gis-based air pollution monitoring using static stations and mobile sensor in tehran/iran

8/10/2019 GIS-Based Air Pollution Monitoring using Static Stations and Mobile Sensor in Tehran/Iran

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International Journal of Scientific Research in Environmental Sciences, 2(12), pp. 435-448, 2014

Available online at http://www.ijsrpub.com/ijsres

ISSN: 2322-4983; 2014; Author(s) retain the copyright of this article

http://dx.doi.org/10.12983/ijsres-2014-p0435-0448

435

Full Length Research Paper

GIS-Based Air Pollution Monitoring using Static Stations and Mobile Sensor in

Tehran/Iran

Hassan Hamraz1, Abolghasem Sadeghi-Niaraki*1, 2, Mehrnoosh Omati1, Negar Noori1

1GIS Dept., Geoinformation Technology Center of Excellence, Faculty of Geodesy & Geomatics Eng., K.N. Toosi Universityof Technology, Tehran, Iran.

2Dept. of Geo-Informatic Eng., Inha University, South Korea

*Corresponding Author: Phone (+9821-88877070); Email: [email protected] 06 October 2014; Accepted 23 November 2014

Abstract. Air pollution is a major problem in mega cities and its harmful for environment and human health. Usually innowadays, air pollution is monitored by static stations networks and because of high cost of developing and maintenance theamount of these stations is limited. Air pollution is strongly dependent to location and its vary from one to another, there foreless static stations cause poor spatial resolution in published pollution maps. This research paper is for generating a pollutionmap with higher spatial resolution according to mobile measurements through Mobile Data Acquisition Unit (MDAU) andTehran Environmental Protection Agencys measurements. Mobile unit box used for this study is equipped with: handheldGPS, MQ 9 gas sensor (CO detector), Seeeduino Stalker microcontroller and Xbee Bluetooth module, and it was placed on topof a vehicle and the datas were collected in Hemat highway, Tehran/Iran during 15:00 to 16:00 hours, afterwards Mobile andstatic stations measurements were imported and aggregated in GIS environment to generate an air pollution map through Geo-

statistical Analysis. Finally, CO pollution map was validated and compared with pollution map produced via Tehran AirQuality Control Companys data.

Keywords:Air pollution, Gas sensor, GIS, Geostatistics

1. INTRODUCTION

Air pollution in result of industrialization,urbanization growing and etc. is a major problem formega cities. Factories and vehicles are one of the mostimportant factors in air pollution around the world.Vehicles are the main source of Carbon Monoxide

(CO). CO is a harmful gas with no colour and odourand cant be detected by human senses because of notaste and smell, and it effects on our health.

Nowadays, air pollution is monitored through staticstations, their efficiency is high and can accuratelymeasure wide range of pollutants but they havelimitations such as: high costs in accommodation,developing and maintenance, therefore the smallnumber of static stations cause limited spatialresolution in published pollution maps. Air pollutionis strongly dependent to location and its vary from

one to another therefore the pollutant concentrations

near these stations are accurate and the accuracy andreliability decreases as the distance increases. Theselimitations cause movements towards new generationof air pollution sensors. In recent years, lots ofresearch groups start to measure electrochemical

pollutants with low-cost gas sensors. Most of thesesensors are showing electrochemical reaction whenthey are exposed to certain gases and through thesesensors gas concentration obtains. These sensors aresmall, low-cost, portable and can be assembled almosteverywhere for sending datas, therefore these sensors

can be used as an air pollution measurement nodes on

vehicles and etc. they collect large amount of datafrom different places and the result is pollution mapswith high spatial resolution.

