 (-1.02 to 0.67)

0.07 (-0.83 to 0.98)

-0.65 (-1.64 to 0.35)

-0.05 (-1.30 to 1.20)

0.07 (-0.35 to 0.48)

-0.68 (-1.84 to 0.49)

-0.06 (-0.42 to 0.31)

-0.01 (-1.03 to 1.01)

ES (95% CI)

0.04 (-0.33 to 0.42)

0.19 (-0.61 to 0.98)

-0.18 (-0.69 to 0.32)

0.30 (-0.34 to 0.94)

0.26 (-0.66 to 1.17)

0.38 (0.10 to 0.66)

0.00 (-0.61 to 0.61)

0.58 (-0.87 to 2.02)1.37 (0.39 to 2.35)

100.00

3.48

1.84

4.65

6.94

2.12

2.85

1.34

1.89

1.13

1.66

2.85

0.47

2.25

7.03

0.95

0.49

0.83

2.03

17.64

2.94

1.95

0.87

0.76

0.63

0.40

3.62

0.46

4.60

0.60

Weight

4.46

1.00

2.46

1.52

0.75

7.95

1.67

0.300.65

%

0.07 (-0.01 to 0.15)

0.22 (-0.20 to 0.65)

-0.17 (-0.76 to 0.41)

0.02 (-0.35 to 0.38)

0.19 (-0.11 to 0.49)

-0.44 (-0.98 to 0.10)

0.27 (-0.20 to 0.74)

-0.31 (-0.99 to 0.37)

0.10 (-0.47 to 0.67)

0.50 (-0.25 to 1.24)

-0.27 (-0.88 to 0.34)

0.16 (-0.31 to 0.62)

-0.12 (-1.27 to 1.03)

-0.38 (-0.90 to 0.15)

0.13 (-0.17 to 0.43)

0.34 (-0.47 to 1.15)

-0.10 (-1.23 to 1.02)

0.16 (-0.71 to 1.02)

-0.10 (-0.65 to 0.46)

-0.02 (-0.21 to 0.17)

0.41 (-0.05 to 0.87)

0.18 (-0.38 to 0.75)

-0.18 (-1.02 to 0.67)

0.07 (-0.83 to 0.98)

-0.65 (-1.64 to 0.35)

-0.05 (-1.30 to 1.20)

0.07 (-0.35 to 0.48)

-0.68 (-1.84 to 0.49)

-0.06 (-0.42 to 0.31)

-0.01 (-1.03 to 1.01)

ES (95% CI)

0.04 (-0.33 to 0.42)

0.19 (-0.61 to 0.98)

-0.18 (-0.69 to 0.32)

0.30 (-0.34 to 0.94)

0.26 (-0.66 to 1.17)

0.38 (0.10 to 0.66)

0.00 (-0.61 to 0.61)

0.58 (-0.87 to 2.02)1.37 (0.39 to 2.35)

100.00

3.48

1.84

4.65

6.94

2.12

2.85

1.34

1.89

1.13

1.66

2.85

0.47

2.25

7.03

0.95

0.49

0.83

2.03

17.64

2.94

1.95

0.87

0.76

0.63

0.40

3.62

0.46

4.60

0.60

Weight

4.46

1.00

2.46

1.52

0.75

7.95

1.67

0.300.65

%

0-4 -2 0 2 4


Page 44 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

12





Anshan

Yuxi

Urumqi

Shenyang

Changchun

Taiyuan

Qingdao

ChengduQiqihar

Wuhan

Yinchuan

Chongqing

Tongchuan

Yantai

Harbin

Xuzhou

Hangzhou

Zhengzhou

Suzhou

Nanchang

Xining

Liuzhou

Beijing

Maanshan

City

Tangshan

Tianjin

Hohhot

Guilin

Changsha

Shanghai

Changde

Qinhuangdao

Panzhihua

Zaozhuang

Guangzhou

Zunyi

Nanjing

Yichang

0.05 (-0.04 to 0.14)

0.16 (-0.48 to 0.80)

-0.25 (-1.41 to 0.90)

0.25 (-0.23 to 0.72)

-0.08 (-0.65 to 0.50)

0.03 (-0.49 to 0.54)

-0.28 (-0.83 to 0.26)

0.20 (-0.10 to 0.49)

-0.47 (-0.98 to 0.05)-0.59 (-1.58 to 0.41)

-0.13 (-0.50 to 0.24)

0.37 (-0.24 to 0.98)

0.13 (-0.18 to 0.43)

0.23 (-1.02 to 1.49)

0.12 (-0.43 to 0.67)

-0.04 (-0.42 to 0.33)

-0.62 (-1.44 to 0.21)

0.03 (-0.55 to 0.61)

-0.01 (-0.47 to 0.46)

0.14 (-0.29 to 0.56)

-0.31 (-1.01 to 0.38)

-0.01 (-0.86 to 0.85)

0.04 (-0.77 to 0.86)

-0.11 (-0.30 to 0.08)

-0.21 (-1.23 to 0.82)

ES (95% CI)

1.75 (0.78 to 2.72)

0.09 (-0.28 to 0.45)

0.73 (-0.11 to 1.57)

0.09 (-1.06 to 1.24)

-0.01 (-0.60 to 0.58)

