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Nontarget mass spectrometry has great potential to reveal patterns of water contamination globally through community science, but few studies are conducted in low-income countries, nor with open-source workflows, and few datasets are FAIR (Findable, Accessible, Interoperable, Reusable). Water was collected from urban and rural rivers around Dhaka, Bangladesh, and analyzed by liquid chromatography high-resolution mass spectrometry in four ionization modes (electrospray ionization ±, atmospheric pressure chemical ionization ±) with data-independent MS2 acquisition. The acquisition strategy was complementary: 19,427 and 7365 features were unique to ESI and APCI, respectively. The complexity of water pollution was revealed by >26,000 unique molecular features resolved by MS-DIAL, among which >20,000 correlated with urban sources in Dhaka. A major wastewater treatment plant was not a dominant pollution source, consistent with major contributions from uncontrolled urban drainage, a result that encourages development of further wastewater infrastructures. Matching of deconvoluted MS2 spectra to public libraries resulted in 62 confident annotations ( i.e. , Level 1-2a) and allowed semiquantification of 42 analytes including pharmaceuticals, pesticides, and personal care products. In silico structure prediction for the top 100 unknown molecular features associated with an urban source allowed 15 additional chemicals of anthropogenic origin to be annotated ( i.e. , Level 3). The authentic MS2 spectra were uploaded to MassBank Europe, mass spectral data were openly shared on the MassIVE repository, a tool ( i.e. , MASST) that could be used for community science environmental surveillance was demonstrated, and current limitations were discussed.