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| Pub Dom Img adapted by SM |
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in Eng?ish
Ooooh, hallo dear English thinking-speaking-hearing listener, welcome to the podcast number five of me, @sciencemug , the twitter account/ blog that talks about science aaand at the same time dives into the warm ocean of its own inconsistency while duelling with karma in punta di fioretto.
Aaand that does all of this in Eng?ish that is English-question mark, the kind of language that is to real English what “The Martian” is to plausibility and a frappuccino to a good idea. (And a Martian sipping a frappuccino to something I never want to witness. Never.)
Sooo, right now, with the voice of the dumb human I control via a wireless-voodoo trick, I’m gonna tell you the tale of China’s capital megacity Beijing which stubbornly wears a powerful ring of pollution -ooh, my precioussss!- in spite of risking, like that, its own demise…
Mark Z. Jacobson, Son V. Nghiem, Alessandro Sorichetta and Natasha Whitney are four scientists from Stanford, Cal Tech and the University of Southampton who decide to investigate Beijing, the Chinese mega-capital city and the extent and consequences of its tumultuous urban expansion in the new millennium’s first decade.
Sooo, the four science pals, spearheaded by professor Mark Jacobson, who I’m gonna refer to MJ from now on, ‘cause, you know, I’m a big fan of Micheal Jordan and I’m sure he does like that a Stanford’s brain have the same initials of his, well prof MJ and his science buddies do two things.
One, they collect and analyze satellite and geo-located crowd-sourced data about the explosive urbanization of Beijing. Two they then evaluate the effects of such an expansion via a very sophisticated state of the art computer model.
And what they find, dear listener, it’s kinda eerie, ‘cause the Chinese city’s growth has created what the researcher call a “ring of impact”(P) that surrounds the city and it’s strangling it with environmental problems of all sort.
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| Ring of impact (by sciencemug) |
[The ring's pic is a Pub Dom Img adapted by sciencemug; source Wikimedia Commons]
But let’s start with phase one of our researchers’ study: the “collect and analyze data”.
MJ and colleagues need to build a data set that take into consideration the changes of Beijing’s urban coverage in space and time, the time being the 2000-2009 period. To do that, prof MJ’s science squad needs, over the Beijing area, a large high-resolution coverage over a long time, and this need rules out the possibility for the researchers to count on visual satellite data. Visual satellites in fact usually collects high-res data only sporadically in time and space, while they provide wide coverage data only at a low-resolution, that is not precise enough for a reliable study about urban and suburban environments’ changes.
Sooo, professor Jacobson starts asking himself: “Who I gonna call?” while hearing in his head a catchy pop song from the eighties and seeing, always in his head that must be a very intriguing place by the way, a little green blob bouncing around till he, the professor not the little green bouncing blob, and the science bunch have an epiphany and find a solution: they call NASA.
Well, indeed the story it’s not that epic but I think that dropping the space agency’s name is a good way to get your attention back, isn’t it true dear listener? Eh? Eh? Booobs! See that? Ryan Gosling’s abs! See that? NASA! See that? Eh? Aaanyway, dear listener, here’s the real story: prof MJ’s team decides to use NASA’s QuikSCAT satellite’s SeaWinds scatterometer data; then, to make sense of these data, the team processes them with “the dense sampling method” (aka DSM)(P) (1), an innovative method that can estimate urban extent and monitor urban changes over time; finally, done all of this, the researchers can eventually map Beijing’s urban and intraurban area with a resolution of about 1 km.
Wooa wooa wooa, my hyper geeky me, wait a minute wait-a-minute, slow down a bit and restate the last sentences of yours, please. In simple words. With calm. No rush. Make it clear.
Ok! Ok, my so called normal me, ok! So, there was this NASA’s spaceprobe, the QuickSCAT. QuickSCAT was a satellite orbiting Earth at about 800 kilometers of height, launched in 1999 and died of an age-related mechanical failure in 2009. QuickSCAT mission was to check the whole globe and measure near-surface wind speed and direction under all weather and cloud conditions over Earth's oceans. So, NASA’s satellite QuickSCAT, to do its job, mounted an instrument, called SeaWinds. This SeaWinds instrument was a scatterometer, that is a microwave radar, hence it measured its own backscatter, that is the reflection of signals back to the direction from which they come, meaning the radar itself. Which, in the case of SeaWinds, as you know already, was in orbit.
