The PANDA Project Guy P. Brasseur Jan. 2015
Objective of the PANDA Project • To establish a team of European and Chinese scientists who will jointly use space observations and in-situ data as well as advanced numerical models to monitor, analyse and forecast global and regional air quality . • PANDA will disseminate methodologies, tools and data to a variety of users.
Specific Goals (1) • Identify space remote sensing data that are available and could be used to better monitoring air quality (ozone and its precursors, aerosols) at the global and regional scales; • Improve remote sensing data where needed, taking into account specific conditions in Asia; • Identify and collect surface in-situ observations that are available and will complement space observations; • Improve and evaluate the current knowledge on anthropogenic and natural emissions in Asia and at the global scale; • Use remote-sensing and in-situ data to analyse specific air quality situations, using state-of-the-art global and regional chemical transport models.
Specific Goals (2) • Conduct specific air quality analyses in urbanized areas of the Asian continent and assess the importance of intense emissions in the populated areas of Beijing, Shanghai and Guangzhou; • Develop a methodology to downscale existing global scale predictions of air quality (specifically those provided by the EU MACC-II Project) for the East Asian region, including the populated areas of China; • Quantify the impact of regional air pollution emissions in Asia on the rest of the world in response to long-range transport; • Develop user-friendly toolboxes providing easily accessible data related to air quality; • Disseminate information and products to users and stakeholders, including members of national, regional and local environmental agencies and other decision makers
The PANDA Project Coordinator: Guy Brasseur Deputy Coordinator: Prof. Xuemei Wang Period: Jan 2014 - Dec. 2016 Budget: 2 Millions Euros
The Analysis and Prediction System
Satellite Integration and Prediction Consistent fields Model Assimilation Ground based Toolbox for communicating air pollution predictions
Modeling Strategy Global Urban Target ~50km • EMEP (MET NORWAY) Shanghai? • IFS/C-IFS (ECMWF) Target ~2km • MOZART (NUIST) • enviro-HIRLAM (DMI) • MOZART (CNRS) • WRF-CHEM (MPI-M) Boundary Conditions Boundary Condition Sub-regional Beijing / Tianjin Regional Shanghai / YRD Target ~15km Guangzhou / PRD • EMEP (MET NORWAY) Target ~5km Boundary • WRF-CHEM (MPI) • WRF-CHEM E China (SCUEM) Conditions • CHIMERE-ECMWF (KNMI) • WRF-CHEM (SESE-SYSU ) • LOTOS-EUROS-ECMWF (TNO) • Enviro-HIRLAM (DMI ) • AURORA (VITO) • IFS/C-IFS not all species (ECMWF)
Global Forecast of Air Quality for Monday 26 January 2015 EU MACC Project ECMWF Carbon Monoxide Nitrogen Dioxide Aerosol
Study periods Year 2010 : Global • ~2 months January 2010 and July 2010 : Regional • ~2 weeks within the selected months : Local Year 2013 : As far as possible ( emissions… observations ...): • ~2 months January + December 2013 : Global+Regional • ~2 weeks within the selected months : Local Forecasts: • MACC (global) already available and can be used for e.g. demo of AQI on toolbox • besides, main strategy will develop later based on the experience with coupling/nudging/assimilation
Topics: engineering/applied and science • Sensitivity to emissions: high vs low anthropogenic (uncertainty ), top-down / inverse emissions from MarcoPolo, policy oriented / mitigation scenarios and their impacts? • (lateral) boundary conditions, coupling of chemical and aerosol schemes between global / regional / local configurations • Assimilation and nudging approaches • NO 2 in winter, CO underestimation • Link aerosol/haze/meteorology, link with WMO/WGNE and COST. • Field campaign (June 2014)
Downscaling to Regional Scale in Asia Satellite data MACC forecast/reanalysis as IC & BC WRF-Chem 60x60km 20x20km Assimilation in MACC WRF-Chem prediction AQI WRF-Chem prediction 20x20km 7 x 7 km Air Quality Index (AQI)
Choice of Initial Conditions for the Regional Downscaling MACC surface CO analysis MOZART surface CO MACC surface O3 analysis MOZART surface O3
WRF-Chem CO 60x60km 20x20k Downscaling of CO m with different surface emissions: HTAPv2 (left) MACCity (right) CO with HTAPv2 CO with MACCity emissions emissions Observations WRF 60x60km WRF 20x20km WRF 20x20+htap
The Workpackages
WP-1 Remote Sensing Data
Satellites used in the PANDA Project
