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Supplemental Material Spatiotemporal changes in PM2.5 pollution and the associated mortality burden in China between 2015 and 2016 Luwei Feng 1,a, Bo Ye 2,a, Huan Feng 2, FuRen 1,3,4, Shichun Huang 2, Xiaotong Zhang 2, Yunquan Zhang 2, Qingyun Du 1,3,4,5,* and Lu Ma 2,6,* 1 School of Resources and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China; [email protected] (L.F.); [email protected] (F.R.) 2 Department of Epidemiology and Biostatistics, School of Health Sciences, Wuhan University, 185 Donghu Road, Wuchang District, Wuhan 430071, China; [email protected] (B.Y.); [email protected] (H.F.); [email protected] (S.H.); [email protected] (X.Z.); [email protected] (Y.Z.) 3 Key Laboratory of GIS, Ministry of Education, Wuhan University, 129 Luoyu Road, Wuhan 430079, China 4 Key Laboratory of Digital Mapping and Land Information Application Engineering, National Administration of Surveying, Mapping and Geoinformation, Wuhan University, 129 Luoyu Road, Wuhan 430079, China 5 Collaborative Innovation Center of Geospatial Technology, Wuhan University, 129 Luoyu Road, Wuhan 430079, China 6 Global Health Institute, Wuhan University, 8 Donghunan Road, Wuchang District, Wuhan 430072, China * Correspondence: [email protected] (Q.D.); Tel.: + 86-27-8766-4557 (Q.D.); [email protected] (L.M.); Tel.: +86-27-6875-8815 (L.M.) Table of Contents Page Estimates of (α, γ, δ) 2 Estimates of confidence interval for RRIER 2 Table S1 3 Figure S1 4 Figure S2 5 Table S2 5 Reference 12 {rˆ(s) ,...,rˆ (s) ,s = 1,...,S} Estimates of (α, γ, δ): A set of RR estimates 1 Ks and corresponding confidence {z(s) ,...,z (s) ,s = 1,...,S} intervals based on PM2.5 concentrations 1 Ks can be obtained from previous research, where S means different types of PM2.5 sources and Ks is the number of RR estimates available from for source type S. In order to estimate α, δ and γ, flowing steps are needed: First, determine the {rˆ(s) ,...,rˆ (s) ,s = 1,...,S} {γˆ (s),...,γˆ (s) ,s = 1,...,S} logarithm of the relative risk estimates 1 Ks denoted by 1 Ks and then {γˆ (s),...,γˆ (s) ,s = 1,...,S} {νˆ (s),..., νˆ (s) ,s = 1,...,S} denote the standard error of 1 Ks by 1 Ks . After that, generate 1000 realizations of the log-relative risks assuming a normal distribution with mean {γˆ (s),...,γˆ (s) ,s = 1,...,S} {νˆ (s),...,νˆ (s) ,s = 1,...,S} 1 Ks and standard deviation 1 Ks and take their exponents. One thousand estimates of (α, γ, δ) could be obtained for each set of simulated RRs. The estimation routine did not converge in a small percentage of cases (< 5%) due to a set of simulated RR that were not consistent with the risk model form in our practical processing. Thus we continue to simulate relative risks until 1000 estimates of (α, γ, δ) were obtained [1]. Estimates of confidence interval for RRIER: We simulated 1,000 sets of source type–specific RRs based on their point estimates and CIs and fit the IER model to these simulated values, obtaining 1,000 sets of parameter estimates of (α, γ, δ) and zcf. Using these parameters, we then generated Int. J. Environ. Res. Public Health 2017, 14, x; doi: www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2017, 14, x 2 of 9 1,000 IER values over the global concentration range. The mean of these 1000 IER values at a specific concentration was used as our central estimate of relative risk, and the 2.5% and 97.5% values among the 1000 IER values were used to form the lower and upper confidence intervals for each disease at each specific concentration [1]. Table S1. Change of PM2.5 concentration in provinces from 2015 to 2016. Average change of PM2.5 Number Cities with increasing PM2.5 Cities with decreasing PM2.5 Province from 2015 to 2016 per city of cities b Number Rate (%) Number Rate (%) (μg/m3) a Jilin −9.66 9 0 0 