e-Figure 1. Location of the three cities in the study

e-Table 1. Summary statistics of daily lung cancer mortality and environment data in the three cities, China, 2013-2015 *

Variables Mean Min P25 Median P75 MaxBeijingLCM 7.3 0 (1) 5.0 7.0 9.0 19.0PM2.5, μg/m3 81.6 5.2 33.0 62.3 112.6 470.9PM10, μg/m3 96.3 0 44.6 80.3 128.8 464.9SO2, μg/m3 18.7 1.8 4.5 10.1 25.0 136.6O3, μg/m3 54.3 -0.4 24.1 49.8 77.6 168.1Temperature, ℃ 13.1 -10.8 2.4 14.5 23.3 32.8Humidity 36.0 -28.7 14.7 36.2 59.5 75.8ChongqingLCM 15.8 4.0 13.0 15.0 19.0 37.0PM2.5, μg/m3 58.4 8.7 32.4 47.1 71.9 211.9PM10, μg/m3 87.8 10.9 52.7 75.7 107.8 292.5SO2, μg/m3 22.6 4.2 13.3 19.1 28.9 80.0O3, μg/m3 37.0 3.3 16.5 33.0 52.6 119.6Temperature, ℃ 18.9 2.8 12.3 19.5 24.4 35.0Humidity 56.5 25.3 46.7 58.1 67.3 75.7GuangzhouLCM 4.2 0 (18) 3 4 6 12PM2.5, μg/m3 45.1 7.6 26.0 40.5 57.4 158.4PM10, μg/m3 60.0 5.2 37.7 53.8 76.9 185.7SO2, μg/m3 16.4 2.8. 11.1 15.0 20.5 57.1O3, μg/m3 48.5 0 27.4 43.0 63.1 255.5Temperature, ℃ 23.3 5.5 18.5 25.0 28.4 33.5Humidity 64.0 14.9 55.0 68.0 76.1 80.5

*

LCM: lung cancer mortality

e-Figure 2. Daily concentrations of PM2.5, PM10, SO2, and O3 in the three cities, China, 2013-2015

e-Table 2. Correlation coefficients between daily air pollutants and meteorological factors in the three cities, China, 2013-2015PM2.5 PM10 SO2 O3 Temperature

PM10 0.84**SO2 0.56** 0.53**O3 -0.07** -0.02 -0.18**Temperature -0.38** -0.29** -0.40** 0.45**Humidity -0.27** -0.27** -0.35** -0.23** 0.90**

**p < 0.001

e-Table 3. Mixed-effects meta-regression results on city-specific modifiers

Wald test on significance of modifiers (P-value), Cochran Q test for heterogeneity (P-value), and I2

statistic (%) in different meta-regression models.

Pollutant Modifier Significance of modifiers Q-test I2

PM2.5 Intercept-only — 0.0851 30.31%Smoking prevalence, % 0.5126 0.0273 45.80%Percentage of tertiary industry, % 0.1191 0.1438 1.96%Percentage of secondary industry, % 0.3741 0.0340 45.40%Percentage of primary industry, % 0.3244 0.0866 10.10%Proportion of the elderly, % 0.7759 0.0399 22.25%Population density, persons/10,000km2

0.7153 0.0422 20.03%Years of education, years 0.1473 0.1191 2.36%GDP per capita, 10,000yuan/person 0.5031 0.0518 12.25%Annual mean humidity, % 0.0362 0.4626 0%Annual mean temperature, ℃ 0.0298 0.6509 0%Annual mean PM2.5, 10μg/m3

0.0322 0.5607 0%Latitude, ° 0.1317 0.1232 1.57%Longitude, ° 0.0315 0.5845 0%

PM10 Intercept-only — 0.0097 67.69%Smoking prevalence, % 0.3490 0.0023 72.61%Percentage of tertiary industry, % 0.0196 0.0580 0.59%Percentage of secondary industry, % 0.2551 0.0029 73.25%Percentage of primary industry, % 0.2607 0.0242 18.24%Proportion of the elderly, % 0.8325 0.0060 58.17%Population density, persons/10,000km2

0.8786 0.0067 56.58%Years of education, years 0.0939 0.0420 9.29%

GDP per capita, 10,000yuan/person 0.9435 0.0097 50.34%Annual mean humidity, % 0.0035 0.3934 0%Annual mean temperature, ℃ 0.0026 0.6557 0%Annual mean PM10, 10μg/m3

0.0057 0.2352 3.24%Latitude, ° 0.0749 0.0446 7.61%Longitude, ° 0.0028 0.5605 0%

SO2 Intercept-only — 0.0252 54.36%Smoking prevalence, % 0.2821 0.0081 63.93%Percentage of tertiary industry, % 0.0617 0.0494 0.07%Percentage of secondary industry, % 0.1989 0.0125 61.62%Percentage of primary industry, % 0.1405 0.8775 0.29%Proportion of the elderly, % 0.7352 0.0087 52.35%Population density, persons/10,000km2

