Air Quality Monitoring System Within Campus by Using

2019 14th Iberian Conference on Information Systems and Technologies (CISTI)

19 – 22 June 2019, Coimbra, Portugal

ISBN: 978-989-98434-9-3

Air Quality Monitoring System Within Campus by Using Wireless Sensor Networks

Roman A. Lara-Cueva1, Senior Member, IEEE, Patricia B. Meneses1, Marcelo D. Marquez1, Rodolfo X. Gordillo1, Member, IEEE, and Diego S. Benıtez2, Senior Member, IEEE

1Grupo de Investigacion en Sistemas Inteligentes (WiCOM-Energy) and Ad Hoc Networks Research Center (CIRAD) Departamento de Electrica, Electronica y Telecomunicaciones, Universidad de las Fuerzas Armadas ESPE

Campus Sangolquí, Casilla Postal 171-5-231B, Sangolquí -Ecuador 2Colegio de Ciencias e Ingenierıas “El Politecnico”,

Universidad San Francisco de Quito, Campus Cumbaya, Casilla Postal 17-1200-841, Quito, Ecuador email: {ralara, pbmeneses, mdmarquez, rxgordillo}@espe.edu.ec, [email protected]

Abstract — In this paper, we present a solution to monitor CO2 emissions produced by vehicles within a university campus. The system is implemented based on distributed sensor nodes by using ZigBee technology. Several sensors were placed around the main streets on campus in order to monitor CO2 concentrations. Data suggested that speed bumps generate increased levels of CO2, which are emitted by vehicles. We determine that the CO2 levels in our campus are 410 parts per million, which is within tolerable limits. The wireless system developed has been used as a tool to take actions in order to reduce the environmental pollution around our university campus, and to improve traffic by removing the speed bumps or to restrict the circulation of vehicles at peak hours when the system indicates that pollution levels are high.

Keywords - CO2 monitoring, IEEE 802.15.4, Waspmote, WSN, Xbee, Zigbee.

I. INTRODUCTION Since the industrial revolution, the concentration of CO2

in the atmosphere has increased significantly. Nowadays, the CO2 concentration has increased in the atmosphere at least a 30% than in 1975, leading to increase greenhouse gases that contribute to global warming. The main cause of greenhouse is CO2 contamination, which contributes about 64% to this phenomena [1]. Currently, we are aware about climate change around the world, however we often think that pollution does not affect our daily activities, and we indirectly perceive its effects and therefore we are unable to generate effective changes to prevent pollution. It is estimated that in the global context around 24,000 million tonnes of CO2 are emitted per year, in first place by the countries of the Organization for Economic Cooperation and Development (OECD) with 52%, followed by Russia with 14%, and China with 13%. United States CO2 emissions are about 5,500 million tonnes, representing almost a quarter of the global total. Latin America including Mexico, with 360 million tons represents about 1% of global emissions [2].

According to the World Health Organization (WHO), 2.7 million people worldwide die each year due to health problems related with pollution by CO2 emissions [3]. Previous works in the literature are proof of the increasing human concern to try to change the effects caused by CO2 emissions indoor [4-6] or outdoor [7-8]. The gas in larger quantities that has been monitored is CO, for example in [9] a mathematical model for

monitoring CO in indoor scenarios is presented. In [10], an evolution in the treatment of the data produced by using a Zigbee network is showed, however the main problem and limitation of such implementation is the equipment used since the modules had a proprietary code. In [6], a study to determine the Vehicle Ad- hoc Networks (VANET) and to inform contaminated routes to vehicles for not running on these routes is showed. As we can see, in the previous cases, monitoring of CO2 related to vehicles emissions has been already previously conducted.