9 100 Hubei −9.05 13 0 0 13 100 Shanghai c −7.62 1 0 0 1 100 Shandong −7.05 17 0 0 17 100 Qinghai −6.81 8 1 12.50 7 87.50 Heilongjiang −6.50 13 1 7.69 12 92.31 Inner Mongolia −6.38 12 1 8.33 11 91.67 Jiangsu −5.84 13 0 0 13 100 Liaoning −5.51 14 1 7.14 13 92.86 Hunan −4.89 14 1 7.14 13 92.86 Zhejiang −4.06 11 1 9.09 10 90.91 Guangxi −3.32 14 2 14.29 12 85.71 Hainan −2.24 2 0 0 2 100 Guangdong −2.15 21 3 14.29 18 85.71 Yunnan −2.11 16 5 31.25 11 68.75 Hebei −2.11 11 4 36.36 7 63.64 Henan −2.05 17 7 41.18 10 58.82 Gansu −1.92 14 4 28.57 10 71.43 Beijing c −1.11 1 0 0 1 100 Fujian −0.85 9 2 22.22 7 77.78 Anhui −0.29 16 6 37.50 10 62.50 Ningxia −0.25 5 2 40 3 60 Guizhou −0.04 9 4 44.44 5 55.56 Chongqing c 0.14 1 1 100 0 0 Sichuan 0.72 21 12 57.14 9 42.86 Tibet 1.10 7 4 57.14 3 42.86 Jiangxi 1.43 11 8 72.73 3 27.27 Tianjin c 2.79 1 1 100 0 0 Shaanxi 4.74 10 5 50 5 50 Shanxi 5.79 11 10 90.91 1 9.09 Xinjiang 7.55 14 8 57.14 6 42.86 SUM −2.34 336 94 27.98 242 72.02 a “+” reflects an increase in PM2.5 from 2015 to 2016, and “−” reflects a decrease in PM2.5 from 2015 to 2016. b Cities without records of PM2.5 are not included. c Shanghai, Beijing, Chongqing and Tianjin are municipalities of China. Int. J. Environ. Res. Public Health 2017, 14, x 3 of 9 Figure S1. Proportion distributions of hourly PM2.5 concentrations (in the range of ≤10 μg/m3 (a), 10–15 μg/m3 (b), 15–25 μg/m3 (c), 25–35 μg/m3 (d), ＞35 μg/m3 (e)) in 336 cities. Int. J. Environ. Res. Public Health 2017, 14, x 4 of 9 Figure S2. Diurnal variation of PM2.5 concentration in four seasons over 2015 and 2016. Table S2. Changes in deaths caused by fluctuations of PM2.5 concentrations from 2015 to 2016 in 336 Chinese cities. City PM2.5 (μg/m3) Change of death number from 2015 to 2016 Province 2015 2016 LC IHD stroke COPD sum Heilongjiang Yichun 26 17 -22.31 -58.16 -219.54 -24.40 -324.41 Heihe 31 22 -18.12 -47.33 -194.99 -19.18 -279.62 Mudanjiang 48 37 -20.06 -35.90 -138.45 -22.60 -217.01 Shuangyashan 42 33 -17.11 -36.44 -125.95 -13.90 -193.41 Hegang 43 34 -17.11 -27.33 -124.41 -18.38 -187.23 Harbin 65 51 -22.04 -42.97 -85.71 -17.37 -168.08 Daxing’anling 25 21 -11.15 -29.08 -105.56 -9.85 -155.64 Qiqihar 49 41 -13.37 -26.73 -91.76 -13.56 -145.42 Daqing 44 38 -13.58 -27.13 -76.09 -9.19 -125.99 Jixi 27 25 -3.69 -9.62 -45.54 -4.88 -63.73 Suihua 37 34 -7.01 -9.25 -40.52 -4.71 -61.49 Qitaihe 37 36 -3.51 -9.25 -10.13 0.00 -22.88 Kiamusze 29 31 3.65 9.54 33.19 4.84 51.22 Jilin Changchun 54 41 -19.16 -29.64 -105.16 -18.63 -172.60 Yanbian 61 46 -18.74 -36.26 -94.92 -18.34 -168.25 Liaoyuan 59 46 -16.18 -29.21 -88.11 -14.79 -148.29 Siping 44 36 -14.23 -30.30 -87.66 -11.55 -143.75 Jilin 58 46 -16.18 -29.21 -81.23 -14.79 -141.41 Songyuan 51 42 -11.12 -22.39 -83.59 -15.03 -132.13 Baicheng 58 48 -13.48 -29.21 -59.07 -11.09 -112.86 Tonghua 36 31 -8.89 -15.62 -76.88 -7.90 -109.28 Baishan 52 50 -2.76 -7.41 -15.11 -3.76 -29.04 Qinghai Haixi 38 27 -16.51 -28.89 -157.66 -36.56 -239.63 Hainan 42 31 -18.81 -28.46 -136.68 -29.01 -212.95 Haibei 39 31 -13.65 -22.94 -107.18 -21.94 -165.71 Haidong 57 46 -12.73 -16.46 -80.45 -27.61 -137.24 Golog 43 37 -8.06 -11.38 -71.47 -21.57 -112.49 Huangnan 51 45 -7.81 -11.13 -52.68 -21.05 -92.67 Yushu 19 17 -6.05 -6.31 -43.15 -7.92 -63.42 Xining 48 50 2.62 5.61 15.32 0.00 23.55 Hubei Xiaogan 71 46 -32.65 -52.76 -141.51 -30.21 -257.14 Jingmen 69 57 -16.44 -30.15 -52.42 -11.42 -110.43 Wuhan 68 57 -16.44 -30.15 -45.18 -11.42 -103.19 Xiangyang 54 47 -11.54 -15.62 -47.51 -11.79 -86.47 Huangshi 66 57 -13.80 -22.77 -37.85 -7.67 -82.10 Jingzhou 69 60 -10.96 -22.61 -37.44 -7.61 -78.63 Suizhou 64 56 -11.12 -15.29 -38.06 -11.51 -75.98 Ezhou 53 47 -8.72 -15.62 -39.82 -7.92 -72.09 Shiyan 52 47 -8.72 -15.62 -39.82 -7.92 -72.09 Huanggang 57 51 -8.59 -15.51 -38.92 -7.79 -70.82 Int.
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