Mapping the Spatial Heterogeneity of Anthropogenic Soil Nitrogen Net Replenishment Based on Soil Loss: A Coastal Case in the Yellow River Delta, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Region
2.2. Data Collection
2.2.1. Soil Sampling and Laboratory Analysis
2.2.2. Auxiliary Spatial Data
2.3. Interpolation of Soil Sampling Data and Verification
2.3.1. Ordinary Kriging
2.3.2. IDW
2.3.3. GWR and GWRK
2.3.4. Verification
2.4. Soil Nitrogen Loss Risk Index
2.4.1. Soil Erodibility Factor
2.4.2. Slope Factor
2.4.3. Vegetation Coverage Factor
2.4.4. Hydraulic Erosivity Factors
2.4.5. Weight Determination and the SNLRI
2.5. Anthropogenic Soil Nitrogen Net Replenishment Index
2.5.1. STN Change Index
2.5.2. A-SNNRI
2.6. Statistics
3. Results
3.1. Spatiotemporal Distributions of STN and SOM
3.1.1. STN and SOM Characteristics
3.1.2. STN Change and Impact Factor Analysis
3.2. Soil Nitrogen Loss Risks in the YRD
3.2.1. Distribution of Soil Erodibility
3.2.2. SNLRI Analysis
3.2.3. Soil Loss Influence on STN Change
3.3. Spatial Distributions of A-SNNRI
4. Discussion
4.1. Influences of Human Activities on STN Status
4.1.1. Influence of Land Use on STN Distribution
4.1.2. Anthropogenic STN Supplement Analysis
4.2. A-SNNRI Application in the Control of Coastal Nonpoint Source Pollution
4.3. Limitations
5. Conclusions
- (i)
- In the study area, the STN and SOM presented consistent spatial distributions, with lower values on the east coast and the highest values in the southwest and the main city of Dongying. The mean values of STN and SOM were 0.7 g/kg (June and October) and 11.6 g/kg (June), respectively. The larger STN decreases were in the northern area and the east coast, while the STN increases were mainly in the main city and the southwest. Urban villages and arable land held the largest mean STNs and summary changes. Population and land use presented significant influences on STN status. However, sea proximity was a negative factor for the STN content and variety.
- (ii)
- The soil erodibility was relatively high, and the K value was 0.033 t·h·MJ−1·mm−1. Higher soil loss risks were mainly located on the coast, the larger estuaries of the southeast, and the main city of Dongying. The contributions of influencing factors to the SNLRI showed the order of V > K > R > F > S. There were significant negative correlations between the STN changes and the K values. Moreover, except for urban villages and arable land, other land uses showed that the summary STN changes had significant negative correlations with soil loss.
- (iii)
- Higher A-SNNRIs were mainly located on the east coast, the southeast region, and the main city of Dongying, which also had the largest soil loss risks. The contribution weights of SNCI and SNLRI to the A-SNNRI were 0.33 and 0.67, respectively. Larger values of the summary A-SNNRIs were found in arable land, saline-alkali land, and urban villages, while the mean A-SNNRI of arable land was relatively low. However, tidal flat land and industrial and mining storage land had the largest mean A-SNNRIs. The distributions of population and socioeconomic activities presented significant influences on the A-SNNRIs. In the YRD, the pollution risk sources of STN were mainly in urban villages, agriculture, industry, and aquaculture.
- (iv)
- In coastal areas, there are often settlements of human life and socioeconomic production, which are also the main sources of marine pollution. However, due to differences in the regional natural conditions and the development of the social economy, the impacts of such human activities on coastal pollution often present spatial heterogeneity. Therefore, when formulating corresponding land-source management and control strategies for marine pollution in coastal areas, we should determine pollutant sources from the human behaviour perspective in a spatially specific way.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Pearson Correlations (SPSS) | Elevation | DS | Sand | Silt | Clay | NDVI | Rainfall | Population | q Statistic (Geodetector) | Land Use |
---|---|---|---|---|---|---|---|---|---|---|
STN_June a | 0.085 | 0.174 | 0.111 | −0.103 | −0.108 | 0.322 ** | 0.048 | 0.254 ** | STN_June d | 0.392 ** |
STN_October b | 0.205 * | 0.316 ** | 0.020 | −0.017 | −0.016 | 0.343 ** | 0.148 | 0.230 * | STN_October d | 0.377 ** |
SOM_June c | 0.101 | 0.200 * | 0.135 | −0.126 | −0.132 | 0.336 ** | 0.053 | 0.257 ** | SOM_June d | 0.370 ** |
STN change d | 0.362 ** | 0.655 ** | 0.184 ** | −0.190 ** | −0.173 ** | 0.118 * | 0.456** | 0.413 ** | STN change d | 0.127 ** |
Pearson Correlations c | A-SNNRI | Population | GDP | DS |
---|---|---|---|---|
A-SNNRI | 1 | 0.164 * | 0.280 ** | −0.298 ** |
Population | 1 | 0.785 ** | 0.252 ** | |
GDP | 1 | 0.254 ** | ||
DS | 1 |
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Wang, Y.; Huang, C.; Liu, G.; Zhao, Z.; Li, H.; Liu, Q. Mapping the Spatial Heterogeneity of Anthropogenic Soil Nitrogen Net Replenishment Based on Soil Loss: A Coastal Case in the Yellow River Delta, China. Sustainability 2022, 14, 6078. https://doi.org/10.3390/su14106078
Wang Y, Huang C, Liu G, Zhao Z, Li H, Liu Q. Mapping the Spatial Heterogeneity of Anthropogenic Soil Nitrogen Net Replenishment Based on Soil Loss: A Coastal Case in the Yellow River Delta, China. Sustainability. 2022; 14(10):6078. https://doi.org/10.3390/su14106078
Chicago/Turabian StyleWang, Youxiao, Chong Huang, Gaohuan Liu, Zhonghe Zhao, He Li, and Qingsheng Liu. 2022. "Mapping the Spatial Heterogeneity of Anthropogenic Soil Nitrogen Net Replenishment Based on Soil Loss: A Coastal Case in the Yellow River Delta, China" Sustainability 14, no. 10: 6078. https://doi.org/10.3390/su14106078