The Pan-Eurasian Experiment (PEEX) program, initiated in 2012, aims to resolve science, technology and sustainability questions in the Northern Eurasian region (Lappalainen et al., 2016, 2018). Ground-based observations, satellite remote sensing and modeling of atmospheric, land and ocean data are used to obtain information on the state of the Eurasian environment. The PEEX study area is very large and sparsely populated, with few comprehensive ground-based observations. Satellite data can be used to obtain information filling the gaps between ground-based observations. Although the satellite-retrieved information is less detailed than that from a multitude of ground-based instruments, space-based sensors provide data from the same instrument and with the same method over the whole region with a return time depending on swath width. In this contribution, we focus on the use of satellite data to obtain information on atmospheric composition, such as aerosols (AOD, AAI), NO2, CO2, as well as SIF and forest fires, obtained from different instruments. In particular we focus on the warm summer of 2018 to identify whether this led to anomalous behaviour of the concentrations over Eurasia and their spatial distribution.

References:
 Lappalainen, H.K. et al.: Pan-Eurasian Experiment (PEEX): towards a holistic understanding of the feedbacks and interactions in the land–atmosphere–ocean–society continuum in the northern Eurasian region, Atmos. Chem. Phys., 16, 14421-14461, doi:10.5194/acp-16-14421-2016, 2016.
 Lappalainen, H.K., et al. (2018). The Silk Road agenda of the Pan-Eurasian Experiment (PEEX) program, Big Earth Data, DOI: 10.1080/20964471.2018.1437704

The Ozone Monitoring Instrument (OMI), launched on board of NASA’s EOS-Aura spacecraft on July 15, 2004, provides unique contributions to air quality monitoring from Space. The combination of urban scale resolution (13 x 24 km2 in nadir) and daily global coverage proved to be key features for the air quality community. The OMI data is currently used operationally for improving the air quality forecasts, for inverting high-resolution emission maps, UV forecast and volcanic plume warning systems for aviation. Due to its almost 14 year continuous operation OMI provides the longest NO2 and SO2 record from space, which is essential to understand the changes to emissions globally.

In 2017 Tropospheric Monitoring Instrument (TROPOMI), was launched on board ESA’s Sentinel 5 Precursor satellite in October 2017. TROPOMI has a spatial resolution of 3,5x7 km2 in nadir; a more than 12 times improvement over OMI. The high spatial resolution serves two goals: (1) emissions sources can be detected with even better accuracy and (2) the number of cloud-free ground pixels will increase substantially. TROPOMI will continue OMI’s ozone and air quality trace gas records. Added to that TROPOMI will measure the O2 A band for better cloud detection, as well as CO and the second most important greenhouse gas CH4. TROPOMI will therefore be an important satellite mission for the EU Copernicus atmosphere service and will be followed by ESA’s sentinel 4 and 5 satellites. The first measurements of TROPOMI turn out to be above expectation.

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, preferably to a level where sources can be isolated on the < 1 x 1 km2 scale. Currently we are working on new follow-on satellite instrumentation with which we envisage to improve emission monitoring to the < 1 x 1 km2 spatial resolution level (TROPOLITE).

An overview of air quality applications, emission inventories, and trend analyses will be given, based on the excellent OMI data record, followed by first measurements and results of the TROPOMI instrument. An outlook will be presented on the potentials of the TROPOMI and what new satellite instrumentation with a 1 x 1 km2 spatial resolution can bring in the air quality and climate domain.

Tropospheric ozone is the most hazardous gaseous pollutant. Monitoring and understanding the spatiotemporal evolution of ozone pollution is therefore a crucial societal issue. Observation of tropospheric ozone at continental and global scales is only possible by spaceborne remote sensing. However, standard spaceborne observations using single-band approaches using either UV or IR measurements show limited sensitivity to ozone in the atmospheric boundary layer, which is the major concern for air quality.
A new capacity to observe the daily distribution of ozone located at the lowermost troposphere (below 3 km of altitude) is now offered by an innovative multispectral synergism of IASI and GOME-2 measurements at the IR and UV respectively (Cuesta et al., 2013; 2018). This novel method called IASI+GOME2 retrieves ozone at the lowermost troposphere with a low mean bias, a linear correlation of 0.86 and a mean precision of 16% as compared to reference ozonesonde measurements around the world during all seasons. The retrieval sensitivity peaks down to 2 to 2.5 km over land during summer. This multispectral product is available at the IASI spatial resolution (pixels spaced by 25x25 km2) and for cloud fractions below 30%. IASI+GOME2 retrievals also show a good and currently unique agreement with respect to in situ measurements of ozone at the surface, over East Asia and Europe, for both ozone outbreak events and the seasonal evolution. IASI+GOME2 data is publicly available at the French data centre AERIS/ESPRI (http://cds-espri.ipsl.fr).
The current presentation focuses on the analysis of global observations of lowermost tropospheric ozone derived from IASI+GOME2. We study the annual evolution of the main global hotspots of ozone at the lowermost troposphere at the tropics and mi-latitudes. We provide new observational evidence on the origin of these lowermost ozone major hotpots, in link with meteorological and dynamical conditions as well as main emissions of its precursors. Comparisons between the new IASI+GOME2 observations and global chemistry transport models are done for assessing their consistency.

The presentation introduces the first TROPOMI-based “top-down” sulfur dioxide (SO2) emissions estimates for point sources. A total of about 500 continuously emitting point sources releasing from about 30 kT y-1 to more than 4000 kT y-1 of SO2 per year that have been previously identified using Ozone Monitoring Instrument (OMI) data were analyzed using TROPOMI SO2 measurements. The 2018 annual emissions from these sources were estimated and compared to similar estimates from OMI and low-resolution SNPP/ Ozone Mapping Profiling Suite (OMPS) measurements.

