Public Information System about the Air Quality inLisbonFilipe Funenga

Instituto Superior TecnicoUniversidade de Lisboa

Email: filipe.funenga@ist.utl.pt

Abstract—Monitoring air pollution and the appropriate actionto restrain the anthropogenic emissions of chemicals into theatmosphere, has been something that has concerned publichealth officials and the citizens themselves more and more. Themonitoring is currently, in its vast majority, made by stationsof a fixed nature with few points of data collection. The use ofmobile atmospheric monitoring stations integrated on a networkof public buses creates the possibility of monitoring at variouspoints of a city taking advantage of its mobility. URBISNET isa project aiming to assemble a low cost pollution monitoringnetwork, built on a top of a wireless sensor network installed onthe public bus fleet of Lisbon.

One of the challenges that this system has, is storing, analysingand publicly publishing the captured data. If this last step is doneon the Internet, then it has the potential to substantially increasesocial awareness and public involvement in environmental sus-tainability.

This work focused on estimating maps of the atmosphericpollutants and their main characteristics based on samplescollected under the URBISNET project. Data collected from theatmospheric pollutants on the last decade is analysed to define themain characteristics for the geostatistical model. Also, syntheticdata is presented and analysed concerning the quality of thealgorithms used. A comparison between these values and datagenerated with a Callpuff model is presented. An analysis isalso made to the parallel performance of the Ordinary Krigingimplementation made. Finally, the accesss times to the webpagecreated are shown and compared.

The evaluation made to the implemented work proved the cor-rect operation of its features and a very satisfactory performanceby them.

I. INTRODUCTION

Monitoring air pollution and the appropriate action torestrain the anthropogenic emissions of chemicals into theatmosphere has seen, in Portugal, a steady growth in the last12 years, [1], [2]. The impulse to act against air pollution ismotivated by its negative effects on human health, which areundoubtedly a reason for concern.

The conventional systems used to monitor air pollution inurban areas are based on container-style monitoring stations(see figure 1), which usually involve high construction andmaintenance costs. Furthermore, the equipment inside thestations is expensive, occupies a large space and entails highpower consumption. Their overall advantages are precision andfiltering of different pollutant measurements.

In Lisbon, a city with an area of 84 Km2, there are only 8air quality monitoring stations. The URBISNET project aimsto contribute to the monitoring of air pollutants by deploying

Figure 1. Fixed station from QualAr.

an innovative system that will substantially increase the spatialdensity of collected samples.

The core of the project is a low-cost portable station, ofwhich a prototype has already been developed. The reduceddimensions, low power consumption and the mobile communi-cation interfaces are key features that make this station suitablefor installation in a public transport network.

There are numerous possible networks to choose from:buses, trams, trains, taxis, bicycle sharing systems, amongothers. The two main constraints in the architecture of thedistributed monitoring system addressed in this work arespatial range and course predictability. Based on Lisbon’spublic transportation options and the technical advantages ofthe URBISNET’s portable station, the fleet of public urbanbuses was chosen.

This network comprises 78 bus lines which operate overthe city area. These bus lines have intersecting routes and busstops. In order to gather information from the mobile stationsmounted on top of the buses, several relay nodes must beplaced at multiple bus stops across the city. A study on thissubject was developed under the URBISNET project. Whenplacing relay nodes, the goal is to have as many bus routes aspossible within their range. This way, most bus routes shallbe within one hop distance to the relay nodes; the remainingshould be within the smallest distance possible to the previousroutes (i.e. routes which are ”one hop neighbours” of relaynodes).

In an intermediate stage of the project, communicationbetween stations and relay nodes was developed. In addition

to the implementation of modules using GPRS and Wi-Fi, aplatform for remote management was developed. This platformallows direct configuration and live monitoring of the stations.

Once the captured data arrives at the central server, it canbe stored, processed and published. Although measurementsfrom mobile stations have less precision compared to fixedstations, it is possible to estimate maps of the atmosphericpollutants and their main characteristics by collecting sampleswith greater spatial resolution.

