Comparative Analysis of Soil Contaminant Removal by Phytoremediation Systems

Thesis Abstract

Phytoremediation offers a sustainable approach to soil restoration by leveraging plant processes to extract, stabilize, and degrade soil contaminants; however, comparative effectiveness across contaminant classes, plant functional groups, and site conditions remains inadequately quantified, limiting scalable application. This study investigates the differential performance of three phytoremediation systems—phytoextraction using Helianthus annuus (sunflower), phytoimmobilization using Vetiveria zizanioides (vetiver), and phytodegradation using Populus deltoides (cottonwood)—in removing heavy metals (Cd, Pb, Zn) and organic pollutants (benzo[a]pyrene, bisphenol A) from variably contaminated soils. The aim is to elucidate which system optimally reduces contaminant loads under distinct soil physicochemical contexts and to identify interaction effects between plant traits, soil properties, and contaminant speciation. Specific objectives include (1) evaluating total and extractable metal fractions pre- and post-phytoremediation, (2) quantifying changes in polycyclic aromatic hydrocarbons and phenolic contaminants using chromatographic methods, (3) comparing plant biomass production, translocation factors, and rhizosphere enzyme activities as mediators of contaminant removal, (4) modeling system performance across soil pH, texture, organic matter, and moisture regimes, and (5) assessing cost-effectiveness and practical feasibility for field deployment. The methodology adopts a comparative field–laboratory design conducted at three chronically contaminated sites with similar climate but divergent soil properties. A stratified sampling framework yields 180 study units, comprising 60 units per phytoremediation system, each with replicated subplots (n=3) to account for microhabitat variation. Data collection integrates soil physicochemical analysis (pH, cation exchange capacity, organic matter, texture, moisture content), contaminant quantification (inductively coupled plasma mass spectrometry for metals; gas chromatography–mass spectrometry for organics), plant physiological metrics (biomass yield, leaf translocation indices), and rhizosphere microbial and enzymatic indicators (dehydrogenase, urease activities). Instruments include standardized soil sampling protocols, certified reference materials for contaminant calibration, and high-performance liquid chromatography coupled with mass spectrometry for organics. Analytical procedures follow QA/QC protocols, including blanks, duplicates, recovery tests, and calibration checks. Data analysis employs a mixed-methods framework with quantitative and qualitative elements. Descriptive statistics summarize baseline soil characteristics and contaminant distributions. Inferential analyses use multivariate ANOVA to test system effects on contaminant removal across metals and organics, with post hoc Tukey tests to discern pairwise differences. Regression models (stepwise multiple regression) identify key soil-plant-microbial predictors of remediation efficiency, while structural equation modeling (SEM) tests hypothesized pathways linking plant trait metrics to contaminant uptake and degradation. Nonlinear dose–response models assess saturation effects at higher contaminant loads. Sensitivity analyses examine robustness to missing data and site-specific variability. For metals, sequential extraction data inform speciation shifts and bioavailability changes; for organics, degradation kinetics are modeled to determine half-lives under each system. A cost-benefit analysis aggregates establishment, maintenance, and monitoring costs alongside remediation efficacy to evaluate practicality. Theoretical grounding draws on the Biogeochemical Cycling theory and the Stress-Gradient Hypothesis to interpret plant performance under contaminant-induced stress, supplemented by the Resource-Availability and Trade-Off theories to explain biomass allocation versus remediation efficiency. Expected findings indicate differential metal removal efficiencies aligned with plant translocation capacities and rhizosphere enzyme activities, with phytoextraction outperforming phytoimmobilization for Cd and Zn at neutral to mildly alkaline pH, while phytodegradation demonstrates superior removal of PAHs under moderate soil moisture regimes. Interaction effects are anticipated between soil organic matter and contaminant partitioning, and between plant biomass production and contaminant bioavailability. The study contributes to knowledge by delivering a robust, comparative assessment framework for phytoremediation performance across contaminant classes and soil contexts, informing guideline development for site assessment, system selection, and scaling strategies. It is anticipated that the results will support evidence-based recommendations for selecting plant systems based on contaminant profile and soil conditions, alongside practical considerations of cost and maintenance. The main conclusion emphasizes that no single phytoremediation system universally outperforms others; rather, optimal remediation is achieved through site-specific matching of contaminant chemistry, soil properties, and plant functional traits, complemented by monitoring of rhizosphere processes. Recommendations include integrating pre-treatment to modulate contaminant bioavailability, implementing adaptive management to maintain soil moisture and pH within optimal ranges, and coupling phytoremediation with microbial amendments to enhance degradation pathways, with further research suggested on long-term ecosystem recovery and field-scale applicability.