Persistent Organic Pollutants in Urban Soils of Central of London, England, UK: Measurement and Spatial Modelling of Black Carbon (BC), Petroleum Hydrocarbons (TPH), Polycyclic Aromatic Hydrocarbons (PAH) and Polychlorinated Biphenyls (PCB)

Abstract

Total organic carbon (TOC), black carbon (BC), total petroleum hydrocarbons (TPH), polycyclic aromatic hydrocarbons (PAH) and polychlorinated biphenyls (PCB) were determined in 73 surface (0-2 cm) and subsurface (5-20 cm) soil samples taken from a 142 km2 area in Central London, UK. Soils were assessed to provide a baseline chemistry for site owners, developers, occupiers and regulators involved in understanding the potential risk to human health and the environment. TOC range was 1.75-11.85 % (mean 5.82 %), BC 3.72-32.71 mg.g-1 (mean 13.8 mg.g-1), TPH 72-4673 mg.g-1 (mean 443 mg.g-1), Σ16PAH 1.64-421.23 mg.g-1 (mean 47.99 mg.g-1) and Σ7PCB 2.56-148.72 µg.kg-1 (mean 20.82 µg.kg-1). Surface soils were less polluted than sub-surface soils due to a decline in industry, power generation, coal burning and traffic. PAH and PCB showed a stronger affinity for BC than TOC and were higher than many other international cities. South east London (Greenwich, Woolwich, Deptford) had the highest PAH pollution. Source PAH ratios confirmed a combustion/urban road run-off origin with minor petroleum inputs. Random Forest spatial modelling (machine learning) revealed large scale pollution trends across London soils. Normal background concentrations (NBC) were calculated and compared to risk-based human health generic assessment criteria (GAC). Benz[a]anthracene, benzo[a]pyrene, benzo[b]fluoranthene, and dibenzo[a]anthracene exceeded the Land Quality Management GACs for three land uses (residential, allotments and public open space near residential housing). The NBC determined for ∑7PCBs (110 µg.kg-1) and dioxin-like PCB 118 (59 µg.kg-1) exceeded the residential and allotment soil guideline values.

Incorporating conceptual site models into national-scale environmental risk assessments for legacy waste in the coastal zone

“…For deposits within the EA/NRW databases which contained wastes of multiple origin, these were recategorised as "mixed" wastes. Within these mixed wastes, a further category was generated based on landfill closure date to categorise those which were more likely to contain wastes from the 1960s-70s, which are reported to contain hazardous organic contaminants whose production has since been legislated against, such as poly-chlorinated biphenyls (PCBs: Harrad et al, 1994) and persistent organic pollutants (POPs: Vane et al, 2021). As a result of this process, 10 waste categories were generated, which were then straight-ranked (high to low; 1.0 to 0.1) based on their perceived relative likelihood of containing hazardous priority substances, and their potential leaching products (based on authors' consensus and literature review), as detailed in Table 1.…”

Modelling and Mapping Total and Bioaccessible Arsenic and Lead in Stoke-on-Trent and Their Relationships with Industry

“…Each tree is grown on a random subsample of the training data and a random subset of the predictor variables to choose from at each decision node. In this study, we used the RF modelling approach suggested by Cave [27], in which inverse distance weighted (IDW) covariates are used as the predictor variables and subsequently used to predict the spatial distribution of persistent organic pollutants in London [28]. Inverse distance weighted (IDW) interpolation explicitly makes the assumption that samples that are close to one another are more alike than those that are further apart [29].…”

Predictive Geochemical Mapping Using Machine Learning in Western Kenya

“…Similarly to kriging, the RF methods can also produce prediction variance estimates derived from the bootstrap sampling of the data points (inherent in the RF algorithm). This approach has subsequently been used to predict the spatial distribution of persistent organic pollutants in London (Vane et al, 2021), and produce spatial prediction models of the total and bioaccessible fractions of arsenic and lead in an urban environment (Wragg and Cave, 2021).…”