The Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT) [1] is a computer model that is used to compute air parcel trajectories to determine how far and in what direction a parcel of air, and subsequently air pollutants, will travel. HYSPLIT is also capable of calculating air pollutant dispersion, chemical transformation, and deposition. [2] The HYSPLIT model was developed by the National Oceanic and Atmospheric Administration (NOAA) Air Resources Laboratory and the Australian Bureau of Meteorology Research Centere in 1998. [3] The model derives its name from the usage of both Lagrangian and Eulerian approaches.
Early interest in computing air parcel trajectories stemmed from the nuclear arms race of the Cold War. In 1949, the United States government used wind data from radiosonde balloon measurements to determine the likely sources of air parcel trajectories to find a Soviet atomic test site. [4] The initial version of HYSPLIT (HYSPLIT1) was developed in 1982 and obtained meteorological data solely from rawinsonde measurements, and its dispersion calculations assumed uniform daytime mixing and no mixing at night. [5] The second version of HYSPLIT (HYSPLIT2) improved upon HYSPLIT1 by varying the mixing strength. [6] The third version of HYSPLIT (HYSPLIT3) utilized numerical weather prediction models to compute meteorology rather than rawinsonde data alone, improving spatial and temporal resolution of the model. [7] HYSPLIT4, created in 1998, serves as the basis for current model versions. [3]
The HYSPLIT model is widely used for both research applications and emergency response events to forecast and establish source-receptor relationships from a variety of air pollutants and hazardous materials. [1] Examples of use include:
The HYSPLIT model can be run interactively on the Real-Time Environmental Applications and Display System (READY) web site [12] or installed on PC, Mac, or Linux applications, which use a graphical user interface, or automated through scripts ('PySPLIT' package in Python, 'openair' and 'splitr' packages in R). HYSPLIT is rather unusual in that it may be run in client-server mode (HYSPLIT-WEB) from the NOAA website, allowing members of the public to select gridded historical or forecast datasets, to configure model runs, and retrieve model results with a web browser. Annual trainings on the installation, configuration, and use of the modeling system and its applications are offered by HYSPLIT developers. [13]
The HYSPLIT model is extensively used by United States Land Management Agencies to forecast potential human health impacts from wildland fire smoke. Smoke from wildland fires can directly impact both the public and wildfire personnel health. [14] The U.S. Department of Agriculture Forest Service AirFire Research Team uses HYSPLIT as a component of its BlueSky modeling framework to calculate the likely trajectories of smoke parcels given off by a fire. [15] When combined with various other independent models of fire information, fuel loading, fire consumption, fire emissions, and meteorology within the BlueSky framework, the user can calculate the downwind concentrations of several pollutants emitted by a fire, such as Carbon Dioxide or Particulate Matter. This information is useful for land management and air regulatory agencies to understand the impacts from both planned and unplanned wildland fires and the smoke-related consequences of a spectrum of wildfire management tactics and mitigation strategies. [16] In emergency response situations, incident management teams can deploy technical specialist Air Resource Advisors to assist with predicting and communicating smoke impacts to a wide variety of stakeholders, including incident teams, air quality regulators, and the public. Air Resource Advisors are specially trained to interpret BlueSky forecasts to provide timely smoke impact and forecast information to address public health risks and concerns.