0.7847 0.0093 50.39%Years of education, years 0.0985 0.0371 1.60%GDP per capita, 10,000yuan/person 0.9723 0.0117 39.04%Annual mean humidity, % 0.0127 0.3111 0.93%Annual mean temperature, ℃ 0.0083 0.5231 0%Annual mean SO2, 10μg/m3

0.7789 0.0092 50.62%Latitude, ° 0.0838 0.0391 0.61%Longitude, ° 0.0092 0.4453 0%

O3 Intercept-only — 0.0299 56.18%Smoking prevalence, % 0.1867 0.0397 36.86%Percentage of tertiary industry, % 0.0105 0.4928 0%Percentage of secondary industry, % 0.2895 0.0258 46.85%Percentage of primary industry, % 0.0082 0.8902 0%Proportion of the elderly, % 0.0194 0.0215 0.46%

Population density, persons/10,000km2

0.0279 0.2453 12.07%Years of education, years 0.0087 0.7078 0%GDP per capita, 10,000yuan/person 0.0126 0.3738 0%Annual mean humidity, % 0.3311 0.0266 37.11%Annual mean temperature, ℃ 0.6028 0.0143 51.06%Annual mean O3, 10μg/m3

0.0089 0.6675 0%Latitude, ° 0.0090 0.6660 0%Longitude, ° 0.5202 0.0170 47.71%

e-Table 34. Association between air pollution (each 10μg/m3 increase) and lung cancer mortality in the by alternative dfs, sensitivity pooled

estimatesanalyses in of the three cities, China, 2013-2015

ModelsPooled estimate (95% CI)

PM2.5 PM10 SO2 O3

Main model 0.52 (0.06, 0.99) 0.53 (-0.05, 1.12) 1.37 (-0.72, 3.51) 0.08 (-0.97, 1.14)

df for temporal trends

df=7/year 0.47 (0.03, 0.90) 0.48 (-0.09, 1.06) 1.46 (-0.74, 3.71) 0.12 (-0.97, 1.22)

df=8/year 0.40 (0.05, 0.75) 0.45 (-0.07, 0.97) 1.34 (-0.66, 3.38) 0.14 (-0.92, 1.21)

df=9/year 0.42 (0.02, 0.82) 0.43 (-0.07, 0.94) 1.32 (-0.67, 3.35) 0.07 (-0.90, 1.04)

df for temperature

df=4 0.53 (0.06, 1.01) 0.54 (-0.05, 1.14) 1.42 (-0.72, 3.60) 0.09 (-0.99, 1.17)

df=7 0.52 (0.06, 0.98) 0.54 (-0.05, 1.14) 1.33 (-0.72, 3.43) 0.14 (-0.93, 1.21)

df for relative humidity

df=4 0.53 (0.06, 1.01) 0.54 (-0.04, 1.14) 1.42 (-0.71, 3.59) 0.08 (-0.97, 1.15)

df=5 0.51 (0.08, 0.95) 0.53 (-0.03, 1.09) 1.42 (-0.68, 3.57) 0.12 (-0.95, 1.19)

e-Table 5. Association between air pollution (each 10μg/m3

increase) and lung cancer mortality by alternative dfs by city (lag=0), China, 2013-2015

Models PM2.5 PM10 SO2 O3

Beijing

Main model 0.20 (-0.21, 0.61) 0.01 (-0.36, 0.39) -0.45 (-2.05, 1.18) 0.87 (-0.23, 1.98)

df=7/year for temporal trend 0.18 (-0.23, 0.59) -0.03 (-0.41, 0.35) -0.46 (-2.06, 1.18) 0.84 (-0.27, 1.96)

df=4 for temperature 0.20 (-0.21, 0.61) 0.01 (-0.36, 0.38) -0.44 (-2.05, 1.19) 0.87 (-0.22, 1.98)

df=4 for relative humidity 0.19 (-0.21, 0.60) 0.01 (-0.37, 0.38) -0.45 (-2.05, 1.18) 0.88 (-0.22, 1.99)

Chongqing

Main model 0.80 (0.17, 1.44) 0.72 (0.27, 1.17) 2.00 (-0.04, 4.07) -1.08 (-2.14, -0.02)

df=7/year for temporal trend 0.76 (0.10, 1.41) 0.69 (0.23, 1.16) 2.03 (-0.04, 4.14) -1.12 (-2.18, -0.04)

df=4 for temperature 0.81 (0.18, 1.44) 0.72 (0.27, 1.17) 2.02 (-0.02, 4.11) -1.11 (-2.17, -0.04)

df=4 for relative humidity 0.82 (0.20, 1.46) 0.75 (0.30, 1.20) 2.09 (0.05, 4.18) -1.08 (-2.13, -0.01)

Guangzhou

Main model 1.74 (0.06, 3.44) 1.61 (0.30, 2.94) 6.06 (0.76, 11.64) 0.62 (-0.89, 2.16)

df=7/year for temporal trend 1.67 (-0.02, 3.40) 1.53 (0.21, 2.86) 6.30 (0.96, 11.93) 0.83 (-0.69, 2.38)

df=4 for temperature 1.77 (0.09, 3.47) 1.64 (0.33, 2.96) 6.15 (0.84, 11.74) 0.68 (-0.86, 2.24)

df=4 for relative humidity 1.72 (0.04, 3.43) 1.60 (0.29, 2.93) 6.01 (0.68, 11.62) 0.63 (-0.89, 2.17)

e-Table 46. Excess risk of lung cancer mortality associated with each 10μg/m3 increase of 8-h average concentration of O3 in the three cities by season,

