I went to see my Professor friend on a Sunday morning. Idea was to chit chat and have delicious dosai prepared by his wife with a tangy green chutney and ending the breakfast with a strong south Indian filter coffee.
Professor was busy as usual and was glued to his laptop doing some frantic Google search.
I asked “Professor, what’s your search today, You look real desperate”.
“Well Dr Modak, I am looking for data on sales of Asthalin in the city of Mumbai over past 10 years.” Professor responded while glaring at the computer screen
Some of you may know that Asthalin is a product of Cipla Pharmaceuticals that has been a life saver inhaler to combat asthma.
I was surprised about Professors interest in sale of Asthalin.
“I am teaching the subject of air pollution tomorrow and I badly need this data” Professor said
I did not want to disturb him and so kept shut.
A few minutes passed by and Professor seemed to have found that he was looking for.
“Aha, I finally hit on the data I wanted, now let us get to the dining table for the breakfast” Professor seemed to be relived
While relishing the Mysore plain dosai, I asked Professor the connection …
“Well, Dr Modak, You probably know that I always teach the subject of environment with the help of meta data.” Professor said.
Meta data – what’s that Professor? I knew a bit about this term, but wanted to have a better explanation
“Metadata is simply data about data. It means it is a description and context of the data. It helps to organize, find and understand data. In most instances, meta data is used to search data, but I use meta data to make understand data better.” Professor explained
Take a case of continuous air quality monitoring data. When we see sudden spikes in the concentration levels, then you may check the “meta data” that tracks the “surround” situation e.g. may be a truck was standing next to the monitoring station puffing emissions over 10 minutes. This meta data could be in the form of a video file that records the surrounding as much as the air pollutant concentration is recorded by the monitoring instrument. Another example could be an instance of a sudden fall in the particulate concentration. This may indicate a shower of rain (like a spell of a drizzle) washing out the particulates. So, records of the rain events become a useful meta data.
“Oh yes, understood Professor” I said. “So, what you are saying is that meta data is required or is very important to understand the environmental data we monitor. In many instances. we don’t pay attention to this kind of data. We don’t record or we overlook”.
“Indeed. So, tomorrow when I teach air pollution, I will be showing map of city of Mumbai with air quality trends over 12 monitoring stations and show at the same time the information or trend in the sale of asthalin inhaler at some of the major chemists. Probably, the air quality (especially the particulates) near to the chemist shops may be correlated with the sale of asthalin. But I am not very sure. I plan to show last 10-year trend between the two, based on monthly average data. It may throw interesting relationship between PM10 or PM2.5 or ratio between PM2.5 and PM10 with the sale of asthalin”
Professor showed me a map that he was attempting to prepare. I thought this was a great idea to make students understand the air pollution in Mumbai and raise a debate. Merely looking at the air pollutant concentrations wouldn’t perhaps give a deeper understanding of the problem.
I thought of similar associations. I remembered that we got some statistics from Western Railways in Mumbai about the frequency of cable coating they had to follow to combat cable corrosion. When Mumbai had moderately high Sulphur dioxide concentration, the frequency of cable recoating had increased. A plot between average seasonal Sulphur dioxide concentration and expenditures on per unit length for recoating showed an interesting proportional relationship.
Professor continued. ”There are known relationships that show coupling e.g. per capita income and per capita waste generation. So richer you get more becomes the waste generation. But if you start digging more, you may find even more interesting relationships. For instance, value of goods purchased through e-commerce websites may explain the rising fraction of plastic in the Municipal Solid Waste (MSW). So, it may worth to expose the student to the meta data on e-commerce platforms to understand the changing composition of the MSW. Patterns and modes of consumption help to know the generation of waste”
Professor was right. I remembered that increasing cost of raw water treatment reflected the deteriorating quality of river water. More dosage of flocculants and disinfectant had to be used to combat the pollution released upstream of the raw intake works.
Professor had another example. In the city of Hubli in India, he had found that high concentrations of Total Dissolved Solids (TDS) in drinking water were related to the insurance claims made by patients for the treatment of kidney stones. That showed serious health and economic implications to justify investing in a TDS management plan.
Sometimes we assess the effectiveness of a regulation and a degree of enforcement by examining the trend in the fines collected or number of non-compliance cases filed. I analyzed the data on the number of cases filed to the National Green Tribunal (NGT) over 5 years across India and this statistic showed the “hot spots” or the “troubled areas” that we should worry.
Extent of night illumination at industrial estates (detected through the satellite imageries), the amount of octroi collected on the road entering the industrial estate and the water cess records provide a good measure to assess the resource intensity. These are interesting elements of meta data to serve as a proxy. You can then compare the resource intensities and potential environmental impacts of two industrial estates on this basis.
Professor said that it is necessary that the Teacher should use the “associativity” and appropriate meta data to make students think beyond the silos, be creative and learn to question or inquire. This style is perhaps most desirable to explain the complex subject of environment and its management. Remember that examining meta data also helps to check the “quality” of the data and validate some of the hypothesis. We need to build a number of interesting teaching case studies for this purpose. Professor lighted his cigar
“Oh Professor, since you mentioned about validating the hypothesis, I must share with you something funny” I said while sipping the filter coffee. Generally, higher is the number of environmental professionals available in a country, the national Environmental Performance Index (EPI) should improve. [EPI is a measure developed by the Yale University. EPI for each country is estimated every year and the index has been published for more than 15 years].
And so, what was your finding for India data Dr Modak? Professor asked.
“Well, I found that as the number of environmental professionals increased, the levels of EPI for India deteriorated! Quite contrary to the hypothesis”
“Aha, you did not use the right meta data Dr Modak. Professor exclaimed. “If you had used meta data on corruption and scams in India, then you would have certainly found a relationship between corruption in the country and the deteriorating EPI”
I thought Professor was absolutely right.
Cover image sourced from https://tech.ebu.ch/groups/pmag
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