How Do We Measure & Report Emissions?
Flare Stock Monitoring
Flaring systems, including flare stacks and flame arrestors, are a common piece of equipment in the oil and gas industry. They are used to burn gases before they enter the atmosphere to ensure no harmful chemicals are released into the environment. This is why the safe operation of the flaring system is essential. Flares that do not ignite properly tend to release harmful gases into the atmosphere, often putting the infrastructure and personnel at risk. This also leads to non-compliance with environmental regulations. Thus, it is essential to confirm that the main flare or pilot flare remains lit at all times.
Once the acid gases come in contact with the water present in the atmosphere, it forms acid rain. It is harmful to animals, humans, and the environment. Continuous monitoring of flared gases and pilot flames can help ensure that the gases are ignited properly and make sure they do not cause much harm when released into the atmosphere. Also, it assures compliance with essential government regulations.
Engineering Emissions Factors
Emissions factors have long been the fundamental tool in developing national, regional, state, and local emissions inventories for air quality management decisions and in developing emissions control strategies. More recently, emissions factors have been applied in determining site-specific applicability and emissions limitations in operating permits by federal, state, local, and tribal agencies, consultants, and industry. These users have requested guidance on the use of emissions factors and other emissions quantification tools (e.g., emissions testing and monitoring, mass balance techniques) in developing permits that are practical in their enforcement.
An emissions factor is a representative value that attempts to relate the quantity of a pollutant released to the atmosphere with an activity associated with the release of that pollutant. These factors are usually expressed as the weight of pollutant divided by a unit weight, volume, distance, or duration of the activity emitting the pollutant (e.g., kilograms of particulate emitted per megagram of coal burned). Such factors facilitate estimation of emissions from various sources of air pollution. In most cases, these factors are simply averaging of all available data of acceptable quality, and are generally assumed to be representative of long-term averages for all facilities in the source category (i.e., a population average).
The general equation for emissions estimation is:
E = A x EF x (1-ER/100)
• E = emissions;
• A = activity rate;
• EF = emission factor, and
• ER =overall emission reduction efficiency, %
Compilation of Air Pollutant Emissions Factors (AP-42)
AP-42, Compilation of Air Pollutant Emissions Factors, has been published since 1972 as the primary compilation of EPA’s emissions factor information. It contains emissions factors and process information for more than 200 air pollution source categories. A source category is a specific industry sector or group of similar emitting sources. The emissions factors have been developed and compiled from source test data, material balance studies, and engineering estimates.
The Fifth Edition of AP-42 was published in January 1995. Since then, EPA has published supplements and updates to the fifteen chapters available in Volume I, Stationary Point and Area Sources.
Air Quality Models
Air quality models use mathematical and numerical techniques to simulate the physical and chemical processes that affect air pollutants as they disperse and react in the atmosphere. Based on inputs of meteorological data and source information like emission rates and stack height, these models are designed to characterize primary pollutants that are emitted directly into the atmosphere and, in some cases, secondary pollutants that are formed as a result of complex chemical reactions within the atmosphere. These models are important to the air quality management system because they are widely used by agencies tasked with controlling air pollution to both identify source contributions to air quality problems and assist in the design of effective strategies to reduce harmful air pollutants.
For example, air quality models can be used during the permitting process to verify that a new source will not exceed ambient air quality standards or, if necessary, determine appropriate additional control requirements. In addition, air quality models can also be used to predict future pollutant concentrations from multiple sources after the implementation of a new regulatory program, in order to estimate the effectiveness of the program in reducing harmful exposures to humans and the environment.
The most commonly used air quality models include the following:
Dispersion Modeling – These models are typically used in the permitting process to estimate the concentration of pollutants at specified ground-level receptors surrounding an emissions source.
Photochemical Modeling – These models are typically used in regulatory or policy assessments to simulate the impacts from all sources by estimating pollutant concentrations and deposition of both inert and chemically reactive pollutants over large spatial scales.
Receptor Modeling – These models are observational techniques which use the chemical and physical characteristics of gases and particles measured at source and receptor to both identify the presence of and to quantify source contributions to receptor concentrations.
Dispersion models are important to governmental agencies tasked with protecting and managing the ambient air quality. The models are typically employed to determine whether existing or proposed new industrial facilities are or will be in compliance with the National Ambient Air Quality Standards (NAAQS) in the United States and other nations.
Much of the data reported by oil and gas operators and accepted by regulatory agencies comes from these emissions estimates. The problem that is surfacing today is the estimates from newer technology observations (usually satellites) suggest the traditional approach tends to underestimate the total emission impact.