Fire risks are quite high these days in Southern California. And some fires have even already been observed by the Sentinel-3 (S3) B satellite, from the European Copernicus programme. Here below are some Red-Green-Blue (RGB) pictures from the Level 1 (L1) measurements of the optical S3B sensors, disseminated by EUMETSAT: the Ocean and Land Colour Instrument (OLCI) and the oblique view of the Sea & Land Surface Temperature Radiometer (SLSTR). These images show smoke aerosol particles spreading from North of Los Angeles over Pacific.
RGB image composite of the OLCI Sentinel-3 B L1 product: 2019.10.11, California, North of Los Angeles.
RGB image composite of the oblique view from SLSTR Sentinel-3 B L1 product: 2019.10.11, California, North of Los Angeles.
As illustrated by @weatherchannel, these areas are located in “Critical” fire danger zones.
On 2019.02.05, a remarkable dust outbreak issued from the Western Sahara coast spread over Gran Canaria islands. This thick plume, with heavy load of particles, and larger than 1.000 km width, was well observed via a series of satellite images:
Aerosol Index UVAI retried from TROPOMI Sentinel-5 Precursor over the Saharan dust outbreak of 2019.02.05. Credit SentinelHub.. Source: https://apps.sentinel-hub.com/eo-browser
Several days later, on 2019.03.02, another Saharan dust was transported over Cabo Verde. Similarly, a very large and thick plume was captured in the images from the NASA SUOMI VIIRS sensor, and measured by the aerosol index UVAI from Tropomi.
RGB composite image from SUOMI VIIRS of the Saharan dust transport of 2019.03.02. Credit: NASA EODIS Worldview Source: https://worldview.earthdata.nasa.gov
Aerosol Index UVAI retried from TROPOMI Sentinel-5 Precursor over the Saharan dust transport of 2019.03.02. Credit SentinelHub.. Source: https://apps.sentinel-hub.com/eo-browser
Biomass burning is a major source of trace gases & aerosol particles on a regional and a global scale (Seiler and Crutzen, 1980; Logan et al., 1981; Crutzen and Andreae, 1990; Andreae, 1991). Interannual variations in biomass burning within specific regions of the world can be dramatic, depending on factors such as rainfall and political incentives to clear land. The forest fires in Indonesia during 1997–1998 and those in Mexico during 1998, both related to the El Nino Southern Oscillation (ENSO) induced drought, are well known examples of extreme fire events (e.g. Levine, 1999; Nakajima et al., 1999; Peppler et al., 2000; Cheng and Lin, 2001).
The principal biomass burning areas can be observed in the Amazonian region and in central Africa. Among the trace gases released, NO2 – nitrogen dioxide & CO – carbon monoxide abundances can be very high. Satellite observations are a helpful tool for the identification of these sources in the troposphere and to follow their transport. In addition, these intensive biomass burning episodes release a large quantity of aerosol particles, at fine size and with absorbing properties.
Below are the animations of NO2 and CO columns as observed by the TROPOMI sensor, on-board the Sentinel-5 Precursor mission from the European Copernicus program. These animations cover ~1 month of biomass burning over Central Africa. They are extracted from the SentinelHub Earth Observation (EO) browser.
Animation Tropospheric NO2 vertical column density from TROPOMI Sentinel-5 Precursor from 2019.01.01 to 2019.02.07. Credit SentinelHub. Source: https://apps.sentinel-hub.com/eo-browser/
Additionally, you can visualise here animations based on the NASA SUOMI VIIRS observations showing the fire detected pixels (in red) and the detection of fine absorbing particles in large concentrations. Note that SUOMI and Sentinel-5 P are flying together on the same orbit / same track with only a few minutes apart.
Animation RGB image composite from SUOMI VIIRS from 2019.01.01 to 2019.02.07. Credit NASA EODIS WorldView. Source: https://worldview.earthdata.nasa.gov/?p=geographic&l=VIIRS_SNPP_CorrectedReflectance_TrueColor,MODIS_Aqua_CorrectedReflectance_TrueColor(hidden),MODIS_Terra_CorrectedReflectance_TrueColor(hidden),VIIRS_SNPP_Thermal_Anomalies_375m_Day,OMPS_Aerosol_Index(hidden),AIRS_CO_Total_Column_Day(hidden),OMI_Nitrogen_Dioxide_Tropo_Column(hidden),MODIS_Terra_Thermal_Anomalies_All(hidden),MODIS_Terra_Aerosol_Optical_Depth_3km(hidden),Reference_Labels(hidden),Reference_Features(hidden),Coastlines&t=2019-01-25-T00%3A00%3A00Z&z=3&v=-80.65378411193339,-57.40841307238665,84.29515205827931,42.15408692761331&ab=on&as=2019-01-01T00%3A00%3A00Z&ae=2019-02-07T00%3A00%3A00Z&av=3&al=false
Animation UV Absorbing Aerosol Index (UVAI – Red = absorbing aerosols detected in large amounts) from SUOMI VIIRS from 2019.01.01 to 2019.02.07. Credit NASA EODIS WorldView. Source: https://worldview.earthdata.nasa.gov/?p=geographic&l=VIIRS_SNPP_CorrectedReflectance_TrueColor,MODIS_Aqua_CorrectedReflectance_TrueColor(hidden),MODIS_Terra_CorrectedReflectance_TrueColor(hidden),VIIRS_SNPP_Thermal_Anomalies_375m_Day,OMPS_Aerosol_Index(hidden),AIRS_CO_Total_Column_Day(hidden),OMI_Nitrogen_Dioxide_Tropo_Column(hidden),MODIS_Terra_Thermal_Anomalies_All(hidden),MODIS_Terra_Aerosol_Optical_Depth_3km(hidden),Reference_Labels(hidden),Reference_Features(hidden),Coastlines&t=2019-01-25-T00%3A00%3A00Z&z=3&v=-80.65378411193339,-57.40841307238665,84.29515205827931,42.15408692761331&ab=on&as=2019-01-01T00%3A00%3A00Z&ae=2019-02-07T00%3A00%3A00Z&av=3&al=false
More information?