0.27 (-0.01 to 0.56)

-0.00 (-0.62 to 0.61)

-0.37 (-1.29 to 0.55)

0.30 (-1.15 to 1.75)

-0.36 (-1.12 to 0.40)

0.57 (0.11 to 1.03)

-0.28 (-1.20 to 0.65)

0.12 (-0.30 to 0.53)

0.53 (-0.59 to 1.65)

100.00

1.87

0.61

3.17

2.26

2.73

2.48

6.52

2.780.82

4.75

2.07

6.29

0.52

2.45

4.64

1.17

2.25

3.23

3.79

1.62

1.09

1.20

10.91

0.77

Weight

0.85

4.79

1.13

0.62

2.19

6.88

2.02

0.95

0.39

%

1.36

3.33

0.95

3.88

0.66

0.05 (-0.04 to 0.14)

0.16 (-0.48 to 0.80)

-0.25 (-1.41 to 0.90)

0.25 (-0.23 to 0.72)

-0.08 (-0.65 to 0.50)

0.03 (-0.49 to 0.54)

-0.28 (-0.83 to 0.26)

0.20 (-0.10 to 0.49)

-0.47 (-0.98 to 0.05)-0.59 (-1.58 to 0.41)

-0.13 (-0.50 to 0.24)

0.37 (-0.24 to 0.98)

0.13 (-0.18 to 0.43)

0.23 (-1.02 to 1.49)

0.12 (-0.43 to 0.67)

-0.04 (-0.42 to 0.33)

-0.62 (-1.44 to 0.21)

0.03 (-0.55 to 0.61)

-0.01 (-0.47 to 0.46)

0.14 (-0.29 to 0.56)

-0.31 (-1.01 to 0.38)

-0.01 (-0.86 to 0.85)

0.04 (-0.77 to 0.86)

-0.11 (-0.30 to 0.08)

-0.21 (-1.23 to 0.82)

ES (95% CI)

1.75 (0.78 to 2.72)

0.09 (-0.28 to 0.45)

0.73 (-0.11 to 1.57)

0.09 (-1.06 to 1.24)

-0.01 (-0.60 to 0.58)

0.27 (-0.01 to 0.56)

-0.00 (-0.62 to 0.61)

-0.37 (-1.29 to 0.55)

0.30 (-1.15 to 1.75)

-0.36 (-1.12 to 0.40)

0.57 (0.11 to 1.03)

-0.28 (-1.20 to 0.65)

0.12 (-0.30 to 0.53)

0.53 (-0.59 to 1.65)

100.00

1.87

0.61

3.17

2.26

2.73

2.48

6.52

2.780.82

4.75

2.07

6.29

0.52

2.45

4.64

1.17

2.25

3.23

3.79

1.62

1.09

1.20

10.91

0.77

Weight

0.85

4.79

1.13

0.62

2.19

6.88

2.02

0.95

0.39

%

1.36

3.33

0.95

3.88

0.66

0-4 -2 0 2 4


Page 45 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

13

Figure SM7. Percentage Change in Daily Number of All-Cause Deaths per 10- 𝜇𝜇𝑚𝑚/𝑚𝑚3 Increase in 3-day Moving Average PM10 (lags 0, 1, and 2) in 38 Chinese Cities

Note: Maximum likelihood estimates and 95 percent confidence intervals of the city-specific mean lag PM10 effects on total mortality, the largest 38 cities in China. The dependent variable is the percentage change in the number of daily all-cause deaths. X-axis is percentage change. Each solid square represents an effect size. Horizontal lines indicate 95 percent CIs. ES=Effect Size.



Nanchang

Hangzhou

Qinhuangdao

Chongqing

Chengdu

Qingdao

Changsha

Tangshan

Taiyuan

Hohhot

Suzhou

Panzhihua

Liuzhou

Zhengzhou

Qiqihar

Wuhan

Harbin

Tongchuan

Urumqi

ShanghaiAnshan

Yantai

Changde

Shenyang

Tianjin

MaanshanGuangzhou

Beijing

City

Zunyi

Xining

YichangNanjing

Zaozhuang

Yuxi

Yinchuan

Changchun

Xuzhou

Guilin

0.45 (0.28 to 0.62)

0.29 (-0.60 to 1.17)

0.60 (-0.17 to 1.37)

-0.04 (-1.21 to 1.13)

0.45 (0.12 to 0.78)

-0.19 (-0.81 to 0.42)

0.20 (-0.19 to 0.58)

0.63 (-0.08 to 1.35)

-0.84 (-2.17 to 0.50)

0.66 (-0.02 to 1.34)

-0.02 (-1.19 to 1.15)

0.62 (0.04 to 1.20)

-1.39 (-3.35 to 0.57)

2.14 (1.16 to 3.12)

0.14 (-0.46 to 0.74)

-0.35 (-1.56 to 0.85)

0.58 (0.14 to 1.03)

0.26 (-0.26 to 0.79)

0.11 (-1.52 to 1.73)

-0.19 (-0.78 to 0.41)

0.36 (-0.05 to 0.78)0.34 (-0.52 to 1.19)

0.37 (-0.36 to 1.11)

0.50 (-0.24 to 1.23)

0.74 (0.03 to 1.45)

0.64 (0.13 to 1.14)

