EMMA
Integrated Environmental Monitoring,
Forecasting and Warning Systems in Metropolitan Areas

Keywords:Urban air quality, traffic management,monitoring forecasting and warning system
Application site: Leicester, UK; Genoa, I; Madrid, E; Stockholm, S
Website: http://www.mdx.ac.uk/emma
E-mail: d.matteucci@cnuce.cnr.it or nhodges@lccatc.demon.co.uk (for the Leicester site)

Context
Monitoring networks are available in many European cities, providing information on air and water quality. In most cases these networks were developed independently over the years, utilising different communication and informatic standards. A system uniformly applicable in various cities should be more efficient for decision making purposes. Efficient management, including 24-48 hour air pollution forecasts provides crucial information to policy makers and planners to make informed decisions on the day-to-day management, e.g. imposing traffic restrictions and informing the public with regard to critical health pollution levels.

Objectives
The project targets are:

• The availability of integrated systems to identify in almost real-time concentrations of different kinds of pollutants in urban areas
• The creation of an integrated modular system characterised by an open architecture for the forecasting and management of air quality at city section level and by taking into account the need for local micro-meteorological forecasts Quality control and quality assurance of real-time environmental data
• The availability of accurate air quality data and meteorological data on ozone, SO 2 , NOx, O 3 , CO, PM10, benzene and toluene data, humidity, wind, temperature, turbulence, and vertical meteorological conditions
• To produce accurate forecasts for short term actions (24 and 48 hours) and long term strategy.
• The capability to accept and utilise standard messages, procedures and protocols and to disseminate information to the general public through different and preferably friendly means of communication.
• The creation of "Air Quality Indicators", usable during the whole year at any climatic condition, compliant with international and National Rule, and understandable by common citizens To influence transport patterns and drivers behaviour on the basis of present and forecasted air quality situation.

Results
The demonstrator, based in Genoa has focused on developing an integrated Air Quality System, that includes an emission database and pro g n o s t i c dispersion models, with GIS-functionality using the existing monitoring system. In Stockholm, the local partners emphasised the software enhancement of the remote sensing technique (SODAR) and on improved forecasting capabilities. The major challenge for the Leicester-based partner has been to develop software interfaces between the air quality system and the traffic management system, and to demonstrate that the two systems work well together and can produce output (such as forecasts for air quality) reliable enough for immediate measures to be taken. Daily air quality forecasts are produced describing current, 24 hour and 48 hour ahead levels to anticipate the need to implement emergency restrictions and minimise the effect of false alarms. Pollutants monitored in Leicester are CO, NOx, O3, PM10 and SO2 .

The Madrid-based demonstrator operates a complex, highly sophisticated non-hydrostatic meteorological forecasting model whose results help to increase the accuracy of the air quality forecasts. Air quality is measured and forecasted at city section level, including the needs of interoperability between two existing air quality and meteorological networks plus the necessity to inform drivers via Variable Message Signs.

EMMA, like EFFECT, highlighted that traffic emissions are a significant part of the pollution in a city environment. The work on importing weather and pollution forecasts highlighted the influence both can have on the local air quality. Care is needed in incorporating the data into the air quality modelling. The UK Meteorological Office tracked back the source of imported pollution when Leicester's monitored values exceeded those initially modelled using only local emissions database.

The Madrid EMMA Air Quality Model provided a forecast including the effects of chemical reactions in the atmosphere, and illustrated the effect of the mountains to the north of the city. In Genoa it was the effect of the coastal mountains and the valleys running down to the Mediterranean Sea which was modelled. Stockholm showed the value of the Mini-SODAR in understanding the atmospheric layers, whilst its AIRVIRO model was used to assist in optimising the engineering planning of a northern section of the city ring road.

Technical characteristics
The enhanced AIRVIRO Air Quality Model gives near real-time predictions of Air Quality to be used together with Traffic Demand Strategies for reducing pollution levels.

