Lanthanum-modified biochar for dual removal of particulate/dissolved phosphorus in agricultural runoff: Performance and reuse as slow-release fertilizer.

The coordinated development of agricultural economic growth and non-point source (NPS) pollution is an important task in enhancing pollution prevention. Significantly, agricultural economic growth and NPS pollution are interrelated, and their mechanism will be affected by financial development. For this reason, the current study established a panel smooth transformation regression (PSTR) model to reveal the mechanical evolution under different financial development levels. It was found that the impact of agricultural economic growth on NPS pollution was significantly positive in the low level of financial development, which is manifested as an “intensification effect”. Fortunately, when the level of financial development reaches the medium and high thresholds, agricultural economic growth will inhibit agricultural NPS pollution. At the same time it was also found that the impact of agricultural economic growth on NPS pollution is manifested as an “inhibition effect” at the overall level, but presenting significant structural differences. Specifically, the impact on the eastern and central regions of China is manifested as an “inhibition effect”, whereas the impact in the western region of China is characterized as an “intensification effect”. Finally, the elasticity analysis showed that the influence of financial development on agricultural NPS pollution was significantly positive, and that its intensification effect is ubiquitous.

PDF HTML阅读 XML下载 导出引用 引用提醒 基于农业面源污染分区的三峡库区生态农业园建设研究 DOI: 10.5846/stxb201306091483 作者: 作者单位: 西南大学 农业部西南耕地保育重点实验室，资源环境学院,重庆市北碚区水利局,西南大学 农业部西南耕地保育重点实验室，资源环境学院,西南大学 农业部西南耕地保育重点实验室，资源环境学院,西南大学 农业部西南耕地保育重点实验室，资源环境学院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家水体污染控制与治理科技重大专项课题：三峡库区及上游流域农村面源污染控制技术与工程示范（2012ZX07104-003） The construction of the eco-agricultural yards in three gorges reservoir area based on agricultural non-point source pollution zones Author: Affiliation: Key Laboratory of Arable Land Conservation (Southwest China), Ministry of Agriculture, College of Resources and Environment, Southwest University, Chongqing 400715, China,Water Conservancy bureau of Beibei, Chongqing,Key Laboratory of Arable Land Conservation (Southwest China), Ministry of Agriculture, College of Resources and Environment, Southwest University, Chongqing 400715, China,Key Laboratory of Arable Land Conservation (Southwest China), Ministry of Agriculture, College of Resources and Environment, Southwest University, Chongqing 400715, China,Key Laboratory of Arable Land Conservation (Southwest China), Ministry of Agriculture, College of Resources and Environment, Southwest University, Chongqing 400715, China Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:在三峡库区建立生态农业园是解决库区农业、环境和生态问题的有效措施。以农业面源污染分区为基础，探讨三峡库区重庆段生态农业园的建设模式和对策。结果表明：（1）根据地形地貌特征，将生态农业园规划为三大生态农业区，低海拔平坝丘陵生态农业区适宜发展“粮果蔬-猪-沼气-粮果蔬+休闲旅游”复合型高效农业模式，中海拔低山生态农业区可采用“粮-林-畜-沼气-草”的生态农业园建立模式，高海拔高山生态农业区适宜发展林草牧药复合型高效生态农业发展模式。（2）结合农业产业发展，确定了生态种植园、生态养殖园和生态综合园为该区的生态农业园建设方向。划分归并后的五大农业面源污染产污区中，库首外围丘陵山地产污区适宜以生态种植园为主导发展方向；库尾丘陵山地产污区可主营生态养殖园；余下3个产污区的发展核心都宜放在生态综合园建设。（3）三大生态农业区中的中海拔低山生态农业区和五大产污区中的库中平行岭谷产污区是库区生态农业园的建设重点。（4）为保障生态农业园的建设，应积极促进国家、政府、企业和农户之间的合作关系，不断创新和提升农业面源污染防控和农业生产技术。 Abstract:As agriculture is increasingly challenged by constraints of resources, environment and population emerging from its modern development, it is imperative to explore a kind of sustainable agriculture such as ecological agricultural yards that could accommodate economic, social and ecological needs within an already stressed natural resource basement. To balance the relationships among ecological protection, economic increasing and agricultural development, the establishment of eco-agricultural yards is an effective measure in Three Gorges Reservoir Area. Based on agricultural non-point source pollution zones, this paper dealt with the construction modes and countermeasures of the eco-agriculture yards in Three Gorges Reservoir Area of Chongqing. The results showed: (1) The Three Gorges Reservoir Area of Chongqing was divided into "one line, three zones and six pillar industries" in the whole. And according to the landform, the eco-agriculture yards were designed for three eco-agriculture districts, including the eco-agriculture district in low altitude hilly area, the eco-agriculture district in middle altitude mountain area and the eco-agriculture district in high altitude mountain area. Moreover, the complex agriculture mode with "crop, fruit, vegetables-pigs-biogas-crop, fruit, vegetables+leisure tourism" was suitable for the eco-agricultural development in low altitude hilly area. The eco-agricultural mode with "crop-forest-livestock-biogas-grass" could obtain well effective in middle altitude mountain area. And the eco-agricultural mode with "forest, grass, and livestock" complex mode was available for high altitude mountain area. Considering the development of agricultural industrialization, the ecological plantation, the ecological aquaculture and the ecologically integrated yards were suggested as the construction orientation of eco-agricultural yards in the future in Three Gorges Reservoir Area of Chongqing. (2) On the basis of the existing agricultural non-point source pollution status, Three Gorges Reservoir Area of Chongqing was subdivided into five agricultural non-point source pollution producing zones, involving upstream peripheral hilly area, along the river in upstream hilly area, midstream paralleled ridge-valley area, downstream hilly area, and downstream of the main urban area. Pollution producing regions in upstream peripheral hilly area were suitably dominated by the ecological plantation.More attention should be paid to agricultural non-point source pollution form fertilizers in these regions. Pollution producing regions in downstream hilly area could concentrate on the ecological aquaculture. The eco-agriculture district in low altitude hilly area should regard agricultural non-point source pollution from rural households and runoff as the center of contamination controlling in these regions. And the rest regions were appropriate to the establishment of the ecologically integrated yard. Furthermore, greater monitoring efforts should be put into the pollution producing regions in midstream paralleled ridge-valley area. And, more controlling measures of agricultural non-point source pollution from fertilizers could be applied to eco-agricultural districts in middle altitude mountain area of pollution producing regions along the river in upstream hilly area, while residential areas in middle altitude mountain area were the core for agricultural non-point source pollution controlling in pollution producing regions in downstream of the main urban area. (3) Totally, the eco-agricultural districts in middle altitude mountain area, and the pollution producing regions in midstream paralleled ridge-valley area were the key zones to be constructed, owing to their extensive distribution, favorable natural conditions and more serious agricultural non-point source pollution status. (4) To promote the development of the eco-agricultural yards in Three Gorges Reservoir Area of Chongqing, two aspects were necessary to be considered. That is, the cooperative relationships among the country, government, enterprises and farmers were positively built. And, the high-tech for agricultural production and non-point source pollution controlling measures should be continuously innovated and improved. 参考文献 相似文献 引证文献