1.75 (0.45 to 3.05)1.66 (1.08 to 2.24)

0.25 (-0.02 to 0.51)

ES (95% CI)

0.98 (-0.15 to 2.11)

-0.04 (-1.25 to 1.18)

0.79 (-0.55 to 2.13)0.80 (0.26 to 1.34)

1.33 (0.39 to 2.27)

2.10 (0.68 to 3.52)

-0.53 (-1.38 to 0.33)

0.57 (-0.11 to 1.26)

-0.60 (-1.71 to 0.50)

0.50 (-0.91 to 1.91)

100.00

2.28

2.67

1.55

4.84

3.33

4.54

2.88

1.26

3.04

1.54

3.51

0.66

1.99

3.39

1.48

4.24

3.79

0.92

3.44

4.382.36

2.80

2.80

2.92

3.89

1.333.49

5.18

Weight

1.62

1.47

1.263.70

2.09

1.15

2.35

3.02

1.68

1.16

%

0.45 (0.28 to 0.62)

0.29 (-0.60 to 1.17)

0.60 (-0.17 to 1.37)

-0.04 (-1.21 to 1.13)

0.45 (0.12 to 0.78)

-0.19 (-0.81 to 0.42)

0.20 (-0.19 to 0.58)

0.63 (-0.08 to 1.35)

-0.84 (-2.17 to 0.50)

0.66 (-0.02 to 1.34)

-0.02 (-1.19 to 1.15)

0.62 (0.04 to 1.20)

-1.39 (-3.35 to 0.57)

2.14 (1.16 to 3.12)

0.14 (-0.46 to 0.74)

-0.35 (-1.56 to 0.85)

0.58 (0.14 to 1.03)

0.26 (-0.26 to 0.79)

0.11 (-1.52 to 1.73)

-0.19 (-0.78 to 0.41)

0.36 (-0.05 to 0.78)0.34 (-0.52 to 1.19)

0.37 (-0.36 to 1.11)

0.50 (-0.24 to 1.23)

0.74 (0.03 to 1.45)

0.64 (0.13 to 1.14)

1.75 (0.45 to 3.05)1.66 (1.08 to 2.24)

0.25 (-0.02 to 0.51)

ES (95% CI)

0.98 (-0.15 to 2.11)

-0.04 (-1.25 to 1.18)

0.79 (-0.55 to 2.13)0.80 (0.26 to 1.34)

1.33 (0.39 to 2.27)

2.10 (0.68 to 3.52)

-0.53 (-1.38 to 0.33)

0.57 (-0.11 to 1.26)

-0.60 (-1.71 to 0.50)

0.50 (-0.91 to 1.91)

100.00

2.28

2.67

1.55

4.84

3.33

4.54

2.88

1.26

3.04

1.54

3.51

0.66

1.99

3.39

1.48

4.24

3.79

0.92

3.44

4.382.36

2.80

2.80

2.92

3.89

1.333.49

5.18

Weight

1.62

1.47

1.263.70

2.09

1.15

2.35

3.02

1.68

1.16

%

0-4 -2 0 2 4

All Causes

PM10 Effect (10μg/m3, 3-day Moving Average)

Page 46 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

14

Figure SM8. Percentage Change in Daily Number of All-Cause Deaths per 10- 𝜇𝜇𝑚𝑚/𝑚𝑚3 Increase in 4-day (Lag 3-6 Days) Moving Average of PM10

Note: Maximum likelihood estimates and 95 percent confidence intervals of the city-specific mean lag PM10 effects on total mortality, the largest 38 cities in China. The dependent variable is the percentage change in the number of daily all-cause deaths. X-axis is percentage change. Each solid square represents an effect size. Horizontal lines indicate 95 percent CIs. ES=Effect Size.



Maanshan

Yantai

Liuzhou

Shenyang

Panzhihua

Changchun

Anshan

Zaozhuang

Zhengzhou

City

Guangzhou

Chengdu

Yinchuan

NanchangZunyi

Harbin

Shanghai

Guilin

TangshanYichang

Chongqing

Beijing

Changde

Yuxi

Qingdao

XiningXuzhou

Tianjin

Suzhou

Urumqi

Tongchuan

Hangzhou

Qinhuangdao

Taiyuan

Qiqihar

Changsha

Hohhot

Wuhan

Nanjing

0.19 (-0.02 to 0.40)

1.62 (-0.97 to 4.21)

-0.36 (-1.53 to 0.81)

0.65 (-0.91 to 2.21)

0.23 (-0.84 to 1.30)

-0.67 (-3.44 to 2.09)

-0.09 (-1.19 to 1.00)

-0.49 (-1.80 to 0.82)

0.80 (-0.45 to 2.04)

0.26 (-0.68 to 1.20)

ES (95% CI)

0.90 (-0.23 to 2.04)

0.42 (-0.50 to 1.33)

-1.58 (-2.89 to -0.27)

0.30 (-1.19 to 1.79)0.34 (-1.38 to 2.06)

0.04 (-0.70 to 0.78)

-0.06 (-0.77 to 0.66)

1.55 (-0.82 to 3.92)

1.20 (-0.66 to 3.06)0.95 (-1.38 to 3.29)

-0.34 (-1.04 to 0.35)

0.28 (-0.16 to 0.72)

