Exam 1: ESOH 315 Environmental Sampling

chemical, biological, physical

3 types of environmental pollutants are…

pesticides, heavy metals, industrial waste

Chemical pollutants include…

bacteria, viruses, invasive species

biological pollutants include…

particulate matter, noise, thermal pollution

physical pollutants include…

Pathways of Exposure

Route a pollutant takes from its source to a human receptor

air, water, soil, food chain

Common pathways are…

inhalation, ingestion, injection, and dermal absorption

Routes of Exposure include

Routes of Exposure

How pollutants enter the human body

Planning, Implementation, and Assessment

EPAs Phases of Data Collection

Data Quality Objective Development and Sampling reparation

EPA Planning tasks include

Data Collection, QA/QC, Lab Analysis

EPA Implementation tasks include

Data Evaluation and Data Quality Analysis

EPA Assessment tasks include

Data Quality Objectives

Define a purpose, establish criteria, and guide decision-making

State the Problem

Step 1 of DQO Process

Identify the Decision

Step 2 of DQO Process

Identify Inputs

Step 3 of DQO

Define Boundaries

Step 4 of DQO

Decision Rule

Step 5 of DQO

Decision Errors

Step 6 of DQO

Optimize Design

Step 7 of DQO

sampling methods, QA/QC procedures, and data validation steps

Quality Assurance Project Plans include

outliers, missing values, and anomalies

Environmental Data Validation identifies

Random Error

Error that cannot be fixed and is unpredictable

Systematic Error

Error that (for the most part) can be fixed

Precision

measure of agreement between repeated measurements under similar conditions

Bias

systematic of persistent distortion of a measurement process that causes errors in one direction

Accuracy

measure of overall agreement of a measurement to a known value

Representativeness

degree to which data accurately and precisely represent a characteristic of a population

Comparability

describes measure of confidence with which on data point is compared to another

Completeness

measure of amount of valid data needed from a measurement system

Sensitivity

capability of a method or instrument to differentiate between different concentrations of an analyte

Judgmental Sampling

based on expert knowledge

Random Sampling

Equal chance for all locations

Systemic Sampling

Regular intervals or grid-based

Stratified Sampling

Divides area into subgroups

Adaptive Cluster Sampling

Adds samples near “hits”

Composite Sampling

Combines multiple samples

Estimate, Delineate, Detect, Compare, and Support

Sampling Design Objectives include…

contamination, environmental conditions, and equipment malfunction

Challenges in Sampling Include…

Quality Assurance

Proactive policies and procedures to ensure data integrity

Quality Control

Reactive measures to detect and correct issues

Blanks

Detect contamination

Duplicates

Assess precision of measurements

Spikes

Evaluate accuracy of by adding known quantities of analytes

Feild Blanks

Clean media exposed to field conditions to detect possible contamination

Equipment Blanks

Clean water passed through sampling equipment to check for contamination

Trip Blanks

Clean sample transported with field samples, unopened, to detect contamination during transport

Matrix Spike

Known analyte added to a real sample to test recovery and matrix interference

Duplicate Sample

Second sample from same location to test precision and reproducibility