Chapter 23 The Death of Stars
23.6 The Mystery of the Gamma-Ray Bursts
Learning Objectives
By the end of this section, you will be able to:
- Give a brief history of how gamma-ray bursts were discovered and what instruments made the discovery possible
- Explain why astronomers think that gamma-ray bursts beam their energy rather than it radiating uniformly in all directions
- Describe how the radiation from a gamma-ray burst and its afterglow is produced
- Explain how short-duration gamma-ray bursts differ from longer ones, and describe the process that makes short-duration gamma-ray bursts
- Explain why gamma-ray bursts may help us understand the early universe
Everybody loves a good mystery, and astronomers are no exception. The mystery we will discuss in this section was first discovered in the mid-1960s, not via astronomical research, but as a result of a search for the tell-tale signs of nuclear weapon explosions. The US Defense Department launched a series of Vela satellites to make sure that no country was violating a treaty that banned the detonation of nuclear weapons in space.
Since nuclear explosions produce the most energetic form of electromagnetic waves called gamma rays (see Radiation and Spectra), the Vela satellites contained detectors to search for this type of radiation. The satellites did not detect any confirmed events from human activities, but they did—to everyone’s surprise—detect short bursts of gamma rays coming from random directions in the sky. News of the discovery was first published in 1973; however, the origin of the bursts remained a mystery. No one knew what produced the brief flashes of gamma rays or how far away the sources were.
From a Few Bursts to Thousands
With the launch of the Compton Gamma-Ray Observatory by NASA in 1991, astronomers began to identify many more bursts and to learn more about them ([link]). Approximately once per day, the NASA satellite detected a flash of gamma rays somewhere in the sky that lasted from a fraction of a second to several hundred seconds. Before the Compton measurements, astronomers had expected that the most likely place for the bursts to come from was the main disk of our own (pancake-shaped) Galaxy. If this had been the case, however, more bursts would have been seen in the crowded plane of the Milky Way than above or below it. Instead, the sources of the bursts were distributed isotropically; that is, they could appear anywhere in the sky with no preference for one region over another. Almost never did a second burst come from the same location.
For several years, astronomers actively debated whether the burst sources were relatively nearby or very far away—the two possibilities for bursts that are isotropically distributed. Nearby locations might include the cloud of comets that surrounds the solar system or the halo of our Galaxy, which is large and spherical, and also surrounds us in all directions. If, on the other hand, the bursts occurred at very large distances, they could come from faraway galaxies, which are also distributed uniformly in all directions.
Both the very local and the very distant hypotheses required something strange to be going on. If the bursts were coming from the cold outer reaches of our own solar system or from the halo of our Galaxy, then astronomers had to hypothesize some new kind of physical process that could produce unpredictable flashes of high-energy gamma rays in these otherwise-quiet regions of space. And if the bursts came from galaxies millions or billions of light-years away, then they must be extremely powerful to be observable at such large distances; indeed they had to be the among the biggest explosions in the universe.
The First Afterglows
The problem with trying to figure out the source of the gamma-ray bursts was that our instruments for detecting gamma rays could not pinpoint the exact place in the sky where the burst was happening. Early gamma-ray telescopes did not have sufficient resolution. This was frustrating because astronomers suspected that if they could pinpoint the exact position of one of these rapid bursts, then they would be able to identify a counterpart (such as a star or galaxy) at other wavelengths and learn much more about the burst, including where it came from. This would, however, require either major improvements in gamma-ray detector technology to provide better resolution or detection of the burst at some other wavelength. In the end, both techniques played a role.
The breakthrough came with the launch of the Italian Dutch BeppoSAX satellite in 1996. BeppoSAX included a new type of gamma-ray telescope capable of identifying the position of a source much more accurately than previous instruments, to within a few minutes of arc on the sky. By itself, however, it was still not sophisticated enough to determine the exact source of the gamma-ray burst. After all, a box a few minutes of arc on a side could still contain many stars or other celestial objects.
However, the angular resolution of BeppoSAX was good enough to tell astronomers where to point other, more precise telescopes in the hopes of detecting longer-lived electromagnetic emission from the bursts at other wavelengths. Detection of a burst at visible-light or radio wavelengths could provide a position accurate to a few seconds of arc and allow the position to be pinpointed to an individual star or galaxy. BeppoSAX carried its own X-ray telescope onboard the spacecraft to look for such a counterpart, and astronomers using visible-light and radio facilities on the ground were eager to search those wavelengths as well.
Two crucial BeppoSAX burst observations in 1997 helped to resolve the mystery of the gamma-ray bursts. The first burst came in February from the direction of the constellation Orion. Within 8 hours, astronomers working with the satellite had identified the position of the burst, and reoriented the spacecraft to focus BeppoSAX’s X-ray detector on the source. To their excitement, they detected a slowly fading X-ray source 8 hours after the event—the first successful detection of an afterglow from a gamma-ray burst. This provided an even-better location of the burst (accurate to about 40 seconds of arc), which was then distributed to astronomers across the world to try to detect it at even longer wavelengths.
That very night, the 4.2-meter William Herschel Telescope on the Canary Islands found a fading visible-light source at the same position as the X-ray afterglow, confirming that such an afterglow could be detected in visible light as well. Eventually, the afterglow faded away, but left behind at the location of the original gamma-ray burst was a faint, fuzzy source right where the fading point of light had been—a distant galaxy ([link]). This was the first piece of evidence that gamma-ray bursts were indeed very energetic objects from very far away. However, it also remained possible that the burst source was much closer to us and just happened to align with a more distant galaxy, so this one observation alone was not a conclusive demonstration of the extragalactic origin of gamma-ray bursts.
On May 8 of the same year, a burst came from the direction of the constellation Camelopardalis. In a coordinated international effort, BeppoSAX again fixed a reasonably precise position, and almost immediately a telescope on Kitt Peak in Arizona was able to catch the visible-light afterglow. Within 2 days, the largest telescope in the world (the Keck in Hawaii) collected enough light to record a spectrum of the burst. The May gamma-ray burst afterglow spectrum showed absorption features from a fuzzy object that was 4 billion light-years from the Sun, meaning that the location of the burst had to be at least this far away—and possibly even farther. (How astronomers can get the distance of such an object from the Doppler shift in the spectrum is something we will discuss in Galaxies.) What that spectrum showed was clear evidence that the gamma-ray burst had taken place in a distant galaxy.
Networking to Catch More Bursts
After initial observations showed that the precise locations and afterglows of gamma-ray bursts could be found, astronomers set up a system to catch and pinpoint bursts on a regular basis. But to respond as quickly as needed to obtain usable results, astronomers realized that they needed to rely on automated systems rather than human observers happening to be in the right place at the right time.
Now, when an orbiting high-energy telescope discovers a burst, its rough location is immediately transmitted to a Gamma-Ray Coordinates Network based at NASA’s Goddard Space Flight Center, alerting observers on the ground within a few seconds to look for the visible-light afterglow.
The first major success with this system was achieved by a team of astronomers from the University of Michigan, Lawrence Livermore National Laboratory, and Los Alamos National Laboratories, who designed an automated device they called the Robotic Optical Transient Search Experiment (ROTSE), which detected a very bright visible-light counterpart in 1999. At peak, the burst was almost as bright as Neptune—despite a distance (measured later by spectra from larger telescopes) of 9 billion light-years.
More recently, astronomers have been able to take this a step further, using wide-field-of-view telescopes to stare at large fractions of the sky in the hope that a gamma-ray burst will occur at the right place and time, and be recorded by the telescope’s camera. These wide-field telescopes are not sensitive to faint sources, but ROTSE showed that gamma-ray burst afterglows could sometimes be very bright.
Astronomers’ hopes were vindicated in March 2008, when an extremely bright gamma-ray burst occurred and its light was captured by two wide-field camera systems in Chile: the Polish “Pi of the Sky” and the Russian-Italian TORTORA [Telescopio Ottimizzato per la Ricerca dei Transienti Ottici Rapidi (Italian for Telescope Optimized for the Research of Rapid Optical Transients)] (see [link]). According to the data taken by these telescopes, for a period of about 30 seconds, the light from the gamma-ray burst was bright enough that it could have been seen by the unaided eye had a person been looking in the right place at the right time. Adding to our amazement, later observations by larger telescopes demonstrated that the burst occurred at a distance of 8 billion light-years from Earth!
To Beam or Not to Beam
The enormous distances to these events meant they had to have been astoundingly energetic to appear as bright as they were across such an enormous distance. In fact, they required so much energy that it posed a problem for gamma-ray burst models: if the source was radiating energy in all directions, then the energy released in gamma rays alone during a bright burst (such as the 1999 or 2008 events) would have been equivalent to the energy produced if the entire mass of a Sun-like star were suddenly converted into pure radiation.
For a source to produce this much energy this quickly (in a burst) is a real challenge. Even if the star producing the gamma-ray burst was much more massive than the Sun (as is probably the case), there is no known means of converting so much mass into radiation within a matter of seconds. However, there is one way to reduce the power required of the “mechanism” that makes gamma-ray bursts. So far, our discussion has assumed that the source of the gamma rays gives off the same amount of energy in all directions, like an incandescent light bulb.
