Journal of Geographical Sciences >
Application of modified export coefficient model to estimate nitrogen and phosphorus pollutants from agricultural non-point source
Zhao Xiaoyuan (2000-), Master Candidate, specialized in watershed management. E-mail: zhaoxiaoyuan22@mails.ucas.ac.cn |
Received date: 2023-02-08
Accepted date: 2023-06-19
Online published: 2023-10-08
Supported by
Key Research and Development Program of Hubei Province(2020BCA073)
Independent Innovation Research Program of Changjiang Institute of Survey, Planning, Design and Research Co., Ltd.(CX2019Z05)
There is a great uncertainty in generation and formation of non-point source (NPS) pollutants, which leads to difficulties in the investigation of monitoring and control. However, accurate calculation of these pollutant loads is closely correlated to control NPS pollutants in agriculture. In addition, the relationships between pollutant load and human activity and physiographic factor remain elusive. In this study, a modified model with the whole process of agricultural NPS pollutant migration was established by introducing factors including rainfall driving, terrain impact, runoff index, leaching index and landscape intercept index for the load calculation. Partial least squares path modeling was applied to explore the interactions between these factors. The simulation results indicated that the average total nitrogen (TN) load intensity was 0.57 t km-2 and the average total phosphorus (TP) load intensity was 0.01 t km-2 in Chengdu Plain. The critical effects identified in this study could provide useful guidance to NPS pollution control. These findings further our understanding of the NPS pollution control in agriculture and the formulation of sustainable preventive measures.
ZHAO Xiaoyuan , ZHANG Zhongwei , LIU Xiaojie , ZHANG Qian , WANG Lingqing , CHEN Hao , XIONG Guangcheng , LIU Yuru , TANG Qiang , RUAN Huada Daniel . Application of modified export coefficient model to estimate nitrogen and phosphorus pollutants from agricultural non-point source[J]. Journal of Geographical Sciences, 2023 , 33(10) : 2094 -2112 . DOI: 10.1007/s11442-023-2167-x
Figure 1 Location of the Chengdu Plain, southwest China |
Table 1 Descriptive statistics of factors for modified export coefficient model |
Parameter | αTN | αTP | β | RITN | RITP | LI | LIITN | LIITP |
---|---|---|---|---|---|---|---|---|
Mean | 1.0406 | 1.0442 | 0.9020 | 0.4701 | 0.1042 | 0.5215 | 0.9460 | 0.9455 |
Standard deviation | 0.3975 | 0.4141 | 0.5387 | 0.2117 | 0.0971 | 0.1480 | 0.1477 | 0.1481 |
Coefficient of variation (%) | 38.20 | 39.66 | 59.72 | 45.03 | 93.19 | 28.38 | 15.61 | 15.66 |
Maximum | 2.1651 | 2.2250 | 2.8407 | 1 | 0.9429 | 1 | 1 | 1 |
Minimum | 0.3363 | 0.3161 | 0 | 0 | 0 | 0 | 0 | 0 |
Figure 2 Box plots of factors including rainfall driving factor (α), terrain impact factor (β), runoff index (RI), leaching index (LI) |
Figure 3 The spatial distributions of total nitrogen load (a) and total phosphorus load (b) in the Chengdu Plain |
Figure 4 The spatial distributions of hot and cold spots of total nitrogen load (a) and total phosphorus load (b). The number in parentheses stands for corresponding confidence |
Figure 5 Total nitrogen and total phosphorus loads from different pollution sources in the Chengdu Plain |
Figure 6 The partial least squares path modeling for the effects of different modified export coefficient model factors on pollutant loads. The red arrows stand for positive effect and blue arrows stand for negative effect. The wider the arrow, the stronger the effect. The number in parentheses represent the t value. * stands for statistical significance at p < 0.05 and *** stands for statistical significance at p < 0.001. |
Table 2 Comparison on pollutant loads of simulation accuracy between export coefficient model and modified export coefficient model in Minjiang River watershed (2020) |
Pollutant | Observation (t) | ECM (t) | Re (%) | Modified ECM (t) | Re (%) |
---|---|---|---|---|---|
Total nitrogen | 5053.78 | 16844.73 | 233.31 | 4208.97 | -16.72 |
Total phosphorus | 454.47 | 2166.58 | 376.73 | 87.15 | -80.82 |
Table S1 CN2 values corresponding to different land uses and soil types |
Land use type | Soil type | |||
---|---|---|---|---|
A | B | C | D | |
Cropland | 59 | 70 | 78 | 81 |
Forest | 36 | 60 | 73 | 79 |
Grassland | 76 | 85 | 90 | 93 |
Water area | 100 | 100 | 100 | 100 |
Impervious land | 59 | 74 | 82 | 86 |
Bareland | 60 | 74 | 81 | 85 |
Table S2 Interception efficiency of forest and grassland to total nitrogen and total phosphorus |
Land use type | Total nitrogen | Total phosphorus |
---|---|---|
Forest | 0.83 | 0.75 |
Grassland | 0.79 | 0.70 |
Table S3 Pollutant export coefficients from agricultural sources |
Type | Pollution Source | Unit | Total nitrogen | Total phosphorus |
---|---|---|---|---|
Rural living | Population | kg/(person·a) | 5.00 | 0.45 |
Livestock | Pig | kg (head·a) | 6.42 | 1.62 |
Cattle | 37.66 | 7.44 | ||
Sheep | 25.94 | 3.72 | ||
Rabbit | 0.34 | 0.05 | ||
Chicken | 0.34 | 0.05 | ||
Cropland | Dry field | kg km-2 | 997.01 | 55.22 |
Paddy field | 1392.54 | 11.49 |
Figure S1 The spatial distribution of DEM in the Chengdu Plain, southwest China |
Figure S2 The spatial distribution of soil erosion amount (A) in the Chengdu Plain, southwest China |
Figure S3 The spatial distribution of rainfall in the Chengdu Plain, southwest China |
Figure S4 The spatial distributions including rainfall driving factor (α), terrain impact factor (β), runoff index (RI), leaching index (LI) and landscape intercept index (LII) in the Chengdu Plain, southwest China |
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