Assessment of heavy metals uptake by cauliflower (Brassica oleracea var. botrytis) grown in integrated industrial effluent irrigated soils: A prediction modeling study

Abstract

This study explored the heavy metals uptake by cauliflower (Brassica oleracea var. botrytis) grown in integrated industrial effluent (IE) irrigated soils of Haridwar, India. Water, soil and vegetable samples were collected from four study sites namely Location 1 (bore well-irrigated taken as control), and 2, 3, and 4 (IE irrigated). Standard sampling and analytical techniques were adopted to perform all fields and laboratory works. Regression modeling, Principal Component Analysis, (PCA) and Accumulation Nutrient Elements (ANE) methods were implemented. The results showed that after irrigation, the nutrient-rich IE significantly (P < 0.05, 0.01, and 0.001) enhanced the soil fertility. The heavy metals contents in B. oleracea tissues grown in IE irrigated soils had higher accumulation as compared to bore wellwater irrigated soils. Prediction models with high R2 (0.59–0.99), high F values, low P values (<0.05), and less difference among measured vs. predicted values (γ) were developed. PCA technique was used to extract three independent data components (PC 1, PC 2 and PC 3) for the heavy metal data of water, soil and B. oleracea tissues based on the extracted eigenvalues and variance (%). ANE method showed that among all heavy metals, Fe had the highest chemical share with 33.24%, 46.61%, and 36.50%, while Cd had the lowest share 0.62%, 0.15%, and 0.19% participation for roots, leaves and florescence parts of B. oleracea, respectively. The methodology is helpful to model heavy metals uptake by B. oleracea grown in IE irrigated soils.