-0.22 (-1.36 to 0.92)

3.16 (0.69 to 5.63)

0.11 (-0.60 to 0.81)

-1.22 (-3.34 to 0.90)-1.57 (-3.48 to 0.34)

0.58 (-0.22 to 1.37)

-0.94 (-1.99 to 0.11)

0.48 (-0.39 to 1.35)

0.16 (-2.17 to 2.49)

0.02 (-1.58 to 1.62)

1.34 (-0.31 to 2.99)

0.08 (-1.00 to 1.16)

0.44 (-1.18 to 2.07)

0.62 (-0.65 to 1.88)

2.22 (0.74 to 3.70)

0.01 (-0.83 to 0.85)

0.56 (-0.45 to 1.56)

100.00

0.62

2.60

1.58

2.98

0.55

2.87

2.14

2.33

3.64

Weight

2.71

3.79

2.14

1.711.33

5.02

5.27

0.73

1.150.75

5.45

8.53

2.69

0.68

5.38

0.911.10

4.61

3.07

4.04

0.75

%

1.51

1.44

2.94

1.47

2.27

1.74

4.27

3.26

0.19 (-0.02 to 0.40)

1.62 (-0.97 to 4.21)

-0.36 (-1.53 to 0.81)

0.65 (-0.91 to 2.21)

0.23 (-0.84 to 1.30)

-0.67 (-3.44 to 2.09)

-0.09 (-1.19 to 1.00)

-0.49 (-1.80 to 0.82)

0.80 (-0.45 to 2.04)

0.26 (-0.68 to 1.20)

ES (95% CI)

0.90 (-0.23 to 2.04)

0.42 (-0.50 to 1.33)

-1.58 (-2.89 to -0.27)

0.30 (-1.19 to 1.79)0.34 (-1.38 to 2.06)

0.04 (-0.70 to 0.78)

-0.06 (-0.77 to 0.66)

1.55 (-0.82 to 3.92)

1.20 (-0.66 to 3.06)0.95 (-1.38 to 3.29)

-0.34 (-1.04 to 0.35)

0.28 (-0.16 to 0.72)

-0.22 (-1.36 to 0.92)

3.16 (0.69 to 5.63)

0.11 (-0.60 to 0.81)

-1.22 (-3.34 to 0.90)-1.57 (-3.48 to 0.34)

0.58 (-0.22 to 1.37)

-0.94 (-1.99 to 0.11)

0.48 (-0.39 to 1.35)

0.16 (-2.17 to 2.49)

0.02 (-1.58 to 1.62)

1.34 (-0.31 to 2.99)

0.08 (-1.00 to 1.16)

0.44 (-1.18 to 2.07)

0.62 (-0.65 to 1.88)

2.22 (0.74 to 3.70)

0.01 (-0.83 to 0.85)

0.56 (-0.45 to 1.56)

100.00

0.62

2.60

1.58

2.98

0.55

2.87

2.14

2.33

3.64

Weight

2.71

3.79

2.14

1.711.33

5.02

5.27

0.73

1.150.75

5.45

8.53

2.69

0.68

5.38

0.911.10

4.61

3.07

4.04

0.75

%

1.51

1.44

2.94

1.47

2.27

1.74

4.27

3.26

0-4 -2 0 2 4

PM10 Effect (10μg/m3, Lag 3-6 Days Moving Average)

Page 47 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


Figure SM9. Percentage Change in Daily Number of Deaths per 10- 𝜇𝜇𝑚𝑚/𝑚𝑚3 Increase in Concurrent Day PM10 in 38 Chinese Cities: Males vs. Females

Note: Maximum likelihood estimates and 95 percent confidence intervals of the city-specific mean lag PM10 effects on total mortality for cardiorespiratory and non-cardiorespiratory deaths separately, the largest 38 cities in China. The dependent variable is the percentage change in the number of daily deaths for males and females separately.



Nanjing

TaiyuanShanghai

Zunyi

Maanshan

Wuhan

Tongchuan

Tianjin

Xuzhou

Chengdu

City

Yuxi

Yichang

Harbin

Xining

Zhengzhou

Shenyang

Chongqing

Qingdao

Yinchuan

Guangzhou

Qiqihar

Urumqi

Hangzhou

Qinhuangdao

Changsha

Tangshan

Nanchang

Beijing

Liuzhou

Panzhihua

Yantai

Guilin

Zaozhuang

ChangdeChangchun

Hohhot

Suzhou

Anshan

0.39 (0.23 to 0.54)

0.64 (0.06 to 1.22)

0.42 (-0.24 to 1.08)0.43 (0.02 to 0.83)

0.11 (-1.06 to 1.29)

1.48 (0.18 to 2.78)

0.50 (0.01 to 0.99)

-0.21 (-1.71 to 1.29)

0.80 (0.33 to 1.27)

-1.12 (-2.24 to -0.00)

0.20 (-0.38 to 0.78)

ES (95% CI)

2.19 (0.62 to 3.76)

0.09 (-1.35 to 1.53)

-0.04 (-0.54 to 0.45)

-0.36 (-1.51 to 0.79)

-0.13 (-0.72 to 0.47)

0.60 (-0.11 to 1.32)

0.47 (0.08 to 0.86)

0.27 (-0.14 to 0.67)

