Huiming Li

Bioaccessibility, sources and health risk assessment of trace metals in urban park dust in Nanjing, Southeast China

Arsenic, Cd, Co, Cr, Cu, Mn, Ni, Pb, V, and Zn total concentrations and bioaccessibilities in 15 urban park dust samples were determined. The oral bioaccessibility measured by the Simple Bioaccessibility Extraction Test (SBET) decreased in the order of Pb>Cd>Zn>Mn>Cu>Co>V>Ni>As>Cr. The Tomlinson pollution load index (PLI) and geoaccumulation index (I(geo)) were calculated to evaluate the pollution extent to which the samples were contaminated. Sources were identified using principal component analysis and Pb isotope compositions. Most elements except Co and V were considered to mainly originate from anthropogenic sources. Non-carcinogenic and carcinogenic risks to humans through urban park dust exposure were assessed using the oral bioaccessibilities of the elements. Ingestion was the main pathway for non-carcinogenic risk. The hazard quotients were below the safe level (=1) for all elements, however, Pb (0.154) and As (0.184) posed potential higher risks to children than adults. The carcinogenic effects occurring were below the acceptable level (10(-4)) for As and <10(-6) for Cd, Co, Cr, and Ni.

Influence of pollution control on lead inhalation bioaccessibility in PM2.5: A case study of 2014 Youth Olympic Games in Nanjing.

Pollution controls were implemented to improve the air quality for the 2014 Youth Olympic Games (YOG) in Nanjing. To investigate the influence of pollution control on Pb inhalation bioaccessibility in PM2.5, samples were collected before, during, and after YOG. The objectives were to identify Pb sources in PM2.5 using stable isotope fingerprinting technique and compare Pb inhalation bioaccessibility in PM2.5 using two simulated lung fluids. While artificial lysosomal fluid (ALF) simulates interstitial fluid at pH 7.4, Gambles solution simulates fluid in alveolar macrophages at pH 4.5. The Pb concentration in PM2.5 samples during YOG (88.2ngm(-3)) was 44-48% lower than that in non-YOG samples. Based on stable Pb isotope ratios, Pb in YOG samples was mainly from coal combustion while Pb in non-YOG samples was from coal combustion and smelting activities. While Pb bioaccessibility in YOG samples was lower than those in non-YOG samples (59-79% vs. 55-87%) by ALF, it was higher than those in non-YOG samples (11-29% vs. 5.3-21%) based on Gambles solution, attributing to the lower pH and organic acids in ALF. Different Pb bioaccessibility in PM2.5 between samples resulted from changes in Pb species due to pollution control. PbSO4 was the main Pb species in PM2.5 from coal combustion, which was less soluble in ALF than PbO from smelting activities, but more soluble in Gambles solution. This study showed it is important to consider Pb bioaccessibility during pollution control as source control not only reduced Pb contamination in PM2.5 but also influenced Pb bioaccessibility.

Prediction of size-fractionated airborne particle-bound metals using MLR, BP-ANN and SVM analyses.

Size-fractionated heavy metal concentrations were observed in airborne particulate matter (PM) samples collected from 2014 to 2015 (spanning all four seasons) from suburban (Xianlin) and industrial (Pukou) areas in Nanjing, a megacity of southeast China. Rapid prediction models of size-fractionated metals were established based on multiple linear regression (MLR), back propagation artificial neural network (BP-ANN) and support vector machine (SVM) by using meteorological factors and PM concentrations as input parameters. About 38% and 77% of PM2.5 concentrations in Xianlin and Pukou, respectively, were beyond the Chinese National Ambient Air Quality Standard limit of 75xa0μg/m3. Nearly all elements had higher concentrations in industrial areas, and in winter among the four seasons. Anthropogenic elements such as Pb, Zn, Cd and Cu showed larger percentages in the fine fraction (ø≤2.5xa0μm), whereas the crustal elements including Al, Ba, Fe, Ni, Sr and Ti showed larger percentages in the coarse fraction (ø > 2.5xa0μm). SVM showed a higher training correlation coefficient (R), and lower mean absolute error (MAE) as well as lower root mean square error (RMSE), than MLR and BP-ANN for most metals. All the three methods showed better prediction results for Ni, Al, V, Cd and As, whereas relatively poor for Cr and Fe. The daily airborne metal concentrations in 2015 were then predicted by the fully trained SVM models and the results showed the heaviest pollution of airborne heavy metals occurred in December and January, whereas the lightest pollution occurred in June and July.

