0123)4567&81 9:;?1@9AB@C1 1users.isr.ist.utl.pt/~jpg/proj/urbisnet/pubs/techrep/... · 2013-11-24 · 5 / 49 2.1. Description of the acquired sensors and their components

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Table of contents

1. PROPOSED OBJECTIVES ................................................................................................................. 4

2. DESCRIPTION OF WHAT WAS DONE UNTIL AUGUST 2013 ............................................................. 4

2.1. DESCRIPTION OF THE ACQUIRED SENSORS AND THEIR COMPONENTS ........................................................... 5 2.2. DESCRIPTION OF THE TESTS CONDUCTED UNDER LABORATORY CONDITIONS ................................................. 7 2.3. DESCRIPTION OF FIELD TRIALS ........................................................................................................... 10 2.4. DESCRIPTION OF THE SOFTWARE DEVELOP FOR CREATING MAPS WITH THE MEASUREMENTS MADE IN THE FIELD 10 2.5. DESCRIPTION ABOUT THE DATA COLLECTION IN PUBLIC AND PRIVATE ORGANIZATIONS .................................. 11

3. RESULTS OBTAINED UNTIL AUGUST 2013 .................................................................................... 11

3.1. RESULTS OF LABORATORY TESTS FOR M1 WITH CO_1 OR NO2_1 .......................................................... 12 3.2. RESULTS OF LABORATORY TESTS FOR (M2/CO3), (M4/CO4), (M3/NO2_2) AND (M5/NO2_5) ............. 21 3.3. RESULTS OF THE FIELD TRIALS USING A BICYCLE AND M1 WITH CO1/NO2_1 ............................................ 30 3.4. RESULTS OF THE FIELD TRIALS FOR THE SENSORS ACQUIRED IN 2013 ........................................................ 41 3.5. THE DATA OBTAINED FROM THE PUBLIC AND PRIVATE ORGANIZATIONS ..................................................... 42

4. DISCUSSION OF THE RESULTS AND CONCLUSIONS ...................................................................... 44

5. UNFINISHED TASKS ...................................................................................................................... 46

6. POSSIBLE INTERESTED PARTY AND HIS EXPECTATIONS ............................................................... 48

7. REFERENCES ................................................................................................................................ 49

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Acknowledgments

I would like to express my gratitude to my supervisor, Prof. João Pedro Gomes, for the trust that he have placed in me, also whose expertise, understanding and patience, added greatly to my experience during the time I spent in ISR.

Special thanks to Eng. Tânia M. Frinha and Eng. Célia R. Soares from ISQ, whose presences created one of the best working environments inside a lab that I have experience so far, and also for their guidance during the lab tests.

I would also like to thank João Carvalho for his first’s lessons in programming with Matlab, which gave the necessary push to continue on my own, and Fábio Gameiro for his knowledge and support.

In conclusion, I extend also my tanks to Marcelina Almeida and Dr José Domingos Pereira from Antral, Eng. Paulo Simões from Lisbon City Hall, architect Isabel Seabra from IMTT and Hannah from Aeroqual, for their time and support.

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1. Proposed objectives Development and integration of a set of sensors for a mobile/portable air quality

station Development of the prototype hardware for a mobile/portable air quality station and

mechanical/electrical integration of these stations in vehicles. Develop auxiliary hardware support for remote programming of mobile and gateway

nodes Planning, assistance and reporting in field testing; data archival and retrieval

2. Description of what was done until August 2013 Under controlled laboratory conditions, tests were carried out to a previously acquired set of gas sensors, in order to assess their reliability for the proposed objectives and obtain their calibration curves.

A comparison was made in the performance of these acquired sensors with other sensors on the market, of which two of the sensors compared were the same as those that were installed on the stations of air quality in Lisbon at that time (1). The comparisons were made under controlled laboratory conditions in ISQ (Instituto de Soldadura e Qualidade) (2). Other comparisons were made in the field, putting the sensors near the air quality stations in Lisbon.

To simulate the operation mode of our sensors during the movement across the streets of Lisbon, a bicycle was used as vehicle to transport them. The chosen circuit included five fixed stations, with a total distance of about 40 km traveled per session. During these sessions, it was possible to obtain data on the amount of CO (carbon monoxide) and NO2 (Nitrogen Dioxide) measured by our sensors.

Based on the data obtained from traveling through the streets of Lisbon, a computer program was developed in order to generate maps containing the values obtained for each sensor at a specific point in space and time, as well as other relevant information.

Further tests were conducted in laboratory conditions in order to try to understand issues with our sensors. Throughout this period, communication lines were established with the supplier of the sensors (Aeroqual) (3), in order to better understand the results, and also perform new tests that the company asked for.

During the course of the previous works, it was also provided a support in gathering information within public and private organizations that possessed data on the direct impact that mobility has on air quality in Lisbon.

Meetings were held with a potential future client, in order to obtain information about what they hope to get from the final product.

As result of the information obtained by the test made with our sensors as well as the information provided by Aeroqual (3), a decision was made to obtain upgraded sensors from them. Again, in order to assess their reliability for the proposed objectives and obtain their calibration curves, the same tests that were made to previous sensors were made for the new ones

Meanwhile, a communication protocol was initiated between Aeroqual (3) sensors and a GSM/GPS Module from “Round Solutions” (4). The plan for an enclosure of all apparatus, sensors and communication/memory modules, were initiated as well.

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2.1. Description of the acquired sensors and their components In a first stage it was acquired from Aeroqual (3) three sensors to measure the concentrations of O3 (ozone), CO and NO2 in the atmosphere, as well as one handheld monitor (AQL‐S505) that makes the communication/interpretation of the sensors. It also was acquired a probe that measures the temperature and relative humidity. In Figure 2.1 it is possible to see the acquired monitor mounted with a gas sensor and with the temperature & relative humidity probe.

Figure 2.1 ‐ ‐AQL‐S505 monitor mounted with a gas sensor and temperature & relative humidity probe

The handheld monitor acquired in the first stage came with a rechargeable battery (Ni‐MH) and internal memory which allows it to store values acquired during sessions of measurements. The time scale of the data acquisition only takes a minimum of 1 minute between each measurement.

In the second stage and based on the results of the first stage, an upgraded and new equipment was purchased from Aeroqual (3) again. The equipment was composed of 4 upgraded monitors, 9 sensors, 2 water and dust proof enclosures and cables.

In order to resolve some problems, this new monitors came with a new firmware/hardware that among other things, allows it to incorporate all the T/RH (temperature & relative humidity) probe without resorting to AQL‐S505 monitors series (that were a special group in the AQL‐S500 series and the only ones that allowed T/RH probes), meaning that from then on, all of that series were named AQL‐S500. This news monitors came with improved batteries (Lithium Polymer), since the previous ones required 12 hours to recharge and run for approximately 4‐6 hours when fully charged, these new ones only need 3 hours to recharge and can run for approximately 8 hours if fully charged, and also had a better life expectancy.

The water and dust proof enclosure was acquired not only for a high level of protection from the environment, but also as an effort to resolve some inconsistent results obtained with the first handheld monitor (AQL‐S505) which at that time were believed to be linked to the turbulence of the surrounding environment. The appearance as well as the dimensions are represented in Figure 2.2, with a slight difference from the ones that had been used, which is that comes with two further entries in the lower part of the enclosure alongside the power supply entry. This new entries were for the T/RH probe and commutations cables.

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Figure 2.2 – Appearance and dimensions of the water and dust proof enclosure with a handheld monitor

(mounted with a gas sensor) inside – courtesy of AEROQUAL (3). All sensor heads obtained are described in Table 2.1, being represented there the manufacture specifications.

Gas sensor Carbon monoxide (CO)

Nitrogen Dioxide (NO2)

Ozone (O3)

Quantity 5 5 2

Type Gas Sensitive Electrochemical

Gas Sensitive Electrochemical

Gas Sensitive Semiconductor

Range (ppm) 0‐25 0‐1 0‐0.5 and 0‐0.15

Minimum detection Limit (ppm) 0.02 0.002 0.001

Resolution (ppm) 0.01 0.001 0.001

Operational Range: Temperature (ºC) ‐10 to 50 ‐10 to 50 ‐5 to 40

Operational Range: RH (%) 10 to 90 10 to 90 5 to 95

Table 2.1 – Aeroqual (3) specifications for all acquired sensors heads The O3 sensors were not tested in laboratorial conditions nor in the field, instead it were verified only if they work, by connecting then to the monitor and see if the reacted as expected.

In order to better follow witch sensors and monitors were being used at the time of the test, a table (Table 2.2) was created that shows the names given to each monitor and sensors heads acquired.

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Type Ref. given Acquisition date Description

CO Sensor CO_1 01‐04‐2012 To use with M1

NO2 Sensor NO2_1 01‐04‐2012 To use with M1

O3 sensor O3_1 01‐04‐2012 To use with M1

Monitor (S505) M1 01‐04‐2012 First handheld monitor acquired (AQL‐S505)





O3 sensor O3_2 26‐06‐2013 To use with M2 or M3

Monitor (S500) M2 26‐06‐2013 Handheld monitor AQL‐S500 ‐ to freely use

Monitor (S500) M3 26‐06‐2013 Handheld monitor AQL‐S500 ‐ to freely use





Monitor (S500) M4 26‐06‐2013 AQL‐S500 monitor – to use inside enclosure

Monitor (S500) M5 26‐06‐2013 AQL‐S500 monitor – to use inside enclosure

Table 2.2 – References given to sensors heads and monitors

2.2. Description of the tests conducted under laboratory conditions The tests that will be described below were all made in partnership with ISQ (2) and were performed in their metrology laboratory gas installations. These tests basically consisted of placing the sensor (sometimes either alone or with other reference sensor) in a sealed volume and inject known concentrations of the gas to which the sensor is sensitive, and collect data for different concentrations of injected gas, also taking into account the response time of the sensor during the all process.

