Due to relatively stable levels of some AAs and ARG levels across 13 months monitoring time, limited correlations were observed between absolute loads of ARGs and total loads of associated AAs by class (figure ). However, correlations between individual associated AAs and ARGs demonstrated some potential correlations where seasonal variability was reported (table ). Statistically significant positive correlations were observed for ermB and the average monthly loads of the macrolides CLR and dmCLR (p=0.45, 0.48 and 0.58 for CLR, dmCLR r≤0.05). Other statistically significant correlations again showed weakly positive correlations between aSPY and sul1 (p=0.28, r≤0.05) and OFX and qnrS (p=0.35, r≤0.05). Weaker positive correlations were observed between CLR/dmCLR and ermB when correlating all the individual sampling points (0.28 and 0.46 for CLR and dmCLR respectively, r≤0.05) with no statistically significant results observed for aSPY and sul1 and OFX and qnrS (table ). Several studies have investigated relationships in environmental settings between certain AA classes and respective genes in wastewater . For instance, correlations between abundance of TET genes and tetracycline levels and sul genes and sulphonamides . Regarding macrolides, Rodriguez-Mozaz et al. reported significant positive correlations between CIP and qnrS, OFX and qnrS, CLR and ermB, and SMX and sul1 in wastewater streams, but no significant correlation between AZM and ermB . Other studies however have observed weak correlations, or not statistically significant relationships, between TET genes and tetracyclines and sul genes and sulphonamides . AGR levels in this study (other than qnrS) did not exhibit seasonal variation. Statistical tests in this study have demonstrated that for several AAs, the levels do not change much (or at all), it is therefore not expected to observe significant seasonal changes in ARG levels. This longitudinal study focused on temporal observations of changes to daily AAs and ARGs loads. The population remains relatively constant (<±20%) as the city of Bath is studied across only a 13-month period. This is opposed to our previous studies which studied more locations with varying population sizes but focused on a shorter time period . positive correlations between fluoroquinolones and qnrS loads between different locations (r = 0.997, p < 0.004) . The study also observed strong positive correlations between macrolide AAs and ermB (r=0.928, p <0.0002). However, it was highlighted that strong positive correlations were observed with both AA and gene loads with population size. It was theorised that correlations between AAs and gene loads are likely linked to population size as a key driving force. Furthermore in the case for ermB, it has been reported that abundance of this gene in influent wastewater could be heavily influenced by the presence of ermB in common gut bacteria . Other studies have found that global variation between gene abundance strongly correlates with socio-economic, environmental, and health factors , but these do not vary significantly between these two neighbouring sites.