TROPOMI, on-board the Copernicus Sentinel-5 Precursor satellite, here
In winter time, cold temperature leads to an increase in using heaters of course. And when the electricity source is notably based on coal power plants, then gas emissions (and particles) increase as well leading to higher pollutant concentration.
Air pollution in China is well known. Satellite observations as evidence show a strong increasing trend of NO2 column concentrations since 1995 in China (Irie et al., 2005; Richter et al., 2005; van der A et al., 2006). The main anthropogenic emissions of NOx in China are from transport and coal-fired power plants (Liu et al., 2015; Saikawa et al., 2017; Li et al., 2017). Because of the rapid implementation of new technologies and air quality control regulations for power plants and vehicles in China, their emission factors and activities are also changing with time. In spite of major reductions the last 7 years thanks to very supportive governmental decisions, it still remains an issue.
Pictures below, from NASA MODIS Aqua let us imagine how suffocating this air may remain these days. TROPOMISentinel-5 Precursor observations indeed show high concentrations of not only NO2 – nitrogen dioxide, and CO – carbon monoxide. Efforts in reducing emissions due to the electricity generation and vehicles must continue to ensure a better health for the whole population.
2019.01.17 NASA MODIS Aqua false RGB composite image. Credit NASA EOS WorldView. Source: https://worldview.earthdata.nasa.gov/?p=geographic&l=VIIRS_SNPP_CorrectedReflectance_TrueColor(hidden),MODIS_Aqua_CorrectedReflectance_TrueColor,MODIS_Terra_CorrectedReflectance_TrueColor(hidden),MODIS_Aqua_Angstrom_Exponent_Ocean(hidden),MODIS_Aqua_AOD_Deep_Blue_Land(hidden),MODIS_Aqua_Aerosol_Optical_Depth_3km(hidden),MODIS_Aqua_AOD_Deep_Blue_Combined(hidden),AIRS_CO_Total_Column_Day(hidden),OMI_Nitrogen_Dioxide_Tropo_Column(hidden),MODIS_Terra_Thermal_Anomalies_All,MODIS_Terra_Aerosol_Optical_Depth_3km(hidden),Reference_Labels(hidden),Reference_Features(hidden),Coastlines&t=2019-01-17-T00%3A00%3A00Z&z=3&v=97.78198716466228,19.823136129740995,139.01922120721545,44.71376112974098
Our last research paper focused on the challenging topics of aerosol layer height (ALH) retrieval from hyperspectral visible measurements, aerosol correction, and tropospheric NO2 retrieval from satellite sensors like OMI was published in the Atmospheric Measurement Techniques (AMT) journal. This work relies on the activities achieved during the last months of my thesis research with my colleagues of the Geoscience and Remote Sensing (GRS) department of TU Delft and KNMI: Dr. J. Pepijn Veefkind, Dr. Johan de Haan, Dr. Piet Stammess, and Prof. Dr. Pieternel F. Levelt.
This paper is based on the last developments we published during 2016, 2017, and 2018. During these years, not only the OMI cloud algorithm was improved (Veefkind et al., 2016), but also an OMIaerosol layer height (and optical thickness) neural network algorithm was developed (Chimot et al., 2017, 2018). This time, we directly evaluate the impacts of these developments to correct of aerosol absorption and scattering effects in the visible spectral range in view of retrieving troposphericNO2, an important trace gas affecting air quality in urban and industrialised areas.
What are the main conclusions? Aerosol correction on troposphericNO2 retrieval from OMI has been greatly improving the last 2-3 years for UV-Vis air quality satellites. Notably thanks to the updated effective cloud retrievals. But also there is a clear potential from the ALH based on the 477 nm O2-O2 band. However, the decision for the future processors is not necessarily easy to take: accuracy vs. radiance closure budget are clearly competing.
