Use of the lichen Xanthoria mandschurica in monitoring atmospheric elemental deposition in the Taihang Mountains, Hebei, China

Abstract

Air pollution is a major concern in China. Lichens are a useful biomonitor for atmospheric elemental deposition but have rarely been used in North China. The aim of this study was to investigate the atmospheric depositions of 30 trace elements (Al, Ba, Ca, Cd, Ce, Co, Cr, Cs, Cu, Fe, K, La, Mg, Mn, Mo, Na, Ni, P, Pb, Rb, Sb, Sc, Sm, Sr, Tb, Th, Ti, Tl, V and Zn) in a region of the Taihang Mountains, Hebei Province, China using lichens as biomonitors. Epilithic foliose lichen Xanthoria mandschurica was sampled from 21 sites and analyzed using inductively coupled plasma mass spectrometry (ICP-MS). The results show that 1) eight elements (Cd, Cr, Cu, Mo, P, Pb, Sb and Zn) are of atmospheric origin and are highly influenced by the atmospheric transportation from the North China Plain, as well as local mining activities, while 2) the remaining 22 elements are primarily of crustal origin, the concentration of which has been enhanced by local mining and quarrying activities. These results clearly validate the applicability of lichens in biomonitoring of atmospheric elemental deposition and demonstrate the spatial pattern for air pollution in the region.

Spatial-Temporal Patterns of Element Concentrations in Xanthoparmelia camtschadalis Transplanted along Roads

“…Our results suggest severe air pollution at the exposure sites. A comparison with the relevant studies shows that the median values for the POST-cs of most elements in XPc were higher than the documented values from "clean sites" [9,12,20,[24][25][26] and "polluted sites" [15,[27][28]. This conclusion is consistent with the results of instrumental air quality monitoring (Fig.…”

Lichen elemental composition distinguishes anthropogenic emissions from dust storm inputs and differs among species: Evidence from Xilinhot, Inner Mongolia, China

“…This is also true for Pb, which had an EF SS of >5 in XPC and XPT. A large body of relevant literature has attributed the enrichment of these metals in lichens to anthropogenic pollutants released from industrial activities or traffic 5 9 11 13 17 18 19 20 . However, this region is a rural, pastoral area without intense industrial operations and is far from range fires and the application of chemical fertilizers and pesticides.…”

“…A comparison with values from different lichens in diverse ecosystems shows that our data are higher than or at the upper range of the literature values for 16 crustal metals (Al, Ba, Ce, Cs, Fe, K, La, Mn, Na, P, Sc, Sm, Tb, Th, Ti and V) 7 8 9 12 18 21 22 23 24 25 26 27 28 29 30 31 32 . To minimize species effects, XM was compared between XILRB and a region of the Taihang Mountains where the air quality has long been influenced by local anthropogenic emissions and heavy air pollution in the adjacent North China Plain 20 . The results also show higher or similar concentrations in XILRB for 11 crustal elements (Al, Ba, Cs, Fe, K, Mn, Na, Ni, P, Sc and Ti; Fig.…”

“…They had a lower enrichment capability of atmospheric metals than other lichens, as indicated by the lower EF SS s and concentrations ( Tables 1 and 2 ). Although the applicability of XM in the biomonitoring of natural and anthropogenic emissions has been reported in the Taihang Mountains 20 , care should be taken in this region because the relative importance of anthropogenic contributions may be obscured by the high crustal input due to frequent dust storm events.…”

Spatial Association Pattern of Air Pollution and Influencing Factors in the Beijing–Tianjin–Hebei Air Pollution Transmission Channel: A Case Study in Henan Province

“…Given the importance of non-attainment days for air pollution exposure, a hierarchical clustering analysis was conducted by combining the mean seasonal NAQI and seasonal non-attainment days in each year of 2015 and 2016 for the 18 cities of Henan Province to investigate their spatial association pattern. The hierarchical clustering was performed with SPSS software version 16.0 using the Euclidean distance of the Z-score standardized variables to measure dissimilarities and Ward's criterion to construct the hierarchical tree [33].…”