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Int. J. Environ. Res., 8(4):1215-1222, Autumn 2014ISSN: 1735-6865

Received 29 March 2014; Revised 16 June 2014; Accepted 20 June 2014

*Corresponding author E-mail: margom@cartif.es

New Strategy for a good Management and Control of Pollution Caused byurban Traffic

Gómez, M.* and Hidalgo, D.

Environment Area, CARTIF Technology Center, Parque Tecnológico de Boecillo,205 – 47151Boecillo, Valladolid, Spain

ABSTRACT: Pollution control is based on correctly measuring pollutant concentrations but, at the moment,the principal European cities only monitor pollution levels by means of a few fixed stations co-located inselected points of the urban layout far from hotspots and/or by means of meteorological prediction models offuture pollution. Few cities integrate the information from both sources. The implementation of wirelesssensor network provides an alternative solution by deploying a larger number of disposable sensor nodes andit is the key that enable more flexible real-time environmental monitoring. This paper presents a new systemfor sustainable traffic management by using models that predict pollution levels, which are fed with data,gathered by the air quality sensors network and may help to take early action. This new system has beenimplemented in Salamanca (Spain), thus allowing, to test in real urban environments the collection and processingof data from 35 motes located in experimental roads during a year-round period. Monitoring results allowbuilding a database with the temporal evolution of the different environmental indicators registered. In generalthe prediction model has an average error around 20% for predictions at one hour and 30% at three hours. Theresults highlight the very good performance of the prediction model. The strategic approach proposed ishighly innovative and embodies great scientific and technological advances. The use and integration of measuredand modeled data thus becomes a key element in the future management of atmospheric pollution.

Key words: Pollution control, Sensors network, Sustainable traffic management

INTRODUCTIONTons of polluting agents are discharged into the

air of principal cities every day. Furthermore, it has beenconfirmed that emissions from motor vehicles are themain cause of air degradation, especially now thatindustrial emissions have been reduced thanks to theprogressive displacement of industrial facilities outsideurban areas (Jacquemin, 2007). Loss of environmentalquality is one of the biggest threats of our century tohealth and human well-being, together withenvironmental impacts. Several analyses have provena relationship between air pollution and the emergenceand/or aggravation of respiratory diseases (Molitor etal., 2006) (Gehring et al., 2010), as well as other relatedillnesses, such as cardiovascular diseases or cancer(Vineis & Husgafvel, 2005) (Wild, 2009). Furthermore,its relationship to the increase in allergies and, thus,the decline in many people´s quality of life, appears tobe very clear (Gehring et al., 2010). On the other hand,polluting agents affect historical buildings causingsevere deterioration and the correspondingly high

maintenance costs of the city’s architectural heritage.In general, air quality in an area depends on thegeographical distribution of emission sources, theamount of pollutants emitted, the physical-chemicalprocesses taking place in the atmosphere and theclimatology and topography that very much determinethe dispersion and transport processes (OSE, 2007).Vehicular traffic is increasing around the world,especially in urban areas. This increase results in ahuge traffic congestion, which has dramaticconsequences on economy, human health andenvironment (Amine & Challal, 2012). The highestpercentage of air pollution comes directly from roadtraffic and not anymore from large industries, currentlyplaced outside metropolitan and urban areas. Roadtraffic is considered to be responsible for 22.4% of allemissions in Spain (CNE, 2013).

European concern regarding this matter hasresulted in the creation of new legislation, aimed atadding new chemical compounds to the list ofpollutants to be controlled and promoting the

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reduction of threshold emission levels, in order toreduce atmospheric pollution (Greening, 2004 ; Twigg,2007). In 2008, the EU Directive (2008/50/EC) onambient air quality and cleaner air for Europe wasadopted. This Directive aimed to show the EuropeanUnion’s strong commitment to improving air quality inthe EU. In response to this, substantial policy measureshave taken to reduce air pollution. Europe-wide policesinclude emission standards for motor vehicles anddefinition of national emission ceilings. In addition,local polices have been implemented to reduce airpollution concentrations at busy urban roads(Boogaard et al., 2012). Environment monitoring hasbecome an important field of control and protection,providing real time system and control communicationswith the physical world. An intelligent and smartWireless Sensor Network system can gather andprocess a large amount of data from the beginning ofthe monitoring and manage air quality, the conditionsof traffic, to weather situations.

From the studies, it has been proved to be analternative way to replace the conventional method(Fauzi & Khairunnisa, 2012).