Taking the Yellow River Delta as a typical research area, we constructed a coastal agricultural TN and TP non-point source pollution estimating model by analyzing the relationship between the surface soil pollutant loss risks and the monitored pollutant flux into the sea. On this basis, TN and TP non-point source export coefficients of paddy fields, irrigated land, and dry land were calculated, and the verification showed that the estimated export coefficients were acceptable. In the study area, the TN and TP export coefficients into the sea of arable land were 18.33 kg·(hm2·a)-1 and 1.02 kg·(hm2·a)-1, respectively. The agricultural non-point source pollution loads of arable land were relatively high in summer. The sub-basins with larger agricultural non-point source pollution loads were mainly located in the control areas of the Zhimai River, Guangli River, and Xiaodao River. The administrative regions with larger total agricultural TN and TP loads were mainly in the northern Huanghekou Town and Yong'an Town, and areas with larger loads per unit area were in the southwest. Therefore, it is necessary to pay more attention to the temporal effects of agricultural non-point source pollution, simultaneously coordinate the social and economic development, and formulate comprehensive agricultural non-point source pollution prevention and control strategies from the perspective of sub-basins and administrative units. This will allow us to improve the offshore pollution status from the perspective of land and sea coordination.

The Chinese government has made great efforts to improve the scale efficiency of land through various measures during recent years, hoping to realize the coordinated developing goal of promoting agricultural benefits and protecting the environment. Statistics show that China’s land scale efficiency has steadily increased, but agricultural nonpoint source (NPS) pollution has also increased, which seems contrary to the expected outcome. Can increasing scale efficiency really curb agricultural NPS pollution? This study uses provincial-level data from China, together with a panel model and spatial econometric model, to investigate the relationship between scale efficiency and agricultural NPS pollution. It is found that the increase of scale efficiency aggravates the agricultural NPS pollution, and the conclusion still holds after considering spatial effect. The results of spatial analysis shows that the agricultural NPS pollution is spatially dependent. Further decomposition of the spatial effect shows that the scale efficiency not only intensifies the local agricultural NPS pollution, but also has a spillover effect (though not statistically significant) on agricultural NPS pollution in the surrounding areas. It is worth noting that financial policy, raising wage income and upgrading industrial structure can effectively curb agricultural NPS pollution in this region and adjacent areas, which also deserves our attention in the control of agricultural NPS pollution. In addition, it is necessary to make financial and fiscal support policies specifically for the governance of agricultural NPS pollution, adjust the distorted prices of input factors such as chemicals and pesticide, and accelerate the transformation of small-sized farmers to family farms, in order to maximize the inhibitory effect of scale efficiency on relieving agricultural NPS pollution.