Publishing the captured data on the Internet has the po-tential to substantially increase social awareness and publicinvolvement in environmental sustainability.

A good public information system concerning air qualitymust be considered intuitive by its common viewers, thecity’s inhabitants. This is the central motivation for this thesis.The main objective is to present an estimation of various airpollutants’s concentration based on recent captured data.

II. STATE OF THE ART

A. Informational Feedback

Until the end of the 1990’s, data collection for air qualityassessment was limited to a reduced network of monitoringstations in the main urban and industrial areas and to some raremeasurement campaigns. Under the scope of the Portuguesedecree no 276/99, a work group was created in 2000, coordi-nated by the Portuguese Agency for the Environment, knownas APA, (formerly known as Instituto do Ambiente), aiming toanalyse and discuss coherent and harmonized methodologiesfor an appropriate national strategy.

An information system was created integrating the Por-tuguese Online Database about the Air Quality (know asQualAr), based on the Portuguese Environment Agency, whichpublishes data from the fixed stations operated by the Com-missions for Coordination and Regional Development, as wellas a daily Air Quality Index for zones and agglomerations.

The air quality index, known as IQAr, is a quantitativetool used in QualAr that simplifies classification and publicunderstanding of air quality for agglomerations in the country.One of the reasons for creating this measure was meeting legalobligations imposed by the European Union.

It is important to note that the relation between colourscale and corresponding concentration values is not the samefor all pollutants. In figure 2 it is shown that for CO, thecolours related with a bad air quality start when the valuesfor pollutant concentration exceed half of the maximum limit.Conversely, in the case of NO2, bad air quality colours startsooner, corresponding to values bellow half the maximumlimit. O3 maintains a linear relation between colour and itscorresponding concentration.

B. URBISNET Project

The main scientific goal of this project is to develop toolsfor estimating a diffusive field, such as the concentration ofgases over a certain area, using a set of mobile sensors. Theenvisioned application proposed is pollution monitoring in the

Figure 2. IQAr spectrum for the three pollutants.

city of Lisbon based on measurements taken by the fleet ofpublic urban buses.

A prototype mobile system was developed whose primaryfunction is to acquire atmospheric data through a set ofintegrated sensors, sending it to a server to be stored. Itcomprises a few sensing units that include sensors for carbondioxide (CO2), carbon monoxide (CO), ozone (O3), carbondioxide (NO2 ), temperature and relative humidity. It is alsoequipped with a GPS receiver in order to georeference thecollected samples and a clock to provide time tags.

In terms of communications, the station has GPRS and Wi-Fi interfaces, which are used for different purposes. Althoughthe main goal is to transfer samples from the mobile stations tothe central server, there is still a need to perform tasks relatedto live monitoring and remote configuration. These requiredpermanent connectivity to mobile stations, which is supportedby GPRS.

C. Related Projects

1) OpenSense Project: OpenSense project, from the SwissFederal Institute of Technology Zurich, funded by Nano-Tera, the Swiss Scientific Initiative in Health, Security andEnvironmental Systems, is an open platform whose majorscientific objective is to efficiently and effectively monitorair pollution using wireless and mobile sensors by adoptingcomplex utility driven approaches towards sensing and datamanagement.

The major similarity to URBISNET is the fact that thisproject uses the public urban transport infrastructure as ameans to gather samples about the atmospheric pollutants:specifically, a tram, also known as a tramcar or trolleycar,houses the measurement stations. Like buses, this transporthas fixed routes over rails that allow the use of a vehicularnetwork/gateway approach, like the one explained earlier forthe URBISNET project. Also, if the tram network is wideenough (compared to the bus network) this solution can besaid to have the advantage of using electricity, which is notan on-the-spot source of atmospheric pollution, unlike buses.

2) Haze Watch Project: The Haze Watch project is aninitiative by a team of students from the School of ElectricalEngineering at the University of New South Wales, Australia.This project aims to implement a mobile air pollution sensorattached to motor vehicles using it to gather air pollutionreadings. The main pollutants measured are Carbon Monox-ide (CO), Ozone(O3), Sulphur Dioxide (SO2) and NitrogenDioxide (NO2).

