Crosswinds can also occur when travelling on roads, especially on large bridges and highways, which can be dangerous for motorists because of possible lift force created as well as causing the vehicle to change direction of travel.

Generally, a crosswind is any wind that is blowing perpendicular to a direction.

Each aircraft has a uniquely stated maximum crosswind component derived from flight test experiments. For example a Boeing 727-200 has a maximum crosswind component of 35 knots (17.8 m/s), while a Cessna 172 has a maximum crosswind component of 17 knots (8.7 m/s).

Wind coverage is defined as the percentage of time that crosswinds are below an acceptable velocity. According to the FAA standards (FAA AC 150/5300-13), the minimum wind coverage considering all the observations is 95 percent. This means that for the 95% of the time, the crosswind component must be smaller than the maximum crosswind component of the aircrafts landing in a specific airport.

In designing runway orientation, the most desirable runway is one that has the largest wind coverage and minimum crosswind components.

The WindRose PRO software can be used for analysing a long series of data and calculating, for each possible runway direction, the wind coverage and the crosswind components (maximum, average and median). The software can also be used to evaluate the correct orientation of an existing runway. Moreover, since WindRose PRO allows date/time filtering of the input data, it is possible to evaluate the wind coverage and the crosswind components even for airports which work only in particular seasons (for example during summer) or only during day time. For each direction, the WindRose PRO output contains information about the wind coverage, the maximum crosswind from left and right, the average and the median (i.e. the 50^th percentile) of the absolute value of the crosswind components, the maximum headwind, the maximum tailwind and the average and the median of the absolute value of the headwind components.

As an example, we considered the Cagliari Elmas airport (Sardinia, Italy) whose runway is oriented from South East to North West. We collected the METAR data of the airport for the period 2008-2011, decoded them and prepared for WindRose PRO. More than 98% of the data were valid. The derived wind rose is represented in the following figure (left). It is observed that the prevailing wind direction is aligned with the runway, as it must be. The wind rose of the maximum (red) and average (green) speed shows that the maximum wind speed observed from 2008 to 2011 is 18 m/s, and comes from North West.

The wind coverage has been calculated for a hypothetical aircraft with a maximum crosswind speed of 8 m/s. WindRose PRO has been instructed to test 72 possible runway orientations, starting from 0 degree with steps of 5 degrees. It is observed that orientation N must be intended as the orientation from N degrees to (N+180) degrees. The following figure (left) shows that the maximum wind coverage, which is equal to 100%, is obtained for the two directions 115 degrees and 120 degrees. Only directions from 25 degrees to 50 degrees have a wind coverage smaller than the 95% indicated by the FAA. Finally, an example of numerical results of WindRose PRO is also presented in the figure (right) as a table. For each runway orientation the following parameters are calculated: wind coverage, maximum crosswind from left and right, average and median of the absolute values of crosswinds, maximum headwind and tailwind, average and median of the absolute values of headwind.

For any additional information on WindRose PRO, visit this page or contact us.

Acknowledgements: the foreground image has been taken from the Crosswind landing article on Wikipedia.

A wind rose is a chart which gives a view of how wind speed and wind direction are distributed at a particular location over a specific period of time. It is a very useful representation because a large quantity of data can be summarised in a single plot.

The first step to plot a wind rose with an electronic data sheet is to organise the wind data in a table according to their direction and speed classes. In other words the joint distribution of wind direction and speed must be calculated, as shown for example in the next figure. Each yellow cell contains the number of events observed over a specific time period for a specific combination of wind direction and speed. For example, wind blowing from North (N) with a speed smaller than 1 m/s has been observed 51 times, while wind blowing from North East (NE) with speed between 1 m/s and 2 m/s has been observed 159 times. If available, the user may also specify the average wind speed for each direction, as shown for example in the green cells. The total number of events and the corresponding percentages for each direction and wind speed class are automatically updated.

The example file uses 16 directions and 6 wind speed classes, but their number and contents can be easily modified.

Once the number of observations for each direction and wind speed class has been specified for each yellow cell, three charts are produced: the wind rose, the wind direction distribution and the wind speed distribution. If the average wind speed for each direction is also specified, then a fourth chart is produced representing the rose of the average wind. Examples of these four charts are reported in the following images.

