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.

Antibiotics in water and soil are persistent, bioaccumulative and toxic to aquatic organisms and human health. To address it, as one of the new technologies, green synthesized magnetic Fe₃O₄ nanoparticles by Excoecaria cochinchinensis extract used to remove rifampicin (RIF) was investigated in this study. Results showed the adsorption efficiency of RIF reached 98.4% and the maximum adsorption capacity is 84.8 mg/g when 20 mL of RIF at a concentration of 20 M was adsorbed by 10 mg Fe₃O₄ at a temperature of 303 K. The morphology of the green Fe₃O₄ characterized by SEM demonstrated the dimensions ranging from 20 to 30 nm. The N₂ adsorption/desorption isotherms revealed that the surface area of Fe₃O₄ was 111.8 m²/g. In addition, adsorption studies indicated that the kinetics fitted the pseudo second-order and isothermal adsorption conformed to the Langmuir isotherm. Furthermore, due to their magnetic properties, the Fe₃O₄ nanoparticles were easily separated and reused and the mechanism for removing RIF occurred through adsorption rather than chemical redox reaction. Finally, the reusability of Fe₃O₄ for adsorption of RIF showed that the removal efficiency decreased to 61.5% after five cycles.

This article gives an overview of nanotechnologies applied in remediation of water contaminated by poly- and perfluoroalkyl substances (PFASs). The use of engineered nanomaterials (ENMs) in physical sorption and photochemical reactions offers a promising solution in PFAS removal because of the high surface area and the associated high reactivities of the ENMs. Modification of carbon nanotubes (CNTs) (e.g., oxidation, applying electrochemical assistance) significantly improves their adsorption rate and capacity for PFASs removal and opens a new door for use of CNTs in environmental remediation. Modified nanosized iron oxides with high adsorption capacity and magnetic property have also been demonstrated to be ideal sorbents for PFASs with great recyclability and thus provide an excellent alternative for PFAS removal under various conditions. Literature shows that PFOA, which is one of the most common PFASs detected at contaminated sites, can be effectively decomposed in the presence of either TiO₂-based, Ga₂O₃-based, or In₂O₃-based nano-photocatalysts under UV irradiation. The decomposition abilities and mechanisms of different nano-photocatalysts are reviewed and compared in this paper. Particularly, the nanosized In₂O₃ photocatalysts have the best potential in PFOA decomposition and the decomposition performance is closely related to the specific surface area and the amount of photogenerated holes on the surfaces of In₂O₃ nanostructures. In addition to detailed review of the published studies, future prospects of using nanotechnology for PFAS remediation are also discussed in this article.

In this study, carbon nanotube-based adsorbents, oxidized multi-walled carbon nanotube (OMWCNT) with non-magnetic property and OMWCNT-Fe₃O₄ and OMWCNT-κ-carrageenan-Fe₃O₄ nanocomposites with magnetic property, having different structural and surface properties were prepared and their adsorptive properties for the removal of toxic diquat dibromide (DQ) herbicide from water by adsorption were determined in detail. For each adsorption system, the effects of initial DQ concentration, contact time and temperature on the adsorption processes were determined. Equilibrium time was found to be 300 min for DQ solutions. OMWCNT showed faster adsorption and higher maximum adsorption capacity value than magnetic adsorbents. With increasing initial herbicide concentration from 5.43 mg.L⁻¹ to 16.3 mg.L⁻¹, the values of initial sorption rate exhibited a decrease from 29.1 mg.g⁻¹.min⁻¹ to 4.28 mg.g⁻¹.min⁻¹ for OMWCNT-DQ system, from 1.21 mg.g⁻¹.min⁻¹ to 0.823 mg.g⁻¹.min⁻¹ for OMWCNT-Fe₃O₄-DQ system and from 0.674 mg.g⁻¹.min⁻¹ to 0.612 mg.g⁻¹.min⁻¹ OMWCNT-κ-carrageenan-Fe₃O₄ system. Maximum adsorption capacity value of OMWCNT was approximately 2.8-fold higher than magnetic OMWCNT-Fe₃O₄ and 5.4-fold higher than magnetic OMWCNT-κ-carrageenan-Fe₃O₄ at 25 °C. Adsorption kinetic and isotherm data obtained for all adsorption systems were well-fitted by pseudo second-order and Langmuir models, respectively. Thermodynamic parameters indicated that the adsorption of DQ onto carbon nanotube-based adsorbents was spontaneous and endothermic process. Furthermore, OMWCNT having the highest herbicide adsorption capacity could be regenerated and reused at least five times. This study showed that carbon nanotube-based adsorbents with magnetic and non-magnetic property were of high adsorption performance for the removal of DQ from water and could be promising adsorbent materials for the efficient removal of herbicides from wastewaters.

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.

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.

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.

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.

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.

Although air pollution decreased in some cities that shifted from an industrial to a service-based economy, and vehicular emission regulation became more restrictive, it is still a major risk factor for mortality worldwide. In central São Paulo, Brazil, air quality monitoring stations and tree-ring analyses revealed a decreasing trend in the concentrations of particulate matter and metals. Such trends, however, may not be observed in industrial districts located in the urban periphery, where the usual mobile sources may be combined with local stationary sources. To evaluate environmental pollution in an industrial district in southeastern São Paulo, we assessed its spatial variability, by measuring magnetic properties and concentrations of Al, Ba, Ca, Cl, Cu, Fe, K, Mg, Mn, P, S, Sr, Zn in the bark of 62 trees, and its temporal trends, by measuring Cd, Cu, Ni, Pb, V, Zn in tree rings of three trees. Source apportionment analysis based on tree barks revealed two clusters with high concentrations of metals, one related to vehicular and industrial emissions (Al, Ba, Cu, Fe, Zn) in the east side of the industrial cluster, and the other related to soil resuspension (Cu, Zn, Mn) in its west side. These patterns are also supported by the magnetic properties of bark associated with iron oxides and titanium-iron alloy concentrations. Dendrochemical analyses revealed that only the concentrations of Pb consistently decreased over the last four decades. The concentrations of Cd, Cu, Ni, V, and Zn did not significantly decrease over time, in contrast with their negative trends previously reported in central São Paulo. This combined biomonitoring approach revealed spatial clusters of metal concentration in the vicinity of this industrial cluster and showed that the local population has not benefited from the decreasing polluting metal concentrations in the last decades.