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| by sciencemug |
Anyway, we are done with the satellite and the radar stuff. Now let’s go to the “the dense sampling method” (aka DSM) which prof MJ and colleagues use to make sense of these satellite and radar data. DMS is math stuff, so I’m not even
trying to explain it ‘cause to explain it, well, I should get it first, and there’s nooo way I get it (I’m a dumb blog after all). I tell you only this: “the dense sampling method” works -uh-oh big revelation here- on the backscattered data form the radar, yeah yeah, ok, I’ve told you that already just few seconds ago… But repetita iuvant, and, above all, what you don’t know yet is that DMS provides two outputs: a "mean value"(P) of the various measurements done by the just hyper-mentioned satellite’s radar, and an "index of variability"(P) (called IV) of such measurements. The mean value is the average value over an annual period of measurements, while the IV represents the variations around that mean. Ok? Ok!
Soo, "why the DMS has to produce two outputs, wasn't the mean value enough?" are you asking? Well, dear listener, the answer is that the mean value by itself is not enough to identify uniquely urban areas. Why? Well, listen to this. Urban areas have higher mean values than those of the rural areas, ‘cause, well, cities have more buildings than countryside, right? Right, but sometimes nature's tricky and natural landscapes too may have a high mean value, like, a field covered by a fat slice of snow can trick the poor radar and be mistaken for a big roof or something else "urban". So, a high "mean value" is not always an index of actual urbanization. And here is where the “index of variability” comes in handy, because IV, and I quote, “is also typically high in an urban environment due to the large backscatter variability associated with human-made targets (like different sides of buildings, orientation of building corners, and vehicle traffic)”(P). Sooo, when a spot on the ground has a high mean value aaand a hig IV, well, that's definitely a good piece of real estate!
In other words, dear normal me and listener: boobs! Ryan Gosling’s abs! NASA! By combining the mean values and the indexes of variability our good DMS, the "dense sampling method", can efficiently help to provide a map of urban extent and typology of a city (in prof MJ's case, Beijing).
Therefore, my dear dear listener, the DSM is math. And it works. And it runs on data collected between 2000 and 2009 by a NASA’s satellite’s radar.
Thus, to summarize, a dead NASA’s satellite’s radar provide prof MJ and his mates with the kind of high-resolution+wide coverage data set they need for they study on Beijing, and the “dense sampling method”, allows them to use these data to delineate and map, over time and with a resolution of about 1 km, the urban extent of the China’s capital as well as its interurban and intraurban changes.
Or, in simpler words, our fine researchers can figure out and precisely discriminate the changing presence over 10 years, in the Beijing area, of physical infrastructures such as houses, factories, shopping centers, skyscrapers and so on.
Sooo, what do our nice and clever researchers find out after using space probes, microwave radars and math stuff?
The answer after the break!
You
just listened a sample of “The sound of silence”. Download it for only
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| by sciencemug |
199.99 euros at iGoldeneardrum!
“The sound of silence”, for who’s tired of the noisy cities. Or to hear.
Ok, ok, dear listener, now let’s see what professor Jacobson and his collegues find out thanks to QuikSCAT’s data and DSM’s math.
The scientists establish that the actual urban extent within the Beijing Municipality increases of the 400% between 2000 and 2009, passing from 1105 to 4139 square kilometers. The remaining 12 thousands and something square kilometers of the official administrative Beijing municipality are non urban stuff, like rural areas, nature reserves, and mountains with just a bunch of people living on them.
So, now our researchers have numbers about the rooftops extent and about the vegetated coverage of the China’s capital city area. But, to be able to end the “collect and analyze data” phase one of their work and pass to phase two, that is the “put the data into the holy state of the art computer model and wait for a muffin and an answer different from a predictable 42 about the consequences on Beijing of those above mentioned numbers”, well to pass to phase two of their study our fellas scientists need first to gather another piece of information about the urbanization of Beijing. The researchers, indeed, also need to know the total road surface area of the Beijing municipality.
To get that professor Jacobson and colleagues go use OpenStreetMap (aka OSM) road data. OSM is a project started in 2004 and supported by the OpenStreetMap Foundation, a UK-registered not-for-profit organization. The goal of OpenStreetMap is to create, and I quote, “a free open-access database of worldwide geographic information entirely contributed on a voluntary basis”(P).
Moreover, and I keep quoting, “OSM […] road data [are] obtained primarily from GPS-enabled portable devices, satellite images, and commercial and governmental data sets [aaaaand the data are] contributed only by registered members [who do that freely] and [the data] are not reviewed before being made public on the Internet”(P). The good quality and general reliability of OpenStreetMap road data have been verified by different independent studies that focused on Europe, US and China. One of this studies in particular is published in 2014 and assesses the completeness of OSM road data about Beijing, and establishes a rate of accuracy of OSM data superior to the 70%.
