Traceability offers significant information about the quality and safety of Chinese Angelica, a medicine and food homologous substance. In this study, a systematic four-step strategy, including sample collection, specific metal element fingerprinting, multivariate statistical analysis, and benefit-risk assessment, was developed for the first time to identify Chinese Angelica based on geographical origins. Fifteen metals in fifty-six Chinese Angelica samples originated from three provinces were analyzed. The multivariate statistical analysis model established, involving hierarchical cluster analysis (HCA), principal component analysis (PCA), and self-organizing map clustering analysis was able to identify the origins of samples. Furthermore, benefit-risk assessment models were created by combinational calculation of chemical daily intake (CDI), hazard index (HI), and cancer risk (CR) levels to evaluate the potential risks of Chinese Angelica using as traditional Chinese medicine (TCM) and food, respectively. Our systematic strategy was well convinced to accurately and effectively differentiate Chinese Angelica based on geographical origins.

Choosing native vascular plants as nutrient and toxic element accumulators for passive biomonitoring of urban river quality is not an easy task in Mediterranean rivers, due to the particular climate determining high variations in river hydrology. To identify potential biomonitors for this area, the roots of seven species (Angelica sylvestris, Apium nodiflorum, Tradescantia fluminensis, Nasturtium officinale, Persicaria lapathifolia, Arctium lappa, Typha latifolia), growing in seven sites along the River Irno (Southern Italy), were collected in July 2010 and analyzed regarding their capability to accumulate Cd, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, V, and Zn through atomic absorption spectrometry. Notwithstanding the expected different accumulation degree among the species, they highlighted similar spatial contamination gradients, and all of them appeared suitable, alone or in combination, for river passive biomonitoring. A. nodiflorum, in particular, appeared the best biomonitor for the River Irno, where severe anthropogenic impacts were detected: high Cu and Cd contamination from vine cultivation in the upper stretch, and Pb, Zn, and Mn contamination in the medium stretch from airborne dusts coming from a cast iron foundry.