But as we discuss in Pulsars and the Discovery of Neutron Stars, not all sources of radiation in the universe are like this. Some produce thin beams of radiation that are concentrated into only one or two directions. A laser pointer and a lighthouse on the ocean are examples of such beamed sources on Earth ([link]). If, when a burst occurs, the gamma rays come out in only one or two narrow beams, then our estimates of the luminosity of the source can be reduced, and the bursts may be easier to explain. In that case, however, the beam has to point toward Earth for us to be able to see the burst. This, in turn, would imply that for every burst we see from Earth, there are probably many others that we never detect because their beams point in other directions.
Long-Duration Gamma-Ray Bursts: Exploding Stars
After identifying and following large numbers of gamma-ray bursts, astronomers began to piece together clues about what kind of event is thought to be responsible for producing the gamma-ray burst. Or, rather, what kind of events, because there are at least two distinct types of gamma-ray bursts. The two—like the different types of supernovae—are produced in completely different ways.
Observationally, the crucial distinction is how long the burst lasts. Astronomers now divide gamma-ray bursts into two categories: short-duration ones (defined as lasting less than 2 seconds, but typically a fraction of a second) and long-duration ones (defined as lasting more than 2 seconds, but typically about a minute).
All of the examples we have discussed so far concern the long-duration gamma-ray bursts. These constitute most of the gamma-ray bursts that our satellites detect, and they are also brighter and easier to pinpoint. Many hundreds of long-duration gamma-ray bursts, and the properties of the galaxies in which they occurred, have now been studied in detail. Long-duration gamma-ray bursts are universally observed to come from distant galaxies that are still actively making stars. They are usually found to be located in regions of the galaxy with strong star-formation activity (such as spiral arms). Recall that the more massive a star is, the less time it spends in each stage of its life. This suggests that the bursts come from a young and short-lived, and therefore massive type of star.
Furthermore, in several cases when a burst has occurred in a galaxy relatively close to Earth (within a few billion light-years), it has been possible to search for a supernova at the same position—and in nearly all of these cases, astronomers have found evidence of a supernova of type Ic going off. A type Ic is a particular type of supernova, which we did not discuss in the earlier parts of this chapter; these are produced by a massive star that has been stripped of its outer hydrogen layer. However, only a tiny fraction of type Ic supernovae produce gamma-ray bursts.
Why would a massive star with its outer layers missing sometimes produce a gamma-ray burst at the same time that it explodes as a supernova? The explanation astronomers have in mind for the extra energy is the collapse of the star’s core to form a spinning, magnetic black hole or neutron star. Because the star corpse is both magnetic and spinning rapidly, its sudden collapse is complex and can produce swirling jets of particles and powerful beams of radiation—just like in a quasar or active galactic nucleus (objects you will learn about Active Galaxies, Quasars, and Supermassive Black Holes), but on a much faster timescale. A small amount of the infalling mass is ejected in a narrow beam, moving at speeds close to that of light. Collisions among the particles in the beam can produce intense bursts of energy that we see as a gamma-ray burst.
Within a few minutes, the expanding blast from the fireball plows into the interstellar matter in the dying star’s neighborhood. This matter might have been ejected from the star itself at earlier stages in its evolution. Alternatively, it could be the gas out of which the massive star and its neighbors formed.
As the high-speed particles from the blast are slowed, they transfer their energy to the surrounding matter in the form of a shock wave. That shocked material emits radiation at longer wavelengths. This accounts for the afterglow of X-rays, visible light, and radio waves—the glow comes at longer and longer wavelengths as the blast continues to lose energy.
Short-Duration Gamma-Ray Bursts: Colliding Stellar Corpses
What about the shorter gamma-ray bursts? The gamma-ray emission from these events lasts less than 2 seconds, and in some cases may last only milliseconds—an amazingly short time. Such a timescale is difficult to achieve if they are produced in the same way as long-duration gamma-ray bursts, since the collapse of the stellar interior onto the black hole should take at least a few seconds.
Astronomers looked fruitlessly for afterglows from short-duration gamma-ray bursts found by BeppoSAX and other satellites. Evidently, the afterglows fade away too quickly. Fast-responding visible-light telescopes like ROTSE were not helpful either: no matter how fast these telescopes responded, the bursts were not bright enough at visible wavelengths to be detected by these small telescopes.
Once again, it took a new satellite to clear up the mystery. In this case, it was the Swift Gamma-Ray Burst Satellite, launched in 2004 by a collaboration between NASA and the Italian and UK space agencies ([link]). The design of Swift is similar to that of BeppoSAX. However, Swift is much more agile and flexible: after a gamma-ray burst occurs, the X-ray and UV telescopes can be repointed automatically within a few minutes (rather than a few hours). Thus, astronomers can observe the afterglow much earlier, when it is expected to be much brighter. Furthermore, the X-ray telescope is far more sensitive and can provide positions that are 30 times more precise than those provided by BeppoSAX, allowing bursts to be identified even without visible-light or radio observations.
On May 9, 2005, Swift detected a flash of gamma rays lasting 0.13 seconds in duration, originating from the constellation Coma Berenices. Remarkably, the galaxy at the X-ray position looked completely different from any galaxy in which a long-duration burst had been seen to occur. The afterglow originated from the halo of a giant elliptical galaxy 2.7 billion light-years away, with no signs of any young, massive stars in its spectrum. Furthermore, no supernova was ever detected after the burst, despite extensive searching.
What could produce a burst less than a second long, originating from a region with no star formation? The leading model involves the merger of two compact stellar corpses: two neutron stars, or perhaps a neutron star and a black hole. Since many stars come in binary or multiple systems, it’s possible to have systems where two such star corpses orbit one another. According to general relativity (which will be discussed in Black Holes and Curved Spacetime), the orbits of a binary star system composed of such objects should slowly decay with time, eventually (after millions or billions of years) causing the two objects to slam together in a violent but brief explosion. Because the decay of the binary orbit is so slow, we would expect more of these mergers to occur in old galaxies in which star formation has long since stopped.
While it was impossible to be sure of this model based on only a single event (it is possible this burst actually came from a background galaxy and lined up with the giant elliptical only by chance), several dozen more short-duration gamma-ray bursts have since been located by Swift, many of which also originate from galaxies with very low star-formation rates. This has given astronomers greater confidence that this model is the correct one. Still, to be fully convinced, astronomers are searching for a “smoking gun” signature for the merger of two ultra-dense stellar remnants.
There are two examples we can think of that would provide more direct evidence. One is a very special kind of explosion, produced when neutrons stripped from the neutron stars during the violent final phase of the merger fuse together into heavy elements and then release heat due to radioactivity, producing a short-lived but red supernova sometimes called a kilonova. (The term is used because it is about a thousand times brighter than an ordinary nova, but not quite as “super” as a traditional supernova.) Hubble observations of one short-duration gamma-ray burst in 2013 show suggestive evidence of such a signature, but need to be confirmed by future observations.
The second “smoking gun” has been even more exciting to see: the detection of gravitational waves. As will be discussed in Black Holes and Curved Spacetime, gravitational waves are ripples in the fabric of spacetime that general relativity predicts should be produced by the acceleration of extremely massive and dense objects—such as two neutron stars or black holes spiraling toward each other and colliding. The first example of gravitational waves has been observed recently from the merger of two large black holes. If a gravitational wave is observed one day to be coincident in time and space with a gamma-ray burst, this will not only confirm our theories of the origin of short gamma-ray bursts but would also be among the most spectacular demonstrations yet of Einstein’s theory of general relativity.
Probing the Universe with Gamma-Ray Bursts
The story of how astronomers came to explain the origin of the different kinds of bursts is a good example of how the scientific process sometimes resembles good detective work. While the mystery of short-duration gamma-ray bursts is still being unraveled, the focus of studies for long-duration gamma-ray bursts has begun to change from understanding the origin of the bursts themselves (which is now fairly well-established) to using them as tools to understand the broader universe.
The reason that long-duration gamma-ray bursts are useful has to do with their extreme luminosities, if only for a short time. In fact, long-duration gamma-ray bursts are so bright that they could easily be seen at distances that correspond to a few hundred million years after the expansion of the universe began, which is when theorists think that the first generation of stars formed. Some theories predict that the first stars are likely to be massive and complete their evolution in only a million years or so. If this turns out to be the case, then gamma-ray bursts (which signal the death of some of these stars) may provide us with the best way of probing the universe when stars and galaxies first began to form.
So far, the most distant gamma-ray burst found (on April 29, 2009) originated a remarkable 13.2 billion light-years away—meaning it happened only 600 million years after the Big Bang itself. This is comparable to the earliest and most distant galaxies found by the Hubble Space Telescope. It is not quite old enough to expect that it formed from the first generation of stars, but its appearance at this distance still gives us useful information about the production of stars in the early universe. Astronomers continue to scan the skies, looking for even more distant events signaling the deaths of stars from even further back in time.
Key Concepts and Summary
Gamma-ray bursts last from a fraction of a second to a few minutes. They come from all directions and are now known to be associated with very distant objects. The energy is most likely beamed, and, for the ones we can detect, Earth lies in the direction of the beam. Long-duration bursts (lasting more than a few seconds) come from massive stars with their outer hydrogen layers missing that explode as supernovae. Short-duration bursts are believed to be mergers of stellar corpses (neutron stars or black holes).