Trace metals and magnetic particles in PM 2.5 : Magnetic identification and its implications

Magnetic measurement was combined with geochemical analysis to investigate the trace metal pollution of PM2.5. The study was carried out in Nanjing, China, where the average PM2.5 concentrations in summer and winter in 2013–2014 were 66.37 and 96.92u2009μg/m3, respectively. The dominant magnetic mineral in PM2.5 had a low-coercivity pseudo-single domain and consisted of magnetite and hematite. Iron-oxide magnetic particles comprised spherical as well as angular particles. Stable Pb isotopic ratio determinations showed that Pb in summer samples derived from coal emissions while the main sources of winter samples were smelting industry and coal emissions. The magnetic properties of the particles correlated strongly with trace metals derived from anthropogenic activities, such as industrial emission, coal combustion, and traffic vehicle activities, but poorly with those derived from natural sources. In the multiple linear regression analysis, Cr and Fe had higher correlation coefficients (training Ru2009>u20090.7) in contrast to the low training R of As, Cd, Ni, Sr, and Ti (<0.5) determined using the PM2.5 concentrations and magnetic parameter values as the decision variables. Our results support the use of environmental magnetism determinations as a simple and fast method to assess trace metals in urban particulate matter.

Response of magnetic properties to metal deposition on urban green in Nanjing, China

Environmental magnetism is a simple and fast method that can be used to assess heavy metal pollution in urban areas from the relationships between magnetic properties and heavy metal concentrations. Leaves of Osmanthus fragrans, one of the most widely distributed evergreen trees in Nanjing, China, were collected from four different district types, i.e., residential, educational, traffic, and industrial. The magnetic properties and heavy metal concentrations were measured both for unwashed (dust-loaded) and washed leaves. Scanning electron microscopy with energy-dispersive X-ray spectroscopy confirmed that unwashed leaves accumulated much dust due to atmospheric deposition. The value of magnetic properties and heavy metal concentrations in unwashed leaves was significantly higher than those of washed leaves, indicating that these characteristics were mainly derived from atmospheric particulate matter. Saturation isothermal remanent magnetization (SIRM) values obtained from unwashed and washed leaves ranged from 209.14xa0×xa010−6 to 877.85xa0×xa010−6xa0Am2xa0kg−1 and from 69.50xa0×xa010−6 to 501.28xa0×xa010−6xa0Am2xa0kg−1, respectively. High concentrations of heavy metals, such as Pb and Fe, the Tomlinson pollution load index, and the SIRM of unwashed leaves occurred in the traffic and industrial districts. A preliminary principal component analysis identified the source categories and suggested that industrial activities may be more related to the release of particulate matter rich in Fe. The heavy metal concentrations and pollution load index showed significant positive correlations with the low-frequency magnetic susceptibility and SIRM of unwashed leaves, indicating that these properties can be used to semi-quantify atmospheric heavy metal pollution. Our study suggests that it is possible to employ magnetic measurements as a useful tool for the monitoring and assessment of atmospheric heavy metal pollution.

Particulate Matter Triggers Depressive-Like Response Associated With Modulation of Inflammatory Cytokine Homeostasis and Brain-Derived Neurotrophic Factor Signaling Pathway in Mice

