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Compositional data analysis of smoke emissions from debris piles with low-density polyethylene.

David R WeiseHeejung JungJavier Palarea-AlbaladejoDavid R Cocker
Published in: Journal of the Air & Waste Management Association (1995) (2021)
Data describing the composition of smoke are inherently multivariate and always non-negative parts of a whole. The data are relative and the information is contained in the ratios between parts of the composition. A prior analysis of smoke emissions produced from the burning of manzanita wood mixed with low-density polyethylene plastic applied traditional statistical methods to the compositional data and found no effect. The current paper applies compositional data techniques to these smoke emissions to determine if the prior analysis was accurate. Analysis of variance of the isometric log-ratios showed that LDPE significantly affected the CO2 emission ratio for 8 of the 191 trace gases; this analysis showed none of the gases identified in the previous analysis were affected by LDPE. LDPE did not affect the CO2 emission ratios for the alkanes, alkenes, alkynes, aldehydes, cycloalkanes, cycloalkenes, diolefins, ketones, MAHs, and PAHs. Compositional data analysis should be used to analyze smoke emissions data. Burning contaminant-free LDPE should produce emissions like wood. Implications Reanalysis of impact of burning LDPE plastic in silvicultural debris piles using appropriate statistical techniques confirmed previously published results from inappropriate techniques that LDPE did not change the composition of the smoke emissions. Being able to dispose of these LDPE-covered forest debris by burning can save thousands of dollars in labor costs annually. Disposal of pesticide-free agricultural LDPE plastic by burning should only produce wood-like smoke emissions. This applies to LDPE/total mass ratios of 0.25- 2.5% as studied.
Keyphrases
  • data analysis
  • municipal solid waste
  • electronic health record
  • life cycle
  • big data
  • heavy metals
  • risk assessment
  • climate change
  • systematic review
  • machine learning
  • deep learning
  • drinking water
  • health information