Estimating PM2.5 Concentrations Using Spatially Local Xgboost Based on Full‐Covered SARA AOD at the Urban Scale

Article Estimating PM2.5 Concentrations Using Spatially Local Xgboost Based on Full‐Covered SARA AOD at the Urban Scale

Zhiyu Fan 1,2, Qingming Zhan 1,2,*, Chen Yang 3, Huimin Liu 4 and Muhammad Bilal 5

1 School of Urban Design, Wuhan University, 8 Donghu South Road, Wuhan 430072, China; [email protected] 2 Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China 3 College of Urban and Environmental Sciences, Peking University, Beijing 100871, China; [email protected] 4 Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, NT, Hong Kong, China; [email protected] 5 School of Marine Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China; [email protected] * Correspondence: [email protected]

Table of Contents

Corresponding Section in Name Type Article Figure S1. Regression scattering plots of SARA AOD and Figure 2.2.2 AERONET AOD in Beijing site. Table S1. Data information and related variable acronym Table 2.2.5 Table S2. Parameters selection in XGBoost Table 2.3.1 Table S3. Variables description Table 3.1 Table S4. Seasonal and annual AOD coverage Table 3.1 Table S5. Cross‐validation result comparison of XGBoost Table 4 and Random Forest (RF)

Figure S1. Regression scattering plots of SARA AOD and AERONET AOD in Beijing site.

Remote Sens. 2020, 12, x; doi: FOR PEER REVIEW www.mdpi.com/journal/remotesensing Remote Sens. 2020, 12, x FOR PEER REVIEW 2 of 3

Table S1. Data information and related variable acronym.

Temporal Spatial Data Source Variable Acronym in Article Resolution Resolution

Ground‐level Each PM2.5 1 day PM2.5 Measurement Station

SARA AOD (Terra) MODIS 1 day 500 m AOD

(1) temperature at 2 m (T2M), (2) relative humidity (RH), (3) surface pressure (SP), (4) European Centre eastward wind at 10 m above for Medium‐ Meteorological displacement height (U10M), Range Weather 6 hours 0.125° Parameters (5) northward wind at 10 m Forecasts above displacement height (ECMRWF) (V10M), and (6) planetary boundary layer height (PBLH)

NDVI MODIS 16 days 500 m NDVI

Shuttle Radar DEM Topographic ‐ 90 m DEM Mission (SRTM)

Resource and Coverages of (1)water bodies Environment (CWB), (2) impervious land Land use Data Cloud ‐ 30 m (CII), (3)farmland (CFA) and Platform (4) forest (CFO) (REDCP)

Population REDCP ‐ 1 km POP

Gross Domestic REDCP ‐ 1 km GDP Product

Road Network REDCP ‐ 1 km Road length (RL)

Density of residential areas Point of Amap ‐ Shapfile and restaurants (DRR), Interest density of industries (DI)

Table S2. Parameters selection in XGBoost.

AOD gap‐fill model AOD‐PM2.5 model

booster XgbLinear XgbLinear

n_trees 500 300

gamma 0.5 0.9

max_depth 5 7

eta 0.05 0.05

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Table S3. Variables description.

Variable Minimum Maximum Mean Std VIF PM2.5 (μg/m3) 3 506 78.348 69.511 ‐ AOD (‐) 0.00005 2.98 0.70 0.64 2.242 RH (%) 0.182 0.952 0.526 0.156 2.583 SP (hpa) 863.021 1031.712 962.605 769.903 3.779 T2M (°) ‐14.598 29.637 11.127 10.898 6.187 U10M (m/s) ‐3.798 4.735 0.731 0.908 1.165 V10M (m/s) ‐4.835 3.588 ‐0.177 1.130 1.300 PBLH (m) 63.035 4206.958 1219.989 666.892 2.194 NDVI (‐) 0.001 0.953 0.443 0.222 3.038 DEM (m) 10 2154 368.012 253.790 2.890 POP (people) 45.543 32050.407 1317.289 2840.279 18.992* GDP (¥) 104.175 807531.669 13117.186 47791.822 14.538* RL (m) 0.014 24801.283 1467.169 1548.537 2.966 DRR (‐) 0 538.985 16.567 44.907 2.167 DI (‐) 0 3.065 0.320 0.399 1.887 CWB (%) 0.004 1 0.293 0.290 93.834* CII (%) 0.003 1 0.551 0.378 11069* CFA (%) 0.004 1 0.528 0.336 5024.640* CFO (%) 0.004 1 0.845 0.277 3655.713* Please refer Table S1 for the descriptions of these variable names. * means that VIF larger than 7.5 so the corresponding variables won’t be used in GWR.

Table S4. Seasonal and annual AOD coverage.

Period Minimum Maximum Mean Std Spring 0.32 0.53 0.45 0.02 Summer 0.24 0.54 0.42 0.03 Autumn 0.20 0.44 0.35 0.04 Winter 0.07 0.5 0.33 0.07 Annual 0.28 0.47 0.39 0.03

Table S5. Cross‐validation result comparison of XGBoost and Random Forest (RF) in SARA AOD gap‐fill process.

Time use (s) R2 RMSE (μg/m3) MPE (μg/m3) Period XGBoost RF XGBoost RF XGBoost RF XGBoost RF Spring 1804 40732 0.93 0.93 0.10 0.09 0.05 0.05 Summer 1568 34181 0.90 0.90 0.15 0.15 0.09 0.09 Autumn 1260 28125 0.94 0.94 0.10 0.10 0.06 0.05 Winter 1037 26900 0.92 0.90 0.07 0.08 0.04 0.05 Annual 7281 122434 0.86 0.86 0.15 0.15 0.10 0.09