For the control of the concentrations of injected gas it was used a SONIMIX 2106B programmer gas divider (Figure 2.1), with nitrogen and air as the background gas used for creating the concentrations of CO and NO2 desired.

Figure 2.3 – SONIMIX 20106B Programmer gas divider

The description of the tests performed will be presented in chronological order, from oldest to newest:

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1) Was carried out a series of tests to ascertain the sensors (CO_1 and NO2_1) response across its operating range, taking into account the maximum and minimum values that the device can measure. To obtain this, were injected gas concentrations near the minimum that the sensors can handle and then gradually increasing the concentrations up to the maximum allowed by the device. To obtain a volume isolated from the ambient gases, it was used a sealed Box, as is shown in Figure 2.4, with the sensor inside. The timescale for the change between concentrations depended on the stabilization of the values measured by the sensor at the time during that specific session.

Figure 2.4 – Box used to isolate the sensor from the ambient gases

2) Based on the results of the series of tests described above, we proceeded to another group of tests that were intended to check the consistency of the values obtained (see chapter 3) to the sensors CO_1 and NO2_1. One of the procedures chosen was to pick up two different gas concentration values and switch between these values over a specific time to check the consistency of the sensor when brought to the same concentration value. In order to obtain a lower volume of gas, thus increasing the response time of the sensor, a sealed bag was used, as shown in Figure 2.5. For concentrations of CO was possible to use the sensors “GrayWolf IQ‐610” (5) and “BW gasalertmicroclip XT” (6) as comparison. During these tests it was also observed the reaction of the sensor when motion (repetitive small movements) were applied.

Figure 2.5 – Sealed bag with our sensor and a “Gasalertmicroclip XT” ❶ sensor inside; upstream of the gas flow

is the Gray Wolf ❷ sensor. 3) The third group of tests focused on comparing the performance of the sensors CO_1 and

NO2_1 with those that were installed on the fixed stations of air quality in Lisbon at that time (“Horiba AP MA‐360” and “Horiba AP NA‐360”) (7). For this purpose, and similarly to that described in the last paragraph it was also used a bag sealed for ours sensors during

Gas entry

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the measurements. Due to the large size of the Horiba sensors, the gas was injected directly into the Horiba sensors, using for that a binary gas divider to divert the gas flow into the Horiba sensors.

4) As a result of the observed problems in previous tests, related to the movement, and also because of conversations with Aeroqual (3), came the need to seek answers to these new problems. Therefore, these tests were made accordantly Aeroqual specifications, so the tubes that carried the gas were change for metallic ones and the isolated box was used again (Figure 2.6). Although, this time the box was filled with only one concentration of CO and waited for the time needed for the sensor to stabilize and then applied motion to the whole box.

Figure 2.6 – The use of metallic tubes and the isolation box, as Aeroqual request it

5) This final group of tests were made to the new equipment, obtain in the second stage and explain in chapter 2.1. In the first part of the tests, were placed in the box two sensors that measured the same gas (Figure 2.7), thereby compare the performance of each sensor for the same volume of gas, but this time the maximums chosen were a reflexing of the gas legal limits in the city (For CO=9ppm and NO2=0.3ppm). The second part of the tests were an attempt to directly inject gas into the sensor (Figure 2.7), thereby eliminating the time it takes the gas volume to fill the box or the bag, thus evaluating the responsiveness of the sensor as well as the ability of the sensor absorbing the gas by its fan. And finally, in the last part, a sealed bag was used to compare the results with the directly gas injection, the sealed bag was prepared in the same way that is represented in Figure 2.5.

Figure 2.7 – At the right are 2 sensors inside the isolation box. In the left is the representation of the assembly

taken to inject gas directly at the sensor

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2.3. Description of field trials The tests performed in the field with the sensors had two objectives, the first one was to compare the performance of our sensors with the ones in fixed stations, and second one was to test our sensors during movement, trough the roads of Lisbon. To the second objective it was used a backpack containing a sensor, placed in a net to permit air circulation, and a GPS (Garmin GPSMAP 62s) (8) attached near the sensor as shown in figure 3.

Figure 2.8 – Backpack with the sensor ❷ and GPS❶.

To simulate the operation of the sensors through the streets of Lisbon and acquire data, a bicycle was used to go through a circuit that includes 5 fixed stations as "checkpoints", with short stops at each station to ensure a few readings close to each station.

To directly compare the performance of ours sensors with the stations obtaining an average trough at a least an hour near a station, all sensors were placed near a station and left in the same place without moving it. The station chosen was the Entrecampos station as show in Figure 2.9.

Figure 2.9 –Sensors near Entrecampos air quality station in Lisbon.

2.4. Description of the software develop for creating maps with the measurements made in the field

In order to be able to visualize spatially the measurements made on the streets of Lisbon, as well as relate that measurements with the fixed stations, it was created in Matlab a software that allows us to use these measurements and represent then in Google Earth. This program allows visualization either by points or by areas of the regions measured, and also represents the location of the fix stations with the corresponding measurements at the time the sensors

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station of air quality

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were near, allowing the definition of a circle of influence to each fixed station. The areas in the software are created by grouping spot measurements of equal value.

These data on Google earth are represented by "Layers", that can be enabled or disabled for better visualization and understanding of the results. The obtained values can be represented by a color scale which can be set to the maximum value obtained in each daily session, whose maximum value is the maximum of the set of ours sensors and/or of the fixed stations measurements. On the other hand, there is always the possibility of applying this color scale to the predefined legal limits or even to other values of choice.

Currently the color scale is composed of 5 colors, in order to represent the legal limits, ie each color corresponds to the legal limits, and the scale is as follows: "excellent" (dark green), "good" (green), "medium" (yellow), "weak" (orange) and "bad" (red). The black color is also used when the values are not valid or do not exist.

Starting from the measurements made over a daily session, the "Layers" are created taking into account the time it lasted each session. So the layers can show the entire session of each sensor/station, or represent only hour by hour during that daily session.

The need to rearrange the values by hours during the day comes to facilitate the comparison between the values of the fixed stations and ours sensors, this is because the fix stations are represented by an average of each hour of the day.

The “KML toolbox” was used in our software, it is class‐based toolbox that allows us to create many different plots in Google Earth, by automatically creating the required xml‐based KML, and was developed by Rafael Oliveira (9).

2.5. Description about the data collection in public and private organizations

Within the framework of the project related to the collection of parameters on the impact of mobility on air quality in Lisbon, some contacts were made with organizations that might have data on public transport circulating in Lisbon, specifically linked to taxis and/or buses. In the field concerning the taxis, was first established contact with the largest taxis trade union in the country, Called Antral (10).

From Antral, the most relevant data that was expect to be found were the ones related to the amount of kilometers that each taxi travels on average during a day in Lisbon.

In parallel, meetings were schedule with the Lisbon City Hall (11) so as to achieve the exact amount of licensed taxis operated in Lisbon at the time, and retrieve their identifications.

Once the list with the identifications were retrieved, new meetings were schedule, but this time with IMTT (Institute for Mobility and Land Transport) (12) in order to finally obtain the technical data of each taxi, and them calculate the real impact on air quality.

On the other hand, and in the field concerning the buses, there was only one company (Public) that operates in Lisbon, and that company is Carris (13). Obtaining data from Carris was a facilitated process, because the company every year emits the “annual sustainability report”, and from it is possible to get all the information needed regarding the impact of buses on air quality.

3. Results obtained until August 2013 In this chapter it will be shown the results regarding the tests made under laboratory conditions, the results of the field trials and the data collected from public and private organizations.

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The results of the laboratory tests described in chapter 2.2 will be presented in a form of graphs, in which they are identified with the paragraph number from that chapter. It should be noted, however, that ISQ Lab is an external laboratory to IST‐ISR, and has its own autonomy and also commercial commitments, therefore, test schedules and freedom to take certain decisions, especially the assembly of experimental apparatus, were limited to the internal procedures. Therefore, not all the tests were conduct in the expected way.

Some results of the field trails it will be shown has a screen capture of Google Earth, with some valid measurements made in the field with the bicycle as well the measurements of the fix stations at the time. On the other hand, the results from the sensors, when they were left static in a place near the quality air station from Entrecampos, it will be presented as a graph too. The results from the fix stations were obtain in Qualar website (1), and to confirm the results just consult the dates on the tables and graphs were the results are being presented.

The data that was collected from public and private organizations will be shown as well as the results of some calculations made.

3.1. Results of laboratory tests for M1 with CO_1 or NO2_1 To obtain the concentrations of CO and NO2 required for this tests it was used only Nitrogen as a neutral gas.

