For rough estimates, work backward based on data for say Cd (or Ni) contamination level in water sources.
Perform a regression analysis for not too current years linking Ni/Cd battery production to contamination levels probably with a multi year lag effect
(for example, a spike in battery sales in year 6 may be observed as contributing to years 7 to 10 growth in Cd pollution). You then do a reverse
regression to estimate battery sales based on pollution levels in other geographic areas and total sale numbers. This is akin to a missing data
problem in statistics using auxiliary data that is believed to be correlated (like more sales, means more dumping, increased leaching into water
sources, and finally tested higher levels of Cd).
Get the statistical standard deviation estimates for the regression coefficients. From there derive an estimate (or expected distribution) of sales. I
would use Monte Carlo sampling to estimate the latter.
Assumption: There is a constant % of Ni/Cd batteries that are improperly dumped which eventually find there way into the local water supply.
You may be able to improve your analysis by looking at the % of just Ni/Cd versus all battery sales for whatever available data you can obtain.
Also, the sale of electrical devices may be correlated to Ni/Cd sales/comsumption.
[Edited on 23-8-2018 by AJKOER] |