Magnetic properties of urban street dust can be used as a proxy of urban pollution. In this study, magnetic measurements on 160 street dust samples, collected from five different functional areas (industrial, traffic, commercial, residential and clean areas) in sixteen administrative districts in Shanghai, China, were systematically conducted. It is showed that magnetic carriers were predominately coarse-grain ferrimagnetic particles. Meantime, concentration-related magnetic parameters showed significant variations among the functional areas and administrative units. Magnetic susceptibility (χ) decreased in the following order: industrial area (IA) > traffic area (TA) > commercial area (CoA) > residential area (RA) > clean area (ClA). Moreover, combined with the analyses conducted using a scanning electron microscope and an energy dispersive X-ray spectrometer (SEM-EDX), it is found that spherical magnetic particles originating mainly from anthropogenic sources were abundant in industrial areas. Baoshan district, which is heavily impacted by industrial activities, showed the highest χ value among the administrative units. Additionally, the correlations of street dust χ value with population, value of industrial output and the gross domestic product (GDP) in Shanghai and other cities indicated that χ is positively correlated with the city GDP as well as the population size (PS) to some extent. This study demonstrates that magnetic parameters of street dust can be used as an effective tool for monitoring environmental pollution and industrial activities in urban environments.

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.

Magnetic properties of road dusts from 26 urban sites in Bulgaria are studied. Temporal variations of magnetic susceptibility (χ) during eighteen months monitoring account for approximately 1/3rd of the mean annual values. Analysis of heavy metal contents and magnetic parameters for the fraction d < 63 μm reveal significant correlations (p < 0.05) between χ and Fe, Mn and PLI index. The highest negative correlation (R2 = −0.84) is observed between the ratio ARM/χ and Pb content. It suggests that Pb is related to brake/tyre wear emissions, releasing larger particles and higher Pb during slow driving – braking. Bulk χ values of road dusts per city show significant correlation with population size and mean annual NO2 concentration on a log-normal scale. The results demonstrate the applicability of magnetic measurements of road dusts for estimation of mean NO2 levels at high spatial density, which is important for pollution modelling and health risk assessment.

A combined magnetic-chemical study of 15 daily, simultaneous PM10–PM2.5–PM1 urban background aerosol samples has been carried out. The magnetic properties are dominated by non-stoichiometric magnetite, with highest concentrations seen in PM10. Low temperature magnetic analyses showed that the superparamagnetic fraction is more abundant when coarse, multidomain particles are present, confirming that they may occur as an oxidized outer shell around coarser grains. A strong association of the magnetic parameters with a vehicular PM10 source has been identified. Strong correlations found with Cu and Sb suggests that this association is related to brake abrasion emissions rather than exhaust emissions. For PM1 the magnetic remanence parameters are more strongly associated with crustal sources. Two crustal sources are identified in PM1, one of which is of North African origin. The magnetic particles are related to this source and so may be used to distinguish North African dust from other sources in PM1.

This study focuses on magnetic susceptibility processing and analysis towards fast and cost-efficient discrimination and semi-quantification of anthropogenic heavy metal loads in soil. Spatial variability of magnetic susceptibility was investigated on sets of soil cores from both “polluted” and “less polluted” forest soil close to a steel mill near Leoben, Austria. Test sites of 10 m2 represent “site scale” dimensions. Statistical analysis of magnetic data provides a boundary depth indicating the transition from the “polluted” to the deeper, “unpolluted” zone in contaminated natural soil. Introduction of a block master curve simplifies the complex variations of individual curves, and represents magnetic susceptibility at “site scale”. For linking the block master curve to heavy metals we only require magnetic susceptibility data from one soil core and heavy metal data from two sub-samples from the same core. Our optimized magnetic susceptibility data processing scheme provides an applicable tool to semi-quantify anthropogenic heavy metal loads in soil.

The influence of anthropogenic outdoor sources on the geochemical composition of house dust material in large cities is poorly understood. In this study, we investigate the magnetic signature and the concentrations of potentially toxic elements (PTEs) in randomly selected house dust samples from the metropolitan area of Athens, the most populated city in Greece. Environmental magnetic measurements, including isothermal remanent magnetization and thermomagnetism, indicated that the main magnetic mineral is coarse-grained low-coercivity magnetite. Detailed microscopic observations of the magnetically extracted material revealed the presence of three different kinds of Fe-rich particles deriving from both combustion-related and non-exhaust vehicular sources: irregularly-shaped grains and spherules of Fe-oxides, and particles consisting of metallic Fe. Further study of the morphology of single anthropogenic magnetic spherules (size > 30 μm) identified the presence of magnetite spherical particles, typically formed by industrial combustion processes. Enrichment factors (EFs) for the PTEs calculated against the Athens urban soil showed that the house dusts were very highly enriched in Cd, Cu, Zn and significantly enriched in Pb (median EF values of 34.1, 26.2, 25.4 and 10.3, respectively). The oral bioaccessibility of PTEs in the house dust, evaluated using a simulated gastric solution (0.4 M glycine), was in the order Pb > Zn > Mn > Cd > Ni > Cu > Cr > Fe. Concentrations of Pb increased with the house age. Principal component and cluster analysis demonstrated the close association of anthropogenic Cu, Pb and Zn with the magnetic susceptibility of the house dusts. We conclude that both traffic-related and industrial sources trigger the occurrence of magnetic Fe/PTEs- rich particles in house dust. These results reinforce the use of environmental magnetism determinations for assessing anthropogenic contamination of PTEs in the indoor environment in large cities.

