Md. Saiful Islam*
Department of Soil Science, Patuakhali Science and Technology University, Dumki, Patuakhali-8602, Bangladesh
Ram Proshad
Department of Soil Science, Patuakhali Science and Technology University, Dumki, Patuakhali-8602, Bangladesh
Mohammad Asadul Haque
Department of Soil Science, Patuakhali Science and Technology University, Dumki, Patuakhali-8602, Bangladesh
Md. Fazlul Hoque
Department of Soil Science, Patuakhali Science and Technology University, Dumki, Patuakhali-8602, Bangladesh
Md. Shahin Hossin
Department of Soil Science, Patuakhali Science and Technology University, Dumki, Patuakhali-8602, Bangladesh
Md. Nazirul Islam Sarker
School of Public Administration, Sichuan University, Chengdu, China
Heavy metals, Health hazard, Foods, Bangladesh
Tangail district, Bangladesh
Risk Management in Agriculture
2.1. Study area and sampling: The area of Tangail district is 3414.28 km2 and located at the center point in Bangladesh. Tangail district is one of the most densely polluted area in Bangladesh where the density of population is 1,100/km2 (2011 census). The study area is situated between 24° 01′ and 24° 47′ N latitudes and between 89° 44′ and 90° 18′ E longitudes (Wikipedia). For present study, agricultural fields were selected around industrial vicinity of Tangail district, Bangladesh. Several industries like textiles, metals processing, dyeing, brick kiln, battery manufacturing, and leathers are situated near the selected fields. At each sampling station, same species of cereal and vegetables were collected as sub-samples and were thoroughly mixed to form a composite sample. Seventy five samples of eight different agricultural crops i.e. rice (Oryza sativa), sponge guard (Luffa cylindrical), bitter gourd (Momordica charantia), papaya (Carica papaya), okra (Abelmuschus esculentus), bean (Phaseolus vulgaris), brinjal (Solanum melongena) and chili (Capcicum frutescens) were collected by hand from the selected agricultural fields during March-April, 2016. All samples were kept in polythene zip-bags and brought to the laboratory on the day of sampling. Distilled water was applied to wash each sample and the consumable parts of agricultural crops were cut into small pieces and was kept in oven at 70–80 °C to attain constant weight (Tiwari et al., 2011). The fresh and dry weights were recorded to calculate moisture contents. The processed samples were brought to Yokohama National University, Japan for chemical analysis. 2.2. Sample analysis Analytical grade reagents were used for sample analysis and Milli-Q (Elix UV5 and MilliQ, Millipore, USA) water was used for solution preparation. For food, 0.2∼0.3 g of dried sample was digested with 1.5 mL 69% HNO3 (Kanto Chemical Co, Tokyo, Japan) and 4.5 mL 35% HCl (Kanto Chemical Co, Tokyo, Japan) in a closed Teflon vessel. Microwave Digestion System (Berghof speedwave® , Eningen, Germany) was used for sample digestion. The digested solution was then filtered by using syringe filter (DISMIC®—25HP PTFE, pore size=0.45 μm) Toyo Roshi Kaisha, Ltd., Japan and stored in 50-mLpolypropylene tubes (Nalgene, New York). 2.3. Quality control and instrumental analysis For heavy metals, samples were analyzed using inductively coupled plasma mass spectrometer (ICP-MS, Agilent 7700 series). Multi-element Standard XSTC-13 (Spex CertiPrep® , Metuchen, USA) solutions were used for preparing standard curve. Internal calibration standard solutions containing 1.0 mg/L of indium, yttrium, beryllium, tellurium, cobalt and thallium were purchased from Spex Certi Prep® USA. Plant materials (NIST, 1547 Peach leave) of National Institute and Technology were used as CRM in each sample batch to prove the accuracy and precision of the digestion procedure and for subsequent analyses. 2.5. Statistical analysis Statistical package SPSS 16.0 (International Business Machines Corporation [IBM] Armonk, NY, USA) was used for statistical analysis. The means and standard deviations of the metal concentrations in foods were calculated. A Pearson bivariate correlation was used to evaluate the inter-element relationship in cereal and vegetables. Statistical significance differences of heavy metals concentrations among food samples were determined by Multivariate Post Hoc Turkey tests. Multivariate methods in terms of principal component analysis (PCA) were used to interpret the potential sources of heavy metals in studied foods. Cluster analysis (CA) was used to divide the food samples into several groups with distribution of heavy metals by obtaining similarities or differences in samples.
Geocarto International,
Journal