Rawlins, BG; Vane, Christopher
ORCID: https://orcid.org/0000-0002-8150-3640; Kim, Alexander; Tye, Andrew; Kemp, Simon
ORCID: https://orcid.org/0000-0002-4604-0927; Bellamy, P.
2008
Methods for estimating types of soil organic carbon and their application to
surveys of UK urban areas.
Soil Use and Management, 24 (1).
47-59.
10.1111/j.1475-2743.2007.00132.x
Abstract
The occurrence of substantial quantities of black carbon (BC) in urban soil due to local dispersal following
incomplete combustion of fossil fuel complicates the determination of labile soil organic carbon
(SOC). Estimates of SOC content were made from loss on ignition (LOI) analyses undertaken on samples
(0–15 cm depth) from comprehensive soil geochemical surveys of three UK urban areas. We randomly
selected 10 samples from each decile of the LOI distribution for each of the surveys of
Coventry (n = 808), Stoke-on-Trent (n = 737) and Glasgow (n = 1382) to investigate the proportions
of labile SOC and BC. We determined their total organic carbon (TOC) and BC contents, and
by difference the labile SOC content, and investigated the linear relationship of the latter with SOC
estimates based on LOI analyses. There was no evidence for a difference in the slope of the regression
for the three urban areas. We then used a linear regression of labile SOC based on LOI analyses
(r2 = 0.81) to predict labile SOC for all survey samples from the three urban areas. We attribute the
significantly higher median BC concentrations in Glasgow (1.77%, compared with 0.46 and 0.59% in
Coventry and Stoke-on-Trent) to greater dispersal of coal ash across the former. An analysis of the 30
samples showed that LOI at 450 C accounts for a consistent proportion of BC in each sample
(r2 = 0.97). Differences between TOC (combustion at 1050 C after removal of inorganic carbon) and
an LOI estimate of SOC may be a cost-effective method for estimation of BC. Previous approaches to
estimation of urban SOC contents based on half the mean SOC content of the equivalent associations
under pasture, underestimate the empirical mean value.
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