The joint distribution of wind direction and speed must be determined by the user. This task might require long times, particularly for large time series of data. In a wind rose the length of each arm is proportional to the number of events, or the frequency, at which wind was observed from that direction. For a specific direction, the different wind speed frequencies sum up to give the total length of the arm. The wind rose plotted with the Microsoft Excel or Open Office Calc files does have such feature. If you need more professional wind roses and more complex analysis of your data, you might want to evaluate WindRose PRO.

In order to obtain a harmonised approach in air quality modelling over Europe, the Forum for Air Quality Modelling in Europe (Fairmode) was established in 2008 as a joint action of the European Environment Agency and the European Commission’s Joint Research Centre (JRC). The technical reference guide is an output of that joint action

Air quality models are very important tools, since they allow to:

• Assessing the existing air quality situation – for example showing exceedances of EU or national air quality standards, calculating population exposure to pollution and health impacts, and identifying contributions of air pollutants from different sources.

• Air quality forecasting – many national, regional and local authorities have established forecasting systems to warn the public when air pollution episodes are expected.

• Air quality planning – identifying possible measures to reduce emissions, and developing emissions reduction scenarios.

Precisely because of their importance, it is expected that AQ models be comparable, well documented, and validated for their required applications in order to achieve reliable modelling results.

The EEA technical report provides an overview of the use of models with regard to the Directive 2008/50/EC on ambient air quality and cleaner air for Europe. The guide has three key aims:

• provide common technical guidance for the use of air quality (AQ) modelling in relation to the EU’s AQ Directive;

• provide a central reference point for the development of a harmonised approach to modelling;

• promote good practice in AQ modelling.

The technical report is available from the EEA web site.

A wind rose is a chart which gives a view of how wind speed and wind direction are distributed at a particular location over a specific period of time. It is a very useful representation because a large quantity of data can be summarised in a single plot. This type of plot can be used not only to plot wind speed against wind direction, but to plot any variable which depends on wind direction, as for example the average or the maximum wind speed measured over a time period. If a monitoring station measures both wind and concentrations of air pollutants, it is possible to plot the concentration levels of a specific pollutant against wind direction to investigate if higher levels could be related to any specific source.

Wind roses contain important information and are used in different fields as, for example, in air quality studies, in designing energy saving buildings, and in positioning wind turbines.

The simple on line tool available in this web site allows to create wind roses with 16 directions, each one representing an arc 22.5 degrees wide. The first direction is centred on North (i.e. 0 degree), and the last one on NNW (i.e. 337.5 degree). As usual, the wind direction represents the direction from which the wind blows. The colours of the wind speed classes cannot be modified, they are red, yellow, green, cyan, and blue.

Two examples of usage of the tool will be shown in the following.

Example of usage: wind rose

When used for plotting a wind rose, the on line tool requires that the joint frequency distribution of wind direction and speed must be inserted. Therefore the user must analyse the wind data, group them according to the 16 direction, and then according to the wind speed classes adopted. This binning of the wind data is not done automatically by the on line tool, while other software packages (e.g. WindRose PRO) are capable to create the wind rose directly by reading the wind data in many formats.

When you access the page of the on line wind rose tool, some data are preloaded and, to save time, we are going to use such data in this example. Anyway, you can modify such values according to your needs. The first step is to assign a title to the plot we are going to create, we must write it within the Plot title text box, as an example we will simply write Wind rose. We will leave the Smoothing check-box unchecked. On the contrary, all the Draw check-boxes and the Fill check-boxes will be checked, so that the data of all the five columns will be represented and filled. We will change the text of the Labels, which will be used for the legend, inserting for example 0-1 in place of Class 1, then 1-2, 2-3, 3-4 and >4 in place of Class 5.

Click the Proceed button, and you will obtain the following plot. Try to uncheck some of the check-boxes to see how the plot changes.

In a wind rose the length of each arm is proportional to the number of events, or the frequency, at which wind was observed from that direction. For a specific direction, the different wind speed frequencies sum up to give the total length of the arm. The wind rose plotted with the on line tool does not have such feature, since all the frequencies are plotted starting from the centre of the plot, therefore it is more properly a radar plot.