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Graph 3.1 –The complete results of the tests made to M1/CO_1 with different concentrations of CO (2012‐04‐27)

Results from what has been described in the paragraph 1 of chapter 2.2

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10:19 10:48 11:16 11:45 12:14 12:43 13:12 13:40 14:09 14:38 15:07 15:36 16:04 16:33

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Measurements of CO for M1/CO_1 ‐ Complete Session

CO near zero PPM (room air: near 45% of RH and near 23,8C of temperature)

CO = 0 PPM (box: near 11% of RH and near 24C of temperature)

CO = 2,1 PPM (box: near 2% of RH and near 23,9C of temperature)



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Graph 3.2 ‐ The complete results of the tests made to M1/NO2_1 with different concentrations of NO2 (2012‐05‐16)


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Measurements of NO2 for M1/NO2_1 ‐ Complete Session

Warming phase (Room: near 50% of RH and near 22,2C of temperature)NO2 = 0,0061 PPM (box: near 15% of RH and near 23,9C of temperature)NO2 = 0,0111 PPM (box: near 6% of RH and near 25C of temperature)NO2 = 0,0230 PPM (box: near 4% of RH and near 25,5C of temperature)NO2 = 0,0541 PPM (box: near 3% of RH and near 26,1C of temperature)NO2 = 0,0877 PPM (box: near 2% of RH and near 26,5C of temperature)NO2 = 0,1218 PPM (box: near 1% of RH and near 26,8C of temperature)NO2 = 0,1598 PPM (box: near 1% of RH and near 27,4C of temperature)NO2 = 0,2005 PPM (box: near 1% of RH and near 27,8C of temperature)NO2 = 0,3000 PPM (box: near 0% of RH and near 28,4C of temperature)NO2 = 0,5022 PPM (box: near 0% of RH and near 28,9C of temperature)NO2 = 0,9034 PPM (box: near 0% of RH and near 29,4C of temperature)NO2 = 0 PPM (box: near 0% of RH and near 29,6C of temperature)Moving the sensor to a bag (near 29,7C of temperature)NO2 = 0 PPM (bag: near 0% of RH and near 29,7C of temperature)NO2 = 0,5022 PPM (bag: near 0% of RH and near 30,7C of temperature)

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NO2 = 0,5022 PPM (box: near 0% of RH and near 28,9C of temperature)

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NO2 = 0,5022 PPM (bag: near 0% of RH and near 30,7C of temperature)

Graph 3.3 – Comparison of two sections of Graph 3.2 for the same concentrations of NO2 in different containers.(isolation box and bag)

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Graph 3.4 – Comparisons between our sensor (AQL‐S505 = M1/CO_1) and the others handheld sensors (GrayWolf IQ‐610 and gasalertmicroclip) (2012‐06‐18).


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Comparisons with different sensors for the same concentrations of CO

AQL‐S505 sensor with CO = 7,7 PPM AQL‐S505 sensor with CO = 2,1 PPMMovement ‐> AQL‐S500 sensor with CO = 7,7 PPM GrayWolf IQ‐610 sensor with CO = 7,7 PPMGrayWolf IQ‐610 sensor with CO = 2,1 PPM gasalertmicroclip XT sensor with CO = 7,7 PPMgasalertmicroclip XT sensor with CO = 2,1 PPM

Movement applied to the sensor

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Graph 3.5 – The results of the tests made to M1/NO2_1 with 3 different concentrations of NO2 (2012‐06‐18)


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Measurements of NO2 for M1/NO2_1

NO2 = 0,2005 PPM (room: near 56% of RH and near 23,2ºCof temperature)

NO2 = 0 PPM (room: with near 54% of RH and near 23,4ºCof temperature)

NO2 = 0,1045 PPM (room: near 56% of RH and near 23,9ºCof temperature)

Movement: NO2 = 0,1045 PPM (room: near 56% of RH andnear 24,2ºC of temperature)


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Graph 3.6 ‐ Comparison between our sensor (AQL‐S505 = M1/CO_1) and Horiba APNA‐360 sensor (2012‐06‐27).


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Comparisons with different sensors for the same concentrations of CO ‐ Complete Session

AQL‐S505 sensor with CO = 0,1097 PPM Horiba APNA‐360 sensor with CO = 0,1097 PPM AQL‐S505 sensor with CO = 0 PPMHoriba APNA‐360 sensor with CO = 0 PPM AQL‐S505 sensor with CO = 1,0071 PPM Horiba APNA‐360 sensor with CO = 1,0071 PPMAQL‐S505 sensor with CO = 2,0037 PPM Horiba APNA‐360 sensor with CO = 2,0037 PPM AQL‐S505 sensor with CO = 3,0002 PPMHoriba APNA‐360 sensor with CO = 3,0002 PPM AQL‐S505 sensor with CO = 4,0035 PPM Horiba APNA‐360 sensor with CO = 4,0035 PPMAQL‐S505 sensor with CO = 5,0027 PPM Horiba APNA‐360 sensor with CO = 5,0027 PPM AQL‐S505 sensor with CO = 6,0055 PPMHoriba APNA‐360 sensor with CO = 6,0055 PPM AQL‐S505 sensor with CO = 7,0012 PPM Horiba APNA‐360 sensor with CO = 7,0012 PPMAQL‐S505 sensor with CO = 8,0057 PPM Horiba APNA‐360 sensor with CO = 8,0057 PPM AQL‐S505 sensor with CO = 9,0058 PPMHoriba APNA‐360 sensor with CO = 9,0058 PPM AQL‐S505 sensor with CO = 0,1097 PPM Horiba APNA‐360 sensor with CO = 0,1097 PPM

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Graph 3.7 ‐ Comparison between our sensor (AQL‐S505 = M1/NO2_1) and Horiba APMA‐360 sensor (2012‐06‐27).


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Comparisons with different sensors for the same concentrations of NO2 ‐ Complete Session

AQL‐S505 sensor with NO2 = 0 PPM Horiba APMA‐360 sensor with NO2 = 0 PPM AQL‐S505 sensor with NO2 = 0,0061 PPMHoriba APMA‐360 sensor with NO2 = 0,0061 PPM AQL‐S505 sensor with NO2 = 0,0111 PPM Horiba APMA‐360 sensor with NO2 = 0,0111 PPMAQL‐S505 sensor with NO2 = 0,0541 PPM Horiba APMA‐360 sensor with NO2 = 0,0541 PPM AQL‐S505 sensor with NO2 = 0,0988 PPMHoriba APMA‐360 sensor with NO2 = 0,0988 PPM AQL‐S505 sensor with NO2 = 0,1479 PPM Horiba APMA‐360 sensor with NO2 = 0,1479 PPMAQL‐S505 sensor with NO2 = 0,2005 PPM Horiba APMA‐360 sensor with NO2 = 0,2005 PPM AQL‐S505 sensor with NO2 = 0,2492 PPMHoriba APMA‐360 sensor with NO2 = 0,2492 PPM AQL‐S505 sensor with NO2 = 0,3 PPM Horiba APMA‐360 sensor with NO2 = 0,3 PPMAQL‐S505 sensor with NO2 = 0,0111 PPM Horiba APMA‐360 sensor with NO2 = 0,0111 PPM

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Graph 3.8 ‐ The complete results of the tests made to M1/CO_1 with the same concentration of CO inside the box (2012‐07‐20)


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Measurements of CO for M1/CO_1

AQL‐S505 sensor during the period of placement inside the gas box (room with near 45% of RH and near 23,5ºC of temperature)AQL‐S505 sensor with CO = 4 PPM (controlled gas box with the room with near 7% of RH and near 24,1ºC of temperature)Movement to the box ‐> AQL‐S505 sensor with CO = 4 PPM (controlled gas box with the room with near 3% of RH and near 24ºC of temperature)


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3.2. Results of laboratory tests for (M2/CO3), (M4/CO4), (M3/NO2_2) and (M5/NO2_5)

This final group of tests were made to the new equipment, obtain in the second stage and explain in chapter 2.1. In the first part, were placed in the box two sensors that measured the same gas (Figure 2.7), thereby comparing the performance of each sensor for the same volume of gas, but this time the maximums chosen were a reflexing of the gas legal limits in the city (CO=9ppm and NO2=0.3ppm). The second part of the tests were an attempt to directly inject gas into the sensor (Figure 2.7), thereby eliminating the time it takes the gas volume to fill the box or the bag, thus evaluating the responsiveness of the sensor as well as the ability of the sensor to absorbing the gas by its fan (but the based gas used were air). And finally, in the last part a sealed bag was used to compare the results with the injection of the gas directly, the sealed bag was prepared in same way that is represented in Figure 2.5.

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Graph 3.9 ‐ Comparison between M4/CO_4 and M2/CO_3 inside the same box (2013‐07‐02).