This project relates to the URBISNET project because ofthe web interface used. The combination of the map of a city

with a raster representation of atmospheric pollution will leadto an understanding and interpretation of spatial phenomenathat are simply not apparent when individual spatial data typesare considered in isolation.

3) The Copenhagen Wheel Project: The CopenhagenWheel transforms ordinary bicycles into hybrid e-bikes thatalso function as mobile sensing units. It allows to capturethe energy dissipated while cycling and braking, and saveit for when a boost is needed. The system is similar to theKinetic Energy Recovery System (KERS), which allows tostore the energy in batteries used in braking for reuse later,thus facilitating travelling in uneven paths.

The major difference between URBISNET and this projectis the use of urban bicycles to carry the sensors on the domainunder analysis. Since these are normal citizen bicycles, thereis no predefined static network or schedule where the samplesare expected to be collected.

III. PUBLIC INFORMATION SYSTEM

The chosen solution for the client application was thus tointegrate a web-map view element on the web page presented,which is a similar interface to famous web services like GoogleMaps or Bing Maps. With this interface, the user is able tovisualise at the same time the pollutant’s concentration valueand its location.

In the initial architectural breakdown, an organisational unitwas defined: the execution domain, which agglomerates themain computational tasks into small sets. Separating proce-dural stages allows a higher focus to be given to the imple-mentation of each part. The architecture can be summarisedin three execution domains: Availabe Data, Web-Server andWeb-Browser.

Tile Map Service is a specification for storing and retrievingcartographic data developed by the Open Source GeospatialFoundation which provides a solution to serve digital mapsonline using predefined image tiles.

The Tile Map Service advertises its available tiles througha standardized declaration, that defines the type of contentin each layer, in each graphical representation style, in eachformat, in each coordinate reference system, at each scale,and over each geographic fragment of the total covered area.This protocol fills a gap between the simplistic standard usedby OpenStreetMap tiles and the complexity of the Web MapService standard, providing simple URLs to tiles while alsosupporting alternate spatial referencing system.

When generating estimated pollution maps, data samplesof the atmospheric pollutants need to be selected withinboth spatial and temporal ranges. For this purpose, the mapgenerator needs to execute a scheduler that will run thespatial estimation tasks at predefined times and with predefinedtemporal ranges. The scheduling definition was implementedthrough basic configuration text files that are loaded by theprogram when execution starts.

Each task is launched separately in a new process us-ing environment variables to define the period of analysis.Other solutions were analysed to transmit these initialization

Figure 3. IQAr spectrum for the three pollutants.

parameters (e.g. passing command line arguments, writingto a temporary file or using a pipe between processes) butenvironment variables were the simpler solution and workedout-of-box for all possible implementations of the algorithmsimagined (C, Python, R, Matlab and others).

To attain the required computational efficiency, interpola-tion algorithms were implemented using the C programminglanguage, although other implementations could easily beused: Matlab, the R programming language and the Pythonprogramming language.

Regardless of the implementation, any algorithm needs toretrieve samples from the available data execution domain,stored in a MySQL database, and generate png images, usingmethods spatial estimation, accompanied by the respectiveworldfile and an optional text file with technical informationthat provides an in depth summary about the estimationperformed.

The result in figure 3 shows only one area, around the fixedstation of Avenida da Liberdade, where the levels of NO2 arehigher. It is important to note that few samples were used tocover a big area and that the data collected refers to a timeof the year with stable atmospheric conditions. The analysedpollutant thus presents lower values, usually characterized withthe green color of the IQAr chromatic scale.

IV. SPATIAL ESTIMATION

The purpose of estimation in the URBISNET project is toestimate values for the atmospheric pollutants in unknownlocations within the area covered by existing observations.The expected result is a discrete assignment of an unknownfunction u in the study region D.