The green development of FinTech empowerment has become a compelling theme in economic development. In this study, based on the weighted least squares (WLS) and threshold regression methods of cross-sectional data, we empirically examine the impact of FinTech development on agricultural nonpoint source (NPS) pollution, a major cause of impaired surface water quality. Our results show that there is an inverted “U” shape relationship between the development of FinTech and agricultural NPS pollution. That is, after crossing a “threshold value”, the level of FinTech development can curb agricultural NPS pollution. At the structural level, the availability of FinTech services, the FinTech infrastructure, and the agricultural NPS pollution also have an inverted “U” shape relationship. At the threshold effect, in the developing stage of an agricultural economy, the overall level of FinTech development, the use of FinTech services, the availability of FinTech services, and the FinTech infrastructure have an inverted “U” shape relationship with agricultural NPS pollution. On the other hand, in the developed stage of an agricultural economy, the impact of FinTech development and its structure on agricultural NPS pollution is insignificant. Hence, we can conclude that FinTech development can help reduce agricultural NPS pollution in under-developed regions. However, due to the fact that a “U” shape relationship always exists between FinTech service quality and agricultural NPS pollution, the quality of FinTech service should be the main focus to reduce agricultural NPS pollution more effectively.

Agricultural non-point source (NPS) pollution directly affects the quality of soil and water, ecological balance and human health, and is a key challenge to achieve sustainable environmental development and efficient resource management. Taking the Nansi Lake Basin (NLB) as the study area, this study explores the main sources of agricultural NPS pollution and its influencing factors, aiming to provide scientific basis for the management of water resources in the basin. Current studies usually use the runoff pollution partitioning method to estimate agricultural NPS pollution loads in runoff, but the accuracy of the analyses is limited by the incompleteness of water quality monitoring data, especially the lack of complete runoff records in some years. To compensate for this deficiency, this study simulated the river runoff based on the Long-Term Hydrological Impact Assessment (L-THIA) model, and applied the simulation results to the quantitative calculation of agricultural NPS pollution loads after verifying the model reliability through accuracy calibration. Based on L-THIA model, the spatial and temporal distribution data of agricultural NPS pollution in the basin from 2010 to 2020 were obtained, the distribution characteristics of chemical oxygen demand (COD) and ammonia nitrogen (NH3-N) were quantitatively assessed, and the impacts of natural and socio-economic factors on them were analyzed. A regression model was developed to simulate future agricultural NPS pollution through multiple regression analysis. The results showed that the total agricultural NPS pollution in the NLB showed an increasing trend during the study period. In particular, among the socio-economic factors, COD and NH3-N were significantly correlated with fertilizer application, pesticide use, rural employment and total population. Among the natural factors, topographic index, watershed area and gully density were positively correlated with pollutants, while slope and soil organic matter were negatively correlated. The results of this study raise awareness of the contribution of influencing factors and allow researchers and planners to focus on the most important NPS pollution sources and influencing factors. The study provides an important reference for the prevention and control of agricultural NPS pollution in the NLB, which is of great practical importance.

An inexact simulation-based stochastic optimization method for identifying effluent trading strategies of agricultural nonpoint sources

Modeling the response of agricultural non-point source pollution to planting structure and fertilization level in Erhai Lake Basin under low-latitude plateau climate

Agriculture, as an important activity on which human beings depend for their livelihood, brings serious environmental problems while meeting the needs of human survival, among which agricultural non-point source (NPS) pollution is one of the most urgent environmental problems. This study quantitatively assessed the loading characteristics spatial and temporal evolution patterns of two agricultural NPS pollutants, chemical oxygen demand (COD) and ammonia nitrogen (NH3-N), from 2010 to 2020 in the Nansi Lake Basin as an example, and constructed a system dynamics (SD) simulation model to simulate and analyze agricultural NPS pollution under different development and treatment scenarios, based on an investigation of the regional prevention and control strategy of agricultural NPS pollution and the technological system. The results show that the current status of agricultural NPS pollution load in the Nansi Lake Basin is poor, and the level of pollution load is high, showing obvious geographical differences. In terms of temporal changes, the pollution loads of the two pollutants showed a decreasing trend from 2010 to 2020, among which the pollution load of NH3-N showed the largest change. Spatially, the spatial distribution of each type of pollutant has some similarities, with smaller pollution loads in Jining and Zaozhuang and relatively larger pollution loads in Heze and Ningyang. The main source of COD pollution in the Nansi Lake Basin is rural life, with an emission proportion of 52.85%, and the main sources of NH3-N pollution from agricultural NPS pollution in the area are rural life and livestock and poultry farming, with emission proportions of 47.55% and 35.36%, respectively. Under the status quo continuum scenario, the pollution load values for COD are consistently higher than those for NH3-N, so the relative impact of COD is greater. In this study, the principles and methods of SD in system science are adopted to deal with the agricultural NPS pollution of Nansi Lake Basin, and the evolution of its behavioral characteristics are simulated, forecasted, and predicted, and policy experiments are conducted, with a view to providing references for the prevention and control of agricultural NPS pollution in Nansi Lake Basin and further research.