In this sense, we propose to use open source equipment; in this way we can use multiple platforms and development environments for its implementation according to the requirements. The use of Wireless Sensor Networks (WSN) allows an easy deployment of nodes with the Zigbee communication protocol in order to send and receive data. The most common health problems related to CO2 exposure are: asthma, allergy, stress, among others [11]. In such sense, the aim of this work was to determine the level of pollution generated by vehicles circulating around campus, and to report these levels and the times where the CO2 emissions increase. We divided our study in two stages, the former in order to acquire information about the air pollutants, specifically CO2 gas, a real-time wireless air pollution monitoring system was designed and developed, and the latter the analysis of data sensed.

This paper is organized as follows. Section II presents the description of the wireless air-pollution monitoring system developed in this work. Section III shows the experimentation and results obtained. Finally, section IV highlights our main conclusion and future work.

II. REAL-TIME WIRELESS AIR-POLLUTION SYSTEM In this section, we describe the hardware and software used

in order to develop the real-time monitoring system.

A. Wireless Communication Module

For the development and implementation of the system, we used XBee and Waspmote modules to monitor air quality. The Waspmote modules are manufactured by Spanish company Libelium [12], their main features are: ATmega1281 Microcontroller with a frequency of 14.7456 MHz, a 4KB EEPROM, and a 32kHz real-time clock. These modules have a

The authors would like to thank the financial support of Universidad de las Fuerzas - ESPE in the development of this work, under Project grants 2013-PIT-014 and 2016-EXT-038.



ISBN: 978-989-98434-9-3

Fig. 1. Waspmote Top View.

Fig. 2. XBee module.

central chip that initializes all variables in the environment and thus enable the necessary ports to power sensors and sensor boards, one of the biggest advantages of these modules is that they support different communication protocols, Fig. 1 shows a photo of the current Waspmote module, the battery consumption levels are low (ON: 15mA, Sleep: 55μA), and it is possible to connect a solar panel to recharge the battery.

The American company Digi manufactures the XBee modules. These devices have embedded RF modules with different wireless protocols, in outdoor environments they can reach up to 1.6 km with line of sight. For our System, we use XBee Pro series 2 modules, which is depicted in Fig. 2, them use the ZigBee protocol for communicate with each other, and a firmware called DigiMesh which allows mesh networking, this ensures that all modules are communicating even if one fails, the other nodes supply this missing [13].

B. Gas Sensor Board

The gas board used in this research was the Waspmote Gases 2.0, which is designed to monitor environmental parameters such as temperature, humidity, atmospheric pressure and 14 types of gases. Fig. 3 shows the board, w h i c h have multiple sockets where the various sensors were placed.

In our case, we used the socket 1A with a CO2 sensor model TGS4161 [14], which is calibrated from factory. This sensor provides a voltage proportional to the concentration of atmospheric CO2 is given in parts per million (ppm), the measurement range is from 350 to 10000 50 ppm.

Fig. 3. Gas Sensor Board

Fig. 4. Block diagram of the data acquisition system.

According to [15], the sensor consists of two electrodes, when exists a change in the electromotive force (EMF), it can measure the concentration of CO2 gas in the atmosphere.

We identified the relationship to transform from voltage to ppm within the sensors by adding the following instructions to the computer program:

void loop(){ co2Val = SensorGasv2.

readValue(SENS_CO2; sensor=(0.220-co2Val); value_co2=pow(10,(

(sensor+158.631)/62.877)); Utils.float2String(

value_co2, C02_str,3); }

C. Data Acquisition

The Waspmote modules collect information from the CO2 sensor in voltage and with proper scaling it was converted to ppm concentration, this information was then sent via Zigbee to the coordinator module, which was placed with a computer, the serial port was used for reading data. A block diagram of the data acquisition system is showed in Fig. 4, for data acquisition and storage we used Matlab .



ISBN: 978-989-98434-9-3

Fig. 5. Example of an area where the system was tested.

The data collection was set for 10 minutes, during periods where exist more traffic on the university campus. Matrices for each sensor were constructed, indicating the number of times that the port was read, the timestamps of the readings, the number of readings obtained at that time, and the CO2 readings in ppm. Finally, the collected data was stored in multiple files for further analysis.