Due to a very high spatial resolution, TROPOMI produces the same number of mostly cloud-free observations over a certain area over one month as OMI produces over one year. The uncertainties of TROPOMI SO2 measurements in the tropical region are about 0.5-1.0 DU, i.e., comparable to those from OMI. As a result, monthly emissions for many tropical sites can be estimated with uncertainties similar to the uncertainties of annual emissions estimates from OMI. This is not, however, the case for middle and high latitudes where TROPOMI uncertainties are higher. There are also systematic biases between TROPOMI and OMI over some regions, e.g., over the Persian Gulf.

High spatial resolution of TROPOMI also makes it possible to identify smaller sources and estimate individual emissions for cases when several sources are located just 20 km apart. In case of OMI, such separation is possible only for sources located 50-70 km apart. Also, SO2 plumes from TROPOMI data appear narrower (with the width under 15 km) that from OMI data (more than 20 km).

Several case studies will be also presented. As one example, monthly emissions are estimated for twelve power plants located near Johannesburg, South Africa, within a ~40 km radius. The emissions show a high correlation (0.82) with the power plant capacities as illustrated by the Figure where a scatter plot of TROPOMI-based estimates of SO2 emissions for March 2018 vs. power plant capacity for a cluster of twelve power plants is shown. Note that monthly emission estimates for individual sources located in such close proximity are not possible using OMI data.

Satellite imagery is commonly used to detect and quantify emissions of sulphur dioxide (SO₂) from volcanic eruptions. Emissions into the troposphere, such as those from smaller explosive eruptions or non-eruptive degassing, are often measured with ultraviolet sensors. However, there are advantages to using infrared instruments, for example, they are able to make measurements at night and during high latitude winters. Previous applications of retrievals developed for the Infrared Atmospheric Sounding Interferometer (IASI) have demonstrated that these can be successfully applied to large eruptions, but there is little literature available on how these can be applied to smaller SO₂ emissions and no systematic studies of this instrument’s monitoring capability despite over a decade of measurements and now three instruments in orbit. This study applied a ‘fast’ linear retrieval developed for IASI, across the globe for a ten-year period. The results were dominated by emissions from large explosive eruptions, but elevated signals can also be identified from smaller eruptions and industrial centres, suggesting the technique has promise for detecting lower level emissions. A more systematic approach was then taken, rotating the linear retrieval output at a selection of volcanoes across the globe with the local wind field. Comparison of the upwind and downwind signal has been used to evaluate whether an elevated signal has been identified and subsequently, build up an inventory which shows when IASI is able to identify volcanic emissions. This helps to demonstrate the strengths and limitations of the instrument and can also begin to show changing activity at different volcanoes. The linear retrieval is a fast method for use in near real time, or for exploring large datasets to identify interesting activity which can be explored further. Regularly identified with this IASI retrieval, were elevated emissions in Ecuador and Kamchatka and so these were selected for further study with an iterative retrieval which is able to quantify the mass of SO₂. At Tungurahua, in Ecuador, these results were compared against the Ozone Monitoring Instrument (OMI) and ground-based flux measurements. While the magnitudes differed, all three exhibited the same trend, demonstrating that the IASI iterative retrieval is capable of capturing relative changes in activity at Tungurahua. The IASI and OMI trends also agreed well in Kamchatka, where OMI, and other UV sensors, are unable to operate for three to four month each year due to the lack of sunlight in winter months. Therefore, these results suggest that IASI could be used to complement other instruments for evaluating changes in volcanic SO₂ emissions.

Monitoring the effects of human activities on the atmospheric composition is becoming more and more important for protecting both the environment and human health. Satellite observations have been extensively used to monitor air polluting emissions from different type of point sources. Satellite observations are currently freely available and offer an opportunity to build financially sustainable services.

We present here the results of ILMApilot project (funded by the Key funding programme by the Academy of Finland “Forging ahead with research”), which aims at increasing the societal impact of satellite-based atmospheric observations for air pollution monitoring.

In particular, we show the application of space-based sulfur dioxide (SO2) observations to evaluate the efficacy of cleantech solutions in reducing air polluting emissions from metal smelting. We analyze the Ozone Monitoring Instrument (OMI) satellite-based SO2 observations over Tsumeb (Namibia) and Bor (Serbia) copper smelters, where two sulfur-capture plants, designed to transform gaseous SO2 emissions into sulfuric acid, were implemented in 2015. We observe a reduction in the annual SO2 emissions by up to 90% after 2015 at both smelters, as a result of the implementation of the sulfuric acid plants. The OMI-based emission estimates are mostly in line with those reported at facility-level and reproduce the same year-to-year variability. This variability is driven by the changes in the copper production, the sulfur-to-copper ratio and by the technology employed to reduce the SO2 emissions. OMI observations are directly used by the company operating the sulfuric acid plants to confirm the efficacy of the employed technology using independent satellite-based observations.

The results demonstrate how satellite-based observations are able to detect relative changes in SO2 emissions and can be used to verify and complete existing emission informations. The approach presented here can be applied to other sources on global scale to support cleantech companies as well as decision-makers involved in environmental policies and sustainable development. Future activities include the application of the new TROPOMI/Sentinel5P SO2 retrievals, which are expected to improve SO2 point source detection due to the improved spatial resolution and sensitivity.