This paper proposes a methodology using computational fluid dynamics (CFD)</span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style=...This paper proposes a methodology using computational fluid dynamics (CFD)</span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">-</span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">FLUENT to simulate the dispersion of particulate matter </span><span style="font-family:Verdana;">releasing</span><span style="font-family:Verdana;"> from a biosolid applied agricultural field and predict the particulate concentrations for different ranges of particle sizes. The discrete phase model</span></span></span></span><span><span><span style="font-family:""> </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">(</span><span style="font-family:Verdana;">Lagrangian</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">-</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">Eulerian</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> approach) was used in combination with each of the four turbulence models:</span></span></span><span><span><span style="font-family:""> </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">Standard </span><i><span style="font-family:Verdana;">kε</span></i><span style="font-family:Verdana;"> (</span><i><span style="font-family:Verdana;">kε</span></i><span style="font-family:Verdana;">), Realizable </span><i><span style="font-family:Verdana;">kε</span></i><span style="font-family:Verdana;"> (</span><i><span style="font-family:Verdana;">Rkε</span></i><span style="font-family:Verdana;">),</span></span></span></span><span><span><span style="font-family:""> </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">Standard </span><i><span style="font-family:Verdana;">kω</span></i><span style="font-family:Verdana;"> (</span><i><span style="font-family:Verdana;">kω</span></i><span style="font-family:Verdana;">), and Shear-stress transport k-</span><i><span style="font-family:Verdana;">ω</span></i><span style="font-family:Verdana;"> (SST) to predict particulate matter size concentrations for distances downwind of the agricultural field.</span></span></span></span><span><span><span style="font-family:""> </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">In this modeling approach, particulates were simulated as discrete </span><span style="font-family:Verdana;">phase</span><span style="font-family:Verdana;"> and air as </span><span style="font-family:Verdana;">continuous</span> <span style="font-family:Verdana;">phase</span><span style="font-family:Verdana;">. The predicted particulate matter concentrations were compared statistically with their corresponding field study observations to evaluate the performance of turbulence models. The statistical analysis concluded that among four turbulence models, the discrete phase model when used with </span><i><span style="font-family:Verdana;">Rkε</span></i><span style="font-family:Verdana;"> performed the best in predicting particulate matter concentrations for low (</span><i><span style="font-family:Verdana;">u</span></i><span style="font-family:Verdana;"> < 2 m/s) and medium (2 < </span><i><span style="font-family:Verdana;">u</span></i><span style="font-family:Verdana;"> < 5 m/s) wind speeds. For high (</span><i><span style="font-family:Verdana;">u</span></i><span style="font-family:Verdana;"> > 5 m/s) wind speeds, </span><i><span style="font-family:Verdana;">Rkε</span></i><span style="font-family:Verdana;">, </span><i><span style="font-family:Verdana;">kω</span></i><span style="font-family:Verdana;">, and SST showed similar performances. The discrete phase model using </span><i><span style="font-family:Verdana;">Rkε</span></i><span style="font-family:Verdana;"> performed very well and modeled </span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">the </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">best concentrations for the particle sizes (μm)</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">:</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> 0.23, 0.3, 0.4, 0.5, 0.65, 0.8, 1, 1.6, 2, 3, 4, and 5. For particle sizes</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">:</span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;"> 7.5 and 10, the performances of </span><i><span style="font-family:Verdana;">Rkε</span></i><span style="font-family:Verdana;">, </span><i><span style="font-family:Verdana;">kε</span></i><span style="font-family:Verdana;">, </span><i><span style="font-family:Verdana;">kω</span></i><span style="font-family:Verdana;">, and SST were simi</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">lar.展开更多
