Session 3 of the 15th Concawe Symposium took place online on the 18th of March 2024.
Leonard Oirbans of TNO, presented “Urban Strategy: Digital twins for liveable and resilient cities” in Session 3: Ambient air quality: Experiences, actions and future commitments at city level.
More here: https://www.concawe.eu/event/15th-symposium-ambient-air-quality-at-city-level/
It’s truly a pleasure to be able to talk to you today about urb strategy digital twins for liver born resilient cities and the application in ambient air quality and just to start off um this picture on the on the right side of this slide shows two of my colleagues Peter
And H working on uh this digital twin environment and in the background you can see beautiful picture of the air quality actually in the city together with some data layers some Ro layers and then the picture in the front there’s the congestion levels in the in the city uh and this really nicely
Illustrates from my point of view the way these digal twins could could help policym so my talk today will be shortly about TN uh about the urban challenges that we face in our cities uh what is urban strategy exactly and how do we uh simulate air quality within this
Instrument and then I want to dive into some examples of use cases starting with integral urban planning in the city of Amsterdam showing how we deal with realtime air quality data and then two other cases abroad one in Germany weing to N2 emission reduction strategies and one in Asia Singapore uh for zero
Emission strategies for buses so to start off uh about TN uh it is the largest independent research and Technology organization in the Netherlands we have about 4,000 colleagues of mine we were founded by law in 1932 which is the peak of the Great Depression and uh our mission is
To create impact for Innovations for the sustainable well-being and prosperity of society so our slogan is also Innovation for life so what we really try to do is uh capture these scientific approaches make them applicable and bring it back to society based so to go straight on with these
Urban challenges and I’m sure these many are familiar to you so we’re facing urbanization densification in cities and aging population also scarcity of public space so public space uh combating about uh different functions uh of course decarbonization energy accessibility and Equity uh and the housing challenge particularly in the Netherlands but also
Other European cities and today we of course will dive into the air pollution Challenge and what we see around us is that these cities are increasingly complex systems which this um Sim simultanous transitions in multiple demands uh which are also increasingly intertwined so think about for example the mobility domain and the energy
Domain it’s almost impossible to to picture them apart nowadays and this means that we need a balanced and integral approach to form future proof policies so imagine solving one of these Urban challenges just from one perspective could lead to well maybe a suboptimal uh solution or even introduce new problems in different uh
Different areas okay so how how do we deal with so many challenges how can we make uh how can we deal with this complexity but we like to say with Urban strategy which is a unique multimodal domain digital twin approach for interactive and interc planning we like to say we try to make
This complexity manageable and just like in the picture to the right you can see this triangle which is the urban strategy framework basically and um we manag to integrate this data the visualization and the analytics so these are your simulation models in one in one solution and it allows you to construct
Realistic digital replicas of the real world these are these digital twins um and it is very useful because in these digital environments you can try out variety of policy measures and explore the impact integral very special about um Urban strategy in my opinion is that we also use ize high performance Computing uh
Which results in a very large scaling potential and in essence it means that we’re able to really really quickly use this data and visualization and analytics uh integration but also within our simulation models we um highly optimize them for calculation speeds and this is useful because then you can
Explore a wide variety of different solutions uh because you have this speed advantage in your calculations well and that opens up of course the possibility to interactively explore the solution space to help accelerate resolving this societal challenges and I can especially recommend the paper I noted in the bottom uh explaining this this
Framework you know so with this digital twin approach you will get situations like this so you stand around a touch table interactive touch together with different stakeholders um looking at your digital environment and exploring what if we change certain aspects of our cities uh and directly you can then see the
Impacts on Mobility air and so on so this is this is a subset of all the analytics so the simulation models within Urban strategy so you can can see parts of Mobility demand also assignment for example also active modes like cycling and walking of course air quality noise
Quality uh energy like I also mentioned and especially interesting also uh we’re working on Equity indicators so how can we determine for whom in our city this uh different policy interventions actually play out well and of course uh today we will focus on the air quality
