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This week is the BBC's So I Can Breathe season, | :00:00. | :00:31. | |
looking at ways to tackle air pollution around the world. | :00:32. | :00:33. | |
We are out on the streets of London to test a new camera | :00:34. | :00:37. | |
It has a particular sensitivity to a range of gases | :00:38. | :00:41. | |
which are invisible to the human eye. | :00:42. | :00:43. | |
The camera is supposed to be used by experts who know | :00:44. | :00:47. | |
what they're looking for in the numbers | :00:48. | :00:48. | |
and colours that they see and it's really supposed to be used | :00:49. | :00:52. | |
in industrial locations as well, where you are looking for gas leaks. | :00:53. | :00:58. | |
But, I must say, even here I can see sprays | :00:59. | :01:01. | |
coming from some of the exhaust pipes through this camera | :01:02. | :01:04. | |
Now, if you want to tackle air pollution problems across a city, | :01:05. | :01:17. | |
you have to know where the pollution is coming from | :01:18. | :01:20. | |
That is something that Mark Cieslak has been investigating. | :01:21. | :01:27. | |
Poor air quality, as a result of pollution, | :01:28. | :01:29. | |
poses a serious risk to public health. | :01:30. | :01:34. | |
The Global Burden of Disease data now suggests that a lack of | :01:35. | :01:39. | |
clean air is the third leading cause of death in the world | :01:40. | :01:42. | |
after high blood pressure and smoking. | :01:43. | :01:45. | |
But whether it triggers allergies or asthma, | :01:46. | :01:48. | |
understanding the exact challenges pollution causes, | :01:49. | :01:51. | |
The more precise the information is, the better we can come up | :01:52. | :02:02. | |
We can identify areas where there are particular problems. | :02:03. | :02:08. | |
Action to gather that even more precise data about pollution | :02:09. | :02:11. | |
is being taken on the other side of the Atlantic, in Chicago. | :02:12. | :02:18. | |
Because of Chicago's location in the Midwest | :02:19. | :02:20. | |
and the fact that it is a large city, | :02:21. | :02:22. | |
it is something of a transport hub for road, rail and air travellers. | :02:23. | :02:26. | |
All those different types of vehicles | :02:27. | :02:28. | |
don't do the city's air quality any favours. | :02:29. | :02:34. | |
Here, a system is being installed which has been dubbed | :02:35. | :02:37. | |
It is called the Array of Things, and when it is completed | :02:38. | :02:44. | |
it will be a citywide network of sensors, or nodes, | :02:45. | :02:46. | |
The array will monitor a variety of different things, | :02:47. | :02:50. | |
from traffic levels to local climate as well as monitoring | :02:51. | :02:53. | |
Eventually, all of the data the Array gathers will be | :02:54. | :02:59. | |
made available online for anybody to use. | :03:00. | :03:11. | |
We have come just outside of Chicago to the Argonne National Laboratory. | :03:12. | :03:15. | |
It is part of the US Department of Energy | :03:16. | :03:17. | |
and is the birthplace of the Array of Things. | :03:18. | :03:26. | |
The donor is really into air quality, so they are really excited. | :03:27. | :03:34. | |
Here, the team behind the array continue to refine the sensor boxes | :03:35. | :03:38. | |
and the technology they contain, liaising with city officials | :03:39. | :03:41. | |
and arranging the continued roll-out of the network across the city. | :03:42. | :03:45. | |
This is the guts, if you like, of the Array of Things nodes. | :03:46. | :03:50. | |
Which part here is the air quality sensor? | :03:51. | :03:53. | |
Each one here is a specific cell attuned to a specific | :03:54. | :03:58. | |
This a ozone, this is a sulphur dioxide sensor. | :03:59. | :04:02. | |
Nitrogen dioxide sensor and there's a token reducing gases. | :04:03. | :04:09. | |
Installation of the array began towards the end of 2016. | :04:10. | :04:13. | |
By the end of 2018, 500 nodes are planned for the network, | :04:14. | :04:18. | |