Studies for air pollution monitoring by using low-cost gas sensor system assembling in recent years aregrowing. In (Ikram et al., 2012) an air pollutionmonitoring system was developed. This system used aset of low-cost electrochemical gas sensor equippedwith solar panel for measuring pollutants, temperatureand humidity. The measured preliminary data wassent to a server through GSM modem and the server

publishes pollution map after completing analysis and

interpolation. In (Vlgyesi et al., 2008) a mobile airquality monitoring network is presented. This systemwas equipped with gas sensors node mounted on avehicle for measuring pollutants concentration. Themeasurements were sent to the server while the nodes


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are in designed WIFI network coverage and the serveranalysis the data and publish them. In (Al-Ali etal.,2010) an online GPRS-sensor array for air

pollution monitoring was designed. This systemincludes mobile data acquisition unit and internet

pollution monitoring server. The mobile unit isequipped with gas sensors which measure pollutantslevel and sends them to a server with time andlocation via GSM modem. The server makes real-timeaccessibility to the datas for the internet users.

P-sense (Mendez et al., 2011)is a monitoring andcontrolling system for air pollution. In this systemsensing nodes are mobile phones equipped with GPSand gas sensor. The gas sensor linked to the mobile

phone via Bluetooth connection. Each node senses thedatas and sends them to the server. Users can see

variables of interests in real time by sending a requestto the server. In (Tudose et al., 2011) a mobile systemfor air quality and pollution measurement suitable forurban areas is presented. This system includes gassensors that are mounted on vehicles. The pollutantsmeasurement is provided for the driver through adashboard display and its also sent to a web server

via GSM link. The datas are published on the internet

through web services and users can access to them atany time and place as they desire. Open Sense (Li etal., 2012) is a dataset of mobile air qualitymeasurements in Zurich. This system has two types of

stations. Mobile stations equipped with gas sensor ontop of trams and static gas sensor stations next tonational air pollution monitoring network for longterm sensor testing. In (Hasenfratz et al.,, 2012) a

participatory air pollution monitoring system usingsmartphones and gas sensors is represented. Thesensor was mounted on a bicycle and tookmeasurements from different bicycle rides all aroundthe city. N-smarts (Honicky et al., 2008)is a projectthat attach gas sensor to GPS-enabled cell phones togather raw data about urban air pollution.(Devarakonda et al., 2013) presented a real-time air

quality monitoring system through mobile sensing inmetropolitan areas. It has two mobile sensing models;first for deployment on public transportationInfrastructure such as buses and second for relies onair quality aware drivers whom installed personalsensing devices in their cars. The sensing modelsmeasure the concentration of pollutants with gassensors and send them via cellular data link to thecloud server. In (Pummakarnchana et al., 2005)an air

pollution monitoring and GIS modelling system isrepresented. Gas sensor integrated with Personal DataAssistant (PDA) and wireless GIS is used for air

pollution monitoring over a wide area.This research paper is about using the potential of

low cost gas sensors to increase measurements spatial

resolution thereby complementing existing relativelysparse static stations. A pollution map is generatedwith higher spatial resolution according to mobilemeasurements through Mobile Data Acquisition Unit(MDAU) and Tehran Environmental ProtectionAgencys.Furthermore, GIS capabilities are used foranalysing data and generate pollution map.

2. MATERIALS AND METHODS

Nowadays, air pollution is monitored through staticstations. These stations have limitations. Besides

pollutants concentration is highly location-dependent,so accuracy and reliability decrease as distanceincreases .Due to limited number of static stations andlack of proper distribution, the result of published

pollution maps have poor spatial resolution. In thispaper we want to get air pollution datas fromdifferent locations to generate pollution maps withhigher spatial resolution. For this purpose, at first

preliminary studies were completed to design MobileData Acquisition Unit for collecting mobile air

pollution data, then TEPA (Tehran EnvironmentalProtection Agency) air pollution measurements weregotten to calibrate gas sensor and generate pollutionmap, afterwards the MDAU and TEPA datas were

imported and aggregated in GIS environment and withthe help of Geostatistics in ARCGIS 10 CO

concentration maps was generated. Finally, the resultswere evaluated with Tehran Air Qulaity ControlCompanys data. Fig.3 shows an overview of

methodology.

