ISSS608 2016-17 T3 Assign KRUTIKA BALVEER CHOUDHARY
Overview
VAST provides information of sensor, wind, factory and chemical. There are nine sensors and four factories in the Lekagul Wildlife Preserve Area. Their locations are as following:
Chemical Description
Appluimonia
An airborne odor is caused by a substance in the air that you can smell. Odors, or smells, can be either pleasant or unpleasant. In general, most substances that cause odors in the outdoor air are not at levels that can cause serious injury, long-term health effects, or death to humans or animals. However, odors may affect your quality of life and sense of well-being. Several odor-producing substances, including Appluimonia, are monitored under this program.
Chlorodinine
Corrosives are materials that can attack and chemically destroy exposed body tissues. Corrosives can also damage or even destroy metal. They begin to cause damage as soon as they touch the skin, eyes, respiratory tract, digestive tract, or the metal. They might be hazardous in other ways too, depending on the particular corrosive material. An example is the chemical Chlorodinine. It has been used as a disinfectant and sterilizing agent as well as other uses. It is harmful if inhaled or swallowed.
Methylosmolene
This is a trade name for a family of volatile organic solvents. After the publication of several studies documenting the toxic side effects of Methylosmolene in vertebrates, the chemical was strictly regulated in the manufacturing sector. Liquid forms of Methylosmolene are required by law to be chemically neutralized before disposal.
AGOC-3A
New environmental regulations, and consumer demand, have led to the development of low-VOC and zero-VOC solvents. Most manufacturers now use one or more low-VOC substances and Mistford’s plants have wholeheartedly signed on. These new solvents, including AGOC-3A, are less harmful to human and environmental health.
Factory Description
Roadrunner Fitness Electronics
Roadrunner produces personal fitness trackers, heart rate monitors, headlamps, GPS watches, and other sport-related consumer electronics. Roadrunner began as one of the region’s first fitness stores in 1962, with an eye toward outfitting the entire nation with appropriate outdoor gear. After an earthquake nearly destroyed their main warehouse in 1968, Roadrunner turned a bad situation into a glowing success with the first “slightly damaged goods” sale. After which they began to focus on manufacturing; though their “Earthshaking Bargains” business still sells dented, overstocked and refurbished items over the internet and from a small retail shop attached to their front office.
Kasios Office Furniture
Kasios Office Furniture manufactures metal and composite-wood office furniture including desks, tables, and chairs. Kasios wants to do with desk chairs what Starbucks did for coffee – making office furniture what people must have, instead of what they just need. “Office equipment doesn’t need to be ugly!” says founder Ken Kasios. “We have redesigned all office products to be cool, fun, and hip—even your basic stapler.” Kasios business model is focused on in-store merchandising highlighting the beauty and functionality of their “user-centered design”. They recently celebrated the one-year anniversary of a distribution and merchandising agreement with the national office supply chain store PaperKlips.
Radiance ColourTek
Radiance produces solvent based optically variable metallic flake paints. “Metallic paints with an untarnished reputation!” quips ColourTek’s Senior Vice President Arthur Donner. “Radiance ColourTek metallic paints are worth their weight in gold.” Offering a new generation of paints in the 1970s, Radiance out marketed all competitors for three decades until manufacturing process issues began to tarnish their reputation. “We were challenged,” said Donner. “Polishing up our pearlescent pigments caused us to lose luster, but now we have the lowest VOCs (volatile organic compounds) in the industry!”
Indigo Sol Boards
Indigo Sol produces skateboards and snowboards. Founder Billy Keys started off manufacturing wooden wine barrels for northwestern US wineries, but then navigated a course from decorative fiberglass wine barrels to making his first pair of fiberglass skis in 1971. Excellent product and sales decisions rocketed Keys Skis production to unexpected levels, until they were bought out by a large Denver, Colorado-based private investment group. Keys returned to making specialized snowboards in the 1980s, with a small company in Mistford called Indigo Sol. The company has seen modest growth in recent years.
Data Description
Analysis of Sensors
Non-Functional Sensors
As seen in the figure, 2nd and 6th of April, 2nd,4th,7th of August and 2nd and 7th of December are the days when the sensors are recording less readings than the usual number of recordings. When these days were further investigated, the following was seen:
April: 2nd, 4th at 12:00 am none of the sensors recorded any readings.