The present study was conducted in the commune of Kérou in North Benin to measure the bioavailability of agricultural pesticides heavily used in the production of cotton on snails. The test was conducted in peasa...The present study was conducted in the commune of Kérou in North Benin to measure the bioavailability of agricultural pesticides heavily used in the production of cotton on snails. The test was conducted in peasant environment in two villages of the commune. In each site, there are three cages containing 6 snails each. The snails are exposed to soil and plants without special protection and are therefore exposed to environmental conditions. Firstly it’s to evaluate the presence or absence of agricultural pesticides in the viscera of snails after exposure. Secondly it consists to compare the different doses found in the viscera of snails in the different sites. And finally it’s to evaluate the effect of the toxicity of the pesticides on the reproduction of snails. The tests consisted of placing steel cages in the two experimental sites. In each site, there are three cages containing 6 snails each. Snails are exposed to soil and plants without special protection and are therefore exposed to environmental conditions. The observations were made in order to establish a study of transfer kinetics. After the analysis of the data, it appears generally that the accumulation of mineral elements highlighted is not proportional to the mineralogical composition of the soil in which the snail is exposed. Total nitrogen and nitrate were significantly accumulated in the flesh of site 2 snails compared to site 1 snails. These different measures show the bioavailability of soil minerals for living organisms. Accumulations are not necessarily accompanied by toxic effects in terms of survival for snails after 28 days of exposure. However, the absence of immediate toxic effect on snails does not imply a lack of effect in food chains due to the trophic availability of contaminants. The use of snails as bio-indicator of soil quality has therefore proved relevant in the context of soils polluted by minerals.展开更多
Under changing climate,trace elements like selenium(Se)have emerged as vital constituent of agro-ecosystems enabling crop plants to off-set the adverse effects of suboptimal growth conditions.The available form of sel...Under changing climate,trace elements like selenium(Se)have emerged as vital constituent of agro-ecosystems enabling crop plants to off-set the adverse effects of suboptimal growth conditions.The available form of selenium is important for boosting its bioavailability to crop plants having varied agro-botanical traits and root architectural systems.As compared to selenite,the selenate has a weaker soil bonding,higher absorption in the soil solution which results in a comparatively absorption by plant roots.Various factors including dry climate,high pH,optimal ambient air temperature,less accumulation of water,and low concentration of organic matter in the soil tend to boost the selenate ratio in the soil.The use of selenium pelleted seeds has emerged as an interesting and viable alternative to alleviate selenium deficiency in agricultural eco-systems.Similarly,the co-inoculation of a mixture of Selenobacteria and Arbuscular mycorrhizal fungi represents an evolving promising strategy for the bio-fortification of wheat plants to produce selenium-rich flour to supplement human dietary needs.Furthermore,in-depth research is required to assure the effectiveness of biological fertilization procedures in field conditions as well as to explore and increase our understanding pertaining to the underlying main mechanisms and channels of selenium absorption in plants.The focus of this review is to synthesize the recent developments on Se dynamics in soil-plant systems and emerging promising strategies to optimize its levels for crop plants.Recent developments regarding the use of micro-organisms as a biotechnological mean to enhance plant nutrition and crop quality have been objectively elaborated.The study becomes even more pertinent for arid and semi-arid agro-ecosystems owing to the potential role of selenium in providing stress tolerance to crop plants.Moreover,this review synthesizes and summarizes the recent developments on climate change and bioavailability,and the protective role of selenium in crop plants.展开更多
采用BCR(community bureau of reference)连续提取法对梅县玉水铜矿区周边农田土壤重金属Cu、Pb、Zn和Mn的形态分布及其生物有效性进行了分析.结果表明,矿区下游地区农田土壤重金属污染比上游严重,属于重污染区,而上游土壤属于轻度污染...采用BCR(community bureau of reference)连续提取法对梅县玉水铜矿区周边农田土壤重金属Cu、Pb、Zn和Mn的形态分布及其生物有效性进行了分析.结果表明,矿区下游地区农田土壤重金属污染比上游严重,属于重污染区,而上游土壤属于轻度污染.矿区上游和下游农田土壤中,Pb的污染贡献最大.上游和下游土壤中Cu、Zn、Mn都主要分布在残渣态中,Pb主要分布在可还原态.上游和下游土壤中都以Pb的有效性最高,Zn的有效性最低.展开更多