For Further Exploration
Articles
Death of Stars
Hillebrandt, W., et al. “How To Blow Up a Star.” Scientific American (October 2006): 42. On supernova mechanisms.
Irion, R. “Pursuing the Most Extreme Stars.” Astronomy (January 1999): 48. On pulsars.
Kalirai, J. “New Light on Our Sun’s Fate.” Astronomy (February 2014): 44. What will happen to stars like our Sun between the main sequence and the white dwarf stages.
Kirshner, R. “Supernova 1987A: The First Ten Years.” Sky & Telescope (February 1997): 35.
Maurer, S. “Taking the Pulse of Neutron Stars.” Sky & Telescope (August 2001): 32. Review of recent ideas and observations of pulsars.
Zimmerman, R. “Into the Maelstrom.” Astronomy (November 1998): 44. About the Crab Nebula.
Gamma-Ray Bursts
Fox, D. & Racusin, J. “The Brightest Burst.” Sky & Telescope (January 2009): 34. Nice summary of the brightest burst observed so far, and what we have learned from it.
Nadis, S. “Do Cosmic Flashes Reveal Secrets of the Infant Universe?” Astronomy (June 2008): 34. On different types of gamma-ray bursts and what we can learn from them.
Naeye, R. “Dissecting the Bursts of Doom.” Sky & Telescope (August 2006): 30. Excellent review of gamma-ray bursts—how we discovered them, what they might be, and what they can be used for in probing the universe.
Zimmerman, R. “Speed Matters.” Astronomy (May 2000): 36. On the quick-alert networks for finding afterglows.
Zimmerman, R. “Witness to Cosmic Collisions.” Astronomy (July 2006): 44. On the Swift mission and what it is teaching astronomers about gamma-ray bursts.
Websites
Death of Stars
Crab Nebula: http://chandra.harvard.edu/xray_sources/crab/crab.html. A short, colorfully written introduction to the history and science involving the best-known supernova remant.
Introduction to Neutron Stars: https://www.astro.umd.edu/~miller/nstar.html. Coleman Miller of the University of Maryland maintains this site, which goes from easy to hard as you get into it, but it has lots of good information about corpses of massive stars.
Introduction to Pulsars (by Maryam Hobbs at the Australia National Telescope Facility): http://www.atnf.csiro.au/outreach/education/everyone/pulsars/index.html.
Magnetars, Soft Gamma Repeaters, and Very Strong Magnetic Fields: http://solomon.as.utexas.edu/magnetar.html. Robert Duncan, one of the originators of the idea of magnetars, assembled this site some years ago.
Gamma-Ray Bursts
Brief Intro to Gamma-Ray Bursts (from PBS’ Seeing in the Dark): http://www.pbs.org/seeinginthedark/astronomy-topics/gamma-ray-bursts.html.
Discovery of Gamma-ray Bursts: http://science.nasa.gov/science-news/science-at-nasa/1997/ast19sep97_2/.
Gamma-Ray Bursts: Introduction to a Mystery (at NASA’s Imagine the Universe site): http://imagine.gsfc.nasa.gov/docs/science/know_l1/bursts.html.
Introduction from the Swift Satellite Site: http://swift.sonoma.edu/about_swift/grbs.html.
Missions to Detect and Learn More about Gamma-ray Bursts:
- Fermi Space Telescope: http://fermi.gsfc.nasa.gov/public/.
- INTEGRAL Spacecraft: http://www.esa.int/science/integral.
- SWIFT Spacecraft: http://swift.sonoma.edu/.
Videos
Death of Stars
BBC interview with Antony Hewish: http://www.bbc.co.uk/archive/scientists/10608.shtml. (40:54).
Black Widow Pulsars: The Vengeful Corpses of Stars: https://www.youtube.com/watch?v=Fn-3G_N0hy4. A public talk in the Silicon Valley Astronomy Lecture Series by Dr. Roger Romani (Stanford University) (1:01:47).
Hubblecast 64: It all ends with a bang!: http://www.spacetelescope.org/videos/hubblecast64a/. HubbleCast Program introducing Supernovae with Dr. Joe Liske (9:48).
Space Movie Reveals Shocking Secrets of the Crab Pulsar: http://hubblesite.org/newscenter/archive/releases/2002/24/video/c/. A sequence of Hubble and Chandra Space Telescope images of the central regions of the Crab Nebula have been assembled into a very brief movie accompanied by animation showing how the pulsar affects its environment; it comes with some useful background material (40:06).
Gamma-Ray Bursts
Gamma-Ray Bursts: The Biggest Explosions Since the Big Bang!: https://www.youtube.com/watch?v=ePo_EdgV764. Edo Berge in a popular-level lecture at Harvard (58:50).
Gamma-Ray Bursts: Flashes in the Sky: https://www.youtube.com/watch?v=23EhcAP3O8Q. American Museum of Natural History Science Bulletin on the Swift satellite (5:59).
Overview Animation of Gamma-Ray Burst: http://news.psu.edu/video/296729/2013/11/27/overview-animation-gamma-ray-burst. Brief Animation of what causes a long-duration gamma-ray burst (0:55).
Collaborative Group Activities
- Someone in your group uses a large telescope to observe an expanding shell of gas. Discuss what measurements you could make to determine whether you have discovered a planetary nebula or the remnant of a supernova explosion.
- The star Sirius (the brightest star in our northern skies) has a white-dwarf companion. Sirius has a mass of about 2 MSun and is still on the main sequence, while its companion is already a star corpse. Remember that a white dwarf can’t have a mass greater than 1.4 MSun. Assuming that the two stars formed at the same time, your group should discuss how Sirius could have a white-dwarf companion. Hint: Was the initial mass of the white-dwarf star larger or smaller than that of Sirius?
- Discuss with your group what people today would do if a brilliant star suddenly became visible during the daytime? What kind of fear and superstition might result from a supernova that was really bright in our skies? Have your group invent some headlines that the tabloid newspapers and the less responsible web news outlets would feature.
- Suppose a supernova exploded only 40 light-years from Earth. Have your group discuss what effects there may be on Earth when the radiation reaches us and later when the particles reach us. Would there be any way to protect people from the supernova effects?
- When pulsars were discovered, the astronomers involved with the discovery talked about finding “little green men.” If you had been in their shoes, what tests would you have performed to see whether such a pulsating source of radio waves was natural or the result of an alien intelligence? Today, several groups around the world are actively searching for possible radio signals from intelligent civilizations. How might you expect such signals to differ from pulsar signals?
- Your little brother, who has not had the benefit of an astronomy course, reads about white dwarfs and neutron stars in a magazine and decides it would be fun to go near them or even try to land on them. Is this a good idea for future tourism? Have your group make a list of reasons it would not be safe for children (or adults) to go near a white dwarf and a neutron star.
- A lot of astronomers’ time and many instruments have been devoted to figuring out the nature of gamma-ray bursts. Does your group share the excitement that astronomers feel about these mysterious high-energy events? What are some reasons that people outside of astronomy might care about learning about gamma-ray bursts?
Review Questions
1: How does a white dwarf differ from a neutron star? How does each form? What keeps each from collapsing under its own weight?
2: Describe the evolution of a star with a mass like that of the Sun, from the main-sequence phase of its evolution until it becomes a white dwarf.
3: Describe the evolution of a massive star (say, 20 times the mass of the Sun) up to the point at which it becomes a supernova. How does the evolution of a massive star differ from that of the Sun? Why?
4: How do the two types of supernovae discussed in this chapter differ? What kind of star gives rise to each type?
5: A star begins its life with a mass of 5 MSun but ends its life as a white dwarf with a mass of 0.8 MSun. List the stages in the star’s life during which it most likely lost some of the mass it started with. How did mass loss occur in each stage?
6: If the formation of a neutron star leads to a supernova explosion, explain why only three of the hundreds of known pulsars are found in supernova remnants.
7: How can the Crab Nebula shine with the energy of something like 100,000 Suns when the star that formed the nebula exploded almost 1000 years ago? Who “pays the bills” for much of the radiation we see coming from the nebula?
8: How is a nova different from a type Ia supernova? How does it differ from a type II supernova?
9: Apart from the masses, how are binary systems with a neutron star different from binary systems with a white dwarf?
10: What observations from SN 1987A helped confirm theories about supernovae?
11: Describe the evolution of a white dwarf over time, in particular how the luminosity, temperature, and radius change.
12: Describe the evolution of a pulsar over time, in particular how the rotation and pulse signal changes over time.
14: What do astronomers think are the causes of longer-duration gamma-ray bursts and shorter-duration gamma-ray bursts?
15: How did astronomers finally solve the mystery of what gamma-ray bursts were? What instruments were required to find the solution?
Thought Questions
- A star with no nuclear reactions going on in the core, which is made primarily of carbon and oxygen.
- A star of uniform composition from center to surface; it contains hydrogen but has no nuclear reactions going on in the core.
- A star that is fusing hydrogen to form helium in its core.
