We performed a risk assessment of metal exposure to population subgroups living on, and growing food on, urban sites. We modeled uptake of cadmium, copper, nickel, lead, and zinc for a selection of commonly grown allotment and garden vegetables. Generalized linear cross-validation showed that final predictions of Cd, Cu, Ni, and Zn content of food crops were satisfactory, whereas the Pb uptake models were less robust. We used predicted concentrations of metals in the vegetables to assess the risk of exposure to human populations from homegrown food sources. Risks from other exposure pathways (consumption of commercially produced foodstuffs, dust inhalation, and soil ingestion) were also estimated. These models were applied to a geochemical database of an urban conurbation in the West Midlands, United Kingdom. Risk, defined as a "hazard index," was mapped for three population subgroups: average person, highly exposed person, and the highly exposed infant (assumed to be a 2-year-old child). The results showed that food grown on 92% of the urban area presented minimal risk to the average person subgroup. However, more vulnerable population subgroups (highly exposed person and the highly exposed infant) were subject to hazard index values greater than unity. This study highlights the importance of site-specific risk assessment and the "suitable for use" approach to urban redevelopment.

Summary Isotopically exchangeable cadmium and zinc (‘E values’) were measured on soils historically contaminated by sewage sludge and ones on zinc‐rich mine spoil. The E‐value assay involves determining the distribution of an added metal isotope, e.g. 109Cd, between the solid and solution phases of a soil suspension. The E values for both metals were found to be robust to changes in the position of the metal solid⇔solution equilibrium, even though the concentration of dissolved metal varied substantially with electrolyte composition and soil:solution ratio. Concentration of labile metal was also invariant over isotope equilibration times of 2–6 days. The use of a submicron filtration procedure, in addition to centrifuging at 2200 g, proved unnecessary if 0.1 m Ca electrolyte was used to suspend the soils. The proportion of ‘fixed’ metal, in non‐labile forms, apparently increased with increasing pH, although there was considerable variation in both sets of contaminated soil. Zinc and cadmium in the sludged soils were similarly labile. Several possible methods for the measurement of chemically reactive metal were explored for comparison with E values, including single extraction with 1 m CaCl2 and a ‘pool depletion’ (PD) method. The latter involves comparing solid⇔solution metal equilibria in two electrolytes with differing degrees of (solution) complex formation, 0.1 m Ca(NO3)2 and CaCl2. Both the single extraction and the PD method gave good estimates of E value for Cd, although the single extraction was more consistent. Neither technique was a useful substitute for determining labile Zn, because of weak chloro‐complexation of Zn2+. We therefore suggest that 1 m CaCl2 extraction of Cd alone be used as an alternative to E values to avoid the inconvenience of isotopic dilution procedures.

No abstract

Conversion of tropical peat swamp forest to drainage-based agriculture alters greenhouse gas (GHG) production, but the magnitude of these changes remains highly uncertain. Current emissions factors for oil palm grown on drained peat do not account for temporal variation over the plantation cycle and only consider CO2 emissions. Here, we present direct measurements of GHGs emitted during the conversion from peat swamp forest to oil palm plantation, accounting for CH4 and N2O as well as CO2. Our results demonstrate that emissions factors for converted peat swamp forest is in the range 70–117 t CO2 eq ha−1 yr−1 (95% confidence interval, CI), with CO2 and N2O responsible for ca. 60 and ca. 40% of this value, respectively. These GHG emissions suggest that conversion of Southeast Asian peat swamp forest is contributing between 16.6 and 27.9% (95% CI) of combined total national GHG emissions from Malaysia and Indonesia or 0.44 and 0.74% (95% CI) of annual global emissions.

No abstract

We demonstrate the application of a high-resolution X-ray Computed Tomography (CT) method to quantify water distribution in soil pores under successive reductive drying. We focus on the wet end of the water release characteristic (WRC) (0 to 275 kPa) to investigate changes in soil water distribution in contrasting soil textures (sand and clay) and structures (sieved and field structured) and to determine the impact of soil structure on hydraulic behavior. The 3-D structure of each soil was obtained from the CT images (at a 10 lm resolution). Stokes equations for flow were solved computationally for each measured structure to estimate hydraulic conductivity. The simulated values obtained compared extremely well with the measured saturated hydraulic conductivity values. By considering different sample sizes we were able to identify the smallest possible representative sample size which is required to determine a globally valid hydraulic conductivity.

No abstract

In many cases, quantitative information on production can only be obtained through crop simulation studies Mechanistic crop growth models have many potential uses for crop and long-term climatic records (MacDonald and Hall, management. These models can aid in preseason and within-season management decisions for cultural practices such as fertilizer and 1980;Matis et al., 1985; Bouman et al., 1995). Underirrigation applications and pest and disease management. When mak-standing the impacts of weather on crop production by ing these management decisions, maximizing yield and net return as applying simulation models provides a credible basis for a function of inputs and production costs is one of the fundamental a quantitative estimate of the range of yields farmers goals. Reliable yield forecasting within the growing season would can expect for a given set of management conditions enable improved planning and more efficient management of grain (Arkin and Dugas, 1981, Hammer et al., 1996; Tsuji et production, handling, and marketing. The objective of this study was al., 1998). to determine if the dynamic simulation model CERES-Wheat could The use of crop simulation models for predicting crop be used to forecast final grain yield and crop biomass within the yield as function of weather and climate has been studied growing season for environmental and management conditions in the extensively (Hoogenboom, 2000). These applications United Kingdom (UK). Experimental data for three seasons and four sites were used for model calibration and evaluation. A stochastic range from predicting yield at a farm level to predicting approach was applied, based on multiple years of weather data gener-regional and national yield levels although large-scale ated with the weather generator SIMMETEO. Yield forecasts were predictions are normally more common (Travasso and conducted for five different developmental stages within the growing Delecolle, 1995; Supit, 1997). Most of these prediction season. For each forecast date, observed weather data were used up applications include forecasts that are conducted before to the forecast date and supplemented with generated weather data planting while some simulations are conducted during until final harvest was predicted. Eighty-nine different sequences of the growing season. The improved understanding of El generated weather data were used for each forecast. Predicted grain Nino and the Southern Oscillation phenomenon has yield had a root mean square difference (RMSD) ranging from 0.95 especially led to many applications that are based on t ha Ϫ1 for the first forecast date to 0.68 t ha Ϫ1 for the final forecast seasonal climate forecasts (Hammer et al., 1996; Meinke date while the RMSD for total predicted biomass ranged from 3.59 to 2.09 t ha Ϫ1 . An analysis of predicted final grain yield and biomass tailored for the specific needs of the farming community