gis-based air pollution monitoring using static stations ... · pdf file... seeeduino stalker...

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 University

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

*Corresponding Author: Phone (+9821-88877070); Email: [email protected]

Received 06 October 2014; Accepted 23 November 2014

Abstract. Air pollution is a major problem in mega cities and it’s harmful for environment and human health. Usually in

nowadays, air pollution is monitored by static stations networks and because of high cost of developing and maintenance the

amount of these stations is limited. Air pollution is strongly dependent to location and it’s vary from one to another, therefore

less static stations cause poor spatial resolution in published pollution maps. This research paper is for generating a pollution

map with higher spatial resolution according to mobile measurements through Mobile Data Acquisition Unit (MDAU) and

Tehran Environmental Protection Agency’s measurements. Mobile unit box used for this study is equipped with: handheld

GPS, MQ 9 gas sensor (CO detector), Seeeduino Stalker microcontroller and Xbee Bluetooth module, and it was placed on top

of a vehicle and the data’s were collected in Hemat highway, Tehran/Iran during 15:00 to 16:00 hours, afterwards Mobile and

static 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 Air

Quality Control Company’s data.

Keywords: Air pollution, Gas sensor, GIS, Geostatistics

1. INTRODUCTION

Air pollution in result of industrialization,

urbanization growing and etc. is a major problem for

mega cities. Factories and vehicles are one of the most

important 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 odour

and can’t be detected by human senses because of no

taste and smell, and it effects on our health.

Nowadays, air pollution is monitored through static

stations, their efficiency is high and can accurately

measure wide range of pollutants but they have

limitations such as: high costs in accommodation,

developing and maintenance, therefore the small

number of static stations cause limited spatial

resolution in published pollution maps. Air pollution

is strongly dependent to location and it’s vary from

one to another therefore the pollutant concentrations

near these stations are accurate and the accuracy and

reliability decreases as the distance increases. These

limitations cause movements towards new generation

of air pollution sensors. In recent years, lots of

research groups start to measure electrochemical

pollutants with low-cost gas sensors. Most of these

sensors are showing electrochemical reaction when

they are exposed to certain gases and through these

sensors gas concentration obtains. These sensors are

small, low-cost, portable and can be assembled almost

everywhere for sending data’s, therefore these sensors

can be used as an air pollution measurement nodes on

vehicles and etc. they collect large amount of data

from different places and the result is pollution maps

with high spatial resolution.

Studies for air pollution monitoring by using low-

cost gas sensor system assembling in recent years are

growing. In (Ikram et al., 2012) an air pollution

monitoring system was developed. This system used a

set of low-cost electrochemical gas sensor equipped

with solar panel for measuring pollutants, temperature

and humidity. The measured preliminary data was

sent to a server through GSM modem and the server

publishes pollution map after completing analysis and

interpolation. In (Völgyesi et al., 2008) a mobile air

quality monitoring network is presented. This system

was equipped with gas sensors node mounted on a

vehicle for measuring pollutants concentration. The

measurements were sent to the server while the nodes

Hamraz et al.

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

436

are in designed WIFI network coverage and the server

analysis the data and publish them. In (Al-Ali et

al.,2010) an online GPRS-sensor array for air

pollution monitoring was designed. This system

includes mobile data acquisition unit and internet

pollution monitoring server. The mobile unit is

equipped with gas sensors which measure pollutants

level and sends them to a server with time and

location via GSM modem. The server makes real-time

accessibility to the data’s for the internet users.

P-sense (Mendez et al., 2011) is a monitoring and

controlling system for air pollution. In this system

sensing nodes are mobile phones equipped with GPS

and gas sensor. The gas sensor linked to the mobile

phone via Bluetooth connection. Each node senses the

data’s and sends them to the server. Users can see

variables of interests in real time by sending a request

to the server. In (Tudose et al., 2011) a mobile system

for air quality and pollution measurement suitable for

urban areas is presented. This system includes gas

sensors that are mounted on vehicles. The pollutants

measurement is provided for the driver through a

dashboard display and it’s also sent to a web server

via GSM link. The data’s are published on the internet

through web services and users can access to them at

any time and place as they desire. Open Sense (Li et

al., 2012) is a dataset of mobile air quality

measurements in Zurich. This system has two types of

stations. Mobile stations equipped with gas sensor on

top of trams and static gas sensor stations next to

national air pollution monitoring network for long

term sensor testing. In (Hasenfratz et al.,, 2012) a

participatory air pollution monitoring system using

smartphones and gas sensors is represented. The

sensor was mounted on a bicycle and took

measurements from different bicycle rides all around

the city. N-smarts (Honicky et al., 2008) is a project

that attach gas sensor to GPS-enabled cell phones to

gather raw data about urban air pollution.

