Hubungan Penggunaan Dan Penanganan Pestisida Pada Petani Bawang Merah Terhadap Residu Pestisida Dalam Tanah Di Lahan Pertanian Desa Wanasari Kecamatan Wanasari Kabupaten Brebes

Excessive use of pesticides causing pollution and environmental damage agriculture. Examination in Brebes on 31 samples of fruits and vegetables, found 22% of samples contain detectable residues of organophosphate and found two soil samples (10%) contained residues organochlorin. The purpose of this study was to determine the relationship of the use and handling of pesticides on their onion farmers against pesticide residues in the soil on agricultural land Wanasari Village, District Wanasari, Brebes. This study is observational method with cross sectional approach. The population in this study were all farmers in the Wanasari conducting spraying. Collecting data using the tool Banu questionnaire and examination of pesticide residues in soil using GC-MS Gas Chromatography - Mass Spectrometry. The results of this study are of 55 69.1 onion farmers use pesticides are not good. The use of pesticides covering 80% is not good in mixing pesticides, 87.3% use a smaller dose, 49.1% use pesticides that are not registered with the Ministry of Agriculture, 87.3% is not good in the way of spraying and 87.3 does well in frequency spraying. Handling pesticides in agricultural land is not good 59.1%, ie 74.5% is not good in handling pesticide containers, 90.9% is not good in storage of pesticides, 89.1% is not good in handling a spill and 87.3% did not either in place to clean pesticide containers. The research result is negative soil samples pesticide residues. The conclusion was that no pesticide residue class organochlorin

High Throughput Heuristics for Prioritizing Human Exposure to Environmental Chemicals

The risk posed to human health by any of the thousands of untested anthropogenic chemicals in our environment is a function of both the hazard presented by the chemical and the extent of exposure. However, many chemicals lack estimates of exposure intake, limiting the understanding of health risks. We aim to develop a rapid heuristic method to determine potential human exposure to chemicals for application to the thousands of chemicals with little or no exposure data. We used Bayesian methodology to infer ranges of exposure consistent with biomarkers identified in urine samples from the U.S. population by the National Health and Nutrition Examination Survey (NHANES). We performed linear regression on inferred exposure for demographic subsets of NHANES demarked by age, gender, and weight using chemical descriptors and use information from multiple databases and structure-based calculators. Five descriptors are capable of explaining roughly 50% of the variability in geometric means across 106 NHANES chemicals for all the demographic groups, including children aged 6–11. We use these descriptors to estimate human exposure to 7968 chemicals, the majority of which have no other quantitative exposure prediction. For thousands of chemicals with no other information, this approach allows forecasting of average exposure intake of environmental chemicals

Subcellular concentration of APAP and Phase I metabolites in REFSIM simulation.

<p>Four cells were monitored in each of the three regions; (A), periportal (PP), midzonal (MZ) and perivenous (PV), and the average concentration in each group is plotted. (B) APAP, (C) GSH, (D) NAPQI, (E) NAPQI-GSH, (E) APAP-Glucuronide, and (E) APAP-Sulfate. Error bars are the standard deviation of the four cells in a region.</p

Time course of a standalone simulation of the sinusoid model in CC3D using the parameters set REFSIM.

<p>A simulated 3 second square pulse of APAP was pushed into the left end of the vessel lumen for three seconds starting one second into the simulation. The concentration of APAP in the blood and hepatocytes is given by the heat map scale at left and time progresses from top to bottom. Blood components are created at the periportal (left) end and a constant force is exerted on the blood components to induce blood flow through the simulated sinusoid. The temporal scales was adjusted so that the blood speed in the simulation was equivalent to 200 <i>μ</i>m/s, giving a transit time of a blood component through the sinusoid of one second.</p

Standalone simulation of sub-cellular model.

<p>Results of the standalone run of the sub-cellular model using parameter set <b>REFSIM</b> and an initial concentration of APAP of 0.1mM (15<i>μ</i>g/ml).</p

The diffusion model for the multicell scale sinusoid model.

<p>Transfer between each of the three cell, or pseudo-cell, <i>types</i> is described by this transfer map. Subscripts indicate that there are multiple cells of each of the types and transfer is calculated between all pairs of cells that are in contact at a particular instant. The paired arrows represent passive transport that equilibrates across pairs of cell types if they are in contact. Looped arrows represent passive transport between adjacent cells of the same type. The single arrow labeled “Active Transport” represents the Michaelis-Menten modeled import of APAP from the serum into hepatocytes.</p

Sensitivity comparisons for formation of NAPQI-GSH.

<p>Comparison of the sensitivities for the formation of NAPQI-GSH (NAPQIGSH_Sum) versus the average parameter sensitivities about the fixed point <b>REFSIM</b>. Axis are as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0162428#pone.0162428.g013" target="_blank">Fig 13</a>.</p

Plasma concentrations calculated using parameter set LNsim23.

<p>This parameter set represents a hypothetical chemical species with ADME behavior significantly different than APAP or the hypothetical species in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0162428#pone.0162428.g009" target="_blank">Fig 9</a>. Symbols are as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0162428#pone.0162428.g007" target="_blank">Fig 7</a> and the APAP <i>in</i> <i>vivo</i> data is included for comparison.</p

CC3D Parameters.

<p>CC3D Parameters.</p

Plasma concentrations calculated using parameter set LNsim8.

<p>This parameter set represents a hypothetical chemical species with ADME behavior significantly different than APAP. Symbols are as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0162428#pone.0162428.g007" target="_blank">Fig 7</a> and the APAP <i>in</i> <i>vivo</i> data is included for comparison.</p