Exposure risk assessment and evaluation of the best management practice for controlling pesticide runoff from paddy fields. Part 2: Model simulation for the herbicide pretilachlor

2010 ◽  
Vol 67 (1) ◽  
pp. 70-76 ◽  
Author(s):  
Thai Khanh Phong ◽  
Son Hong Vu ◽  
Satoru Ishihara ◽  
Kazuaki Hiramatsu ◽  
Hirozumi Watanabe
2006 ◽  
Vol 41 (7) ◽  
pp. 1315-1326 ◽  
Author(s):  
JEN-YANG LIN ◽  
YEN-CHANG CHEN ◽  
WALTER CHEN ◽  
TSU-CHUAN LEE ◽  
SHAW L. YU

Author(s):  
Shehan Jayasekera ◽  
Edward Hensel ◽  
Risa Robinson

Background: Natural environment inhalation topography provides useful information for toxicant exposure, risk assessment and cardiopulmonary performance. Commercially available wearable respiratory monitors (WRMs), which are currently used to measure a variety of physiological parameters such as heart rate and breathing frequency, can be leveraged to obtain inhalation topography, yet little work has been done. This paper assesses the feasibility of adapting these WRMs for measuring inhalation topography. Methods: Commercially available WRMs were compiled and assessed for the ability to report chest motion, data analysis software features, ambulatory observation capabilities, participant acceptability, purchasing constraints and affordability. Results: The following WRMs were found: LifeShirt, Equivital EQ02 LifeMonitor, Smartex WWS, Hexoskin Smart Garment, Zephyr BioHarness, Nox T3&A1, BioRadio, SleepSense Inductance Band, and ezRIP & zRIP Durabelt. None of the WRMs satisfied all six assessment criteria in a manner enabling them to be used for inhalation topography without modification and development. Conclusions: The results indicate that there are WRMs with core technologies and characteristics that can be built upon for ambulatory inhalation topography measurement in the NE.


2014 ◽  
Vol 11 (1) ◽  
pp. 91-107 ◽  
Author(s):  
F. Cui ◽  
X. Zheng ◽  
C. Liu ◽  
K. Wang ◽  
Z. Zhou ◽  
...  

Abstract. Contemporary agriculture is shifting from a single-goal to a multi-goal strategy, which in turn requires choosing best management practice (BMP) based on an assessment of the biogeochemical effects of management alternatives. The bottleneck is the capacity of predicting the simultaneous effects of different management practice scenarios on multiple goals and choosing BMP among scenarios. The denitrification–decomposition (DNDC) model may provide an opportunity to solve this problem. We validated the DNDC model (version 95) using the observations of soil moisture and temperature, crop yields, aboveground biomass and fluxes of net ecosystem exchange of carbon dioxide, methane, nitrous oxide (N2O), nitric oxide (NO) and ammonia (NH3) from a wheat–maize cropping site in northern China. The model performed well for these variables. Then we used this model to simulate the effects of management practices on the goal variables of crop yields, NO emission, nitrate leaching, NH3 volatilization and net emission of greenhouse gases in the ecosystem (NEGE). Results showed that no-till and straw-incorporated practices had beneficial effects on crop yields and NEGE. Use of nitrification inhibitors decreased nitrate leaching and N2O and NO emissions, but they significantly increased NH3 volatilization. Irrigation based on crop demand significantly increased crop yield and decreased nitrate leaching and NH3 volatilization. Crop yields were hardly decreased if nitrogen dose was reduced by 15% or irrigation water amount was reduced by 25%. Two methods were used to identify BMP and resulted in the same BMP, which adopted the current crop cultivar, field operation schedules and full straw incorporation and applied nitrogen and irrigation water at 15 and 25% lower rates, respectively, than the current use. Our study indicates that the DNDC model can be used as a tool to assess biogeochemical effects of management alternatives and identify BMP.


2018 ◽  
Vol 35 (12) ◽  
pp. 2315-2331 ◽  
Author(s):  
Daiani Cecchin Ferreira ◽  
Karine Primieri Nicolli ◽  
Érica A. Souza-Silva ◽  
Vitor Manfroi ◽  
Claudia Alcaraz Zini ◽  
...  

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