Comprehensive analysis and modeling of landfill leachate

Landfill leachate data compiled from 220 different landfills from 46 countries in Europe, Middle East, Asia, Africa, and America was analysed by multivariate statistical approaches. Data pre-treatment procedure such as handling of outliers, completion of missing data, and standardization of data was applied to prepare the raw data matrix for the complex statistical analyses including cluster and principal component analyses (PCA). Regression modeling was conducted to estimate leachate parameter values. Results show that usually inorganic parameters, if included in the PCA, dominated the first components indicating the highest correlations as well as accounting for majority of the variation in the data. Those highly correlated parameters in landfill leachate could be important in evaluation of their pathways into leachate in terms of transport and biodegradation mechanisms as well as their elimination potential from sampling and analytical procedures during monitoring activities at landfills. Some leachate parameters having significantly high concentrations, such as organics, salts, and some inorganics, impacted the formation of components in PCA. This in turn provides important information about the specific characteristics of leachate samples and the landfills to which they belong.

Automated summary

This study analyzed landfill leachate data from 220 landfills in 46 countries across Europe, Middle East, Asia, Africa, and America using multivariate statistical approaches. The results showed that inorganic parameters dominated the principal component analyses (PCA) and accounted for the majority of the variation in the data. These highly correlated parameters could provide important information for evaluating the transport and biodegradation mechanisms of leachate, as well as their elimination potential during monitoring activities at landfills.