Abstract
Atmospheric pollutants deposited on impermeable surfaces can be an important source of pollutants to stormwater runoff; however, modelling atmospheric pollutant loads in runoff has rarely been done, because of the challenges and uncertainties in monitoring their contribution. To overcome this, impermeable concrete boards (≈1m2) were deployed for 11months in different locations within an urban area (industrial, residential and airside) throughout Christchurch, New Zealand, to capture spatially distributed atmospheric deposition loads in runoff over varying meteorological conditions. Runoff was analysed for total and dissolved Cu, Zn, Pb, and total suspended solids (TSS). Mixed-effect regression models were developed to simulate atmospheric pollutant loads in stormwater runoff. In addition, the models were used to explain the influence of different meteorological characteristics (e.g. antecedent dry days and rain depth) on pollutant build-up and wash-off dynamics. The models predicted approximately 53% to 69% of the variation in pollutant loads and were successful in predicting pollutant–load trends over time which can be useful for general stormwater planning processes. Results from the models illustrated the importance of antecedent dry days on pollutant build-up. Furthermore, results indicated that peak rainfall intensity and rain duration had a significant relationship with TSS and total Pb, whereas, rain depth had a significant relationship with total Cu and total Zn. This suggested that the pollutant speciation phase plays an important role in surface wash-off. Rain intensity and duration had a greater influence when the pollutants were predominantly in their particulate phase. Conversely, rain depth exerted a greater influence when a high fraction of the pollutants were predominantly in their dissolved phase. For all pollutants, the models were represented by a log–arctan relationship for pollutant build-up and a log–log relationship for pollutant wash-off. The modelling approach enables the site-specific relationships between individual pollutants and rainfall characteristics to be investigated.
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