Brown, A S; Barker, S J; Brown, R J C; Wyche, K P; Butterfield, D M (2024) A robust regression analysis method to determine the significance of trends concentrations of heavy metals in UK ambient air and improve network design and emissions inventories. Environmental Monitoring and Assessment, 196. 101

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Abstract

A novel application of the Theil-Sen robust regression method for determining the temporal trends in the concentration of heavy metals in UK ambient air over the period 2005-2020 is presented and compared to other regression methods. We have demonstrated improvements over non-robust methods of regression, proving the ability to tease out trends that are small with respect to the variability of the concentration measurement. The method is used to identify, in general, large and significant trends in the concentrations of Ni, As, Pb and V over the period 2005-2020, either across the UK as a whole or at groupings of site classifications in the UK. These trends have been compared to trends in emissions data determined in the same manner. Although the results for most metals provide confidence that the UK metals network of monitoring sites is successful in appropriately capturing changes in emissions, a key finding of this work is the disagreement between trends in measured concentrations and emissions for Cu, Mn & Ni, for which we suggest improvements in future network design. The results also indicate that UK emissions data for V should be reviewed, as we propose that the rate of reduction of vanadium emissions is likely to have been overestimated.

Item Type:	Article
Keywords:	Air quality; Metals; Emissions; Temporal trends; Regression; Theil-Sen
Subjects:	Environmental Measurement > Air Quality and Airborne Particulates
Divisions:	Atmospheric Environmental Sciences
Identification number/DOI:	10.1007/s10661-023-12248-9
Last Modified:	06 Sep 2024 13:57
URI:	https://eprintspublications.npl.co.uk/id/eprint/10001