(Devarakonda et al., 2013) presented a real-time air

quality monitoring system through mobile sensing in

metropolitan areas. It has two mobile sensing models;

first for deployment on public transportation

Infrastructure such as buses and second for relies on

air quality aware drivers whom installed personal

sensing devices in their cars. The sensing models

measure the concentration of pollutants with gas

sensors and send them via cellular data link to the

cloud server. In (Pummakarnchana et al., 2005) an air

pollution monitoring and GIS modelling system is

represented. Gas sensor integrated with Personal Data

Assistant (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 relatively

sparse static stations. A pollution map is generated

with higher spatial resolution according to mobile

measurements through Mobile Data Acquisition Unit

(MDAU) and Tehran Environmental Protection

Agency’s. Furthermore, GIS capabilities are used for

analysing data and generate pollution map.

2. MATERIALS AND METHODS

Nowadays, air pollution is monitored through static

stations. These stations have limitations. Besides

pollutants concentration is highly location-dependent,

so accuracy and reliability decrease as distance

increases .Due to limited number of static stations and

lack of proper distribution, the result of published

pollution maps have poor spatial resolution. In this

paper we want to get air pollution data’s from

different locations to generate pollution maps with

higher spatial resolution. For this purpose, at first

preliminary studies were completed to design Mobile

Data Acquisition Unit for collecting mobile air

pollution data, then TEPA (Tehran Environmental

Protection Agency) air pollution measurements were

gotten to calibrate gas sensor and generate pollution

map, afterwards the MDAU and TEPA data’s were

imported and aggregated in GIS environment and with

the help of Geostatistics in ARCGIS 10 CO

concentration maps was generated. Finally, the results

were evaluated with Tehran Air Qulaity Control

Company’s data. Fig.3 shows an overview of

methodology.

2.1. Mobile Data Acuisition Unit

Mobile data acquisition unit (MDAU) was designed

for collecting data from different locations. MDAU

box is equipped with: handheld GPS and laptop,

seeeduino stalker board, ATmega328 P

microcontroller, 3.7 V battery, XBEE BLUETOOTH,

potentiometer, capacitor and MQ-9 carbon monoxide

gas sensor. (See Fig.2)

2.1.1. Potentiometer

Gas sensors show varied reactions in different

temperatures and causes interrupts in sensors

performance, a calibration for avoiding this matter is

necessary and to obtain correct functioning a 10KΩ

potentiometer is used for MQ 9 gas sensor in order to

calibrate gas sensor in different temperature

conditions. Potentiometer acts as a variable resistor.

For minimizing errors and reducing effective source

impedance a capacitor is in use.


437

Fig. 1: Overview of methodology

Fig. 2: Components of designed box

Hamraz et al.


438

Fig. 3: Calibration procedure

Fig. 4: MDAU measurements and Tehran Environmental Protection agency’s 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 one

has different operational principles. They can be

classified 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 are

related to their types (Choi et al., 2009). MQ_9 gas

sensor that was used in this study is semiconductor

type. This sensor has high sensitivity to carbon


439

monoxide (CO) and detect CO gas in low

temperature.MQ-9 sensor evaluates gas

concentrations by measuring the electrical

conductivity of a sensitive layer such as tin

dioxide(sno2).with CO molecules absorption by this

metal-oxide, sensor conductivity (resistance)

changes. Table1 shows some sensor specification.

Fig. 5: CO Histogram

Fig. 6: CO Normal QQ Plot

Hamraz et al.