-0.12 (-0.93 to 0.69)

1.71 (1.08 to 2.34)

-0.01 (-1.25 to 1.24)

-0.10 (-0.69 to 0.48)

0.65 (-0.11 to 1.40)

0.10 (-0.99 to 1.18)

0.60 (-0.19 to 1.38)

-0.10 (-1.37 to 1.17)

0.41 (-0.46 to 1.28)

0.21 (-0.05 to 0.47)

1.81 (0.78 to 2.84)

-1.55 (-3.40 to 0.30)

0.40 (-0.34 to 1.14)

0.82 (-0.69 to 2.32)

0.61 (-0.26 to 1.49)

0.64 (-0.11 to 1.39)0.64 (-0.04 to 1.31)

0.32 (-0.70 to 1.35)

-0.05 (-0.67 to 0.57)

0.32 (-0.50 to 1.15)

100.00

3.56

3.124.70

1.43

1.21

4.13

0.96

4.27

1.55

3.56

Weight

0.88

1.03

4.08

1.49

3.48

%

2.86

4.80

4.72

2.44

3.29

1.31

3.54

2.66

1.61

2.56

1.26

2.22

5.79

1.74

0.66

2.75

0.95

2.20

2.683.04

1.77

3.35

2.39

0.39 (0.23 to 0.54)

0.64 (0.06 to 1.22)

0.42 (-0.24 to 1.08)0.43 (0.02 to 0.83)

0.11 (-1.06 to 1.29)

1.48 (0.18 to 2.78)

0.50 (0.01 to 0.99)

-0.21 (-1.71 to 1.29)

0.80 (0.33 to 1.27)

-1.12 (-2.24 to -0.00)

0.20 (-0.38 to 0.78)

ES (95% CI)

2.19 (0.62 to 3.76)

0.09 (-1.35 to 1.53)

-0.04 (-0.54 to 0.45)

-0.36 (-1.51 to 0.79)

-0.13 (-0.72 to 0.47)

0.60 (-0.11 to 1.32)

0.47 (0.08 to 0.86)

0.27 (-0.14 to 0.67)

-0.12 (-0.93 to 0.69)

1.71 (1.08 to 2.34)

-0.01 (-1.25 to 1.24)

-0.10 (-0.69 to 0.48)

0.65 (-0.11 to 1.40)

0.10 (-0.99 to 1.18)

0.60 (-0.19 to 1.38)

-0.10 (-1.37 to 1.17)

0.41 (-0.46 to 1.28)

0.21 (-0.05 to 0.47)

1.81 (0.78 to 2.84)

-1.55 (-3.40 to 0.30)

0.40 (-0.34 to 1.14)

0.82 (-0.69 to 2.32)

0.61 (-0.26 to 1.49)

0.64 (-0.11 to 1.39)0.64 (-0.04 to 1.31)

0.32 (-0.70 to 1.35)

-0.05 (-0.67 to 0.57)

0.32 (-0.50 to 1.15)

100.00

3.56

3.124.70

1.43

1.21

4.13

0.96

4.27

1.55

3.56

Weight

0.88

1.03

4.08

1.49

3.48

%

2.86

4.80

4.72

2.44

3.29

1.31

3.54

2.66

1.61

2.56

1.26

2.22

5.79

1.74

0.66

2.75

0.95

2.20

2.683.04

1.77

3.35

2.39

0-4 -2 0 2 4

Males




City

Yinchuan

Nanchang

Harbin

Chongqing

Hangzhou

Shenyang

Panzhihua

TangshanSuzhou

Changsha

Zhengzhou

Changde

Anshan

Guilin

Wuhan

Xining

Qingdao

Yuxi

Xuzhou

Guangzhou

Taiyuan

Yantai

Qiqihar

Tongchuan

Qinhuangdao

Liuzhou

Changchun

Zunyi

Zaozhuang

Tianjin

Nanjing

Shanghai

Urumqi

Yichang

Maanshan

Hohhot

ChengduBeijing

0.51 (0.34 to 0.68)

ES (95% CI)

-0.92 (-1.95 to 0.10)

-0.08 (-1.17 to 1.02)

0.29 (-0.27 to 0.85)

0.50 (0.07 to 0.94)

0.32 (-0.56 to 1.20)

0.55 (-0.28 to 1.37)

-0.16 (-2.45 to 2.13)

-0.23 (-1.71 to 1.25)0.99 (0.35 to 1.64)

0.47 (-0.41 to 1.35)

0.67 (-0.04 to 1.37)

0.81 (-0.07 to 1.69)

-0.17 (-1.18 to 0.84)

-0.21 (-2.08 to 1.67)

1.12 (0.58 to 1.66)

0.69 (-0.82 to 2.19)

0.11 (-0.34 to 0.57)

1.28 (-0.47 to 3.03)

-0.05 (-1.27 to 1.17)

1.58 (0.87 to 2.29)

1.30 (0.47 to 2.13)

0.05 (-0.77 to 0.88)

-0.61 (-2.13 to 0.91)

0.92 (-1.16 to 2.99)

0.75 (-0.58 to 2.07)

1.48 (0.24 to 2.72)

0.64 (-0.12 to 1.40)

0.97 (-0.48 to 2.41)

1.80 (0.82 to 2.78)

