Road dust (RD) retained on noise barriers was used as a monitor of emission of traffic-related metals from expressway. By using SEM/EDX analysis it has been revealed that the main components of this particulate were irregular fine aggregates and tire debris with a ragged porous structure and with inclusions derived from the road surface. The results of chemical fractionation showed that driving conditions influence strongly a distribution pattern of Cu, whereas the atmospheric corrosion process affects a distribution pattern of Zn. The distribution pattern of Cu originating only from vehicle braking emission was “isolated” from the distribution pattern of road traffic copper. The predicted comparative mobilities of the emitted metals form the order: Zn >> Cu ≈ Mn > Pb >> Fe. The high mobility of zinc (K = 0.61) may create a current inhalation hazard and may be a source of future environmental hazard in the areas adjacent to heavily trafficked roads.

Historical trends in trace element deposition were analyzed using herbaria specimens. We determined Al, Fe, Mg, Mn, Ca, Na, P, K, S, As, Cd, Cr, Cu, Ni, Pb and Zn contents in leaves of eight specimens collected in 1941. To assess changes, we collected the same plants from a botanical garden in 2012. The concentrations of major elements showed large species variability. However, temporal trends were predominately detected for heavy metals. The Cd, Ni and Cr contents in the 2012 leaves were 10, 13 and 16 times higher, respectively, than in 1941. Urban activities have substantially raised the levels of these metals in urban atmospheres due to changes in human activities over 70 years of urban growth. Nevertheless, Pb has decreased (−126%) in recent decades thanks to controlled lead fuel combustion. In short, metal deposition trend to increase Cr, Ni and Cd levels.

Field crops represent one of the highest contributions to dietary metal exposure. The aim of this study was to develop specific regression models for the uptake of metals into various field crops and to compare the usability of other available models. We analysed samples of potato, hop, maize, barley, wheat, rape seed, and grass from 66 agricultural sites. The influence of measured soil concentrations and soil factors (pH, organic carbon, content of silt and clay) on the plant concentrations of Cd, Cr, Cu, Mo, Ni, Pb and Zn was evaluated. Bioconcentration factors (BCF) and plant-specific metal models (PSMM) developed from multivariate regressions were calculated. The explained variability of the models was from 19 to 64% and correlations between measured and predicted concentrations were between 0.43 and 0.90. The developed hop and rapeseed models are new in this field. Available models from literature showed inaccurate results, except for Cd; the modelling efficiency was mostly around zero. The use of interaction terms between parameters can significantly improve plant-specific models.

Soil heavy metal pollution due to long-term sewage irrigation is a serious environmental problem in many irrigation areas in northern China. Quickly identifying its pollution status is an important basis for remediation. Visible-near-infrared reflectance spectroscopy (VNIRS) provides a useful tool. In a case study, 76 soil samples were collected and their reflectance spectra were used to estimate cadmium (Cd) concentration by partial least squares regression (PLSR) and back propagation neural network (BPNN). To reduce noise, six pre-treatments were compared, in which orthogonal signal correction (OSC) was first used in soil Cd estimation. Spectral analysis and geostatistics were combined to identify Cd pollution hotspots. Results showed that Cd was accumulated in topsoil at the study area. OSC can effectively remove irrelevant information to improve prediction accuracy. More accurate estimation was achieved by applying a BPNN. Soil Cd pollution hotspots could be identified by interpolating the predicted values obtained from spectral estimates.

Fluorescence excitation-emission matrix (EEM) spectra coupled with parallel factor analysis (PARAFAC) was used to characterize dissolved organic matter (DOM) derived from macrophyte decomposition, and to study its complexation with Cu (II) and Cd (II). Both the protein-like and the humic-like components showed a marked quenching effect by Cu (II). Negligible quenching effects were found for Cd (II) by components 1, 5 and 6. The stability constants and the fraction of the binding fluorophores for humic-like components and Cu (II) can be influenced by macrophyte decomposition of various weight gradients in aquatic plants. Macrophyte decomposition within the scope of the appropriate aquatic phytomass can maximize the stability constant of DOM-metal complexes. A large amount of organic matter was introduced into the aquatic environment by macrophyte decomposition, suggesting that the potential risk of DOM as a carrier of heavy metal contamination in macrophytic lakes should not be ignored.