Example of usage: average wind speed

Now suppose you want to plot the average wind speed for each direction. In the Plot title text box write Wind speed. Since there is only one variable to plot, only one Draw check-box must be checked, we will check only the first one. Uncheck the corresponding Fill check-box, so that the curve will be not filled. In the first label insert Average. Then fill in the first column with the following values for the average wind speed 1.3, 1.5, 2.5, 3.3, 2.6, 1.8, 1.7, 1.4, 1.3, 1.7, 2.0, 2.0, 2.3, 1.3, 1.1, 1.0. The values are in m/s and have been calculated using WindRose PRO starting from hourly data for a whole year. The average wind speeds must be inserted from N to NNW, so that 1.3 m/s is the value corresponding to N, and 1.0 is the value corresponding to wind coming from NNW.

Click the Proceed button, and you will obtain the following plot.

This same procedure can be followed for plotting air pollution concentration data if their average, or peak, values are known for each direction.

This article shows how to use the simplified version of the COPERT 4 methodology available on the Enviroware web site. The methodology is based on the contents of the EMEP/CORINAIR Emission Inventory Guidebook – 2007, available on the internet site of the European Environment Agency, more precisely on chapter 7, concerning Road transport. Chapter 7 provides the methodology, emission factors and relevant activity data to calculate:

• the emissions produced by the exhaust systems of road vehicles (SNAP codes 0701 to 0705),

• the non-exhaust emissions such as fuel evaporation from vehicles (SNAP code 0706) and

• the component attrition, which means tyre and brake wear and road abrasion (SNAP codes 0707 and 0708).

The simplified methodology allows to calculate the only exhaust emissions. For many European countries, it gives the bulk emission factors in terms of grams of pollutants emitted per kg of fuel consumed. The emission factors at national level have been obtained applying the detailed COPERT 4 methodology using the activity data derived from TREMOVE. Therefore the detailed COPERT4 methodology has been a-priori applied to obtain the simplified emission factors.

The vehicles categories considered by the simplified COPERT4 methodology are Gasoline Passenger Cars (gPC), Diesel Passenger Cars (dPC), Gasoline Light Duty Vehicles (gLDV), Diesel Light Duty Vehicles (dLDV), Diesel Heavy Duty Vehicles (dHDV), Buses, Mopeds and Motorcycles. The simplified methodology does not deal with LPGs, 2-stroke and gasoline heavy-duty vehicles because of their small contribution to a national inventory.

The simplified methodology allows to calculate the exhaust emissions of carbon monoxide (CO), nitrogen oxides (NOX), non-methane volatile organic compounds (NMVOC), methane (CH4), particulate matter (PM), and carbon dioxide (CO2). All PM emissions refer to PM_2.5, as the coarse fraction (PM_2.5-10) is negligible in vehicle exhaust.

The application of the simplified COPERT 4 methodology must be done keeping in mind that the emission factors

• correspond to a fleet composition estimated for year 2005, therefore their accuracy deteriorates as time distance increases from such year because new technologies appear and the contribution of older technologies decreases;

• correspond to national-wide applications including mixed conditions driving (from urban congestion to free flow highway).

The methodology can be useful for example in simplified emission inventories, where rough estimate of the transport contribution is required. It is observed that the methodology is not suitable to be applied over small areas (e.g. a single town), or for a small time period (e.g. few days), because in such cases it would be even more approximated.

Example of input data

The emission factors are given as function of fuel used by the transport sector, therefore the first step is to obtain information about the total amount of fuel used. Considering for example Italy, for the whole country and for year 2008, such information can be obtained from the internet site of the Italian Oil Union (Unione Petrolifera). In 2008 Italy has consumed, for the road transport sector, 11044 Gg of gasoline, and 25934 Gg of diesel.

Since we want to estimate the emissions in Italy, an assumption that we have to do is that all this fuel has been consumed in Italy, even if a fraction of it has been consumed abroad. Similarly there will be a fraction of fuel sold abroad and consumed in Italy.

Other assumptions are needed to split the fuel consumption among the vehicle classes listed above (gPC, dPC, gLDV, dLDV, dHDV, buses, mopeds and motorcycles). A precise calculation of the consumption split is beyond the scope of this article, however it is worth to say that there are methodologies and software which allow a reliable estimate of the consumption of each vehicle class. The EMITRA software, for example, uses the actual fuel consumption, the number of vehicles and the vehicles fleet as known input data, then calculates the total consumption starting from assumed values of the average speed on different road types and of the average trip length for each vehicle type. If the calculated consumption is equal to the actual consumption, or at least comparable within a degree of acceptability, the fuel split is automatically obtained. Otherwise the procedure is repeated using different values for the average speeds and the average trip lengths until the convergence is reached.