August: on 2nd August only monitor 3 recorded any readings for 12:00 am while for both 4th and 7th none of the sensors worked at 12:00 am.
December: On 2nd December at 12:00 am none of the sensors recorded any readings whereas on 7th December only monitor 6 and 7 recorded some readings at 12:00 am.
Sensors with Erroneous Function
Here, the sensors have been analysed to see if at any given point in time, multiple readings for a chemical are detected. This could be explained by some problem in the functioning of the sensor.
As seen in the figure on the left, Sensors 5 and 6 seem to record multiple readings for AGOC-3A quite often as compared to the other sensors. It is noticed the Methylosmolene has days with less recordings which could be due to the wind speed/direction.
As seen on the figure on the left, in August the sensors on a whole do not perform as well as they did in April. Sensors 4,5,6 have a lot of instances of multiple recordings of the same chemical at the same time on the same day. Sensors 7 and 8 are not shown in the plot because they seem to perform consistently across August.
As seen on the figure on the left, in December the sensors on a whole seem to perform better than August but still not as consistent as April. Sensors 4,5,6,9 have a lot of instances of multiple recordings of the same chemical at the same time on the same day. Sensors 7 and 8 are not shown in the plot because they seem to perform consistently across December.
Sensors 4,5,6 are the most inconsistent in terms of performance as compared to other sensors across the 3 months. Further analysis needs to be done to understand the reason for the inconsistency in the recordings by these sensors.
Analysis of Chemicals
In the hourly overall graph we can spot two patterns as follows: AGOC-3A is emitted during 06:00 to 21:00 whereas Methylosmolene is emitted majorly during 21:00 to 6:00. Sufficient information is not available to understand if the two chemicals have a relationship or which function of the factory they are produced in which might increase their emission during certain times of the day.
AGOC-3A
As seen in the daily average graph, this chemical seems to be majorly detected by sensors 3,4,5,6 while the highest peaks indicating the detection of chemical reading above average level are mostly sen in the month of August. This could be attributed to either the functioning of the factory and the wind. The anomaly seen in the month of August by sensor 3 could be due to the sensor's placement or its functioning. Further analysis could be done on this.
In the hourly graph for both overall and by chemical and the monitor, we can see that AGOC-3A is primarily emitted during 06:00 to 21:00. Sensor 6 shows a very inconsistent recording of readings on an hourly basis, this could be because of the factory that emits this chemical and is detected by 6 do not emit consistently or the detection by 6 is due to the wind speed and wind direction which can be inconsistent.
Methylosmolene
According to the daily graph, the chemical is comparatively consistent as compared to the latter months of August and December. For this chemical as well, sensor 6 seems to have very inconsistent readings and it is seen that for this sensor particularly, the maximum peaks are in the first half of the month. This again could be attributed to the factory that is influencing the readings of sensor 6 and the wind direction in combination.
According to the hourly graph, the peaks are mostly during the night time i.e from 21:00 to 6:00 which is also the pattern seen in the overall hourly graph. Further investigation can be done into this to understand whether the time of the day the chemical is emitted impacts its potency towards the environment and whether there are certain factories that increasing their emissions particularly at night.
Appluimonia
According to the daily graph, this chemical is released fairly consistently across all sensors and all months except Sensor 3. Sensor 3 exhibits the most readings and the highest variations as well. It can also be seen that the emissions have the highest variation on an hourly basis as detected by Sensor 3.
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 Sensor 4 has increased dramatically from April to December in both daily averages as well as hourly averages. Sensor 6 seems to have a lot of high's and low's which are consistent across all months and all the other chemicals as well. Sensor 6 should be analysed to understand the reason behind the inconsistency int he readings.
Analysis of Factories
Using the meteorological data, the influence range of a particular factory in 3 hour intervals can be determined. I assumed that the originating point of the wind is from the sensor and only the readings recorded every 3 hours were considered for this part of the analysis. On the basis of the pollution plume model, the angle of the fence is considered to be 30 degree by default and wind speed is converted to miles per hour and then scaled to the grid.