China, 2013-2015*

Beijing Chongqing Guangzhou

All period Lag02 0.41 (-0.56, 1.38) -0.07 (-0.66, 0.53) 0.33 (-0.93, 1.61) Lag01 0.83 (0.02, 1.65) -0.11 (-0.63, 0.42) 0.37 (-0.69, 1.45) Lag2 -0.49 (-1.15, 0.17) 0.04 (-0.42, 0.50) -0.03 (-0.87, 0.83) Lag1 0.49 (-0.19, 1.17) -0.12 (-0.57, 0.34) 0.08 (-0.83, 1.01) Lag0 0.66 (0.02, 1.30) -0.04 (-0.50, 0.42) 0.39 (-0.45, 1.24)

Warm season

Lag02 1.12 (-0.04, 2.30) -1.66 (-2.81, -0.49) 0.46 (-3.10, 4.16) Lag01 1.43 (0.48, 2.39) -0.90 (-1.76, -0.04) 0.45 (-0.96, 1.87) Lag2 -0.42 (-1.18, 0.36) -0.55 (-1.23, 0.13) -0.23 (-1.29, 0.83) Lag1 1.00 (0.22, 1.79) -0.76 (-1.48, -0.04) 0.23 (-0.92, 1.40) Lag0 0.83 (0.10, 1.56) -0.36 (-0.97, 0.25) 0.30 (-0.74, 1.36)Cool season Lag02 -0.97 (-3.40, 1.52) -1.56 (-3.65, 0.58) 0.84 (-1.14, 2.87) Lag01 -0.68 (-2.73, 1.41) -1.15 (-2.82, 0.56) 0.45 (-1.32, 2.25) Lag2 -0.41 (-2.00, 1.21) -0.40 (-1.68, 0.90) 0.80 (-0.70, 2.32) Lag1 -1.14 (-2.79, 0.53) -1.31 (-2.71, 0.11) -0.11 (-1.69, 1.50) Lag0 0.24 (-1.37, 1.88) -0.22 (-1.46, 1.04) 0.72 (-0.76, 2.23)

*

Warm season: April-September; Cool season: October-March; df (time): 6/year, df (temperature0-2): 3, df (humidity0-2):3

e-Table 7. Association between air pollution (each 10μg/m3

increase) and lung cancer mortality in the three cities (lag=0), China, by using two-years data

(2014-2015) *

Beijing Chongqing Guangzhou

PM2.5 0.14 (-0.34, 0.62) 0.74 (0.01, 1.49) 1.13 (-0.91, 3.22)

PM10 -0.03 (-0.46, 0.40) 0.63 (0.09, 1.17) 0.98 (-0.56, 2.54)

SO2 -0.50 (-2.53, 1.57) 2.84 (0.09, 5.67) 5.48 (-1.84, 13.35)

O3 1.51 (0.11, 2.93) -1.35 (-2.69, 0.02) 0.31 (-2.00, 2.68)*

df (time): 6/year, df (temperature0-2): 3, df (humidity0-2):3

e-Table 8. Association between air pollution (each 10μg/m3

increase) and lung cancer mortality (lag=0) by alternative season classification in the three

cities, China, 2013-2015*

Beijing Chongqing Guangzhou Pooled analysis

PM2.5

Warm season 0.35 (-0.35, 1.06) -0.04 (-1.29, 1.23) 1.49 (-1.79, 4.89) 0.30 (-0.30, 0.91)

Cool season 0.09 (-0.41, 0.59) 1.05 (0.36, 1.75) 2.10 (0.19, 4.05) 0.71 (-0.02, 1.45)

PM10

Warm season 0.37 (-0.36, 1.10) -0.01 (-0.89, 0.88) 1.39 (-1.19, 4.04) 0.27 (-0.28, 0.82)

Cool season -0.18 (-0.62, 0.26) 0.99 (0.49, 1.49) 1.62 (0.10, 3.17) 0.61 (-0.19, 1.41)

SO2

Warm season 1.31 (-4.25, 7.19) -2.33 (-6.12, 1.62) 6.59 (-2.49, 16.52) -0.27 (-3.26, 2.82)

Cool season -0.55 (-2.23, 1.16) 3.92 (1.57, 6.32) 7.95 (1.34, 14.99) 2.66 (-0.70, 6.13)

O3

Warm season 1.70 (0.37, 3.05) -1.09 (-2.34, 0.18) 0.47 (-1.92, 2.93) 0.32 (-1.16, 1.83)

Cool season -1.19 (-3.18, 0.84) -1.14 (-3.09, 0.86) 1.91 (-0.19, 4.06) -0.17 (-1.77, 1.45)*

Warm season: May-October; Cool season: November-April; df (time): 6/year, df (temperature0-2): 3, df (humidity0-2):3