Making better decisions, faster, to tackle air pollution

Traffic within the City of Portici has been modelled and forecasted using traffic simulation [2] This is used off-line (i.e. outside the UIAQ system).

A number of baseline traffic scenarios have been developed, based on actual traffic data, analysed over a period of a year, [3] to reflect variances in traffic during different seasons, days and hours. Initially, traffic flows have been determined for roads with traffic data, and subsequently traffic flows have been estimated for roads with no traffic data. Hence, for each typical day the simulation provides forecasted traffic and velocity data in each road per hour.

Furthermore, a number of policy reduction scenarios are currently being developed, including traffic control policies (such as diverting traffic or limiting the use of specific streets) and vehicle restriction measures (for instance, banning vehicles of a specific Euro emission standard, or of a specific fuel type such as diesel from driving in and through the city centre). The model subsequently calculates traffic and velocity data in each road per hour under each reduction measure (i.e. policy reduction scenario).

Therefore, traffic scenarios simulate traffic conditions each day, with and without reduction measures.

In order to estimate air quality emissions per traffic scenario, COPERT [4] has been used to compute emission factors, considering vehicle characteristics and when relevant the traffic/vehicle constraints introduced by the reduction measure. 

Estimated emissions for both the baseline traffic scenario and the respective policy reduction scenarios are used in the air quality model to evaluate local air quality.

Typically, air quality models are steady-state dispersion models, incorporating a simplistic description of the dispersion process and approximating plume behaviour. As a result, some of the fundamental assumptions made do not accurately reflect reality. For example, when calculating hourly ground-level concentrations, air pollutants released during the previous hours are not considered. Another example is that these models usually assume a minimum wind speed, as they ‘break down’ during low wind speed or calm conditions.

In order to improve estimates of ground-level concentrations of air pollutants, a new “box” model was developed. [5] The new air quality model was developed using a process based modelling approach. This ensures that the model simulates the detailed physical and biological processes that explicitly describe air pollutants dispersion and removal. Furthermore, a Compartment Flow/System Dynamics approach has been employed. This enables conceptualising the system in terms of compartments (stocks) and flows (processes). From a modelling point of view, the main advantage of this approach is that the graphical representation of the system variables and processes helps the model development within an interdisciplinary workgroup. From a mathematical point of view, the use of differential equations means that the air pollutant concentrations calculated in each moment of time depend on concentrations calculated before, allowing for the accumulation of pollutants to be considered even in calm weather conditions.

In order to develop the model, the key elements and processes involved were identified. Experts from various scientific disciplines were consulted to determine which processes are fundamental to capture the dynamics of the system. In essence, the following processes have been incorporated into the model:

• Dry deposition of pollutants on vertical surfaces. Consequently, the air quality model takes into account the contribution of plants and vegetation in removing air pollutants, in particular particulate matter;

• Wet deposition (through precipitation, for example because of rain);

• Simplified chemical reactions that air pollutants undergo;

• Movement of pollutants by simple diffusion; and

• Movement of pollutants due to wind/air masses.

The model also includes 3D wind flow calculations, which take into account the geometry of buildings, considering advection and turbulent flows, as well as horizontal and vertical flows. 

In terms of input, the model uses:

• Modelled emissions from local traffic

• Meteorological data; and

• Terrain and land use data (e.g. vegetation and buildings).

A key input to any air quality model is emissions of air pollutants. The model developed primarily considers traffic emissions (incorporated as line sources), as it has been assumed that air pollution in Portici is mainly from traffic. [6] However, Portici is the centre of an urban area within the Naples metropolitan area, thus there are other pollution sources within and outside Portici that affect local air quality. Consequently, urban accumulation and the regional background is also incorporated in the model. More specifically, data from the air quality monitoring stations are used to calibrate the model (with night measurements being the starting point of the simulation) in order to account for urban accumulation. The regional background is obtained from the Copernicus Atmosphere Monitoring Service (CAMS). [7]

Topography, land use and meteorology are important when determining the dispersion of air pollutants, their dry/wet deposition and removal by vegetation. Even more so for the City of Portici that is characterised by complex terrain and meteorological conditions, including sea and volcano breezes.

As such, the terrain of the city has been incorporated in the air quality model with data on vegetation and buildings, derived from a Digital Elevation Model, [8] along with data on the road network.

Meteorological conditions outside the city of Portici have been provided by the Weather Research and Forecasting model (WRF) to incorporate in the air quality model. More specifically, the model focuses in a 60km x 50 km domain centred on Portici, with a spatial resolution of 1 km x 1 km and an hourly temporal resolution. This uses amongst other the Global Forecast System (GFS), global meteorological data (NCEP), local meteorological data and satellite data. WRF is run once a day, producing four days weather forecasts that are then used in the air quality model, including data on temperature, wind speed, precipitation, relative humidity, and solar radiation.

In summary, the air quality model combines monitored and modelled data. Monitored data provide information on the current situation (including weather conditions, traffic density and air quality concentrations) and models are used to forecast traffic, air quality and assess the effectiveness of policy interventions.

The outputs of the model are two-dimensional maps of pollutant concentration forecasts at a height of 1.5 meters with a spatial resolution between 2 and 5 meters.

A future improvement that may be explored is the use of fixed monitoring stations to undertake real time calibration of the air quality model, including to forecasts as time passes

Besides calibrating the air quality model, monitored data are used in the UIAQ tool to provide information on air quality levels at different areas across the city of Portici.   

More specifically, monitored data from the extensive ARPAC [9] air quality monitoring network, consisting of fixed and mobile regulatory monitoring stations, are incorporated in the UIA tool. Using data from both fixed and mobile stations has numerous advantages. For instance, fixed stations provide a complete temporal coverage, whilst mobile stations can be positioned in different locations to identify and monitor air quality hotspots.

Moreover, the city of Portici has procured seven fixed nodes that will be used to monitor local air pollution across the city. [10] Results from these will also be incorporated in the UIAQ tool. Finally, the use of portable air quality monitors, [11] tested within the framework of AIR-HERITAGE, is also being explored.

The UIA tool is a web application that enables policy makers, urban planners and the technical department of the municipally to monitor local congestion, identify air pollution hotspots, and evaluate the effectiveness of different policy measures. As such, optimal measures for reducing local air pollution and managing air quality exceedances can be identified. Results are visualised in the form of high-resolution maps that show air quality levels if no action is taken, but also if policy measures are implemented. The maps also contain different layers for presenting modelled and monitored data.

The UIAQ tool has different user functions built-in, so the user can focus on a specific part of the city, or can modify policy scenarios and run new ones. For instance, the user can configure different settings, such as restrict the circulation of vehicles that fall within a specific vehicle type, or have a certain engine type, or use a particular fuel type, and simulate air quality levels to evaluate the effectiveness of these actions. However, it should be noted that such additional policy scenarios need some time to run, as traffic emissions and the air quality model need to be re-run.

Finally, the tool will also be accessible through a mobile application, so that citizens have access to information on air pollution levels across the city modelled both real time and forecasted for the following 4 days. In particular, citizens will be able to use the application to see their exposure to air pollution (in the form of pollutant concentration maps), whilst moving along the streets of the city (using mobile phone tracking). Citizens will also be able to monitor air pollution levels on different paths, and see pollution forecasts. The idea is for citizens to have readily available information on highly polluted roads and routes, which will hopefully help them choose a greener path to use on the day.