2.1. Mobile Data Acuisition Unit

Mobile data acquisition unit (MDAU) was designedfor collecting data from different locations. MDAU

box is equipped with: handheld GPS and laptop,seeeduino stalker board, ATmega328 Pmicrocontroller, 3.7 V battery, XBEE BLUETOOTH,

potentiometer, capacitor and MQ-9 carbon monoxidegas sensor. (See Fig.2)

2.1.1. Potentiometer

Gas sensors show varied reactions in differenttemperatures and causes interrupts in sensors

performance, a calibration for avoiding this matter isnecessary and to obtain correct functioning a 10K

potentiometer is used for MQ 9 gas sensor in order tocalibrate gas sensor in different temperatureconditions. Potentiometer acts as a variable resistor.For minimizing errors and reducing effective sourceimpedance a capacitor is in use.


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Fig. 1: Overview of methodology

Fig. 2: Components of designed box


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Fig. 3: Calibration procedure

Fig. 4: MDAU measurements and Tehran Environmental Protection agencys monitoring stations

2.1.2.GAS Sensor

Portable gas sensors are used to detect toxic gases.There are wide varieties of gas sensors that each onehas different operational principles. They can beclassified according operational characteristic in three

types: heating semiconductor, non-dispersive infrared(NDIR) and light emitting diode (LED). Size,accuracy and power consumption of gas sensors arerelated to their types (Choi et al., 2009). MQ_9 gassensor that was used in this study is semiconductortype. This sensor has high sensitivity to carbon


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monoxide (CO) and detect CO gas in lowtemperature.MQ-9 sensor evaluates gasconcentrations by measuring the electricalconductivity of a sensitive layer such as tin

dioxide(sno2).with CO molecules absorption by thismetal-oxide, sensor conductivity (resistance)changes. Table1 shows some sensor specification.

Fig. 5: CO Histogram

Fig. 6:CO Normal QQ Plot


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2.1.3. Microcontroller

In this study, ATmega328 P microcontroller is used. Itis in class of AVR microcontrollers. Programming inmega IC was written with BASIC language inBASCOM environment. Since gas sensors areanalogue in nature and have output resistance,therefore an ADC (ANALOGUE DIGITALCONVERTER) port is embedded and used forinterfacing between microcontroller and gas sensor, it

converts analogue signal to digital. CO concentrationis usually describes as parts per million(PPM).microcontroller was programmed with AVR Inorder to convert MQ 9 output resistance to PPMaccording to MQ 9 datasheet. Microcontroller sendssensor measurements through XBEE BLUETOOTHto the laptop. The connection between XBEEBLUETOTH and laptop is established viaHyperTerminal. HyperTerminal continuously showssensor measurements on laptop.

Table1: Sensor specificationSENSOR TYPE SENSITIVITY RANGE POWER

MQ-9 SEMI

CONDUCTOR

CO 20-2000

PPM

1.4V

Table2:TEPA monitoring stations dataID Station name X Y CO(ppm)

1 Elmo Sanat University 51.51143 35.73981 0.83

2 Razi park 51.38939 35.67016 1.83

3 Shahid beheshti University 51.39514 35.80338 1.62

4 Pasdaran 51.47336 35.78966 0.78

5 Shokoofe 51.45076 35.68574 2.1

6 Shahrak cheshmeh 51.26075 35.75079 1.47

7 Shahrdari Mantaghe 15 51.47996 35.64108 0.96

8 Tehran University 51.39776 35.70336 1.81

9 Shahre rey 51.42769 35.59301 4.45

Table 3:TEPA monitoring stations data

ID X Y CO(ppm)

80 51.49031 35.75839 2.02020281 51.49252 35.75879 1.01010182 51.49388 35.75891 2.02020283 51.49717 35.75884 1.01010184 51.51012 35.75748 1.01010185 51.5155 35.75496 1.01010186 51.52115 35.75327 1.010101

87 51.5236 35.75292 2.02020288 5152556 35.75269 1.010101

Table 4:parameter value for stable modelNUGGET RANGE PARTIAL SILL

0.1473352 0.0023022 0.7021622

2.2. Gas Sensor Calibration

The main problem with gas sensors are their lowaccuracy and stability. Therefore calibration

techniques are required to increase their accuracy.There are two common ways for calibration of gassensors in purpose of urban air pollution monitoring.First, laboratory calibration which gas sensors are

calibrated in laboratory and in comparison togenerated gas mixture (Choi et al., 2009). Second,Field calibration that in this approach, sensor is placednear reference stations with high quality concentration

measurements (Kamionka et al., 2006). In this study, asecond approach was selected to improve data qualitythat gathered by MQ 9 gas sensors because in this waysensor performance can be observed under real


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conditions and in presence of other gases. MQ_9sensor was calibrated via ENVIRO S_A sensor(Tehran Environmental Protection Agencys station).

