The enrichment of the biosphere with reactive nitrogen from anthropogenic origin, in combination with increased consumption of vegetables and (preserved) animal products, has led to increased intake by humans of nitrite and nitrate. Nitrate and nitrate-forming salts are among the key components of fertilizers and the increased dependency of farming practices on such fertilizers over several decades has led to increasing levels of human exposure. This arises from consumption of crops and from nitrate-contaminated drinking water due to agricultural land runoff. For years, people have viewed dietary sources of nitrate as harmful to humans causing methemoglobinemia and cancers. However, methemoglobinemia is rare and evidence suggests a relation with infective enteritis rather than with nitrate alone. Also, epidemiological evidence for an association between cancers of the digestive tract and nitrate intake is inconclusive in terms of increased risks of cancer although the International Agency for Research on Cancer concluded “ingested nitrite or nitrate under conditions that result in endogenous nitrosation is probably carcinogenic to humans (Group 2A)”. The discovery of the nitric oxide pathway in the early 1980s revealed that nitrate is produced endogenously in the body changing our perception of nitrate safety. Recently benefits of dietary sources of nitrate for cardiovascular health and protection against infections have been unveiled, calling for an assessment of the risk and benefits associated with nitrate in our food and water supply. The scope of this article is to review the current state of the science on nitrate in human health and disease.

Role of Nitrate in Human Health

河床上的沉没植被的存在能改变水流动结构并且改变沉积运动的状态。在这研究，沉积的早期的运动在一个实验室实验面对在开的隧道的沉没灵活植被被调查。植被与柱体在平行数组安排了的灵活橡胶被模仿。植被密度，水深度，和早期的运动上的沉积谷物尺寸的效果被调查。试验性的结果显示沉积的早期的运动速度作为植被密度减少和水深度和沉积谷物尺寸增加增加。与灵活植物，沉积的早期的运动速度比它没有植被的低，并且比它与僵硬植被的大。一个一般早期的运动速度方程被导出，它能被用于灵活、僵硬的植被条件。

Incipient motion of sediment in presence of submerged flexible vegetation

Combining Radial Basis Function Neural Network Models and Inclusive Multiple Models for Predicting Suspended Sediment Loads

An inclusive multiple model for predicting total sediment transport rate in the presence of coastal vegetation cover based on optimized kernel extreme learning models

Effective management of Japanese black pine (Pinus thunbergii Parlat.) coastal forests considering tsunami mitigation.

Effects of different forest cover configurations on reducing the solitary wave-induced total sediment transport in coastal areas: An experimental study

The increase in population and the growth of agricultural activities, and consequently the increase in demand for safe water, has made the use of unconventional water to solve the problems of water...

Nitrate removal from agricultural effluent using sugarcane bagasse active nanosorbent

Phosphate Removal from Agricultural Drainage Using Biochar

Planting vegetation is one of the practical solutions for reducing sediment transfer rates. Increasing vegetation cover decreases environmental pollution and sediment transport rate (STR). Since sediments and vegetation interact complexly, predicting sediment transport rates is challenging. This study aims to predict sediment transport rate under vegetation cover using new and optimized versions of the group method of data handling (GMDH). Additionally, this study introduces a new ensemble model for predicting sediment transport rates. Model inputs include wave height, wave velocity, density cover, wave force, D50, the height of vegetation cover, and cover stem diameter. A standalone GMDH model and optimized GMDH models, including GMDHhoney badger algorithm (HBA), GMDHrat swarm algorithm (RSOA), GMDHsine cosine algorithm (SCA), and GMDHparticle swarm optimization (GMDH-PSO), were used to predict sediment transport rates. As the next step, the outputs of standalone and optimized GMDH were used to construct an ensemble model. The MAE of the ensemble model was 0.145 m3/s, while the MAEs of GMDH-HBA, GMDH-RSOA, GMDH-SCA, GMDH-PSOA, and GMDH in the testing level were 0.176 m3/s, 0.312 m3/s, 0.367 m3/s, 0.498 m3/s, and 0.612 m3/s, respectively. The Nash– Sutcliffe coefficient (NSE) of ensemble model, GMDH-HBA, GMDH-RSOA, GMDH-SCA, GMDH-PSOA, and GHMDH were 0.95 0.93, 0.89, 0.86, 0.82, and 0.76, respectively. Additionally, this study demonstrated that vegetation cover decreased sediment transport rate by 90%. The results indicated that the ensemble and GMDH-HBA models could accurately predict sediment transport rates. Based on the results of this study, sediment transport rate can be monitored using the IMM and GMDH-HBA. These results are useful for managing and planning water resources in large basins.

Rohollah Fattahi

Papers

Effects of different forest cover configurations on reducing the solitary wave-induced total sediment transport in coastal areas: An experimental study

An inclusive multiple model for predicting total sediment transport rate in the presence of coastal vegetation cover based on optimized kernel extreme learning models

Nitrate removal from agricultural effluent using sugarcane bagasse active nanosorbent

Phosphate Removal from Agricultural Drainage Using Biochar

Application of Group Method of Data Handling and New Optimization Algorithms for Predicting Sediment Transport Rate under Vegetation Cover