One popular use of HYSPLIT is to establish whether high levels of air pollution at one location are caused by transport of air contaminants from another location. HYSPLIT's back trajectories, combined with satellite images (for example, from NASA's MODIS satellites), can provide insight into whether high air pollution levels are caused by local air pollution sources or whether an air pollution problem was blown in on the wind. [17] Analyzing back trajectories over extended periods of time (month-year) can begin to show the geographic origin most associated with elevated concentrations. Several methods for identifying the contribution of high concentrations exist, [18] including frequency based approaches, potential source contribution function, concentration weighted trajectory, and trajectory clustering.[ citation needed ]
For example, HYSPLIT back trajectories show that most air pollution in Door County, Wisconsin originates from outside the county. This map shows how air travels to the pollution monitor in Newport State Park. [19] Because the monitor at Newport State park is near the shore, only the red lines (which show the lower air currents) meaningfully depict the path of ozone to the monitor. Unfortunately, as shown on the map, these lower air currents carry polluted air from major urban areas. But further inland, the air from higher up mixes more, so all color lines are significant when tracing the path of air pollution further inland. Fortunately, these higher air currents (shown in green and blue) blow in from cleaner, mostly rural areas. [20]
Although the HYSPLIT model has been improved since its inception in the 1980s, there are several considerations for users. [21] Key among them are the model's inability to account for secondary chemical reactions and reliance on the input meteorological data's resolution, which can have coarse temporal and spatial resolution. Users should evaluate results carefully in areas with complex terrain. Despite its use in a wide range of emergency response events, HYSPLIT is not a U.S. Environmental Protection Agency (U.S. EPA) preferred or recommended model for regulatory purposes. AERMOD, a steady-state gaussian plume dispersion model, is the US EPA's preferred model for estimating point source impacts for primary emitted pollutants. [22] Photochemical grid models, like the Community Multi-scale Air Quality Model (CMAQ), can simulate the complex chemical and physical processes in the atmosphere (including secondary formation of air pollutants) at a large scale.
Numerical weather prediction (NWP) uses mathematical models of the atmosphere and oceans to predict the weather based on current weather conditions. Though first attempted in the 1920s, it was not until the advent of computer simulation in the 1950s that numerical weather predictions produced realistic results. A number of global and regional forecast models are run in different countries worldwide, using current weather observations relayed from radiosondes, weather satellites and other observing systems as inputs.
An air quality index (AQI) is an indicator developed by government agencies to communicate to the public how polluted the air currently is or how polluted it is forecast to become. As air pollution levels rise, so does the AQI, along with the associated public health risk. Children, the elderly and individuals with respiratory or cardiovascular problems are typically the first groups affected by poor air quality. When the AQI is high, governmental bodies generally encourage people to reduce physical activity outdoors, or even avoid going out altogether. When wildfires result in a high AQI, the use of a mask outdoors and an air purifier indoors are also encouraged.
Atmospheric dispersion modeling is the mathematical simulation of how air pollutants disperse in the ambient atmosphere. It is performed with computer programs that include algorithms to solve the mathematical equations that govern the pollutant dispersion. The dispersion models are used to estimate the downwind ambient concentration of air pollutants or toxins emitted from sources such as industrial plants, vehicular traffic or accidental chemical releases. They can also be used to predict future concentrations under specific scenarios. Therefore, they are the dominant type of model used in air quality policy making. They are most useful for pollutants that are dispersed over large distances and that may react in the atmosphere. For pollutants that have a very high spatio-temporal variability and for epidemiological studies statistical land-use regression models are also used.
The Air Quality Modeling Group (AQMG) is in the U.S. EPA's Office of Air and Radiation (OAR) and provides leadership and direction on the full range of air quality models, air pollution dispersion models and other mathematical simulation techniques used in assessing pollution control strategies and the impacts of air pollution sources.
This page is out of date and should be considered an historic reference only
Roadway air dispersion modeling is the study of air pollutant transport from a roadway or other linear emitter. Computer models are required to conduct this analysis, because of the complex variables involved, including vehicle emissions, vehicle speed, meteorology, and terrain geometry. Line source dispersion has been studied since at least the 1960s, when the regulatory framework in the United States began requiring quantitative analysis of the air pollution consequences of major roadway and airport projects. By the early 1970s this subset of atmospheric dispersion models was being applied to real-world cases of highway planning, even including some controversial court cases.
Air stagnation is a meteorological condition that occurs when there is a lack of atmospheric movement, leading to the accumulation of pollutants and particles that can decline the air quality in a particular region. This condition typically correlates with air pollution and poor air quality due to the possible health risks it can cause to humans and the environment. Due to light winds and lack of precipitation, pollutants cannot be cleared from the air, either gaseous or particulate.
CALPUFF is an advanced, integrated Lagrangian puff modeling system for the simulation of atmospheric pollution dispersion distributed by the Atmospheric Studies Group at TRC Solutions.
The AERMOD atmospheric dispersion modeling system is an integrated system that includes three modules:
NAME atmospheric pollution dispersion model was first developed by the UK's Met Office in 1986 after the nuclear accident at Chernobyl, which demonstrated the need for a method that could predict the spread and deposition of radioactive gases or material released into the atmosphere.
