Supplementary Materials for Fine Particulate Matter and Ozone Pollution in the 18 Cities of the Sichuan Basin in Southwestern China: Model Performance and Characteristics

Xue Qiao1,2,3, Hao Guo3, Pengfei Wang3, Ya Tang4, Qi Ying5, Xing Zhao6, Wenye Deng7, Hongliang Zhang3,8,*

1Institute of New Energy and Low-Carbon Technology & Healthy Food Evaluation Research Center, Sichuan University, No. 24, South Section One, First Ring Road, Chengdu, Sichuan 610065, China 2State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, China 3Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA 70803, USA 4College of Architecture and Environment & Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China 5Zachry Department of Civil Engineering, Texas A&M University, College Station, TX 77843, USA 6Department of Epidemiology and Biostatistics, West China School of Public Health, Sichuan University, No. 17, Section 3, South Renmin Road, Chengdu, Sichuan 610041, PR China 7Xinjiang Academy of Environmental Protection Science, Urumqi 830011, China 8 Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China *Corresponding author. Tel: +1-225-578-0140

E-mail address: [email protected]

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Table S1. The area, population, and economic development, and the concentrations of PM2.5 and O3 of the 18 prefectural cities in the SCB in 2015.

GDP Centr Annu Annu 90th 90th Cit Populatio Area (×100 al al al percentil percentil y City n (km2 millio urban PM2.5 PM2.5 e of 8-h e of 8-h ID (×1000) ) n area (μg (μg O3 O3 Group 1: upwind cities 1 Bazhong 3795 1229 501.34 160.29 35 57.2 2 Dazhou 6828 1658 1350.7 645.99 59 50.8 3 Guangyua 3053 1631 605.43 216.7 21 64.4 4 Mianyang 5455 2024 1700.3 465 47 45 64.0 63.1 Group 2: downwind cities 5 Chengdu 12281 1211 10801. 862.19 64 61 85.5 85.0 6 Deyang 3900 5910 1605.0 179.7 53 53 72.9 72.5 7 Guangan 4674 6341 1005.6 141.81 43 75.0 8 Leshan 3538 1272 1301.2 368.42 55 64.4 9 Luzhou 5057 1223 1353.4 411.38 62 59 56.5 60.2 10 Meishan 3491 7140 1029.8 292.59 60 73.9 11 Nanchong 7423 1247 1516.2 420 61 58 44.9 44.9 12 Neijiang 4204 5385 1198.5 278.93 60 70.7 13 Suining 3788 5323 915.81 316 49 61.1 14 Yibin 5521 1326 1525.9 1268 58 56 57.5 55.3 15 Zigong 3275 4381 1143.1 778.32 74 72 55.6 51.2 16 Ziyang 5037 7960 1270.3 186.87 40 72.8 Others 17 Ya’an 1549 1504 502.58 196.89 34 32.1 18 Chongqin 33720 8240 15717. 7026.6 57 53 59.3 59.5 #data source: China Statistical Yearbook 2016 , http://www.stats.gov.cn/tjsj/ndsj/2018/indexeh.htm; *calculated by using the data published on the air quality data releasing platform, Ministry of Ecology and Environment of the People’s Republic of China. http://106.37.208.233:20035/.

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Table S2. Major Physics Options for WRF Simulations Physics parameterization Option Meaning of the option

Microphysics mp_physics=8 New Thompson et al. scheme

Long wave radiation ra_lw_physics=1 RRTM scheme

Shortwave radiation ra_sw_physics=2 Goddard shortwave

Surface layer sf_sfclay_physics=1 Monin-Obukhov similarity theory

Land surface sf_surface_physics=2 MM5 Land Surface Model

Planetary boundary layer bl_pbl_physics=1 Yonsei University scheme

Cumulus Parameterization cu_physics=3 Grell-Devenyi ensemble scheme

Urban Surface sf_urban_surface=0 Not enabled

Pressure top (in Pa) to use in the p_top_requested=1000 The pressure top is 10 hPa model

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Table S3. Statistical indices used for model performance evaluation in this study