Calibration procedure is shown in Fig.3. Forcalibration, both data sets are performed to calculatethe mean and standard deviation of readings from theMQ_9 and S_A sensor as follows (Ikram et al., 2012)(*) 1=Mean of Reference station readings

(*) 1=Standard deviation of Reference stationreadings(*) 2=Mean of Gas sensor readings(*) 2=Standard deviation of Gas sensor readings=1/2 (1)=1-(2*) (2)Calibrated reading=*(un-calibrated reading) + (3)

Fig. 7:Trend Analysis of Co data

2.3. Study Area

Tehran is a city with population over 10 million andits located on 51 to 5140 E longitudes and 3530to 3551 N latitudes, with industrial zones and heavy

traffic two worst factors in air pollution , therefore its

the most polluted city of Iran. There are 17 monitoringstations across Tehran which are operated by TEPA

but only 9 of them measures CO pollutantconcentration correctly .These stations collect datafrom different parts of the city and archive them daily.these stations do not have proper location distribution

in the city, therefore MDAU was used to have themobile observations and the 90 samples points withgeographic coordinates during 15:00 to 16:00 hours(rush hour starting time) from Hemat highway (one ofthe most important and high-traffic roads in Tehranthat connect west to east) at 9thJuly 2014 (as a sampleof work day) were collected and added to TEPAsstatic stations measurements. Fig.4 represents MDAUmeasurements and Tehran Environmental ProtectionAgencys monitoring stations.

2.4. Geostatistical Analysis

After collecting CO concentrations data, spatialdistribution of this pollutant in experimental areashould be analysed and CO level on non-sampledlocations should be estimated. Selecting optimal

method for predicting values associated with spatialphenomena is very important. Therefore, one of themost common methods for predicting pollutantconcentrations is Geostatistic. It is kind of statisticthat consist of different technique for modelingregional variations and spatial analysis.Geostatisticaltools qualify spatial connection between observedvalues and estimates reliable value for not measured

points from neighboring samples(Bohling, 2005;Garca, Gonzlez and Rodrguez, 2008). Preparationof pollution map is possible if spatial correlation

between pollutant concentrations is known.Determining spatial pattern of variables is done withestimating non-sampled location value based onsampled points (Interpolation) (Garca et al., 2008).Geostatistical methods like kriging has severaladvantages over deterministic methods likeIDW(Inverse Distance Weighting) and Spline(Isaaksand Srivastava, 1989; Li and Australia, 2008).Manystudies have shown better results for Geoestatisticlamethods like kriging in comparison to deterministictechniques like IDW and spline (i.e Horlek et al.,2007; Wang and Zhang, 2012). There are different

methods in Geostatistic for understanding spatialcorrelation and map generation. The best method iskriging (spatial regression) especially for COconcentrations. It gives the best unbiased predictionwith minimum variance at each location and consider


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spatial correlation between data at different locations.It also provides information on interpolation errors.Kriging has various forms. In pollution applications,ordinary kriging method is used for estimation ofvariability and predicting pollutants (Pang et al., 2010;Pope and Wu, 2013; Song, 2008). A structured

process based on (Garca et al., 2008)was followed togenerate CO concentrations map with Geostatistics:

1) Exploratory analysis of data. Its for checking

data consistency, identifying statistical distributionand Trend of data. Normal QQ plot and histogram can

be used to check normality of data. Trend analysisenables identifying presence or absence of trends inthe input dataset.