440

2.1.3. Microcontroller

In this study, ATmega328 P microcontroller is used. It

is in class of AVR microcontrollers. Programming in

mega IC was written with BASIC language in

BASCOM environment. Since gas sensors are

analogue in nature and have output resistance,

therefore an ADC (ANALOGUE DIGITAL

CONVERTER) port is embedded and used for

interfacing between microcontroller and gas sensor, it

converts analogue signal to digital. CO concentration

is usually describes as parts per million

(PPM).microcontroller was programmed with AVR In

order to convert MQ 9 output resistance to PPM

according to MQ 9 datasheet. Microcontroller sends

sensor measurements through XBEE BLUETOOTH

to the laptop. The connection between XBEE

BLUETOTH and laptop is established via

HyperTerminal. HyperTerminal continuously shows

sensor measurements on laptop.

Table1: Sensor specification

SENSOR TYPE SENSITIVITY RANGE POWER

MQ-9 SEMI

CONDUCTOR

CO 20-2000

PPM

1.4V

Table2: TEPA monitoring stations data

ID 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 2.020202

81 51.49252 35.75879 1.010101

82 51.49388 35.75891 2.020202

83 51.49717 35.75884 1.010101

84 51.51012 35.75748 1.010101

85 51.5155 35.75496 1.010101

86 51.52115 35.75327 1.010101

87 51.5236 35.75292 2.020202

88 5152556 35.75269 1.010101

Table 4: parameter value for stable model

NUGGET RANGE PARTIAL SILL

0.1473352 0.0023022 0.7021622

2.2. Gas Sensor Calibration

The main problem with gas sensors are their low

accuracy and stability. Therefore calibration

techniques are required to increase their accuracy.

There are two common ways for calibration of gas

sensors in purpose of urban air pollution monitoring.

First, laboratory calibration which gas sensors are

calibrated in laboratory and in comparison to

generated gas mixture (Choi et al., 2009). Second,

Field calibration that in this approach, sensor is placed

near reference stations with high quality concentration

measurements (Kamionka et al., 2006). In this study, a

second approach was selected to improve data quality

that gathered by MQ 9 gas sensors because in this way

sensor performance can be observed under real


441

conditions and in presence of other gases. MQ_9

sensor was calibrated via ENVIRO S_A sensor

(Tehran Environmental Protection Agency’s station).

Calibration procedure is shown in Fig.3. For

calibration, both data sets are performed to calculate

the mean and standard deviation of readings from the

MQ_9 and S_A sensor as follows (Ikram et al., 2012)

(*) µ1=Mean of Reference station readings

(*) σ1=Standard deviation of Reference station

readings

(*) µ2=Mean of Gas sensor readings

(*) σ2=Standard deviation of Gas sensor readings

α=σ1/σ2 (1)

β=µ1-(µ2*α) (2)

Calibrated reading=α*(un-calibrated reading) +β (3)

Fig. 7: Trend Analysis of Co data

2.3. Study Area

Tehran is a city with population over 10 million and

its located on 51° to 51°40′ E longitudes and 35°30′

to 35°51′ N latitudes, with industrial zones and heavy

traffic two worst factors in air pollution , therefore it’s

the most polluted city of Iran. There are 17 monitoring

stations across Tehran which are operated by TEPA

but only 9 of them measures CO pollutant

concentration correctly .These stations collect data

from 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 the

mobile observations and the 90 samples points with

geographic coordinates during 15:00 to 16:00 hours

(rush hour starting time) from Hemat highway (one of

the most important and high-traffic roads in Tehran

that connect west to east) at 9th July 2014 (as a sample

of work day) were collected and added to TEPA’s

static stations measurements. Fig.4 represents MDAU

measurements and Tehran Environmental Protection

Agency’s monitoring stations.

2.4. Geostatistical Analysis

After collecting CO concentrations data, spatial

distribution of this pollutant in experimental area

should be analysed and CO level on non-sampled

locations should be estimated. Selecting optimal

method for predicting values associated with spatial

phenomena is very important. Therefore, one of the

most common methods for predicting pollutant

concentrations is Geostatistic. It is kind of statistic

that consist of different technique for modeling

regional variations and spatial analysis. Geostatistical

tools qualify spatial connection between observed

values and estimates reliable value for not measured

points from neighboring samples(Bohling, 2005;

García, González and Rodríguez, 2008). Preparation

of pollution map is possible if spatial correlation

between pollutant concentrations is known.

