Atmospheric Pollution variation
Pollution refers to the introduction or presence of a substance which has harmful effects on the environment. Pollution can be both naturally (e.g. as a result of a volcanic eruption) or artificially produced (e.g. as a consequence of human industrial activity). Among the many substances that are considered as pollutants, the ones that have the largest effect on the health of the population and nature are Nitrous Oxide (NO2), Ozone (O3), Nitric Acid (HNO3), Sulfur Dioxide (SO2), and aerosols or particulate matters (PM). These pollutants are the ones are most present in the atmosphere and its presence causes severe health problems in the population worldwide.
Satellites have greater coverage than fixed pole stations and, because of this, they are more suited to regional measurements of the atmospheric composition. While fixed stations measure the air quality with greater accuracy at lower altitudes, satellites are able of measuring larger areas at different altitudes and thus, generate an atmospheric map of air quality and pollution.
Satellite observation data can be used to measure the presence of pollution in the different layers of the atmosphere throughout the world and how the composition of the atmosphere varies during the year with the different seasons. Comparing the data obtained pre and post pandemic will give us a strong indicator of how the human activity (industry) and land-based movement (traffic) has been affected by the COVID-19. In addition, the data will show the effect of the policies the different countries have had in response to the pandemic.
There are several projects currently working on measuring the Air Quality and composition of the atmosphere.
During the past months, most countries have had to implement severe restrictions in human activity and movement, with some countries declaring a total lockdown. As a result of this, most non-essential businesses have stopped operating (while some still continue to be inoperative) and movement of people has been significantly reduced. The reduction of human activity caused by the COVID-19 has had a positive effect in the Earth’s atmospheric composition and, Earth observation satellites have detected a decrease in the pollution emissions worldwide. This reduction has been most significant in large cities, highly industrialised, that have been most severely affected by the pandemic such as China, Italy or Spain.
Night-time light brightness
Nighttime light (NTL) observation data provides a measure of artificial night lighting brightness on the earth’s surface, as seen from space. Although observation of NTL can be achieved by conventional panchromatic imaging, more specialised sensors augmented by post-processing algorithms are able to produce datasets with wider usefulness. For example, imagers with high-sensitivity sensors and high spatial resolution, such as the Chinese panchromatic JL1-3B satellite and cubesat LJ1-01, with spatial resolutions of 0.92m and 130m respectively. Data from LJ1-01 is available freely, however data from JL1-3B is only available on a commercial basis (Zhao et al).
The current state of the art in NTL data is provided by the NASA Black Marble product, containing imagery obtained by the visible-infrared VIIRS instrument onboard the Suomi National Polar Partnership satellite, coupled with data from Landsat-8, Sentinel-2 and other earth observing satellites. VIIRS has a minimum spatial resolution of 375m and a very-sensitive low-light Day/Night frequency band. Uncorrected nightly images are available, as well as monthly and annual cloud-free and moonlight-corrected composites (NASA Black Marble).
Regarding the use of NTL data in observing the effect of the COVID-19 pandemic on human movement, we expect that a comparison of previous data to data obtained during the pandemic will reveal that established seasonal patterns in NTL change are broken, with reduced NTL brightness along roads and at popular tourist destinations and increased brightness in rural areas as people move away from large cities.
NTL can be a reliable proxy for socioeconomic activities such as population density and human movements, including road traffic. NTL data has previously been used to study seasonal epidemics such as measles through the estimation of population density fluctuations (Bharti et al).
Unfortunately, it was not possible to validate our assumptions regarding observable changes using the nightly imaging available at EOSDIS Worldview, mainly due to cloud coverage and inconsistent image exposure. Attempts to recreate published images of the Wuhan lockdown effects on road traffic were also unclear. Additionally, the in-flight calibration of the sensor and the continuously improving image quality make it hard to objectively compare data from different years.
Visual variation in human related infrastructure and movement
This parameter is chosen as it is the most distinct change that can be observed using earth observation satellites. However, care must be taken when drawing conclusions as a visual change may not only be associated with a change due to the pandemic but as a result of many factors like seasonal changes. Hence data for the other variable factors will need to be collected and used when drawing a meaningful conclusion.
What will be measured?
We will be able to track vehicle influxes into a city centre, how often there are traffic jams, how the city traffic changes throughout the year and distinguish between pattern changes due seasons and hence use machine learning to differentiate between changes due to a pandemic and those that occur annually.
This can be extended to the shipping industry especially in the vicinity of a port, and aircraft on the ground can also be monitored using the same techniques.
Using the above satellites, the concentration of people in a city can also be measured using a chorographical method as the resolution of the satellite allows this to be achieved as its resolution is 0.31 m.
Advantages
Limitations to this method
Equipment required
There are 2 satellites currently in orbit that can be used for this mission.
