Urban rivers are exposed to an increasing load of organic micropollutants from wastewater effluent posing an ecological as well as public health hazard. One-off surveys can capture a snapshot of the pollution profile but fail to reveal the full scale of spatial and temporal heterogeneity. In the present study, 41 micropollutants (non-steroid anti-inflammatory drugs (NSAID), antihypertensives, antiepileptic, antidiabetic, antibiotics, iodinated contrast media (ICM), corrosion inhibitors, pesticides) were monitored every two weeks for one-year upstream and downstream of the Budapest metropolitan area in Danube River (336 samples total). ICMs, benzotriazoles and metamizole degradation products were detected in highest concentration regularly exceeding 100 ng/L. Median concentration of other pharmaceuticals ranged from <1 to 26 ng/L, while pesticides were typically below 10 ng/L. Variability of micropollutant concentration was primarily temporal, exhibiting two different patterns: (1) inverse correlation to river discharge, observed for corrosion inhibitors and carbamazepine (r = -0.505 to -0.665) or (2) inverse correlation to water temperature, observed primarily for ICMs, antihypertensives and antibiotics, r = -0.654 to -0.904). Temperature dependence was also significant after correcting for river discharge. Relative increase of pharmaceuticals was 2-134% after the metropolitan area, partially explained by emission estimates calculated from retail data and metabolization rates. The concentration of five ICMs (iopamidol in 100, iodixanol in 96, diatrizoate in 22, iomeprol in 21 and iohexol 13% of the samples) and two NSAIDs (ibuprofen and diclofenac (in 31.5 and 23% of the samples) exceeded the predicted no environmental effect concentration, posing a risk to algae (HQ = 1.2-6) and fish (HQ = 1.4-1.9), respectively. Results suggest that risk-based monitoring and risk management efforts should focus on ICMs, NSAIDs and industrial chemicals, taking into account that sampling in cold periods and during low flow provides the worst-case estimates.