Study Area
The study was carried out at Kwaru stream, Hayin Dan-Mani, Kaduna. Kwaru stream is an urban stream that is impacted by many anthropogenic activities such as dumping domestic solid wastes, wastewater from residential areas, car wash, and it is an important source of drinking water for cattle and diverse irrigated crops in the dry season. Kaduna is located in the northern guinea savanna region of Nigeria, and it is characterized by two distinct seasons in the year; the dry and rainy seasons. The dry season runs from November to April and is characterized by cold and dry conditions, and the harmattan wind blows from the northeast toward the southwest. On the other hand, the rainy season, which runs from May to early October, is depicted by warm and humid conditions with the wind blowing from the southwest towards the northeast. The average monthly temperature of the region is between 26˚C and 34˚C (Bununu et al. 2015). Five sampling stations were selected along the irrigated stretch of Kwaru Stream based on the activities along the stream's catchment. Station 1 (10˚32'41.5"N, 007˚24'26.5"E) is upstream and receives run-off mainly from the farms upstream stream. Station 2 (10˚32'36.0"N, 007˚24'25.5"E) is characterized by irrigation farming, dumping and burning waste. Station 3 (10˚32'21.6"N, 007˚24'22.6"E) is depicted by dumping wastes such as polythene bags, waste food, and cattle grazing and watering. The activities in station 4 (10˚32'13.0"N, 007˚24'16.4"E) include car and upholstery wash points, irrigation water pump sites, and open human and cattle defecation. In station 5 (10˚32'15.5"N007˚24'12.3" E), we found an inflow of wastewater from residential and agricultural areas.
Water and vegetable sample collection
Surface water of Kwaru stream and vegetable samples were collected in March and April 2019, representing the driest months and the peak of the dry season. These months also coincide with when most irrigated vegetables attain harvest age and size. The samples of Leafy vegetables collected included Amaranthus hybridus (spinach), Brassica oleracea (cabbage), Lactuca sativa (lettuce), Hibiscus cannabinus (kenaf), and Vernonia amygdalina (bitter leaf). Fruit vegetable samples included Daucus carota (carrot) and Lycopersicum esculentum (tomatoes). The samples were collected in triplicates upstream (stations 1, 2 and 3) and downstream (stations 4 and 5). Sample identification was authenticated at the Herbarium of the Department of Botany, Ahmadu Bello University, Zaria, Nigeria.
Microcystins Analysis
Aliquots (5 ml) of water samples were frozen and thawed severally to break the microcystins cells (Chia and Kwaghe 2015). Furthermore, cellular contents were extracted in 75% methanol following (Chia and Kwaghe 2015). Next, vegetable samples were rinsed with tap water followed by distilled water. Then, 2g of each sample was cut with a razor blade, weighed and homogenized using a mortar and pestle in 80% methanol to obtain a homogenous mixture (Díez-Quijada et al. 2018). The choice of 80% methanol was to dissolve the intracellular toxins (Turner et al. 2018). Finally, the homogenate was centrifuged at 2000 rpm (10 minutes), and the supernatant was extracted and preserved at -40°C until ready for analysis.
Extracts were subjected to microcystins analysis using a 96 well Abraxis Microcystins-ADDA Enzyme-Linked Immunosorbent Assay (ELISA) kit, following the manufacturer's instructions. The absorbance of the colour reaction at the end of the ELISA procedure was read at 450 nm in a Bio-Rad iMark™ Microplate reader (Bio-Rad Laboratories, Inc., Hercules CA, USA), and the concentrations expressed per cell quota of potential microcystins producing species (Chia et al. 2019).
Data Analyses
The statistical significance of changes in microcystins accumulated in vegetables and total microcystins in water was determined using analysis of variance (ANOVA). When significant differences existed, the Tukey's post-hoc test was used to perform a multiple comparison. The significance level for all analyses was set at p 0.05. The total daily intake (TDI) limit of 0.04 g kg-1 body weight, determined by the World Health Organization, was used to estimate the risk of eating contaminated vegetables(WHO 2011). The first part of the analysis was based on the premise that an adult of 60 kg consumes at least 40 g of vegetable per day and a microcystin content threshold of 60 g kg-1 in vegetable tissues.
\({TDI}_{fish}={MCs}_{{40g}^{-1}}/BM\) Equation 1
Where MCs40g−1 is the concentration of MCs per 40g of vegetables and BM is body mass of an adult weighing at least 60kg.
The monthly and seasonal risks of eating microcystins-contaminated vegetables from study area were predicted using the hierarchical/multilevel modeling method. Equation 2 shows the model that was generated:
Equation 2
Note: The veggies are represented by the MCs. MCs represents the content of the ith observation from the jth group (stream location or month), β0 represents the overall mean or intercept, ßj represents the group-type mean,
& represents within-group variance, and represents between-group variance. The lmer function of the lme4 package (Bates 2010) of R was used to perform hierarchical modeling, with restricted maximum likelihood (REML) set to FALSE. Analysis of variance with the ranova function of the jtools package of R was used to determine the significance of the various multilevel tests. Using cumulative distribution functions, the monthly and stream location risks of consuming infected veggies from the analyzed water source were also demonstrated (CDF).
Using Pearson's correlation coefficient and the R base stats function, the correlation between vegetable tissue bound microcystins and cyanobacterial cell bound microcystins was found. R (https://www.r-project.org) software for macOS was used for all statistical studies.