Tag – you’re it! Chasing atmospheric tracers

Beijing pollution

Beijing, a megacity of ~20 million inhabitants, when I was there last September. How to keep track of the pollution being released?

This is a blog post I originally wrote for GeoLog (the most excellent official blog of the EGU), which appeared on 22 March 2013 (in a slightly edited form). 

I’ve been ruminating over the idea for this post for some time now; since last October in fact, when the EGU Twitter Journal Club discussed a paper about tagging (You can find the Storify for the discussion here). Not tagging as in the playground favourite, but the idea of keeping track of certain molecules in your chemical transport model, so you can follow them as they move through the atmosphere and undergo chemical transformations.

After deliberating, cogitating and digesting, I’ve decided to offer my opinion on tagging, which I’ve come to through reading, listening and discussing the topic with colleagues. I thought this might be of general interest, as the concept of tagging frequently sparks debates (in my experience) and seems to arouse stronger and more varied views than I would usually expect from a modelling technique. So with this post, I want to answer the question “what is tagging and what is it used for?” and at least attempt to answer “why does it generate such mixed feelings?”.

I think that the answer to the second may lie in the answer to the first, so let’s start with that. One way of describing tagging is as an accounting method. Doesn’t sound very geophysical? Well, we’re talking the model world here, and we can keep track of – or account for – every little thing we do in our model world. To try and understand the composition of the Earth’s atmosphere, people have constructed computer models to describe both the physical and chemical processes in the atmosphere. I’m particularly interested in trace gases like ozone, which is found in parts-per-billion quantities in the troposphere (roughly the lowest 10-15 km of the atmosphere). Tropospheric ozone is a popular species to study, as it is a greenhouse gas, it’s bad for human and plant health, and it’s one of the key oxidants in the atmosphere.

Another interesting thing about ozone is that it’s not emitted directly. Many other pollutants and greenhouse gases, like methane or nitrogen oxides, are emitted by both natural and anthropogenic sources, but ozone is formed through photochemical reactions, which occur in the presence of both sunlight and nitrogen dioxide. So, to study the ozone in the atmosphere using a model, it needs to include emissions of relevant gases, motions in the atmosphere that move these gases around, chemical reactions that transform one gas into another, as well as other processes like deposition of certain gases to surfaces or removal in rain.

With all this going on, it can be hard to disentangle one process from another, particularly since all the different processes are interlinked. This is where tagging can come in handy as an accounting tool. Say we are interested in how much ozone is added to the atmosphere as a result of activity in a particular city – let’s call it Mega-City One. How could you find this out? Well, one quite common way is to run the model as normal, and then to repeat the simulation but with Mega-City One removed. So you take out any emissions coming from Mega-City One and see what difference it makes. This tells you what would happen if you simply removed that city.

However, you may not want to know about this unrealistic “annihilation” situation, as a city won’t simply disappear overnight. You may be more interested in your normal run (which you think best represents the real world) and what is contributing to the ozone in that run. The non-linearly minded amongst you will see the subtle difference between the two questions. If we were talking about an inert tracer, there would be no difference, as what was emitted by Mega-City One would just stay in the atmosphere unchanged, and would be directly attributable to Mega-City One. The difference with ozone is that it’s not emitted, and the chemistry that creates and destroys it can and does behave nonlinearly.

So to answer the attribution question (in my model simulation, how much ozone is a result of Mega-City One emissions?), tagging is one method that is used. The idea is that you ‘tag’ tracers that you are interested in, and follow them through the model. So you can ‘tag’ all the emissions from Mega-City One, and when one of the nitrogen dioxide molecules from this city undergoes photolysis to make an oxygen atom, which then reacts with molecular oxygen (O2) to form ozone (O3), you know that this ozone molecule can be attributed to Mega-City One. This is done by solving a set of equations for the tags alongside the usual chemistry scheme. This does not disturb the normal running of the model, and you will find that the total amount of ozone attributed to Mega-City One will be different from calculating the difference in ozone between your normal and your annihilation runs.

Why would you want to do either of these things anyway? Well, you might want to know what effect different cities or other sources are actually having, or would have on the atmosphere if they were built. So you’d use the annihilation method. Or, you might want to know what the biggest culprit was for particularly bad air quality in a particular place. So you’d use tagging to find out if it was Mega-City One or Mega-City Two, or if it was the road transport or the power stations that was the biggest source.

I hope I’ve achieved my first aim of answering my first question, about what tagging is and what it’s used for. The second question is not quite so straightforward, but I’ll give my thoughts on the matter: I think the key to why – to put in bluntly – some people aren’t so keen on tagging, and some people aren’t so keen on the annihilation method, stems from different notions of what tagging is really being used for. Ultimately, I think this boils down to finding ways of communicating complex ideas and their applications, without being misconstrued. Existing pre-conceptions probably also play a part, as these will (rightly or wrongly) fill the gaps when someone else’s explanation is incomplete.

Successful communication of complex ideas isn’t easy! It takes time and effort, but it really pays off in the end. Just think, how often have you heard (or taken part in) a heated discussion in which you find everyone was actually in agreement all along, they just didn’t know it yet?

