Air Quality I

Monitoring the effect of human activity on the atmospheric composition has become more and more important for protecting both environment and human health. Satellite observations have been extensively used to monitor air quality because of their mapping capability and availability with global coverage. They are particularly important over areas where ground-based measurements are not performed or not publicly available. For example, satellite observations are suitable for monitoring the changes in polluting emissions from different kind of sources, such as car traffic, industry, ships and energy production. Furthermore, satellite observations are freely available and offer an opportunity to build financially sustainable services.

This work presents several successful applications of satellite-based data for air quality monitoring to support both public and private sector, with particular focus on Finnish society. The activities are implemented as pilot projects, based on the interaction between researchers and identified users. The results are mostly based on the space-based observations of atmospheric concentrations of nitrogen and sulfur dioxide.

Some of the topics covered in these applications are: (1) Satellite-based air quality monitoring in Helsinki area for local environmental authorities and oil sector company operating in the area. (2) Satellite-based SO₂ emission monitoring to support Finnish Cleantech companies operating in the metal smelting industry sector. (3) Satellite-based air pollution observations as background information and training material in FMI international cooperation project with developing countries.

 Also, we will present the most recent results from TROPOMI/S5P observations in Helsinki, including their comparison with ground-based measurements and their potential for societal applications. Also, we will show how the experience gained in users’ engagement will be useful for future applications, e.g. using TROPOMI data and in preparation for planned anthropogenic CO₂ missions.

These activities are funded under of the Key project funding by the Academy of Finland “Forging ahead with research” through the ILMApilot project. The goal is to boost the societal impact of research by promoting scientific research and its application. The research activities have been also supported by ESA, via the Living Planet fellowship programme.

Operational satellite-based Earth Observation products for air quality applications tend to have a relatively coarse spatial resolution on the order of several kilometers to tens of kilometers. While the Sentinel-5P/TROPOMI instrument with its 7 km x 3.5 km footprint at nadir provides significantly improved spatial detail compared to previous satellite instruments, it is not yet sufficient for air quality applications within a city, where spatial gradients are often sharp and citizen exposure to air pollution varies significantly from street to street.

In order to address this issue we have developed a spatial downscaling technique for satellite-based air quality products. This was carried out as part of the ESA-funded project SAMIRA (“SAtellite based Monitoring Initiative for Regional Air quality”). The method applies geostatistics and a temporally static or dynamic high-resolution proxy dataset to downscale the satellite information from the original Level-2 pixel geometry to any arbitrary (usually regular) high-resolution pixel geometry. The method applies a combination of area-to-point kriging and regression and essentially combines a high-resolution but often biased proxy dataset with the coarse-resolution but assumed to be unbiased satellite observations, and as such adds value to both datasets. The method can be used directly on Level-2 swath data without the need for prior gridding. It is further capable of providing pixel-level uncertainty estimates taking into account the original product uncertainty and the uncertainty introduced by the downscaling.

We demonstrate the methodology with data from Aura/OMI and Sentinel-5/TROPOMI. We focus here primarily on nitrogen dioxide (NO₂), but the algorithm is generic and can be used for other species as well. As spatial proxy datasets we use high-resolution output from the EPISODE and WRF-Chem models as temporally dynamic proxy data, as well as long-term average high-resolution NO₂ maps for Europe as time-invariant proxy information. We demonstrate the algorithm for selected regions in Europe with significant air quality issues (Po Valley, Ruhr area, Silesia, Warsaw, and others).

Initial results indicate that the method is capable of providing qualitatively realistic high-resolution maps (e.g. 100 m), for which the absolute values are provided by the satellite observations and the sub-grid spatial patterns are inherited from the fine-scale spatial proxy dataset. Re-aggregating the downscaled results to the original satellite pixel geometry reproduces the original values. Quantitatively, a comparison against stations observations of NO₂ shows that the correlation for the downscaled maps increases significantly compared to the correlation between the original satellite dataset and the station data. Overall, the method is a first step towards exploiting the Sentinel-5P/TROPOMI data for applications at the urban scale.

Due to its large spatial coverage, satellite remote sensing provides opportunities to monitor the large-scale variabilities of ground-level air quality. China is suffering from serious air pollution dominated with aerosol particles in autumn and winter, and Ozone in summer that adversely affects human health. China has built near 1500 ambient monitoring stations over 367 cities to supervise air quality improvement in China. Hourly average concentrations of air pollutants including PM2.5, NO2, SO2, and O3 from these stations are available in the National air quality publishing platform. This gives us an unprecedented opportunity to improve the space-borne estimation of air quality over China. In this presentation, aerosol optical depth (AOD) data from Moderate Resolution Imaging Spectroradiometer (MODIS) and Geostationary Ocean Color Imager (GOCI) are used to estimate ground-level PM2.5 concentration. Tropospheric NO2 and O3 columns from Ozone Monitoring Instrument (OMI) are used to estimate ground-level NO2 and O3 concentrations. In addition, meteorological variables including planetary boundary layer height, relative humidity, wind speed, air pressure, and temperature, Normalized Difference Vegetation Index (NDVI), Digital Elevation Model (DEM) and Multi-resolution Emission Inventory for China are included. Geographically and temporally weighted regression (GTWR) model and deep learning method are constructed to build the relationship between satellite products and ground measurements. Much improved estimations are achieved with large coefficient of determination (R2) and small root mean square error (RMSE). The results have profounding implication for improving our understanding of human exposure to air pollutant.