Determining spatial pattern of variables is done with

estimating non-sampled location value based on

sampled points (Interpolation) (García et al., 2008).

Geostatistical methods like kriging has several

advantages over deterministic methods like

IDW(Inverse Distance Weighting) and Spline(Isaaks

and Srivastava, 1989; Li and Australia, 2008).Many

studies have shown better results for Geoestatisticla

methods like kriging in comparison to deterministic

techniques like IDW and spline (i.e Horálek et al.,

2007; Wang and Zhang, 2012). There are different

methods in Geostatistic for understanding spatial

correlation and map generation. The best method is

kriging (spatial regression) especially for CO

concentrations. It gives the best unbiased prediction

with minimum variance at each location and consider

Hamraz et al.


442

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 of

variability and predicting pollutants (Pang et al., 2010;

Pope and Wu, 2013; Song, 2008). A structured

process based on (García et al., 2008) was followed to

generate CO concentrations map with Geostatistics:

1) Exploratory analysis of data. It’s for checking

data consistency, identifying statistical distribution

and Trend of data. Normal QQ plot and histogram can

be used to check normality of data. Trend analysis

enables identifying presence or absence of trends in

the input dataset.

2) Structural Analysis of data. It is used to analyse

spatial distribution of variables. Spatial

autocorrelation between measured sample points can

be quantified with semivariogram /covariance clouds.

The variogram represent degradation of spatial

correlation between pairs of points when the

separation distance increases. After defining

experimental variogram, a model should fit to the

points. The fitted model provides information about

spatial connection for kriging interpolation.

3) Prediction. With the aid of geostatistics, values

for non-sampled locations can be estimated that

consider spatial distribution pattern and integrating

and trends. There are lots of geostatistics methods for

interpolation but the best one is Kriging that in

addition of estimated value gives variance, or its

square root, the kriging standard deviation for each

location. In this study ordinary kriging was used. This

method considers the mean fluctuates locally. before

generating the final map cross validation were used to

validate the accuracy of all interpolations .In the

phase, the value is estimated at each location with the

remaining data and after calculation between

estimated and actual value for all data points and with

computation some statistics are tested that how well

values at unknown locations are predicted by the fitted

model.

The Geostatistical Analysis includes all above

process that was done with Geostatistical Analysis

extension in ArcGIS (version 10) and finally CO

predictions map is generated.

3. RESULTS AND DISCUSSIONS

TEPA has 17 air pollution monitoring stations; among

these stations only 9 of them sense CO correctly and

with this few numbers of station pollution maps are

generated with poor resolution. Therefore MDAU was

designed 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 calibration

and improving accuracy as described above.

Afterwards the data with MDAU were collected in the

afternoon from Hemat Highway in Tehran. Finally, 90

sample points were collected via MDAU. Table 2

shows TEPA monitoring stations data and table 3 is

an example of MDAU measurements.

3.1. Exploratory Analysis of Data

Histogram and QQ plot are useful tools for checking

distribution and normality of data. Although normality

is not necessary for Kriging, but normality causes

better estimates. In Histogram depicted in the Fig.5

mean and median values are approximately the same

and SKEWNESS value is app. zero, these are normal

distribution signs. Figure 5 shows histogram for CO

samples. In Normal QQ plot as shows in Fig.6, the

closer points to the straight line (45 degree) follows

normal distribution.

If Tend exists in our data, it is deterministic

component and can be represented by mathematical

formula. Fig.7 shows Trend Analysis of CO data. It

shows while we rotate points, trends always exist as a

downside-up U-shape. This trend should be removed

in prediction step with second order polynomial.

3.2. Structural Analysis of Data

Figure 8 represents directional semivariogram and

model of CO. Semivariogram/covariance modelling

determines the best choice of a variogram model that

fit to sample points. In this study, stable model is the

most suitable with associated parameter value in table

4.

