Monitoring and Assessment of Regional air quality in China using space Observations. Project Of Long-term sino-european cooperation

Transcription

1 Monitoring and Assessment of Regional air quality in China using space Observations Project Of Long-term sino-european cooperation

2 MarcoPolo partners Royal Netherlands Meteorological Institute (KNMI), The Netherlands Aristotle University of Thessaloniki (AUTH), Greece Belgian Institute for Space Aeronomy (IASB-BIRA), Belgium Danish Meteorological Institute (DMI), Danmark Democritus University of Thrace (DUTH), Greece Finnish Meteorological Institute (FMI), Finland Institute of Atmosphere Physics (IAP-CAS), P.R. China IsardSAT (ISAT), Spain National Observatory Athens (NOA), Greece Hefei Institute of Physical Sciences, Anhui Institute of Optics and Fine Mechanics (HIPS-AIOFM), P.R. China Netherlands Organization for Applied Scientific Research (TNO), Netherlands Peking University (PKU), P.R. China Tsinghua University (THU), P.R. China Flemish Institute for Technological Research (VITO), Belgium London Metropolitan University (LMU), United Kingdom

3 Objectives of MarcoPolo Improved air quality modelling/forecasting by: New emission inventory Emission estimates from satellites Statistical information from the ground (GIS, MEIC) Monitoring, Validation, and air quality studies Strengthening of co-operation between China and Europe Dissemination of MarcoPolo end products

4 Overview of research approach WP2 WP3 Meterological data Satellite data Monitoring of air quality Emission estimates Ground-based data WP4 Emission inventories WP5 Assessment & forecasting WP7 User service Users WP6 Validation of observations and emissions

5 Monitoring and analysing air quality Satellites used: UV-VIS: GOME, SCIAMACHY, GOME-2, OMI, FY-3 Aerosol data: AATSR, CALIPSO, MODIS, MISR Future application: Sentinels, ADM-Aeolus, EarthCARE Monitoring products: Derived satellite products for: NO 2, HCHO, CHOCHO, O 3, SO 2, aerosols Analyses: Climatology vertical distribution aerosols and clouds Temporal patterns in aerosols concentrations Effect of aerosols on trace gas retrievals

6 Monitoring HCHO, GOME-2 A, EUMETSAT AATSR AOD

7 Sulphur dioxide (SO2) Main source of SO2: coal power plants

8 Tropospheric ozone from GOME-2 Jan. Apr. July Oct. Seasonal cycle of the tropospheric ozone (0-2.5km) derived by assimilating ozone profiles from GOME2 For the method: see poster of Jacob van Peet

9 Satellite products on the portal

10 Emission Estimates from space For estimates will be derived of the key species in air pollution: VOC emissions Aerosol emissions NOx emissions SO2 emissions CO emissions Method: Various techniques: adjoint model, data assimilation of concentrations or emissions.

11 Changes w.r.t. to apriori NOx Emissions

12 North vs South Korea

13 How effective were the air quality measures during the YOG2014 Case Study: Location: Nanjing Event in 2014: the Youth Olympic Games in August. Studied: 1. ground observations 2. satellite observations 3. emission estimates

14 Yangtse River Delta

15 Yangtse River Delta

16 Case study: How? See poster of Jieying Ding The monthly NO x emission estimates in Nanjing for 2013 and 2014, and the monthly NO x emission of the MEIC inventory of 2010.

17 NOx emission trends in East Asia

18 Inversion of satellite observations Chemistry Transport Model Concentrations Satellite observations Inversion algorithm Emissions

19 Emission Inventory Satellite based NOx emissions East China Source: KNMI» Top-down emissions:» monthly emissions of several species» at 0.25 or 0.5 resolution» estimates using satellite data» Bottom-up emissions:» monthly emissions of several species» at 0.25 resolution» through bottom-up approach (MEIC)» MarcoPolo emission database:» Combining top-down with bottom-up databases» Downscaling to high resolution Bottom-up NOx emissions transport China Source: THU

20 Source attribution Source attribution for PM2.5 over the categories: Power Residential Industry Transport Agriculture

21 Air quality modelling and forecasting Regional modelling: East China, 0.25 degree CHIMERE model adapted to China Including forecasts Urban-scale assessment: Yangzhou in the Yangtze River Delta Modelling with new emission inventory Urban/City-scale modelling: Beijing, Shanghai and Pearl River Delta Enviro-HIRLAM and SILAM model Study on relationship of air quality and meteorology

22 Bilingual forecast service on the web-portal

23 Validation 50+ ground data sets available for validation 72 time series (satellite or model) uploaded to be validated Validation of Satellite retrievals Emission data Model results Using MAX-DOAS measurements In-situ observations Sunphotometers Mobile DOAS Aeronet-like network of stations Bottom-up emission inventories Biogenic flux measurements NO,NO2,SO2 (ppb) 年月 Yearmonth NO SO2 NO2 O O3 (ppb)

24 Intercomparisons Satellite Ground Model Emissions using satellites Bottom-up Emissions

25 Example Validation results Diurnal cycle in comparison between model and MAX-DOAS NO2 (Beijing) Comparison between model and MAX-DOAS for different cloud filtering limits (Hefei)

26 Data dissemination Data dissemination Web-portal Toolbox Ftp Study on how to present AQ results: Exploitation via multiple media types for use by professionals (policy makers, environmental agencies) and/or general public Results from EU-project AirINFORM

27 Leaflet

28 Summary MarcoPolo Dragon 3 project (ESA-NRSCC) : June 2012 June 2016 Collaborative Project of FP7-Space (EU) : January 2014 December 2016 Objectives: Improved air quality modelling/forecasting in China by: New emission inventory Emission estimates from satellites Statistical information from the ground (GIS, MEIC) Monitoring, validation, and air quality studies Strengthening of co-operation between China and Europe Dissemination of end products in cooperation with EU project Panda on web portal