Particulate matter (PM) exposure may contribute to depressive-like response in mice. However, few studies have evaluated the adaptive impacts of long-term PM exposure on depressive-like response associated with systemic inflammation and brain-derived neurotrophic factor (BDNF) signaling pathway. We studied the association among depressive-like behaviors, mRNA levels of pro and anti-inflammatory cytokines, and the expression of BDNF signaling pathway in mice by long-term PM exposure. C57BL/6 male mice were exposed to ambient air alongside control mice breathing air filtered through a high-efficiency air PM (HEPA) filter. Depressive-like behaviors were assessed together with proinflammatory, anti-inflammatory cytokine mRNA levels and the modulation of BDNF pathway in hippocampus and olfactory-bulb of mice exposed to PM for 4, 8, and 12 weeks. Exposure to HEPA-filtered air for 4 weeks may exert antidepressant like effects in mice. Proinflammatory cytokines were up-regulated while the expression of BDNF, its high-affinity receptor tropomyosin-related kinase B (TrkB), and the transcription factor (cyclic adenosine monophosphate)-response element-binding protein (CREB) were down-regulated in ambient air mice. However, after 8 weeks, there was no significant difference in the rate of depressive-like behaviors between the 2 groups. After 12 weeks, mice exposed to ambient air again had a higher rate of depressive-like behaviors, significant up-regulation of proinflammatory cytokines, down-regulation of interleukin-10, BDNF, TrkB, and CREB than HEPA mice. Ultrafine PM in brain tissues of mice exposed to ambient air was observed. Our results suggest continuous high-level PM exposure alters the depressive-like response in mice and induces a damage-repair-imbalance reaction.

Leaf magnetic properties as a method for predicting heavy metal concentrations in PM2.5 using support vector machine: A case study in Nanjing, China

The aim of this study was to establish a method for predicting heavy metal concentrations in PM2.5 (particulate matter with a diameter of less than 2.5u202fμm) using support vector machine (SVM) models combined with magnetic properties of leaves. In this study, PM2.5 samples and the leaves of three common evergreen tree species were collected simultaneously during four different seasons in Nanjing, China. A SVM algorithm was used to establish models for the prediction of airborne heavy metal concentrations based on leaf magnetic properties, with or without meteorological factors and pollutant concentrations as input variables. Results showed that the annual average PM2.5 concentration was 58.47u202fμg/m3. PM2.5 concentrations, leaf magnetic properties, and nearly all airborne heavy metals had higher concentrations in winter than in spring, summer, or fall. Ferrimagnetic minerals preponderant in dust-loaded leaves were sampled from the three tree species. Models using magnetic properties of leaves from Ligustrum lucidum Ait and Osmanthus fragrans Lour yielded better prediction effects than those based on the leaves of Cedar deodara G. Don, showing relatively higher correlation coefficient (R) values and lower errors in both training and test stages. Fe and Pb concentrations were well-simulated by the prediction models, with R valuesu202f>u202f0.7 in both training and test stages. By contrast, the concentrations of V, Co, Sb, Tl, and Zn were relatively poor-simulated, with most R valuesu202f<u202f0.7 in both training and test stages. Predictions for the main urban areas of Nanjing showed that the highest heavy metal concentrations occurred near industrial and traffic pollution sources. Our results provide a cost-effective approach for the prediction of airborne heavy metal concentrations based on the biomagnetic monitoring of tree leaves.

Use of portable X-ray fluorescence spectroscopy and geostatistics for health risk assessment

Laboratory analysis of trace metals using inductively coupled plasma (ICP) spectroscopy is not cost effective, and the complex spatial distribution of soil trace metals makes their spatial analysis and prediction problematic. Thus, for the health risk assessment of exposure to trace metals in soils, portable X-ray fluorescence (PXRF) spectroscopy was used to replace ICP spectroscopy for metal analysis, and robust geostatistical methods were used to identify spatial outliers in trace metal concentrations and to map trace metal distributions. A case study was carried out around an industrial area in Nanjing, China. The results showed that PXRF spectroscopy provided results for trace metal (Cu, Ni, Pb and Zn) levels comparable to ICP spectroscopy. The results of the health risk assessment showed that Ni posed a higher non-carcinogenic risk than Cu, Pb and Zn, indicating a higher priority of concern than the other elements. Sampling locations associated with adverse health effects were identified as hotspots, and high-risk areas were delineated from risk maps. These hotspots and high-risk areas were in close proximity to and downwind from petrochemical plants, indicating the dominant role of industrial activities as the major sources of trace metals in soils. The approach used in this study could be adopted as a cost-effective methodology for screening hotspots and priority areas of concern for cost-efficient health risk management.