III. EXPERIMENTATION AND RESULTS A. Sensors Deployment

The area considered for the study was defined by the existence of speed bumps to limit speed traffic around campus, thus it was assumed that exist more gas combustion close this areas due to the vehicle acceleration required to pass over the bumps. Fig. 5 shows an example of the area in which the system was tested.

After several test, we determined that the best location to place the modes were found around the trees surrounding the speed bumps, with a maximum distance for the nodes of 10 m among each other, as illustrated in Fig. 6. With this distance we achieved most readings, however for future works is proposed to used hops to enable the communication with more sensors and to use a gateway. In addition, the street has an inclination of 20 degrees, where vehicles moved on in an upward direction from node H to node A.

We define a star topology, where all terminal nodes measure the CO2 concentration; therefore all nodes send the collected information to the coordinator without jumping. By using this type of network, traffic problems were not considered, since 30 seconds were enough to detect significant changes in CO2 concentration [16].

B. Data Analysis

Data were collected during August 2016, the main features of this month were that the weather was dry, the days were sunny, and the average temperature range was from 14 ºC to 28 ºC. During this month, sensors were set to take data every 500 ms and stored the data in a matrix, around 4 readings each sensor were obtained every 500 ms for the 10 minutes of data collection.

Fig. 6. Sensors deployment locations.

Fig. 7. CO2 concentration readings (in ppm) per node.

At the end of the test, the data were extracted and plotted. Examples of the signals obtained are showed in Figs. 7 and 8.

In this trajectory, vehicles have to decelerate, even stop, when arrive to the speed bump, for that reason nodes F, G, and H detect values around 310 ppm, then the vehicle start to accelerate and nodes D and E still remain presented values around 310 ppm, but node C, which was located 25 m from the speed bump, presents the largest value around 410 ppm as depicted in Fig. 8, the main reason is vehicles have to accelerate in order to reach the near maximum speed for our campus of 50 km per hour and then maintain that speed. In this sense, sensor C detected the burning of fuel required by the cars engine to reach the maximum speed allowed, but later the emissions decrease as the cars keep the speed and move away from the bump.

Finally, by analyzing data from Fig. 8, it is possible to conclude that pollution may be reduced in 60 ppm by removing the speed bump on the road tested, since sensors F, G, and H showed concentrations of CO2 that were within tolerable limits as we move away from the speed bump.

In our case, when vehicles enter to the campus, they must travel a distance of about 2 km per day, if we take into account that a vehicles consume on average 32 km/gal of gas [17], its possible to determine that the average consumption is:



ISBN: 978-989-98434-9-3

Fig. 8. Example of CO2 sensors readings (in ppm) versus time.

32 × 13.8 = 8.42 . If we analyze this consumption in one year we have: 730 × 18.42 = 86.7 ,

This is equivalent to 0.23 ppm of CO2 per year.

Therefore, in average a vehicle that travel 2 km within the campus distance emits 0.23 ppm of CO2 per year; thus it can be determined that a difference of 60 ppm of CO2 contamination is considerably.

IV. CONCLUSION

The use of WSN in this research allows no cables and electrical connections near the nodes, thus CO2 could be monitored in an environment without any constraint; the use of Zigbee technology also allows the sensors to enter into sleep mode to save batteries and just collect data from time to time, since the change in CO2 concentration is only observed after a certain time and therefore it is not necessary for the system to keep values all the time.

By using the proposed system, it was demonstrated that the use of speed bumps to limit speed traffic around campus increase CO2 emissions, since the car engines require more acceleration to pass over the bumps. Although, the variation of 60 ppm detected in this work is minimal, as the number of speed bumps increase around campus, the related air pollution also increases proportionally; therefore other solutions for reducing vehicle speeds should be implemented to reduce the CO2 emissions.

As future work, we plan to implement a similar application for Smart Cities, in which can be obtained information about

other parameters related with activities within the city. Similarly, other sensors and systems for vehicular mobility could also be add to the air quality monitoring system developed in this work to reduce CO2 emissions.

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Air Quality Monitoring System Within Campus by Using