A. Inverse Distance Weighting

Let the problem be stated as:

u(x) =

∑Ni=1 pi(x)× ui∑N

i=1 pi(x)

wherepi(x) =

1

d(x, xi)h

is called the weight function. d(x, xi) is the Cartesian distancebetween x and a sample location xi.

The weight function tries to mimic the influence of sampleson the unknown locations using proximity (inverse of thedistance) as a decision rule. Here, weight decreases as distanceincreases from the samples and the parameter h, called thedistance-decay parameter, allows further control over the shapeof the influence.

Notice that as x approaches a sample location xi, the ith

terms in the numerator and denominator exceed all boundswhile the other terms remain bounded. Therefore limx→xi

=ui as desired, and the function u(x) is continuous.

B. Ordinary Kriging

The following optimisation problem can be solved to esti-mate any point:

minimizeW

Cov{0}+WTCov{X}W − 2WTCov{x,X}(1a)

subject to WT · 1 = 1 (1b)

Minimizing the previous problem amounts to zeroing theN+1 partials derivatives with respect to wi and µ, obtaining asystem of N+1 equations with N+1 unknowns that solve theweights and, at the same time, respects the unbiased constraintwhile minimising the variance of estimation.

The solution for matrix W is easily obtained by ignoringthe last row of matrix M in:

M =

[W−µ

]= K−1 · Z (2)

With this vector, it becomes possible to obtain an estimationof U(x) and its variance:σ

2(x) = Cov(0)−[Cov{x1, x} · · · Cov{xN , x} 1

]·

[W

µ

]U(x) =WT · UX

V. RESULTS AND PERFORMANCE

The experimental variograms with 10 lags were processedand fitted with a theoretical model (spherical, exponential orgaussian) for the three pollutants based on the available datafrom the fixed stations.

Table IEXPERIMENTAL VARIOGRAMS PARAMETERS.

Pollutant Model Range [Km] SillNO2 exponential 6 400CO exponential 6 100000O3 exponential 7 140

In all the works analysed, the same problem related withpoor spatial continuity was noticed: the values presented forthe range of the variogram are similar. The major differencesappear in the values defined for the sill.

The use of an empirical variogram model in the estimationprocess is a central decision that influences the quality of the

estimation. To assess the robustness of the chosen variogramfor each pollutant, simulations were made using synthetic dataand some error indicators were evaluated.

Cross-validation is an approach where the generated syn-thetic samples are partitioned into complementary subsets,performing the analysis on one subset — called the trainingset — and validating the analysis on the other subset — calledthe validation set. With the ability to produce synthesiseddata, it was possible to repeat the simulation process untilcross-validation error indicators, obtained from the differencesbetween the validation set and the estimated values, started toconverge.

Two measurements were proposed to remove the effectof measurement units and the sensitivity to the changes inmeasurement unit or scale: the RMAE and the RRMSE.

RMAE

RMAE =1

N

N∑i=1

estimadedi − observedi

observedi

RRMSE

RRMSE =

√√√√ 1

N

N∑i=1

(estimadedi − observedi

observedi

)2

VI. CONCLUSION

This work aimed at building a system to characterize theair quality of a city monitored, not only by fixed stations butalso, by a mobile sensor network like URBISNET.

This chapter summarises the main conclusions obtained inthis work and its respective contributions to the state of theart in prediction of air quality at a spatial level. The presentwork was develop having three goals in mind:• Design a public, intuitive and light interface to visualise

estimated pollution maps;• Implement a resilient and reconfigurable server applica-

tion responsible for coordinating the periodic generationof pollution maps;

• Develop algorithms for creating pollution maps and com-pare the performance and quality of these under differentconditions.

The proposed objectives were carried out successfully andthe main conclusions are presented as follows:

1) An interface to visualise estimated pollution maps wasdeveloped. Since nowadays many devices and virtuallyall computer platforms have a web-browser installed,the selection of an HTTP based protocol — TMS —and the respective web server for the interface foun-dation easily enables a public use of the application.By aggregating location and quantification of estimateddata in a web map view — using Openlayers — theinterface became intuitive, unlike reading numbers froma table. Finally, by maintaining a scheduler process inthe server to generate pollution maps and responding touser requests with pre-processed data it was possible tokeep a lightweight client side.