For this example we will simply assume that the fuel is consumed as summarised in the following pie charts, which is, for gasoline, gPC: 93%, gLDV: 4 %, mopeds: 1%, motorcycles: 2%, and for diesel, dPC: 41%, dLDV: 11%, dHDV: 43% and buses: 5%.

Therefore the Gg of fuel consumed by the different vehicle classes is gPC 10270.9; gLDV 441.8; mopeds 110.4; motorcycles 220.9; dPC 10632.9; dLDV 2852.7; dHDV 11151.6 and buses 1296.7.

These numbers are then used as input data for the on line procedure. We need to select Italy among the available countries, then we must insert the fuel consumption for each vehicle class, paying attention to the units because the above numbers are in Gg (i.e. kilotonnes), while the system needs them in Mg (i.e. tonnes). An example of the input mask is shown in figure.

Results

The results of the on line simplified COPERT 4 methodology are given both in numerical terms and graphically. A table (see the figure) gives the total emissions calculated for each pollutant and for each vehicle class. The emission units are automatically decided by the software starting from their values, they can be kg, Mg and Gg. Moreover, six pie charts, one for each pollutant, show the amount of emissions due to each vehicle class.

Using the input data discussed before, the amount of estimated emission due to road transport over the whole Italy are 1910.9 Gg of carbon monoxide, 703.2 Gg of nitrogen oxides, 206.7 Gg of NMVOC, 15.0 Gg of methane, 26.8 Gg of particulate matter and 116.3 Tg of carbon dioxide. As shown by the pie charts below, which are automatically produced by the on line simplified COPERT 4 methodology, the greatest amount of carbon monoxide is emitted by gasoline passenger cars (gPC), which is responsible for the emission of more than 81% of the total. More than 52% of nitrogen dioxides is emitted by diesel heavy duty vehicles, while passenger cars, both gasoline and diesel, are responsible for the emission of about 17% each one. Methane and NMVOC are mostly emitted by gasoline passenger cars (about 60% of the total). Important emissions of particulate matter, which is all PM_2.5, are due to heavy duty vehicles (more than 37% of the total) and to diesel passenger cars (more than 34% of the total). Finally, the greatest emissions of carbon dioxide are due to heavy duty vehicles (30.1%), diesel passenger cars (28.7%) and gasoline passenger cars (27.9%).

In a typical emission inventory, now that we have the total emissions of each pollutant, other steps would follow. Among these steps, four important ones are:

• the spatial disaggregation of the emissions (i.e. how they distribute over the territory);
• the temporal disaggregation of the emissions (i.e. how they distribute over the months, the days of the week and the hours of the day);
• the chemical speciation of the NMVOC (i.e. the determination of the chemical species within this pseudo-species which contains all the volatile organic compounds but methane);
• the NOX speciation into NO and NO2.

Concerning the chemical speciation, the CORINAIR methodology contains the fraction of species (alkanes, cycloalkanes, alkenes, alkines, aldehydes, ketones and aromatics) for each vehicle category and fuel type. Even the NOX speciation, indications are given within the CORINAIR methodology.

The size speciation of particulate matter would be another task in an emission inventory but, as stated above, all the road traffic exhaust emissions of PM refers to PM_2.5. Finally, concerning the PM speciation in elemental and organic carbon, the CORINAIR methodology contains ratios for different vehicle technologies.

This text appeared for the first time in February 2008 on the Enviroware internet site. A PDF document of the original version is available.

