Statistical optimisation of operational parameters using response surface methodology and phytoremediation of arsenic in constructed wetland

Phytoremediation using the robust perennial grass <i>Pennisetum</i> <i>purpureum</i> has been researched as a substitute for traditional treatment approaches for arsenic (As) pollution remediation in the Constructed wetland system. In the current investigation, As absorption and removal through <i>P. purpureum</i> were measured and optimisation was done using Response surface methodology (RSM). To maximise the absorption and removal of As, the Box-Behnken design (BBD) was applied, with 3 key factors (As concentration in treatment sand: 5, 22, and 39 mg kg<sup>−1</sup>; exposure period: 14, 28, and 42 days; aeration rate: 0, 1, and 2 L min<sup>−1</sup>) in the optimisation. The estimated optimum conditions were 39 mg kg<sup>−1</sup> As concentration, 42 days of retention time, and 0.18 L min<sup>−1</sup> aeration rate, with an actual biological As accumulation and removal were 5,088.19 mg kg<sup>−1</sup> dry weight (DW) and 91.45%, respectively. These amounts were comparable to the amount predicted through RSM 5,245.94 mg As kg<sup>−1</sup> DW and 94.29%, respectively. The variance between the test and expected results was 3.10%, demonstrating that RSM could estimate the optimum biological accumulation of As correctly with relatively minimum error. Factors, like As concentration, retention time, and aeration rate, could influence the performance of arsenic phytoremediation through <i>P. purpureum</i>.

Publication Details

Journal
Figshare
Published
2025-01-01
DOI
https://doi.org/10.6084/m9.figshare.29038736
Primary Topic
E-commerce and Technology Innovations
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Statistical optimisation of operational parameters using response surface methodology and phytoremediation of arsenic in constructed wetland

Figshare
E-commerce and Technology Innovations
article

Statistical optimisation of operational parameters using response surface methodology and phytoremediation of arsenic in constructed wetland

article en

Abstract

Phytoremediation using the robust perennial grass <i>Pennisetum</i> <i>purpureum</i> has been researched as a substitute for traditional treatment approaches for arsenic (As) pollution remediation in the Constructed wetland system. In the current investigation, As absorption and removal through <i>P. purpureum</i> were measured and optimisation was done using Response surface methodology (RSM). To maximise the absorption and removal of As, the Box-Behnken design (BBD) was applied, with 3 key factors (As concentration in treatment sand: 5, 22, and 39 mg kg<sup>−1</sup>; exposure period: 14, 28, and 42 days; aeration rate: 0, 1, and 2 L min<sup>−1</sup>) in the optimisation. The estimated optimum conditions were 39 mg kg<sup>−1</sup> As concentration, 42 days of retention time, and 0.18 L min<sup>−1</sup> aeration rate, with an actual biological As accumulation and removal were 5,088.19 mg kg<sup>−1</sup> dry weight (DW) and 91.45%, respectively. These amounts were comparable to the amount predicted through RSM 5,245.94 mg As kg<sup>−1</sup> DW and 94.29%, respectively. The variance between the test and expected results was 3.10%, demonstrating that RSM could estimate the optimum biological accumulation of As correctly with relatively minimum error. Factors, like As concentration, retention time, and aeration rate, could influence the performance of arsenic phytoremediation through <i>P. purpureum</i>.

Figshare
Clean water and sanitation
Openalex Percentile: Top 31%
E-commerce and Technology Innovations
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.