Trends in NOx emissions and SO2 concentrations in China
1KNMI, Netherlands, The; 2AUTH, Greece; 3BIRA-IASB
To monitor air quality trends in China for the period 2005-2015 we derived SO2 columns and NOx emissions on a provincial level. To put these trends into perspective they are compared with public data on energy consumption and the environmental policies of China. We distinguish the effect of air quality regulations from economic growth by comparing them relatively to fossil fuel consumption. Pollutant levels, per unit of fossil fuel, are used to assess the effectiveness of air quality regulations. We note that the desulphurisation regulations enforced in 2005-2006 only had a significant effect in the years 2008-2009 when a much stricter control of the actual use of the installations began. For national NOx emissions a distinct decreasing trend is only visible since 2012, but the emission peak year differs from province to province. The last three years show both a reduction in SO2 and NOx emissions per fossil fuel unit, since the authorities have implemented several new environmental regulations. Despite an increasing fossil fuel consumption and a growing transport sector, the effects of air quality policy in China are clearly visible.
Evaluation of RSD-DRFs technique using deterioration experimental data
1Climate Research Group, Division of Environmental Physics and Meteorology, Faculty of Physics, National & Kapodistrian University of Athens, Greece; 2Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, China
As a part of our research during the implementation of the DRAGON 3 project we have developed a new technique of using satellite observations to estimate the level of deterioration of the materials used in constructions and cultural monuments. This technique is mainly based on the already developed Dose Response Functions (DRFs) in which the ground-based measurements of various atmospheric pollutants (e.g. nitrogen oxides, sulphur dioxide, ozone) and several climatic parameters, such as air temperature and others, are often used as input data. The values of DRFs of specific materials provide a measure of their corrosion or soiling caused by their outdoor exposure to weather and the air pollution factors. In this work, we evaluate the performance of our proposed technique using the available deterioration experimental data from more than 10 European sites. These data were obtained during different periods since 2005 for cases of four materials (carbon steel, limestone, zinc, modern glass). The term “Remotely Sensed Data-Dose Response Functions (RSD-DRFs)” is proposed for this technique.
Ensemble of ESA/AATSR Aerosol Optical Depth (AOD) Products Based on the Likelihood Estimate Method with Uncertainties
RADI, China, People's Republic of
Within the ESA Climate Change Initiative (CCI) project Aerosol_cci, there are three Aerosol Optical Depth (AOD) datasets of Advanced Along Track Scanning Radiometer (AATSR) data. These are obtained using the ATSR-2/ATSR dual-view aerosol retrieval algorithm (ADV) by the Finnish Meteorological Institute (FMI)/University of Helsinki (UHEL), the Oxford-RAL Retrieval of Aerosol and Cloud algorithm (ORAC) by the University of Oxford/ Rutherford Appleton Laboratory (RAL) and the Swansea algorithm (SU) by the University of Swansea. The three AOD datasets vary widely. Each has unique characteristics, so none is significantly better than the others, and each has shortcomings that limits the scope of its application. To address this, we propose a method for converging these three products to create a single dataset with higher spatial coverage and better accuracy. The fusion algorithm consists of three parts: the first part is to remove the system errors; the second part is to calculate the uncertainty and fusion of datasets using the maximum likelihood estimate (MLE) method; and the third part is to mask outliers with a threshold of 0.12. The ensemble AOD results show that the spatial coverage of fused dataset after mask is 148%, 13% and 181% higher than ADV, ORAC and SU respectively, and the root mean square (RMSE), mean absolute error (MAE), mean bias error (MBE) and relative mean bias (RMB) are superior to the three original datasets. Thus, the accuracy and spatial coverage of the fused AOD dataset after mask are improved compared to the original data. Finally, we discuss the selection of mask thresholds.
Spatial and temporal variations of aerosols over China from multi-satellite observations.
1Finnish Meteorological Institute (FMI), Helsinki, Finland; 2National Observatory of Athens (NOA), Athens, Greece; 3Laboratory of Atmospheric Physics, Department of Physics, Aristotle University of Thessaloniki, Thessaloniki, Greece; 4Laboratory of Atmospheric Physics, Department of Physics, University of Patras, Patra, Greece; 5Department of Environmental Engineering, School of Engineering, Democritus University of Thrace, Xanthi, Greece; 6University of Derby, UK; 7Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences (RADI/CAS), Beijing, China; 8Royal Netherlands Meteorological Institute (KNMI), De Bilt, The Netherlands
Satellite data from several different instruments are used to study the spatial and temporal distribution of aerosols over China since 1995. In particular ATSR-2 (1995-2003), AATSR (2002-2012), MODIS (2000-present) are used to provide the spatial distribution of the AOD, while CALIOP (2007-present) also provides information on the vertical structure of aerosols, including aerosol type information and in particular dust. The AOD data sets are validated and evaluated versus sun photometer data from AERONET and the Chinese network CARSNET. This is particularly valuable because aerosol retrieval algorithms are developed and validated over areas where many independent ground-based observations are available, such as over the eastern USA and Europe. However, over these areas the AOD levels are often relatively low as compared to China where the occurrence of very high AOD, combined with the variation in aerosol type and surface characteristics, poses particular problems as regards data selection and discrimination between high AOD and the occurrence of clouds.