为科学评估农用地土壤重金属复合暴露对儿童的非致癌健康风险,以华南某生态观光园类农用地为研究对象,对其表层土壤中As、Cd、Cr、Cu、Pb、Ni、Zn、Hg的含量进行检测,采用单项污染指数法和内梅罗综合污染指数法评估其污染程度,并引入二...为科学评估农用地土壤重金属复合暴露对儿童的非致癌健康风险,以华南某生态观光园类农用地为研究对象,对其表层土壤中As、Cd、Cr、Cu、Pb、Ni、Zn、Hg的含量进行检测,采用单项污染指数法和内梅罗综合污染指数法评估其污染程度,并引入二元证据权重(binary weight of evidence,BINWOE)法和重金属生物可给性对儿童非致癌健康风险进行修正.结果表明:①研究区表层土壤中As、Cd、Cr、Cu、Pb、Ni、Zn、Hg的含量分别为1.72~19.40、0.07~19.00、4.00~52.00、4.00~42.00、36.60~1.07×10^(4)、8.00~23.00、62.00~1.52×10^(3)、0.01~0.49 mg/kg,8种重金属的传统非致癌健康风险值的范围为0.65~78.80,其中部分点位As、Cd、Cr及Pb的儿童非致癌风险处于不可接受水平(HQ>1).②4种重金属(As、Cd、Cr、Pb)引入BINWOE法修正的儿童非致癌健康风险值是传统方法的0.67~3.31倍.③基于重金属生物可给性的儿童非致癌健康风险值(0.70~75.00)是基于重金属总量的儿童非致癌健康风险值(1.72~116.10)的0.38~0.92倍.研究显示,对存在多种重金属污染的农用地开展儿童非致癌健康风险评估时,需考虑重金属间的相互作用及生物可给性,以避免直接套用传统风险评估方法低估(高估)污染土壤对儿童的实际健康风险.展开更多
文摘This paper proposes a methodology using computational fluid dynamics (CFD)</span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">-</span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">FLUENT to simulate the dispersion of particulate matter </span><span style="font-family:Verdana;">releasing</span><span style="font-family:Verdana;"> from a biosolid applied agricultural field and predict the particulate concentrations for different ranges of particle sizes. The discrete phase model</span></span></span></span><span><span><span style="font-family:""> </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">(</span><span style="font-family:Verdana;">Lagrangian</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">-</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">Eulerian</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> approach) was used in combination with each of the four turbulence models:</span></span></span><span><span><span style="font-family:""> </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">Standard </span><i><span style="font-family:Verdana;">kε</span></i><span style="font-family:Verdana;"> (</span><i><span style="font-family:Verdana;">kε</span></i><span style="font-family:Verdana;">), Realizable </span><i><span style="font-family:Verdana;">kε</span></i><span style="font-family:Verdana;"> (</span><i><span style="font-family:Verdana;">Rkε</span></i><span style="font-family:Verdana;">),</span></span></span></span><span><span><span style="font-family:""> </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">Standard </span><i><span style="font-family:Verdana;">kω</span></i><span style="font-family:Verdana;"> (</span><i><span style="font-family:Verdana;">kω</span></i><span style="font-family:Verdana;">), and Shear-stress transport k-</span><i><span style="font-family:Verdana;">ω</span></i><span style="font-family:Verdana;"> (SST) to predict particulate matter size concentrations for distances downwind of the agricultural field.</span></span></span></span><span><span><span style="font-family:""> </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">In this modeling approach, particulates were simulated as discrete </span><span style="font-family:Verdana;">phase</span><span style="font-family:Verdana;"> and air as </span><span style="font-family:Verdana;">continuous</span> <span style="font-family:Verdana;">phase</span><span style="font-family:Verdana;">. The predicted particulate matter concentrations were compared statistically with their corresponding field study observations to evaluate the performance of turbulence models. The statistical analysis concluded that among four turbulence models, the discrete phase model when used with </span><i><span style="font-family:Verdana;">Rkε</span></i><span style="font-family:Verdana;"> performed the best in predicting particulate matter concentrations for low (</span><i><span style="font-family:Verdana;">u</span></i><span style="font-family:Verdana;"> < 2 m/s) and medium (2 < </span><i><span style="font-family:Verdana;">u</span></i><span style="font-family:Verdana;"> < 5 m/s) wind speeds. For high (</span><i><span style="font-family:Verdana;">u</span></i><span style="font-family:Verdana;"> > 5 m/s) wind speeds, </span><i><span style="font-family:Verdana;">Rkε</span></i><span style="font-family:Verdana;">, </span><i><span style="font-family:Verdana;">kω</span></i><span style="font-family:Verdana;">, and SST showed similar performances. The discrete phase model using </span><i><span style="font-family:Verdana;">Rkε</span></i><span style="font-family:Verdana;"> performed very well and modeled </span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">the </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">best concentrations for the particle sizes (μm)</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">:</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> 0.23, 0.3, 0.4, 0.5, 0.65, 0.8, 1, 1.6, 2, 3, 4, and 5. For particle sizes</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">:</span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;"> 7.5 and 10, the performances of </span><i><span style="font-family:Verdana;">Rkε</span></i><span style="font-family:Verdana;">, </span><i><span style="font-family:Verdana;">kε</span></i><span style="font-family:Verdana;">, </span><i><span style="font-family:Verdana;">kω</span></i><span style="font-family:Verdana;">, and SST were simi</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">lar.