Wireless Sensor Networks (WSN) is an emergenttechnology with an effective potential and have a greatadded value to intelligent transportation systems (ITS)(Amine & Challal, 2012). It is clear that ITS´s goal isenabling smart cities and in this respect, pollutionprevention is becoming a sensible field and needs moreand more attention (Amine & Challal, 2013). Theimplementation of wireless sensor network providesan alternative solution by deploying a larger numberof disposable sensor nodes. Wireless sensor a networkis an emerging technology that transforming the waywe measure and actively participates in creating a smartenvironment (Oliveira & Rodrigues, 2011) (El-Bendaryet al., 2013). This air-quality sensor network togetherwith prediction models will help to define a new UrbanTraffic Management and Control Strategy based onthe prevention of high pollution episodes caused byurban traffic. The use and integration of measured andmodelled data thus becomes a key element in the futuremanagement of atmospheric pollution.

Therefore, taking into account this fact, the aim ofthe present work has been to test in real urbanenvironments the quality of the information suppliedby 35 motes located in experimental roads of city ofSalamanca (Spain) during a year-round period, as anew tool for traffic pollution control.

MATERIALS & METHODSPollution control is based on correctly measuring

pollutant concentrations but, at the moment, theprincipal European cities only monitor pollution levels

by means of a few fixed stations co-located in selectedpoints of the urban layout far from hot-spots and/orby means of meteorological prediction models of futurepollution. Few cities integrate the information from bothsources (Berkowicz et al., 2006) (Zito et al., 2008), aimedat attaining the permanent control of traffic flows, asthese can cause emergency episodes related topollution levels surpassing the legal thresholds. Themain handicaps to date have been the lack of reliabilityof the prediction models used, as well as the difficultyof making precise online measurements of urban “hot-spots” available. On the other hand, traditional trafficmanagement systems are usually based on mobilitymodels, their main purpose being to solve trafficvolume demands on urban roads, although not takinginto consideration environmental points of view.

It is evident that departments in charge of urbanmobility cannot integrate in their traffic managementmodels predictions that are difficult to compare withreal time data, as their decisions are based on instantmobility data in particular. An innovative strategyconsists of achieving the sustainable management oftraffic in cities by using two key elements: a pervasiveair-quality network of sensors and prediction models.Such a development will help to define a new UrbanTraffic Management and Control Strategy based onthe prevention of the usually high pollution episodescaused by urban traffic. Air quality control is currentlyrestricted to the collection of meteorological andpollutant concentration data at several fixed stationsaround the city. On rare occasions, such a controlincorporates real density or traffic flow data from thosespots, and it therefore becomes unfeasible to deduceany empirical causal relationship between the datagathered and traffic in the “hot-spots”. On the otherhand, many cities have developed air quality predictionmodels based on real or estimated data referring totraffic and car emissions, as well as to weatherforecasts, producing mid-term predictions (meso-models for 24 to 72 hours) which are difficult to contrastempirically against live measures from fixedmeasurement stations. The relevance of such modelsis scarce in practice, since they are not used to taketraffic management control measures in most cases,but as a forward indicator of high pollution episodes.Traditional environmental prediction models allow forcoverage of a broad area but they are very time-demanding and difficult to implement. These modelsneed to be provided with very precise and extensivedata to obtain acceptable results. This is why, for manycities, the cost associated with modeling activities isnot justified. On the other hand, most of the existingair quality models were originally developed to predictemissions and concentrations of pollutants discharged

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by fixed sources. However, air quality problems todayin urban areas are mainly caused by vehicles, which isthe reason for the better modeling of traffic emissions.Emissions are higher when vehicles speed up, slowdown or are left in neutral, specifically under congestedtraffic conditions, and not many air quality models canwork out these situations. Moreover, few models cantake into consideration pollutant dispersion within thecomplex topography of a city, including the well-known“canyon effect” in street sectors flanked by highbuildings, which cause the accumulation of traffic-related pollutants, due to limited air exchange causedby the isolation of the air stock. Furthermore, formodeling road traffic emissions with more complexmodels, the lack of dynamic data will limit the models´ability to predict pollution levels in the vicinity ofstreets (i.e. sidewalks). Consequently, predictions ofair pollutant concentrations in urban areas are oftentoo imprecise to allow for re-distributing traffic flows.

