- Particulate Matter (PM10) In the period 2001-2011, 20-44 % of the urban population in EU-27 was potentially exposed to ambient concentrations of PM10 in excess of the EU limit value set for the protection of human health
- Nitrogen dioxide (NO2) In the period 2001-2011, 5-23 % of the urban population in EU-27 was potentially exposed to ambient NO2 concentrations above the EU limit value set for the protection of human health
- Ozone (O3) In the period 2001-2011, 14-65 % of the urban population in EU-27 was exposed to ambient O3 concentrations exceeding the EU target value set for the protection of human health
So while the basic incentives via different kinds of regulations are set, it seems that policy makers either do too little to improve overall air quality, or cannot control peak concentrations.
While overall trends for air quality tend to improving across the EU, large agglomeration centres, like Paris, are the troublemakers. During most of the year the air quality in even those larger cities meets the European regulation, but when the weather is just right and Parisiens are not on strike or holidays, then air pollution reaches unsafe levels.
Thus, policy makers tend to have trouble to control peak concentrations. In order to control these, a variety of tools are in their toolbox, but they all rely too much on short-term measures.
For example, one of these very short-term measures to curb air pollution is `car number plate alternation’. Basically, if the air quality in some area decreases below a certain threshold, then many countries/cities implement the regulation that only cars with odd number plates are allowed to drive into urban centres on certain days, while cars with even number plates are allowed to drive on the other days.
However, this regulation is implemented if the regulatory thresholds are already crossed. In a sense, this is like going to the dentist when the tooth hurts and not when the semi-annual control demands it.
What is thus needed is a predictive model of air pollution, which is able to say that under certain weather conditions the likelihood that a regulatory threshold will be crossed is sufficiently high so that the regulator can act preemptively.
And this is entirely possible. We now have very detailed data on air pollution, we have a good knowledge of traffic congestion, and we have weather forecasts which are reliable at least for a couple of days ahead. Combining these information should be a simple task and the result could be that a regulator could now act before a threshold is crossed.
Or, in the words of The Hollies: