12 research outputs found
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
Evaluation of the Lorber-Egeghy-East Model R Package (LEEM-R) exposure estimates across data richness, sample size, and exposure factors
Presentation to ISES on Sept. 25-29 in Lisbon, Portugal
Science Inventory, CCTE products: https://cfpub.epa.gov/si/si_public_search_results.cfm?advSearch=true&showCriteria=2&keyword=CCTE&TIMSType=&TIMSSubTypeID=&epaNumber=&ombCat=Any&dateBeginPublishedPresented=07/01/2017&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&DEID=&personName=&personID=&role=Any&journalName=&journalID=&publisherName=&publisherID=&sortBy=pubDate&count=25
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Predicting Exposure Pathways with Machine Learning
Presentation at International Society of Exposure Science annual meeting October 201
Predicting Exposure Pathways Allows Risk-Based Prioritization
Presentation at the Toxicology Forum Winter meeting Jan 201
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
SHEDS-HT: An Integrated Probabilistic Exposure Model for Prioritizing Exposures to Chemicals with Near-Field and Dietary Sources
United
States Environmental Protection Agency (USEPA) researchers
are developing a strategy for high-throughput (HT) exposure-based
prioritization of chemicals under the ExpoCast program. These novel
modeling approaches for evaluating chemicals based on their potential
for biologically relevant human exposures will inform toxicity testing
and prioritization for chemical risk assessment. Based on probabilistic
methods and algorithms developed for The Stochastic Human Exposure
and Dose Simulation Model for Multimedia, Multipathway Chemicals (SHEDS-MM),
a new mechanistic modeling approach has been developed to accommodate
high-throughput (HT) assessment of exposure potential. In this SHEDS-HT
model, the residential and dietary modules of SHEDS-MM have been operationally
modified to reduce the user burden, input data demands, and run times
of the higher-tier model, while maintaining critical features and
inputs that influence exposure. The model has been implemented in
R; the modeling framework links chemicals to consumer product categories
or food groups (and thus exposure scenarios) to predict HT exposures
and intake doses. Initially, SHEDS-HT has been applied to 2507 organic
chemicals associated with consumer products and agricultural pesticides.
These evaluations employ data from recent USEPA efforts to characterize
usage (prevalence, frequency, and magnitude), chemical composition,
and exposure scenarios for a wide range of consumer products. In modeling
indirect exposures from near-field sources, SHEDS-HT employs a fugacity-based
module to estimate concentrations in indoor environmental media. The
concentration estimates, along with relevant exposure factors and
human activity data, are then used by the model to rapidly generate
probabilistic population distributions of near-field indirect exposures
via dermal, nondietary ingestion, and inhalation pathways. Pathway-specific
estimates of near-field direct exposures from consumer products are
also modeled. Population dietary exposures for a variety of chemicals
found in foods are combined with the corresponding chemical-specific
near-field exposure predictions to produce aggregate population exposure
estimates. The estimated intake dose rates (mg/kg/day) for the 2507
chemical case-study spanned 13 orders of magnitude. SHEDS-HT successfully
reproduced the pathway-specific exposure results of the higher-tier
SHEDS-MM for a case-study pesticide and produced median intake doses
significantly correlated (<i>p</i> < 0.0001, <i>R</i><sup>2</sup> = 0.39) with medians inferred using biomonitoring
data for 39 chemicals from the National Health and Nutrition Examination
Survey (NHANES). Based on the favorable performance of SHEDS-HT with
respect to these initial evaluations, we believe this new tool will
be useful for HT prediction of chemical exposure potential
Conceptual Framework To Extend Life Cycle Assessment Using Near-Field Human Exposure Modeling and High-Throughput Tools for Chemicals
Life
Cycle Assessment (LCA) is a decision-making tool that accounts
for multiple impacts across the life cycle of a product or service.
This paper presents a conceptual framework to integrate human health
impact assessment with risk screening approaches to extend LCA to
include near-field chemical sources (e.g., those originating from
consumer products and building materials) that have traditionally
been excluded from LCA. A new generation of rapid human exposure modeling
and high-throughput toxicity testing is transforming chemical risk
prioritization and provides an opportunity for integration of screening-level
risk assessment (RA) with LCA. The combined LCA and RA approach considers
environmental impacts of products alongside risks to human health,
which is consistent with regulatory frameworks addressing RA within
a sustainability mindset. A case study is presented to juxtapose LCA
and risk screening approaches for a chemical used in a consumer product.
The case study demonstrates how these new risk screening tools can
be used to inform toxicity impact estimates in LCA and highlights
needs for future research. The framework provides a basis for developing
tools and methods to support decision making on the use of chemicals
in products
High-Throughput Models for Exposure-Based Chemical Prioritization in the ExpoCast Project
The
United States Environmental Protection Agency (U.S. EPA) must
characterize potential risks to human health and the environment associated
with manufacture and use of thousands of chemicals. High-throughput
screening (HTS) for biological activity allows the ToxCast research
program to prioritize chemical inventories for potential hazard. Similar
capabilities for estimating exposure potential would support rapid
risk-based prioritization for chemicals with limited information;
here, we propose a framework for high-throughput exposure assessment.
To demonstrate application, an analysis was conducted that predicts
human exposure potential for chemicals and estimates uncertainty in
these predictions by comparison to biomonitoring data. We evaluated
1936 chemicals using far-field mass balance human exposure models
(USEtox and RAIDAR) and an indicator for indoor and/or consumer use.
These predictions were compared to exposures inferred by Bayesian
analysis from urine concentrations for 82 chemicals reported in the
National Health and Nutrition Examination Survey (NHANES). Joint regression
on all factors provided a calibrated consensus prediction, the variance
of which serves as an empirical determination of uncertainty for prioritization
on absolute exposure potential. Information on use was found to be
most predictive; generally, chemicals above the limit of detection
in NHANES had consumer/indoor use. Coupled with hazard HTS, exposure
HTS can place risk earlier in decision processes. High-priority chemicals
become targets for further data collection
High-Throughput Models for Exposure-Based Chemical Prioritization in the ExpoCast Project
The
United States Environmental Protection Agency (U.S. EPA) must
characterize potential risks to human health and the environment associated
with manufacture and use of thousands of chemicals. High-throughput
screening (HTS) for biological activity allows the ToxCast research
program to prioritize chemical inventories for potential hazard. Similar
capabilities for estimating exposure potential would support rapid
risk-based prioritization for chemicals with limited information;
here, we propose a framework for high-throughput exposure assessment.
To demonstrate application, an analysis was conducted that predicts
human exposure potential for chemicals and estimates uncertainty in
these predictions by comparison to biomonitoring data. We evaluated
1936 chemicals using far-field mass balance human exposure models
(USEtox and RAIDAR) and an indicator for indoor and/or consumer use.
These predictions were compared to exposures inferred by Bayesian
analysis from urine concentrations for 82 chemicals reported in the
National Health and Nutrition Examination Survey (NHANES). Joint regression
on all factors provided a calibrated consensus prediction, the variance
of which serves as an empirical determination of uncertainty for prioritization
on absolute exposure potential. Information on use was found to be
most predictive; generally, chemicals above the limit of detection
in NHANES had consumer/indoor use. Coupled with hazard HTS, exposure
HTS can place risk earlier in decision processes. High-priority chemicals
become targets for further data collection
High-Throughput Models for Exposure-Based Chemical Prioritization in the ExpoCast Project
The
United States Environmental Protection Agency (U.S. EPA) must
characterize potential risks to human health and the environment associated
with manufacture and use of thousands of chemicals. High-throughput
screening (HTS) for biological activity allows the ToxCast research
program to prioritize chemical inventories for potential hazard. Similar
capabilities for estimating exposure potential would support rapid
risk-based prioritization for chemicals with limited information;
here, we propose a framework for high-throughput exposure assessment.
To demonstrate application, an analysis was conducted that predicts
human exposure potential for chemicals and estimates uncertainty in
these predictions by comparison to biomonitoring data. We evaluated
1936 chemicals using far-field mass balance human exposure models
(USEtox and RAIDAR) and an indicator for indoor and/or consumer use.
These predictions were compared to exposures inferred by Bayesian
analysis from urine concentrations for 82 chemicals reported in the
National Health and Nutrition Examination Survey (NHANES). Joint regression
on all factors provided a calibrated consensus prediction, the variance
of which serves as an empirical determination of uncertainty for prioritization
on absolute exposure potential. Information on use was found to be
most predictive; generally, chemicals above the limit of detection
in NHANES had consumer/indoor use. Coupled with hazard HTS, exposure
HTS can place risk earlier in decision processes. High-priority chemicals
become targets for further data collection