The established analysis pipeline was applied in proof-of-principle studies of two different urine sample sets from geographically different areas, Austria and Nigeria. Both studies included several individuals (Austria (4) and Nigeria ( )) who donated samples at four different time points. In the Austrian samples, 17 different xenobiotics belonging to four compound categories were identified (Figure /B, Table ). Most exposures were either personal care product ingredients or phytoestrogens. The Nigerian urine samples were contaminated with more than twice as many chemicals (n=48) coming from diverse sources (Figure , Table ), but personal care product ingredients and phytoestrogens were still the dominant classes. Concentrations covered five orders of magnitude from the sub-ppb to the ppm range. The Nigerian samples were contaminated with a broader diversity of chemicals, including air pollutants (1-OH-pyrene), mycoestrogens and industrial side products (2-naphthol) and higher concentration levels. In particular, the maximum values were frequently 10-100times higher than the Austrian samples. However, especially the Austrian dataset was small and homogenous compared to the Nigerian dataset. Moreover, the demographic data of the individuals involved in both studies and storage times varied and the stability of analytes was not specifically assessed in this work. Therefore, the available data is not sufficiently representative to estimate population-wide exposure levels. However, the results highlight the potential of the dual-column approach to capture environmental contaminants in different urine samples. The Austrian samples were considered a low-exposure scenario due to strict regulation and enforcement on the use/tolerance of e.g. pesticides and mycotoxins, whereas the Nigerian samples were regarded as high-exposure samples as often food/environmental safety regulations are lacking or not adequately enforced. Each detected xenobiotic was, on average, detected in 31 out of 77 Nigerian samples. Four analytes (enterolactone, BPA, nonylphenol and propylparaben) were present in > 90% of all samples (Figure ). In the Austrian samples, two thirds were contaminated with 4-tert-octylphenol, BPA and propylparaben. Besides, all samples contained nonylphenol and dibutyl phthalate. MS2 spectra further supported identification (Figure ), although for all analytes discovered here reference standards were used for retention time confirmation. The number and level of the detected phytoestrogens were restricted due to the regulated diet with no vegetable and fruit intake except for a smoothie on day 2 before the Austrian study and on the first two study days. Approximately 70 diverse endogenous metabolites were quantified in the urine samples of both studies (Table , Table ). Thirteen individuals completed the full longitudinal sampling with four time points. The variation over two days is exemplified for six individuals and analytes of distinct origins, including the plasticizer bisphenol A, the phytoestrogen genistein, the personal care product ingredient propylparaben and the air pollutant 1-OH-pyrene in Figure . The concentrations spanned over three orders of magnitude within the sample individual. Genistein and propylparaben differed vastly between the timepoints in particular. The intraindividual variation was like the interindividual variation demonstrating the importance of longitudinal sampling for exposure assessment for several participants. The diversity and high dynamic of the exposome even within the same individual was described previously ) and further supported the need for time-resolved testing to capture dynamic exposure in spot urine samples or 24 h urine.