Variation in commercial aviation and passenger maritime industries
The work carried out using this parameter includes contrasting air traffic and shipping traffic pre-pandemic data (late 2019-early 2020) with current data in order to show how COVID19 has affected these two industries. This analysis also includes explanation of key events represented in these sets of data which are related to the different actions taken by governments during the evolution of the pandemic. The data used to carry out this analysis has been obtained by software (FlightRadar24 and MyShipTracking) that uses Satellite Navigation Systems to provide active tracking of aircraft and ships in these industries.
An example of a satellite constellation that provides aircraft tracking is the Iridium constellation which has been fitted with ADS-B transponders. These transponders are the ones that allow the FlightRadar24 software to actively track all aircraft flying around the world with significant accuracy. This system also allows aircraft to be tracked in remote places of the world where there are no local radar stations near the designated flight path (patches of the Indian Ocean and the Pacific Ocean).
The shipping industry also has its specific Navigation satellites that allow live tracking of large vessels and their specific cargo (oil, cargo, LNG or passengers). An example of a satellite that aids ship navigation in the European region is EGNOS. These satellites allow software such as MyShipTracking or MarineTraffic to actively track ships all around the world.
This parameter has been chosen because the most common policies adopted by governments to contain the spread of the disease directly affect it. Most countries have imposed nationwide lockdowns and closure of borders, these actions have virtually put the commercial aviation industry and the passenger maritime industry in temporary hibernation due to the large risk of infection they possess as a consequence of their global nature.
Covid-19 has had a dramatic impact on the daily lives of people across the globe.
We wanted to take advantage of our knowledge in the field of aerospace and earth observation to contribute towards the global response to the pandemic.
Having strongly felt the effects of the lockdown in our home regions through the movement restrictions, we were interested in investigating how human movement can be indirectly observed using non-traditional datasets and how to make full use of the potential that satellite technology can afford in observing the evolution of the pandemic and its effect on different regions.
Once the challenge was chosen, we broke down our devised solution to the following steps:
To justify the significance of the chosen parameters (step 3), data from the relevant missions and observation programs was used. To determine pollution levels (NO2, in particular), the Tropomi data and images from Sentinel-5P (Copernicus) were used, combined with models of atmospheric chemistry. Atmospheric Ozone presence was studied from the data from the Aura satellite.
For the nighttime light brightness, nightly uncorrected imaging data obtained from the VIIRS instrument onboard the Suomi-NPP satellite was used, as part of the NASA Black Marble product suite, as presented in EOSDIS Worldview.
For the active monitoring of movement in the commercial aviation and maritime industries, two tracking software applications were used: Flightradar24 (making use of publicly available ADSB data), Marine traffic and Myshiptracking databases (GNSS data).
For the observation of human infrastructure and movement data from raw satellite photos were used. This data was used obtained from the ‘Planet Explorer’ website. This data was of high resolution. However, higher resolution is available for future use.
Among the different problems and achievements faced during the weekend, we wanted to highlight the following:
-Finding non-traditional data to expose changes in human movement patterns.
-Justifying the significance of nighttime light was difficult. A direct correlation between brightness levels and human movements was unclear in the available imaging data.
-A steep learning curve to overcome and an overload of information to choose from and pinpoint a solution strategy.
-We are satisfied with the structure that we chose in developing our solution.
-Working remotely from different areas of the world under different lockdown restrictions was a challenge but one that we overcame to submit a solution.
Here is a link to our slideshow. (must be downloaded for gifs to animate)
https://earthobservatory.nasa.gov/images/146362/airborne-nitrogen-dioxide-plummets-over-china
https://earthdata.nasa.gov/learn/articles/feature-articles/nighttime-images-wuhan
https://www.eurocontrol.int/covid19#data-insights
https://www.nasa.gov/mission_pages/aura/main/index.html
https://viirsland.gsfc.nasa.gov/Products/NASA/BlackMarble.html
Explaining Seasonal Fluctuations of Measles in Niger Using Nighttime Lights Imagery
https://earthdata.nasa.gov/learn/pathfinders
https://www.flightradar24.com/data/statistics
https://www.eurocontrol.int/covid19#data-insights
https://worldview.earthdata.nasa.gov/
http://www.esa.int/SPECIALS/Eduspace_EN/SEM1NP3Z2OF_0.html
https://www.planet.com/explorer/#/zoom/2.58
https://www.satimagingcorp.com/satellite-sensors/worldview-3/
https://www.satimagingcorp.com/satellite-sensors/superview-1/
https://www.myshiptracking.com/
https://www.marinetraffic.com/
https://www.gsa.europa.eu/segment/maritime
http://.%20https:/aireon.com/resources/overview-materials/its-just-ads-b/