0.22 (-0.29 to 0.72)

0.53 (-0.08 to 1.14)

0.17 (-0.29 to 0.62)

0.24 (-0.46 to 0.94)

1.01 (-0.61 to 2.63)

2.06 (0.45 to 3.67)

-0.14 (-1.52 to 1.24)

0.43 (-0.24 to 1.09)0.39 (0.10 to 0.67)

100.00

Weight

2.04

1.85

4.37

5.39

2.55

2.79

0.51

1.133.77

2.56

3.41

2.55

2.11

0.74

4.50

1.09

5.22

0.84

1.56

3.40

2.75

2.79

1.08

0.61

1.37

1.53

3.10

1.18

2.19

4.79

4.00

5.18

3.44

0.97

0.98

1.27

3.666.73

%

0.51 (0.34 to 0.68)

ES (95% CI)

-0.92 (-1.95 to 0.10)

-0.08 (-1.17 to 1.02)

0.29 (-0.27 to 0.85)

0.50 (0.07 to 0.94)

0.32 (-0.56 to 1.20)

0.55 (-0.28 to 1.37)

-0.16 (-2.45 to 2.13)

-0.23 (-1.71 to 1.25)0.99 (0.35 to 1.64)

0.47 (-0.41 to 1.35)

0.67 (-0.04 to 1.37)

0.81 (-0.07 to 1.69)

-0.17 (-1.18 to 0.84)

-0.21 (-2.08 to 1.67)

1.12 (0.58 to 1.66)

0.69 (-0.82 to 2.19)

0.11 (-0.34 to 0.57)

1.28 (-0.47 to 3.03)

-0.05 (-1.27 to 1.17)

1.58 (0.87 to 2.29)

1.30 (0.47 to 2.13)

0.05 (-0.77 to 0.88)

-0.61 (-2.13 to 0.91)

0.92 (-1.16 to 2.99)

0.75 (-0.58 to 2.07)

1.48 (0.24 to 2.72)

0.64 (-0.12 to 1.40)

0.97 (-0.48 to 2.41)

1.80 (0.82 to 2.78)

0.22 (-0.29 to 0.72)

0.53 (-0.08 to 1.14)

0.17 (-0.29 to 0.62)

0.24 (-0.46 to 0.94)

1.01 (-0.61 to 2.63)

2.06 (0.45 to 3.67)

-0.14 (-1.52 to 1.24)

0.43 (-0.24 to 1.09)0.39 (0.10 to 0.67)

100.00

Weight

2.04

1.85

4.37

5.39

2.55

2.79

0.51

1.133.77

2.56

3.41

2.55

2.11

0.74

4.50

1.09

5.22

0.84

1.56

3.40

2.75

2.79

1.08

0.61

1.37

1.53

3.10

1.18

2.19

4.79

4.00

5.18

3.44

0.97

0.98

1.27

3.666.73

%

0-4 -2 0 2 4

Females


Page 48 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


16

Figure SM10. Percentage Change in Daily Number of Deaths per 10- 𝜇𝜇𝑚𝑚/𝑚𝑚3 Increase in Concurrent Day PM10 in 38 Chinese Cities: Elderly vs. Young

Note: Maximum likelihood estimates and 95 percent confidence intervals of the city-specific mean lag PM10 effects on total mortality for cardiorespiratory and non-cardiorespiratory deaths separately, the largest 38 cities in China. The dependent variable is the percentage change in the number of daily deaths for elderly and young people separately.



Zunyi

Guangzhou

Panzhihua

Tangshan

Shanghai

Beijing

Xuzhou

City

Yinchuan

Maanshan

GuilinHarbinZhengzhou

Changchun

Qingdao

Taiyuan

Chengdu

Shenyang

AnshanYichang

Hohhot

Qinhuangdao

Hangzhou

Xining

Yantai

Qiqihar

Tianjin

Urumqi

Changde

Chongqing

Nanchang

Yuxi

Tongchuan

Zaozhuang

Changsha

Liuzhou

Nanjing

Suzhou

Wuhan

0.50 (0.34 to 0.66)

0.49 (-0.56 to 1.55)

1.80 (1.27 to 2.33)

-1.09 (-2.81 to 0.63)

-0.57 (-1.69 to 0.55)

0.39 (0.05 to 0.73)

0.29 (0.08 to 0.50)

-0.42 (-1.35 to 0.52)

ES (95% CI)

-0.01 (-0.75 to 0.74)

1.73 (0.63 to 2.83)

0.06 (-1.30 to 1.41)0.12 (-0.32 to 0.55)0.19 (-0.34 to 0.73)

0.84 (0.27 to 1.42)

0.22 (-0.12 to 0.56)

0.96 (0.35 to 1.57)

0.27 (-0.28 to 0.81)

0.69 (0.06 to 1.32)

0.34 (-0.41 to 1.10)0.35 (-0.88 to 1.57)

-0.52 (-1.51 to 0.47)

0.06 (-0.92 to 1.03)

0.58 (-0.05 to 1.20)

0.35 (-0.65 to 1.36)

0.44 (-0.19 to 1.07)

-0.61 (-1.82 to 0.61)

0.54 (0.14 to 0.93)

-0.13 (-0.66 to 0.40)

0.99 (0.31 to 1.67)