2) Structural Analysis of data. It is used to analysespatial distribution of variables. Spatial

autocorrelation between measured sample points canbe quantified with semivariogram /covariance clouds.The variogram represent degradation of spatialcorrelation between pairs of points when theseparation distance increases. After definingexperimental variogram, a model should fit to the

points. The fitted model provides information aboutspatial connection for kriging interpolation.

3) Prediction. With the aid of geostatistics, valuesfor non-sampled locations can be estimated thatconsider spatial distribution pattern and integratingand trends. There are lots of geostatistics methods for

interpolation but the best one is Kriging that inaddition of estimated value gives variance, or itssquare root, the kriging standard deviation for eachlocation. In this study ordinary kriging was used. Thismethod considers the mean fluctuates locally. beforegenerating the final map cross validation were used tovalidate the accuracy of all interpolations .In the

phase, the value is estimated at each location with theremaining data and after calculation betweenestimated and actual value for all data points and withcomputation some statistics are tested that how wellvalues at unknown locations are predicted by the fitted

model.The Geostatistical Analysis includes all above

process that was done with Geostatistical Analysisextension in ArcGIS (version 10) and finally CO

predictions map is generated.

3. RESULTS AND DISCUSSIONS

TEPA has 17 air pollution monitoring stations; amongthese stations only 9 of them sense CO correctly andwith this few numbers of station pollution maps aregenerated with poor resolution. Therefore MDAU wasdesigned to cover up this problem with minimum

budget. Before collecting data with MADU in the city,the unit was placed near University of Tehran

monitoring station (operated by TEPA) for calibrationand improving accuracy as described above.Afterwards the data with MDAU were collected in theafternoon from Hemat Highway in Tehran. Finally, 90sample points were collected via MDAU. Table 2shows TEPA monitoring stations data and table 3 isan example of MDAU measurements.

3.1. Exploratory Analysis of Data

Histogram and QQ plot are useful tools for checkingdistribution and normality of data. Although normalityis not necessary for Kriging, but normality causes

better estimates. In Histogram depicted in the Fig.5mean and median values are approximately the sameand SKEWNESS value is app. zero, these are normal

distribution signs. Figure 5 shows histogram for COsamples. In Normal QQ plot as shows in Fig.6, thecloser points to the straight line (45 degree) followsnormal distribution.

If Tend exists in our data, it is deterministiccomponent and can be represented by mathematicalformula. Fig.7 shows Trend Analysis of CO data. Itshows while we rotate points, trends always exist as adownside-up U-shape. This trend should be removedin prediction step with second order polynomial.

3.2. Structural Analysis of Data

Figure 8 represents directional semivariogram andmodel of CO. Semivariogram/covariance modellingdetermines the best choice of a variogram model thatfit to sample points. In this study, stable model is themost suitable with associated parameter value in table4.

Also direction semivariogram is used to explorethe different direction influence on the data and defineacceptable direction variogram. At different locations,the highest correlation of data points with theirneighbors occur when CO values are shifted west-

east. This shift is in direction and distance thatcalculated cross-covariance is at its max value. Afterspecifying SEMIVARIOGRAM model fitted andnumber of points neighbouring with different weightfor predicting values at unmeasured locations, crossvalidation determinates that which model provides themost accurate estimations and assesses validity of

prediction errors. As can be seen in Figure9 meanerror close to zero that is indicate unbiased predictionand root-mean-square standardized error close to 1that shows standard errors are accurate. Also at QQ

plot tab is seen that most points are close to straightline and indicate that prediction errors are close tonormal distribution. Fig.10 provides summary ofmodel information that was used to create surface.


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(a) (b)Fig. 8: a) South-North semi-variogram of CO. b) West-east semivariogram CO

3.3. Prediction

Finally, Figure 11 shows predicted CO map using anOrdinary KRIGING model in Tehran between 3-4PM. By means of prediction standard error surfacecan be quantified the uncertainty for each location. Asis seen in figure 12, locations near static stations and

sample points have lower error and with increasingdistance from these points, we see more error values.In other words, mobile measurements reduce

prediction errors and causes increasing in spatialresolution.

