Influencing factors and prediction of arsenic concentration in Pteris vittata: A combination of geodetector and empirical models

Zeng, Wei-Bin; Wan, Xiaoming; Lei, Mei; Gu, Gaoquan; Chen, Tongbin

doi:10.1016/j.envpol.2021.118240

Cited by 30 publications

(9 citation statements)

References 45 publications

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“…At the same time, all parameters indicating the high performance of the model (R 2 , ME, MNAE, F and P-values) were considered, and the selected soil factors (As concentration, pH, OC, CC, and CEC) had a certain effect on the As concentrations in wheat/maize tissues. Other researchers have reported that the above-related parameters impact the As absorption of crops (for example, Zeng et al 2022). The prediction models for As absorption of wheat/maize used in this study are comparable or better than those for As absorption of many other crops.…”

supporting

confidence: 55%

Prediction of arsenic accumulation in a calcareous soil-wheat/maize rotation system with continuous amendment of sewage sludge

Chen¹,

Huang²,

Song³

et al. 2022

PLANT SOIL ENVIRON

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A potted experiment was conducted to explore the accumulation of arsenic (As) and predict the uptake of As by a wheat-maize rotation system in calcareous soil with different rates of sewage sludge (SS) amendment over two consecutive years. The SS amendment decreased the pH value of calcareous soil but increased the cation exchange capacity (CEC), calcium carbonate (CC), organic carbon (OC) and As accumulation in soil and crops with increasing SS addition. The As bioconcentration factor (BCF) of wheat and maize had a significant negative correlation with pH, CC and a significant positive correlation with OC. Soil CEC had a significant positive correlation only with the As BCF of wheat. Regression analysis showed that soil As, pH, OC, CC and CEC were good predictors of the As concentration in wheat/maize. The regression model for each part of the wheat/maize plants had a high model efficiency value and explained 67~88% of the variability. The R2 values of the wheat and maize grain prediction models were 79% and 76%, respectively. Thus, these models contribute to the study of As risk assessment for sewage sludge utilisation in calcareous soil-wheat/maize rotation systems.

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supporting

confidence: 55%

Prediction of arsenic accumulation in a calcareous soil-wheat/maize rotation system with continuous amendment of sewage sludge

Chen¹,

Huang²,

Song³

et al. 2022

PLANT SOIL ENVIRON

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“…In addition, the risk of HFRS is closely affected by climate factors [18,21], with temperature, wind speed, and humidity being essential drivers. Geodetector is an emerging technique in recent years and has become a reliable tool for detecting and exploiting spatial heterogeneity and determinants [22][23][24][25]. Geodetector could identify relationships between diseases and their associated factors by detecting similarities in the spatial distribution of independent and dependent variables, using the power of determinant as a metric.…”

Section: Introductionmentioning

confidence: 99%

The Spatiotemporal Pattern and Its Determinants of Hemorrhagic Fever With Renal Syndrome in Northeastern China: Spatiotemporal Analysis

Wang¹,

Wei²,

Jia³

et al. 2023

JMIR Public Health Surveill

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Background Hemorrhagic fever with renal syndrome (HFRS) is a significant zoonotic disease mainly transmitted by rodents. However, the determinants of its spatiotemporal patterns in Northeast China remain unclear. Objective This study aimed to investigate the spatiotemporal dynamics and epidemiological characteristics of HFRS and detect the meteorological effect of the HFRS epidemic in Northeastern China. Methods The HFRS cases of Northeastern China were collected from the Chinese Center for Disease Control and Prevention, and meteorological data were collected from the National Basic Geographic Information Center. Times series analyses, wavelet analysis, Geodetector model, and SARIMA model were performed to identify the epidemiological characteristics, periodical fluctuation, and meteorological effect of HFRS in Northeastern China. Results A total of 52,655 HFRS cases were reported in Northeastern China from 2006 to 2020, and most patients with HFRS (n=36,558, 69.43%) were aged between 30-59 years. HFRS occurred most frequently in June and November and had a significant 4- to 6-month periodicity. The explanatory power of the meteorological factors to HFRS varies from 0.15 ≤ q ≤ 0.01. In Heilongjiang province, mean temperature with a 4-month lag, mean ground temperature with a 4-month lag, and mean pressure with a 5-month lag had the most explanatory power on HFRS. In Liaoning province, mean temperature with a 1-month lag, mean ground temperature with a 1-month lag, and mean wind speed with a 4-month lag were found to have an effect on HFRS, but in Jilin province, the most important meteorological factors for HFRS were precipitation with a 6-month lag and maximum evaporation with a 5-month lag. The interaction analysis of meteorological factors mostly showed nonlinear enhancement. The SARIMA model predicted that 8,343 cases of HFRS are expected to occur in Northeastern China. Conclusions HFRS showed significant inequality in epidemic and meteorological effects in Northeastern China, and eastern prefecture-level cities presented a high risk of epidemic. This study quantifies the hysteresis effects of different meteorological factors and prompts us to focus on the influence of ground temperature and precipitation on HFRS transmission in future studies, which could assist local health authorities in developing HFRS-climate surveillance, prevention, and control strategies targeting high-risk populations in China.