0

2

4

6

8

10

12

11:20 11:34 11:49 12:03 12:18 12:32 12:46 13:01 13:15 13:30Time 24h (hh:mm)

Measurements of CO for M2/CO_3 Near the gas output

Putting in the Box (Averages: RH=47,6% and temperature= 24,3º) CO=0 PPM (Averages: RH=43,4% and temperature= 24,8º) CO=0,5122 PPM (Averages: RH=25% and temperature= 25,1º)CO=1,0461 PPM (Averages: RH=15,6% and temperature= 25,4º) CO=1,9512 PPM (Averages: RH=11,4% and temperature= 25,6º) CO=2,9561 PPM (Averages: RH=9,2% and temperature= 25,7º)CO=3,9518 PPM (Averages: RH=7,7% and temperature= 25,8º) CO=4,9530 PPM (Averages: RH=6,8% and temperature= 26º) CO=5,9532 PPM (Averages: RH=6% and temperature= 26,1º)CO=6,9571 PPM (Averages: RH=5,4% and temperature= 26,2º) CO=7,9540 PPM (Averages: RH=4,9% and temperature= 26,2º) CO=8,9586 PPM (Averages: RH=4,5% and temperature= 26,3º)CO=0 PPM (Averages: RH=3,9% and temperature= 26,4º)

0

2

4

6

8

10

12

11:20 11:34 11:49 12:03 12:18 12:32 12:46 13:01 13:15 13:30

PPM

Measurements of CO for M4/CO_4 Near the gas input

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Graph 3.10 ‐ Comparison between M5/NO2_4 and M3/NO2_2 inside the same box (2013‐07‐03).


0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

13:55 14:09 14:24 14:38 14:52 15:07 15:21 15:36

PPM

Time 24h (hh:mm)

Measurements of NO2 for M5/NO2_4 Near the gas input

Putting in the Box (Averages: RH=53,1% and temperature= 27,9º) CO=0 PPM (Averages: RH=33,4% and temperature= 28,4º) CO=0,0483 PPM (Averages: RH=18,1% and temperature= 28,8º)CO=0,0712 PPM (Averages: RH=12,7% and temperature= 29º) CO=0,1093 PPM (Averages: RH=10% and temperature= 29,3º) CO=0,2019 PPM (Averages: RH=8,4% and temperature= 29,5º)CO=0,3036 PPM (Averages: RH=7,4% and temperature= 29,7º) CO=0 PPM (Averages: RH=6,3% and temperature= 29,9º)

0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

13:55 14:09 14:24 14:38 14:52 15:07 15:21 15:36

Measurements of NO2 for M3/NO2_2 Near the gas output

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Graph 3.11 ‐ The complete results of the tests made to M2/CO_3 (inside the bag) with different concentrations of CO (2013‐07‐02)


0

2

4

6

8

10

12

15:36 15:50 16:04 16:19 16:33 16:48 17:02 17:16 17:31 17:45 18:00

PPM

Time 24h (hh:mm)

Measurements of CO for M2/ CO_3 in a Bag

CO=0 PPM (Averages: RH=0,2% and temperature= 26,2º) CO=0,5122 PPM (Averages: RH=0% and temperature= 26,4º) CO=1,0461 PPM (Averages: RH=0% and temperature= 26,3º)

CO=1,9512 PPM (Averages: RH=0% and temperature= 26,2º) CO=2,9561 PPM (Averages: RH=0% and temperature= 26,3º) CO=3,9518 PPM (Averages: RH=0% and temperature= 26,4º)

CO=4,9530 PPM (Averages: RH=0% and temperature= 26,1º) CO=5,9532 PPM (Averages: RH=0% and temperature= 26º) CO=6,9571 PPM (Averages: RH=0% and temperature= 26º)

CO=7,9540 PPM (Averages: RH=0% and temperature= 26º) CO=8,9586 PPM (Averages: RH=0% and temperature= 26,2º) CO=0 PPM (Averages: RH=0% and temperature= 26,5º)

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Graph 3.12 ‐ The complete results of the tests made to M4/CO_4 (inside the bag) with different concentrations of CO (2013‐07‐03)


0

2

4

6

8

10

12

10:04 10:33 11:02 11:31 12:00 12:28

PPM

Time 24h (hh:mm)

Measurements of CO for M4/CO_4 in a Bag

Putting in the Bag (Averages: RH=23,5% and temperature= 26,8º) CO=0 PPM (Averages: RH=4,2% and temperature= 27,2º) CO=0,5122 PPM (Averages: RH=1,9% and temperature= 27,8º)CO=1,0461 PPM (Averages: RH=0,9% and temperature= 27,9º) CO=1,9512 PPM (Averages: RH=0,5% and temperature= 28º) CO=2,9561 PPM (Averages: RH=0,2% and temperature= 28,2º)CO=3,9518 PPM (Averages: RH=0% and temperature= 28,4º) CO=4,9530 PPM (Averages: RH=0% and temperature= 28,7º) CO=5,9532 PPM (Averages: RH=0% and temperature= 28,7º)CO=6,9571 PPM (Averages: RH=0% and temperature= 29º) CO=7,9540 PPM (Averages: RH=0% and temperature= 29,2º) CO=8,9586 PPM (Averages: RH=0% and temperature= 29,1º)CO=0 PPM (Averages: RH=0% and temperature= 29,2º)

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Graph 3.13 ‐ The complete results of the tests made to M3/NO2_2 (inside the bag) with different concentrations of NO2 (2013‐07‐03)


0

0,05

0,1

0,15

0,2

0,25

16:33 16:40 16:48 16:55 17:02 17:09 17:16 17:24 17:31

PPM

Time 24h (hh:mm)

Measurements of NO2 for M3/NO2_2 in a Bag

Putting in the Bag (Averages: RH=30% and temperature= 30º) CO=0 PPM (Averages: RH=50% and temperature= 28º) CO=0,0483 PPM (Averages: RH=6,9% and temperature= 28º)CO=0,0712 PPM (Averages: RH=5,2% and temperature= 28º) CO=0,1093 PPM (Averages: RH=4,2% and temperature= 28º) CO=0,2019 PPM (Averages: RH=3,4% and temperature= 28º)CO=0,3036 PPM (Averages: RH=2,9% and temperature= 28º) CO=0 PPM (Averages: RH=2,3% and temperature= 28º)

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Graph 3.14 ‐ The complete results of the tests made to M5/NO2_4 (inside the bag) with different concentrations of NO2 at different times


0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

15:07 15:21 15:36 15:50 16:04 16:19 16:33 16:48

PPM

Time 24h (hh:mm)

Measurements of NO2 for M5/NO2_4 in a Bag (2013‐07‐03)

Putting in the Bag (Averages: RH=31% and temperature= 29,5º) NO2=0 PPM (Averages: RH=6,7% and temperature= 29,4º) NO2=0,0483 PPM (Averages: RH=1,5% and temperature= 29,2º)NO2=0,0712 PPM (Averages: RH=1% and temperature= 29,1º) NO2=0,1093 PPM (Averages: RH=0,8% and temperature= 29,2º) NO2=0,2019 PPM (Averages: RH=0,7% and temperature= 29,1º)NO2=0,3036 PPM (Averages: RH=0,5% and temperature= 29,1º) NO2=0 PPM (Averages: RH=0,3% and temperature= 29,2º)

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

11:16 11:31 11:45 12:00 12:14 12:28 12:43 12:57

Measurements of NO2 for M5/NO2_4 in a Bag (2013‐07‐24)

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Graph 3.15 ‐ The complete results of the tests made to M4/CO_4 (direct injection of gas) with different concentrations of CO (2013‐07‐23)

Results from what has been described in the paragraph 5 of chapter 2.2 (but the background gas used was Air)

0

2

4

6

8

10

12

14

14:38 14:52 15:07 15:21 15:36 15:50 16:04 16:19 16:33 16:48 17:02

PPM

Time 24h (hh:mm)

Measurements of CO for M4/CO_4 Direct "T"

Putting the tubes (Averages: RH=48,8% and temperature= 25,7º) Direct with CO=0 PPMDirect with CO=0,5089 PPM Direct with CO=1,0398 PPMDirect with CO=2,0023 PPM Direct with CO=2,9999 PPMDirect with CO=4,0016 PPM Direct with CO=5,0054 PPMDirect with CO=6,0010 PPM Direct with CO=7,0038 PPMDirect with CO=8,0035 PPM Direct with CO=9,0055 PPM

29 / 49

Graph 3.16 ‐ The complete results of the tests made to M5/NO2_4 (direct injection of gas) with different concentrations of NO2 (2013‐07‐24)

Results from what has been described in the paragraph 5 of chapter 2.2 (but the background gas used was Air)

0

0,05

0,1

0,15

0,2

0,25

9:36 9:50 10:04 10:19 10:33 10:48 11:02 11:16 11:31

PPM

Time 24h (hh:mm)

Measurements of NO2 for M5/NO2_4 Direct "T"

Putting the tubes (Averages: RH=58,6% and temperature= 24,2º) Direct with NO2=0 PPM

Direct with NO2=0,0483 PPM Direct with NO2=0,0712 PPM

Direct with NO2=0,1093 PPM Direct with NO2=0,2019 PPM

Direct with NO2=0,3036 PPM Direct with NO2=0 PPM

30 / 49

3.3. Results of the field trials using a bicycle and M1 with CO1/NO2_1 It is presented below some examples of the results obtained in the field and processed by the program described in chapter 2.4, using later Google earth to represent them. The next results are all from CO measures, this is because the results obtained with CO, although they are not the best ones regarding what were expected to find in the field, they are the ones that better show the potential of the program used, due to the high levels of CO obtained, thus showing better the effect of the color scales that can be used.

So, in Figure 3.1 you can see a representation by areas during a daily session of measurements of CO through the streets of Lisbon. These areas are built by connecting all adjacent points that have the same measured value. And in this case, when it is observed the entire daily session, the values presented in the fixed stations are an arithmetic average of the values measured by the station throughout the session (hour by hour).