0.66 (0.33 to 0.99)

0.19 (-0.62 to 1.01)

1.44 (0.11 to 2.77)

0.26 (-1.27 to 1.79)

1.19 (0.39 to 1.99)

0.52 (-0.16 to 1.19)

2.06 (1.12 to 3.00)

0.60 (0.12 to 1.07)

0.72 (0.23 to 1.21)

0.95 (0.54 to 1.36)

100.00

1.62

3.34

0.75

1.49

4.26

4.82

1.90

Weight

2.47

1.53

1.123.793.33

3.16

4.27

3.00

3.28

2.93

2.451.30

%

1.76

1.80

2.93

1.72

2.93

1.32

4.00

3.35

2.72

4.33

2.25

1.15

0.92

2.28

2.74

1.89

3.62

3.54

3.93

0.50 (0.34 to 0.66)

0.49 (-0.56 to 1.55)

1.80 (1.27 to 2.33)

-1.09 (-2.81 to 0.63)

-0.57 (-1.69 to 0.55)

0.39 (0.05 to 0.73)

0.29 (0.08 to 0.50)

-0.42 (-1.35 to 0.52)

ES (95% CI)

-0.01 (-0.75 to 0.74)

1.73 (0.63 to 2.83)

0.06 (-1.30 to 1.41)0.12 (-0.32 to 0.55)0.19 (-0.34 to 0.73)

0.84 (0.27 to 1.42)

0.22 (-0.12 to 0.56)

0.96 (0.35 to 1.57)

0.27 (-0.28 to 0.81)

0.69 (0.06 to 1.32)

0.34 (-0.41 to 1.10)0.35 (-0.88 to 1.57)

-0.52 (-1.51 to 0.47)

0.06 (-0.92 to 1.03)

0.58 (-0.05 to 1.20)

0.35 (-0.65 to 1.36)

0.44 (-0.19 to 1.07)

-0.61 (-1.82 to 0.61)

0.54 (0.14 to 0.93)

-0.13 (-0.66 to 0.40)

0.99 (0.31 to 1.67)

0.66 (0.33 to 0.99)

0.19 (-0.62 to 1.01)

1.44 (0.11 to 2.77)

0.26 (-1.27 to 1.79)

1.19 (0.39 to 1.99)

0.52 (-0.16 to 1.19)

2.06 (1.12 to 3.00)

0.60 (0.12 to 1.07)

0.72 (0.23 to 1.21)

0.95 (0.54 to 1.36)

100.00

1.62

3.34

0.75

1.49

4.26

4.82

1.90

Weight

2.47

1.53

1.123.793.33

3.16

4.27

3.00

3.28

2.93

2.451.30

%

1.76

1.80

2.93

1.72

2.93

1.32

4.00

3.35

2.72

4.33

2.25

1.15

0.92

2.28

2.74

1.89

3.62

3.54

3.93

0-4 -2 0 2 4

Elderly (>=60 years)




City

Zhengzhou

Liuzhou

Urumqi

Qiqihar

Nanchang

Zunyi

Tianjin

Qingdao

Harbin

Changchun

Tangshan

Yuxi

Beijing

Wuhan

Chongqing

YichangAnshan

Panzhihua

Hohhot

Taiyuan

Maanshan

Qinhuangdao

Shenyang

Yantai

HangzhouNanjing

Xining

Changsha

ChengduTongchuan

Zaozhuang

Guilin

Changde

Suzhou

Xuzhou

Guangzhou

Yinchuan

Shanghai

0.19 (-0.02 to 0.40)

ES (95% CI)

0.26 (-0.68 to 1.20)

0.65 (-0.91 to 2.21)

0.48 (-0.39 to 1.35)

0.44 (-1.18 to 2.07)

0.30 (-1.19 to 1.79)

0.34 (-1.38 to 2.06)

0.58 (-0.22 to 1.37)

0.11 (-0.60 to 0.81)

0.04 (-0.70 to 0.78)

-0.09 (-1.19 to 1.00)

1.20 (-0.66 to 3.06)

3.16 (0.69 to 5.63)

0.28 (-0.16 to 0.72)

0.01 (-0.83 to 0.85)

-0.34 (-1.04 to 0.35)

0.95 (-1.38 to 3.29)-0.49 (-1.80 to 0.82)

-0.67 (-3.44 to 2.09)

2.22 (0.74 to 3.70)

0.08 (-1.00 to 1.16)

1.62 (-0.97 to 4.21)

1.34 (-0.31 to 2.99)

0.23 (-0.84 to 1.30)

-0.36 (-1.53 to 0.81)

0.02 (-1.58 to 1.62)0.56 (-0.45 to 1.56)

-1.22 (-3.34 to 0.90)

0.62 (-0.65 to 1.88)

0.42 (-0.50 to 1.33)0.16 (-2.17 to 2.49)

0.80 (-0.45 to 2.04)

1.55 (-0.82 to 3.92)

-0.22 (-1.36 to 0.92)

-0.94 (-1.99 to 0.11)

-1.57 (-3.48 to 0.34)

0.90 (-0.23 to 2.04)

-1.58 (-2.89 to -0.27)

-0.06 (-0.77 to 0.66)