- A star that is fusing helium to carbon in the core and hydrogen to helium in a shell around the core.
- A star that has no nuclear reactions going on in the core but is fusing hydrogen to form helium in a shell around the core.
17: Would you expect to find any white dwarfs in the Orion Nebula? (See The Birth of Stars and the Discovery of Planets outside the Solar System to remind yourself of its characteristics.) Why or why not?
18: Suppose no stars more massive than about 2 MSun had ever formed. Would life as we know it have been able to develop? Why or why not?
19: Would you be more likely to observe a type II supernova (the explosion of a massive star) in a globular cluster or in an open cluster? Why?
20: Astronomers believe there are something like 100 million neutron stars in the Galaxy, yet we have only found about 2000 pulsars in the Milky Way. Give several reasons these numbers are so different. Explain each reason.
22: The Large Magellanic Cloud has about one-tenth the number of stars found in our own Galaxy. Suppose the mix of high- and low-mass stars is exactly the same in both galaxies. Approximately how often does a supernova occur in the Large Magellanic Cloud?
23: Look at the list of the nearest stars in Appendix I. Would you expect any of these to become supernovae? Why or why not?
24: If most stars become white dwarfs at the ends of their lives and the formation of white dwarfs is accompanied by the production of a planetary nebula, why are there more white dwarfs than planetary nebulae in the Galaxy?
25: If a 3 and 8 MSun star formed together in a binary system, which star would:
- Evolve off the main sequence first?
- Form a carbon- and oxygen-rich white dwarf?
- Be the location for a nova explosion?
26: You have discovered two star clusters. The first cluster contains mainly main-sequence stars, along with some red giant stars and a few white dwarfs.  What are the relative ages of the clusters? How did you determine your answer?
27: A supernova remnant was recently discovered and found to be approximately 150 years old. Provide possible reasons that this supernova explosion escaped detection.
28: Based upon the evolution of stars, place the following elements in order of least to most common in the Galaxy: gold, carbon, neon. What aspects of stellar evolution formed the basis for how you ordered the elements?
29: What observations or types of telescopes would you use to distinguish a binary system that includes a main-sequence star and a white dwarf star from one containing a main-sequence star and a neutron star?
Figuring for Yourself
31: The ring around SN 1987A ([link]) initially became illuminated when energetic photons from the supernova interacted with the material in the ring. The radius of the ring is approximately 0.75 light-year from the supernova location. How long after the supernova did the ring become illuminated?
32: What is the acceleration of gravity (g) at the surface of the Sun? (See Appendix E for the Sun’s key characteristics.) How much greater is this than g at the surface of Earth? Calculate what you would weigh on the surface of the Sun. Your weight would be your Earth weight multiplied by the ratio of the acceleration of gravity on the Sun to the acceleration of gravity on Earth. (Okay, we know that the Sun does not have a solid surface to stand on and that you would be vaporized if you were at the Sun’s photosphere. Humor us for the sake of doing these calculations.)
33: What is the escape velocity from the Sun? How much greater is it than the escape velocity from Earth?
34: What is the average density of the Sun? How does it compare to the average density of Earth?
35: Say that a particular white dwarf has the mass of the Sun but the radius of Earth. What is the acceleration of gravity at the surface of the white dwarf? How much greater is this than g at the surface of Earth? What would you weigh at the surface of the white dwarf (again granting us the dubious notion that you could survive there)?
36: What is the escape velocity from the white dwarf in [link]? How much greater is it than the escape velocity from Earth?
37: What is the average density of the white dwarf in [link]? How does it compare to the average density of Earth?
38: Now take a neutron star that has twice the mass of the Sun but a radius of 10 km. What is the acceleration of gravity at the surface of the neutron star? How much greater is this than g at the surface of Earth? What would you weigh at the surface of the neutron star (provided you could somehow not become a puddle of protoplasm)?
39: What is the escape velocity from the neutron star in [link]? How much greater is it than the escape velocity from Earth?
40: What is the average density of the neutron star in [link]? How does it compare to the average density of Earth?
41: One way to calculate the radius of a star is to use its luminosity and temperature and assume that the star radiates approximately like a blackbody. Astronomers have measured the characteristics of central stars of planetary nebulae and have found that a typical central star is 16 times as luminous and 20 times as hot (about 110,000 K) as the Sun. Find the radius in terms of the Sun’s. How does this radius compare with that of a typical white dwarf?
42: According to a model described in the text, a neutron star has a radius of about 10 km. Assume that the pulses occur once per rotation. According to Einstein’s theory of relatively, nothing can move faster than the speed of light. Check to make sure that this pulsar model does not violate relativity. Calculate the rotation speed of the Crab Nebula pulsar at its equator, given its period of 0.033 s. (Remember that distance equals velocity × time and that the circumference of a circle is given by 2πR).
43: Do the same calculations as in [link] but for a pulsar that rotates 1000 times per second.
44: If the Sun were replaced by a white dwarf with a surface temperature of 10,000 K and a radius equal to Earth’s, how would its luminosity compare to that of the Sun?
45: A supernova can eject material at a velocity of 10,000 km/s. How long would it take a supernova remnant to expand to a radius of 1 AU? How long would it take to expand to a radius of 1 light-years? Assume that the expansion velocity remains constant and use the relationship: $\text{expansion time}=\frac{\text{distance}}{\text{expansion velocity}}.$
46: A supernova remnant was observed in 2007 to be expanding at a velocity of 14,000 km/s and had a radius of 6.5 light-years. Assuming a constant expansion velocity, in what year did this supernova occur?
47: The ring around SN 1987A ([link]) started interacting with material propelled by the shockwave from the supernova beginning in 1997 (10 years after the explosion). The radius of the ring is approximately 0.75 light-year from the supernova location. How fast is the supernova material moving, assume a constant rate of motion in km/s?
48: Before the star that became SN 1987A exploded, it evolved from a red supergiant to a blue supergiant while remaining at the same luminosity. As a red supergiant, its surface temperature would have been approximately 4000 K, while as a blue supergiant, its surface temperature was 16,000 K. How much did the radius change as it evolved from a red to a blue supergiant?
49: What is the radius of the progenitor star that became SN 1987A? Its luminosity was 100,000 times that of the Sun, and it had a surface temperature of 16,000 K.
50: What is the acceleration of gravity at the surface of the star that became SN 1987A? How does this g compare to that at the surface of Earth? The mass was 20 times that of the Sun and the radius was 41 times that of the Sun.
51: What was the escape velocity from the surface of the SN 1987A progenitor star? How much greater is it than the escape velocity from Earth? The mass was 20 times that of the Sun and the radius was 41 times that of the Sun.
52: What was the average density of the star that became SN 1987A? How does it compare to the average density of Earth? The mass was 20 times that of the Sun and the radius was 41 times that of the Sun.
53: If the pulsar shown in [link] is rotating 100 times per second, how many pulses would be detected in one minute? The two beams are located along the pulsar’s equator, which is aligned with Earth.


1.8 How Cases Move Through the System
Shanell Sanchez; Kate McLean; and Pamela Simek
How Cases Move Through the System
The criminal justice process is not what gets portrayed on television, and most cases do not go to trial or result in a prison sentence. Part of the problem is that our current system is overloaded, and ensuring both due process and crime control can be more challenging than one thinks. In order to effectively process cases through the criminal justice system, discretion is an important tool for police, prosecutors, judges, and correctional officials. Discretion provides freedom to make decisions; specifically, it is the power to use one’s judgment in making decisions within legal guidelines. A police officer may use their discretion to search an individual who appears to be engaged in specific, criminally suspicious behaviors in public—or to let a speeding motorist go with only a warning. Similarly, prosecutors exercise their discretion in dropping a case that they believe they cannot win in court, or in charging an individual with the violation of every possible criminal law. Many people see discretion as the most powerful, and least regulated, tool of the criminal justice system (Kessler & Piehl, 1998; Gottfredson & Gottfredson, 1988).
Discretion in Action
Provide an example of discretion, which can be from a professor, a college administrator, a police officer, a judge, or a boss. Describe how this exercise of discretion impacted the outcome of your situation, for better or for worse. Do you think that discretion was fairly exercised in this case? Why or why not?
Ethics refers to our understanding of what constitutes good or bad, moral or immoral, behaviors. Ethical behavior is incredibly important in the criminal justice system, because people working in the system get authority, power, and discretion from the government (Sellers, 2015). Imagine a case wherein a police officer let a severely intoxicated driver go with a warning, because the offender explained that they had gotten drunk after a bad break-up. Would it be ethical for police to allow the driver to simply drive off, when they represent a real threat to public safety? Or should the officer use their discretion and express empathy for an individual whose behavior is compromised by personal circumstances? Ethics and discretion often go hand-in-hand.