The availability of more precise and shorter-termprediction models based on live traffic and car emissiondata referred to the real vehicle traffic conditions, aswell as the recent appearance on the market of a widerange of measurement tools for the differentcontaminating agents which are far less expensive thanthose used in fixed stations, together with today’scommunication systems technology, means that a largeamount of online pollution measurementscorresponding to the “hot-spots” within the city arenow available. When comparing these measures withthe predictions from a shorter than usual term model (3to 5 hours), it is possible to predict some hours inadvance, and with a high degree of reliability, the highpollution occurrences for different places of all urbanareas, as well as to draw up alternative scenarios based

on simulated traffic flow deviations or restrictions,across those or nearby areas which influence them. Inthis way, the current urban traffic management andcontrol system would have at its disposal anotherabsolutely reliable decision element, complementaryto the traditional mobility and accessibility decisionalgorithms, which would help to develop a more rationaland environmentally friendly management of trafficflows. The application of the current legislationregarding air quality conditions in cities, whichcompels them to draw up Air Quality Plans for severalforthcoming years, will therefore provide anothermanagement tool that will undoubtedly contribute toimproving the air breathed by citizens and act uponthe most important urban contaminating agent (i.e.traffic) by organizing traffic flows following the“ecological capacity” of the city corresponding to eachweather condition, thus allowing only sustainableflows through the “hot-spots”. The practicalimplementation of this strategy includes threecomponents: monitoring, modeling and informationfeedback. Monitoring refers to the measurement ofparameters (pollutant concentrations, traffic flows,meteorological conditions, etc.) by means of aninnovative and extensive low-cost network of sensorsdeployed in the area under study. The informationgathered will be a dynamic input to the modeling phasein order to generate pollution predictions in real timeand to calculate the effects of a variety of hypotheses(scenarios) of traffic regulation. The impact of theselected pollution scenarios at “hot-spots” will becontrasted with the new data collected by the samemeasuring instruments. Finally, by introducing afeedback into the regulation system, a fine-tuningamong pollution results and traffic control measurestaken in real time will be achieved.

Fig.1. One of the streets under study with some motes identified in circle

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35 motes have been installed on two different roads ofSalamanca with high traffic density (Fig.1), which hasallowed to test in real urban environments the collectionand process of data during a year-round period.

Motes information is essential for traffic monitoringand control, since urban data diagnosis is the basis ofthe mobility plan. Information refers to the followingparameters collected from pollution “hot-spots”:carbon monoxide (CO), nitrogen dioxide (NO2), ozone(O3), particulates (PM), noise, humidity, andtemperature. Other variables can be measured accordingto local circumstances. Design and manufacture of themotes is based on a commercial model (LibeliumWaspmote).

Each mote is composed of seven sensors: CO (FigaroTGS 2422), NO2 (e2V MICS-2710), O3 (e2V MICS-2610),particles (Sharp GP2Y1010AU0F), noise (PanasonicWM-61B), temperature (Microchip MCP9700A) andhumidity (Sencera 808H5V5).

The sensor node sends the collected data to therouter. The router (Meshlium, Libelium) includes Wi-Fitechnology that allows (2.4 GHz, 5GHz), 502.15·4/ZigBee, GPRS, Bluetooth and GPS in the sameequipment. Multi-sensors networks can be used in atwo-fold approach to city pollution abatement:pollution characterization and traffic monitoring andcontrol. During the testing period (one year), more thantwo millions data were collected and processed in theproject data center.

Air pollution monitoring is considered as a verycomplex task. Traditionally data loggers were used tocollect data periodically and this was very timeconsuming and quite expensive. The use of wirelesssensor networks can make less complex air pollutionmonitoring and a reading can be obtained instantly.The motes are integrated into a power system andprotective enclosure and which in turn are coupled toa post similar to parking meters. In this way, the mote isprotected of possible acts of vandalism and alsofacilitates the placement of the power system as wellas to know the real concentration of a pollutant that isinhaling a passer. The data transmission system usedis Waspmote XBee-802.15.4, consisting of threeelements: microcontroller (ATmega 1281), antenna(XBee-802.15.4) and gases board (port adapted formicrosensors). This system allows the datatransmission through radio signals that are beingtransmitted from a mote to another until the signalarrives at the sites where the motes information iscollected and transmitted to the data processingsystem. Each of the roads under study has a routerthat is connected to a single database. The routersstore data temporarily and later send the information

via GPRS or Ethernet to the database located in thetraffic management center.