3.4. Evaluation

Tehran Air Quality Control Companys data is used tovalidate our results. Fig.13 shows CO concentrationmap generated from those data. As can be seen inFig.11 and Fig.13, they are similar to each other andhave similar results and both have more CO

concentrations in west of Tehran but fig.11 is moredetailed. So high spatial resolution maps can begenerated with minimum budget with the aid ofsystems proposed in this research. As regards vehiclesis the main source of CO pollutant. It is expected thathigh CO concentrations usually are in areas that trafficis more intense. In the evening traffic follow areheavy from west to east in Tehran. This factor causesmore CO and pollution in west of Tehran.


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Fig. 9: Prediction errors and normal QQ plot

Fig. 10:Summary of model information


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Fig. 11:Predicted CO map (scale:1/250000)

Fig. 12:Prediction standard error surface


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4. CONCLUSION

Air pollution has many harmful effects, so it isnecessary to have pollution maps with higherresolution to act appropriate actions based on enoughand complete information. High cost for establishingstatic stations severely limits the number of thesestations and causes pollution map with poor spatialresolution. A useful way to improve pollution

products and increase our observations and knowledgeis utilizing low cost portable gas sensor. In this study,CO gas sensor was used and a Mobile DataAcquisition Unit was designed to collect mobile dataon CO concentrations. These measurements were

collected with TEPAs data in GIS environment and

with the help of Geostatistics Analysis in ARCGIS 10,CO concentrations map were generated with higherspatial resolution. High resolution map and moredetailed data can help regulations and people to make

better decision based on these data. Because of somelimitations, only one mobile data acquisition unit andonly one sensor was used, better results can beobtained with more data acquisition unit throughdifferent path. It is recommended to add bidirectionalGSM/GPRS links to mobile data acquisition unit forhaving real-time data in order to have a web-basedclient-server system with GIS capabilities can befuture researches.

Fig. 13: CO concentration map (scale:1/250000) produced from Tehran Air Quality Control Companys data

ACKNOWLEDGEMENTS

This work was supported by an Inha University

research grant.

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Dr. Abolghasem Sadeghi-Niarakihas a PhD in Geospatial Information System (GIS)Engineering and IT (Geo-informatics) from Inha University,South Korea. He obtained his B.Sand M.S degree from Dept. of GIS, Geodesy and Geomatics Eng. Faculty, K.N. Toosi ofTechnology, Tehran, Iran. He is an Assistant Professor at K.N. Toosi University of Technology.His research interests include Ubiquitous GIS(UbiGIS), U-City, Brain City, Spatial enable

Society, U-SDI, Semantic GIS Web Service, Context-awareness, Ontology, LBS, M2M, MotionSensors & Indoor Positioning, Smartphone based Remote Sensing and Ubiquitous Health.

Hassan Hamrazreceived the B.S degree in Geomatic Eng. from Department of GIS, Geodesyand Geomatic Engineering Faculty, K.N.T Universtity of Technology, Tehran, Iran. He is nowGIS M.S student at Department of GIS, Geodesy and Geomatic Engineering Faculty, K.N.TUniverstity of Technology, Tehran, Iran. He works on designing air pollution frameworks,

models, Analysis, visualization and statistical analysis. He also works on Ubiquitous air pollution,electrochemical gas sensors and designing sensor node in air pollution field.

Mehrnoosh Omaticurrently is studying for Master of Science degree at Department of RemoteSensing, Geodesy and Geomatic Engineering Faculty, K.N.Toosi University of Technology,Tehran, Iran. She obtained her first degree in Geomatic Eng. from Faculty of Geodesy andGeomatic Engineering, K.N.T University of Technology, Tehran, Iran in 2014.

Negar Noori received the B.S degree in Geomatic Eng. from Department of GIS, Geodesy andGeomatic Engineering Faculty, K.N.T Universtity of Technology, Tehran, Iran. M.S student atDepartment of photogrammetry, Geodesy and Geomatic Engineering Faculty, K.N.T Universityof Technology, Tehran, Iran.

gis-based air pollution monitoring using static stations and mobile sensor in tehran/iran

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