Also direction semivariogram is used to explore

the different direction influence on the data and define

acceptable direction variogram. At different locations,

the highest correlation of data points with their

neighbors occur when CO values are shifted west-

east. This shift is in direction and distance that

calculated cross-covariance is at its max value. After

specifying SEMIVARIOGRAM model fitted and

number of points neighbouring with different weight

for predicting values at unmeasured locations, cross

validation determinates that which model provides the

most accurate estimations and assesses validity of

prediction errors. As can be seen in Figure9 mean

error close to zero that is indicate unbiased prediction

and root-mean-square standardized error close to 1

that shows standard errors are accurate. Also at QQ

plot tab is seen that most points are close to straight

line and indicate that prediction errors are close to

normal distribution. Fig.10 provides summary of

model information that was used to create surface.


443

(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 an

Ordinary KRIGING model in Tehran between 3-4

PM. By means of prediction standard error surface

can be quantified the uncertainty for each location. As

is seen in figure 12, locations near static stations and

sample points have lower error and with increasing

distance from these points, we see more error values.

In other words, mobile measurements reduce

prediction errors and causes increasing in spatial

resolution.

3.4. Evaluation

Tehran Air Quality Control Company’s data is used to

validate our results. Fig.13 shows CO concentration

map generated from those data. As can be seen in

Fig.11 and Fig.13, they are similar to each other and

have similar results and both have more CO

concentrations in west of Tehran but fig.11 is more

detailed. So high spatial resolution maps can be

generated with minimum budget with the aid of

systems proposed in this research. As regards vehicles

is the main source of CO pollutant. It is expected that

high CO concentrations usually are in areas that traffic

is more intense. In the evening traffic follow are

heavy from west to east in Tehran. This factor causes

more CO and pollution in west of Tehran.

Hamraz et al.


444

Fig. 9: Prediction errors and normal QQ plot

Fig. 10: Summary of model information


445

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

Fig. 12: Prediction standard error surface

Hamraz et al.


446

4. CONCLUSION

Air pollution has many harmful effects, so it is

necessary to have pollution maps with higher

resolution to act appropriate actions based on enough

and complete information. High cost for establishing

static stations severely limits the number of these

stations and causes pollution map with poor spatial

resolution. A useful way to improve pollution

products and increase our observations and knowledge

is utilizing low cost portable gas sensor. In this study,

CO gas sensor was used and a Mobile Data

Acquisition Unit was designed to collect mobile data

on CO concentrations. These measurements were

collected with TEPA’s data in GIS environment and

with the help of Geostatistics Analysis in ARCGIS 10,

CO concentrations map were generated with higher

spatial resolution. High resolution map and more

detailed data can help regulations and people to make

better decision based on these data. Because of some

limitations, only one mobile data acquisition unit and

only one sensor was used, better results can be

obtained with more data acquisition unit through

different path. It is recommended to add bidirectional

GSM/GPRS links to mobile data acquisition unit for

having real-time data in order to have a web-based

client-server system with GIS capabilities can be

future researches.

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

ACKNOWLEDGEMENTS

This work was supported by an Inha University

research grant.

REFERENCES

Al-Ali A, Zualkernan I, Aloul F (2010). A mobile

GPRS-sensors array for air pollution

monitoring. Sensors Journal, IEEE, 10(10):

1666-1671.

Bohling, G. (2005). Introduction to geostatistics and

variogram analysis. Kansas geological survey,

20p.

Choi S, Kim N, Cha H, Ha R (2009). Micro sensor

node for air pollutant monitoring: Hardware

and software issues. Sensors, 9(10): 7970-7987.

Devarakonda S, Sevusu P, Liu H, Liu R, Iftode L,

Nath B (2013). Real-time air quality monitoring

through mobile sensing in metropolitan areas.

Paper presented at the Proceedings of the 2nd

ACM SIGKDD International Workshop on

Urban Computing.

García FM, González PV, Rodríguez FL (2008).

Geostatistical Analysis and Mapping of

Groundlevel Ozone in a Medium Sized Urban

Area. Paper presented at the Proceeding of

World Academy of Science, Engineering and

Technology.

Hasenfratz D, Saukh O, Sturzenegger S, Thiele L

(2012). Participatory air pollution monitoring

using smartphones. Mobile Sensing.

