ISSS608 2016-17 T3 Assign ONG GUAN JIE JASON Observations & Insights
ISSS608 Assignment 2 Ong Guan Jie Jason
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Observations & Insights
This section aims to use the dashboard described in the previous section to reveal some interesting observations and insights with regards to the questions that were raised for this mini challenge.
Contents
Question 1: Characterizing the Monitors' Performance and Operation
Monitors' Performance
There are a total of 9 monitors installed in the vicinity of the four factories. The trellis plot allows us to observe the performance of the monitors at a high level.
Fig 1.1 shows a trellis plot capturing emission readings of the chemical AGOC-3A for the month of April. Immediately we can deduce that monitors 1 and 2 are working normally because emission readings are very close to the average line. Similarly, monitors 3, 4 and 6 should be working as intended because fluctuations about the average line seem normal.
We can verify the findings above by performing a drill-in analysis of the trellis plot which brings us to the cycle plot illustrated in Fig 1.2. We can see that emission readings are relatively flat for monitors 1 and 2 whereas fluctuations are regular for monitors 3, 4 and 6 which lends further credence to the earlier findings.
The combined view of the dashboard allows us to perform both level of analysis easily at the same time.
Unexpected Behavior
Scenarios whereby monitors are exhibiting unexpected behavior could be instances of sudden spikes in readings happening erratically during the month. Fig 1.3 shows an illustration of this. The trellis plot above shows that there is a sudden spike in emission readings for chemical AGOC-3A on day 17 for the month of april. The cycle plot below allows a deeper understanding of what is happening and we can see that there were two occurrences of abnormally high readings captured at 06:00 and 07:00 on the 17th April.
Another clearer example can be seen in Fig 1.4. We can see that there is only one instance of abnormally high reading for chemical Methylosmolene captured by both graphs on the 11th of April at 03:00. This is unexpected because the graph depicts a relatively smooth trend for the rest of the month.
However, there will also be blurred scenarios and are not so easily to discern unlike the above examples. For example. Fig 1.5 shows that emission readings for chemical AGOC-A are relatively flat and stable for monitors 1,2 and 3. However for each of them, there is one occurrence of abnormally high reading detected. Is this a simple case of faulty monitors? The cycle plot suggests otherwise. This anomalous event actually occurred at the same timing, 06:00 on 5th December for all monitors. Is it possible that all 3 monitors who are within close proximity to each other were faulty at the same time or is there some other underlying reason?
Question 2: Patterns of Chemicals' Releases
Detected Chemicals
We can leverage on the trellis plot to have a quick overview which chemicals are being detected by which monitors. Fig 1.6 shows a representation of the detection of chemical AGOC-3A across the 3 months.
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Monitors 3-7 and 9 are able to detect AGOC-3A fairly well. Monitors 3 and 6 were picking up a highest concentration for April at an average of 32.8ppm daily. Monitor 3 continued to pick up the highest concentration at 51.6ppm for August but monitor 4 picked up a higher concetration at 60.3ppm for December.
Similarly for Appluimonia, monitors 3 and 4 were picking up a higher concentration of this chemical compared to the other monitors across these 3 months. Monitors 5-9 were able to detect a considerably but lesser concentration as well.
For Chlorodinine, all monitors except for 1 were able to detect a considerable amount of this chemical. Monitor 3 continued to pick up the highest concentration for all months.
Monitors 3,6 and 7 were able to detect Methylosmolene at a much higher concentration compared to others for April. But at the later months, monitors 4 and 5 were starting to pick up a substantial amount of this chemical as well with the former detecting the highest concentration out of all monitors during December.
Release Patterns
The cycle plot is used to observe the time patterns in the release of the different chemicals.
- AGOC-3A
This chemical is released typically between 06:00 to 21:00 based on the 3 months of data.
- Appluimonia
This chemical is being released constantly throughout the month and exhibits the same trend for all 3 months. Moreover, the release of this chemical has increased dramatically during the month of December with almost all the monitors registering an increase compared to the previous two months.
- Chlorodinine
Similar to the previous chemical, Chlorodinine is also being released constantly throughout the 3 months of data. The release of this chemical typically spikes near the start and approaching the end of the month. Also, the level of concentration detected by monitor 4 has increased dramatically from April to December.
- Methylosmolene
This chemical's release pattern is typically between 22:00 and 05:00 based on the 3 months of data. This is coincidentally the opposite pattern of AGOC-3A which suggests that both chemicals may share a relationship with one another.
Question 3: Factories Responsible for Chemical Releases
The way the dashboard can be used to trace the release of the pollutants to the polluters is to look for spikes in the monitors readings via the cycle plot then set the filters for the air plume model to the corresponding day and hour. After that, we observe if the polygons overlap any factories in their trajectories. This could be a possible indication that the factory is responsible for releasing that pollutant.
Chemical Release Associated with each Factory
- AGOC-3A
Roadrunner and Kasio were located very close to one another which makes it very difficult to discern whether there is only one actual factory releasing the chemical or both are releasing the chemicals. The dashboard has managed to pinpoint the potential culprits to both of these companies multiple times but due to their close proximity which is highlighted in Fig 2.1, it makes it very difficult to find cases to isolate one factory. On 6th April at 06:00, the wind was blowing from 270 degrees with a speed of 0.9m/s and monitor 6 captured a reading of 228.8ppm. However, the polygon we are interested in which is colored in red overlaps two factories in its trajectory therefore we are able to tell if one or both contributed to the pollution.
There is one particular instance illustrated in Fig 2.2 which manages to provide considerable evidence to lay charges on one of the factories. On the 22nd April at 09:00, the polygon overlaps only one factory in its trajectory which is subsequently captured by monitor 9. Therefore, we can be fairly positive Roadrunner is one of the factories releasing AGOC-3A.
Another instance on the 23rd August at 06:00 shows evidence of Kasio releasing this chemical as well.
- Appluimonia
One of the highest level of concentration was captured on 29th April at 09:00. The reading was 26.85ppm which is highest for that month and wind speed was 0.2m/s. This suggests that Radiance is responsible for emitting this chemical since it is the only factory covered by the trajectory as shown in fig 2.4.
However, at this particular point of time, the trajectory of the polgyon is very close to both Radiance and Indigo. In fact, if we were to adjust the angle parameter to make the polygon has a larger spread, the two polygons will overlap and moreover owing to the low wind speed, it is very possible that chemicals emitted by Indigo may be carried by the polygon 6 hence Radiance is innocent.
On the 7th April at 03:00, the reading was 6.96ppm and the polygon contained only Indigo in its trajectory hence this provides ample evidence that Indigo is one of the factories responsible for producing this chemical.
- Chlorodinine
Kasio is highly likely to be one of the factories responsible for producing this chemical.
Radiance starts to produce this chemical in August.
- Methylosmolene
Indigo is responsible for producing this chemical. Kasio is responsible as well.
