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Environmental Engineering and Management Journal October 2020, Vol. 19, No. 10, 1915-1926
http://www.eemj.icpm.tuiasi.ro/; http://www.eemj.eu
“Gheorghe Asachi” Technical University of Iasi, Romania
UTAQ, A TOOL TO MANAGE THE SEVERE
AIR POLLUTION EPISODES
Fabrizio Ferrari, Giuseppe Maffeis1, Johannes Flemming2, Roberta Gianfreda1
1TerrAria srl, via Melchiorre Gioia 125, Milan, 20125, Italy
2European Centre for Medium-Range Weather Forecasts, Reading, RG2 9AX, UK
Abstract
The Urban Tool for Air Quality (UTAQ) is a project funded by CAMS (Copernicus Atmosphere Monitoring Service) - ECMWF
(European Centre for Medium-Range Weather Forecasts). UTAQ is a web-based tool with a user-friendly interface that allows users
to evaluate current and forecasted air quality for the following 4 days at urban scale with high resolution (10-50 m). UTAQ, in the
mobile version, allows citizens to evaluate and then to limit their air quality exposition thanks to the personal position tracking of
the mobile device. UTAQ, in the decision makers’ version, allows local authorities to evaluate and find the best traffic limitation
strategies to be implemented in the short-term to manage emergency conditions of air quality exceedances. To support this process,
UTAQ produces high-resolution maps of air quality both on the current situation and on the forecasted next 4 days. These maps are
the combination of (1) the background concentrations supplied by the European CAMS ENSEMBLE model in real time analysis
and 4-days forecasts (2) the peak concentration due to traffic through specific hourly source-receptor functions, to make the
calculation fast and reliable even at high resolution (3) observed air quality data monitored by urban stations. UTAQ supports the
authorities to increase the degree of awareness of its citizens communicating the air quality forecasts and the benefit obtained thanks
to the emission abatement strategies adopted. A first version of UTAQ has been trained and validated on a 10x10 km2 domain close
to Monza in Lombardy region, including 7 municipalities (for a total of 180 thousand of inhabitants) with the air monitoring station
of Meda (ARPA Lombardia). This work describes an improved version of UTAQ, trained and validated on a domain centered on
the metropolitan area of Milan.
Key words: air pollution episode forecast, CAMS, mitigation strategy, source-receptors models, traffic limitation policies
efficiency, urban dispersion modelling
Received: February, 2020; Revised final: June, 2020; Accepted: July, 2020; Published in final edited form: October, 2020
1. Introduction
Despite significant improvement of air
quality in Europe in recent decades, air pollution still
causes about 400,000 premature deaths every year and
more than 75.000 in Italy alone (EEA Report, 2019).
In the past, exceedances of air quality thresholds
occurred across larger areas of the continent. In recent
years, the exceedances are only found confined to
heavily urbanized areas such as the Po Valley,
southern Poland and Benelux in terms of particulate
matter concentration and big cities in terms of nitrogen
dioxide (Kiesewetter et al., 2013). The transport sector
Author to whom all correspondence should be addressed: e-mail: f.ferrari@terraria.com
is the main source for the emission of many primary
pollutants, which lead to poor air quality, particularly
in urban areas with high road traffic volumes (Fig. 1).
The EU threshold for the annual average of nitrogen
dioxide (40 µg m-3), one of the most critical air
pollutants which is typically associated with emissions
of road vehicles, has been widely exceeded in 2017,
with 86% of exceedances observed at monitoring
stations close to road infrastructures (EEA Report,
2019).
The infringement procedure by the European
Commission against Italy, because of the persistent
excess of PM10 daily limit values in many regions in
Ferrari et al./Environmental Engineering and Management Journal 19 (2020), 10, 1915-1926
1916
May 2018 (IP/18/3450, 2018), and the decision of the
European Commission decision to refer Italy to the
EU Court of Justice, for failure to respect limit values
for nitrogen dioxide (EC Case, 2019), are both clear
signs of how serious the Italian air quality problem is.