Figure 3.1 ‐ A daily session (2012‐12‐03) measurements of CO in Google earth, with representation by areas. The

circular areas represent the base stations By clicking in the areas/points or the fixed station, it is possible to get access to a more detailed description of the measurements, such has the hours that the value were obtain, the value with more precision and in the case of the fixed stations the type of station that it is as well as all values measured during the daily session.

The menu on the left gives access to a representation by layers, meaning that it is possible to show by areas, points, entire sessions or hour to hour sessions, sensor or even the fix stations by themselves.

The representation by points as it can be seen in Figure 3.2 normally makes the overview more difficult, but it can be quite useful when zoomed in over a particular region, because the values obtained punctually can be seen in greater detail. For example one can see in Figure 3.3 the values for CO at a given moment along a road in Lisbon represented simultaneously by points and areas

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Figure 3.2 ‐ A daily session (2012‐12‐03) measurements of CO in Google earth, with representation by points. The

circular areas represent the base stations

Figure 3.3 – Zoomed area where one can see what was measured along that road with both representations, by

points and areas. The next tables, ranging from Table 3.1 up to Table 3.7, are a numeric representation of the valid gas values, obtained during the bicycle trails, through the streets of Lisbon, and they are composed at is base by two columns, with time and gas value, but whenever the bicycle was near the fix stations, it was added to the right of the columns an adjacent table with information about the comparisons between the values measured by the station with the mean values obtained by our sensors in that specific time. This adjacent tables shows the averages of the values obtained by our sensors parked near the stations (shown in green), the average of all values (composed by the green + gray values) represented in gray and the values obtain for that hour by the fix station. The grey values in the based columns were obtained during the approach to the stations (with movement).

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Beginning of the Table 3.1 Time CO(ppm) (2012‐05‐11) 15:23 5,27

15:24 5,11 Entrecampos 15:25 4,97 Average

[Green] (ppm)

Average [Total] (ppm)

Station CO (ppm)

15:26 5,49 15:27 5,27 15:28 4,07

1,97 0,39

15:29 3,79 15:30 3,24

1,96

15:31 3,12 15:32 3,06 15:33 3,04 15:34 3,25 15:34 3,25 15:35 3,18 15:36 3,24 15:37 3,28 15:38 3,64 15:40 3,31 15:41 2,81 15:42 2,42 15:43 2,03 15:44 1,57 15:45 1,92 15:46 1,8 15:47 1,87 15:48 1,4 15:49 0,53 15:50 0,22 15:51 0 15:52 0 15:53 0 15:54 0,37 15:55 0,24 15:56 0,02 15:57 0,17 15:57 0,17 15:58 0,39

1,32 1,41 Standard deviation (ppm) 15:59 0,44

16:00 0,56 16:01 1,14 16:02 1,76 16:03 1,58 16:04 1,18 16:05 0,87 16:06 0,46 16:07 0,8 16:08 0,9 16:09 0,92 16:10 0,79 16:11 0,55

16:12 0,65 Benfica 16:13 0,55 Average

[Green] (ppm)


Station CO (ppm)

16:14 0,48 16:15 0,69 16:16 0,63

1,33 0,37

16:17 0,65 16:18 1,63

1,37

16:19 0,71 16:19 0,71 16:20 0,78 16:21 0,82 16:22 1,18 16:23 1,22 16:24 1,31 16:25 1,27 16:26 1,54 16:27 2,43 16:28 3,32 16:29 1,6 16:30 1,36 16:31 1,31 16:33 1,62 16:34 1,68 16:35 1,41 16:36 1,37 16:37 1,43 16:38 1,24 16:39 1,04 16:40 1,02 16:41 1,05 16:42 0,95 16:42 0,95 16:43 1,16 16:44 1,49 16:45 1,78 16:46 1,61 16:47 1,31 16:48 1,58 16:49 1,79


16:51 0,62 16:52 0,84 16:53 2,19 16:54 1,18 16:55 0,21 16:56 0,03 16:56 0,03 16:57 0,15 16:58 0,44 16:59 0 17:00 1,05 17:01 0,82 17:02 0,99 17:03 1,11 17:04

1,23

33 / 49

17:05 1,27 Av. Liberdade 17:06 1,24 Average

[Green] (ppm)


Station CO (ppm)

17:07 1,36 17:08 0,71 17:09 1,45

1,18 0,47

17:10 1,52 17:11 1,2

1,12

17:12 0,68 17:13 0,3 17:14 0 17:15 0,15 17:16 0,47 17:17 0,75 17:18 0,93 17:19 0,81 17:20 0,48 17:21 0,64 17:22 0,75 17:23 0,7 17:25 0,77 17:26 0,86 17:27 1,1 17:28 1,33 17:28 1,33 17:29 1,81 17:30 2,03 17:31 2,04 17:32 1,99 17:33 2,04 17:34 2,09 17:35 1,83 17:36 1,48 17:37 1,37 17:38 1,25 17:39 1,2 17:40 1,24 17:41 1,74 17:42 1,73 17:43 2,15


17:45 2,95 17:46 2,26 17:47 2,53 17:48 3,46 17:49 3,72 17:50 4,17 17:50 4,17 17:51 3,13 17:52 2,66 17:53 2,94 17:54 1,64 17:55 1,54 17:56 0,89 17:57 0,72 Table 3.1 ‐ End

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Beginning of the Table 3.2 Time CO(ppm) (2012‐11‐13)

11:02 0,8 Entrecampos 11:03 1,32 Average

[Green] (ppm)


Station CO (ppm)

11:04 0,94 11:05 0,18 11:06 0

0 0,30

11:07 0 11:08 0

0

11:09 0 11:10 0 11:11 0 11:11 0 11:12 0 11:13 0 11:14 0 11:15 0

0 0 Standard deviation (ppm) 11:16 0

11:17 0 11:19 0 11:20 0 11:21 0 11:22 0 11:23 0 11:24 0 11:25 0 11:26 0 11:27 0 11:28 0 11:29 0,14 11:30 0,61 11:31 0,96 11:32 0,4 11:33 0 11:34 0 11:35 0 11:36 0,37 11:37 0,62 11:38 0 11:39 0 11:40 0 11:41 0 11:42 0 11:43 0 11:44 0 11:45 0 11:46 0 11:47 0 11:48 0 AV. Liberdade

11:49 0 Average [Green] (ppm)


Station CO (ppm)

11:50 0 11:51 0 11:52 0

0,05 0,30

11:53 0 11:54 0

0,03 11:55 0 11:56 0,08 11:57 0,11 11:58 0,18 11:59 0,17


12:01 0,17 12:02 0,12 12:03 0,17 12:04 0,11

12:05 0,06 12:06 0,3 12:07 1,06 12:08 0,91 12:09 0,9 12:11 0,72 12:12 0,73 12:12 0,73 12:13 1,42 12:14 1,07 12:15 0,82 12:16 0,92 12:17 1,2 12:18 1,02 12:19 0,66 12:20 1,15 12:21 1,39 12:22 1,67 12:23 1,94 12:24 1,95 12:25 1,62 12:26 1,26 12:27 0,91 12:28 0,2 12:29 0,77 12:30 0,79 12:31 0,91 12:32 0,35 12:33 0,57 12:34 1,34 12:35 1,67 12:36 1,96 Olivais

12:37 1,83 Average [Green] (ppm)


Station CO (ppm)

12:38 1,65 12:39 1,67 12:40 1,15

0,28 0,24

12:41 0,5 12:42 0,41

0,17 12:43 0,42 12:44 0 12:45 0 12:46 0 12:47 0 12:48 0 12:49 0,05


12:51 0 12:52 0,01 12:53 0 Table 3.2 ‐ End

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14:25 0,63 14:26 0,93 14:27 1,08 14:28 1,13 14:51 0 Entrecampos



Station CO (ppm)

14:53 0 14:54 0 14:55 0

0

0,76 14:56 0 14:57 0

0 14:58 0 14:59 0 15:00 0

0,69 15:01 0 15:02 0 15:03 0 15:04 0


15:06 0 15:07 0 15:07 0 15:08 0 15:09 0 15:09 0 15:10 0 15:11 0 15:12 0 15:13 0 15:14 0 15:15 0 15:15 0 15:16 0 15:17 0 15:18 0 15:19 0 15:20 0 15:21 0 15:22 0 15:23 0 15:24 0 15:24 0 15:25 0 15:26 0 15:27 0 15:28 0 15:29 0 15:30 0 15:30 0 15:31 0 15:33 0 15:34 0 15:35 0 15:36 0 15:37 0 15:38 0 15:39 0 15:40 0 15:41 0 15:42

0

15:43 0,08 AV. Liberdade 15:44 0 Average

[Green] (ppm)


Station CO (ppm)

15:45 0 15:46 0 15:47 0,08

0,04 0,61

15:48 0,1 15:49 0,1

0,03

15:50 0 15:51 0,02 15:52 0 15:52 0 15:53 0,04 15:54 0 15:55 0,04 15:56 0,22


15:58 0,34 15:59 0,12 16:00 0,12 16:01 0,91 16:01 0,91 16:02 1,17 16:03 1,24 16:04 1,2 16:05 1,33 16:06 1,13 16:07 0,88 16:07 0,88 16:08 0,38 16:09 0,17 16:10 0,62 16:11 0,02 16:12 0 16:13 0 16:13 0 16:14 0 16:15 0 16:16 0 16:17 0 16:18 0 16:19 0 16:19 0 16:20 0 16:21 0 16:21 0 16:22 0 16:23 0 16:25 0 16:26 0 16:27 0 16:28 0 16:28 0 16:29 0 16:30 0