1. Introduction

Any person involved in air quality studies knows that cartographic representation of data (input, output or measured) is very important, time consuming and, most often, difficult. One of the difficulties is to find a suitable cartographic base over which the information (sources, sensible receptors, buildings, isopleths, etc.) must be represented. Sometimes the cartographic base is available, but it is not georeferenced, therefore the user must try to reference it using the coordinates of some known points and the dimension in pixels of the image. This procedure may introduce some positioning mistakes. When things go well, there are georeferenced images (for example tiff images with their world file – or tfw files) which can be loaded in GIS systems. The drawback of the GIS systems is that their use presents some difficulties and they are expensive (with some noticeable exceptions of free systems, such as MapWindow and TatukGIS). Most often the cartographic georeferenced bases are very expensive. Since some years a new powerful tool, Google Earth (abbreviated with GE sometimes in the rest of the document), is available to (virtually) navigate over the globe, finding places and visualising information. Google Earth is a computerised 3D representation of the world that uses satellite, aerial and geographic information system imagery combined with mapping software. Moreover, digital topographic data from NASA and other sources are used to model orography. The Google Earth interface allows to insert very easily graphical and textual information, but the great power of the tool is the KML language (Keyhole Markup Language ), which allows to show very complex information with a relatively simple syntax. Google Earth is a geographic browser, and the KML is its language, just as HTML is the language of the internet browsers. Other geographic browsers exist, as for example Microsoft Live Earth and NASA World Wind, but this document focuses on Google Earth. This document describes some possible uses of Google Earth for representing Air Quality (AQ) data. In particular it shows how Enviroware uses Google Earth to represent AQ data in its air dispersion evaluation studies.

2. Applications

This paragraph describes some example application of Google Earth for representing AQ data. When more information is available on the internet, the links will be indicated, and only few comments will be given in the text.

2.1 Measured and estimated point data

2.1.1 AirNow

The U.S. EPA, NOAA, NPS, and other agencies developed the AIRNow Web site to provide the public with easy access to US air quality information. The Web site offers daily Air Quality Index (AQI) forecasts as well as real-time AQI conditions for many cities within the US. The AQIs are available as KML files and can be viewed with Google Earth. As can be seen by the figure, the user has a global information at a glance, looking to the colours of the circles, and can access more detailed information clicking on a specific point.

2.1.2 Air Quality data over Europe

Air quality statistics of interest for the European Legislation for many pollutants and for years 1995, 2000 and 2005 are available as KML files from the Enviroware’s web site. The statistics are available for benzene, nitrogen dioxide and nitrogen oxides, ozone, lead, PM10 and sulphur dioxide. The KML files have been created starting from the information of the AirBase database. The following figure shows, as an example, the 2005 exceedances of the 1 hour limit value of SO2 over a great part of Europe. The red circles represent monitoring stations where the limit value has been exceeded more than the allowed number of times, and the green circles represent monitoring stations where the 1 hour concentration has been lower than the limit value (or has exceeded the limit value for less than the allowed number of times). The map indicates that the limit has been respected over almost the whole Europe, with few exceptions in South Sardinia (Italy), Spain, France, Bosnia Herzegovina, Slovenia and Bulgaria. Detailed information on the monitoring station (station code, location, number of exceedances, characteristics of the stations, limit value, percent of valid data) is displayed when the user clicks on a specific point. The information is organised by country, therefore the user can choose to visualise only some countries by clicking over the corresponding layer.

2.1.3 UK Air Quality Archive

The UK Air Quality Archive allows to view in GE the latest data and information for all automatic air quality monitoring sites in the UK national network. All the data is automatically refreshed every hour. Circles of different colours over the map indicate the position of the AQ monitoring stations and their pollution level. Clicking over a point will display more detailed information, including a weekly graph of pollutant levels. An example is shown in the image.

2.1.4 Emission inventories

The US-EPA makes available point sources emissions information as KML file. The KML file contains facility level information for the seven major point-source sectors: cement facilities, chemical manufacturing, electric generating units, natural gas pipelines, oil and gas production, petroleum refineries and pulp and paper industries. Each facility is shown as a balloon which height over the ground is proportional to its emissions, therefore tilting the view allows to immediately identify the bigger emitters. The following figure shows a tilted zoomed view of the point sources KML file, and the detailed information about a facility, which shows up after clicking on the corresponding placemark. The US-EPA shows only the point source emissions over GE. It is of course possible to show area emissions using polygons and mobile emissions using lines. Polygons and lines can be represented with different colours depending on their emission levels, and detailed information can be displayed by clicking over them.