An example of the severe situation occurred
in the first months of 2020 in northern Italy (Fig. 2)
when high anthropogenic emissions combined with
the frequent wintertime air stagnation and a long
period without rainfall caused high concentrations of
particulate matter, mainly PM10 in the Po valley.
Even if emissions will be reduced because of a
regional air quality plan in the future, it could be
expected that climate change will lead to a shift of
precipitation patterns and to the decrease of winter
rainfall (ISPRA Report, 2019) and thereby
maintaining occurrence of air pollution events in the
Po Valley during winter.
Fig. 1. Observed concentrations of NO
2
in 2017. Dots in the highest two colors categories correspond to values above the EU
annual limit value: 40 µg m
-3
(EEA Report, 2019)
Fig. 2. An example of a severe PM
10
episode. CAMS (Copernicus Atmosphere Monitoring Service) ensemble analysis for 31
January 2020 (daily average): in dark red values over PM
10
legislation limit (50). In the chart, PM
10
daily average concentration
trend measured by the Milano Senato air quality monitoring station (ARPA Lombardia)
UTAQ, a tool to manage the severe air pollution episodes
1917
The EU air quality standards, adopted by the
member states in 2008 (EC Directive, 2008)), require
that each state establishes an action plan defining the
measures to be taken in the short term to reduce the
population exposure to the concentration exceeding
the limit, for the main pollutants affecting urban areas,
such as NO2, PM10 and O3.
To comply with the short-term limit values set
by the legislation and to reduce the dangerous
concentration peaks, actions must be planned at least
one or two days before. The actions to be taken could
be effectively led by air quality forecasting systems
such as the one implemented in UTAQ. Furthermore,
according to EU directives, local authorities must
provide information to the public on the air quality
status and on the forecasted trend for the following
days.
Mitigation measures are often taken because of
measured concentrations and weather forecasts, when
the air quality critical episode has already occurred
and the efficacy is strongly limited. The availability of
a reliable urban air quality forecasting system could
help decision-makers to plan abatement measures in
time, minimizing both health costs as well as the costs
connected to the unnecessary restrictions on economic
activities.
The Urban Tool for Air Quality (UTAQ)
presented in this paper is such a tool that will support
air quality management by local authorities and will
be instrumental to provide air quality information to
the public.
A first version of UTAQ (Ferrari et al, 2019)
has been trained and validated on a 10x10 km2 domain
close to Monza in Lombardy region, including 7
municipalities (for a total of 180 thousand of
inhabitants) with the air monitoring station of ARPA
Lombardia in Meda. In this first version of UTAQ, 48
hours before observation was assimilated in the
computation. This paper describes an improved
version of UTAQ trained and validated on a domain
centered on the metropolitan area of Milan, with a new
computation of the total concentrations with data
assimilation performed with observations of 24 hours
before.
2. Methodology
2.1. UTAQ architecture
UTAQ is a web-based tool with a user-friendly
interface which provides: (1) a Web version for policy
makers helping them to define the best strategy to
reduce urban air quality pollution peak episodes by
traffic limitation measures, (2) a public Web version
with maps of the current and the predicted air quality
status, (3) a public mobile version in which, through
the device GPS position, citizens can inquire about the
current and forecasted air quality at their position.
UTAQ represent air pollution as combination
of regional, urban and street level contributions (Fig.
3, adopted from Harrison, 2018) in the following way:
I. the regional background concentration
(green part) are obtained from the estimates of the
European scale model ENSEMBLE of the regional
Copernicus Atmosphere Monitoring Service (CAMS)
retrieved from the European Centre for Medium-
Range Weather Forecasts (ECMWF) which provides
four days of hourly concentrations forecasts of several
pollutants, including in particular nitrogen dioxide
NO2 and particulate matter PM10 (Marécal et al, 2015)
- paragraph 2.5;
II. the city increment (light blue-lilac part) is
obtained from the pollutant concentration measured
on the day before from one or more air quality
monitoring stations, therefore taking into account the
temporal urban accumulation of pollutants (Maffeis,
1999) - paragraph 2.6;
III. the street level increments (red-yellow part)
in terms of PM
10 daily average and NO
2 hourly
average (in compliance with the observations
available) are calculated using local traffic emissions
through specific hourly source-receptor functions
(hourly kernels), in order to make the calculation fast
and reliable even at high resolution (20x20 m2).