Using the inversion algorithm DECSO, developed within the GlobEmission project, we derived monthly NOx emissions on a 0.25 x 0.25 degree resolution over East Asia for an 11-year period (2007 to 2017) based on OMI observations. We used these emissions to analyse trends and seasonal cycle of maritime emissions over Chinese seas. No effective regulations on NOx emissions have been implemented for ships in China, which is reflected in the trend analysis of maritime emissions. The effect of maritime emissions on the air quality over land will be discussed. Simulations by an atmospheric chemistry transport model show a notable influence of maritime emissions on air pollution over coastal areas, especially in summer. The satellite-derived spatial distribution and the magnitude of maritime emissions over Chinese seas are in good agreement with bottom-up studies based on the Automatic Identification System of ships. We will further show how the new high resolution observations of TROPOMI on Sentinel 5p are expected to enhance the accuracy of maritime emissions in the future.

Forest fires in Indonesia are a seasonal occurrence, largely due to the agricultural practice of slash and burn in which land is cleared for new planting by cutting back vegetation and setting it on fire. During late 2015, the agricultural fires were particularly severe, due to it being an El-Niño year, impacting regional air quality. 

The land in Indonesia contains a lot of peat, which easily burns to emit a variety of trace gases including hydrogen cyanide (HCN).  Satellite limb instruments such as the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) instrument and Microwave Limb Sounder (MLS) have revealing an unprecedented amount of HCN emitted from Southeast Asia during September–November 2015 which had been transported into the upper troposphere and lower stratosphere.

Here we present nadir observations of HCN total columns derived from the Infrared Atmospheric Sounding Interferometer (IASI) during September–November 2015.  IASI observations of carbon monoxide (CO) using the University of Leicester IASI Retrieval Scheme (ULIRS) are used to calculate enhancement ratios of HCN relative to CO and ultimately derive new emission factors for Indonesian peatland which will be used to improve chemical transport models. These satellite-derived data are compared to those already in emission databases such as GFED.

The growth of human population and the industrialization in the 19th and 20th century has led to dramatic changes in the Earth atmosphere. Especially the chemical composition of the atmosphere is rapidly changing as a result of human activities. We entered the “anthropogenic” epoch, where the activities of humans play a key role in the further development of the ozone layer, air quality and climate change.

The rapid development of megacities and the strong development in the Asian countries are clear examples of the large changes that effected the atmosphere in the last decades and will continue to do so in future. In the coming decades air pollution in megacities will continue to be a major area of concern and the need for timely, high resolution information on emissions will increase. With the Paris climate agreement, the mitigation paragraph of climate change was put on the agenda, and ways to assess globally the effectiveness of emission control policy measures become more and more important, for as well land as ship emissions.

Measurements from space are therefore urgently needed to help society to deal with the challenges we have to overcome in the air quality and climate domain. The current instrumentation will make it possible to really make this step towards society, not only on the global and regional domain, but even on the local domain. In order to understand sentinel 5p/TROPOMI’s measurements and trends, we will need to lay contact with local scientists and scientific and governmental organizations residing in the area’s measured, since a detailed understanding of the emission sources and changes,  local atmospheric circumstances  etc. will often be needed for the correct interpretation of the data. TROPOMI will therefore be a game changer for our field, it will enable the step towards use of satellite data for the challenges society has in order to control its emissions.

In this presentation the possibilities satellite data can provide society to help overcoming its challenges in the next decades in the air quality and climate domain will be presented, using clear examples of the OMI instrument and the first amazing results of the sentinel 5 precursor/TROPOMI instrument. An outlook will be presented on what new satellite instrumentation going towards a 1 x 1 km2 spatial resolution could bring.

CAMS is one of the six thematic services of Copernicus (http://atmosphere.copernicus.eu). It delivers operationally consistent and quality-controlled air quality forecasts at the global and regional (Europe) scales, as well detailed information related to air pollution, solar energy, greenhouse gases, surface emissions and climate forcing. CAMS is a truly European effort: it is implemented by ECMWF on behalf of the European Union and involves over 130 entities from 28 countries through about 50 contracts.

The CAMS global forecasting system is using ECMWF's Integrated Forecasting System (IFS), which is used for Numerical Weather Prediction and has been extended with modules for atmospheric chemistry, aerosols and greenhouse gases. The CAMS system assimilates observations from more than 70 satellite sensors to constrain both the meteorology and the atmospheric composition species. The system is continuously developed to provide the best possible products to the users: current efforts on model developments an on improving the data assimilation system for satellite observations of atmospheric composition will be discussed.

The presentation will especially focus on two topics illustrating the current state-of-play with CAMS. The CAMS reanalysis covering the period 2003-2016 has been released in last September; we’ll illustrate some aspects of the production and of the validation of this dataset, which is expected to be used by several thousand users worldwide. Second, we’ll provide some information about the uptake in CAMS of Sentinel-5P’s key data products (ozone, NO2, SO2, CO, CH4, HCHO) discussing in particular the synergies that have been at play with the developers of the different retrievals and facilitated speedy development, release and uptake of the operational products.

Finally, the success of CAMS is through the uptake of the products and the success of its users. We’ll describe a few success stories, such as the selection of CAMS air quality outputs by The Weather Chanel for its web & mobile platforms as well as for the default weather application of Apple iOS 12, which was released at the end of September. This, together with European Air Quality bulletins broadcast several times daily on Euronews, allows CAMS products reaching an audience of several tens of millions.