Statistic/abbreviation Definition Notes

Fractional Bias (FB) ( ) -200%≤FB≤+200% × ×100 ( ) 2 Pi−Oi N � Pi+Oi Fractional Error (FE) │ │ 0≤FE≤+200% × ×100 ( ) 2 Pi−Oi N � Pi+Oi Gross Error (GE) 2 Concentration units │Pi Oi│ N � − Root Mean Square Error (RMSE) Concentration units 1 ( ) N 2 � � 𝑃𝑃𝑃𝑃 − 𝑂𝑂𝑂𝑂 Mean Bias (MB) 1 Concentration units (Pi Oi) N � − ( ) Normalized Mean Bias (NMB) ×100 -100%≤NMB≤+∞ ∑ 𝑃𝑃𝑃𝑃−𝑂𝑂𝑂𝑂 | ∑ 𝑂𝑂𝑂𝑂 | Normalized Mean Error (NME) ×100 0≤NME≤+∞ ∑ 𝑃𝑃𝑃𝑃−𝑂𝑂𝑂𝑂 ∑ 𝑂𝑂𝑂𝑂 Subscript i represents the pairing of N observations (O) and predictions (P) by site and time.

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Table S4. The WRF model performance for Temperature (T2), Relative Humidity (RH), Wind Speed (WS), and wind direction (WD) in the 12-km domain. The values that do not meet the criteria are denoted in bold.

Winter (December 2014 to Summer Benchmark February 2015) (June to August 2015) December January February June July August No. of 8038 9684 8976 19080 20994 19764 data OBS 72.8 74.8 71.5 68.6 66.9 69.3 RH % PRE 62.1 66.1 65.0 67.5 63.9 66.8 MB -10.8 -8.7 -6.5 -1.1 -2.9 -2.5 RMSE 21.2 19.3 17.5 16.2 14.8 14.3 GE 16.9 15.1 13.6 12.6 11.4 11.0 No. of 19462 22955 21954 25355 26150 25964 data OBS 278.8 278.6 280.0 293.5 294.0 293.5 T2 K PRE 278.0 278.0 279.1 293.4 293.9 293.3 MB -0.8 -0.5 -0.9 -0.1 -0.1 -0.3 ˂±0.5 RMSE 3.3 3.3 3.4 3.3 2.9 2.8 GE 2.6 2.5 2.6 2.5 2.2 2.1 ˂2.0 No. of 9188 12070 12408 13885 12876 12163 data OBS 180.8 184.6 183.7 181.3 179.4 174.6 WD ° PRE 152.4 166.9 172.7 170.1 153.1 148.6 MB -2 2 6 4 -4 -5 ˂±10 RMSE 72.6 72.5 70.9 74.0 77.8 77.1 GE 56 56 54 57 61 60 ˂30 No. of 9291 12123 12528 14036 13112 12376 data OBS 3.1 3.0 3.3 3.1 2.8 2.8 WS m s-1 PRE 4.1 4.1 4.4 4.2 3.4 3.3 MB 1.0 1.1 1.1 1.1 0.6 0.5 ˂±0.5 RMSE 2.3 2.3 2.4 2.3 1.8 1.8 ˂2.0 GE 1.8 1.8 1.9 1.8 1.4 1.4 ˂2.0 There are 101 meteorological stations providing hourly observation data in the 12-km domain. MB is mean bias; GE is gross error; RMSE is root mean square error. The benchmarks are suggested by Emery et al. (2001). The equations of MB, GE, and RMSE can be found in Table S3.

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Table S5. Model performance for 24-h PM2.5 concentrations in the 18 SCB urban centers. The values do not meet the criteria are denoted in white boxes. The predictions used in the statistical analyses are those closes to the daily observations within a 3×3 grid cell region with the grid cell where the monitoring sites are located at the center. Cities Pre Obs NMB NME FB FE N Unit μg m-3 μg m-3 % % % % Goal ˂ ±10 ˂ 35 ˂ ±30 ˂ 50 Criteria ˂ ±30 ˂ 50 ˂ ±60 ˂ 75 Upwind cities Bazhong 63 69 -9 30 -2 31 58 Dazhou 85 115 -26 32 -21 33 58

Guangyuan 53 37 41 50 38 45 58 Mianyang 87 63 37 45 28 36 88 Nanchong 103 94 9 25 10 24 88 Downwind cities