2.2 Meteorology

Many meteorological information can be shown on Google Earth. The software itself contains a layer which allows to show some meteorological information. Within this layer, there are three sub-layers that demonstrate how the weather looks in selected areas. The layers are called as “Clouds”, ”Radar” and “Conditions and Forecasts”. The “Clouds” layer is provided by the Naval Research Laboratory in Monterey, and the “Radar” with the “Conditions and Forecasts” layer is powered by weather.com.

2.2.1 Wind fields

When carrying out an atmospheric dispersion study, it is important to visualise the meteorological fields, in particular the wind fields for selected times. For example, when using the US-EPA recommended CALMET/CALPUFF modelling system, it is important to visualise the wind field reconstructed by CALMET before applying CALPUFF. This can be done with a KML file which shows vectors of specific directions and whose intensity is indicated by their lengths and/or colours. An example of wind field at 10 m above the ground as reconstructed with CALMET for a portion of domain is shown in the image.

2.2.2 Wind roses

A wind rose is a graphic tool used to give a concise view of how wind speed and direction are typically distributed at a particular location, therefore wind roses summarise the occurrence of winds at a location, showing their strength, direction and frequency. Wind roses are important in air quality studies, but they are also essential when placing wind turbines, designing airports and designing energy saving buildings. Wind roses can be described by KML files and visualised in Google Earth over aerial imagery. The KML files representing wind roses for some airports of the world, created processing METAR data, are available at in the Enviroware internet site. A tool which analyses wind data, creates wind roses and automatically creates 2D or 3D wind roses in KML format is WindRose PRO. An example of wind rose created by WindRose PRO in KML format and loaded in Google Earth is shown in the image. The wind rose appears 3D due to the tilting.

2.3 Dispersion models

Google Earth is a useful tool to define input data for dispersion models, to represent their output and to verify both input and output. In the following paragraphs some examples will be shown.

2.3.1 Collecting input data

Google Earth can be used to verify the correct positions of existing sources and sensible receptors. The user is helped in this effort thanks to the possibility to switch the coordinates from lat/lon to Universal Transverse Mercator (UTM), which are used by many dispersion models. Other input data, such as the coordinates of buildings for describing the building downwash effect, can be collected using Google Earth.

2.3.2 Input preparation and verification

GE is also useful to verify the correctness of input data prepared for the atmospheric dispersion models. For example, orography interpolated over grid cells of given sizes can be viewed superimposed to the 3D orography of GE to verify its correctness (e.g. correct representations of valley and peaks, correct representation of coastlines, etc.). The image shows an example of orography prepared interpolating values within a database with 30” resolution, and superimposed to the 3D orography of GE. The outer green line represents the limit of the simulation domain.

The grid plot representation is useful also for checking other input variables, such as the land cover. The land cover plot in GE allows to verify the correct assignments to the model grids; for example it allows to verify that water cells (lakes, sea, etc.), built up cells, forest cells, etc., are correctly assigned. This task can be done assigning different colours to the cells and a degree of transparency (i.e. opacity below 100%), so that it is possible to view the satellite image below the land cover representation. The image shows as an example the land cover representation obtained processing the CORINE database over a specific simulation domain. It allowed to verify the correct representation of a relatively big lake in the south-western part of the domain, and even to recognise a small burnt area (in black close to the domain centre).

2.4 Visualisation of the model outputs

Atmospheric dispersion models produce outputs which require different representations. In the next sub paragraphs some types of output representations within GE will be shown. These representations are routinely used by Enviroware in its atmospheric dispersion studies and for debugging purposes when developing or testing atmospheric dispersion models.

2.4.1 Contours and grids

Predicted concentrations and deposition fields can be represented over Google Earth as grids or contour plots. The grid representation is effective to show the locations where specific limit values are exceeded. For example a grid representation could be used to show the cells where a dispersion models predicts more than 18 exceedances of the 1 hour EU limit value of 200 μg/m3.  In order to prepare plots like the one shown, it is needed a software capable to process the output binary file of a dispersion model (e.g. CALPUFF, AERMOD, ISC3 Short Term or Long Term) to get all the relevant statistics (hourly maximum, daily maximum, percentiles of of 1-hour average concentrations and of of 24-hours average concentrations, number of exceedances of a given threshold, running averages of a specified number of hours, and others).