Identifying particulate matter (PM) emitted from industrial processes into the atmosphere is an important issue in environmental research. This paper presents a passive sampling method using simple artificial samplers that maintains the advantage of bio-monitoring, but overcomes some of its disadvantages. The samplers were tested in a heavily polluted area (Linfen, China) and compared to results from leaf samples. Spatial variations of magnetic susceptibility from artificial passive samplers and leaf samples show very similar patterns. Scanning electron microscopy suggests that the collected PM are mostly in the range of 2–25 μm; frequent occurrence of spherical shape indicates industrial combustion dominates PM emission. Magnetic properties around power plants show different features than other plants. This sampling method provides a suitable and economic tool for semi-quantifying temporal and spatial distribution of air quality; they can be installed in a regular grid and calibrate the weight of PM.

Increasing urbanization and industrialization over the world has caused many social and environmental problems, one of which drawing particular concern is the soil pollution and its ecological degradation. In this study, the efficiency of magnetic methods for detecting and discriminating contaminates in the arid and semi-arid regions of northwestern China was investigated. Topsoil samples from six typical cities (i.e. Karamay, Urumqi, Lanzhou, Yinchuan, Shizuishan and Wuhai) were collected and a systematic analysis of their magnetic properties was conducted. Results indicate that the topsoil samples from the six cities were all dominated by coarse low-coercivity magnetite. In addition, the average magnetite contents in the soils from Urumqi and Lanzhou were shown to be much higher than those from Karamay, Yinchuan, Shizuishan and Wuhai, and they also have relatively higher χlf and χfd% when compared with cities in eastern China. Moreover, specific and distinctive soil pollution signals were identified at each sampling site using the combined various magnetic data, reflecting distinct sources. Industrial and traffic-derived pollution was dominant in Urumqi and Lanzhou, in Yinchuan industrial progress was observed to be important with some places affected by vehicle emission, while Karamay, Shizuishan and Wuhai were relatively clean. The magnetic properties of these latter three cities are significantly affected by both anthropogenic pollution and local parent materials from the nearby Gobi desert. The differences in magnetic properties of topsoil samples affected by mixed industrial and simplex traffic emissions are not obvious, but significant differences exist in samples affected by simplex industrial/vehicle emissions and domestic pollution. The combined magnetic analyses thus provide a sensitive and powerful tool for classifying samples according to likely sources, and may even provide a valuable diagnostic tool for discriminating among different cities.

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.

The non-magnetic capping materials are difficult to be recycled from the water bodies after their application, leading to the increase in the cost of the sediment remediation. To address this issue, a capping material, i.e., magnetic lanthanum/iron-modified bentonite (M-LaFeBT) was prepared by loading lanthanum onto a magnetic iron-modified bentonite (M-FeBT) and used to control the internal phosphorus (P) loading in this study. To determine the capping efficiency and mechanism of M-LaFeBT, the impact of M-LaFeBT and M-FeBT capping on the mobilization of P in sediments was investigated, and the stabilization of P bound by the M-LaFeBT and M-FeBT capping layers was evaluated. Results showed that M-LaFeBT possessed good magnetic property with a saturated magnetization of 14.9 emu/g, and exhibited good phosphate adsorption ability with a maximum monolayer sorption capacity (QMAX) of 14.3 mg P/g at pH 7. Moreover, M-LaFeBT capping tremendously reduced the concentration of soluble reactive P (SR-P) in the overlying water (OL-water), and the reduction efficiencies were 94.7%–97.4%. Furthermore, M-LaFeBT capping significantly decreased the concentration of SR-P in the pore water and DGT (diffusive gradient in thin films)-labile P in the profile of OL-water and sediment. Additionally, most of P bound by the M-LaFeBT capping layer (approximately 77%) was stable under natural pH and reducing conditions. The phosphate adsorption ability for M-LaFeBT was much higher than that for M-FeBT, and the QMAX value for the former was 4.86 times higher than that for the latter. M-LaFeBT capping gave rise to a higher reduction of DGT-labile concentration in the profile of OL-water and sediment than M-FeBT capping. The P adsorbed by the M-LaFeBT capping layer was more stable than that by the M-FeBT capping layer. Results of this study demonstrate that M-LaFeBT is promising for utilization as an active capping material to intercept sedimentary P release into OL-water.