The criminologist Samuel Walker has referred to the criminal justice system as a funnel, and a leaky one at that. In 1967, The President’s Commission on Law Enforcement and the Administration of Justice published a report on the funneling effect of the criminal justice system. The criminal justice system is often referred to as a funnel because most cases do not go through all steps in the system, some because of discretion, and a large portion because they are unknown to police. In other words, just as a funnel is wide at the top and narrow at the bottom, the number of crimes that are formally processed by the criminal justice system decreases at every step, from reporting to punishment (The President’s Commission on Law Enforcement and Administration of Justice, 1967). Questions remain: Is the criminal justice system effective at catching, prosecuting, convicting, and punishing offenders? Does the system properly do its job at all levels? Walker was critical of this report and said the report did not account for the crimes unknown to police, often referred to as the “dark figure of crime.” He also recognized that the most serious crimes are often reported the most, which may confuse the public about the reality of other crimes (Walker, 2015). Others also criticized the report for only looking at reported crimes and adult crimes, but those issues will be highlighted in our next chapter on data in the criminal justice system. It is important to recognize the disparity between crimes that were reported and not reported. This discrepancy was a shock in the 1970s, especially after the United States started asking people about their victimization. The number of crimes people say they experienced far exceeded the crimes they reported to the police (Hansell, Bailey, Kamath, & Corrigan, 2016).
The Funnel Effect in Action
Imagine selling marijuana to friends every week. No one alerts the cops and you never get caught, which means this remains in the category of offenses unknown to police. However, a friend gets busted for selling weed too close to an elementary school, so the offense is immediately classified as known to the police. An officer can choose to arrest or not, depending on the amount. Then, it is up to the prosecutor to decide whether or not to file charges. If charges get filed, your friend may be encouraged to plead guilty and “get it over with.” This would be more likely under a crime control model. However, his/her mom may say, “No, I want you to go to trial,” which would be more likely under a due process model, and now that friend has to decide. If he/she takes the plea bargain, they can skip the trial and go straight to sentencing. Let us say the plea bargain allowed the friend to avoid jail time and serve 300 hours of community service, but if convicted, this friend could serve two years in prison. Many would be tempted by the community service option and choose to be under community supervision such as probation.
The funnel is one way to look at the criminal justice system, but we will see later how it can be much more complicated than this analogy suggests. Without discretion, the criminal justice system would likely collapse under the sheer number of criminal cases, and costs would skyrocket. If the U.S. were to arrest, prosecute, and punish everyone who violated the law, there would not be any money left over for important things like education, healthcare, repairing highways, and so much more. We would see most of our taxpayers paying for just crime control, which may not be the best use of all that money.
The understanding of what constitutes good or bad behavior and helps guide our behaviors


14
Each institution develops its own process for tracking open textbook adoptions. Much depends on the culture of the college or university and at what stage the institution is in terms of open educational awareness and practice.
For some institutions, particularly at the start, tracking adoptions might be as simple as learning about open textbook usage during conversations with instructors. Others conduct routine surveys each term. The advantages of this second method include:
- Keeping the conversation about open education alive
- Learning how OER and open textbook usage is changing on campus (e.g., are new or the same resources being used; which instructors are participating, dropping out, or joining; which disciplines and course levels are involved, and which are not, and why)
- Discovering how the institution can do better or help its faculty, staff, and students in this area (e.g., through the B.C. Ongoing Adoption Program)
Setting up a streamlined adoption procedure with steps and dates gives faculty an established system so they can report their open textbook adoptions on time. That data can then be provided to people and departments (OER team, bookstore, course scheduler, adoption tracker, accessibility department) that need it for the integration of and information about open textbooks in use. [1]
Below is a list of other methods used in B.C. and elsewhere to track open textbook adoptions.
- Provide workshops, lunchtime (brown bag) sessions, or other educational events about open education, open textbooks, and open educational resources (OER). During these sessions, ask faculty if they are currently using or considering adopting an open textbook in their course. Sometimes, institutions report that instructors are using OER and are not aware they are doing so. Several B.C. institutions have scheduled these events, such as the OER workshop at Emily Carr Institute of Art + Design and a session on OER at a daylong event at Vancouver Community College [PDF file].
- Share potential open textbooks and other OER with faculty that might be used in their courses. For help with this, see OER sharing/tracking in the Information Directory.
- Publish contact information for instructors to report adoptions. This information can be found for some B.C. institutions in the Post-Secondary Directory.
- Post an open textbook adoption form. Examples include:
- Okanagan College’s Open Textbook Survey
- BCcampus’ Adoption Form
- Enlist the bookstore’s help. If it is routine for faculty to inform the bookstore which textbook has been assigned to a course, ask if open textbooks can be included (even though they are free). Below is an example of how the Langara College includes open textbooks in its bookstore catalogue—in this case, OpenStax Astronomy for section 001 of the ASTR 1102 course.
Some bookstores also provide hard copies of open textbooks for sale. (The price is typically much lower than standard textbooks.)
- Talk to the library about including sample printed open textbooks, as reference materials, in its stacks for students and instructors to view as well as links to online versions. At Douglas College, several open textbooks are listed in its library’s catalogue, such as OpenStax’ College Physics.
When an instructor adopts an open textbook, inform the library and ask that it be added.
- Mark course sections that use an open textbook or other OER in the course schedule or catalogue. Then, use this resource to record student savings based on actual student enrolment. Below is an example of how SFU includes the open textbook OpenStax College Physics in the course outline and materials section for the D100 section of PHYS 100 offered in the Fall 2019 term.
Also reference the OER & Low-Cost Labeling Implementation Guide from Washington State Board for Community and Technical Colleges.
- Consider OER during course design and redesign with the help of the institution’s instructional designers. If an open textbook or other open educational resource is assigned to a course during its creation or revision, then adoption is a given. (For more information see Course redesign with OER and Course mapping/tracking in the Information Directory.)
- Provide OER grants to encourage faculty to create, adapt, or otherwise consider or use open textbooks or OER. Follow up on completed projects to learn which have been adopted in the classroom. Thompson Rivers University is one institution that has taken this step.
- Celebrate faculty who have contributed to or are using OER, and allow adopting instructors to share why and how they took this step with their colleagues. BCIT has done this with its Open Education Roadshows.
Hidden Adoptions
Open textbooks are expanding at a growing rate, and finding them (in collections, on publishing websites, and posted on a variety of platforms by the authors) and tracking their usage by faculty can be difficult. Or it can be viewed as a puzzle—and a fun one at that. As instructors reveal what OER they are accessing and where they are accessing it, take note and find out if these systems and libraries will share statistics about adopting faculty from your institution. Alternately, review published adoptions as a starting point.
For example:
- OpenStax:
- Open Syllabus Project: Open access/licence resources
- For information about a specific location and institution, select a book and refer to the accompanying map.
Permissions
The following works are reproduced with permission.
- Screenshot of bookstore web page is courtesy of the Langara College Bookstore
- Screenshot of library catalogue is courtesy of the Douglas College Library
- Screenshots of course outlines and materials are courtesy of the Simon Fraser University Library
- Thanks to Amy Hofer, Open Oregon, for this idea. ↵


1.3 Postoperative Care
Postoperative care is the care given after a surgical procedure. The type of postoperative care depends on the type of surgery, as well as health history. It includes pain management and monitoring of the surgical site. Postoperative care begins immediately after surgery and continues until the patient is discharged from the clinic.
Duties of the CDA during the Postoperative Phase:
• Assessment in the postoperative phase begins with monitoring the patient’s airway, breathing, circulation, followed by vital signs and responsiveness (sedation)
• The patient must be constantly monitored until they are awake, responsive, and breathing well (sedation)
• Assess vital signs, bleeding, pain, orientation and mobility and report any concerns
The CDA’s responsibility at the conclusion of the postoperative phase:
• Make sure that patient has a ride home and will remain with a responsible adult (sedation)
• Review and give written Post-Operative recovery instructions
• Review and give written Post-Anesthesia instructions (sedation)
• Ensure that the patient has their prescription for pain medication and post-surgical antibiotics (optional)
The most common postoperative complication following Oral Surgery is nausea and vomiting!
The risk factors include being female, a history of motion sickness, and length of procedure. Maintaining a soft diet following surgery and especially when taking pain control medications is key to avoiding nausea.


2.4 Critical Thinking and Writing
What is done with the obtained information? It is processed and guides an opinion based on the evidence presented. This is a form of critical thinking, which is the ability to construct a skeptical and unbiased analysis by providing factual and empirical evidence to advance knowledge and learning. The proficiency to critically think means to look at a problem or situation that is made known, think through the issues that are related to it by identifying, reasoning, and evaluating. Then, provide a solution that actively addresses the problem or issue. Use the ‘clear image analogy’ when attempting to describe critical thinking. At the beginning of identifying the problem, the image is blurry, after working through the problem with critical thinking, the image becomes clearer.
Within scholarly work, the concept of critical thinking is important for producing effective research. This means the difference between critical writing and descriptive writing. Critical writing analyzes a topic or a potential issue, observes its strengths and weaknesses, and finds potential solutions and alternatives for additional analysis. Descriptive writing, on the other hand, uses the dates, locations, minor statistics, and a summary of events of the issue. Descriptive writing can also be used as a dramatic tool for prose and poetry, describing events with clear detail. It is most commonly found in certain publications, such as news articles. This is not to say that descriptive writing has no place within academia, however, common academic writing relies on the usage of critical writing.