Once the motes were installed, to check thecommunications and validate the information that thesensors collect, they begin to collect continuousinformation of each pollutant concentration levelachieved. These data will be used by the predictionmodels to achieve the sustainable traffic management.Previously, the sensors were calibrated in laboratoryand verified in situ in the streets under study. The newsystem for sustainable traffic management is based onthe use of models that predict pollution levels, whichare fed with data, gathered by the air quality sensorsnetwork and may help to take early action. For this,the modelization of the motes data are based onadvanced statistical techniques. Monitoring resultsallow building a database with the temporal evolutionof the different environmental indicators registered.This allows be studied from the perspective of theBayesian process that maximizes the use of the largeamount of information available at all times.

The result is a prediction model to estimate the shortterm evolution of the different pollutants and implementtraffic control strategies to mitigate the negativeimpacts expected. In the air quality literature, time-seriesanalysis is generally carried out to understand thecause and effect relationships, which in turn helps inforecasting the future concentrations. In this direction,a class of techniques including autoregressiveintegrated moving average (ARIMA) or Box-Jenkinsmodels and structural models have been applied toanalyse air pollutant concentrations. Theseapproaches are widely applied in the air-qualityliterature due to the lack of data on emissions of airpollutants (Chelani & Devotta, 2006). ARIMA is astatistical model that uses variations and statisticalregressions data in order to find patterns for predictingthe future. These methods are applied in cases wherethe data show evidence of being non-seasonal, wherean initial step of differentiation of the series can beapplied to remove the non-seasonal series. The ARIMAmodel needs to identify the coefficients and numberof regressions which will be used. This model is verysensitive to the accuracy with which the coefficientsare determined. As the application of these models isvery common, further details to estimate the parametersand order of the model are given in Box & Jenkins,(1970). ARIMA model has four main components thatneed to be defined: tendency component, cycle,seasonal variation and remainder or erratic compound.

The prediction model allows to the Authorities toknow the prediction of high pollution episodes, at onehour and three hours, for one or several motes andsensors, to show the expert/manager predictions and

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the traffic pollution alarms screen. As any other model,it was carried out an adjustment and optimization ofthe ARIMA model iteratively through a process inwhich we can distinguish four stages: identification,estimation, validation and prediction. In theidentification stage, more than one candidate modelthat can describe the series has been identified, usingdata in chronological order. A data treatment has beenperformed calculating the moving average and applyinga filtrate to obtain manageable values to determine thevalues of the ARIMA model parameters. As a result ofcalculating the model parameters and as the final phaseof the estimation process, the estimation at one andthree hours of model behaviour is performed.

In the estimation stage, considering the appropriatemodel for the time series has been performed inferenceon the parameters. In the validation stage, diagnosticcontrast to validate if the selected model is fitted tothe data predicted with a reliability level higher than95% has been performed. ARIMA model adjustmentshave been modified for each pollutant until achievethe desired level of reliability in the prediction. In theprediction stage, once the ARIMA model has beenadjusted and optimized, it has been also checked itsvalidity by comparing the predictions made in terms ofprobability of future values with measures “in situ” ofthe pollution at the time of predicted time.

Fig.2. Collected data by the NO2 sensor Fig. 3. Collected data by the CO sensor

Fig.4. Collected data by the O3 sensor Fig.5. Collected data by the particles sensor

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The result of this process has been an alternativemodel of traffic management adjusted and optimized.

RESULTS & DISCUSSIONFigs from 2 to 5 show recorded data for some motes

and sensors, in particular NO2 (Fig.2), CO (Fig.3), O3(Fig.4) and particles (Fig.5), collected for two days.

Figures shows that the maximum concentrations aregiven in traffic rush hours (early morning, midday andlate afternoon) as expected, the concentration beingminimal at night. In the case of the ozone (Fig.4), thehighest values were obtained from the morning tomidday, mainly. It is due to a maximum solar radiation(summer) and high traffic. Emissions from traffic in thepresence of sunlight are formed high ozoneconcentrations. A software tool has been created todisplay the results of the prediction model. Maximumvalues for each pollutant were established in thesoftware prediction model. These maximum values aretaking into account a margin above the maximumallowed by law. In this way, when the system predictsthat a value greater than the allowed by law will bereached; an alarm is generated in the software tool forthe corresponding mote. It allows that a traffic managerto assess the need to carry out an action that preventsthe prediction is fulfilled. Following some of theprediction results for some motes and some specificpollutants are gathered (Fig.6 and Fig.7). The predictionof the carbon monoxide is showed in the Fig. 6. Thepoint (•) indicates the prediction to one hour and thesquare ( ) point to three hours. The cross (x) pointindicates the last prediction where the accuracy of theprediction model can be observed.

