Dirty Snow in Arctic and Alpine Regions

In an interesting research paper published on American Association for the Advancement of Science on July 2019, there were important indications on plastic pollution on our planet.

Scientists collected snow samples from Arctic regions of Svalbard and Fram Strait as well as Bremen City, Isle of Heligoland and the Alps (Davos, Tschuggen, Bavaria).

Data and images gathered during the study was processed and analyzed using Python and SimpleITK library showing serious pollution in Arctic and Alpine snow.

There is a risk of mismanaged plastic pollution to triple in amount by 2060 which is a very scary outcome.

Image credit: Melanie Bergmann

Study suggests large concentrations of microplastics being transported via atmospheric routes which suggests microplastics finding their way into soil, aquatic environments and eventually food chain and human and animal bodies.

Scientists also note that there has been unexpectedly little research about the inhalation risk of airborne MPs. 

Although our body has amazing protection mechanisms and mucus handles a great amount of pollutants, especially chronic inhalation of microplastics may cause them to stay in various areas of human body and cause chronic health issues such as allergies, chronic coughing, inflammation, fibrosis, genotoxicity, lung diseases and potentially increased risk of cancer.

Scientists underline the importance of further studies and caution regarding plastic pollution on our planet.

Alp d'Huez, France

Here are some bullet points from the published article:

  • The study provided the first data on contamination of snow by microplastics and microplastic concentrations in snow were very high, indicating significant contamination of the atmosphere. 
  • During its passage through the atmosphere, snow binds airborne particles and pollutants, which are eventually deposited on Earth’s surfaces, a phenomenon termed “scavenging”. Study showed that scavenging represents an important pathway of MPs to land and ocean environments in Europe and the Arctic.
Snow samples showing MP Pollution. Python and SimpleITK were used to identify each pixel with its position, analysis quality and polymer type Enabling the identification, quantification, and size determination. Image credit: Melanie Bergmann


  • One of the problems with plastic is that it persists in the environment for very long periods of time due to its durable structure.
  • Plastic pollutants can be found in urban centers, terrestrial areas, freshwater environments, shores of remote uninhabited islands, the sea surface, water column, and deep seafloor. 
  • Plastic pollutants have also reached polar regions including Arctic beaches, sea ice, water column, sea surface, and the seafloor. 
  • Plastic items fragment into smaller particles and are termed microplastic when attaining sizes below 5 mm. 
  • Polymer composition varied strongly, but varnish, rubber, polyethylene, and polyamide
  • In addition, litter quantities have increased significantly on the deep Arctic seafloor over the past 15 years as highlighted in a time-series study.
  • Mismanaged plastic waste could triple from 60-99 million metric ton in 2015 to 155-265 million metric ton by 2060.
  • In France, MP concentrations in atmospheric fallout increased fivefold after a rain event, indicating that wet deposition could be a pathway of MPs to Earth’s surfaces including the oceans. 

You can read the full article here published by -Scientists Melanie Bergmann, Sophia Mützel, Sebastian Primpke, Mine B. Tekman, Jürg Trachsel and Gunnar Gerdts.

Made in Python: First Black Hole Image in History

The Event Horizon Telescope (EHT) is an impressive collaboration effort which created the first image of a black hole in history, solidifying Einstein’s general relativity theory.

Particular black hole that’s captured is at the center of Messier 87 galaxy, 55 million light years away and 6.5 times bigger than the mass of the sun.

Previously there has been attempts to capture black holes but observations were limited to jets of light coming from somewhere M87 black hole is suspected to be. (Hubble Faint Object Spectrograph was used to measure the rotation velocity of the ionized gas disk -astrophysical jet- at the center of M87 indicating a central black hole with 30% uncertainty.)

Now with the results EHT achieved, it’s official. 8 telescopes, 60 institutions across 20 countries contributed to this groundbreaking discovery. Telescopes used were: ALMAAPEXLarge Millimeter Telescope Alfonso SerranoJames Clerk Maxwell TelescopeIRAM 30-meter telescope, Submillimeter Array, Submillimeter Telescope, and South Pole Telescope.

South Pole Telescope

8 Telescopes synced their data with atomic clocks and each of them saved approximately 350TB of data per day stored on high-performance helium-filled hard drives. Run on 5 nights during a 10 night period in April 2017, 8 telescopes would generate approximately 14 Petabyte of data (1 Petabyte = 1000 Terabyte).

Drives were then flown by commercial planes to supercomputers: Max Planck Institute for Radio Astronomy and MIT Haystack Observatory and that’s where Python comes in.

AIPS is the standard data-reduction and analysis software package most commonly used for radio interferometers, including VLBI (Very-long-baseline interferometry).

Simulation Credit: Hotaka Shiokawa

First written in FORTRAN 66 in 1978, AIPS wasn’t the most suitable to work on EHT data since EHT data had its own characteristics. (Such as wide range of signal to noise, S/N, as opposed to ideal low S/N situation for AIPS)

Because of this scientists created an automated Python script based on AIPS. In addition to this custom pipeline, several standard Python libraries like scipy, pylab, matplotlib, and numpy were utilized during the project.

Black holes are one of the major frontiers in physics and our understanding of universe. Since all the observations in the project are consistent with Einstein’s theory, it also suggests that intense gravity provided by a black hole might be bending spacetime and creating two end points in spacetime.

First black hole image captured ever. (Stitched together in Python)

New millenia became a playground for all Sci-fi plots to come alive and it probably doesn’t get more Sci-fi than wormholes becoming an observed phenomenon.

You can find the Python custom AIPS pipeline on Dr. Intema’s webpage here.
Event Horizon Telescope’s Github repository can be found here.