Descriptive writing, like the news example, follows the subjective details given by individuals who are willing to talk to reporters as sources of information. Most material comes from these sources, and descriptive writing is a second-hand account from another individual. Critical writing employs the concept of critical literacy, which focuses on finding reliable sources, trustworthy data, and creating sound arguments. For example, when writing an academic paper, using one news source might not be enough to effectively critique the subject. At the very least, observing three to five peer-reviewed research articles is the most effective way to garner a general consensus on a topic or issue. News media would be a secondary source to reflect the evidence provided in peer-reviewed research. This uses the forms of logic and analysis within academic writing as an important aspect of learning and education.
- Logic: The acceptance of rationality or preposition, based on reasonable ground
- Analysis: Critically assessing with the five W’s and one H
- Who are the researchers? Is it sponsored by public or private interests?
- What kind of source is it? Is it a book, news report, or peer-reviewed journal article?
- Where was this information published (e.g., CNN vs. Canadian Journal of Higher Education)?
- When was this information published? Is it current?
- Why was this research published? What were the researchers looking to answer?
- How was it published? Was it a peer-review process or self-published?
Critical Thinking’s Impact on Learning
The impact that critical thinking can have on learning is to expand the horizons of people with different knowledge streams. Critical thinking is an important factor towards interdisciplinary learning, as it provides an individual with the tools to develop new knowledge in different areas. In a study of Malaysian medical students, the implementation of critical thinking in the development of research protocols was beneficial to applying new concepts towards higher-order thinking[14]. When all of the factors of critical thinking and critical literacy are taken into account, it can provide a framework for more enhanced learning towards finding new and divergent ways of thinking.


College of Social and Behavioral Science
92 Air Pollution and Cardiometabolic Diseases: Differences by Occupational Group
Bibiana Rodriguez Vega; David Curtis; Ethan Cheung; Susan Walker; and Mari Takano Okubo
Faculty Mentor: David Curtis (Family & Consumer Studies, University of Utah)
Introduction and Literature Review
Air pollution exposure represents a significant, environmental hazard in the U.S., detrimental in daily life and health, but often overlooked. Exposure to air pollution has been associated with increased risk of cardiovascular diseases (CVD) (Brook et al., 2010), metabolic diseases (Ning et al., 2021), and respiratory diseases (Xing et al. 2016). However, not all people are affected to the same degree by air pollution, because of both differential exposure and susceptibility. For instance, fetuses and young children, elderly people, or persons with pre-existing health conditions (e.g. allergies, pulmonary and heart diseases) are at higher risk of being harmed by air pollution due to physiological susceptibility (Makri & Stilianakis, 2008; Peled, 2011). Additionally, socio-economically disadvantaged people are more vulnerable to pollution-related health risks, potentially due to a combination of pre-existing conditions (Makri & Stilianakis, 2008), limited resources, education, access to services and infrastructure, or living and working conditions (Peled, 2011). For example, commuting or residing in places with high pollution levels, time spent outdoors, or working conditions (Makri & Stilianakis, 2008), are often correlated with socio-economic status (SES).
There is an established link between air pollution and adverse effects on individuals’ health, but there is a lack of research on occupational differences in how air pollution is related to disease outcomes. Previous research has focused on the negative effects of occupational exposure to air pollution (risks to a mix of agents, e.g., physical, chemical, or biological) on health outcomes. Most studies have focused on particulate matter and pollutant concentrations within occupational settings (Fang et al., 2010). Studies indicate that occupational exposure increases the risk of incidence and exasperation of CVD and respiratory diseases (Fang et al., 2010; Nishida & Yatera, 2022). Also, there is an adverse association between both SES characteristics (e.g., income, occupation, and education) and respiratory diseases (Zhang et al., 2021; Sidorchuk et al., 2009; Mao et al., 2001).
Occupational differences in air pollution-related health risks may exist because of differences in job strain, work environment exposure, and occupational status. Individually, each of these factors affect mortality (Matthews et al., 2023; Gonzalez-Mulé & Cockburn, 2021; Christ et al., 2012). Job characteristics can act as a buffer on the positive relationship between job strain and mortality. For example, high job control indicates better physical health even with high job demands (Gonzalez-Mulé & Cockburn, 2021). However, not much is known about occupational differences based on ambient air pollution exposure.
The central aim of this study is to examine how occupation moderates the relationship between air pollution exposure and cardiometabolic (CMB) and respiratory disease outcomes, based on differential exposure or susceptibility to ambient fine particulate matter (PM2.5). Also, this study will examine job characteristics (i.e., job strain, environmental exposure at the workplace, occupational status) that may act as moderators, potentially explaining differential disease outcomes from air pollution exposure across occupational groups.
Materials and Methods
Data and Study Population
We used data from Waves 2 and 3 of the Midlife in the United States (MIDUS) study which is a national study that focuses on health and aging processes among US adults during midlife. The first wave of data collection took place in 1995-96, with follow-ups in 2004-05 (Wave 2; M2) and 2013-14 (Wave 3; M3). The MIDUS study includes a random national sample, oversamples in metropolitan areas, siblings of the random sample, a twin sample, and Black/African Americans from Milwaukee (MKE). We restricted the sample to adults younger than age 65 who were employed at M2. For the current study, sample sizes were 2,338 and 1,683 adults when predicting prevalence and incidence, respectively.
Measures
Two outcomes were measured in this study: cardiometabolic and respiratory diseases. Cardiometabolic (CMB) disease was assessed using a summary score of four health indicators: high blood pressure/hypertension, heart trouble, diabetes/high blood sugar, central adiposity. Participants were asked if they had received medical diagnosis or treatment for hypertension and diabetes in the past 12 months. For heart trouble, participants were asked if heart trouble was suspected or confirmed by doctors. Central adiposity was determined by waist circumference, with cutoffs of ≥102cm for males and ≥88 cm for females. Central adiposity is also a more reliable predictor of cardiometabolic disease and mortality compared to general obesity (Ashwell et al., 2012). We standardized these four indicators and calculated the average to create a summary measure of CMB disease. For respiratory disease, participants self-reported if they had experienced or received treatment for lung conditions in the past 12 months: asthma, bronchitis, and emphysema. If participants experienced any of the conditions, they were categorized to have respiratory disease.
PM2.5 air pollution was measured by the annual concentration of fine particulate matter of 2.5 µg/m3 in diameter at the census tract level. We used estimates from the Center for Air, Climate, and Energy Solutions v1 empirical models, which are based on US EPA monitoring sites and air quality monitoring networks. The modeled values were calculated using land use variables and satellite-derived estimates (Kim et al., 2020). We included 5-year averages of PM2.5 air pollution for 2001-05 to predict M2 outcomes, and 2006-10 to predict change in outcomes between M2 and M3.
To assess major occupational group, we used participant-reported current occupation at M2 and M3. Occupation was classified using the 1990 US Census Standard Occupational Classification for M2 (Holman & Hughes, 2021; Scopp, 2003). The occupation classification initially included executive, administrative, and managerial; professional specialty; technician and related support; sales occupation; administrative support including clerical; service occupation; farming, forestry, and fishing; precision production, crafts, and repair; operator, laborer, and military categories. We then grouped these classifications into five categories of interest based on common characteristics: 1) managerial and professional, 2) administration, 3) sales, 4) service work, 5) manual occupations.
To assess major occupational group, we used participant-reported current occupation at M2 and M3. Occupation was classified using the 1990 US Census Standard Occupational Classification for M2 (Holman & Hughes, 2021; Scopp, 2003). The occupation classification initially included executive, administrative, and managerial; professional specialty; technician and related support; sales occupation; administrative support including clerical; service occupation; farming, forestry, and fishing; precision production, crafts, and repair; operator, laborer, and military categories. We then grouped these classifications into five categories of interest based on common characteristics: 1) managerial and professional, 2) administration, 3) sales, 4) service work, 5) manual occupations.
For individual-level control variables, we included the following factors related to CMB and respiratory diseases: age (in year), biological sex (male, female), race (non-Hispanic White, nonHispanic Black, Hispanic, non-Hispanic native Americans, non-Hispanic others), MIDUS sampling types (random digit dialing, sibling, twin, city oversample, Milwaukee [MKE]), marital status (married, separated/divorced, widowed, never married), and current smoking status. We also included three area-level control variables in approximately 2010: tract walkability (compactness composite index), tract median household income (derived from 5-year estimate from 2006-10 American Community Survey), and urban-rural classification (city, suburb, town, rural).
Analysis
Linear regression and linear probability modeling were applied to assess the impact of PM2.5 air pollution on prevalent and incident CMB and respiratory disease, respectively. To assess the moderation of occupational groups in the associations, we created an interaction term to test how the impact of PM2.5 air pollution on each outcome varied by occupational groups. Next, to test underlying process variables, interaction terms by PM2.5 air pollution with job strain, occupational status, and outdoor work environment were added. Standard errors were adjusted to account for clustering in census tracts.