Thanks to the support of the JRC in Ispra, this
approach is borrowed from the SHERPA-City project
(Degraeuwe et al, 2018), where the annual kernels
have been used to calculate the PM10 and NO2 annual
averages - paragraph 2.4.
Fig. 3. Urban pollution schematic profile (adopted from Harrison, 2018)
Ferrari et al./Environmental Engineering and Management Journal 19 (2020), 10, 1915-1926
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The integration of these three contributions
guarantees both scientific validity and efficient
computation time allowing a web operation during the
model computation of the efficiency of traffic
limitation policies chosen by the decision maker.
As described in more detail in the next
paragraph (2.6), the integration method makes sure
that double counting of the CAMS emission
contribution in steps II and III is avoided.
The operation diagram of the UTAQ system is
shown in Fig. 4: starting from the definition of the
calculation domain, different road traffic emission
scenarios can be applied. Next, using the weather
forecast of the IFS (Integrated Forecasting System)-
ECMWF model and the relative hourly source-
receptor functions (kernels), the measured air quality
data and the ENSEMBLE model forecasts,
concentrations maps for the different scenarios of air
quality are produced and made available to policy
makers and citizens.
2.3. Emissions calculation
Due to the overwhelming contribution of the
traffic to air pollution in the city of Milan (Fig. 5),
UTAQ street increment focuses only on traffic sector.
The emissions are calculated on a 20x20 m2
grid in the chosen domain, starting from the road graph
and the related traffic volumes used for the estimation
are taken from OpenTransportMaps (Jedlička et al.,
2016) and the macroscopic model OmniTRANS (de
Graf, 2015), respectively.
Roads are divided into five categories, from
highways to neighborhood streets and for each road
segment the annual average daily traffic (AADT) is
estimated. The calibration of the emission model has
been carried out adjusting the AADT in order to be
consistent with the estimation of the local traffic
emissions inventory provided by INEMAR
Lombardia (Caserini et al., 2012).
Fig. 4. UTAQ architecture
Fig. 5. Emission distribution by CORINAIR SNAP1 sector in 2014 for Milan municipality (INEMAR, 2018)
UTAQ, a tool to manage the severe air pollution episodes
1919
UTAQ grid emissions are estimated using the
average distribution by vehicle type according to the
COPERT4 classification (Gkatzoflias et al., 2012)
with the vehicle emission factors specified by road
type (urban, rural and motorways) and by pollutant
(PM, NO2). Emission factors were taken from JRC
DIONE traffic model database (Thiel et al., 2016)
which incorporates data collected in the European
research project TRACCS (Papadimitriou et al.,
2013). A temporal disaggregation is applied based on
a typical daily profile to obtain hourly emissions.
2.4. Local traffic contribution
Based on previous experience within the
project SHERPA-City (on an annual time scale), it
was decided to adopt hourly Gaussian source-receptor
functions (kernels) to ensure reduced computational
time from an input emission grid. Each kernel,
representative of a different weather condition, depicts
the concentrations Gaussian matrix obtained by
simulating the dispersion of 1 kg/h of a given pollutant
with the Gaussian model IFDM (Lefebvre et al.,
2013).
Therefore, different hourly weather and traffic
conditions can be simulated by model kernels
requiring weather variables such as wind speed, with
direction, temperature and the incident solar radiation.
In UTAQ weather values came from the Integrated
Forecast System (IFS) of ECMWF.
UTAQ is then able to calculate for each cell
and hour the local “street” contribution, responsible
for increased PM10 and NO
2 concentrations
(contribution III in Fig. 3).