Sooooo, prof. MJ and the others get the Beijing OpenStreetMap road dataset of september 2013 and they then overlay it onto the Beijing QuikSCAT urban extents data of 2000 and 2009. Like that the researchers can calculate the total surface covered in the Chinese megacity by 29 different types of roads (regular roads, motorways, bus guideways, cycleways, crossings etc.) in 2000 and 2009 respectively. Jacobson and his group determine thus that, in 2009, almost 7% of Beijing city’s area is covered in roads, the main ones emanating radially from the city center (P).
Aaaaaaaaaaah, dear listener, they did it! They finally did it! Our determined scientists have now numbers about rooftops, roads, vegetated areas and therefore of bare soil surface (that is the total surface minus the other ones) of Beijing from 2000 to 2009, and they thus can pass to phase two, they can finally get to put all these amazing shining brand new numbers into their magnificent hyper-cool sophisticated self medicating state of the art and probably even bewhiskered computer model: the GATOR-GCMOM model (2), the model that can tell: the consequences on Beijing of the just mentioned numbers, who’s gonna win the “Best new act” at the MTV Asia Awards 2016, and whether or not Sheldon, at the end of the show, is going to come out as a sentient robot sent on Earth by a metadimensional race of lichens with opposable thumbs and neat goatees.
Soo, do you wanna know all about the GATOR-GCMOM computer model and its prophecies?
Ahaha, right after the commercial!
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Are you looking for a quiet place were you can hear the birds singing and not some boozer barfing his cheap soul on the landing next your door? A place where, at night, you can actually see the stars instead of the pink neons of some lousy theatre staging something like "The somersaults of a white sock" or "Apocalypse of a gluten free dorito"? A piece of Earth were you can smell the scent of a hundred flowers and not the garlic-onion-after vomit deadly breath of the above mentioned boozer who, by the way, is the author of both the above mentioned plays?
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| Created by SM from a Pub Dom Img |
Well, you have no chance at all to find such a place. Because you are broke.
This is a public announcement from the "LTTTT" the "League of the Truthful Tellers of The True Truth".
Sooo, GATOR-GCMOM computer model. GATOR-GCMOM is an acronym that stands for: “gas, aerosol, transport, radiation, general circulation, mesoscale, and ocean model” (2), where, in meteorology, mesoscale means the study of atmospheric phenomena with dimensions that range from 10 to 1000 kilometers, like thunderstorms and land-see breeze.
The model is born as GATOR (arrhrh! Eheh, sounds like a WWE wrestler covered in a brownish-green costume made of latex and real scales: GATOR! GATOR! GATOR! GATOR vs Mr Crocodile Dundee’s Nephew! GATOR versus ADE! Eheh, got it? Got it? Eeeeeeeh…).
So the model is born as GATOR in 1990 as a regional scale model, then, in 1994, the global scale version of the model comes. The first 2-D version of GATOR-GCMOM appears in 2001. The 3-D version pops out in 2004 and the final and present version of the model dates back to 2011.
GATOR-GCMOM model treats all sort of stuff like gasses, advection, convection, heat fluxes, water vapor fluxes, horizontal and vertical atmospheric gasses’ fluxes, gas-to-cloud conversion, gas-cloud exchange, gas-ocean exchange, gas-to-particle conversion, aerosol particles, aerosol effects on clouds, radiative processes, energy transfer through roads overlying soil, and multiple layers of rooftop material over air, and vegetation, foliage temperatures, foliage and soil humidity, natural emissions from lightning, soils and vegetation (like different chemical compounds, bacteria, dust, pollen, spores and so on), surfaces albedo, ocean energy and chemistry diffusion, ocean-atmosphere exchange and the list keeps going and going.
The GATOR-GCMOM model, in short, deals with interactive feedback among all sort of things and produces an output that quantify the impacts of urbanization on a huge variety of atmospheric parameters.
So Jacobson and his sciencepals, they take all the urban-satellite and the road-GPS data they have collected and processed via DMS about Beijing 2000 to 2009, and they put these numbers into the GATOR-GCMOM model and they run 4 simulations: two simulate what happens in Beijing in January and July 2000, while the other two simulate what happens in the megacity in the same months of 2009. Each simulation is run for one month straight and, I quote again -how awfully odd from me eh?- “the only difference between the [2000] and [2009] simulations is the greater road and roof areas in 2009. All else, including the initial meteorology and emissions, is the same between the two simulations in order to isolate the impacts of infrastructure changes only”(P). Besides, these simulations don’t consider any change in anthropogenic emissions, that are the man-made emissions like those from factories and houses.
Sooo, dear listener, what do these simulations tell? Well, the simulations reveal a sombre picture. Turbo urbanization of a quadrupled Beijing in a decade has created what the Jacobson’s team calls a “ring of impact”(P) around the city. This “ring of impact” is characterized by many factors, all of them nasty, nasty beasts for environment and human health.