The prediction of the nitrogen dioxide is showed inthe Fig. 7. The symbol of the marks indicates the sameas in Fig. 6.

Fig.6. Prediction of the carbon monoxide Fig.7. Prediction of the nitrogen dioxide

The prediction results for ozone and particles havenot been collected because the measures are not good,so the prediction results either. In the case of the ozone,probably due to the specific characteristics of thepollutant, as it is a secondary pollutant. The predictedconcentrations values were compared with themeasured concentrations values for 24 hours, forpredictions at one hour and three hours. In order todetermine the accuracy of the predictions and toevaluate the performance of the proposed model, thefollowing parameters were calculated:Prediction error (PE):

Statistic error (SE):

(2)

Mean absolute error (MAE):

(3)

Mean absolute porcentaje error (MAE %): MAE

% = (4)

Where: N = 24 (in the case of predictions at onehour) and N = 8 (in the case of predictions at threehours).

It is considered that the measured value is the valueof concentration in hour i, where i = 1, 2, 3,…, 24 andthe predicted value is the value of concentration forthe same hour. The results obtained when applyingthese equations, Eqs. (3-4) are presented in Table 1.

(1)

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Table1. Results of the values of the predictions accuracy for 24 hours.

Air pollutant Parameters Predictions at one hour Predictions at three hours

MAE (µg/m3) 9.41 12.22 NO2 MAE (%) 4.67 5.96 MAE (mg/m3) 1.95 3.11 CO MAE (%) 36.57 44.73

Taking into account the results presented in the table

1, it is obvious that predictions at one hour are moreaccurate than predictions at three hours. With respectto the mean absolute percentage error (MAE %) thebest performance is for the NO2, with a value of 4.67%for the predictions at one hour. Obviously, an error inthe sensor measurement may lead to a prediction error.For this reason, it is very important that the sensorsare calibrated correctly and in perfect condition.Prediction method demonstrates the more realisticproblems in the field. The results highlight the verygood performance of the model. As discussed above,the prediction of emission variables are dependent onpast history of the data. It is expected that availabilityof more emission data can improve the performance ofprediction model.

CONCLUSIONThe strategic approach proposed is highly

innovative and embodies great scientific andtechnological advances. The availability of moreaccurate predictive and shorter-term models, based ontraffic data and atmospheric emissions in real trafficconditions and the use of low cost instruments formeasuring pollutant concentrations, together withadvanced communication systems existing today, makeit possible to have a large number of measures relatedto pollution “hot-spots” in the city through a wirelesssensors network. When comparing these measurementswith the predictions of a short-term model (1-3 hours),it is possible to anticipate, with high reliability, highpollution episodes in different points of all urban area,and develop alternative scenarios based on thesimulation of deviations or restrictions of traffic flowsthrough or near their areas of influence. It is very difficultby using traditional means to predict emission levelsfor a specific pollutant, at a given time and location,due to the large amount of factors that are constantlychanging and, thus, affecting the actual pollution. Thisis certainly what this new strategy of traffic pollutioncontrol intends: to create an information platform aimedat understanding, assessing and evaluating the impactderived from actions carried out in order to improveaccessibility, to tackle the congestion problem, toimprove air quality or to implement a Traffic Information

System. The proposal to integrate mobilitymanagement needs and air quality into a single urbantraffic management model is not only necessary toachieve the goal of reducing pollution levels belowthe limits imposed by the European Directives, but itis also absolutely essential to organize the city trafficin a rational manner: i. e., without causing excessivetrouble to the mobility needs of citizens and achievingsustainable traffic levels in a systematical andunequivocal way at any one time. The ultimateobjective is to provide Local Authorities with anefficient and sustainable urban traffic managementsystem in accordance with the existing environmentalpolicies and legislation. In this way, ManagementSystems and Urban Traffic Control may have anotherdecision element absolutely reliable, complementaryto traditional decision algorithms mobility andaccessibility, which helps develop a management, morerational and respectful with the environment of thetraffic flows, being based on active surveillance.

ACKNOWLEDGEMENTSThe authors gratefully acknowledge support of this

work by the LIFE+ Program under the responsibilityof the Directorate General for the Environment of theEuropean Commission through the agreement LIFE 08ENV/E/000107-RESCATAME project.

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