In order to consider the variability of European
weather conditions which affects the main parameters
influencing atmospheric dispersion (wind speed and
direction, solar radiation and temperature), a
contingency table has been pre-built using weather
data of the ERA-Interim data set (ECMWF, 2012).
ERA-interim has spatial resolution of 0.75° of latitude
and longitude and a temporal resolution of six hours.
The following meteorological parameters have been
used:
Wind speed (0-1 m s-1, 1-2 m s-1, 2-3 m s-1,
3-4 m s-1, 4-5 m s-1, 5-6 m s-1, >6 m s-1);
Wind direction (8 direction classes);
Solar radiation (Night, <300 W m-2, 300-600
W m-2, >600 W m-2);
Temperature (<-10°C, -10-0°C, 0-10°C, 10-
20 °C, 20-30 °C, >30 °C).
Four European macro-regions have also been
considered: Northern Europe, Western Europe,
Eastern Europe and Southern Europe.
The combination of all the meteorological
classes is equal to 5,376 cases (7 wind speed classes,
8 wind direction classes, 4 solar radiation classes, 6
temperature classes and 4 European macro-regions).
4,510 kernels have been trained on hourly basis
representing combinations with at least one
occurrence. The trained kernels are used by UTAQ
according to the area in which the chosen domain is.
2.5. CAMS regional background contribution
As previously described, UTAQ uses as
regional background concentration the CAMS
ENSEMBLE forecasting model (Marecal et al, 2015)
which is based on the integration of the outputs of
seven models on a European scale (Table 1). The
adoption of forecast based on ensemble products
reduces the uncertainty of individual models and
consequently improves the reliability and accuracy of
the final results (Leutbecher and Palmer, 2007). The
regional CAMS models use the same (i) weather
parameters (derived from the IFS-ECMWF global
model, the same used by UTAQ), (ii) boundary
conditions of the chemical compounds (CAMS IFS-
MOZART global model) and (iii) emissions (CAMS-
REG).
The ENSEMBLE analysis carried out for the
CAMS products produces forecast concentration maps
for up to 4-days with a spatial resolution of about 10-
20 km (0.1 degrees of latitude and longitude).
Table 1. Models within the CAMS ENSAMBLE model
Model name Institute Spatial
resolution
CHIMERE INERIS (France) 0.15°x0.1°
EMAP MET Norway (Norway) 0.25°x0.1
25°
EURAD-IM RIUUK (Germany) 15 km
LOTOS-
EUROS
KNMI, TNO (The
Netherlands)
0.25°x0.1
25°
MATCH SMHI (Sweden) 0.2°
MOCAGE METEO-FRANCE
(France)
0.2°
SILAM FMI (Finland) 0.1°
2.6. UTAQ concentration calculation
The UTAQ resulting concentration given by
the integration of the three contributions (regional
background, city increment and road increment)
shown in Fig. 3 may be expressed as (Eq. 1):
t
slocaltot atCtCAMStC
)...()()()( (1)
where:
Ctot(t) is the UTAQ resulting hourly
concentration at hour t;
CAMS(t) is the regional hourly concentration
provided by CAMS at hour t (contribution I);
ε is the urban background term that takes into
account the local contribution through the measured
concentrations (contribution II);
αs and β are coefficients (the first varying
with the season s) to take into account the pollutants’
urban accumulation term that is decreasing with
longer predictions.
Ferrari et al./Environmental Engineering and Management Journal 19 (2020), 10, 1915-1926
1920
Clocal(t) is the local traffic concentration
(contribution III) computed as (Eq. 2):
))()(()( tCtCtC avg
trafftrafflocal (2)
where:
Ctraff(t) is the traffic concentration calculated
through the kernel at hour t;
Ctraffavg(t) is the spatial average traffic
concentration on the domain at hour t (the difference
represents the local redistribution of the CAMS
concentration, aimed avoiding the double counting of
the emission already considered in regional CAMS
concentration);
Every day the UTAQ run is initialized (t=0) by
computing the 𝜀 based on the daily mean measured
concentrations of the day before of the air quality
monitoring station located in the domain (Eq. 3):
)1()1()1( * tCtCAMStC localobs
(3)
where the different contributions refer to the daily
average of the day before.