2) The periodic generation of pollution maps was imple-mented in a startup process using flexible and robustsoftware — Upstart , the Python programming languageand the APS module — which makes it very resilient.This application can be configured anytime, not onlyon the start of the service but also, while running. Theconfigurations can be made in a way that allow theadministrator of the service to define periodic jobs tobe launched everyday. Not only the time ranges can bedefined but also the algorithm program can be set, whichallows to create different algorithms that can be used toanalyse multiple air pollutants.

3) The implemented algorithms were developed and ini-tially configured using data provided by QualAr. Thebiggest problem found in this topic was related to thelow spatial resolution that exists in Lisbon’s fixed stationnetwork. Other works were found where the variogramparameters were defined. These were compared with thedefined values in this work, and it can be concluded that:• these parameters are not easy to define and the

values used are not unanimous;• the values used in this work stay inside the range

of values used in other works.Using synthetically generated data, it was observedthat both error indicators RMAE and RRMSE showvalues in ranges [10%, 20%] and [15%, 30%], respec-tively. Moreover, using data generated with a Callpuffmodel, where the experimental variogram is significantlydifferent from the variogram previously defined, it waspossible to run a cross-validation test that presentedresults with the same behaviour as the synthetic data.The implementation of the OK, made use of the paral-lel processing capabilities available in most computersnowadays. The speedup obtained from more parallelism,presents values near Amdahl and Gustafson’s laws the-oretical limits. Also, it was concluded that a highernumber of collected samples allows parallelism intomore threads without losing as much efficiency.Finally, the created webpage shows a low response timewhen compared to general response times.

ACKNOWLEDGMENT

This work describes a public information system for theair quality of Lisbon, Portugal. The system was implementedat Instituto Superior Tecnico under the URBISNET project,lead by Instituto de Sistemas e Robotica with the collabo-ration of Instituto de Telecomunicacoes and Instituto de Sol-dadura e Qualidade. This project was supported by Fundacaopara a Ciencia e Tecnologia under the contract PTDC/EEA-CRO/104243/2008.

I would like to express my gratitude to my supervisor,Prof. Joao Pedro Gomes, whose expertise, understanding, andpatience, added considerably to my academic experience. Iappreciate his vast knowledge and skill in many areas, and hisassistance in writing reports. Also I must thank him for allthe help and persistence to make this work follow through. I

doubt that I will ever be able to convey my appreciation fully,but I owe him my eternal gratitude.

I must also acknowledge Fabio Gameiro, Joao Carvalhoand Sergio Dias for their suggestions and provision of theURBISNET platform in this study.

Thanks also goes out to those who provided me withtechnical advice at times of critical need; I would also liketo thank my friends, namely Pedro Silva, Miguel Verıssimo,Miguel Neto, Mario Ribeiro and Joao Freire, for our technicaldebates and exchanges of knowledge and skills during myacademic program, which helped enrich the experience.

I would also like to thank my family for the support theyprovided me through my entire life and in particular, I mustacknowledge my girlfriend and best friend, Ana Rita Mexia,without whose motivation and encouragement I would nothave been able to finish an academic career in electrotechnicaland computer engineering.

In conclusion, I recognise that this research would not havebeen possible without the support of Instituto de Sistemase Robotica and Instituto Superior Tecnico, and express mygratitude to those institutions.

REFERENCES

[1] D. Jardim, F. Ferreira, A. Dias, C. Martins, and J. Monjardino, “Evolucaoda qualidade do ar em portugal entre 2001 e 2005,” 2008.

[2] A. Russo, “Previsao espacio-temporal da qualidade do ar na regiao urbanade lisboa,” Ph.D. dissertation, Instituto Superior Tecnico, 2011.