Assessing Analytical Methods for the Rapid Detection of Lead Adulteration in the Global Spice Market.

Lead adulteration of spices, primarily via Pb chromate compounds, has been documented globally as a growing public health concern. Currently, Pb detection in spices relies primarily on expensive and time-consuming laboratory analyses. Advancing rapid Pb detection methods, inclusive of their accuracy and precision, would improve field assessments by food safety inspectors, stakeholders, and the public in the hope of reducing Pb exposure risks at its source. Here, we present two field procedures for Pb detection: portable X-ray fluorescence analysis (pXRF) and a simple colorimetric test. We assess their efficacy to detect Pb and its chemical form in seven spice types, including powders, spice-salt mixtures, and dried roots, compared to the proven laboratory technique, inductively coupled plasma mass spectrometry (ICP-MS). Lead concentrations measured using pXRF and ICP-MS were within 5% of each other for spice powders and 24% for dried roots. By pXRF, spice samples were analyzed within collection plastic bags without preparation, resulting in a detection limit of 2 mg Pb/kg for spice powders, which is comparable to national food standards. The colorimetric test utilized here targets hexavalent chromium, making the method selective to Pb chromate adulteration assuming that this is its dominant source in spices. Color development, and thus detection, was observed when Pb concentrations exceeded approximately 5-70 mg/kg in dried turmeric roots and 1000 mg/kg in spice powders; however, it was ineffective for the spice-salt mixture. We show that pXRF analysis and a colorimetric assay provide information that may improve field decisions about Pb adulteration in a range of spice types, helping to minimize Pb exposure.

Soil and Aquifer Properties Combine as Predictors of Groundwater Uranium Concentrations within the Central Valley, California.

With the increasing global need for groundwater resources to fulfill domestic, agricultural, and industrial demands, we face the threat of increasing concentrations of naturally occurring contaminants in water sources and a consequential need to improve our predictive capacity. Here, we combine machine learning and geochemical modeling to reveal the biogeochemical controls on regional groundwater uranium contamination within the Central Valley, California. We use 23 environmental parameters from a statewide groundwater geochemical database and publicly available maps of soil and aquifer physicochemical properties to predict groundwater uranium concentrations by random forest regression. We find that groundwater calcium, nitrate, and sulfate concentrations, soil pH, and clay content (weighted average between 0 and 2 m depths) are the most important predictors of groundwater uranium concentrations. By pairing multivariate partial dependence and accumulated local effect plots with modeled aqueous uranium speciation and surface complexation outputs, we show that regional groundwater uranium exceedances of drinking water standards, 30 µg L-1, are dependent on the formation of uranyl-calcium-carbonato species. The geochemical conditions leading to ternary uranyl complexes within the aquifer are, in part, created by infiltration through the vadose zone, illustrating the critical dependence of groundwater quality on recharge conditions.