Results
The average age of the sample at M2 was 49 years old (SD = 7.90) with a range of 30-64 years old. The sample was 52.6% female, 71.4% were married (15.5% separated/divorced, 2.0% widowed, 11.1% never married), and 53.46% were non-smokers. Table 1 shows that participants across occupational groups had similar exposure to PM2.5 air pollution. Rates of CMB disease and respiratory disease appeared to vary across occupational groups, with the lowest rate among professional occupations at both waves while prevalent respiratory disease was highest for participants in service occupations at M2 and those in sales occupations at M3. 33% of participants with administrative occupation experienced job strain, while those in professional occupation had, on average, higher occupational status. Also, participants with manual occupation spent more time in outdoor work environment (mean = 2.81).
Table 3 shows the associations between PM2.5 air pollution and CMB disease and how it varied by occupational groups and the process factors (job strain, occupational status, outdoor work environment). In Model 2, we found that PM2.5 air pollution did not predict CMB prevalence and incidence. Relative to professional occupational group, participants in administrative, sales, and service occupations likely had higher CMB prevalence. On the other hand, no occupational differences were found for CMB incidence. In addition, as shown in Model 3, occupational factors did not act as moderators for the prevalence and incidence of CMB disease.
As for respiratory disease, PM2.5 air pollution was not a predictor of respiratory disease prevalence and incidence, as shown in Table 4. In terms of occupational difference in Model 2, the only group with a significant difference relative to professional occupations was the administrative group when predicting incidence respiratory diseases. Specifically, compared to professional occupation, participants in administrative occupation likely have 4-percentage-point lower in the risk of incident respiratory disease. We also found two interactive effects by PM2.5 air pollution with sales and service occupational groups. Relative to the professional occupational group, participants in sales and service occupations likely have higher incident respiratory disease. No interactive effect was found for the prevalent models. According to Model 3, job strain was the only process variable that predicted prevalent respiratory disease. Job strain was associated with an increase in respiratory disease prevalence, but not when interacting with PM2.5 air pollution. The other process variables did not have a significant effect on respiratory disease, even when interacting with PM2.5 air pollution.
Discussion
This study adds to the current literature on PM2.5 exposure and health outcomes as it is among the first studies to examine occupational group and occupational characteristics as moderators of non-occupational air pollution exposure. The results of this study generally did not support our expectations that the health risks of PM2.5 exposure would vary be occupational factors. Several estimates were in the expected direction but not significant, which could be attributed to limited statistical power. However, overall, the estimates showed a mixed pattern of relationships that indicate that occupation likely does not moderate the link between residential air pollution exposure and health outcomes.
Air pollution was not associated with CMB disease or respiratory disease, as observed in Table 2. The null relationship between PM2.5 and cardiometabolic disease outcomes among workingage adults with employment stands in contrast to a significant and seemingly stronger relationship that has been reported among the larger MIDUS sample (in a study under review), which includes individuals who are retired or do not currently hold employment—both of which are groups that spend more time at one’s residence where the PM2.5 exposure may be more accurate. It would be interesting to research why fine particulate matter does not have the same effect on cardiometabolic diseases as it does respiratory diseases. Is this due to sample size, or is there a biological reasoning behind this finding? Occupation did not impact the relationship between air pollution and CMB disease outcomes. In relation to professional occupations, administrative, sales, and service occupations had higher CMB prevalence. The prevalence increase could be due to the environment in their jobs — added stress of job demands and the lifestyles of the people who work these jobs. A reason that job characteristics did not moderate the relationship between air pollution and CMB disease outcomes could be due to resources available to employees as they work. We looked at outdoor work environment, job strain, and occupational status. It could be that the participants of the study are not experiencing much distress at their job.
The statistically significant lower incidence of respiratory diseases (Table 3) among administrative occupations, in comparison to the professional group, may suggest that there are occupational differences playing a role in this association across occupations. Based solely on the results of this study, it is challenging to pinpoint an explanation as to why there was a difference specific to the administrative group (and not the other ones) or what specific factors could have contributed to those results.
Job strain was associated with higher prevalence of respiratory diseases (Table 3), an unexpected result as most research to date has indicated that there is no robust evidence for the association between job strain and risk of respiratory diseases (Magnusson Hanson et al., 2017; Heikkilä et al., 2014). On the other hand, the interaction between PM2.5 and job strain, which was not significant, possibly indicates that the combination of air pollution exposure and job strain is not a source/cause of higher risk of respiratory diseases.
Finally, the significant interactive effect of PM2.5 and sales and services occupational groups (Table 3) could mean that those occupations are more often exposed to or affected by air pollution exposure. Given that the air pollution measure of the study was based on residential tracts, those differences could also be explained by socio-economic differences, in which residential areas of lower socio-economic status tend to experience higher concentrations of air pollution (Hajat et al., 2015), assuming that sales and services are linked to low socio-economic status.
This study, however, is not without limitations. The PM2.5 measure was based on residential location, which differs from the workplace air pollution exposure, such that our air pollution measure represents long-term exposure only when within one’s residential neighborhood. Our sample was of moderate size but was less racially/ethnically diverse than the national population, limiting our ability to generalize. Also, the measures of CMB and respiratory diseases were selfreported, creating potential response bias in our dataset. Future research in the topic is needed, as there is limited research addressing this issue, and when doing so, it should address those limitations previously mentioned, for more generalizable conclusions.
Conclusion
In our study, the association between PM2.5 and incident respiratory diseases varied by occupational groups. Meanwhile, the association between PM2.5 and CMB diseases (prevalence and incidence) did not vary by occupational group. PM2.5 more strongly predicts incident respiratory disease for individuals in sales and service occupations compared to professional, but findings were mixed as the same moderation effect was not detected for prevalent respiratory disease. Although participants in administrative, sales, and service occupations had higher prevalent CMB disease in comparison to professional/managerial (white collar) professions, neither occupational group nor occupational factors acted as moderators, as previously hypothesized.
References
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Media Attributions
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172 Putting It Together
Summary
Political parties are an essential component of democracy. Two parties dominate American politics: the Republicans and the Democrats. These major parties are ideologically ambiguous in that they take middle-of-the-road rather than extreme positions on issues.
Societal groups that gravitate toward particular political parties can form partisan coalitions. The weight of these coalitions can shift during critical elections, resulting in an emergent majority that dominates the system for as long as the new coalition holds together.
Third parties offer an alternative to the dominant Republican and Democratic parties, but they have difficulty surviving. They arise to challenge the two major parties when people feel that their interests are not being met. There are numerous challenges faced by third parties in American politics, including winner-take-all elections, legal obstacles, lack of resources, and limited media coverage.
Interest groups are another important component in the political system. Interests groups use lobbyists to influence public officials in all government branches.
Numerous factors determine the success or failure of interest groups in achieving their policy objectives. These include their assets, objectives, alliances, visibility of their involvement in policy decisions, responses to political change and crises, and depictions in the media.
Relatedly, there is a hierarchy of interest groups’ relations with policy makers. Pluralists regard interest groups as essential to American democracy; critics, however, believe that business interest groups are too dominant. Business interest groups have several advantages that enable them to achieve their policy objectives but also several disadvantages, including negative media depictions.


23 Substance Abuse
Juliana Black; Ashley Regan; and Jianing Zhang
Introduction
Substance abuse is a complex disease that heavily impacts a large variety of people. It can simply be stated as excessive use of psychoactive substances, which include alcohol, prescription drugs, and illegal drugs. The dependency can lead to a variety of consequences, but the most concerning side effect is the potential for emotional, physical, and social harm. If you are suffering from substance abuse, please seek help immediately. You are not alone, it can and will get better. Addiction is a lifelong disease that requires active, committed treatment.
What is substance abuse? What does it look like? Who does it affect?
Substance abuse impacts everyone- age, race, and status simply play no factor in this heavy dependency. The typical age range for substance use is 15- 65 years old. There are several factors that can determine one’s resilience for turning directly to a substance when hardship presents itself. College students tend to use stimulants and depressants, such as alcohol, marijuana, prescription medications, and caffeine. Internal and external stressors can increase the likeliness of a true dependency resulting from frequent use. Students, young and vulnerable adults, turn to easily accessible substances that offer no judgment to cope with the extreme stress new experiences can bring on. This can ultimately lead to physical and psychological pain- deteriorating the mind and body of the user. Any recreational drug use has detrimental effects. The brain of a college student which is not nearly fully developed faces permanent damages.
Drugs & Effects
Drug Class: Opiates
- Examples-Heroin, Morphine, Fentanyl, Opium, Oxycodone, Vicodin, and certain prescription pain relievers
- Physical Effects-Decreases pain, pupil dilation, gut motility, and respiratory function
- Psychological Effects- Doses can cause: pain relief, euphoria, and sleepiness
- High doses can result in death due to decreased respiratory function
- Addictive in certain cases
Drug Class: Stimulates
- Examples- Cocaine, Adderall, MDMA (such as molly), Methamphetamines, Amphetamines, Caffeine
- Physical Effects- Increased heart rate, blood pressure, and body temp.