2.4.2 Trajectories

Google Earth is also used to show the trajectories of pollutant parcels. As an example, the image shows the trajectories of three computational particles emitted by the Long Range Lagrangian dispersion model APOLLO2 (which will be used by the Italian I.S.P.R.A. to manage radiological emergencies). The particles are emitted by the same source with temporal distance of few seconds.

2.4.3 Particles

Not only trajectories, but the particle positions at fixed times can be shown in Google Earth. The image shows the positions of the computational particles emitted by the APOLLO2 dispersion model. Different colors represent particles with different heights above the ground. By tilting the view of Google Earth, the particles are represented in 3D with their actual height above the ground. A box with the properties of a particle (lat/lon coordinates, height, age, mass of each component, and others) is shown clicking over it.

3 Conclusions

This document has shown some example of how Google Earth can be used as a powerful tool by the atmospheric dispersion community to show 2D and 3D fields. Of course there are many other possible applications which are not mentioned above and could be described in future revisions of the document. The document did not mention the time trends of variables, but it is possible to show them for specific variables clicking over a given position. For example a placemark could represent the position of a discrete receptor of a model, and clicking over the placemark the time trend of the concentrations predicted at such location would pop-up. A more dynamic example concerns the concentrations measured from a monitoring stations whose KML file describing the time trend could be made available practically in real time through internet.
One of the reasons of the power of the Google Earth tool is that there are layers of information which are always available within the software, and other layers which are made available by the users. The layers provide the ability to overlay additional geographic information like roads, topography, and landmarks.

To create a directional graphic with 3 variables we will use the example file sample_3v, which is composed by 4 columns: Date and time [Date Time], concentration of an air pollutant [Conc (µg/m3)], wind direction [WD (deg)] and wind speed [WS (m/s)].

Open WindRose PRO, click Options and execute the following operations:

Select the Not valid tab and specify the range of acceptability of the data within the Excel file. Invalid data within the example file are written as -999, therefore we need to discard such values. We will accept wind directions within the range [0, 360], wind speeds within the range [0,100] (m/s), and concentration values within the range [0, 1000] (µg/m3).
Be sure to set the filter on valid data before loading the Excel file, because they are filtered while loading.

Select Dirs & type tab, then select Data within the frame Wind rose or data, and specify a point size (20 in the example). Then specify a scale in the text box named Scale for different plots (where different means that we are not producing a wind rose). This scale indicates the number of drawing points for each unit of the variable plotted. We will try with a scale of 250, it will be modified if it is too big or too small.

Select Title & logo and specify the title, its position and its color.

Select Ranges and specify the concentration intervals to plot and their colors. In this example we have chosen to use 5 concentration intervals. The maximum concentration value is 115 µg/m3; we choose to use the following intervals (in µg/m3): 0 – 2; 2 – 5; 5 – 10; 10 – 50; 50 – 120.

Click Ok to set the new options and close the mask.

Click Load, select sample_3v from your PC, then select the Sample worksheet and press Ok. Now select the column containing wind direction (WD) and the one containing wind speed (WS). Check the Use third variable check box and select the column containing the third variable (Conc). We do not need to use date and time for this plot.
Note that the current data filtering options are written at the bottom of the input mask.
Click Read to load the data.

A total of 8760 hourly records (i.e. a whole year) is present within the file. The valid data (i.e. the records with all the fields different from -999) are 99.3% of the total (8703 records), as reported in the lower right corner of the WindRose PRO graphical interface.

Click Analyse to process the data, then click Draw to produce the plot. WindRose PRO creates a plot like the one shown in the next figure. At this point the plot is not very clear yet, but an orange spot corresponding to high concentration values (i.e. greater than 10 µg/m3) is visible in the upper left quadrant.

To isolate the higher concentrations, click again on Options and Ranges, in the Number of intervals drop down select 2, and specify the two higher intervals considered in the previous plot, from 10 to 50 µg/m3 (orange) and from 50 to 120 µg/m3 (red). Then click Ok.

Click Analyse to process the data, then click Draw to produce the plot.

WindRose PRO produces a plot like the one shown in the figure on the left.

The plot indicates an important source of the pollutant considered (e.g. NOX, SO2, etc.) at North West of the point where wind and concentration have been measured.

WindRose PRO can then be used as a tool for evaluating the possible presence of important sources of air pollutants.