Air pollution forecasting

Last updated

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.

Contents

Countries and cities are given forecasts by state and local government organizations, as well as private companies like Airly, AirVisual, Aerostate, Ambee, BreezoMeter, PlumeLabs, and DRAXIS that provide air pollution forecasts.

Motivation

Air pollution is one of the world’s biggest problems, and it causes respiratory problems, lung diseases, and cardiovascular issues and can contribute to mental health issues and aggravate existing health conditions. It can cause depletion to planetary health equally. Therefore, reducing and making people aware of these problems caused by air pollution becomes essential.

With the accurate method of forecasting air pollution, it becomes easier to manage and mitigate the risks of air pollution and ensure a safe level of pollutant concentration in the region. It also helps assess risks to the environment and the climate caused by poor air quality standards. Accurate forecasting can also lead to ease in planning day-to-day activities, avoiding locations with high alert areas, and implementing effective pollution control measures.

Techniques

As with weather forecasting, air pollution forecasting involves the central idea of taking a current snapshot of the atmosphere and using computer simulation to predict what happens next. A typical algorithm uses the following components: [1]

The forecast temporally resolution is usually daily or hourly and the spatial resolution can change from block resolution to dozens of km resolution.

Most forecasts of air quality cover two to five days. [1]

Advanced approaches in air quality forecasting combine historical data with data generated via on-ground sensors and satellite observations to provide insights, analysis, and forecasts from global to street-level air pollution. It also takes into consideration local factors like traffic, regional weather patterns, or emissions in the atmosphere.

Challenges

Meteorological conditions such as thermal inversions can prevent surface air from rising, trapping pollutants near the surface, [6] which makes accurate forecasts of such events crucial for air quality modeling.

Urban air quality models require a very fine computational mesh, requiring the use of high-resolution mesoscale weather models; in spite of this, the quality of numerical weather guidance is the main uncertainty in air quality forecasts. [2]

Uses

By knowing the air quality forecast one can decide how to act, e.g. due to air pollution health effects, one can prepare ahead of time and choose the best time to do an outdoor activity.

See also

Related Research Articles

<span class="mw-page-title-main">European Centre for Medium-Range Weather Forecasts</span> European intergovernmental weather computation organisation based in the UK

The European Centre for Medium-Range Weather Forecasts (ECMWF) is an independent intergovernmental organisation supported by most of the nations of Europe. It is based at three sites: Shinfield Park, Reading, United Kingdom; Bologna, Italy; and Bonn, Germany. It operates one of the largest supercomputer complexes in Europe and the world's largest archive of numerical weather prediction data.

<span class="mw-page-title-main">Meteorology</span> Interdisciplinary scientific study of the atmosphere focusing on weather forecasting

Meteorology is a branch of the atmospheric sciences with a major focus on weather forecasting. The study of meteorology dates back millennia, though significant progress in meteorology did not begin until the 18th century. The 19th century saw modest progress in the field after weather observation networks were formed across broad regions. Prior attempts at prediction of weather depended on historical data. It was not until after the elucidation of the laws of physics, and more particularly in the latter half of the 20th century the development of the computer that significant breakthroughs in weather forecasting were achieved. An important branch of weather forecasting is marine weather forecasting as it relates to maritime and coastal safety, in which weather effects also include atmospheric interactions with large bodies of water.

<span class="mw-page-title-main">Weather forecasting</span> Science and technology application

Weather forecasting is the application of science and technology to predict the conditions of the atmosphere for a given location and time. People have attempted to predict the weather informally for millennia and formally since the 19th century.

<span class="mw-page-title-main">General circulation model</span> Type of climate model

A general circulation model (GCM) is a type of climate model. It employs a mathematical model of the general circulation of a planetary atmosphere or ocean. It uses the Navier–Stokes equations on a rotating sphere with thermodynamic terms for various energy sources. These equations are the basis for computer programs used to simulate the Earth's atmosphere or oceans. Atmospheric and oceanic GCMs are key components along with sea ice and land-surface components.

<span class="mw-page-title-main">Numerical weather prediction</span> Weather prediction using mathematical models of the atmosphere and oceans

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.

<span class="mw-page-title-main">Air quality index</span> Measure of air pollution

An air quality index (AQI) is used by government agencies to communicate to the public how polluted the air currently is or how polluted it is forecast to become. AQI information is obtained by averaging readings from an air quality sensor, which can increase due to vehicle traffic, forest fires, or anything that can increase air pollution. Pollutants tested include particulates, ozone, nitrogen dioxide, carbon monoxide, sulphur dioxide, among others.

<span class="mw-page-title-main">Cooperative Institute for Research in Environmental Sciences</span> Research institute

The Cooperative Institute for Research in Environmental Sciences (CIRES) is a research institute that is sponsored jointly by the National Oceanic and Atmospheric Administration (NOAA) Office of Oceanic and Atmospheric Research (OAR) and the University of Colorado Boulder (CU). CIRES scientists study the Earth system, including the atmosphere, hydrosphere, cryosphere, biosphere, and geosphere, and communicate these findings to decision makers, the scientific community, and the public.

Parameterization in a weather or climate model is a method of replacing processes that are too small-scale or complex to be physically represented in the model by a simplified process. This can be contrasted with other processes—e.g., large-scale flow of the atmosphere—that are explicitly resolved within the models. Associated with these parameterizations are various parameters used in the simplified processes. Examples include the descent rate of raindrops, convective clouds, simplifications of the atmospheric radiative transfer on the basis of atmospheric radiative transfer codes, and cloud microphysics. Radiative parameterizations are important to both atmospheric and oceanic modeling alike. Atmospheric emissions from different sources within individual grid boxes also need to be parameterized to determine their impact on air quality.