Honicky R, Brewer EA, Paulos E, White R (2008). N-

smarts: networked suite of mobile atmospheric

real-time sensors. Paper presented at the

Proceedings of the second ACM SIGCOMM

workshop on Networked systems for

developing regions.


447

Horálek J, Denby B, de Smet P, de Leeuw F, Kurfürst

P, Swart R, van Noije T (2007). Spatial

mapping of air quality for European scale

assessment: Citeseer.

Ikram J, Tahir A, Kazmi H, Khan Z, Javed R, Masood

U (2012). View: implementing low cost air

quality monitoring solution for urban areas.

Environmental Systems Research, 1(1): 1-8.

Isaaks EH, Srivastava RM (1989). An introduction to

applied geostatistics.

Kamionka M, Breuil P, Pijolat C (2006). Calibration

of a multivariate gas sensing device for

atmospheric pollution measurement. Sensors

and Actuators B: Chemical, 118(1): 323-327.

Li J, Australia G (2008). A review of spatial

interpolation methods for environmental

scientists (Vol. 137): Geoscience Australia

Canberra.

Li JJ, Faltings B, Saukh O, Hasenfratz D, Beutel J

(2012). Sensing the Air We Breathe-The

OpenSense Zurich Dataset. Paper presented at

the AAAI.

Mendez D, Perez AJ, Labrador MA, Marron JJ

(2011). P-sense: A participatory sensing system

for air pollution monitoring and control. Paper

presented at the Pervasive Computing and

Communications Workshops (PERCOM

Workshops), 2011 IEEE International

Conference on.

Pang W, Christakos G, Wang JF (2010). Comparative

spatiotemporal analysis of fine particulate

matter pollution. Environmetrics, 21(3‐4): 305-

317.

Pope R, Wu J (2013). Characterizing air pollution

patterns on multiple time scales in urban areas:

a landscape ecological approach. Urban

Ecosystems, 1-20.

Pummakarnchana O, Tripathi N, Dutta J (2005). Air

pollution monitoring and GIS modeling: a new

use of nanotechnology based solid state gas

sensors. Science and Technology of Advanced

Materials, 6(3): 251-255.

Song S (2008). A GIS Based Approach to Spatio-

Temporal Analysis of Urban Air Quality in

Chengdu Plain. The International Achieves of

the Photogrammetry, Remote Sensing and

Spatial Information Sciences, 37.

Tudose DS, Patrascu TA, Voinescu A, Tataroiu R,

Tapus N (2011). Mobile sensors in air pollution

measurement. Paper presented at the

Positioning Navigation and Communication

(WPNC), 2011 8th Workshop on.

Völgyesi P, Nádas A, Koutsoukos X, Lédeczi Á

(2008). Air quality monitoring with sensormap.

Paper presented at the Proceedings of the 7th

international conference on Information

processing in sensor networks.

Wang R, Zhang H, Zhang J (2012). Air pollution

interpolation analysis based on measured and

simulated sites in Nanjing. J. Environ. Sci. Eng.

B., 1: 1119-1123.

Hamraz et al.


448

Dr. Abolghasem Sadeghi-Niaraki has a PhD in Geospatial Information System (GIS)

Engineering and IT (Geo-informatics) from Inha University,South Korea. He obtained his B.S

and M.S degree from Dept. of GIS, Geodesy and Geomatics Eng. Faculty, K.N. Toosi of

Technology, 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, Motion

Sensors & Indoor Positioning, Smartphone based Remote Sensing and Ubiquitous Health.

Hassan Hamraz received the B.S degree in Geomatic Eng. from Department of GIS, Geodesy

and Geomatic Engineering Faculty, K.N.T Universtity of Technology, Tehran, Iran. He is now

GIS M.S student at Department of GIS, Geodesy and Geomatic Engineering Faculty, K.N.T

Universtity 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 Omati currently is studying for Master of Science degree at Department of Remote

Sensing, 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 and

Geomatic 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 and

Geomatic Engineering Faculty, K.N.T Universtity of Technology, Tehran, Iran. M.S student at

Department of photogrammetry, Geodesy and Geomatic Engineering Faculty, K.N.T University

of Technology, Tehran, Iran.

gis-based air pollution monitoring using static stations ... · pdf file... seeeduino stalker...

Documents