Now, I would like to tell you about some results of applying the author’s simulation-based methodology for an environmental information technology for the city of Moscow.
According to the requests of municipal government of Moscow city an information technology for real-time atmospheric air quality assessment (AIR-ECO) has been developed. This slide will help you to get a better idea about the objectives of AIR-ECO.
This was a very large project, which lasted for several years. Five large research organizations participated in it. The total number of contributors to this project reached about 100 people. I was the scientific leader of this project.
Primary objectives of this project were: integration of heterogeneous measuring systems and databases into a uniform technological complex; reception of real-time environmental information from the entire territory of the city without increasing the number of existing measuring stations; support of decision making on improving the natural environment of Moscow.
This slide shows the main sources of input information for AIR-ECO. These are very different sources. A part of them are independent real-time measuring information systems. Another part - traditional databases. Others - are represented by some administrative or technological information.
TV-tower in Ostankino hosts a specialized measuring system providing a real-time acquisition and processing of meteorological and environmental information from 8 high-altitude levels (from 503 meters down to 0 meter). In other words, this system provides a vertical distribution of atmospheric parameters in the air of Moscow. I have a somewhat special attitude to this system, because this is my own development made 25 years ago, which I used to prepare and defend my first PhD in Computer Science.
Further in this slide you can see measuring systems of meteorological and environmental stations on the territory of Moscow.
Statistical information is represented by meteorological and environmental retrospective databases for the last 5 years, databases with information on the sources of emissions, etc.
This slide shows the main functional modules included to the structure of AIR-ECO. Here you see the modules of this system. Names of the most modules reflect their functions. Object-oriented methods were used to design these modules, therefore their functionality can be considered autonomously to a certain extent.
There is an intra-system information channel used for the data exchange and integration of all modules into a uniform information technology. Let me tell you a little more about the functionality of some of the modules.
AIR-ECO uses meteorological data on the speed and direction of wind and the air temperature. This information is received in real time from Ostankino TV-tower and from 5 measuring stations located on territory of Moscow.
You can see a rough layout of the stations in this slide. The data from the TV-tower and meteo-stations is fed to AIR-ECO every 3 hours.
Module “Source” is a very interesting module and I will discuss it in more detail.
For real-time environmental computer systems, such as AIR-ECO, there is a serious methodological problem of obtaining the input data on parameters of the emission sources. Even at the conceptual level it is clear, that if you calculate a pollutant concentration field with discretization of several hours, then the data on the dynamics of emissions must have at least the same discretization. In practice (at least, for Moscow) we have only had annual average values for the emissions of large industrial enterprises and transportation. There is a problem - how to obtain the data necessary for the system’s operation?
There are three possible answers to this question.
First one is the simplest, but extremely expensive in cost and practically not feasible – install an emission parameter measuring system on every source of emission. Second – obtain several dozens of mobile systems for remote measuring, which is also too expensive.
And at last – try to use some imprecise and partial information about the sources of emissions to reconstruct the emission distribution functions.
Base simulation model - is used to obtain initial values of the concentration fields of different types of pollutants in the atmosphere of Moscow.
The computation is conducted on the basis of classical equations for emission and distribution of concentrations from point sources for actual current meteorological conditions.
Transportation and diffusion is calculated for every node of a regular grid. The values of pollutant concentration resulting from emission of different sources are added up in the nodes of the grid. Therefore, it is possible to obtain a general picture of the air pollution over the entire territory of the city.
However, due to the specificity and known abstractness of Gauss equation, obtained results require some adjustment.
Module “Relief” allows the system to take into account the features of topographical relief of the earth surface where Moscow is located. In addition to that, data on the density and height of buildings (both residential, and industrial) on the entire territory of the city is used.
In urban areas, the speed and direction of wind in the near-ground atmospheric layer depend on the features of topography and buildings.
Therefore, calculations of actual pollutant concentrations in the atmospheric air in each node of the regular grid take into account: topographical height of the given location, integral density and height of building, as well as current values of speed and direction of wind.
Module “Control” is intended for computations of possible operating strategies and generation of recommendations on the atmospheric air quality control in specific districts of Moscow.
In case if exceeding pollutant concentrations are at a specific level, preliminary computations are performed to determine how long these increased concentrations may exist in the given district. If this time exceeds one day, the system generates possible solutions to reduce these high levels, taking into account existing organizational constraints.
This slide shows how the high levels of pollutant concentration were decreased in a specific district after taking corresponding measures at the enterprises – sources of emissions.
In this slide you can see sample results of computing the concentration of nitrogen oxides in the atmosphere produced by AIR-ECO.
These computations correspond to some mid-summer period.
What can we notice analyzing this map?
This slide shows the results of computing concentrations of nitrogen oxides on the same day zoomed into the north-eastern part of Moscow. Several major sources of pollution are clearly visible. They produce air pollution exceeding PPM by 50-100%. It is possible to see some integrated pollution of this area by several small-size sources.
This slide shows a map of the atmospheric air pollution with nitrogen oxides in one of the industrial areas of Moscow.
There are several large industrial enterprises, which determine the increased concentrations in a relatively large area of the city. In the center of this area, concentrations exceed PPM by 5 times, on its borders - by 2 times.
Here you may also see (in Russian), that the system displays an average value of the nitrogen oxides concentration in the given administrative district and the level of concentration in the selected point.
This slide shows the results of actual computations, obtained by AIR-ECO of the atmospheric air pollution by SO2 – sulfur dioxide.
The same north-eastern area is shown. As you may see, air pollution by sulfur dioxide is significantly lower than pollution by nitrogen oxides. In this area, there are only 2 enterprises polluting the air with this gas. However, their emissions do not produce dangerous levels of sulfur dioxide in atmosphere. It is confirmed by long-term statistics – the air of Moscow is almost clear from sulfur dioxide.
AIR-ECO is a fundamental high-end scientific information technology, many results of which could be applied in the USA. What are possible areas for such application?
I have identified 4 potential areas, although probably there are many more of them.