The Atmospheric Dispersion Modelling Liaison Committee (ADMLC) is composed of representatives from government departments, agencies and private consultancies. The ADMLC's main aim is to review current understanding of atmospheric dispersion and related phenomena for application primarily in the authorization or licensing of pollutant emissions to the atmosphere from industrial, commercial or institutional sites.

<span class="mw-page-title-main">Atmospheric model</span>

An atmospheric model is a mathematical model constructed around the full set of primitive dynamical equations which govern atmospheric motions. It can supplement these equations with parameterizations for turbulent diffusion, radiation, moist processes, heat exchange, soil, vegetation, surface water, the kinematic effects of terrain, and convection. Most atmospheric models are numerical, i.e. they discretize equations of motion. They can predict microscale phenomena such as tornadoes and boundary layer eddies, sub-microscale turbulent flow over buildings, as well as synoptic and global flows. The horizontal domain of a model is either global, covering the entire Earth, or regional (limited-area), covering only part of the Earth. The different types of models run are thermotropic, barotropic, hydrostatic, and nonhydrostatic. Some of the model types make assumptions about the atmosphere which lengthens the time steps used and increases computational speed.

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.

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.

<span class="mw-page-title-main">Atmospheric infrared sounder</span> Science instrument on NASAs Aqua satellite

The atmospheric infrared sounder (AIRS) is one of six instruments flying on board NASA's Aqua satellite, launched on May 4, 2002. The instrument is designed to support climate research and improve weather forecasting.

A chemical transport model (CTM) is a type of computer numerical model which typically simulates atmospheric chemistry and may give air pollution forecasting.

<span class="mw-page-title-main">History of numerical weather prediction</span> Aspect of meteorological history

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 as well as include 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.

The Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT) 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. 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. The model derives its name from the usage of both Lagrangian and Eulerian approaches.

A land use regression model is an algorithm often used for analyzing pollution, particularly in densely populated areas.

AirQ+ is a free software for Windows and Linux operating systems developed by the World Health Organization (WHO) Regional Office for Europe. The program calculates the magnitude of several health effects associated to exposure to the most relevant air pollutants in a given population. AirQ+ has been used in the BreatheLife campaign and in numerous studies aimed at measuring long-term exposure to ambient particulate matter PM2.5. The first version of the program, AirQ, was distributed in a Microsoft Excel spreadsheet program in 1999, followed by another version of AirQ for Windows in 2000. A substantial difference between AirQ and AirQ+ is that AirQ+ contains a new graphical user interface with several help texts and various features to input and analyse data and illustrate results. AirQ+ version 1.3 was released in October 2018, version 2.0 in November 2019 and version 2.1 in May 2021. It is available in English, French, German and Russian.

<span class="mw-page-title-main">CHIMERE chemistry-transport model</span>

CHIMERE is a chemistry-transport model. It is a computer code that unites a set of equations representing the transport and the chemistry of atmospheric species making it possible to quantify the evolution of air masses and pollution plumes as a function of time on different scales. Using meteorological inputs and emission fluxes, CHIMERE calculates three-dimensional concentrations of pollutants in the atmosphere. Due to the input data used, the number of equations that are solved and the physico-chemistry included in the model, CHIMERE is considered to be a mesoscale model, i.e. simulating the troposphere for a horizontal resolution of 1 to 100 km and over study areas ranging from the city to the hemisphere. 

References

  1. 1 2 3 4 Kumar, Rajesh; Peuch, Vincent-Henri; Crawford, James H.; Brasseur, Guy (September 2018). "Five steps to improve air-quality forecasts". Nature. 561 (7721): 27–29. Bibcode:2018Natur.561...27K. doi: 10.1038/d41586-018-06150-5 . PMID   30181644.
  2. 1 2 Baklanov, Alexander; Rasmussen, Alix; Fay, Barbara; Berge, Erik; Finardi, Sandro (September 2002). "Potential and Shortcomings of Numerical Weather Prediction Models in Providing Meteorological Data for Urban Air Pollution Forecasting". Water, Air, & Soil Pollution: Focus. 2 (5): 43–60. doi:10.1023/A:1021394126149. S2CID   94747027.
  3. Suanno, Chiara; Aloisi, Iris; Fernández-González, Delia; Del Duca, Stefano (September 2021). "Pollen forecasting and its relevance in pollen allergen avoidance". Environmental Research. 200: 111150. Bibcode:2021ER....200k1150S. doi:10.1016/j.envres.2021.111150. PMID   33894233.
  4. Kolehmainen, M; Martikainen, H; Ruuskanen, J (1 January 2001). "Neural networks and periodic components used in air quality forecasting". Atmospheric Environment. 35 (5): 815–825. Bibcode:2001AtmEn..35..815K. doi:10.1016/S1352-2310(00)00385-X.
  5. Daly, Aaron & Paolo Zannetti (2007). Ambient Air Pollution (PDF). The Arab School for Science and Technology and The EnviroComp Institute. p. 16. Retrieved 2011-02-24.
  6. Marshall, John; Plumb, R. Alan (2008). Atmosphere, ocean, and climate dynamics: an introductory text . Amsterdam: Elsevier Academic Press. pp.  44–46. ISBN   978-0-12-558691-7.
  7. "Dermalogica & BreezoMeter partner to educate on pollution's skin effects" . Retrieved 31 May 2018.
  8. "Clean Air Route Finder". Greater London Authority. 14 July 2017.
  9. "Air Pollution Maps: Users Love Them, Your Brand Needs Them".
  10. "An Artificial Intelligence Framework to Forecast Air Quality".