- Psychological Effects- High doses can: increase agitation, paranoia, anxiety & may cause hallucinations and seizures & Low doses can: increase alertness, cause mild sensation of euphoria, decrease appetite
- If a user takes a high dose of MDMA brain toxicity and death can occur
- Addictive in certain cases
Drug Class: Depressants
- Examples- Alcohol, Benzodiazepines (such as xanax), Barbiturates
- Physical Effects- Decreased heart rate, blood pressure, and respiratory functions
- Psychological Effects- High doses can: Induce sleep, cause disturbance of motor functions, and memory loss & Low doses can: Increase relaxation & decrease inhibitions
- High doses can cause death
- Addictive in certain cases
Drug Class: Hallucinogens
- Examples- LSD, DMT, Peyote, dissociative anesthetics ( such as PSP & ketamine), marijuana
- Physical Effects- Increased heart rate and blood pressure that can dissipate over time
- Psychological Effects- Doses can cause: Mild to extreme perceptual changes
- Variability in effects is based on drug strain, the method of ingestion, and individual differences
- Addictive in certain cases
Side Effects of Substance Abuse
Drugs can cause large amounts of dopamine, which is a neurotransmitter that helps people regulate mood, motivation, and a sense of pleasure. Drugs can change the way the brain works and interfere with people’s ability to choose, leading to a strong desire for drugs. Over time, this behavior may turn into substance dependence or drug addiction. Addiction can impair cognitive control and inhibitory control of behavior. Substance abuse can lead to numerous physical, social, and emotional harms. The long-term effects of substance abuse will vary depending on the drug being abused. Common effects of drug abuse include liver disease, hepatitis, HIV, trauma, cancer, heart attack, seizures, and even death. Drug use can also lead to unemployment, divorce, domestic abuse, and bad relationships with loved ones. For students who use drugs, this may result in lower grades, lower attendance, and lower academic performance.
The Psychology Behind Addiction
If an addictive drug is used repeatedly, the user may develop a codependent relationship with the effects that the substance can elicit. Certain substances evoke the dopamine transmitters in the brain which then stimulates its “reward systems”. This process can lead to a build-up of tolerance and dependence on a particular drug. Once dependent on a drug, repeated use is most likely to occur because of the user’s desire to achieve the pleasurable “high” of the drug and get away from the “lows” of withdrawal symptoms. However, if the subject removes the withdrawal symptoms there is still no guarantee a relapse won’t happen. A theory suggests that relapse occurs because it is the brain’s conditioned response to craving the drug.
What kind of treatment is available to those suffering from substance abuse?
Treatment for such a disease is such an incredibly difficult piece to ask for. Taking responsibility for what you have been suffering from is the first step to recovery. Acceptance that there is a problem. Recovery takes a physical and mental toll on the body. You are going through intense, dangerous detox, slowly straying away from the constant use. Withdrawal symptoms must be closely monitored to ensure the safety of your body so you can proceed with healing. Rehab centers are an excellent choice.
Step two is more therapeutic. The gathering of a dependable support system is essential, including outside sources (therapy, narcotics anonymous, alcoholics anonymous, al-anon/ Alateen) and close family/ friends necessary for emotional support and stability. The total cost you can expect will vary. Medical rehab centers are equipped to treat the harsh side effects of withdrawal and can provide a safety net for patients. Therapy is also effective in actively fighting the urge to revert to old addiction habits. Communicating openly with a stranger about issues that may be faced is how you continuously choose to fight your sickness. For many college students, high costs can be detrimental to the future they are working so hard to build. Expensive treatment may not be the most practical resources for students. However, there are local resources that are open to helping those that need it. Paradise Valley Community College deeply cares for the well-being of all students. They have several resources you can easily access by reaching out to the staff such as social workers and more than qualified therapists. The community is here for you.
Conclusion
Substance abuse is a complex illness that users choose to fight on a daily basis. Individuals make the choice to actively fight the urge to use. Utilizing the community or personal resources that are available, and properly educating yourself about substance abuse is crucial to recovery. Though it is a long, continuous road, it is possible to heal from addiction and lead an ordinary life.
Helpful local resources:
- PVCC Counseling: The counselors can help you make correct and positive decisions and resolve all questions and challenges to prevent crises including drug dependence. The service is free, voluntary, and confidential.
- Arizona Behavioral Counseling and Education Inc.: This Company offers a variety of drug abuse recovery programs. The duration and intensity of these procedures range from 18 hours to 90 hours.
- TERROS Substance Abuse Prevention: TERROS is a community-based behavioral health organization that can help people recover from drug abuse, mental illness, and other behavioral health problems. Drug abuse prevention education for teenagers and families.
- Family Service Agency: Family service agencies provide home treatment, relapse prevention, medication, and psychiatric evaluation. They provide individual, group, and intensive drug abuse treatments.
- The Haven : The Haven provides women (and their families) with gender-specific substance use services in a safe, supportive environment without judgment and stigma.


4X
What Is Continuity?
I’m sure that in your first pre-calculus class, you learned about the continuity of functions. If asked to define what this meant, most people would say, “A function is continuous if the graph of said function can be drawn without lifting the pencil or having any jumps.” Up until the late seventeenth century, this definition was more than sufficient. The Intermediate Value Property is one way to more precisely define continuity.
Definition 5.27: (Intermediate Value Property) Let f be defined on an interval I. Suppose that for each a, b ∈ I with f(a) 6= f(b), and for each d between f(a) and f(b), there exists c between a and b for which f(c) = d. We then say that f has the intermediate value property (IVP) on I. (Thompson et al. 305)
Example 5.28: Let F(x) = sin x−1 if x =/ 0
0 if x = 0.
The graph of F is shown below. You may wish to verify that F has the IVP. In particular, F
assumes every value in the interval [−1, 1] infinitely often in every neighborhood of x = 0. ◭
We haven’t yet made precise the phrase “the graph has no jumps,” but the IVP seems to convey that
idea well enough. Since this property is so easy to describe and appears to have content that is easy to
visualize, why not take it as the definition of continuity?
Before attempting to answer that question, let us offer a competing phrase to capture the idea of
continuity: “If x is near x0, then f(x) is near f(x0).” As stated, this phrase is not precise, but we can
make it precise using the limit concept. This phrase could be interpreted really as asserting that
f(x0) = lim f(x).
x→x0
According to this criterion our function F of Example 5.28 would not be continuous at x0 = 0, because
F(0) = 0, but limx→0 F(x) does not exist.
We shall see presently that the definition based on limits allows the development of a useful theory.
We’ll see that the class of continuous functions [as defined using equation (2)] is closed under addition
and multiplication, and that such functions have many other desirable properties. For example, the class
is closed under certain kinds of limits of sequences, and every continuous function on [a, b] is integratible.
On the other hand (as is shown in the exercises), none of the analogous statements is valid for the class of functions defined by IVP. Thus a theory of continuity based on the limit concept allows a rich structure and enjoys wide applicability, whereas one based on the IVP is rather limited. In addition, the fundamental notion of limit extends to much more general settings than R. In contrast, extensions of IVP, while possible, are peripheral to mathematical analysis. (Thompson et al. 306-307)
Continuity goes beyond just a overall generalization of the behavior of a function. It is also important that the points on a graph themselves are continuous.
Definition 5.29: (Continuous) Let f be defined in a neighborhood of x0. The function f is continuous at x0 provided limx→x0 f(x) = f(x0). (Thompson et al. 309)
This means that the function is continuous at a point x only if the limit of the function as it approaches that point is equal to the value of x itself. With this definition, it is possible for x to not be continuous in a couple of ways:
- If f is not defined at x
- limx→x0 f(x) does not exist
- F is defined at x and limx→x0 f(x) exists but
limx→x0 f(x) =/ f(x0) (Def. 5.29)
Examples (UCDavis p. 24):
Example 3.9. The sign function sgn : R → R, defined by sgn x = 1 if x > 0, 0 if x = 0, −1 if x < 0, is not continuous at 0 since limx→0 sgn x does not exist (see Example 2.6). The left and right limits of sgn at 0, lim x→0− f(x) = −1, lim x→0+ f(x) = 1, do exist, but they are unequal. We say that f has a jump discontinuity at 0
Example 3.10. The function f : R → R defined by f(x) = { 1/x if x ̸= 0, 0 if x = 0, is not continuous at 0 since limx→0 f(x) does not exist (see Example 2.7). Neither the left nor right limits of f at 0 exist either, and we say that f has an essential discontinuity at 0.
These definintions and examples relate to interior points in the function. There are also cases in which the points we speak of are not the interior points of the function but any arbitrary point or set.
We already know how to find the limit as our function approaches a point and use that information to see if the function is continuous at the point. What we don’t know yet however, is how to handle a situation in which our function has clearly defined endpoints. There is no way to check from both sides where the limit as our function approaches enpoints seeing as there is only one way to approach an end point. We change up our search for the limits as such:
lim x→a+ f(x) = f(a) and lim x→b− f(x) = f(b).
In this case, we’re approaching our endpoints from within the domain (a,b) in the real numbers.
https://math.stackexchange.com/questions/107296/can-continuity-be-proven-in-terms-of-closed-sets
This site has a nice discussion thread that goes through the proof of continuity using closed sets.
https://www.dpmms.cam.ac.uk/~wtg10/easyanalysis1.html
This site goes over the proof of of continuity using open sets
https://www.math.ucdavis.edu/~hunter/m125a/intro_analysis_ch3.pdf
Pages 21-24 give some more insight on the definitions related to continuity as well as a couple examples of its use.