Numerous studies have shown that urban soils can contain elevated concentrations of heavy metals (HMs). Christchurch, New Zealand, is a relatively young city (150 years old) with a population of 390,000. Most soils in Christchurch are sub-urban, with food production in residential gardens a popular activity. Earthquakes in 2010 and 2011 have resulted in the re-zoning of 630 ha of Christchurch, with suggestions that some of this land could be used for community gardens. We aimed to determine the HM concentrations in a selection of suburban gardens in Christchurch as well as in soils identified as being at risk of HM contamination due to hazardous former land uses or nearby activities. Heavy metal concentrations in suburban Christchurch garden soils were higher than normal background soil concentrations. Some 46% of the urban garden samples had Pb concentrations higher than the residential land use national standard of 210 mg kg⁻¹, with the most contaminated soil containing 2615 mg kg⁻¹ Pb. Concentrations of As and Zn exceeded the residential land use national standards (20 mg kg⁻¹ As and 400 mg kg⁻¹ Zn) in 20% of the soils. Older neighbourhoods had significantly higher soil HM concentrations than younger neighbourhoods. Neighbourhoods developed pre-1950s had a mean Pb concentration of 282 mg kg⁻¹ in their garden soils. Soil HM concentrations should be key criteria when determining the future land use of former residential areas that have been demolished because of the earthquakes in 2010 and 2011. Redeveloping these areas as parklands or forests would result in less human HM exposure than agriculture or community gardens where food is produced and bare soil is exposed.
Hybrid broadband simulation methods typically compute high-frequency portion of ground-motions using a simplified-physics approach (commonly known as “stochastic method”) using the same 1D velocity profile, anelastic attenuation profile and site-attenuation (κ0) value for all sites. However, these parameters relating to Earth structure are known to vary spatially. In this study we modify this conventional approach for high-frequency ground-shaking by using site-specific input parameters (referred to as “site-specific”) and analyze improvements over using same parameters for all sites (referred to as “generic”). First, we theoretically understand how different 1D velocity profiles, anelastic attenuation profiles and site-attenuation (κ0) values affects the Fourier Acceleration Spectrum (FAS). Then, we apply site-specific method to simulate 10 events from the 2010-2011 Canterbury earthquake sequence to assess performance against the generic approach in predicting recorded ground-motions. Our initial results suggest that the site-specific method yields a lower simulation standard deviation than generic case.
Geospatial liquefaction models aim to predict liquefaction using data that is free and readily-available. This data includes (i) common ground-motion intensity measures; and (ii) geospatial parameters (e.g., among many, distance to rivers, distance to coast, and Vs30 estimated from topography) which are used to infer characteristics of the subsurface without in-situ testing. Since their recent inception, such models have been used to predict geohazard impacts throughout New Zealand (e.g., in conjunction with regional ground-motion simulations). While past studies have demonstrated that geospatial liquefaction-models show great promise, the resolution and accuracy of the geospatial data underlying these models is notably poor. As an example, mapped rivers and coastlines often plot hundreds of meters from their actual locations. This stems from the fact that geospatial models aim to rapidly predict liquefaction anywhere in the world and thus utilize the lowest common denominator of available geospatial data, even though higher quality data is often available (e.g., in New Zealand). Accordingly, this study investigates whether the performance of geospatial models can be improved using higher-quality input data. This analysis is performed using (i) 15,101 liquefaction case studies compiled from the 2010-2016 Canterbury Earthquakes; and (ii) geospatial data readily available in New Zealand. In particular, we utilize alternative, higher-quality data to estimate: locations of rivers and streams; location of coastline; depth to ground water; Vs30; and PGV. Most notably, a region-specific Vs30 model improves performance (Figs. 3-4), while other data variants generally have little-to-no effect, even when the “standard” and “high-quality” values differ significantly (Fig. 2). This finding is consistent with the greater sensitivity of geospatial models to Vs30, relative to any other input (Fig. 5), and has implications for modeling in locales worldwide where high quality geospatial data is available.
