To what extent can China's near-term air pollution control policy protect air

Transcription

1 Supplementary information for: To what extent can China's near-term air pollution control policy protect air quality and human health? A case study of the Pearl River Delta region Xujia Jiang 1,2, Chaopeng Hong 1,2, Yixuan Zheng 1, Bo Zheng 1,2, Dabo Guan 1,3, Andy Gouldson 4, Qiang Zhang 1,5*, Kebin He 2,5 1. Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing , People s Republic of China 2. State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing , People s Republic of China 3. School of International Development, University of East Anglia, Norwich NR4 7TJ, UK 4. Sustainability Research Institute, School of Earth and Environment, University of Leeds, Leeds, UK 5. Collaborative Innovation Center for Regional Environmental Quality, Beijing , China * Correspondence to: Q. Zhang (qiangzhang@tsinghua.edu.cn) and K. He (hekb@tsinghua.edu.cn) NUMBER OF PAGES: 23 NUMBER OF FIGURES: 3 NUMBER OF TABLES: 8

2 1 Contents 2 3 Details on satellite-based PM 2.5 concentrations retrieval 4 5 Details on the CMAQ model simulation Table S1. Main parameters (energy use and product outputs) used for emission estimation in 2012 and 2017 (10 4 t) Table S2. Main parameters (end-pipe control technologies and their penetration) used for emission estimation in 2012 and 2017 (%) Table S3. Emission factors of main sources in the PRD. Table S4. Monthly averaged PM 2.5 observation and CMAQ simulation Table S5. The CMAQ model performance evaluation. Table S6. Fitted parameters and baseline incidence rates used in health impact models Table S7. Main emission reductions from air pollution control Table S8. PM 2.5 speciation derived from the CMAQ model Figure S1. Satellite-based PM 2.5 concentration in the PRD in Figure S2. PM 2.5 daily concentration time-series comparison of surface measurements and CMAQ simulations over grids with monitoring sites for four months of Figure S3. CO 2 emissions in 2012 and The 2017 BAU, a hypothetical scenario, assumes the energy consumption is at the same level as 2017; however, it excludes 1

3 intervention of the Action Plan such as natural gas replacement (e.g. E1-E5), vehicle standard upgrade (P-17) and structural change (I-1 and I-2). 2

4 Details on satellite-based PM 2.5 concentrations retrieval The gridded satellite-derived PM 2.5 concentrations, shown in Figure S1, were obtained from a linear mixed-effects (LMEs) model, which is documented in Zheng et al (2015). The model employed to predict for the PRD gridded surface PM 2.5 integrates monitored ground-level PM 2.5 measurements, satellite-derived AOD data, model-generated meteorological parameters (i.e., relative humidity at 2 m above ground and wind speed at 10 m above ground), and satellite-derived annual average tropospheric NO 2 column density as a proxy of anthropogenic emissions. The LMEs model with random intercepts and slopes for AOD and meteorological parameters accounts for the day-to-day variability within the relationships between surface PM 2.5 and daily varying independent variables (i.e. AOD and meteorological parameters) (Lee et al 2011, Hu et al 2014). The impacts of the non-stochastic missing AOD values on annual PM 2.5 prediction were taken into consideration using the measurement-derived correction factors. Compared with the ground monitoring measurements, the satellite-based PM 2.5 shows good agreement with the monitoring data, with the R 2 and normalized mean bias (NMB) value of 0.71 and 8.4%, respectively, for annual PM 2.5 prediction by using the Leave-one-out cross-validation (LOOCV) Details on the CMAQ model simulation The CMAQ model was employed to obtain the PM 2.5 concentration ratios of 2013 and We applied the CMAQ model V ( with CB05 chemical mechanism and AER06 aerosol module, and offline-coupled Weather Research and Forecasting (WRF) model V ( to make the air quality simulation. The latest meteorological data of 2013 was used for the simulation, which is close to the climatological condition over past decade (Guangdong Meteorological Service 2014, 2015). The CMAQ model consists of grid cells with a resolution of 12 km that cover the entire PRD region. The simulations were run for January, April, July and October four mouths to obtain annual PM 2.5 concentrations. The meteorological fields at 12-km horizontal grid 3