100.00

Weight

3.64

1.58

4.04

1.47

1.71

1.33

4.61

5.38

5.02

2.87

1.15

0.68

8.53

4.27

5.45

0.752.14

0.55

1.74

2.94

0.62

1.44

2.98

2.60

1.513.26

0.91

2.27

3.790.75

2.33

0.73

2.69

3.07

1.10

%

2.71

2.14

5.27

0.19 (-0.02 to 0.40)

ES (95% CI)

0.26 (-0.68 to 1.20)

0.65 (-0.91 to 2.21)

0.48 (-0.39 to 1.35)

0.44 (-1.18 to 2.07)

0.30 (-1.19 to 1.79)

0.34 (-1.38 to 2.06)

0.58 (-0.22 to 1.37)

0.11 (-0.60 to 0.81)

0.04 (-0.70 to 0.78)

-0.09 (-1.19 to 1.00)

1.20 (-0.66 to 3.06)

3.16 (0.69 to 5.63)

0.28 (-0.16 to 0.72)

0.01 (-0.83 to 0.85)

-0.34 (-1.04 to 0.35)

0.95 (-1.38 to 3.29)-0.49 (-1.80 to 0.82)

-0.67 (-3.44 to 2.09)

2.22 (0.74 to 3.70)

0.08 (-1.00 to 1.16)

1.62 (-0.97 to 4.21)

1.34 (-0.31 to 2.99)

0.23 (-0.84 to 1.30)

-0.36 (-1.53 to 0.81)

0.02 (-1.58 to 1.62)0.56 (-0.45 to 1.56)

-1.22 (-3.34 to 0.90)

0.62 (-0.65 to 1.88)

0.42 (-0.50 to 1.33)0.16 (-2.17 to 2.49)

0.80 (-0.45 to 2.04)

1.55 (-0.82 to 3.92)

-0.22 (-1.36 to 0.92)

-0.94 (-1.99 to 0.11)

-1.57 (-3.48 to 0.34)

0.90 (-0.23 to 2.04)

-1.58 (-2.89 to -0.27)

-0.06 (-0.77 to 0.66)

100.00

Weight

3.64

1.58

4.04

1.47

1.71

1.33

4.61

5.38

5.02

2.87

1.15

0.68

8.53

4.27

5.45

0.752.14

0.55

1.74

2.94

0.62

1.44

2.98

2.60

1.513.26

0.91

2.27

3.790.75

2.33

0.73

2.69

3.07

1.10

%

2.71

2.14

5.27

0-4 -2 0 2 4

Young (<60 years)


Page 49 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nlyFigure SM11. City-Specific Air Pollution Estimates and Average PM10 Concentrations

Note: The figure plots estimated effect size over the mean PM10 concentrations. The solid line is a linear regression.

Anshan

Beijing

Changchun ChangdeChangsha

Chengdu

Chongqing

Guangzhou

Guilin

Hangzhou

Harbin

Hohhot

Liuzhou Maanshan

Nanchang

Nanjing

Panzhihua

Qingdao

Qinhuangdao

Qiqihar

Shanghai

Shenyang

Suzhou

Taiyuan

Tangshan

Tianjin

Tongchuan

Urumqi

Wuhan

Xining

Xuzhou

Yantai

Yichang

Yinchuan

Yuxi

Zaozhuang

Zhengzhou

Zunyi

-1

-.5

0

.5

1

1.5

2

Estim

ated

Coe

ffici

ent

70 80 90 100 110 120 130 140 150PM10 Mean Concentration (μg/m3)

Estimated Effect Fitted Line 95% CI

Page 50 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

18

Figure SM12. City-Specific Air Pollution Estimates and Average PM10 Concentrations: North vs. South

Note: The figure plots estimated effect size over the mean PM10 concentrations. The solid dots are northern cities. The hollow triangles are southern cities. The solid line is a linear regression for northern cities. The dashed line is for southern cities.

Anshan

Beijing

Changchun

Harbin

HohhotQingdao

Qinhuangdao

Qiqihar

ShenyangTaiyuan

Tangshan

Tianjin

Tongchuan

UrumqiXining

Xuzhou

Yantai

Yinchuan

Zaozhuang

Zhengzhou

ChangdeChangsha

Chengdu

Chongqing

Guangzhou

Guilin

Hangzhou

Liuzhou Maanshan

Nanchang

Nanjing

Panzhihua

Shanghai Suzhou

Wuhan

Yichang

Yuxi

Zunyi

-1

-.5

0

.5

1

1.5

2

Estim

ated

Coe

ffici

ent

70 80 90 100 110 120 130 140 150PM10 Mean Concentration

Estimated Effect, North Linear Fit, NorthEstimated Effect, South Linear Fit, South

Page 51 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960


nly

19

Figure SM13. Socio-economic Factors and Marginal Pollution Effects

-1

-.5

0

.5

1

1.5

2

Estim

ated

Coe

ffici

ent

1 2 3 4 5 6 7 8GDP Per Capita (10,000 Yuan)


-1

-.5

0

.5

1

1.5

2

Estim

ated

Coe

ffici

ent

0 .02 .04 .06 .08 .1 .12 .14 .16 .18Share of Workers Employed in Construction Industry


Page 52 of 52


BMJ

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

particulate air pollution and mortality in china: a time- series … › sites › default › files...

Documents