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

Reconstruction of a century of air pollution history in Nanjing, China, using trace elements in situ leaf specimens of Platanus × hispanica and Pittosporum tobira

Leaves can specifically uptake trace elements from the surrounding environment. And tree leaves are a good biological indicator for air pollution. Therefore, chemical analysis of leaf specifications can be used to reproduce a historical record of air pollution. To better understand the history of urban air pollution from the 1920s to the 2020s in Nanjing, China, leaf samples of two woody plants, Platanus × hispanica and Pittosporum tobira, were collected in this study as environmental indicators from different historical periods. These included historical herbarium specimens and current leaves from live trees. The concentrations of 10 trace elements were determined in the samples using ICP‒MS. Pollution indices were calculated, yielding the key findings. The historical leaf samples showed continuously increasing mean concentrations of the 10 trace elements over time, which significantly correlating with automobile quantities and the number of large-scale industrial enterprises (p < 0.05). Moreover, modern leaf trace element concentrations were significantly correlated with PM10, PM2.5, automobiles, large-scale industrial enterprises, and atmospheric factors, confirming these as sources. In addition to the historical growth trend, spatial heterogeneity was revealed in historical Platanus × hispanica leaf samples from the 14 sites in Nanjing. Changes in heavy metal trace element pollution distributions were consistent with transportation and industrial expansion, with homologous patterns across elements. Specifically, post 1980s increases were observed in the representative NJ2 (Zhongshan Botanical Garden) and the NJ5(Nanjing University) sites, with higher concentrations occurring at in the NJ5 contaminated site than at the NJ2 uncontaminated site. After 2009, the 10 element (except Cd) pollution indices in Platanus × hispanica leaves fluctuated but declined overall. This reconstruction of Nanjing's air pollution history demonstrates that ample environmental information can be extracted from plant leaf markers over time and space.

Is biomagnetic leaf monitoring still an effective method for monitoring the heavy metal pollution of atmospheric particulate matter in clean cities?

The development of a reasonable method for predicting heavy metals (HMs) pollution in atmospheric particulate matter (PM) remains challenging. This paper presents an elution-filtration method to collect PM from the surface of Osmanthus fragrans in a very clean area (Guiyang, China). The aim is to evaluate the effectiveness of biomagnetic leaf monitoring as a simple and rapid method for assessing HMs pollution in clean cities. For this purpose, we determined the magnetic parameters and concentrations of selected HMs in PM samples to investigate their relationships. The results showed that the magnetic minerals in PM samples were mainly low coercivity ferrimagnetic minerals, with a small amount of high coercivity minerals. The types of magnetic minerals were generally single, and the magnetic domain state was pseudo-single domain (PSD). There was a significant correlation between magnetic parameters and the heavy metal (HM) concentrations in PM. Low-field magnetic susceptibility (χ) could be used as an ideal proxy for determining anthropogenic HM pollution. Traffic emissions were the main atmospheric pollution source in urban Guiyang. Due to the incomplete traffic network and large traffic flow, traffic congestion (TC) often occurred at road intersections in the northwest and southwest corners of the city, resulting in the highest concentration of magnetic minerals and the most severe PM pollution. To mitigate atmospheric PM pollution and protect public health, it is strongly recommended that municipal authorities prioritize urban planning and traffic management to address TC. Measures should be implemented urgently to alleviate stop-and-go traffic.

Assessing magnetic properties of biomonitors and road dust as a screening method for air pollution monitoring

Particulate matter (PM) pollution is one of the world's most serious environmental challenges. Among PM components, atmospheric heavy metals (HMs) are considered one of the main pollutants responsible for causing significant negative impacts on human health, and ecological quality. This study aimed to assess environmental magnetism as a simple and rapid method that can be used to evaluate heavy metal contamination in urban areas from the relationships between magnetic properties and heavy metal concentrations. For this purpose, road dust and leaf samples of two common evergreen species (Cupressus lusitanica/Casuarina equisetifolia) were sampled simultaneously for 2 years at sites with different levels of traffic pollution. The results found significant statistical correlations between the magnetic properties and the chemical substances of the plants studied, as Fe, Cr and V showed an r ≥ 0.9 and Cr and Zn r ≥ 0.7 with χlf in C. equisetifolia. The frequency-dependent magnetic susceptibility was found to be between 0% and 14% for plants, and 0% and 2% for road dust, suggesting a rather dissimilar particle size distribution for plants, and a less important contribution from the more hazardous ultrafine superparamagnetic magnetite for both. Confirming that magnetic analyses can be used to distinguish different degrees of urban air pollution.

Comparison between machine linear regression (MLR) and support vector machine (SVM) as model generators for heavy metal assessment captured in biomonitors and road dust

Exposure to suspended particulate matter (PM), found in the air, is one of the most acute environmental problems that affect the health of modern society. Among the different airborne pollutants, heavy metals (HMs) are particularly relevant because they are bioaccumulated, impairing the functions of living beings. This study aimed to establish a method to predict heavy metal concentrations in leaves and road dust, through their magnetic properties measurements. For this purpose, machine learning, automatic linear regression (MLR), and support vector machine (SVM) were used to establish models for the prediction of airborne heavy metals based on leaves and road dust magnetic properties. Road dust samples and leaves of two common evergreen species (Cupressus lusitanica/Casuarina equisetifolia) were sampled simultaneously during two different years in the Great Metropolitan Area (GMA) of Costa Rica. MLR and SVM algorithms were used to establish the relationship between airborne heavy metal concentrations based on single (χlf) and multiple (χlf y χdf) leaf magnetic properties and road dust. Results showed that Fe, Cu, Cr, V, and Zn concentrations were well-simulated by SVM prediction models, with adjusted R² values ≥ 0.7 in both training and test stages. By contrast, the concentrations of Pb and Ni were not well-simulated, with adjusted R² values < 0.7 in both training and test stages. Heavy metal predicción models using magnetic properties of leaves from Casuarina equisetifolia, as collectors, yielded better prediction results than those based on the leaves of Cupressus lusitanica and road dust, showing relatively higher adjusted R² values and lower errors (MAE and RMSE) in both training and test stages. SVM proved to be the best prediction model with variations between single (χlf) and multiple (χlf y χdf) magnetic properties depending on the element studied.

