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

Leng, Xiang'zi; Qian, Xin; Yang, Meng; Wang, Cheng; Li, Huiming; Wang, Jinhua

doi:10.1016/j.envpol.2018.07.007

Cited by 39 publications

(11 citation statements)

References 53 publications

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“…The inhaled magnetic particles were evidenced to be directly associated with brain amyloid plaques. This pathological structure is regarded as a critical feature of many neurodegenerative diseases, such as Alzheimer’s disease. − Furthermore, airborne magnetic particles can precipitate on various targets, such as tree leaves, , topsoil, , and road dust, , giving rise to potential negative impacts on the ecosystem and environment. , …”

Section: Introductionmentioning

confidence: 99%

Magnetic Particles Unintentionally Emitted from Anthropogenic Sources: Iron and Steel Plants

Zhang

et al. 2021

Environ. Sci. Technol. Lett.

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Magnetic particles suspended in the atmosphere have attracted public attention due to their adverse health impacts. However, the origin of these magnetic particles has not been addressed. This study first reports the magnetic properties and chemical characteristics of emitted dusts from various anthropogenic sources. On the basis of the combination of magnetic parameter measurements and PM2.5 emission inventories, the iron and steel industry contributes >80% of the total magnetic particle emission among the studied emission source categories. In contrast, the magnetic particles emitted from other sectors make up a significantly smaller portion. Magnetite is the dominant magnetic contributor, as suggested by X-ray diffraction, Mössbauer spectra, and X-ray absorption studies. Morphological studies further indicate that nanoscale magnetite contained in steel plants derived particles is consistent with that found in human tissues. Our results suggest that applying a priority control strategy in the iron and steel industry can decrease the unintended emission of magnetic particles.

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Section: Introductionmentioning

confidence: 99%

Magnetic Particles Unintentionally Emitted from Anthropogenic Sources: Iron and Steel Plants

Zhang

et al. 2021

Environ. Sci. Technol. Lett.

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“…If we compare only data from deciduous leaves, magnetic susceptibility values obtained on Maroua's leaves are in the same range as data obtained in Portugal (Porto, Braga and Valongo, [84]) and Nanjing (<8 M inhabitants, China, [85]) or Kathmandu [82] but lower than those measured in Rome [28,77]. While relatively isolated medium-sized African cities such as Maroua are not emission "hot spots", such as large cities in West Africa and sub-Saharan Africa [86], our data imply that these cities display locally values comparable to the particulate emissions of some Asian or European cities.…”

Section: Neem Bark and Leaves As Complementary Biocollectorsmentioning

confidence: 81%

Tracking Airborne Pollution with Environmental Magnetism in A Medium-Sized African City

et al. 2021

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As in other parts of the world, air pollution over West and Central Africa has major health and meteorological impacts. Air quality assessment and its possible sanitary impact have become essential even in medium-sized towns, therefore amplifying the need for easy-to-implement monitoring methods with low environmental impact. We present here the potential of magnetic methods to monitor air quality at street level in the medium-sized city of Maroua (northern Cameroon) affected by dust-laden desert winds. More than five hundred (544) samples of bark and leaves taken from Neem trees in Maroua were analyzed. Magnetic susceptibility, saturation remanence, and S-ratio were found to determine the concentration and nature of magnetic particles. They are dominated by magnetite-like particle signals as a part of particulate emissions due to urban activities, including both traffic, composed of a substantial proportion of motorcycles, and wood burning for food preparation. We show that both bark and leaves from Neem trees are adequate passive bio-recorders. The use of both enables different times and heights to be sampled, allowing for the high-resolution monitoring, in terms of spatialization, of various urban environments. Particle emissions require assessment and screening that could be carried out rapidly and efficiently by magnetic methods on bio-recorders, even in cities impacted by dust-laden wind.

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“…Recently, many models have been used to estimate soil attributes and their spatial distribution from geophysical data (gamma ray, κ, and ECa) and soil attributes, including machine learning algorithms, such as the support vector machine (SVM; Priori et al, 2014;Heggemann et al, 2017;Li et al, 2017;Leng et al, 2018;Zare et al, 2020), random forest (Lacoste et al, 2011;Viscarra Rossel et al, 2014;Harris and Grunsky, 2015;Sousa et al, 2020), KNN and artificial neural network (ANN) (Dragovic and Onjia, 2007), and Cubist (Wilford and Thomas, 2012) methods.…”

Section: Introductionmentioning

confidence: 99%

A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes

Mello¹,

Veloso²,

Lana³

et al. 2022

Geosci. Model Dev.

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Abstract. Geophysical sensors combined with machine learning algorithms were used to understand the pedosphere system and landscape processes and to model soil attributes. In this research, we used parent material, terrain attributes, and data from geophysical sensors in different combinations to test and compare different and novel machine learning algorithms to model soil attributes. We also analyzed the importance of pedoenvironmental variables in predictive models. For that, we collected soil physicochemical and geophysical data (gamma-ray emission from uranium, thorium, and potassium; magnetic susceptibility and apparent electric conductivity) by three sensors (gamma-ray spectrometer, RS 230; susceptibilimeter KT10, Terraplus; and conductivimeter, EM38 Geonics) at 75 points and analyzed the data. The models with the best performance (R2 0.48, 0.36, 0.44, 0.36, 0.25, and 0.31) varied for clay, sand, Fe2O3, TiO2, SiO2, and cation exchange capacity prediction, respectively. Modeling with the selection of covariates at three phases (variance close to zero, removal by correction, and removal by importance) was adequate to increase the parsimony. The results were validated using the method “nested leave-one-out cross-validation”. The prediction of soil attributes by machine learning algorithms yielded adequate values for field-collected data, without any sample preparation, for most of the tested predictors (R2 values ranging from 0.20 to 0.50). Also, the use of four regression algorithms proved to be important since at least one of the predictors used one of the tested algorithms. The performance values of the best algorithms for each predictor were higher than those obtained with the use of a mean value for the entire area comparing the values of root mean square error (RMSE) and mean absolute error (MAE). The best combination of sensors that reached the highest model performance was that of the gamma-ray spectrometer and the susceptibilimeter. The most important variables for most predictions were parent material, digital elevation, standardized height, and magnetic susceptibility. We concluded that soil attributes can be efficiently modeled by geophysical data using machine learning techniques and geophysical sensor combinations. This approach can facilitate future soil mapping in a more time-efficient and environmentally friendly manner.

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

Cited by 39 publications

References 53 publications

Magnetic Particles Unintentionally Emitted from Anthropogenic Sources: Iron and Steel Plants

Magnetic Particles Unintentionally Emitted from Anthropogenic Sources: Iron and Steel Plants

Tracking Airborne Pollution with Environmental Magnetism in A Medium-Sized African City

A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes

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