5 spacing generated by the WRF with 23 vertical layers was based on the reanalysis data of the U.S. National Centers for Environmental Prediction (NCEP, and the initial and boundary conditions were derived from the final analysis data (FNL) of NCEP, which was further used to drive the CMAQ. The land use/land cover and topographical data were from the default WRF input dataset. The MEIC model provided anthropogenic source emissions in the PRD, which was further divided into two scenarios, the baseline emission in 2012 and emission with air pollution control in Emission changes outside of the PRD in Guangdong province during were also considered while such changes have not been calculated in neighboring provinces as the backward industry in the surrounding provinces would not change dramatically (compared to the level of the PRD) during the Action Plan s performing. The natural source emission was adopted from the Model of Emissions of Gases and Aerosols from Nature (MEGAN). Open biomass burning has not been considered in this study. By using the surface observations ( updated hourly, more details see Table S4), the CMAQ performance is evaluated (Table S5, Figure S2). We also compared annual average CMAQ-modeled and satellite-derived PM 2.5 concentrations over the domain. The domain-wide annual mean PM 2.5 concentration from CMAQ is 35.9μg/m 3, 13% lower than satellite-based concentration (40.6 μg/m 3 ). The correlation coefficient between two datasets is 0.79 with RMSE of 11.1μg/m 3, indicating the acceptable accuracy of CMAQ simulation. The model performance, in brief, is considered adequate for our application, especially since we only used the relative reduction ratio from the CMAQ model. 4

6 89 Table S1. Main parameters (energy use and product outputs) used for emission estimation in 2012 and 2017 (10 4 t a ) Sectors Power Industry Urban residential Rural residential Energy/products Related Policy (No.) PRD Non-PRD a PRD Non-PRD b Coal Natural gas c Coal Natural gas c Fuel oil Kerosene E-3 Diesel LPG c Other petroleum product Steel I-1 Cement I-2 Coal Natural gas c LPG c Crude oil Diesel Fuel oil Other petroleum product Coal Natural gas c LPG d Crop residential Wood On-road Gasoline E-1&E-2 E-5-5

7 Diesel Off-road Diesel a 10^4t fuel or product (i.e. steel and cement) b non-prd represents the rest of cites in Guangdong beside PRD. c unit of natural gas 10^8 m 3 d Liquid petroleum gas 6

8 95 Table S2. Main parameters (end-pipe control technologies and their penetration) used for emission estimation in 2012 and 2017 (%) Sectors Technology End-pipe control technology PRD Non-PRD a PRD Non-PRD a Related Policy Power Industry (boiler) >300 MW MW MW FGD P-1 LNB LNB+SCR LNB SCR LNB+SCR < 100 MW SCR Natural gas unit auto grate boiler CFB WET ESP ESP SCR P-4 FGD P-6 LNB LNB+SCR P-7 CYC WET P-8 ESP CYC WET P-8 ESP Industry dry process LNB P-13 P-2 P-3 7

9 96 (process) cement SNCR Vehicles FAB ESP Steel sinter FGD P-14 Steel Control-CYC Control-WET Control-ESP Control-FAB Fugitive-with control Fugitive-without control CYC flat glass WET ESP FAB Euro Euro Gasoline Euro Euro Euro Euro Euro Euro Diesel Euro Euro Euro Euro a non-prd represents the rest of cites in Guangdong beside PRD. - P-15 P-15 P-12 P-17 8

10 97 98 Table S3_1. Emission factors of main sources in the PRD (SO 2 ) Power Industry Residential boiler Urban Rural Transportation 2012 Raw Coal LPG Gasoline Diesel Natural gas (g/m 3 ) Raw Coal LPG Gasoline Diesel Natural gas (g/m 3 )

11 Table S3_2. Emission factors of main sources in the PRD (NOx) Power Industry Residential Urban Rural Transportation 2012 Raw Coal LPG Gasoline Diesel Natural gas (g/m 3 ) Raw Coal LPG Gasoline Diesel Natural gas (g/m 3 )

12 Table S3_3. Emission factors of main sources in the PRD (PM2.5) Power Industry Residential Urban Rural Transportation 2012 Raw Coal LPG Gasoline Diesel Natural gas (g/m 3 ) Raw Coal LPG Gasoline Diesel Natural gas (g/m 3 )

13 105 Table S4 Monthly averaged PM 2.5 observation and CMAQ simulation SiteID CityName January April July October Frequency (%) Obs Sim Obs Sim Obs Sim Obs Sim 986 Guangzhou Guangzhou Guangzhou Shenzhen ** Shenzhen ** ** Foshan Foshan Jiangmen Jiangmen Huizhou Huizhou Guangzhou Jiangmen Huizhou Guangzhou Guangzhou Guangzhou Guangzhou Guangzhou Guangzhou Guangzhou Guangzhou Guangzhou Guangzhou Guangzhou Guangzhou Shenzhen Shenzhen Shenzhen Shenzhen Shenzhen Shenzhen Shenzhen Shenzhen Shenzhen Shenzhen Shenzhen Zhuhai Zhuhai Zhuhai Zhuhai

14 1371 Foshan Foshan Foshan Foshan Foshan Foshan Foshan Foshan Zhuhai Zhuhai Zhuhai Zhuhai Jiangmen Jiangmen Jiangmen Jiangmen Zhuhai Zhuhai Zhuhai Zhuhai Zhuhai Huizhou Huizhou Huizhou Huizhou Huizhou Zhaoqing Zhaoqing Zhaoqing Zhaoqing ** observation data is missing