spread across different parts of the city. | :04:19. | :04:22. | |
Charlie Catlett is the Array of Things project lead. | :04:23. | :04:24. | |
of some of the city's earlier sensor sites. | :04:25. | :04:32. | |
So, Charlie, this is the site of one of your first sensors, | :04:33. | :04:35. | |
This one here does the air quality, not just the general air quality | :04:36. | :04:43. | |
but this one will tell us seven different gases and so that means | :04:44. | :04:48. | |
we can say, well, this one is reading this gas | :04:49. | :04:51. | |
particularly high and we know that that that is associated | :04:52. | :04:53. | |
The new ones that we are putting in, we have added a new sensor | :04:54. | :04:59. | |
What we can do with this particle sensor is we can look | :05:00. | :05:03. | |
at the very fine particles that are measured | :05:04. | :05:05. | |
The smaller particles are the ones you cannot see | :05:06. | :05:10. | |
but they are really the most dangerous one. | :05:11. | :05:13. | |
They will go straight into your bloodstream. | :05:14. | :05:16. | |
The large ones are what triggers allergies. | :05:17. | :05:19. | |
So if you are somebody that's got allergies related to asthma, | :05:20. | :05:22. | |
you will be able to use the data from these nodes to look at pollen | :05:23. | :05:26. | |
across the city and you might decide to change your cycle route you take | :05:27. | :05:30. | |
to school or work, based on maybe where the pollen concentration | :05:31. | :05:33. | |
Chicago is not alone when it comes to pollution monitoring. | :05:34. | :05:40. | |
For example, in London, we there's a system called Nowcast, | :05:41. | :05:43. | |
which combines historical pollution data with current pollution | :05:44. | :05:47. | |
measurements to provide an hourly update of pollution levels | :05:48. | :05:49. | |
Array of Things nodes have been installed in other US cities | :05:50. | :06:00. | |
with one in Seattle and another in Denver and there is interest | :06:01. | :06:03. | |
in the system internationally as well. | :06:04. | :06:06. | |
The data generated by the Array of Things will be used | :06:07. | :06:10. | |
by researchers, scientists and healthcare professionals to get | :06:11. | :06:13. | |
a better picture of the effects of poor air quality and pollution. | :06:14. | :06:19. | |
When it comes to turning this information into action, | :06:20. | :06:22. | |
Brennna Berman and Tom Schenk both work for the city of Chicago | :06:23. | :06:32. | |
and are figuring out how the Array of Things can help the city | :06:33. | :06:36. | |
We have pockets of increased rates of asthma among our children that | :06:37. | :06:43. | |
doctors have known about for quite some time but they do not have a lot | :06:44. | :06:48. | |
of information about why they happen in certain areas of the city. | :06:49. | :06:51. | |
The role of the Array of Things is really to help us understand | :06:52. | :06:55. | |
the patterns and issues with air quality in Chicago | :06:56. | :06:57. | |
at a detailed level because you cannot fix a problem | :06:58. | :07:00. | |
if you cannot define it and understand it. | :07:01. | :07:02. | |
We might be thinking about how heavy pollutant vehicles can | :07:03. | :07:05. | |
The City of Chicago has installed hundreds of miles of bike lanes, | :07:06. | :07:10. | |
across the city of Chicago but there is some very clear | :07:11. | :07:13. | |
research showing that inhaling diesel fumes, | :07:14. | :07:16. | |
especially by cyclists as they are riding alongs traffic, | :07:17. | :07:18. | |
So it really helps us picture and take a good look | :07:19. | :07:23. | |
at where the bike avenues are and how that corresponds | :07:24. | :07:26. | |
If you have a school or another sort of vulnerable location very close | :07:27. | :07:32. | |
to an area that has increased air quality challenges, | :07:33. | :07:36. | |
the data from the Array of Things will give us the ability to define | :07:37. | :07:40. | |
