Parametric fate and transport profiling for selective groundwater monitoring at closed landfills: A case study

Monitoring contaminant concentrations in groundwater near closed municipal solid waste landfills requires long term monitoring program which can require significant investment for monitoring efforts. The groundwater monitoring data from a closed landfill in Florida was analyzed to reduce the monitoring efforts. The available groundwater monitoring data (collected over 20 years) were analyzed (i.e., type, concentration and detection level) to identify the trends in concentrations of contaminants and spatial mobility characteristics of groundwater (i.e., groundwater direction, retardation characteristics of contaminants, groundwater well depth, subsoil characteristics), to identify critical monitoring locations. Among the 7 groundwater monitoring well clusters (totaling 22 wells) in landfill, the data from two monitoring well clusters (totaling 7 wells) located along direction of groundwater flow showed similarities (the highest concentrations and same contaminants). These wells were used to assess the transport characteristics of the contaminants. Some parameters (e.g., iron, sodium, ammonia as N, chlorobenzene, 1,4-dichlorobenzene) showed decreasing trends in the groundwater due to soil absorption and retardation. Metals were retarded by ion exchange and their concentration increased by depth indicating soil reached breakthrough over time. Soil depth did not have a significant effect on the concentrations of volatile organic contaminants. Based on the analyses, selective groundwater monitoring modifications were developed for effective monitoring to acquire data from the most critical locations which may be impacted by leachate mobility. The adjustments in the sampling strategy reduced the amount of data collected by as much as 97.7% (i.e., total number of parameters monitored). Effective groundwater sampling strategies can save time, effort and monitoring costs while improving the quality of sample handling and data analyses for better utilization of post closure monitoring funds. (C) 2015 Elsevier Ltd. All rights reserved.