ISSS608 2017-18 T3 Assign See Kwan Yen Visualization

Trends: Do you see any trends of possible interest in this investigation?

Patterns	Visualization
1. Overview of chemical tracking across the Years An overview of the water sensor readings across the years will give us a snapshot of the level of chemicals in the wwterway. Figure 1.1 shows the average water sensor reading values by year of all the measures by units of mg/l and locations. It is apparent that iron had the highest value among all the measures in 2003. This is followed by high amount of Total Coliforms and Total Dissolved Salts. Figure 1.2 shows the average water sensor reading values by year of the measure by units of microgram per liter.Aluminium has the highest amount of value in 2009, followed by Barium in 2009, Boron in 2012 and Zinc across multiple years. Some observation on the data: (a) Consistent readings Measures such as those of Cadmium,Copper and Lead are consistent throughout the years in all the locations, though values may vary with different measures across time (b) Present for a certain period of time Measures such as those of PCB and Endrin are observed to be only present in a period of time or in certain location. Measures of these pattern may hint at contamination which have to be examined. (c) Rare readings Measures such as those of Barium and PAHS are rarely observed. Only a few instances were recorded by the water sensors. Measures of this pattern may still hint at a possible contamination though with such little data.
2. Chemicals by Locations Since Iron has the highest level previously, figure 1.3 shows the trend of Iron across all locations. Iron had been consistently low for Boonsri, Busarakhan, Chai, Kannika, Kohsoom, Sakda, and Somchair. Kohsoom has the highest level of Iron among all locations. It is almost non-existent in Achara, Decha and Tansanee. However, a spike in the value of Iron is observed among 6 of the locations as shown in chart. Investigating further on 2003 period shows that values Copper have also spiked during the same day for Kohsoom. While we do not know the cause, the correlation of such values seem to suggest a contamination incident around that period.
3. Measure Insight - Increasing presence of Arsenic In figure 1.4, Arsenic has seen an increasing trend across many locations. Further investigation as to the possible cause of such continuous growth would be advised as to prevent the ecosystem of the waterways and the habitat from being contaminated further.
4. Location Insight - Achara & Kohssom There is an elevated level of Mercury in Achara in 2010 to 2011. Prior to that, there is no data for Mercury in this location. Kohsoom also saw an increased level of mercury in 2006.

Anomalies: What anomalies do you find in the waterway samples dataset? How do these affect your analysis of potential problems to the environment? Is the Hydrology Department collecting sufficient data to understand the comprehensive situation across the Preserve? What changes would you propose to make in the sampling approach to best understand the situation?

Patterns	Visualization
1. Missing data for specific years and locations The figure 2.1 provides a clear view of missing samples of Sulphates and uneven sampling. This is a cause of major concern. We can clearly see that are missing values for samples in Achara, Decha and Tansanee. Plus a very low level of samples taken in those sites compared to the other locations. At the same time, we can appreciate a high level of samples in Boonsri, Chai, Kannika, Sakda specially in a range of years from 2002 to 2009. This scenario occurred for many other chemicals too.
2.Methylosmoline reading started from 2014 onwards but inconsistent Methylosmoline is the major contaminant chemical. However, there are only 3 years of samples available: 2014, 2015 and 2016. The sampling distribution of Methylosmoline varies across the locations. We can possibly traced it back to when Kasios started setting up its factories in the area and also when they started producing Methylosmoline. There is a high probability that if they coincide, then Kasios is highly probable to be found guilty of the production of Methylosmoline.
3.Other chemicals have uneven distributions in different locations The figure shows Aluminium and Iron distributions of samples taken by locations, as we can see they are very differently distributed with outliers clearly marked in locations like Chai or Boonsri