Influence factor analysis of soil heavy metal based on categorical regression

Xu, Jie; Xiao, Pengnan

doi:10.1007/s13762-021-03640-4

Cited by 6 publications

(3 citation statements)

References 69 publications

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“…A total of 14 possible explanatory variables were selected from two aspects (Table 1) [36,37]: (i) possible sources of As, including distance from an industrial enterprise (Dis_IE), road density (RD), population density (PD), land use types (LU), total nitrogen (TN), total phosphorus (TP), and soil types (ST); (ii) migration-related factors of As, including clay content, silt content, pH, soil organic matter (SOM), elevation, and topographic wetness index (TWI). We crawled the locations of industrial enterprises in the study area from Amap, as shown in Figure 2a.…”

Section: Sources and Pre-processing Of Environmental Variablesmentioning

confidence: 99%

Identifying the Local Influencing Factors of Arsenic Concentration in Suburban Soil: A Multiscale Geographically Weighted Regression Approach

Zhu,

Liu,

Jin

et al. 2024

Toxics

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Exploring the local influencing factors and sources of soil arsenic (As) is crucial for reducing As pollution, protecting soil ecology, and ensuring human health. Based on geographically weighted regression (GWR), multiscale GWR (MGWR) considers the different influence ranges of explanatory variables and thus adopts an adaptative bandwidth. It is an effective model in many fields but has not been used in exploring local influencing factors and sources of As. Therefore, using 200 samples collected from the northeastern black soil zone of China, this study examined the effectiveness of MGWR, revealed the spatial non-stationary relationship between As and environmental variables, and determined the local impact factors and pollution sources of As. The results showed that 49% of the samples had arsenic content exceeding the background value, and these samples were mainly distributed in the central and southern parts of the region. MGWR outperformed GWR with the adaptative bandwidth, with a lower Moran’s I of residuals and a higher R2 (0.559). The MGWR model revealed the spatially heterogeneous relationship between As and explanatory variables. Specifically, the road density and total nitrogen, clay, and silt contents were the primary or secondary influencing factors at most points. The distance from an industrial enterprise was the secondary influencing factor at only a few points. The main pollution sources of As were thus inferred as traffic and fertilizer, and industrial emissions were also included in the southern region. These findings highlight the importance of considering adaptative bandwidths for independent variables and demonstrate the effectiveness of MGWR in exploring local sources of soil pollutants.

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Section: Sources and Pre-processing Of Environmental Variablesmentioning

confidence: 99%

Identifying the Local Influencing Factors of Arsenic Concentration in Suburban Soil: A Multiscale Geographically Weighted Regression Approach

Zhu,

Liu,

Jin

et al. 2024

Toxics

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“…Multiple environmental factors collectively contribute to the spatial distribution of soil heavy metals [26,27]. Existing research primarily selects environmental factors across both natural and socio-economic aspects.…”

Section: Introductionmentioning

confidence: 99%

Spatial Distribution Prediction of Soil Heavy Metals Based on Random Forest Model

Nie,

Chen,

Sun

et al. 2024

Sustainability

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Mastering the spatial distribution of soil heavy metal content and evaluating the pollution status of soil heavy metals is of great significance for ensuring agricultural production and protecting human health. This study used a machine learning model to study the spatial distribution of soil heavy metal content in a coastal city in eastern China. Having obtained six soil heavy metal contents, including Cr, Cd, Pb, As, Hg, and Ni, environmental variables such as precipitation, soil moisture, and population density were selected. Random forest (RF) was used to model the spatial distribution of soil heavy metal content. The research findings indicate that the RF model demonstrates a robust predictive capability in discerning the spatial distribution of soil heavy metals, and environmental factor variables can explain 60%, 52.3%, 53.5%, 63.1%, 61.2%, and 51.2% of the heavy metal content of Cr, Cd, Pb, As, Hg, and Ni in soil, respectively. Among the chosen environmental variables, precipitation and population density exert notable influences on the predictive outcomes of the model. Specifically, precipitation exhibits the most substantial impact on Cr and Ni, whereas population density emerges as the primary determinant for Cd, Pb, As, and Hg. The RF prediction results show that Cr and Ni in the study area are less affected by human activities, while Cd, Pb, As, and Hg are more affected by human industrial and agricultural production. Research has shown that using RF models for predicting soil heavy metal distributions has certain significance.

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“…1 central document of the State Council of China proposed a variety of measures in 2022, such as giving priority to the overall control of agricultural non-point source pollution and emphasizing the green development of agriculture and rural areas [ 2 ]. Promoting the low-carbon utilization of cultivated land resources is not only the top priority to achieve high-quality agricultural development, but is also in line with the requirement of government policies [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Characteristics, Decoupling Effect and Driving Factors of Carbon Emission from Cultivated Land Utilization in Hubei Province

Xiao

Zhang

Qian

et al. 2022

IJERPH

Self Cite

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The carbon emission level and spatiotemporal characteristics in Hubei Province were estimated and studied using the Intergovernmental Panel on Climate Change (IPCC) carbon emission coefficient technique based on county data from Hubei Province from 2000 to 2020. The relationship between carbon emissions from cultivated land utilization and agricultural economic growth was examined using the Tapio decoupling index, and the factors influencing carbon emissions in Hubei Province were further examined using the Logarithmic Mean Divisia Index (LMDI model). The results demonstrate that: (1) Spatiotemporal variations in carbon emissions are evident. In terms of time, the volume of carbon emissions in Hubei Province is still substantial, and the transition to low-carbon land use is quite gradual. Geographically, the high-value region of the middle east coexists with the low-value zone of the west, with apparent regional contrasts. (2) The decoupling between carbon emissions and agricultural economic growth is becoming more and more obvious in Hubei Province. The number of counties and cities in a negative decoupling state has significantly decreased, and the majority of counties are now in a strong decoupling condition. (3) Agricultural production efficiency is the most significant driving factor for restricting carbon emission, according to the decomposition results of carbon emission driving factors based on the LMDI model. In addition, the results of sample decomposition based on topographic characteristics indicate that agricultural production efficiency is primarily responsible for the suppression of carbon emissions in flat regions. The increase in carbon emissions in hilly regions is primarily influenced by agricultural productivity. The increase in carbon emissions in mountainous regions is mostly influenced by agricultural labor intensity. This study′s finding has enlightening implications for the high-quality growth of agriculture.

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Influence factor analysis of soil heavy metal based on categorical regression

Cited by 6 publications

References 69 publications

Identifying the Local Influencing Factors of Arsenic Concentration in Suburban Soil: A Multiscale Geographically Weighted Regression Approach

Identifying the Local Influencing Factors of Arsenic Concentration in Suburban Soil: A Multiscale Geographically Weighted Regression Approach

Spatial Distribution Prediction of Soil Heavy Metals Based on Random Forest Model

Spatiotemporal Characteristics, Decoupling Effect and Driving Factors of Carbon Emission from Cultivated Land Utilization in Hubei Province

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