Using the observed concentration values for a
one-year period the seasonal adjustment coefficients α
and β are estimated with an optimization procedure
minimizing the absolute error between the UTAQ
daily average concentration and the measured daily
average observation during the training period.
This new formulation implemented in UTAQ
foresees the computation of the total concentrations on
the base of the error ε estimated respect to the 24 hours
before observation, attenuated over time thanks to the
β exponent.
3. Results and discussion
This section reports the first results obtained
from calibration and subsequent validation of the
UTAQ model considering PM10 on a domain set on the
urban area of Milan. A first assessment of the tool for
PM2.5 and NO2 was done during COVID19 outbreak
in Milan (AMAT, 2020).
3.1. Calibration on Milan domain
The system has been trained for the year 2018
using alternatingly one of the four ARPA air quality
stations present in the 10x10 km2 domain centered on
the metropolitan area of Milan: Marche, Pascal,
Senato and Verziere (Fig. 6). Pascal station is
classified as background station, while Marche,
Senato and Verziere stations are classified as traffic
stations. The αs and β coefficients computed in the
training period are applied in the validation period
(first half of 2019) considering each air quality
monitoring station individually as the reference used
in equation (3) in order to evaluate which
configuration give the best performances.
To evaluate with which monitoring station the
adjustment coefficient ε produces the best result, R2
and the Root Mean Square Error RMSE are taken into
account (Fig. 7). The graph shows that good results
were obtained for all monitoring stations using Milano
Senato station because higher R2 and lower RMSE
were reached. In contrast, lower R2 and higher RMSE
values were obtained when using Milano Verziere
station. The α coefficient estimated using Milano
Senato station measured concentrations in winter and
summer (Table 2).
Fig. 6. The domain used for the training and validation of UTAQ model. The first three levels of the roads are represented in the
map with the 4 ARPA air quality monitoring stations
Table 2. α and β coefficients of Senato station
Semester Period α β
Winter October 15 - April 15 0.9365 0.2
Summer April 16 - October 14 0.8423 0.2
UTAQ, a tool to manage the severe air pollution episodes
1921
Based on these α and β coefficients estimated
with the Senato station (the one with the best
performance), the final concentration has been
calculated for every hour of 2018. UTAQ’s
performance in the calibration year are summarized in
Figs. 8 -9.
They show the aggregated modelled PM10
concentration and the daily observed data. In addition
to the UTAQ evaluation, the CAMS ENSEMBLE
forecast has been included in the analysis. Overall, the
modeling system implemented in UTAQ reproduces
the summer and winter trends better than the CAMS
model specially in wintertime thanks to the
introduction of the observed data and the higher
spatial resolution.
Fig. 9 shows UTAQ January 2018 simulation
compared with the measured concentrations detailing
the three different contributions described in
paragraph 2: the regional background concentration
obtained from CAMS (I), the urban contribution (II)
and the street level contribution (III). Compared to the
annual average, the contributions respectively
contribute 82%, 14% and 4%, meaning that on the
average CAMS is the principal contribution, while the
urban contribution (City) is dominating the peak days
of January.
Fig. 7. R
2
and RMSE values in 2019 (first half) using each of the 4 ARPA’s stations
Fig. 8: scatter plot between observed daily average PM
10
concentrations (Milano Senato ARPA air quality monitoring station -
Year 2018) and CAMS/UTAQ data in wintertime (left part) and in summertime (right part) from the training data set
Ferrari et al./Environmental Engineering and Management Journal 19 (2020), 10, 1915-1926
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Fig. 9. Observed daily average PM
10
concentrations in Milano Senato ARPA station and UTAQ modelled data
split into the three contributions from the training data set
3.2. Validation of the model
To evaluate the actual performance in the
predictive phase, UTAQ has been validated on the
same domain from January to June of 2019 with the
summer and winter α coefficients estimated on 2018
data. As for the calibration phase (Fig. 8), CAMS
model performance of the CAMS ensemble was also
evaluated (Table 3 and Fig. 10).