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16:31 0 AV. Liberdade 16:32 0 Average

[Green] (ppm)


Station CO (ppm)

16:33 0 16:34 0,12 16:35 0

0,06 0,34

16:36 0,15 16:37 0,26

0,09 16:38 0,1 16:39 0 16:40 0 16:41 0 16:41 0 16:42 0

0,12 0,10 Standard deviation (ppm) 16:43 0

16:44 0 16:45 0 16:46 0,13 16:47 0 16:48 0 16:49 0 16:50 0 16:51 0 16:52 0 16:53 0,14 16:54 0 16:54 0 16:55 0 16:56 0,03 16:57 0,04 16:58 0,17 16:59 0,36 17:00 0,48 17:01 0,34 17:02 0,34 17:03 0,48 17:04 0,32 Table 3.3 ‐ End

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15:09 4,53 15:10 4,53 15:11 4,39 15:12 4,17 15:13 3,84 15:14 3,6 15:15 3,39 15:16 3,96 15:17 3,93 15:18 3,57 15:19 3,19 15:20 2,76 15:21 1,86 15:22 0,55 15:23 0 15:24 0 15:26 0 15:27 0 15:28 0 15:29 0 15:30 0 Entrecampos



Station CO (ppm)

15:32 0 15:33 0 15:34 0

0 0,52

15:35 0 15:36 0

0 15:37 0 15:38 0 15:39 0 15:40 0 15:41 0 15:42 0 15:43 0


15:45 0 15:46 0 15:47 0 15:48 0 15:49 0 15:50 0 15:51 0 15:52 0 15:53 0 15:54 0 15:55 0 15:56 0 15:57 0 15:58 0 15:59 0 16:00 0 16:01 0 16:02 0 16:03 0 16:04 0 16:05 0 16:06 0 16:07 0 16:08 0 16:09 0 16:10 0 16:11 0 16:12 0

16:13 0,05 16:14 0 16:15 0 16:16 0,05 16:18 0 Av. Liberdade



Station CO (ppm)

16:20 0 16:21 0 16:22 0

0 0,51

16:23 0 16:24 0

0 16:25 0 16:26 0 16:27 0 16:28 0 16:29 0 16:30 0


16:32 0 16:33 0 16:34 0 16:35 0 16:36 0 16:37 0 16:38 0,1 16:39 0,33 16:40 0,26 16:41 0,49 16:42 1,01 16:43 0 16:44 0 16:45 0,1 16:46 0,17 16:47 0,29 16:48 0 16:49 0 16:50 0 16:51 0 16:52 0 16:53 0 16:54 0 16:55 0,05 16:56 0,31 16:57 0,48 16:58 0,21 16:59 0,08 17:00 0,1 17:01 0 17:02 0 17:03 0 17:04 0,04 17:05 0,23 17:06 0,97 17:07 0,16 17:08 0,27 17:10 0,39 17:11 0,58 17:12 0,56 17:13 0,63 17:14 0 17:15 0 17:16 0,04 17:17 0,04 Table 3.4 ‐ End

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Beginning of the Table 3.5 Time CO(ppm) (2012‐12‐18) 13:13 0

13:14 0 13:15 0 13:16 0 13:17 0 13:18 0 13:19 0 13:20 0 Entrecampos



Station CO (ppm)

13:22 0 13:23 0 13:24 0

0 0,37

13:25 0 13:26 0

0 13:27 0 13:28 0 13:29 0 13:30 0 13:31 0


13:33 0 13:34 0 13:35 0 13:36 0 13:37 0 13:39 0 13:40 0 13:41 0 13:42 0 13:43 0 13:44 0 13:45 0 13:46 0 13:47 0 13:48 0 13:49 0 13:50 0 13:51 0,71 13:52 0,56 13:53 0,26 13:54 0 13:55 0,19 13:56 0,36 13:57 0,01 13:58 0 13:59 0 14:00 0 14:01 0 14:02 0 Av. Liberdade



Station CO (ppm)

14:04 0,91 14:05 0,91 14:06 0,34

0,33 0,19

14:07 0,42 14:08 0,71

0,41 14:09 0,29 14:10 0,24 14:11 0,2 14:12 0,09 14:13 0,1


14:15 0,03 14:16 0

14:17 0 14:18 0 14:19 0 14:20 0 14:21 0 14:22 0,19 14:23 0,37 14:24 0,35 14:25 0,7 14:26 0,59 14:27 0,41 14:28 0,12 14:29 0,43 14:31 0,2 14:32 0 14:33 0,35 14:34 0,39 14:35 0,26 14:36 0,14 14:37 0,27 14:38 0,96 14:39 0,3 14:40 0 14:41 0 14:42 0 14:43 0,41 14:44 1,01 14:45 0,51 14:46 0,28 14:47 0,12 14:48 0 14:49 0 14:50 0 14:51 0 14:52 0 Olivais



Station CO (ppm)

14:54 0 14:55 0,14 14:56 0,5

0,36 0,21

14:57 0,85 14:58 0,12

0,30

14:59 0,16 15:00 0,18 15:01 0,1 15:02 0,09 15:03 0,38 15:04 0,53 15:05 0,57 15:06 0,55 15:07 0,48 15:08 0,26 15:09 0,23

0,19 0,23 Standard deviation (ppm)

Table 3.5 ‐ End

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12:30 2,15 12:31 2,16 12:32 2,13 12:33 2,13 12:34 2,14 12:35 2,1 12:36 2,03 12:37 1,91 12:38 1,78 12:39 0 12:40 0 12:41 0 12:42 0 Entrecampos



Station CO (ppm)

12:44 0 12:45 0 12:46 0

0 0,41

12:47 0 12:48 0

0 12:49 0 12:50 0 12:51 0 12:52 0 12:53 0 12:54 0


12:56 0 12:57 0 12:58 0 12:59 0 13:00 0 13:01 0 13:02 0 13:03 0 13:04 0,3 13:05 0 13:06 0 13:07 0 13:08 0 13:09 0 13:10 0 13:11 0,48 13:12 0,12 13:13 0 13:14 0 13:15 0,48 13:17 0,04 13:18 0 13:19 0 13:19 0 13:20 0 13:21 0 13:22 0,42 13:23 0,14 13:24 0,16 13:25 0,3 13:25 0,3 13:26 0,1 13:27 0,03 13:28 0,4

13:29 0,95 AV Liberdade 13:30 0,77 Average

[Green] (ppm)


Station CO (ppm)

13:31 0,36 13:31 0,36 13:32 0,39

0,18 0,37

13:33 0,31 13:34 0,29

0,19 13:35 0,14 13:36 0,15 13:37 0 13:38 0,01 13:39 0,03


13:41 0,42 13:42 0,17 13:43 0,03 13:44 0 13:45 0,2 13:46 0,09 13:47 0,1 13:48 0,07 13:49 0,27 13:50 0,2 13:50 0,2 13:51 0,53 13:52 0,89 13:53 0,6 13:54 0,25 13:55 0 13:56 0 13:56 0 13:57 0 13:58 0 13:59 0 14:00 0,12 14:01 0,68 14:02 0,51 14:02 0,51 14:03 0,33 14:04 0,41 14:05 0,09 14:06 0 14:07 0 14:09 0 14:10 0,58 14:11 0 14:12 0 14:13 0 14:14 0 14:15 0,43 14:16 0,1 14:17 0,07 14:18 0,3 14:19 0,26 14:20 0,32 14:21 0,78 14:22 0,73 14:23 0,74 14:24 0,89 14:25 0,58 14:26 1,42 14:27 0,74 14:28 0,57 Table 3.6 ‐ End

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Beginning of the Table 3.7 Time NO2(ppm) (2012‐05‐15) 18:09 0,125

18:10 0,139 18:11 0,128 18:12 0,13 18:13 0,15 18:14 0,125 18:15 0,131 18:16 0,126 18:17 0,126 18:18 0,139 18:19 0,132 18:20 0,131 18:21 0,15 18:22 0,166 18:23 0,256 Entrecampos



Station NO2 (ppm)

18:25 0,261 18:26 0,238 18:27 0,219

0,147 0,033

18:28 0,206 18:29 0,18

0,141

18:29 0,18 18:30 0,123 18:31 0,126 18:32 0,127 18:33 0,144 18:34 0,141 18:35 0,144 18:36 0,143 18:37 0,144 18:38 0,137 18:39 0,134 18:40 0,149 18:41 0,141 18:42 0,146 18:43 0,144 18:44 0,13 18:46 0,134 18:47 0,138 18:48 0,145 18:49 0,134 18:50 0,135 18:51 0,134 18:52 0,127 18:53 0,166 18:54 0,153 18:55 0,189


18:57 0,182 18:58 0,185 18:59 0,179 19:00 0,177 19:01 0,179 19:02 0,131 19:03 0,151 19:04 0,188 19:05 0,175 19:06 0,151 19:07 0,154