Paper that initially inspired the discussion:

Grewe, V, Dahlmann, K, Matthes, S and Steinbrecht, W. (2012) Attributing ozone to NOx emissions: Implications for climate mitigation measures. Atmospheric Environment. Vol. 59, pp 102-107.

Megacities at EGU 2012

I recently attended the EGU’s General Assembly in Vienna (great conference, guys!), and wrote some posts for their GeoLog blog. Here’s the first, which originally appeared here on 26 April 2012.

Almost a whole day’s worth of sessions on megacities – where to begin? I certainly couldn’t pick just one talk to write about, so here’s a mish-mash of the session in general and a few talks in particular.

First things first: what is a megacity? Officially defined (by who, I don’t know) as a city of 5 million people or more, there are only two of them in Europe (London and Paris), and both are among the most polluted cities in Europe. There are other European places that embody megacity characteristics without adhering to the strict definition, so the MEGAPOLI project has focused on two of these alongside the two bona-fide megacities. The Po Valley in Italy, surrounded by mountains on three sides, is populated by 16 million people and contains 37% of the country’s industry. The mountains disrupt the large-scale meteorology so that local winds are often slack, which combines with the high levels of industrial, agricultural and residential emissions to cause worse air quality than in either Paris or London.

Loss in life expectancy from PM2.5

Loss in life expectancy (months) attributable to exposure to anthropogenic PM2.5 for year 2000 emissions (Source: EC, IIASA)

The air quality is similarly poor in the Rhine-Ruhr valley in Germany, an industrial region with about 10 million inhabitants. This region suffers not only from local emissions, but often from pollution transported from London, Paris and the Netherlands in the prevailing winds. (Thanks to the MEGAPOLI website for the info about these locations).

The reasons why these non-megacities have been brought into the fold highlight the complexities of trying to understand what might happen in the coming years as the world becomes increasingly urbanised. It’s not only the amount of stuff being pumped into the atmosphere that causes air quality issues. It’s equally how much stuff gets vented out of the boundary layer (the lowest layer of the atmosphere, where people live), and how much gets washed out in rain. And what happens to the stuff before it gets removed? And this is not even considering the climate impacts of all this stuff is getting higher up into the atmosphere, where it has a longer lifetime and can be transported long distances, potentially also affecting air quality downwind. All these interactions could be broadly categorised into: emissions, boundary layer meteorology, deposition, chemistry, global transport, and climate.

Several talks in the session were related to emissions evaluations, as how can we hope to understand anything if we’re putting the wrong amount of stuff into the atmosphere? Any by “stuff”, I mean NOx (the sum of NO and NO2, which are pollutants emitted from both anthropogenic and natural sources, and can react to produce ozone, which has adverse health effects) and particulates (the shorthand for particulate matter is PM2.5/PM10 for those with a radius less than 2.5/10 microns, also bad for health), as these were the main topics in the session.

Generating emissions inventories is no trivial task, as is evidenced by the continual work going in to this area. In his talk, S Sahu described the development of an emissions inventory for Delhi and the surrounding areas, which is home to a staggering 30 million people in an area of 70 km x 65 km. For 6 months, an army of 250 students surveyed the residents and businesses to determine a sample of the emission-generating activity in the region. They combined this new data with the existing literature and government statistics to develop a GIS-based emissions inventory. Their results showed that there are 5.7 million vehicles on the roads, and 1.5 million living in slums and cooking with wood, kerosene or LPG (in order of decreasing precedence). The PM2.5 emissions total was 68.1 Gg/year, the largest portion of which was from transport at 30.25 Gg/year. Wind-blown dust and residential emissions were also large contributors. The inventory was used to forecast for the Commonwealth games in 2010 and is currently available for both science and policy uses.

Policy issues were the driver behind R Friedrich’s talk, which directly addressed questions of whether air quality policies could result in the desired policy outcome – surely an important factor in decision-making. As part of the EU MEGAPOLI project, his work took a “full chain approach”, whereby the scenario with and without the policy measure was modeled to determine the effectiveness of a policy. The reference scenario assumes the current EU energy and climate package was taken forward. Then each policy was added to the model, and the difference can be described in monetary terms or by DALYs (disability adjusted life years).

The study generated some surprising results. Twenty four policy measures were ranked in terms of avoided DALYs for Paris, and the best measure by this metric was to change to efficient combustion of gaseous fuels (which generate less PM than wood), followed by biomass fuels. However, different metrics paint a different picture. Calculating the efficiency of each measure in monetary terms put coke dry quenching (as opposed to wet quenching which generates PM) in the top spot, followed by use of biofuels, use of district-wide heating networks, an aviation kerosene tax and a switch to electric vehicles. The least efficient measure was a passenger car toll (which, for example, London has had since 2003). Interestingly, the implementation of a low emissions zone was shown to have a negative or neutral effect. On the other hand, the speaker recommended the improvement of traffic management as an efficient measure.

Another EU project, CityZEN, also linked the science with policy needs by producing some 2 page policy briefs on ozone, PM, observations and the East Mediterranean air pollution hotspot, and was discussed by several speakers. Other talks and poster covered the links between meteorology and chemistry, observations and models, but I’m afraid this is all I have time for… See you next time, on the GeoLog.