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Artyukhov et al. Carbon Footprints 2026, 5, 8 Page 15 of 21

with high anthropogenic load, specifically intense road traffic. The radial differentiation coefficient indicated
accumulation of Zn (R = 11) and Pb (R = 27) in the upper horizons, likely due to retention by organic matter.
Along the profile, pH increased from 3.9 to 4.9. The highest Zc value (Zc = 40) was observed in Histic
Cryosol, which had a neutral pH of 5.6-6.2, although radial differentiation was not pronounced. In acidic
(pH 4.0-4.4) organogenic soils with Zc = 31, V accumulated in the middle of the profile, while other heavy
metals showed minor accumulation in the upper horizons. Acidic (pH 4.0-5.4) sandy soils of the southern
hypoarctic tundras were characterized by moderate total contamination (Zc ranging from 0 to 24; acceptable
to moderately hazardous), with pronounced redistribution of Pb (R = 1.4-9) throughout the profile and
accumulation of Zn (R = 2.6; 2.9) and V (R = 5.6; 1.4) in the upper horizons. In the mountain tundra soils of
the Polar Urals, with varying particle size distributions, near-neutral pH (6.0-7.2), and low total
contamination levels (Zc < 16), no significant radial redistribution of the studied elements (R = 0-2.75) was
observed.

Interplay of SOM mineralization and metal content
The combined data on SOM mineralization kinetics and the Zc allow a preliminary assessment of potential
interactions between soil contamination and carbon cycling in the studied soils. While a detailed statistical
correlation analysis is beyond the primary scope of this manuscript, several noteworthy observations can be
made. Soils with moderate to high contamination levels (e.g., Sal_C1, Gk_C7, Zc > 16) did not consistently
show suppressed mineralization potential compared to less contaminated soils from similar landscape
positions. For instance, the Cryic Histosol (Gk_C7) exhibited one of the highest PMC values despite its
elevated Zc, suggesting that the organic-rich matrix in these soils may bind heavy metals, reducing their
immediate bioavailability and toxicity to microbial decomposers .
[68]

It has been demonstrated that total heavy metal/metalloid concentrations in contaminated soils do not fully
reflect toxicity, as bioavailability varies due to physico-chemical interactions with the soil matrix . Previous
[69]
studies indicate that soil contamination with heavy metals and metalloids can alter microbial communities:
tolerant microorganisms may replace more susceptible ones and increase in abundance [69,70] . These
microorganisms can immobilize heavy metals or convert them into less toxic forms . Analyses of the Yamal
[71]
soil microbiome revealed dominant phyla including Acidobacteria (> 10% of total microorganisms),
Gemmatimonadetes (> 4%), and other bacterial groups . Gemmatimonas are highly sensitive to Cd, Pb, Zn,
[72]
and Hg, whereas Acidobacteria are highly tolerant to these metals . This highlights SOM as a key factor
[69]
influencing heavy metal bioavailability, which in turn affects microbial community structure.

A weak linear relationship was observed between Zc and PMC values; however, a stronger correlation was
observed under temperature conditions typical of the sampling region. For incubation at 25 °C, the
Spearman correlation coefficient was 0.24 (R = 0.06), whereas for incubation at 10 °C, it was 0.46 (R = 0.21).
2
2
The correlation coefficient between Zc and Q10 was 0.28 (R = 0.28). The Spearman correlations of the
2
studied parameters are shown in Figure 7.
There was almost no correlation between pollutants such as lead, nickel, cobalt, manganese oxide, and
vanadium, as well as Zc, and the mineralization parameter Q , with low statistical significance (Spearman’s r
10
ranging from -0.13 to 0.04, P = 0.70-0.91). For strontium and arsenic, weak and moderate positive
correlations, respectively, were observed between their concentrations and Q (Spearman’s r = 0.32-0.46, P =
10
0.21-0.41), while zinc and chromium showed moderate negative correlations (Spearman’s r = -0.56 to -0.46, P
= 0.10-0.24). All studied pollutants, as well as pH values, exerted a stronger influence on PMC than on
10
PMC , likely because PMC was measured under conditions closer to those in the study region.
25
10