Average maps of MODIS AOD(550nm), OMI DOMINO NNvO2 and differences after applying the implicit (with OMCLDO2-New) or explicit (with NNMODIS,SSA=0.95) aerosol correction over China in summertime (June–July–August) 2006–2007. (a) MODIS AOD(550nm), (b) OMI DOMINO NNvO2, (c) OMI NNvO2 differences due to changes between OMCLDO2-New and DOMINO implicit aerosol corrections, (d) NNvO2 differences between explicit aerosol correction based on the NNMODIS,SSA=0.95 aerosol parameters (i.e. aerosol forward model assuming SSA = 0.95, MODIS AOD(550 nm) and retrieved ALH) and implicit aerosol correction implemented in DOMINO.
MODIS Terra RGB False colour composite, India 2019.01.18. Source NASA EODIS WorldView https://worldview.earthdata.nasa.gov/?p=geographic&l=VIIRS_SNPP_CorrectedReflectance_TrueColor(hidden),MODIS_Aqua_CorrectedReflectance_TrueColor(hidden),MODIS_Terra_CorrectedReflectance_TrueColor,AIRS_CO_Total_Column_Day(hidden),OMI_Nitrogen_Dioxide_Tropo_Column(hidden),MODIS_Fires_Terra,MODIS_Terra_Aerosol_Optical_Depth_3km(hidden),Reference_Labels(hidden),Reference_Features(hidden),Coastlines&t=2016-01-16&z=3&v=-144.91024116847828,-98.2219769021739,179.4795346467391,100.9030230978261
MODIS Terra Aerosol Optical Depth (AOD) (550 nm). Source: NASA EODIS WorldView https://worldview.earthdata.nasa.gov/?p=geographic&l=VIIRS_SNPP_CorrectedReflectance_TrueColor(hidden),MODIS_Aqua_CorrectedReflectance_TrueColor(hidden),MODIS_Terra_CorrectedReflectance_TrueColor,AIRS_CO_Total_Column_Day(hidden),OMI_Nitrogen_Dioxide_Tropo_Column(hidden),MODIS_Fires_Terra,MODIS_Terra_Aerosol_Optical_Depth_3km(hidden),Reference_Labels(hidden),Reference_Features(hidden),Coastlines&t=2016-01-16&z=3&v=-144.91024116847828,-98.2219769021739,179.4795346467391,100.9030230978261
Tropospheric NO2 vertical column density from Sentinel-5 Precursor TROPOMI, India 2019.01.18. Source: Copernicus SentinelHub https://sentinel-hub.com
But a lot of times air pollution is not directly “visible” with our eyes. A special zoom of 2019.01.15 with TROPOMI on two cities that I know well in South West France, Bordeaux and Toulouse. Very clear NO2pollution can be detected thanks to the fine pixels. Such isolated spots clearly suggest local emissions (mostly cars). Note also the hot-spots in the North of Spain (e.g. Bilbao, SanSebastian , Barcelona)
This study evaluated the possibility to improve OMItroposphericNO2 retrieval over China by creating and exploiting an aerosol vertical profile climatology database from 9 years of CALIOP observations. Among other elements, it shows the potential benefits to use satellite observations in a synergistic way (OMI–MODIS-CALIOP) and how to constrain better aerosol models in view of correcting aerosol scattering and absorption effects in UV-vis satellite measurements.
This notably leads to an update of the POMINO dataset from Lin et al. (2014, 2015).
Research Thesis book cover – Julien Chimot – July 2018
Almost 3 months ago, on 2018.09.10, I had the privilege to defend my research thesis at Delft University of Technology. A big moment after 4 years of intensive collaboration with my colleagues of the Geoscience & Remote Sensing Department and KNMI and in the presence of several friends and relatives. An very strict protocol to follow according to the Dutch rules and tradition.
My thesis book is now available online as an Ebook here! Feel free to have a look if you are interested by aerosol layer height retrieval, UV-Vis satellite measurements such as OMI, troposphericNO2, air quality and climate observations. My main papers are concatenated there.
Analyses of the trends is possible but overall a challenging and sensitive task. Over 20 years, very different sensor techniques, instrument specificities & degradations, variable pixel sizes, cloud detection possibilities etc… A lot of works to harmonise these data!
Atmosphericaerosol are particles suspended in the air. Their sources are very mixed. Aerosol can be man-made or natural: e.g. smoke, desert dust, sea spray, nitrates and sulfates. The aerosol effects on the sunlight modify the shortwave radiation field in the atmosphere. This directly impacts the climate and the satellite observations devoted to ocean surface, land surface, vegetation, and atmospheric gases. Furthermore, heavy load of aerosols affects our air quality.
In spite of many progresses during the last 10-20 years, aerosol observations from space-borne instruments remain incredibly complex. One of the main reasons is their heterogeneity: aerosols are everywhere, but with very variable quantities spatially (horizontally and vertically!), and temporally. And, as highlighted by this NASA picture, aerosol types are also very heterogeneous! Retrieving all these parameters from single satellite measurements, without ambiguity with respect to surface characteristics and clouds, is the difficult task of the scientists working with atmospheric satellite measurements. Many works to continue to do…
More information
NASA WebPage “Just another Day on Aerosol Earth” here
Homepage Julien Chimot: a journey in Earth observation satellite science 