So let’s dive in and just like we saw we have this emissions from from traffic leading to different pollutants carbon dioxide Nitro nitrogen oxides um but when we look at Health impacts well just like presented by the previous speakers we saw that especially NO2 and the particular matter are U source for the
Health so also in my presentation I will focus on these ones also a nice takeway maybe from the first presentation is that when we see that this PM 2.5 gets these Norms will be stricter you can imagine this will be uh quite high on the agenda probably also for
Cities because then how how do we meet these requirements in cities quite quite a change so how do we do it in our strategy basically we start off with the spatial plan so this is where are your houses your jobs your amenities and so on and um from this you can get your
Transportation demand so where people want to traffic then if you confront this with your transportation Network so these are the lines on the left is the road layout in in Amsterdam and to the right is the public transport Network so when you confront this Demand with the network you can um
Say visualize the traffic flows and this shows the number of people this is car intensities in the morning R shower in the city of Amsterdam traveling between their original destination points and The Wider the bar the more traffic there is so you can clearly see at the sides
Of the city these highways ling up but also quite some car intensities actually in the city center around the Central Station country Etc okay so if we know where people are driving and we know these uh characteristics like f composition or Street type or congestion
That one and so on we can calculate the TA pipe emissions but then how do we get from emissions to concentrations well the short answer is that we use culation points that we name receptors but for the sake of this theme we will dive a little bit more into
This so how this works work is basically we take the road Network and I draw two Road segments here and we place a receptor at a certain distance and then we chop up these um Road segments in sections of 10 Metter and we assume that
Every 10 m will be well an AIT and this receptor calculates the paths towards it to get to the concentrations but in order to do that we’re missing a large piece which is of course the dispersion effects from this road to the receptor point and we have two standard
Calculation methods uh for this um so this was approached in a wind tunnel where they would create or recreate this Urban form and then see okay how does this how does these how does the dispersion occur and uh it turns out that you can distinguish two types um one
Uh the name is SRM srm1 and the name is car so it’s not just for for cars but it’s an abbreviation but you can say well we have this Urban Roads with the urban form uh with speeds lower than 70 kilm per hour and in that those cases
You can Define four typologies in the picture to the left U the street Canyon effect is actually the predominant um effect for dispersion where for roads like rural roads or highways with higher speeds um you can approach this like a chingle and that’s what we mean with the cion plume in srm2 method
But I will dive into that later on so really shortly this is uh how you calculate that uh from a formula I will not go into all the details but you can see these different typologies are captured in some parameters so captured the dispersion effect in parameters but then what’s especially interesting from
This formula is that the concentration of the road emissions also is influenced by a Tre Factor okay so let’s uh look into the picture to the right we can see that when you have some trees alongside the road and they’re quite Spar you have a
Factor of one but when you place them a little bit closer to each other within 15 M this will already go up to 1.25 and when they’re actually touching the branches a little bit you will have 1.5 okay well think about combating Urban heat for example also quite a challenge
Given the climate change impact in cities then planting trees is a a great way to mitigate this uh heat stress but from this formula we can see that it will impact your um concentrations from the road well big time um so that’s a really nice way to also illustrate how
Important it is to approach these kind of uh design challenges in integral way well back to the different uh methods the gion Blom again a nice formula this is just actually a way to uh to calculate these type of ction plumes and uh of course wind is one of the factors
And maybe I takeway is that this is like a chimney and the height of this chimney is for example influenced by nose barriers for example lifting up this this BL so this is how we how we calculate that so back to our receptor it now knows this road segments it knows the
Fleet characteristics the intensities and it knows when to calculate through the srm1 or the two method so it is calculating quite a lot well let’s bring it back to the digital TM and here it is sitting with some colleague receptors in the park in Amsterdam um and when we zoom out we can
See there are quite a bunch of receptors we can zoom out even further and I think well you know where this is going it basically laid out the entire city of Amsterdam with these receptor points approximately 500 square kilometers with just over 800,000 of these recept points well and then just
