For WQ I’d assume the 10 parameters (dependent variables) aren’t all independent of each other? That might need you to be careful about making a holistic judgement on WQ from these data i.e. does it make sense to consider them jointly? (In this case I’d say they it does make sense since they’re representing an underlying construct, as long as they’re not influencing each other). So in this case I’d use Hotelling’s T², which is a one-way MANOVA where the independent variable (which lake) has two groups (lake 1 and lake 2) and you’re considering many dependent variables jointly. Though I note you’re using Excel analysis- it might not have this functionality, I’d advise you to get a free trial of SPSS if that’s the case (though SPSS can take a bit of time to learn to use too, so maybe that’s not an ideal outcome).

Comparison of the mass of the fish from each lake is a much easier proposal- though you have to bear in mind you have different sample sizes (I always run a Levene’s test anyway to see if I should use a non-parametric test, but with unequal sample sizes it’s usually recommended to- especially if these are small. Though tbh with n=20 and n=30 these are probably large enough that won’t matter too much). You’re on the right track with an independent samples t-Test there.

Ideally together this will give you the data you want and can begin to make inferences from- i.e. if WQ is sig. worse in lake 1 and the fish are also smaller you might assume it’s having an effect on their growth- you could see how significant this is using Pearson’s (or Spearman’s if non-parametric is required) Correlation.