As in the case of calibration, UTAQ tool
improves CAMS performances, which underpredicts
the severe events of local pollutants accumulations,
especially in winter because of its regional-scale
resolution (10 km). CAMS ensemble underestimates
the PM10 concentrations during the first day of forecast
(average bias equal to -14.2 µg m-3 and RMSE equal
to 21.8 µg m-3) and the following forecast days
(average bias over to 12.9 µg m-3 and RSME over to
20.5 µg m-3). UTAQ improves CAMS forecasts
because of the use of air quality observations and of
the benefit of detailed high-resolution traffic emission
data. Considering the UTAQ performance in more
detail, the model underestimates the PM10
concentrations with values that increase for the
forecast days following the first day0 (average bias
from -1.6 for day0 to 7.3 µg m-3 for day3 and RMSE
from 12.3 µg m-3 for day0 to 15.8 for day3) but with
R2 fairly constant (from 0.73 for day0 to 0.65 for
day3).
Table 4 is a contingency table, which shows the
success, and failure rates of predicting the exceedance
of the daily PM10 threshold (50 µg m-3) by UTAQ and
CAMS models. As presented below, for the first
forecast day (day0) UTAQ correctly predicts an
exceedance in 64% of cases and a non-exceedance in
93% of cases (rates are respectively 20% and 100%
for CAMS model) and in the last forecast day (day3)
respectively in 51% and in 100% of cases (31% and
100% for CAMS model).
4. UTAQ tool application
A web application has been developed to
present in a user friendly interface the results of
UTAQ modeling system, which is available on the
website www.utaq.eu and which is accessible for both
public and authorized users (for the run of the traffic
limitation measures).
4.1. Web application
UTAQ application is an online accessible tool
with a user-friendly interface designed for non-
specialist users and it has been entirely developed with
open source technologies. The tool incorporates: a
Web-GIS (Geographical Information System) with
visualization functions (zoom in, zoom out, pan, etc.)
to help the user selecting the inputs and displaying the
outputs. The maps show hourly concentrations and
time series in graphs and diagrams for the 4-days
forecast. The UTAQ web application consists of:
• a login module that allows the “decision-
making/planner” user to access the UTAQ application
and to decide and to apply traffic limitation policies;
• a module to define new emission scenarios
and new policy measures (for instance: limitation of
certain categories of vehicles such as diesel, heavy
vehicles, Euro 1/2/3 or the closure of particular city
areas to create Low Emission Zones);
• a module to present outputs through grid
maps (emission and air quality index maps), tables and
bar graphs (Fig. 11).