文摘The present study was conducted in the commune of Kérou in North Benin to measure the bioavailability of agricultural pesticides heavily used in the production of cotton on snails. The test was conducted in peasant environment in two villages of the commune. In each site, there are three cages containing 6 snails each. The snails are exposed to soil and plants without special protection and are therefore exposed to environmental conditions. Firstly it’s to evaluate the presence or absence of agricultural pesticides in the viscera of snails after exposure. Secondly it consists to compare the different doses found in the viscera of snails in the different sites. And finally it’s to evaluate the effect of the toxicity of the pesticides on the reproduction of snails. The tests consisted of placing steel cages in the two experimental sites. In each site, there are three cages containing 6 snails each. Snails are exposed to soil and plants without special protection and are therefore exposed to environmental conditions. The observations were made in order to establish a study of transfer kinetics. After the analysis of the data, it appears generally that the accumulation of mineral elements highlighted is not proportional to the mineralogical composition of the soil in which the snail is exposed. Total nitrogen and nitrate were significantly accumulated in the flesh of site 2 snails compared to site 1 snails. These different measures show the bioavailability of soil minerals for living organisms. Accumulations are not necessarily accompanied by toxic effects in terms of survival for snails after 28 days of exposure. However, the absence of immediate toxic effect on snails does not imply a lack of effect in food chains due to the trophic availability of contaminants. The use of snails as bio-indicator of soil quality has therefore proved relevant in the context of soils polluted by minerals.
文摘Under changing climate,trace elements like selenium(Se)have emerged as vital constituent of agro-ecosystems enabling crop plants to off-set the adverse effects of suboptimal growth conditions.The available form of selenium is important for boosting its bioavailability to crop plants having varied agro-botanical traits and root architectural systems.As compared to selenite,the selenate has a weaker soil bonding,higher absorption in the soil solution which results in a comparatively absorption by plant roots.Various factors including dry climate,high pH,optimal ambient air temperature,less accumulation of water,and low concentration of organic matter in the soil tend to boost the selenate ratio in the soil.The use of selenium pelleted seeds has emerged as an interesting and viable alternative to alleviate selenium deficiency in agricultural eco-systems.Similarly,the co-inoculation of a mixture of Selenobacteria and Arbuscular mycorrhizal fungi represents an evolving promising strategy for the bio-fortification of wheat plants to produce selenium-rich flour to supplement human dietary needs.Furthermore,in-depth research is required to assure the effectiveness of biological fertilization procedures in field conditions as well as to explore and increase our understanding pertaining to the underlying main mechanisms and channels of selenium absorption in plants.The focus of this review is to synthesize the recent developments on Se dynamics in soil-plant systems and emerging promising strategies to optimize its levels for crop plants.Recent developments regarding the use of micro-organisms as a biotechnological mean to enhance plant nutrition and crop quality have been objectively elaborated.The study becomes even more pertinent for arid and semi-arid agro-ecosystems owing to the potential role of selenium in providing stress tolerance to crop plants.Moreover,this review synthesizes and summarizes the recent developments on climate change and bioavailability,and the protective role of selenium in crop plants.
文摘采用BCR(community bureau of reference)连续提取法对梅县玉水铜矿区周边农田土壤重金属Cu、Pb、Zn和Mn的形态分布及其生物有效性进行了分析.结果表明,矿区下游地区农田土壤重金属污染比上游严重,属于重污染区,而上游土壤属于轻度污染.矿区上游和下游农田土壤中,Pb的污染贡献最大.上游和下游土壤中Cu、Zn、Mn都主要分布在残渣态中,Pb主要分布在可还原态.上游和下游土壤中都以Pb的有效性最高,Zn的有效性最低.
文摘为科学评估农用地土壤重金属复合暴露对儿童的非致癌健康风险,以华南某生态观光园类农用地为研究对象,对其表层土壤中As、Cd、Cr、Cu、Pb、Ni、Zn、Hg的含量进行检测,采用单项污染指数法和内梅罗综合污染指数法评估其污染程度,并引入二元证据权重(binary weight of evidence,BINWOE)法和重金属生物可给性对儿童非致癌健康风险进行修正.结果表明:①研究区表层土壤中As、Cd、Cr、Cu、Pb、Ni、Zn、Hg的含量分别为1.72~19.40、0.07~19.00、4.00~52.00、4.00~42.00、36.60~1.07×10^(4)、8.00~23.00、62.00~1.52×10^(3)、0.01~0.49 mg/kg,8种重金属的传统非致癌健康风险值的范围为0.65~78.80,其中部分点位As、Cd、Cr及Pb的儿童非致癌风险处于不可接受水平(HQ>1).②4种重金属(As、Cd、Cr、Pb)引入BINWOE法修正的儿童非致癌健康风险值是传统方法的0.67~3.31倍.③基于重金属生物可给性的儿童非致癌健康风险值(0.70~75.00)是基于重金属总量的儿童非致癌健康风险值(1.72~116.10)的0.38~0.92倍.研究显示,对存在多种重金属污染的农用地开展儿童非致癌健康风险评估时,需考虑重金属间的相互作用及生物可给性,以避免直接套用传统风险评估方法低估(高估)污染土壤对儿童的实际健康风险.