A line source, as opposed to a point source, area source, or volume source, is a source of air, noise, water contamination or electromagnetic radiation that emanates from a linear (one-dimensional) geometry. The most prominent linear sources are roadway air pollution, aircraft air emissions, roadway noise, certain types of water pollution sources that emanate over a range of river extent rather than from a discrete point, elongated light tubes, certain dose models in medical physics and electromagnetic antennas. While point sources of pollution were studied since the late nineteenth century, linear sources did not receive much attention from scientists until the late 1960s, when environmental regulations for highways and airports began to emerge. At the same time, computers with the processing power to accommodate the data processing needs of the computer models required to tackle these one-dimensional sources became more available.
Area sources are sources of pollution which emit a substance or radiation from a specified area.Examples of area sources include gas stations, dry-cleaners, print shops, autobody shops, furniture manufactures, and home sources such as wood stoves, pesticides, and cleaners. Area sources contribute to 26 percent of all man-made air toxic emissions according to EPA estimates.
The following outline is provided as an overview of and topical guide to air pollution dispersion: In environmental science, air pollution dispersion is the distribution of air pollution into the atmosphere. Air pollution is the introduction of particulates, biological molecules, or other harmful materials into Earth's atmosphere, causing disease, death to humans, damage to other living organisms such as food crops, and the natural or built environment. Air pollution may come from anthropogenic or natural sources. Dispersion refers to what happens to the pollution during and after its introduction; understanding this may help in identifying and controlling it.
SAFE AIR is an advanced atmospheric pollution dispersion model for calculating concentrations of atmospheric pollutants emitted both continuously or intermittently from point, line, volume and area sources. It adopts an integrated Gaussian puff modeling system. SAFE AIR consists of three main parts: the meteorological pre-processor WINDS to calculate wind fields, the meteorological pre-processor ABLE to calculate atmospheric parameters and a lagrangian multisource model named P6 to calculate pollutant dispersion. SAFE AIR is included in the online Model Documentation System (MDS) of the European Environment Agency (EEA) and of the Italian Agency for the Protection of the Environment (APAT).
A chemical transport model (CTM) is a type of computer numerical model which typically simulates atmospheric chemistry and may give air pollution forecasting.
Assimilative capacity is the ability for pollutants to be absorbed by an environment without detrimental effects to the environment or those who use of it. Natural absorption into an environment is achieved through dilution, dispersion and removal through chemical or biological processes. The term assimilative capacity has been used interchangeably with environmental capacity, receiving capacity and absorptive capacity. It is used as a measurement perimeter in hydrology, meteorology and pedology for a variety of environments examples consist of: lakes, rivers, oceans, cities and soils. Assimilative capacity is a subjective measurement that is quantified by governments and institutions such as Environmental Protection Agency (EPA) of environments into guidelines. Using assimilative capacity as a guideline can help the allocation of resources while reducing the impact on organisms in an environment. This concept is paired with carrying capacity in order to facilitate sustainable development of city regions. Assimilative capacity has been critiqued as to its effectiveness due to ambiguity in its definition that can confuses readers and false assumptions that a small amount of pollutants has no harmful effect on an environment.
The history of numerical weather prediction considers how current weather conditions as input into mathematical models of the atmosphere and oceans to predict the weather and future sea state has changed over the years. Though first attempted manually in the 1920s, it was not until the advent of the computer and computer simulation that computation time was reduced to less than the forecast period itself. ENIAC was used to create the first forecasts via computer in 1950, and over the years more powerful computers have been used to increase the size of initial datasets and use more complicated versions of the equations of motion. The development of global forecasting models led to the first climate models. The development of limited area (regional) models facilitated advances in forecasting the tracks of tropical cyclone as well as air quality in the 1970s and 1980s.
Air pollution forecasting is the application of science and technology to predict the composition of the air pollution in the atmosphere for a given location and time. An algorithm prediction of the pollutant concentrations can be translated into air quality index, same as actual measurements.
SILAM is a global-to-meso-scale atmospheric dispersion model developed by the Finnish Meteorological Institute (FMI).
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