Chengdu 128 100 29 32 29 32 88 Deyang 106 87 22 29 24 30 58 Guangan 99 95 5 30 18 37 58 Leshan 101 92 9 20 16 23 58 Luzhou 125 99 26 30 27 30 88 Meishan 126 96 31 35 33 35 58 Neijiang 127 115 10 23 14 25 58 Suining 104 86 21 40 24 40 58 Yibin 110 95 17 25 22 29 88 Zigong 123 115 7 19 11 21 88

Winter (December 2014 to February 2015) February to 2014 (December Winter Ziyang 114 77 48 58 43 51 58 Others Ya’an 65 58 11 16 13 19 58 Chongqing 139 98 42 44 37 39 88 Upwind cities Bazhong 17 21 -17 26 -22 31 91 Dazhou 26 42 -39 40 -50 51 91 Guangyuan 14 14 3 29 6 23 91

Mianyang 27 32 -15 18 -16 19 91 Nanchong 30 42 -27 28 -33 34 91 Downwind cities

Chengdu 42 42 1 15 1 15 91 Chongqing 45 41 10 14 9 13 91 Deyang 33 37 -10 14 -10 15 91 Guangan 25 28 -11 23 -13 25 91 Leshan 27 34 -22 24 -23 25 91 Luzhou 40 42 -5 15 -3 15 91 Meishan 39 45 -13 18 -14 19 91 Neijiang 32 36 -11 20 -9 21 91

Summer (June to August, 2015) August, to (June Summer Suining 26 35 -25 29 -28 32 91 Yibin 30 35 -14 18 -14 18 91 Zigong 38 46 -18 22 -20 24 91 Others Ya’an 13 18 -27 29 -36 38 91 Ziyang 26 23 11 25 6 24 91 Pre, Predictions, μg m-3; Obs, observations, μg m-3; NMB, Normalized Mean Bias; NME, Normalized Mean Error; FB, Fractional Bias; FE, Fractional Error; N: number of days having both validated simulation and observation data. The simulation goals and criteria are suggested by Emery et al. (2017).

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Table S6. Model performance for 8-h O3 concentrations in the 18 SCB urban centers. The values do not meet the criteria are denoted in white boxes. The predictions used in the statistical analyses are those closes to the daily observations within a 3×3 grid cell region with the grid cell where the monitoring sites are located at the center. A low-cut of 30 ppb is used. Cities Pre Obs NMB NME N Unit ppb ppb % % Goal ˂ ±5 ˂ 15 Criteria ˂ ±15 ˂ 25 Upwind cities Bazhong 54 34 57 57 10

Dazhou NA NA NA NA 0 Guangyuan 50 38 31 31 35 Mianyang 51 36 43 43 22 Nanchong NA NA NA NA 0 Downwind cities Chengdu 52 41 26 27 16 Deyang 54 39 39 39 23 Guangan 50 39 28 29 13 Leshan 66 40 67 67 12 Luzhou 56 39 44 44 11 Meishan 61 42 45 45 12 Neijiang 61 43 41 41 18 Suining 58 36 61 61 8 Yibin 52 36 46 47 12 Zigong 60 35 71 71 16 Ziyang 56 44 28 28 35 Winter (December 2014 to February 2015) February 2014 to (December Winter Others Ya’an NA NA NA NA 0 Chongqing 43 35 24 24 5 Upwind cities Bazhong 50 50 0 11 72 Dazhou 53 53 0 10 62 Guangyuan 52 55 -6 13 76

Mianyang 54 51 4 11 71 Nanchong 55 49 13 19 54 2015 )

Downwind cities Chengdu 61 64 -5 12 80 Deyang 57 58 -1 11 78

August, Guangan 55 64 -14 17 88 Leshan 57 54 7 13 78 Luzhou 56 50 12 16 68 Meishan 57 59 -3 12 80 Neijiang 55 58 -5 11 84 Suining 54 46 16 20 63 Yibin 57 50 15 16 81 Summer (June to Zigong 57 48 18 24 78 Ziyang 53 56 -6 14 85 Others Ya’an 54 35 56 56 35 Chongqing 55 53 4 12 62 N, number of days having both validated observation and simulation data. The simulation goals and criteria are suggested by Emery et al. (2017).