15 109 Table S5. The CMAQ model performance evaluation PM 2.5 μg/m 3 January April July October 4 Months Data Pairs MeanObs MeanSim R MB RMSE NMB (%) NME (%)

16 110 Table S6. Fitted parameters and baseline incidence rates used in health impact models Mortality α γ δ C 0 Reference Incidence rate Reference IHD Burnett et al (2014) Guangdong Provincial Health Statistical Yearbook (2013) Stroke Burnett et al (2014) Guangdong Provincial Health Statistical Yearbook (2013) COPD Burnett et al (2014) Guangdong Provincial Health Statistical Yearbook (2013) LC Burnett et al (2014) Guangdong Provincial Health Statistical Yearbook (2013) Morbidity Coefficients β Reference Incidence rate Reference Health risks of air Hospital admission_respiratory Hospital admission_cardiovascular pollution in Europe HRAPIE project (WHO 2013) Health risks of air pollution in Europe HRAPIE project (WHO 2013) Guangdong Provincial Health Statistical Yearbook (2013) Guangdong Provincial Health Statistical Yearbook (2013) 15

17 113 Table S7. Main emission reductions from air pollution control Measure No. E-2 Types Sectors Reductions (tons) Pollutants PRD Non-PRD SO Power Energy E-3 structure adjustment Industry E-4 Residential I-1 Industrial structure Industry I-2 adjustment P-1 NO x PM VOC SO NO x PM VOC SO NO x PM VOC PM SO NO x PM SO VOC SO P-2 NO x Power P-3 PM P-4 NO x P-5 Industry (boiler) SO P-6 Industry (boiler) SO P-7 Industry (boiler) NO x P-8 P-9 P-10 P-11 Stationary sources end-pipe control Industry (boiler) Industry (ceramics) Industry PM 2.5 SO 2 PM 2.5 SO P-12 (flat glass) PM P-13 Industry (cement) NO x P-14 Industry (sinter) SO P-15 Industry (steel) PM SO P-17 Mobile sources Transport PM NO x VOC

18 V-1 Industry process VOC VOC control V-2 Solvent use VOC SO Emission increase* PM NO x VOC SO Reductions compare PM to 2012 NO x VOC * Emission increase is induced by energy consumption growth from 2012 to

19 118 Table S8. PM 2.5 speciation derived from the CMAQ model. PM 2.5 ( g/m 3 ) OC ( g/m 3 ) BC ( g/m 3 ) NH 4 + ( g/m 3 ) NO 3 - ( g/m 3 ) SO 4 2- ( g/m 3 ) OTHERPMFINE ( g/m 3 ) Reduction 17.0% 10.8% 21.5% 10.5% 1.5% 17.7% 22.8% 18

20 References Burnett R. T., Pope C. A., Ezzati M., Olives C., Lim S. S., Mehta S., et al An integrated risk function for estimating the global burden of disease attributable to ambient fine particulate matter exposure Environ. Health Perspect Department of Health of Guangdong Province Health Statistical Yearbook of Guangdong Province. Flower City Publishing House, Guangzhou. Guangdong Meteorological Service 2014 Guangdong provincial Climate Change Monitoring Bulletin. Available at: Guangdong Meteorological Service 2015 Meteorological yearbook of Guangdong Province (2013). Available at: Hu X., Walker L. A., Lyapustin A., Wang Y., Liu Y Improving satellite-driven PM 2.5 models with Moderate Resolution Imaging Sepectroradiometer fire counts in the southeastern U.S. J. Geophys. Res. 2014JD doi: /2014jd Lee H. J., Liu Y., Coull B. A., Schwartz J., Koutrakis P A novel calibration approach of MODIS AOD data to predict PM2.5 concentrations Atmos. Chem. Phys e8002. WHO 2013 Health risks of air pollution in Europe HRAPIE project New emerging risks to health from air pollution results from the survey of experts. Available at: data/assets/pdf_file/0017/234026/e96933.pdf?ua=1. Zheng Y., Zhang Q., Liu Y., Geng G., He K Estimating ground-level PM 2.5 concentrations over three megalopolises in China using satellite-derived aerosol optical depth measurements Atmos. Environ. doi: /j.atmosenv

21 Figure S1. Satellite-based PM 2.5 concentration in the PRD in x represents the PM 2.5 ground monitoring sites in the PRD in

22 Figure S2. PM 2.5 daily concentration time-series comparison of surface measurements and CMAQ simulations over grids with monitoring sites for four months of

23 Figure S3. CO 2 emissions in 2012 and The 2017 BAU, a hypothetical scenario, assumes the energy consumption is at the same level as 2017; however, it excludes intervention of the Action Plan such as natural gas replacement (e.g. E1-E5), vehicle standard upgrade (P-17) and structural change (I-1 and I-2). 22