19:08 0,163 Benfica 19:09 0,235 Average

[Green] (ppm)


Station NO2 (ppm)

19:10 0,215 19:11 0,167 19:12 0,145

0,142 0,044

19:13 0,154 19:14 0,173

0,140

19:15 0,135 19:16 0,141 19:17 0,137 19:18 0,136 19:19 0,146 19:20 0,137 19:21 0,138 19:22 0,157 19:23 0,135 19:24 0,128 19:25 0,134 19:26 0,134 19:27 0,129 19:28 0,135 19:29 0,142 19:30 0,127 19:31 0,137 19:32 0,15 19:33 0,181 19:34 0,142 19:35 0,15


19:38 0,154 19:39 0,143 19:40 0,15 19:41 0,149 19:42 0,144 19:43 0,15 19:44 0,162 19:45 0,151 19:46 0,149 19:47 0,192 19:48 0,25 19:49 0,181 19:50 0,215 19:51 0,181 19:52 0,166 19:53 0,168 19:54 0,188 19:55 0,187 19:56 0,154 19:57 0,193 19:58 0,194 19:59 0,186 20:00 0,221 20:01 0,163 20:02 0,167 20:03 0,17 20:04 0,176 20:05 0,168 20:06 0,171 20:07 0,167 20:08 0,189 20:09 0,169 20:10 0,188 Table 3.7 ‐ End

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3.4. Results of the field trials for the sensors acquired in 2013 As had been previously explained in chapter 2.3, the sensors were placed near Entrecampos station in the same place without any king of movement and repeated later on, but this time with the M4/CO_4 and M5/NO2_4 inside the water and dust enclosure in which the results for both scenarios were as follows:

Graph 3.17 ‐ Measurements of CO near Entrecampos Station over one hour (2013‐07‐19)

Graph 3.18 ‐ Measurements of CO near Entrecampos Station over one hour (2013‐08‐23)

0

0,5

1

1,5

2

2,5

3

3,5

17:52 18:00 18:07 18:14 18:21 18:28 18:36 18:43 18:50 18:57

ppm

Measurements of CO near Entrecampos Station (M4/CO_4 without water and dust proof enclosure)

M2/CO_2(ppm) M4/CO_4(ppm) Média M2 CO 2(ppm)

Média M4 CO 4(ppm) CO Entrecampos (ppm)

0

0,2

0,4

0,6

0,8

1

1,2

1,4

16:55 17:02 17:09 17:16 17:24 17:31 17:38 17:45 17:52 18:00

ppm

Measurements of CO near Entrecampos Station (M4/CO_4 inside the water and dust proof enclosure)

M2/CO_3(ppm) M4/CO_4(ppm) with enclosureAverage: M2/CO_3(ppm) Average: M4/CO_4(ppm) with enclosureCO Entrecampos (PPM)

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Graph 3.19 ‐ Measurements of NO2 near Entrecampos Station over one hour (2013‐07‐19)

Graph 3.20 ‐ Measurements of NO2 near Entrecampos Station over one hour (2013‐08‐23)

3.5. The data obtained from the public and private organizations To obtain the taxis emissions was necessary to extrapolate based on the experience of the entities that provided the data, this is because the technical data of each taxi were incomplete, and many taxis did not exhibited the values of their emissions in their “Single Car Document”,

0,000

0,020

0,040

0,060

0,080

0,100

0,120

0,140

17:52 18:00 18:07 18:14 18:21 18:28 18:36 18:43 18:50 18:57

ppm

Measurements of NO2 near Entrecampos Station (M5/NO2_4 without water and dust proof enclosure)

Média M3 NO2 2(ppm) Média M5 NO2 4(ppm) NO2 Entrecampos (PPM)

M3 NO2 2(ppm) M5 NO2 4(ppm)

0

0,02

0,04

0,06

0,08

0,1

0,12

0,14

16:48 17:02 17:16 17:31 17:45 18:00 18:14

ppm

Measurements of NO2 near Entrecampos Station (M5/NO_4 inside the water and dust proof enclosure)

M3/NO2_2(ppm) M5/NO2_4(ppm) with enclosure

Average: M3/NO2_2(ppm) Average: M5/NO2_4(ppm) with enclosure

NO2 Entrecampos (PPM)

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and as it can been seen in Table 3.8 from the total of 3439 taxis, only 24,6% had CO values, 28,8% had particles values and 42,8% had CO2 values. In Table 3.8 this values are shown as the “validated counts”. From these values it was obtained the averages for each one and it was also obtained the maximum value for each as well. So, on the assumption that the maximum values obtained were the highest possible values that a taxi could have, it was extrapolated two values, a lowest and highest. The lowest was calculated assuming that the “not validated” data also followed the average. The highest was calculated assuming that the “not validated” data were all equal to maximum value.

The majority of the “not validated” data were composed by oldest cars, and that’s why their averages were taken as a maximum in the extrapolation of the highest value.

Finally, based on information from Antral (10), it was assumed that the average for each taxi were 310km/day, which allow it to take conclusions: Total count (Taxicab): 3439

Validated counts CO (g/km ou

g/kWh)

Particles - diesel engine (g/km or

g/kWh) CO2 (g/km)

Count (numeric) 845 989 1472 Count (% of total) 24,6% 28,8% 42,8% Count (Not Validated) 75,4% 71,2% 57,2% Diesel Engine count (numeric) 3408

Diesel Engine count (% of total) 99,1%

Statistical values from the validated counts

CO (g/km ou g/kWh)

Particles - diesel engine (g/km or

g/kWh) CO2 (g/km)

Average 0,178 0,015 159,7

Standard deviation 0,142 0,029 27,0

Maximum reported 0,844 0,757 248,0

Extrapolations for all taxicabs (daily)

1 taxicab

CO (kg/day) Particles - diesel engine (kg/day) CO2 (kg/day) Range

Average of km/day (source: ANTRAL)

based on the average to all 189,29 15,35 170269,49 Lowest

310

based on the average and maximum 725,20 572,12 224103,65 Highest

Extrapolations for all taxicabs (year)

Days in 1 Year

CO

(tons/year) Particles - diesel

engine (tons/year) CO2

(tons/year) Range

365

based on the average to all 69,09 5,60 62148,36 Lowest

based on the average and maximum 264,70 208,82 81797,83 Highest

Table 3.8 – Estimated range values for emissions of pollutants gases by the taxis in Lisbon

Concerning the buses, the data retrieved from Carris (13) “annual sustainability report” is shown below in Table 3.9.

NOx (tons/year)

CO (tons/year)

CO2 (tons/year)

Particles (tons/year)

Unburned hydrocarbon (tons/year)

371,67 42,47 50378,87 6,17 14,52 Table 3.9 – Emissions of pollutants gases by Carris buses during the year 2011

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4. Discussion of the results and conclusions Before starting the discussion of the results, it would be better to realize what kind of requirements would be expected to be obtained from the sensors in order to get this project moving.

1) Consistency in measurements: It is expected that, for the same amount of gas, the sensor measures the same value.

2) Measurements within the expected: For values that are expected to be obtain daily in the field (see Table 4.1), the sensor must be able to measure it and at the same time have a maximum in its range that can withstand occasional high concentrations without thereby damaging the sensor.

AV. Da Liberdade Station

Legal Limits

Annual Averages Maximum Obtained During The Year

CO (ppm) NO2 (ppm)

Year CO (ppm) NO2 (ppm) CO (ppm) NO2 (ppm)

Levels min max min max

2010 0,33 0,035 2,29 0,223

Bad 8,11 0,213

2009 0,37 0,034 3,41 0,341

Weak 6,89 8,11 0,133 0,212

2008 0,39 0,034 4,42 0,241

Average 5,68 6,89 0,074 0,132

2007 0,44 0,040 3,39 0,266

Good 4,06 5,68 0,053 0,074

2006 0,46 0,037 4,13 0,413

Excellent 0,00 4,05 0,000 0,053

2005 0,51 0,034 4,69 0,218

2004 0,51 0,034 4,20 0,251

2003 0,53 0,037 5,05 0,230

2002 0,60 0,033 6,09 0,185

2001 0,68 0,029 5,34 0,270

Table 4.1 ‐ Values for the legal limits in Portugal (left); The maximums and annual averages obtained in one of the stations of Lisbon in the last 10 years (1) (right)

3) Suitable sensor response: The time response of the sensor must be such that, while moving through the streets of Lisbon, as to be able to measure the concentration of gas in a specific region before leaving that region. In other words, try to create a map of measurements with more resolution, or, at least try to ascertain a road to road measurement. And also, the sensor must be insensible to movement itself.

Thus, the conclusions regarding the performance of the sensors will be a comparison between the obtained results and the requirements presented before.

In terms of consistency, I can only present conclusions regarding the M1 with CO_1/NO2_1, this is because it was impossible to schedule, until the time of this report, the same kind of tests made for M1 with CO_1/NO2_1 described in point 2 of chapter 2.2. So, taking into account the Graph 3.3, Graph 3.4 and Graph 3.5, it is possible to see that the consistency is confirmed for CO_1 above 2ppm and for NO2_1 above 0,5ppm. Below these values it is not certain, but if one looks at all the graphs in chapter 3.1 with measurements below those values, it seems like that the sensors exhibit more random results.