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Table S7. Model performance for 1-h O3 concentrations in the 18 SCB urban centers. The values do not meet the criteria are denoted in white boxes. The predictions used in the statistical analyses are those closes to the daily observations within a 3×3 grid cell region with the grid cell where the monitoring sites are located at the center. A low-cut of 30 ppb is used. Cities Pre Obs NMB NME N Unit ppb ppb % % Goal ˂ ±5 ˂ 15 Criteria ˂ ±15 ˂ 25 Upwind cities Bazhong 56 39 44 44 20

Dazhou NA NA NA NA 0 Guangyuan 54 43 27 27 42 Mianyang 55 39 41 41 32 Nanchong NA NA NA NA 0 Downwind cities Chengdu 57 43 33 33 29 Deyang 57 47 20 29 31 Guangan 54 41 30 30 25 Leshan 66 43 55 55 23 Luzhou 65 43 51 51 21 Meishan 59 43 36 37 30 Neijiang 62 45 37 37 31 Suining 58 41 43 43 19 Yibin 64 40 58 58 16 Zigong 63 35 79 79 32 Ziyang 59 48 25 25 44

Winterto (DecemberFebruary2015) 2014 Others Ya’an 59 34 72 72 4 Chongqing 51 38 32 36 10 Upwind cities Bazhong 52 54 -3 12 75 Dazhou 57 60 -5 14 68 Guangyuan 56 61 -9 15 77

Mianyang 57 58 0 11 74 Nanchong 60 52 14 21 62 Downwind cities Chengdu 69 75 -8 14 82 Deyang 63 68 -6 11 76

2015) August, Guangan 61 72 -14 16 82 Leshan 64 65 -2 14 81 Luzhou 61 58 7 15 75 Meishan 64 68 -6 12 81 Neijiang 61 67 -9 13 81 Suining 57 50 13 17 72 Yibin 63 57 11 14 81

Summer (June to (June Summer Zigong 62 54 14 21 77 Ziyang 57 64 -10 13 80 Others Ya’an 60 38 58 58 48 Chongqing 62 60 3 13 65 N, number of days having both validated observation and simulation data. The simulation goals and criteria are suggested by Emery et al. (2017). 8

Fig. S1. (a) Locations of the three regions and ten city clusters that are most affected by air

pollution in China (1- Beijing-Tianjin-Heibei, 2- Yangtze River Delta, 3- Pearl River Delta, 4- central regions of Liaoning Province, 5- Shandong Province, 6- Wuhan and its peripheral localities, 7- Changsha, Zhuzhou, and Xiangtan in Hunan Province, 8- the Chengdu-Chongqing city cluster in the SCB, 9- western coast of the Taiwan Strait, 10- central and northern regions of

Shansi Province, 11- Guanzhong Plain in Shanxi Province, 12- parts of Gansu and Ningxia provinces, and 13- Urumqi in Xinjiang Province) and (b) locations of national air quality monitoring stations in the 18 SCB cities.

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Fig. S2. Seasonal total emissions of PM2.5, SO2, NOx, and NH3 in the SCB in summer (June to

August 2015) and winter (December 2014 to February 2015). The units are in ton per grid cell.

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Fig. S3. Simulated and observed diurnal variations of O3 (ppb) in the 18 SCB urban centers in summer, 2015. The black lines show observations. The red lines simulations in the urban centers, while green lines show the simulations closest to the observations within 3×3 grid cell regions that surround the urban centers.

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Fig. S4. Spatial variations of simulated wind vectors, PM2.5, and PM2.5 components in the SCB in summer, 2015. Units are in µg m−3. The left and right crosses show the urban centers of

Chengdu and Chongqing, respectively.

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REFERENCES Emery, C., Liu, Z., Russell, A.G., Odman, M.T., Yarwood, G., Kumar, N. (2017).

Recommendations on statistics and benchmarks to assess photochemical model performance.

J. Air Waste Manag., 67: 582–598.

Emery, C., Tai, E., Yarwood, G. (2001). Enhanced meteorological modeling and performance

evaluation for two Texas ozone episodes.

https://www.tceq.texas.gov/assets/public/implementation/air/am/contracts/reports/mm/Enhanc

edMetModelingAndPerformanceEvaluation.pdf.

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