Assessing risk to human health for heavy metal contamination from public point utility through ground dust: a case study in Nantong, China

Heavy metal contamination in ground dust presents potential environmental and human health threats. However, the heavy metal contamination status of ground dust in the vicinity of public point utilities remains poorly explored. Therefore, this study has been designed to analyze the heavy metal contaminations in the ground dust collected monthly near a public bronze sculpture in an urban campus of Nantong, China, using geo-accumulation indexes (Igeo), enrichment factors (EF), potential ecological risk indexes (RI), and health risks (noncarcinogenic risks (HI) and carcinogenic risks (CR)). This study revealed that the maximum Cr, Cu, Mn, Ni, Pb, and Zn concentrations in ground dust samples were 156.2, 708.8, 869.8, 140.8, 180.5, and 1089.7 mg kg-1, respectively, in which the mean Cu and Zn concentrations were 9 and 7 times higher than the background level in the soil. Temporally speaking, for the majority of heavy metals (with the exception of Ni), the high-concentration seasons tend to be mainly summer and autumn. It was observed that Cu and Zn exhibited significant enrichment (EF = 11.7 and 8.4, respectively), moderate-to-strong pollution (Igeo = 2.4 and 2.0, respectively), and moderate- and low-potential ecological risks ([Formula: see text] = 45.6 and 6.6, respectively). The noncarcinogenic risks which adults exposed to the heavy metal concentrations suffered were found to be insignificant. However, the carcinogenic risks related to Ni (1.3E-04) had exceeded the acceptable level. Based on principal component analysis (PCA) and correlation analysis, the heavy metal concentrations in the ground dust of urban campuses could be related to public point utilities, traffic-related exhaust sources, and industrial activities. This study's findings demonstrated that urban public utilities require more attention due to their significant enrichment, ecological risk factors, and the significant carcinogenic risks to the population.

Magnetic properties and its application in the prediction of potentially toxic elements in aquatic products by machine learning

Magnetic measurement was provided to substitute for time-consuming conventional methods for determination of potentially toxic elements. Both the concentrations of 12 elements and 9 magnetic parameters were determined in 700 muscle tissue samples from the snail Bellamya aeruginosa, shrimp species Exopalaemon modestus and Macrobrachium nipponense, and fish species Hemisalanx prognathous Regan, Coilia ectenes taihuensis, and Culer alburnus Basilewsky collected from Chaohu Lake during different hydrological periods. Spherical and irregular iron oxide particles were observed in the muscle tissues of the studied aquatic products. A field survey of the exposure parameters in humans, such as per capita intake dose of local aquatic products, found no evidence that consumption of the tested species poses a potential health risk. Redundancy analysis revealed different degrees of correlation between the magnetic parameters and concentrations of elements in aquatic products. Back-propagation artificial neural network (BP-ANN) and support vector machine (SVM) models were applied to predict elemental concentrations in aquatic products, using magnetic parameters as input. SVM models performed well in predicting the presence of Cr and Ni, with R and index of agreement values of >0.8 in both training and validation stages as well as relatively low errors. The BP-ANN and SVM models both performed relatively poorly in predicting the presence of Cd and Zn in aquatic products, with R values between 0.333 and 0.718 for Cd and between 0.454 and 0.664 for Zn in training and validation stages. For most of the elements, a better R value was obtained with the SVM than with BP-ANN model. The R of Co, Cr, Cu, Ni, and Ti in the training and validation stages of snail in the SVM model were >0.8. This study is a first step in developing a novel approach allowing the rapid monitoring of potentially toxic elements concentrations in aquatic products.

Responses of magnetic properties to heavy metal pollution recorded by lacustrine sediments from the Lugu Lake, Southwest China

Environmental magnetism, which is rapid, sensitive, economical, and non-destructive, has been used to assess heavy metal pollution in lake sediments based on the relationships between magnetic properties and heavy metal concentrations. We conducted a systematic environmental magnetic and heavy metal study of the sediments of the core LGS from Lugu Lake in Southwest China. The results show that the concentration-related magnetic parameters (χ, χ_ARM, and SIRM) in the core LGS showed an increasing trend from bottom to top. The results of rock magnetism indicated that the dominant magnetic particles were magnetite. Two sources of magnetic minerals can be distinguished by the correlations of χ vs. χ_fd% and χ vs. χ_ARM/χ: the surrounding catchment and anthropogenic activities. In addition, Pearson correlation analysis and principal component analysis showed that the concentration-dependent magnetic parameters have significant correlations with heavy metal (Al, Ti, Fe, Cr, Ni, Cu, Zn, and Cd) concentrations as well as the Tomlinson pollution load index (PLI), indicating that there are essential linkages of sources, deposition, and migration between magnetic particles and heavy metals. Based on previously reported ¹³⁷Cs and ²¹⁰Pb data, the historical trends of heavy metal pollution in Lugu Lake were successfully reconstructed, and the causes of heavy metal pollution were mainly agricultural practices and atmospheric metal depositions from anthropogenic sources. The significant correlations between magnetic parameters, heavy metals, and the PLI indicate that magnetic parameters can potentially be used as an index of heavy metal pollution in lacustrine deposits.