Field management of a drinking water reservoir basin based on the investigation of multiple agricultural nonpoint source pollution indicators in north China

Agricultural non-point source (NPS) pollution is a growing environmental problem, contributing much to water eutrophication in China as well as in other parts of the world. NPS pollutants are heterogeneously distributed and discharged from agricultural lands and other sources, and extremely hard to control by technical measures. Hence, control of the water quality problems caused by agricultural NPS pollution becomes difficult if recommended best management practices (BMPs) can’t be well implemented. In this paper, current research progress related to distinguishing agricultural NPS pollution sources was reviewed. Three pivotal methods for identifying agricultural NPS pollution sources were introduced and the advantages and disadvantages of each approach were discussed. Finally suggestions were presented on better identifying agricultural NPS pollution sources in China.

Landscape Planning for Agricultural Non–Point Source Pollution Reduction. II. Balancing Watershed Size, Number of Watersheds, and Implementation Effort

China's intensive agriculture has led to a broad range of adverse impacts upon ecosystems and thereby caused environmental quality degradation. One of the fundamental problems that face land managers when dealing with agricultural nonpoint source (NPS) pollution is to quantitatively assess the NPS pollution loads from different sources at a national scale. In this study, export scenarios and geo-spatial data were used to calculate the agricultural NPS pollution loads of nutrient, pesticide, plastic film residue, and crop straw burning in China. The results provided the comprehensive and baseline knowledge of agricultural NPS pollution from China's arable farming system in 2014. First, the nitrogen (N) and phosphorus (P) emission loads to water environment were estimated to be 1.44Tg N and 0.06Tg P, respectively. East and south China showed the highest load intensities of nutrient release to aquatic system. Second, the amount of pesticide loss to water of seven pesticides that are widely used in China was estimated to be 30.04tons (active ingredient (ai)). Acetochlor was the major source of pesticide loss to water, contributing 77.65% to the total loss. The environmental impacts of pesticide usage in east and south China were higher than other parts. Third, 19.75% of the plastic film application resided in arable soils. It contributed a lot to soil phthalate ester (PAE) contamination. Fourth, 14.11% of straw produce were burnt in situ, most occurring in May to July (post-winter wheat harvest) in North China Plain and October to November (post-rice harvest days) in southeast China. All the above agricultural NPS pollution loadings were unevenly distributed across China. The spatial correlations between pollution loads at land unit scale were also estimated. Rising labor cost in rural China might be a possible explanation for the general positive correlations of the NPS pollution loads. It also indicated a co-occurred higher NPS pollution loads and a higher human exposure risk in eastern regions. Results from this research might provide full-scale information on the status and spatial variation of various agricultural NPS pollution loads for policy makers to control the NPS pollution in China.

“Period-area-source” hierarchical management for agricultural non-point source pollution in typical watershed with integrated planting and breeding

The agricultural non-point source (NPS) pollution caused by non-irrigated farming, such as heavy metals, nitrogen and phosphorus, has posed an extreme threat to the security of agricultural product quality and watershed ecology. Thus, it is urgent to sort out the latest research progress and future development trend to effectively guide future scientific research and technological updates in this field. This study integrates the relevant literature of the Web of Science from 1976 to 2021 and analyzes the research hotspots and development trends in the field of agricultural NPS pollution from non-irrigated farming in combination with CiteSpace. The results showed that the proportion of publications from the United States and China accounted for 58.4%. Science of the Total Environment, Water Science and Technology and Journal of the American Water Resources Association were the most published journals. The research topics and hotspots mainly involve agricultural NPS pollution prevention technology, pollution source identification, pollution load and management and landscape pattern evolution. In the future, agricultural NPS pollution research in non-irrigated farming should combine agricultural big data platforms, spectroscopic methods, artificial intelligence technology, etc. and focus on strengthening soil testing formula fertilization management, the efficient use of livestock and poultry breeding manure, climate change and risk early warning.