The spatial distributions over China varies significantly as a result of the multitude of sources, both natural and anthropogenic, which in turn vary with the season. These include anthropogenic sources such as industry and traffic, agricultural and natural biomass, dust from two major deserts, as well as the seasonal production of precursor gases. In addition, economic development and measures to improve air quality affect the long term variation of aerosol concentrations. Meteorology and large scale circulation including the seasonally progressing monsoon have a substantial effect on the aerosol physical properties as well as on production and removal of the aerosol particles. All of these effects vary with location over China and their seasonal and year-to-year variatons.
An initial analysis of the spatial and vertical variability of the AOD will be presented together with time series showing the variation over representative areas. Satellite derived information on aerosol precursor gases NO2, SO2 and BVOCs will be used in the analysis.
These activities are undertaken as part of the EU-FP7 project MarcoPolo. The main objective of the MarcoPolo project is to improve air quality monitoring, modelling and forecasting over China using satellite-retrieved information on aerosols, NOx, SO2, and biogenic gases. This information will be used in air quality models to invert emission estimates. The results, together with known information from ground-based measurements, will then be used to construct an emission database over China. The MarcoPolo project finished in March 2017 and will in part be continued in the framework of the ESA DRAGON4 initiative.
Bvoc Emissions and O3 in a Subtropical Plantation in China: Measurement and Validation
1LAGEO, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China; 2Department of Earth System Science, University of California, Irvine CA 92697, USA; 32B Technologies, Inc. Boulder, CO 80301, USA; 4Tufts University, Department of Civil and Environmental Engineering, Medford, MA 02155, USA; 5National Center for Atmospheric Research, Boulder, CO 80307, USA; 6National Center for Atmospheric Research, Boulder, CO 80307, USA; 7Royal Belgian Institute for Space Aeronomy, Avenue Circulaire 3, 1180, Brussels, Belgium
Atmospheric pollution is a severe problem in China, it is an important to keep on our monitoring and satellite validation of trace gases, BVOC emissions, aerosols in China, especially at some representative sites. Our main activities are ground observations, validation of satellite retrievals and satellite data applications. To fulfill one of all above tasks, measurements of BVOC emissions, O3 and solar radiation were carried out in a subtropical Pinus plantation in China during 2013-2016. BVOC emissions were measured using a relaxed eddy accumulation (REA) technique and a gradient technique. Monoterpenes were the dominant VOCs in this subtropical Pinus plantation. Isoprene and monoterpene emissions showed obvious diurnal, seasonal and inter-annual variations. In comparison with 2013, annual BVOC emissions decreased in 2015, which were associated with decreases of PAR, temperature and water vapor. O3 concentration above the canopy level also displayed clear diurnal variation. It was found that BVOC emissions were influenced by biomass burning smoke and pine florescence. The mean emission factors determined using the MEGAN model emission algorithms and empirical model of BVOC emissions were 0.71 and 1.19 mg m-2 h-1 for isoprene and 1.39 and 1.65 mg m-2 h-1 for monoterpenes, respectively. Flux measurements of BVOCs at a subtropical bamboo plantation in China were used to evaluate the bottom-up inventory of isoprene emissions and the satellite-based MarcoPolo (Monitoring and Assessment of Regional air quality in China using space Observations) emission inventory for isoprene derived using inversion of satellite columns of formaldehyde. Generally, the space-based inventory provides a satisfactory agreement with the observations in summer. Further validation in this subtropical plantation will be carried out in the future. All measure and validated data will be provided for air quality models, so as to improve our abilities in the forecast of air quality.
Key words: Biogenic volatile organic compounds; emission fluxes; ground measurement; validation; satellite.
Intercomparison of NOx Emission Inventories over East Asia
1Royal Netherlands Meteorological Institute (KNMI); 2Delft University of Technology; 3Japan Agency for Marine-Earth Science and Technology; 4Jet Propulsion Laboratory-California Institute of Technology; 5Nanjing University of Information Sciences and Technology; 6Asia Center for Air Pollution Research; 7Department of Environmental Engineering, Inha University, Inchon; 8Department of Earth System Science, Tsinghua University; 9Institute for Environment and Sustainability, Joint Research Centre
We compare 9 emission inventories of nitrogen oxides including four satellite-derived NOx inventories and the following bottom-up inventories for East Asia: REAS (Regional Emission inventory in ASia), MEIC (Multi-resolution Emission Inventory for China), CAPSS (Clean Air Policy Support System) and EDGAR (Emissions Database for Global Atmospheric Research). Two of the satellite-derived inventories are estimated by using the DECSO (Daily Emission derived Constrained by Satellite Observations) algorithm, which is based on an extended Kalman filter applied to observations from OMI or from GOME-2. The other two are derived with the EnKF algorithm, which is based on an ensemble Kalman Filter applied to observations of multiple species using either the chemical transport model CHASER and MIROC-chem. The temporal behaviour and spatial distribution of the inventories are compared on a national and regional scale. A distinction is also made between urban and rural areas. The intercomparison of all inventories shows good agreement in total NOx emissions over Mainland China, especially for trends, with an average bias of about 20% for yearly emissions. All the inventories show the typical emission reduction of 10% during the Chinese New Year and a peak in December. Satellite-derived approaches using OMI show a summer peak due to strong emissions from soil and biomass burning in this season. Biases in NOx emissions and uncertainties in temporal variability increase quickly when the spatial scale decreases. The analyses of the differences show: the importance of using observations from multiple instruments and a high spatial resolution model for the satellite-derived inventories, while for bottom-up inventories, accurate emission factors and activity information are required. The advantage of the satellite derived approach is that the emissions are soon available after observation, while the strong point the bottom-up inventories is that they include detailed information of emissions for each source category.