Editing Rolling mean in PM2.5 exceedance days

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{| class="wikitable" style="float:right; clear:right; margin:0 0 1em 1em; width:320px;"
|+ SIGNAL Earth Structured Data
|-
! Object type
| Damage Signal
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! SIGNAL Earth ID
| DS-00558
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! Observable type
| PM2.5 exceedance days (threshold event frequency)
|-
! Unit
| days/yr (number of days per year above threshold)
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! Temporal structure
| Annual
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! Monitoring backbone
| Air quality monitoring networks + gridded surfaces
|}
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The {{SignalTerm|type=DS|id=DS-00558|label=Rolling mean in PM2.5 exceedance days}} is an environmental indicator that quantifies the annual average number of days when fine particulate matter (PM2.5) concentrations surpass established health-based thresholds. PM2.5, particulate matter with a diameter of less than 2.5 micrometers, is a significant air pollutant linked to adverse human health outcomes and environmental impacts. Tracking exceedance days provides insight into air quality dynamics and potential exposure risks over time.

This signal captures a state change in air quality conditions by reflecting the frequency of days with elevated PM2.5 levels on a rolling annual basis. It serves as a useful metric for assessing trends in air pollution, informing scientific understanding of atmospheric chemistry, and supporting environmental health assessments globally.

Given its global geographic scope, the rolling mean in PM2.5 exceedance days integrates data from diverse regions and environmental contexts, enabling comparative analyses and monitoring of air quality changes at multiple scales.

== Geographic / System Context ==
The rolling mean in PM2.5 exceedance days is relevant across global geographic contexts, encompassing urban, suburban, and rural environments worldwide. PM2.5 pollution sources vary geographically and include combustion processes, industrial emissions, natural wildfires, and secondary atmospheric formation. The signal reflects air quality conditions in diverse atmospheric and climatic systems, ranging from densely populated metropolitan areas to remote regions.

Because PM2.5 concentrations and exceedance frequencies are influenced by local meteorology, topography, and emission patterns, the geographic scope of this signal facilitates comprehensive monitoring of air quality state changes across different environmental and socio-economic settings.

== Monitoring and Measurement ==
Monitoring of PM2.5 exceedance days relies on a combination of ground-based air quality monitoring networks and gridded surface data products. Fixed-site monitoring stations operated by environmental agencies and research institutions provide direct measurements of PM2.5 concentrations using standardized sampling and analytical methods. These data are supplemented by satellite-derived aerosol optical depth retrievals and chemical transport models that estimate surface-level PM2.5 concentrations globally.

Data integration methods combine observational measurements with modeling outputs to generate spatially continuous surfaces of PM2.5 concentrations. The exceedance days are determined by counting the number of days within a year when PM2.5 levels exceed defined health thresholds, as established by organizations such as the World Health Organization ([https://en.wikipedia.org/wiki/World_Health_Organization WHO]). The rolling mean calculation smooths annual variability to highlight longer-term trends.

Within the SIGNAL system, this phenomenon is treated as a defined environmental signal whose boundaries and measurement conventions are described below.

== Signal Definition ==
The rolling mean in PM2.5 exceedance days is defined as the annual average number of days per year during which PM2.5 concentrations exceed a specified threshold value indicative of potential health risk. This threshold corresponds to established air quality guidelines, commonly set at 25 micrograms per cubic meter for 24-hour mean PM2.5 concentrations by the WHO. The signal represents a state condition within the air quality domain, reflecting the frequency of threshold exceedance events over a moving annual window.

== Boundary Conditions ==
Boundary inclusions for this signal encompass all days within a calendar year where measured or modeled PM2.5 concentrations surpass the defined threshold value. The signal includes data from all geographic areas covered by the monitoring networks and gridded products, regardless of urban or rural classification. Boundary exclusions involve days with missing or invalid PM2.5 data, as well as locations where data quality does not meet established standards. The signal does not include exceedances of other particulate matter size fractions or pollutants, nor does it account for sub-daily exceedances shorter than 24 hours.

== Aggregation Semantics ==
Geographic aggregation of this signal involves spatial averaging of exceedance day counts over defined geographic units such as countries, regions, or grid cells. Temporal aggregation is conducted on an annual basis using a rolling mean to smooth interannual variability and highlight longer-term trends. Cross-signal aggregation is not specified for this signal, as it focuses specifically on PM2.5 exceedance frequency without integrating other environmental signals. Aggregation methods ensure consistency in spatial and temporal resolution to support comparative analyses and trend assessments.

== Observational Status ==
Monitoring of PM2.5 exceedance days is supported by extensive air quality networks and global data products, including those from the Global Burden of Disease (GBD) air pollution exposure datasets and the OpenAQ platform. Scientific literature and model-based exposure assessments provide complementary information on PM2.5 spatial and temporal distribution. Future SIGNAL releases may enhance data resolution, incorporate updated threshold definitions, and integrate additional observational sources to improve accuracy and coverage of this signal.

== Related Signals ==
* None specified

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== Key Associated People ==
* '''Aaron Cohen''' — Steward-candidate (Health Effects Institute) [Domain expert]
* '''Randall Martin''' — Contributor (Washington University in St. Louis) [Domain expert]
* '''William Laurance''' — Contributor (James Cook University) [Domain expert]
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== Sources ==
* [https://www.who.int/publications WHO / UNEP cyanobacteria & eutrophication guidance (context)]
* [https://www.healthdata.org/ GBD air pollution exposure dataset]
* [https://doi.org/10.5194/acp-16-3539-2016 van Donkelaar et al. global PM2.5 exposure models]
* [https://openaq.org/ OpenAQ global air quality platform]
* [https://doi.org/10.1038/s41586-020-03189-9 Global Coral Reef Status Report]
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