Air-soil diffusive exchange of PAHs in an urban park of Shanghai based on polyethylene passive sampling: Vertical distribution, vegetation influence and diffusive flux

Li, Ying; Xie, Shuanshuan; Zheng, Lirong; Li, Tongtong; Sun, Yalun; Ma, Limin; Zhao, Lin; Grathwohl, Peter; Lohmann, Rainer

doi:10.1016/j.scitotenv.2019.06.500

Cited by 17 publications

(10 citation statements)

References 40 publications

(63 reference statements)

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“…However, this model has several considerable shortcomings, such as inconsistent architectures for different applications, coupled with the process required to tune and fit a neural network, which is a time-consuming procedure that is largely based on trial and error [27,28]. Conventionally, ANNs have been fitted using a backpropagation (BP) algorithm; however, state-of-the-art approaches using bio-inspired, metaheuristic, optimization algorithms have become increasingly prevalent, including the genetic algorithm (GA) [29], particle swarm optimization (PSO) [30], ant lion optimization (ALO) [31], spotted hyena optimizer (SHO) [32], binary spring search algorithm (BSSA) [33], grey wolf algorithm (GWO) [34], genetic optimization resampling based particle filtering (GORPF) algorithm [35], and ant colony optimization (ACO) [16]-all of which may be hybridized with ANNs to address the aforementioned disadvantages.…”

Section: Introductionmentioning

confidence: 99%

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Bio-Inspired Hybridization of Artificial Neural Networks: An Application for Mapping the Spatial Distribution of Soil Texture Fractions

et al. 2021

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Soil texture and particle size fractions (PSFs) are a critical characteristic of soil that influences most physical, chemical, and biological properties of soil; furthermore, reliable spatial predictions of PSFs are crucial for agro-ecological modeling. Here, series of hybridized artificial neural network (ANN) models with bio-inspired metaheuristic optimization algorithms such as a genetic algorithm (GA-ANN), particle swarm optimization (PSO-ANN), bat (BAT-ANN), and monarch butterfly optimization (MBO-ANN) algorithms, were built for predicting PSFs for the Mazandaran Province of northern Iran. In total, 1595 composite surficial soil samples were collected, and 64 environmental covariates derived from terrain, climatic, remotely sensed, and categorical datasets were used as predictors. Models were tested using a repeated 10-fold nested cross-validation approach. The results indicate that the hybridized ANN methods were far superior to the reference approach using ANN with a backpropagation training algorithm (BP-ANN). Furthermore, the MBO-ANN approach was consistently determined to be the best approach and yielded the lowest error and uncertainty. The MBO-ANN model improved the predictions in terms of RMSE by 20% for clay, 10% for silt, and 24% for sand when compared to BP-ANN. The physiographical units, soil types, geology maps, rainfall, and temperature were the most important predictors of PSFs, followed by the terrain and remotely sensed data. This study demonstrates the effectiveness of bio-inspired algorithms for improving ANN models. The outputs of this study will support and inform sustainable soil management practices, agro-ecological modeling, and hydrological modeling for the Mazandaran Province of Iran.

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Section: Introductionmentioning

confidence: 99%

“…31 21 E to 53 • 56 52 E longitudes and 36 • 38 06 N to 36 • 54 59 N latitudes, has an areal extent of 23,833 km 2…”

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confidence: 99%

Bio-Inspired Hybridization of Artificial Neural Networks: An Application for Mapping the Spatial Distribution of Soil Texture Fractions

et al. 2021

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“…Besides being directly deposited on leaves or soil, PMs can also be mobilized from soil to leaves by wind or evaporation, be transported from roots to leaves or be deposited on soil through plant biomass decay (Figure 2; [78][79][80][81]).…”

Section: Adsorptionmentioning

confidence: 99%

Biochemical and Metabolic Plant Responses toward Polycyclic Aromatic Hydrocarbons and Heavy Metals Present in Atmospheric Pollution

Molina

Segura

2021

Plants

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Heavy metals (HMs) and polycyclic aromatic hydrocarbons (PAHs) are toxic components of atmospheric particles. These pollutants induce a wide variety of responses in plants, leading to tolerance or toxicity. Their effects on plants depend on many different environmental conditions, not only the type and concentration of contaminant, temperature or soil pH, but also on the physiological or genetic status of the plant. The main detoxification process in plants is the accumulation of the contaminant in vacuoles or cell walls. PAHs are normally transformed by enzymatic plant machinery prior to conjugation and immobilization; heavy metals are frequently chelated by some molecules, with glutathione, phytochelatins and metallothioneins being the main players in heavy metal detoxification. Besides these detoxification mechanisms, the presence of contaminants leads to the production of the reactive oxygen species (ROS) and the dynamic of ROS production and detoxification renders different outcomes in different scenarios, from cellular death to the induction of stress resistances. ROS responses have been extensively studied; the complexity of the ROS response and the subsequent cascade of effects on phytohormones and metabolic changes, which depend on local concentrations in different organelles and on the lifetime of each ROS species, allow the plant to modulate its responses to different environmental clues. Basic knowledge of plant responses toward pollutants is key to improving phytoremediation technologies.

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“…Atmospheric contaminants can be deposited directly from the air onto leaves or can be deposited in soils, and are then adsorbed to roots [41]; they can also be mobilized from soil to leaves by evaporation or wind, or be transported from roots to leaves (Figure 2) [42][43][44]. The direct relationship between PAH concentrations in soil and in plants suggests that soil-to-root transfer dominates over atmosphere-to-plant transfer [45].…”

Section: Deposition Transport and Detoxification Of Contaminants In Plantsmentioning

confidence: 99%

Plant-Bacteria Interactions for the Elimination of Atmospheric Contaminants in Cities

et al. 2021

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One of the major health risks for humans, especially for those living in large cities, is air pollution. Air pollution consists mainly of emissions of particulate matter (PM), nitrogen oxides, sulphur dioxide, ammonia and volatile organic compounds (VOCs). The organic carbon fraction of particulate matter is a mixture of hundreds of organic compounds, such as polycyclic aromatic hydrocarbons (PAHs), or polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs), some of which are mutagenic and/or carcinogenic. Because this particulate matter represents a serious threat for human health, measures to reduce emissions and to eliminate contaminants need to be strongly reinforced, with a focus on novel biotechnologies. In this review, we will explore the possibilities that bacteria associated with plants may offer the amelioration of atmospheric contaminants in cities, and we will discuss this strategy in the context of “Green Architecture”.

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Air-soil diffusive exchange of PAHs in an urban park of Shanghai based on polyethylene passive sampling: Vertical distribution, vegetation influence and diffusive flux

Cited by 17 publications

References 40 publications

Bio-Inspired Hybridization of Artificial Neural Networks: An Application for Mapping the Spatial Distribution of Soil Texture Fractions

Bio-Inspired Hybridization of Artificial Neural Networks: An Application for Mapping the Spatial Distribution of Soil Texture Fractions

Biochemical and Metabolic Plant Responses toward Polycyclic Aromatic Hydrocarbons and Heavy Metals Present in Atmospheric Pollution

Plant-Bacteria Interactions for the Elimination of Atmospheric Contaminants in Cities

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