WP1: Remote Sensing Observations T1.2 - First version of historical satellite data set (Lead: IUP-UB; ISM, CNRS, ULB) [Months: 1-12 D1.2] • Full dataset of O 3 and CO vertical profiles (1-km vertical gridding) from the existing FORLI processing chain (Sept. 2007 - Dec. 2013) (including averaging kernels and error covariance) • NH 3 total columns with suitable error estimates • Ensure that the dataset is suitable for validation activities Monitoring pollution in Asia with IASI - Achievements so far Pollution event as seen by IASI (January 12, 2013) • Extreme concentrations in several pollutants ( CO, SO 2 , NH 3 ) • Temperature inversion increased sensitivity of IASI at surface level CO SO 2 Boynard et al, GRL 2014 FORLI-CO: Available on French database Ether - Cfr . D1.1 Report
Monitoring pollution in Asia with IASI - Achievements so far Population density IASI tropospheric O 3 Trop. O 3 col. (DU) Tropospheric ozone in urban and rural regions in China • Seasonality in tropospheric ozone in several urban centres FORLI-O 3 : Available upon request (ULB/LATMOS) - Cfr . D1.1 and surrounding rural areas, Report including in East Asia Courtesy S. Saffedine
Monitoring pollution in Asia with IASI - Achievements so far Comparisons between ground-based measurements and WRF-Chem in China (7 stations in North China plain, 13 stations in the Pearl River delta) Courtesy S. Saffedine Courtesy S. Saffedine
Monitoring pollution in Asia with IASI - Achievements so far IASI NH 3 total columns 0.05ºx0.05º grid (mg/m²) NH3 emission in 1 km grid cell (kg/yr) (Van Damme et al. , ACP 2014) (Huang et al. , GBC 2012) IASI NH 3 : Available upon request (ULB/LATMOS) - Cfr . D1.1. Report Quick look available on Ether Comparisons with ground-based measurements in China IASI Shangzhuang site in the North China Plain Ground-based meas. (Van Damme et al. , AMTD 2014)
IUP Bremen PANDA contribution NO 2 above EC China • Clear decrease in 2014 • Technological improvements? • Less coal burnt? • Less economic growth? Work on deliverables: • Report on satellite data availability • Preparation of new UV/vis satellite data • Planning and preparation of first summer school • Creation of NO 2 validation data set
Trends in NO 2 tropospheric columns from CAM-Chem and different satellite observations.
Deliverables WP 1
WP-2 In-situ Observations
Ozone weekend effects in the Beijing – Tianjin – Hebei metropolitan area, China The ozone weekend effect (OWE) was first investigated in the metropolitan area of Beijing – Tianjin – Hebei (BTH), China, using in situ measurements from the Atmospheric Environment Monitoring Network from July 2009 to August 2011. An obvious weekly periodical variation in the surface ozone concentration, with a lower ozone concentration from Wednesday to Friday ( weekday ) and a higher concentration from Saturday to Monday ( weekend ); PM Concentrations UV radiation A clear weekly cycle in the fine aerosol concentration was observed, Higher concentrations of aerosol on weekdays can reduce the UV radiation flux by scattering or absorbing, which leads to a decrease in the ozone production efficiency A smaller decrease in volatile organic compounds (VOCs; using CO as a proxy) and much lower NOx concentrations on the weekend may lead to higher VOC/NOx ratio, which can enhance the ozone production efficiency in VOC-limited Ratio of VOCs/NOx regime areas. O3 Y. H. Wang , B. Hu, D. S. Ji, Z. R. Liu, G. Q. Tang, J. Y. Xin, H. X. Zhang, T. Song, L. L. Wang , W. K. Gao, X. K. Wang , and Y. S. Wang （ 2014 ） Ozone weekend effects in the Beijing – Tianjin – Hebei metropolitan area, China 。 Atmos. Chem. Phys., 14, 2419-2429, 2014
Deliverables WP 2
WP-3 Anthropogenic and Natural Emissions
CO emissions in China from 1980 to 2010
Collection of bottom-up emission inventories over China Available emission inventories: - MEIC v.1.0 - EDGAR v4.2 - REAS version 2 - NH 3 emissions from PKU (Peking Univ.) Inter-comparisons of emission estimates in 2008 - Prefer MEIC and REAS, too high estimates of EDGAR
Comparisons of bottom-up emission inventories over China – power plants Comparisons of MEIC, REAS and EDGAR on power plants - Prefer MEIC, which compiled based on unit- based methodology Ratio of SO 2 to CO 2 : Decreasing along with CO 2 emissions in MEIC, reflecting the high FGD implementation on large units
Deliverables WP 3
WP-4 Integration of Observations and Models
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