A good example here in Chicago will actucally be the very quickly | :07:41. | :07:44. | |
growing neighbourhood on the west side. | :07:45. | :07:47. | |
It has quickly evolved into one of our trendiest residential | :07:48. | :07:49. | |
But it is also crisscrossed by any number of street level railroads. | :07:50. | :07:57. | |
By looking at data, by using this data such as the Array of Things, | :07:58. | :08:01. | |
we are going to be able to make thos decisions more confidently | :08:02. | :08:04. | |
and we are going to know that better than in fact many other cities | :08:05. | :08:08. | |
have the ability to know that, because of the data that we look at. | :08:09. | :08:12. | |
Here, the technology clearly has a role to play in the fight | :08:13. | :08:15. | |
But the big pollution-busting powers lay with local | :08:16. | :08:18. | |
Back in London, I'm checking out a pollution monitoring device | :08:19. | :08:33. | |
With this water tank, they can launch their prototype. | :08:34. | :08:42. | |
Oops, I knocked a thing into your tank. | :08:43. | :08:46. | |
They even have their own wind tunnel. | :08:47. | :08:50. | |
Imperial College's AquaMAV is a drone that can fly | :08:51. | :08:53. | |
through the air, dive into the water and then leap out again. | :08:54. | :08:59. | |
All the while, gathering data to give us a greater understanding | :09:00. | :09:05. | |
of pollution levels above and below the surface. | :09:06. | :09:09. | |
The plan is to release a swarm of them into an area of concern. | :09:10. | :09:17. | |
This is our response to extreme environments or post-disaster | :09:18. | :09:19. | |
applications such as after floods, toxic spills, or oil spills, | :09:20. | :09:24. | |
There are different classes of applications and capability to do | :09:25. | :09:31. | |
sampling with an automated, low-cost tool brings an enormous | :09:32. | :09:36. | |
value compared to many other methods such as the human | :09:37. | :09:39. | |
going there with a full protective suit. | :09:40. | :09:48. | |
I was going to say, we have seen a lot of aquatic robots and we have | :09:49. | :09:52. | |
It never occurred to me that is quite difficult to get | :09:53. | :09:56. | |
an underwater robot over great distances quickly and, | :09:57. | :09:59. | |
So, yes, we will just dive it in the water and then dive it out | :10:00. | :10:09. | |
In some applications it is not even accessible through the water, | :10:10. | :10:13. | |
in floods or floating ice, you may not get there via water. | :10:14. | :10:16. | |
On the other side, an aerial beacon may not be able to get | :10:17. | :10:20. | |
the information that local people need, so combining | :10:21. | :10:22. | |
During a dive, the AquaMAV fills with water and then by releasing | :10:23. | :10:28. | |
carbon dioxide from its on-board gas chamber it forces the water back out | :10:29. | :10:33. | |
as a high-powered jet which thrusts the drone back upwards, | :10:34. | :10:37. | |
And then the wings unfold and it comes out of the water and it | :10:38. | :10:43. | |
beautifully becomes this flying birdlike thing. | :10:44. | :10:48. | |
That was a very romantic description. | :10:49. | :10:55. | |
Now you know how romantic I am and what I get excited about. | :10:56. | :10:59. | |
There is a beautiful part of it which makes it elegant. | :11:00. | :11:02. | |
And elegance in nature that makes it effective as well. | :11:03. | :11:05. | |
Having the folding wings might look beautiful but for us it allows us | :11:06. | :11:08. | |
to reduce the drag that it would experience as it dives | :11:09. | :11:11. | |
in the water and allows it to dive more deeply, | :11:12. | :11:15. | |
as well as protecting the wings on impact. | :11:16. | :11:25. | |
The use it for the short cut to click. Join us on Twitter for laser | :11:26. | :11:35. | |
tech news and behind the scenes gossip. Next week join us for two | :11:36. | :11:45. | |
special clicks from India. Thank you for watching. See you then. | :11:46. | :11:48. |