As a highly toxic heavy metal, the pollution and exposure risks of lead are of widespread concern for human health. However, the collection of blood samples for use as an indicator of lead pollution is not always feasible in most cohort or longitudinal studies, especially those involving children health. To evaluate the potential use of urinary lead as an indicator of exposure levels and source apportionment, accompanying with environmental media samples, lead concentrations and isotopic measurements (expressed as 207Pb/206Pb, 208Pb/206Pb and 204Pb/206Pb) were investigated and compared between blood and urine from children living in the vicinities of a typical coking plant and lead-acid battery factory. The results showed urinary lead might not be a preferable proxy for estimating blood lead levels. Fortunately, urinary lead isotopic measurements could be used as an alternative for identifying the sources of children's lead exposure, which coincided well with the blood lead isotope ratio analysis.

Rapid monitoring and discriminating different anthropogenic pollution is a key scientific issue. To detect the applicability and sensitivity of magnetic measurements for evaluating different industrial pollution in urban environment, characteristics of topsoil from three typical fast developing industrial cities (Jinchang, Baiyin and Jiayuguan in Gansu province, northwestern China) were studied by magnetic and geochemical analyses. The results showed that magnetic susceptibility was enhanced near industrial areas, and PSD-MD magnetite dominated the magnetic properties. Magnetic concentration parameters (χlf, SIRM, and χARM) showed different correlations with heavy metals and PLI in the three cities, indicating significantly different magnetic response to different pollution sources. Principal component analysis showed that ferrimagnetic minerals coexist with heavy metals of Fe, As, Cu, Pb, and Zn in Baiyin and Fe, V, Cu, Mn, Pb, and Cr in Jiayuguan. Fuzzy cluster analysis and regression analysis further indicated that the sensitivity of magnetic monitoring to fuel dust is higher than that to mineral dust near non-ferrous metal smelters, and fossil fuel consumption is an important factor for increasing magnetite content. In all the three cities, the sensitivity of magnetic monitoring to pollutants from steel plants is much higher than that from non-ferrous metal plants. Therefore, magnetic proxies provide a rapid means for detecting heavy metal contamination caused by multi-anthropogenic pollution sources in a large scale area, however, the sensitivity was controlled by pollution sources.

We studied magnetic and chemical parameters of sediments from sediments of a water reservoir at Linfen (China) in order to quantitatively reconstruct the atmospheric pollution history in this region. The results show that the main magnetic phases are magnetite and maghemite originating from the surrounding catchment and from anthropogenic activities, and there is a significant positive relationship between magnetic concentration parameters and heavy metals concentrations, indicating that magnetic proxies can be used to monitor the anthropogenic pollution. In order to uncover the atmospheric pollution history, we combined the known events of environmental improvement with variations of magnetic susceptibility (χ) and heavy metals along the cores to obtain a detailed chronological framework. In addition, air comprehensive pollution index (ACPI) was reconstructed from regression equation among magnetic and chemical parameters as well as atmospheric monitoring data. Based on these results, the atmospheric pollution history was successfully reconstructed.

In recent decades, naturally growing mosses have been used successfully as biomonitors of atmospheric deposition of heavy metals and nitrogen. Since 1990, the European moss survey has been repeated at five-yearly intervals. In 2010, the lowest concentrations of metals and nitrogen in mosses were generally found in northern Europe, whereas the highest concentrations were observed in (south-)eastern Europe for metals and the central belt for nitrogen. Averaged across Europe, since 1990, the median concentration in mosses has declined the most for lead (77%), followed by vanadium (55%), cadmium (51%), chromium (43%), zinc (34%), nickel (33%), iron (27%), arsenic (21%, since 1995), mercury (14%, since 1995) and copper (11%). Between 2005 and 2010, the decline ranged from 6% for copper to 36% for lead; for nitrogen the decline was 5%. Despite the Europe-wide decline, no changes or increases have been observed between 2005 and 2010 in some (regions of) countries.

The objective of this research was to assess factors controlling peat and plant chemistry, and vegetation composition in 18 peatlands surrounding Sudbury after more than 30 years of large (>95%) pollution emission reductions. Sites closer to the main Copper Cliff smelter had more humified peat and the surface horizons were greatly enriched in copper (Cu) and nickel (Ni). Copper and Ni concentrations in peat were significantly correlated with that in the plant tissue of Chamaedaphne calyculata. The pH of peat was the strongest determining factor for species richness, diversity, and community composition, although percent vascular plant cover was strongly negatively correlated with surface Cu and Ni concentrations in peat. Sphagnum frequency was also negatively related to peat Cu and Ni concentrations indicating sites close to Copper Cliff smelter remain adversely impacted by industrial activities.