Relationship between Soil Organic Carbon, Soil Nutrients, and Land Use in Linyi City (East China)

Wu, Xinyang; Wang, Lizhi; An, Juan; Wang, Yun; Hu, Song; Wu, Yuanzhi; Liu, Qianjin

doi:10.3390/su142013585

Cited by 14 publications

(4 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This area is subjected to a semi-humid temperate continental monsoon climate, with cold-dry winters and hot-rainy summers. The annual average temperature is 14 • C, accompanied by an annual average precipitation of 813 mm [35]. Most of the precipitation occurs between June and September, accounting for about 73% of the total annual precipitation [36].…”

Section: Study Areamentioning

confidence: 99%

Using Self-Organizing Map and Multivariate Statistical Methods for Groundwater Quality Assessment in the Urban Area of Linyi City, China

Liu,

Li,

Yang

et al. 2023

Water

View full text Add to dashboard Cite

Groundwater holds an important role in the water supply in Linyi city, China. Investigating the hydrochemical characteristics of groundwater, and revealing the factors governing groundwater geochemistry, is a primary step for ensuring the safe and rational exploitation of groundwater resources. This study used a self-organizing map (SOM) and multivariate statistical methods to assess groundwater quality in the urban area of Linyi city. Based on the hydrochemical dataset consisting of nine parameters (i.e., pH, Ca2+, Mg2+, Na+, K+, HCO3−, Cl−, SO42−, and NO3−) from 89 groundwater samples, the SOM was first applied to obtain the weight vectors of the output nodes. Hierarchical cluster analysis (HCA) was used for organizing the nodes into four clusters. The node cluster indices were then remapped to the groundwater samples according to the winner node for each sample. The hydrochemical characteristics and factors controlling the groundwater geochemistry of the four clusters were analyzed using principal component analysis (PCA) and graphical methods including Piper and Gibbs diagrams, as well as binary plots of the major ions in groundwater. Results indicated that groundwater geochemistry in this area is primarily governed by water–rock interactions, such as the dissolution of halite, calcite, and gypsum, along with the influence of municipal sewage and the degradation of organic matter. This study demonstrates that the integration of an SOM and multivariate statistical methods improves the understanding of groundwater geochemistry and hydrochemical evolution in complex groundwater flow systems impacted by utilization.

show abstract

Section: Study Areamentioning

confidence: 99%

Using Self-Organizing Map and Multivariate Statistical Methods for Groundwater Quality Assessment in the Urban Area of Linyi City, China

Liu,

Li,

Yang

et al. 2023

Water

View full text Add to dashboard Cite

show abstract

“…The low precipitation levels in winter accentuate the air pollution problems and contribute to smog formation (Yin et al, 2021;Ji and Li, 2016). Furthermore, in recent years, climate change has led to periods of extremely high temperatures and cold snaps in Linyi (Wu et al, 2022). The Ministry of Environmental Protection of the People's Republic of China has supervised air pollution in Linyi in 2015 (Zhang et al, 2018).…”

Section: Study Areamentioning

confidence: 99%

Application of machine learning to predict hospital visits for respiratory diseases using meteorological and air pollution factors in Linyi, China

Yang

et al. 2023

Environ Sci Pollut Res

View full text Add to dashboard Cite

Urbanization and industrial development have resulted in increased air pollution, which is concerning for public health. This study evaluated the effect of meteorological factors and air pollution on hospital visits for respiratory diseases (pneumonia, acute upper respiratory infections, and chronic lower respiratory diseases). The test dataset comprised meteorological parameters, air pollutant concentrations, and outpatient hospital visits for respiratory diseases in Linyi, China from January 1, 2016 to August 20, 2022. We used support vector regression (SVR) to build regression models to analyze the effect of meteorological factors and air pollutants on the number of outpatient visits for respiratory diseases. To evaluate the model performance, 70% of the dataset was used for training and 30% was used for testing. The Spearman correlation and SVR model results indicated that NO 2 , PM 2.5 , and PM 10 were correlated with the occurrence of respiratory diseases, and the strongest correlation was for pneumonia. An increase in the daily average temperature and daily relative humidity decreased the number of patients with pneumonia and chronic lower respiratory diseases but increased the number of patients with acute upper respiratory infections. The SVR modeling showed potential for predicting the number of respiratory-related hospital visits. This work demonstrated that combining machine learning with meteorological and air pollution data can be used for disease prediction and can serve as a useful tool for policymakers to take preventive measures. HighlightsMachine learning was used to examine correlations between meteorological and air pollution factors and outpatient visits.Pneumonia patient visits were strongly correlated with NO 2 , PM 2.5 , and PM 10 .The SVR model showed the best performance for pneumonia.We examined the link between climate and air pollution factors and respiratory diseases.

show abstract

“…As with SOC, the SAK content was found to gradually increase in closed grasslands without grazing [22]. A significant positive correlation between SOC and total nitrogen (TN) was found in most land-use types [23]. The SOC content was positively correlated with soil nutrients (available N, P, K) [24].…”

Section: Introductionmentioning

confidence: 95%

Change Characteristics of Soil Organic Carbon and Soil Available Nutrients and Their Relationship in the Subalpine Shrub Zone of Qilian Mountains in China

Zhang,

Jia,

Yang

et al. 2023

Sustainability

View full text Add to dashboard Cite

Studying the spatial and temporal distribution of soil organic carbon (SOC) content in high-altitude mountainous areas and its correlation with soil nutrients provides a basis for understanding soil carbon stocks and the factors affecting the local carbon cycle. Based on soil samples collected from a semi-sunny slope and semi-shady slope in the subalpine shrub zone of the eastern Qilian Mountains from May to October 2019, we studied the temporal and spatial changes in SOC and soil available nutrients and their relationships. The results showed that SOC content and soil nutrients were greater on the semi-shady slope than on the semi-sunny slope during the growing season and decreased with an increase in soil depth in different slope directions, showing obvious surface aggregation. The soil available nitrogen (SAN) content was consistent with the SOC content and exhibited greater synchronization. SOC was significantly positively correlated with soil available nutrients in the study area during the whole growing season. However, the correlation between SOC and soil nutrients varied among the different soil layers and slope orientations. The SOC content was more obviously correlated with the SAN content in the soil layer at a depth of 30–40 cm (r = 0.67, p < 0.05) on the semi-shady slope. The SOC content was more obviously correlated with soil-available phosphorus (SAP) content in the soil layer at a depth of 30–40 cm (r = 0.57) on the semi-sunny slop. The SOC content was more obviously correlated with the SAP content in the soil layer at a depth of 60–70 cm (r = 0.55) and with the soil-available potassium (SAK) content in the soil layer at a depth of 70–80 cm (r = 0.84) on the semi-sunny slope.

show abstract

Relationship between Soil Organic Carbon, Soil Nutrients, and Land Use in Linyi City (East China)

Cited by 14 publications

References 49 publications

Using Self-Organizing Map and Multivariate Statistical Methods for Groundwater Quality Assessment in the Urban Area of Linyi City, China

Using Self-Organizing Map and Multivariate Statistical Methods for Groundwater Quality Assessment in the Urban Area of Linyi City, China

Application of machine learning to predict hospital visits for respiratory diseases using meteorological and air pollution factors in Linyi, China

Change Characteristics of Soil Organic Carbon and Soil Available Nutrients and Their Relationship in the Subalpine Shrub Zone of Qilian Mountains in China

Contact Info

Product

Resources

About