Can one sample t test be used to check statistical significance for my data? I have a data set of 10 sampling points for ethyl-benzene concentration in soils (mg/kg). The data is
vector <- c(14.77, 59.05, 94.91, 157.55, 33.89, 0.00, 0.00, 11.36, 4.35, 0.00)

Can a one sample t test be used to test statistical significance difference of the concentrations from the ethyl-benzene Dutch intervention value of 110 mg/kg? How should the zero concentrations be treated? The 0.00 is for concentrations that were Below Detection Limit (BDL)
 A: How the zero concentration should be treated depends on what zero values mean. Is a concentration of zero theoretically possible or does it indicate that something went wrong at that measurement? If the latter is the case I would suggest replacing those values with missings since they show some mistake in the measurement and not the actual concentration.
Assuming zero values make theoretically sense you could use a one sample t test. If you doubt the assumptions of the t test to be met you can alternatively use the one sample sign test. Since your data is really small it is difficult to acually check the assumptions of the t test because tests for normality depend on sample size and I further I would argue that in small sample sizes like yours values easily appear as outliers. Thus I would go with the one sample sign test that uses the median rather than the mean which is a more robust parameter. For this you would need the median concentration of the dutch sample.
If zero is a valid value the R code would be
t.test(vector, mu = 110)
# t(9)= -4.3708, p=.002

library(BSDA)
# assuming that he dutch sample is normally distributed and thus mean= median (or you provide median of dutch data)
SIGN.test(vector, md = 110)
# s= 1, p=.021

EDIT
There has been some discussion and this is why I want to make an update.
It was suggested in the comments that in this case where human and environmental safety is important the mean is to be used and not the median. I would argue that the opposite can be true and this is why. Assume country A has 100 soil-samples with all of them being just right under some cutoff for some risky chemicals in the soil. On the other hand, country B has 99 chemical-free samples while only one soil-sample contains a massive concentration. So in average country B could have a significantly higher mean concentration although only a few people are at risk in this country. But in country A are all people at risk and may all suffer in the long term. Thus to me it is not obvious why the median would be inappropriate here and the mean would be the better measure.
A: I think that the intervention value is a regulatory limit, and it is not obvious what summary statistic should be used unless it is specified in the regulation.  For example, in the U.S., for bacteria in water, often the geometric mean is indicated in the regulation.  For example in New Jersey, the limit for E. coli for some types of waterbodies is

"E. Coli levels shall not exceed a geometric mean of 126 / 100ml or a single sample maximum of 235 / 100ml."

Without this kind of guidance, I don't think you can assume that the relevant statistic is the mean or the median or some other statistic.  In the case of the ethyl-benzene values in the original question, we get a different answer if we use the mean or use the maximum observation.
A: There are many proposed methods for handling truncated data. But we need context.
Ethyl-benzene is toxic, flammable, possibly carcinogenic, etc. The idea of using null hypothesis significance testing is questionable here because... what's the burden of proof? You mention a one sided test, but as a null are concentrations above or below a toxic threshold? The costs on a type 1 and type 2 error are different than in many conventional scientific fields.
In either case, imputing 0 is bad. Better to impute LLD with its lowest detectable value as a conservative measure. Don't know it? Read up on the technology. Then you could apply a log transform to the sample, which is an important convention for concentration data. Inspecting a simple density smoother shows that the distributions are much better behaved on a log scale, so the T-test will better operating characteristics.
