modeling to simulate environmental release conditions and/or impacts

modeling to simulate environmental release conditions and/or impacts Order Description Find a study published in a peer-reviewed journal that relies on modeling to simulate environmental release conditions and/or impacts. Discuss how the model is used in the study and the model’s merits/capabilities and, if possible, limitations.. Please cite all work and sources in the essay. Essay 1, (550 words introduction, body, and conclusions) Exposure-Response Establishing exposure-response relationships is an essential part of environmental risk assessment. This relationship describes how the likelihood and severity of adverse organismal effects are related to the exposure to an agent. In risk assessment, exposure-response relationships are established after hazard identification and before risk characterization. Let’s look at some definitions. Exposure is any condition which provides an opportunity for an external environmental agent to enter the body. An agent is any chemical, biological, or physical material capable of eliciting a biological response. The agent is not the same as the vector or medium - for example, air or water. Dose is the amount of agent actually deposited within the body, and the bridge between exposure and response. Typically, the distinction between exposure and dose is blurred, although, in reality, significantly different doses can result from the same exposure. Response is the biological response to an agent. Before an exposure-response relationship can be established, the following must be done: 1. Exposure setting needs to be characterized; 2. Exposure pathway needs to be identified; 3. Exposure needs to be quantified. Exposure quantification can be expressed in equation form as ‘Exposure = Intensity x Frequency x Duration.’ Intensity is how much exposure. Frequency is how often the exposure occurs. Duration is how long the exposure lasts. Different types of data may be used to estimate actual exposure. In order of usefulness for assessing risk, the types of data are as follows: 1. Quantitative measurement of absorbed dose; 2. Quantitative ambient measurements in vicinity of residence or activity; 3. Quantitative surrogates of exposure, e.g., estimates of drinking water or food consumption; 4. Residence or employment in close proximity to source of exposure; 5. Residence or employment in general geographic area (e.g., county or province) of source of exposure. There are many challenges when attempting to establish dose-response relationships. Standard challenges include characterization of exposure or dose, assessment of response, and selection of an appropriate dose-response model to fit observed data. Challenges that may present themselves include confounding response latency periods and difficulty isolating cause. Establishing causal relationships may be impossible, especially with studies conducted outside a controlled laboratory setting. Two types of models that can be used to establish dose-response relationships are stochastic and non-stochastic. A stochastic model for dose-response assumes no dose is safe and that any dose increases the risk of adverse response. This may or may not be a linear relationship. A non- stochastic model for dose-response assumes that a safe dose (or threshold) does exist. Adverse response occurs only once threshold is exceeded. This is the assumption made by the no-observed-adverse-effect level (NOAEL) approach. The relationship beyond threshold may or may not be linear. Other issues include the following: an adverse response may be present, but not readily observable at low dose; response curves can take many shapes, and contaminants often have an additive or synergistic effect. Benchmark doses are often calculated with dose-response modeling. Benchmark doses are doses at which a particular level of response is likely to occur. Extrapolation can be used where needed, but this technique should be used cautiously. The calculation of benchmark doses is a more robust approach than NOAEL. Exposure-response is often used as a proxy for dose-response, since dose is frequently difficult to determine. Both types of data are used for making business decisions and to set regulatory guidelines.