This inability to measure below 2ppm for CO_1 and 0,5ppm for NO2_1 is a real problem, because if one looks to the Table 4.1, the annual averages are all below this values for each one of them, making them only able to measure the expected maximums that may occur during a day.

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Therefor the requirement that refer to the ability to obtain measurements within the expected is compromised. And the same happens to the new CO and NO2 sensors as it can be seen in all the results.

Looking at the Graph 3.9 and Graph 3.10, one can see that the shape of the graph is identical, so the sensors respond in the exact same way, but with a delay relative to one another and also with a difference in amplitude. Although, the delay could be explained by whether or not the sensor is close to the entrance of the gas in the box, the amplitude, on the other hand can´t be explained by this. In fact, if one looks at the Graph 3.19 and Graph 3.20 it is possible to see that the sensor with the biggest amplitude (M5/NO2_4) maintains this result in the field trials (Graph 3.19 and Graph 3.20), therefor it is a characteristic of the sensor on its own, and nothing to do with the position occupied inside the box.

This difficulty of the sensors CO_1 and NO2_1 to measure the lower values can be seen in all the results presented in chapter 3.3. And, as expected, all the days represented in the results had values within the annual averages. Accordingly to what was been observed in the laboratory it was been reflected in the field trials as well. Looking at the results it look like that the CO presents us with some random results for time to time. The factor “random values” can easily be seen in the values obtained for the standard deviations in the CO sensors. The new CO sensors failed to correct this problem of “random values” as it can be seen in Graph 3.17 and Graph 3.18. Although the NO2 sensors appears to be more consistent, the truth is that the results on the field shows that for the same value the NO2 sensors measure different things as it can be seen in Graph 3.19 and Graph 3.20, showing once more that the values obtain below 0.5 ppm re not to trust.

The “random values” can be a problem even for values above these critical lines, because when disturbed, the sensor exhibit spikes in their measurements which exceeds sometimes their critical lines, thus making impossible to distinguish what is a real measurement from an instability spike. This can be seen in both type of sensor (CO and NO2, old and new).

These instability spikes, which were first seen in Graph 3.2, generated some doubts, which were made available to Aeroqual (3), but until this date have not been answered. An attempt was made to replicate these peaks by applying motion to the sensors, as it can be seen in Graph 3.4 and Graph 3.5, and it was possible to replicate some of that instability, although on smaller scale. It was also proposed by Aeroqual (3) a repetition of the motion test for the CO, but this time using the procedure explain in point 4 of the chapter 2.2, but the result remained the same, with a slight improvement as it can be seen in Graph 3.8.

One possible explanation for these spikes, may have to do with the increase of air flow normally associated with movements, that’s why the slight improvement with the box (less air flow inside the box when movement were applied to the box).

This air flow could explain the not so good results for the new sensors in the bag (Graph 3.11 up to Graph 3.14 ). This is because, at the time of the tests, in order to accelerate all the process (due to a time limit), the bag was periodically deflated to thereby renew faster the gas inside the bag. A good example of this can be seen in Graph 3.14, the results shown at the right were obtain with no deflation action, but I can’t get an explanation for the different results obtained for the same concentration of gas injected.

The enclosure acquired from Aeroqual (3), shown in Figure 2.4, did not mitigate the dynamic effect that the air flow could produce in our sensors, and this can be seen when comparing the Graph 3.17 with Graph 3.18, and Graph 3.19 with Graph 3.20.

The last requirement (time response) was tested by comparing our sensors with other sensors, and as it can be seen in Graph 3.4, Graph 3.6 and Graph 3.7, the time response is identical to the other sensors. Both sensors (CO_1 and NO2_1) had the same response as the other

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sensors in Graph 3.4, Graph 3.7. On the other hand, the bad results shown in Graph 3.6 may be due to fact that the bag, where our sensor was, has undergone some deflation action.

The attempt to directly inject gas into our sensors (Figure 2.7), did not present the expected results, that would be a faster detection of the gas, instead, the results suggest disturbances in the measurements and the inability of the internal fan of the sensor to pull the gas in (Graph 3.15 and Graph 3.16).

One of the things that should be refer, before ending this chapter, is the sensors warm up, that may be another problem, this is because when the sensors are turn on after a power break, they must be warmed up to burn any contaminants. When this appends the sensors take up to 10 min to become fully operational.

5. Unfinished tasks Due to the fact that the funds ended in August 2013, some tasks were not finish. In fact we were stuck inside the first task, which was the development and integration of a set of sensors. As it had been shown in the previous chapter, the first 3 sensors acquired, presented us with some serious problems during the laboratory tests, that were confirm in the filed trails. New tests were required to try to understand these problems, since they were not in accordance with the characteristics submitted by the manufacturer upon purchase of the product (Table 2.1).

New and upgraded sensors should fix these problems but we encounter some new problems in all the process of upgrading, the company had some bugs in there firmware/software that took its time to resolve and when we receive the new and upgraded sensors, we were already very close to August 2013.

The new sensors came with a new protocol, that until the end of August 2013, it was not given to us.

This and other processes took us a lot of time, and force us to cancel tasks, but to better understand all this; a table was created (Table 5.1):

2012

April May June July August September

Theory Lab tests Lab tests

Vacatio

ns

Field Trials Aeroqual Aeroqual

Code Code Com

Networking Networking

2012 2013

October November December January February March

Com Field Trials

vacatio

ns Rep Networking

Aeroqual Aeroqual Com

Code Code Aeroqual

2013

April May June July August

Networking Lab tests Field trials

Rep Aeroqual Aeroqual

Com Com Com

Table 5.1 – Timeline chart

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Label Theory Become familiar with the material and the project; read datasheets and user manuals; other readings

Lab tests Creating protocols; schedules; get material; previous tests; lab tests; data processing, networking

Field trials Testing routes; Testing material; CO and NO2 trials; data processing

Code Learn Matlab; create a script to create kml files (google earth) from the data obtain in the field; data processing

Networking Search for public and private organizations, Establish contacts, Meetings, prepare presentations, data processing

Aeroqual Bureaucracy; Aeroqual troubleshooting ; Aeroqual materials.

Com Learn; Communication protocol between Aeroqual sensors and GSM/GPS module; Learn Linux; Linux problems; special organization; Planning the prototype container

Rep Official Reports

So, the tasks that were initiated but not finished were:

1) Comunication protocol between Aeroqual (3) sensors and GSM/GPS module from “Round Solutions” (4): Using Eclipse a small program was created in C to communicate with Aeroqual S505 monitor. This program was able to communicate with S505, however with the upgrade of the new sensors, the communication protocol changed, and until August 2013 Aeroqual had not yet manage to give us the new protocol.

2) The creation of a protective case: This protective case should contain both CO and NO2 sensors, GSM/GPS module and Wi‐fi, and provide a protection from the weather but allow at the same time gas exchanges with the exterior. An initial sketch was made with some solutions (), but it was put on hold, because of the not so good results with the sensors and the uncertainty that surrounds the communications and the protocol, but also because the protective case needs to evolve to an isolated box with some fans in other to mimic the lab results obtained inside the sealed box, were the most stable results were obtained.

Figure 5.1 – Prototype for a portable station. The green top has to be made of plastic to not interfere with

communications. The Com devices are attached to the top as a solution to Com interferences.

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6. Possible interested party and his expectations From the meetings with the City Hall, they ended up interested in the project. At the time, the City Hall was taking measures to restrict the movement of cars in some roads in Lisbon because of high rates of pollution. And they saw in this project the possibility to have a tool that would allow them to measure street to street at a daily basis, and with that make better decisions of with streets to maintain or restrict traffic. And what they expect to get from the portable stations as a final product is:

1) The results obtain from our sensor should be in line with the ones get from the air quality stations in Lisbon or better (more resolution).

2) A good time response, in order to get values of a street and not from a group of streets.

3) A database easy to access and the ability to show those values on the map.

4) Competitive price and low maintenance.

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7. References 1. QualAr. [Online] 09 de 2013. http://www.qualar.org/.

2. ISQ. [Online] 09 2013. http://www.isq.pt/homepage.aspx.

3. Aeroqual. [Online] 09 de 2013. http://www.aeroqual.com/.

4. Round Solutions. [Online] 09 de 2013. http://www.roundsolutions.com/.

5. Gray Wolf. [Online] 09 de 2013. http://www.wolfsense.com/.

6. GasAlert Microclip. [Online] 09 de 2013. http://www.gasalertmicroclipxt.com/.

7. Horiba. [Online] 09 de 2013. http://www.horiba.com/.

8. Garmin. [Online] 09 de 2013. http://www.garmin.com/.

9. Oliveira, Rafael. [Online] 06 de 2013. http://www.mathworks.com/matlabcentral/fileexchange/34694‐kml‐toolbox‐v2‐7.

10. Antral. [Online] 09 de 2013. http://www.antral.pt/.

11. CML. [Online] 09 de 2013. http://www.cm‐lisboa.pt/.

12. IMTT. [Online] 09 de 2013. http://www.imtt.pt/.

13. Carris. [Online] 09 de 2013. http://www.carris.pt/.

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0123)4567&81 9:;?1@9AB@C1 1users.isr.ist.utl.pt/~jpg/proj/urbisnet/pubs/techrep/... · 2013-11-24 · 5 / 49 2.1. Description of the acquired sensors and their components