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“…It is particularly suited for studying spatial differentiation phenomena and their underlying influencing factors, as it can effectively handle the complexity and variability of spatial data [54,55]. In this study, through the application of the geographical detector, we were not only able to identify the main factors affecting the habitat quality of national nature reserves in Hubei Province but we were also able to gain a deep understanding of how these factors collectively influence habitat quality evolution through various complex interaction mechanisms, providing a scientific basis and guidance for future conservation and management strategies [56,57].…”

Section: )mentioning

confidence: 96%

Spatiotemporal Evolution and Mechanisms of Habitat Quality in Nature Reserve Land: A Case Study of 18 Nature Reserves in Hubei Province

Lin,

Zhang,

Zhu

et al. 2024

Land

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The contribution of biodiversity to the global economy, human survival, and welfare has been significantly increasing. However, nature reserves have long been subject to a sequence of ecological environmental issues caused by human activities. Therefore, quantitatively assessing the spatiotemporal evolution characteristics of habitat quality due to land use changes and exploring the mechanisms of potential influencing factors can provide a scientific basis for the stable and sustainable development of natural ecosystems. This study aims to analyze 18 nature reserves in Hubei Province to identify the spatiotemporal evolution of habitat quality within these reserves and to explore the influence of multifactorial dynamics from nature, humanity, and policy on this evolution. Initially, the study utilizes land use transition matrices and land use dynamic degree methods to understand the spatiotemporal characteristics of land conversion within the study area. Subsequently, it analyzes the spatiotemporal changes in habitat quality from 2000–2020 based on the InVEST model and tools like spatial autocorrelation (Moran’s I) in ArcGIS. Finally, 14 potential influencing factors are selected from natural environment, socio-human, and policy regulation aspects and analyzed in the Geodetector software to understand the factors affecting the spatiotemporal evolution of habitat quality. The results show that, during the study period, the land area of 18 nature reserves in Hubei Province increased from 2000 to 2020, while the water area decreased. There were slight increases in farmland, construction land, and forest land, with significant decreases in grassland and water areas. This reveals the erosion of water bodies due to artificial lake filling during rapid urbanization, leading to a decline in overall habitat quality within the reserves and a gradual increase in spatial heterogeneity. Among the influencing factors, single-factor influences such as land use intensity and distance to county roads and slopes have a strong negative linear relationship with habitat quality, with land use intensity being the most significant human activity factor. The interaction strength among different types of influencing factors in the bivariate interaction detection results is ranked as follows: the interaction between natural geographical and socio-human factors > the interaction within socio-human factors > the interaction within natural geographical factors. This study has diverged from the past focus on the selection of a single continuous natural reserve as the empirical subject. Consequently, it allows for an integrated analysis of physical geographical dimensions such as locational topography with socio-cultural and policy elements including land use and transportation facilities, thereby facilitating a multifactorial assessment of the interactive impacts on habitat quality.

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Influencing factors and prediction of arsenic concentration in Pteris vittata: A combination of geodetector and empirical models

Cited by 30 publications

References 45 publications

Prediction of arsenic accumulation in a calcareous soil-wheat/maize rotation system with continuous amendment of sewage sludge

Prediction of arsenic accumulation in a calcareous soil-wheat/maize rotation system with continuous amendment of sewage sludge

The Spatiotemporal Pattern and Its Determinants of Hemorrhagic Fever With Renal Syndrome in Northeastern China: Spatiotemporal Analysis

Spatiotemporal Evolution and Mechanisms of Habitat Quality in Nature Reserve Land: A Case Study of 18 Nature Reserves in Hubei Province

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