Imagine the number of receptor and Source combinations you would have there to be calculated quite a challenge well we have this emission we have our receptors so finally we can get to these concentration maps in this case the NO2 air quality map any calculation to get from the traffic flow to this
Concentration map on this scale just to 34 seconds well and that’s quite interesting because uh given the speed Advantage we now can explore tons of variants of policy measures and truly explore this the solution the solution space but then if we know of course the concentrations at the different
Locations we can uh attach some other data sets for example buildings and then see what is the exposure to the facade of the building and then if you know how many inhabitants there are which are approximately 500 on this building highlighted in this picture we can go to well potential also health impact
Effects effects such as people above a certain threshold and the picture in the back is the pm10 map this is a very small uh clip of a digital twin we built together with the city of Amsterdam it’s one of our longting uh Innovation Partners on strategy but you can see here a
Representation in the 3D interface showing up these air and noise quality layers and here we are in a 2d interface and we can show different modeling results so again for example these are the traffic intensities calculated by our demand and traffic assignment model and so on this is the
Translation to air quality and these are the noise uh noise levels at a certain point also approach with a similar way with these kind of receptors and unique about this uh approach is that we can not only visualize the data but we can interact
With it so for example this is a part of the highway and it has certain properties speed and capacity and in this example my colleague is closing off the highway so the speed is temporarily changed of this individual link to 0 kilm per hour and then we will see it is
Enabled with a touch on the touch table and immediately all the simulation models that are interested in this data set are triggered so naturally this is the assignment model for example it recognize that that route is unavailable and starts reallocating the routes or the traffic on the network and we can
See of course this uh this impact on traffic intensities but because these traffic intensities also changed our air model also recognized this is something I’m interested in I should recalculate so immediately this train of simulation models starts calculating to giving you an integral perspective and it will end
On this mve on this part you can basically see the impact of closing down this road from the scenario without measures and a scenario with measure and this is just one measure but this can be a well a separate measure so that’s the approach in Amsterdam highly dense City still
Growing how could we get it um livable for big Bo and around of course another example is uh Real Time air so um this is a bigger area and here you can see the pass through time so this is the morning morning Rush Hour showing the sources up and going through
Time to to show actually some more Dynamic data what a little bit stuttery it seems but let’s go back to this morning rush hour so this is approximately at 8:30 in the morning you can see for the Northwest uh industry uh producing and due to the wind direction also having this dispersion effects
Calculated and to the south side of Amsterdam is SLE airport large airport in and we can see that these also from the traffic this int this air quality quite heavily is influenced with this win Direction just to sh that we also deal with this kind of data another interesting uh use case
In Germany four German cities uh and the challenge here was to come up with shortterm uh mitigation measures to improve the air quality but we did it together with PTV and there’s also a nice article about it at the bottom um and in this example we looked
Specifically at um the reduction of NO2 and then it was seen that on the short term of course changing to for example cleaner or electric engines uh well significantly impacted your uh NO2 Emissions on the location um and in another study in Amsterdam we also looked at uh the deployment of shared
Modes for example and there it became apparent that when you just look at NO2 emissions you might improve the situation but uh well if you don’t tune this shared cars properly you will get such an attractive proposition that you can abize on your bike traffic or even your PT traffic uh
Leading of course to higher congestion ratios also emission of PM 10 and 2.5 and then the final one to end my talk is uh in Singapore where they have a fleet of 6,000 buses running on diesel engines and um well they need to be electric at a certain point and you can
See that this is quite uh quite a challenge uh bring together the energy grid um but also all kinds of uh business uh case elements like which type of batteries should we choose which kind of buses should we order what will be the routes uh how should we uh design our charging
Infrastructure quite complex and we um transfer this knowledge also to the land transport Authority in in Singapore so thank you very much for for listening to my story and if this uh well uh triggers you in in some way uh positively of course then I would say let’s let’s collaborate because what I
See is that the Innovation that we work on really thrives on Innovation on collaboration so my final step would be to Let’s improve our cities together and please reach out if you have any questions thank you