UTAQ, a tool to manage the severe air pollution episodes
1923
Table 3 PM
10
UTAQ model performances compared with CAMS model in Milano Senato monitoring station – year 2019
Forecast day BIAS [µg m
-3
] RSME [µg m
-3
] R
2
UTAQ CAMS UTAQ CAMS UTAQ CAMS
day0 [forecast hours 1-24] -1.65 -14.16 12.29 21.84 0.73 0.52
day1 [forecast hours 25-48] -4.34 -13.82 14.82 21.36 0.63 0.52
day2 [forecast hours 49-72] -6.15 -13.41 15.19 20.72 0.65 0.56
day3 [forecast hours 73-96] -7.27 -12.97 15.81 20.50 0.65 0.55
Fig. 10. Validation: scatter plot between observed daily average PM
10
concentrations (ARPA Milano Senato station and modelled
data for forecast day0 (upper-left), day1 (upper-right), day2 (bottom-left) and day3 (bottom-right)
Table 4. Contingency table of PM
10
legal threshold exceedances (50 µg m
-3
) considering observed data (ARPA air quality
monitoring station located in Milano Senato. Year 2019) and UTAQ and CAMS models for each forecast day. The percentage in
parentheses represents the number of occurrences in the models compared respect to the ones in the observations
PM
10
daily average observed data UTAQ model CAMS model
Exceedance Non exceedance Exceedance Non exceedance
day0 Exceedance 32 (64%) 18 (36%) 10 (20%) 40 (80%)
Non exceedance 8 (7%) 104 (93%) 0 (0%) 112 (100%)
day1 Exceedance 28 (57%) 21 (43%) 14 (29%) 35 (71%)
Non exceedance 7 (6%) 106 (94%) 0 (0%) 113 (100%)
day2 Exceedance 26 (53%) 23 (47%) 11 (22%) 38 (78%)
Non exceedance 1 (1%) 112 (99%) 0 (0%) 113 (100%)
day3 Exceedance 25 (51%) 24 (49%) 15 (31%) 34 (69%)
Non exceedance 0 (0%) 113 (100%) 0 (0%) 113 (100%)
Ferrari et al./Environmental Engineering and Management Journal 19 (2020), 10, 1915-1926
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The tool has been designed to ease the work of
decision makers, by making available (i) the road
graph within the urban area, (ii) the traffic volume and
(iii) the related emissions and the 96-hours pollutant
concentration forecast.
4.2. Mobile application
The UTAQ open mobile application (Fig. 12)
allows users to evaluate the estimated air quality
forecast for the following 4 days thanks to an intuitive
interface. The service is freely accessible for the
analyzed domains and provides real time air quality
concentrations (with a spatial resolution of 20x20 m2)
at the users’ position (obtained via the smartphone
GPS device).
5. Conclusions
The paper presents a new version of the tool
UTAQ used to forecast and eventually avoid the
severe air pollution episodes in a high-resolution
urban environment.
Fig. 11. UTAQ Web application screenshots
Fig. 12. UTAQ Mobile application
UTAQ, a tool to manage the severe air pollution episodes
1925
UTAQ has been implemented as web and
mobile application to support policy-makers and local
authorities to evaluate and establish the best traffic
limitation strategies to be implemented in the short-
term, therefore satisfying regulatory requirements and
potentially enabling such limitation policies before air
quality conditions become more severe.
UTAQ could also support local authorities to
increase the awareness of citizens, spreading the air
quality forecasts and communicating the benefit of
short-term mitigation measures.
Current regional or Europeans forecast models,
such as the CAMS ENSEMBLE, cannot fully
reproduce local pollution events in the urban
environment especially related to winter
accumulation, because of their low spatial resolution
and the lack of more detailed local emission inventory.
The high spatial resolution representing the street
level, the evaluation of traffic policies and the use of
observations are the benefits of UTAQ compared to
forecast on larger scales. On the contrary, UTAQ
simplified formulation cannot replace the long-range
transport and physical and chemical transformations
of pollutants considered in a CTM. Two limitations of
UTAQ current version is that it does not take into
account street configuration (e.g. street canyon) and
local variation of the fleet composition and
consequently of the emission factor.
The UTAQ results for PM10 are promising and
encourage the future development of the modeling
tool, the web application and the mobile app too. The
availability of CAMS ENSEMBLE and
OpenTransportMaps data all over the Europe allows
an easy implementation and calibration of UTAQ to
other urban areas.
Acknowledgements
We thank: Bart Degraeuwe and Enrico Pisoni (JRC ISPRA)
for scientific discussions on the methodology and on the
kernels calculation; Angeliki Tsapatsari and Giulia Bonetti
(ECMWF) for the valuable suggestions in using CAMS tool
and improving UTAQ graphical interface; Simone Paleari
and all the staff of the INNOVA21 agency for facilitating
dialogues with stakeholders and the helpful feedbacks about
the identification of the UTAQ added values for the local
authorities.
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