The EMMA dispersion models use meteorological data and emission distributions as input to the simulations. The EMMA emission databases are consistent with Armonia Plus (Italtel), Sice and AIRVIRO (Swedish Met. Office) databases. Measurements of monitored pollutants are used for comparison and validation purposes. Three main models are considered, addressing problems of different scale and topography.

The Street Canyon model simulates dispersion within vertical cross section in a street canyon. The buildings surrounding the street are considered by the model in height as well as the width of the street, being not larger than 2-3 times the height of the surrounding buildings.

The Gaussian (Lagrangean) model which simulates horizontal distributions of pollutants at ground level or – in urban areas – at roof level. In the model the simulation, areas are assumed to be relatively flat and with horizontal scales below 20 x 20 km.

The Grid (Eulerian) model simulates horizontal distributions of pollutants at ground level or – in urban areas – at roof level. It allows dispersion calculations in complex topography and over large areas (specifically in Madrid Area). All three models present more or less uniform type of outputs, thus enabling their direct coupling with the main database.

An HP Workstation plus Meteorological Mast and Monitoring equipment (or data to suit accuracy of output required) is required.

Information and air quality data dissemination is one of the most important functions of the system. For the direct user and customer the data related to air quality predictions are used and available through a Microsoft Windows based GUI. Data for the General Public will be disseminated through graphical display PCs, based and accessible via Internet on dedicated WWW Hyper-text pages. All air pollution data are represented on digital cartographic maps and the Areas of interest are divided into several sub-areas. Traffic flows are represented and monitored on the digital road map. Parks, rivers, important locations, topology and topography are also included on the monitors' representations.

The information is broadcast through a variety of media (Radio Data System – Enhanced Other Network); WWW (World Wide Web); Public Access Terminal; Newspaper etc.) to allow the public to make an informed decision about their effect on the environment, especially travel the following day.

Another main function of the system is to generate actual and future air quality indicators, according to EFSAI categories.

The air quality indicators are related to the overall status of the air pollution through a pre-defined set of conditions which are universal. According to a reference scale for air quality indicators, distinct levels of air quality concentration are introduced.

Cam = annual mean concentration. Annual conditions from past measurements are compared to standard annual mean values set by regulating authorities.
Ctv = target value, related to the standard limit values and applicable to short averaging periods.
Cll = Lower protection limit and
Cul = Upper protection limit, are both values set by standards based on health protection
Civ = intermediate value, equal to the concentration for given vegetation protection. It is the intermediate range between the target concentrations and the annual mean concentration.
Cav = alert value, characterizes situations where the population needs to be warned of critical conditions.This is given via the alert concentration (Cav), which is set by competent authorities for some pollutants.

Based on the measured concentrations at each station site and their direct reference to the scale of Air Quality Indicators, characteristic classes can be calculated and they are indicated in the table below.

Index	Air Quality Indicators	Limits
1	good	Cam>=C
2	moderate	Civ>=C>Cam
3	poor	Cav>=C>Civ
4	critical	Ctv>=C>Cav
5	bad	cll>= C>Ctv
6	extreme	Cul>=C>cll
7	severe	C>Cul

Transferability
EMMA showed that the concept of integrating weather and pollution forecasts within three different Air Quality Modelling systems could be successfully implemented in climates covering Scandanavia, Atlantic Europe and the Mediterranean. The EMMA Website operated at Middlesex University, London, displayed not only pollution forecasts for each of the four cities (Genoa, Leicester, Madrid and Stockholm), but also for sub-areas within the urban conurbation. The Mini-SODAR was proved in Stockholm, where a Regional Air Quality prediction service was operated. Within the Integrated Applications for Digital Sites EQUAL project a version of the EMMA Air Quality Model (similar to that used at Madrid) is to be built at Madrid Technical University (using the internet for communication between Leicester and Madrid) before installing in Leicester to complement the existing suite of air quality models.

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