For starter Beijing’s ground and air temperature is hotter (up to 1 Celsius degree in summer). Urbanization in fact leads to an increment of the roads and rooftops surfaces, while the areas covered by vegetation shrink. This causes the albedo of the city to lower, meaning that the city looses part of its capability to reflect the sunlight and cool down, since roads and rooftops are darker, hence have a lower albedo than the vegetation.
Moreover rooftops and roads have, per se, a lower water content than the soil and urbanization also decreases soil moisture in the ring of impact because road and building surfaces hinder the downward flux of water vapor and precipitation to the soil.
So, to recap, urbanization means low albedos and low moisture contents, and therefore, during the day, lower evaporation rates of road surfaces and roofs than those of soil and vegetated surfaces. Since evaporation, like sweating for humans, is a cooling process, a lower evaporation rate from roads and roofs causes them and the urban ground surface to get warmer, and this, in turn, increases air temperature too.
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| PiPs in Beijing (by sciencemug) |
Urbanization also reduces near-surface wind speed. This, in turn, increases the rate at which wind velocity changes vertically and this powers up the energy of vertical fluxes of air masses which -uhuhuh, gran finale- increases the vertical dilution of nasty stuff, thereby decreasing the surface concentrations of many gas and particle pollutants like ammonia, fine dusts, and carbon monoxide. Well, you’ll say, good, no? Less pollution on the ground means pedestrians breathe better, that’s a win, right? Well, not exactly.
All the just above mentioned wind-air speed game brings the pollutants to concentrate above ground, with a peak at about 300 meters of height. But the just above mentioned wind-air speed game also causes the speed of the wind aloft to slow down even more than the ground one, and this increases air stagnation and reduces the pollutants’ transport out of the megacity. Hence: the bad stuff stays over the ring of impact and the city and doesn’t go away. So, not a win at all...
Another "pleasant" effect of Beijing’s tumultuous urbanization is an increase of the amount of ozone near the surface. And while ozone is a must up in the atmosphere, where it shields our sorry bottoms from those killers UV rays from the Sun, well, too much of it ozone on the ground is no bueno for humans’ lungs, ‘cause, well, it is a toxic gas indeed.
And the list of the downsides of Beijing’s growth goes on.
The mega-city’s urbanization makes even pollen emissions decline a bit, due both to the reduction in vegetation and the reduction in wind speed. Besides, the reduction of vegetation also reduces uptake of CO2 by photosynthesis.
Higher soil temperatures in and around Beijing also increase the emission of methane, another green house gas, but much more potent than CO2. Also, hot soils mean a rise in the bacterial production of pollutant gases like nitric oxide, and, on the other hand, the reduction of bare soil due to urbanization reduces the bacterial uptake of methane itself, ammonia, carbon monoxide and other bad stuff people shouldn't inhale.
In short, to summarize, urbanization make city’s inhabitants breathe worst. And suffer higher temperatures. And overall live a less healthy life.
Aaaand now I know that, being the sensible, wise, smart and slightly passive-aggressive human that you, dear listener, are, I know you’re probably willing to ask me: “why on Earth and any exoplanet yet to discover, have I lost this handful of minutes of my life to listen to a podcast about the consequences of Beijing’s urbanization?" Weell, pal, because you living people are 7.3 billions (I actually don’t know how many of us blogs are around…) and in 2007 (3), for the first time in the human history, more human beings were living in cities than in the rural areas.
Projections say that by 2050 (that is more or less the day after tomorrow) you’ll be 10 billions and two-thirds of you (3)(meaning almost the whole present world’s population) will by piled up in cities and megacities.
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| Urban vs rural population (by sciencemug) |
[For the original graph see (3)]
So, well, I guess it’s a good idea to learn stuff about urbanization and big big big cities? No?
See you buddy, till next time!
(P): THE PAPER THIS POST IS BASED ON
- Jacobson, M. Z., Nghiem, S.V., Sorichetta, A., and Whitney, N. (2015). Ring of impact from the mega-urbanization of Beijing between 2000 and 2009. Journal of Geophysical Research: Atmospheres 120, 5740–5756.
BIBLIOGRAPHY
1- Nghiema, S. V., Balkb, D., Rodrigueza, E., Neumanna, G., Sorichettac, A., Smalld, C., and Elvidgee, C.D. (2009). Observations of urban and suburban environments with global satellite scatterometer data. ISPRS Journal of Photogrammetry and Remote Sensing 64, 367–380.
2- Jacobson, M. Z. (2012). History of, Processes in, and Numerical Techniques in GATOR-GCMOM stanford.edu.
3- VV.AA. (2014). World Urbanization Prospects (highlights). United Nations.
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