Combining Cluster Analysis of Air Pollution and Meteorological Data with Receptor Model Results for Ambient PM2.5 and PM10
| Autor: | Héctor Jorquera, Ana María Villalobos |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
010504 meteorology & atmospheric sciences
Meteorology Health Toxicology and Mutagenesis sustainable urban development air pollution Air pollution health risks lcsh:Medicine 010501 environmental sciences Health benefits medicine.disease_cause Disease cluster 01 natural sciences Article Apportionment medicine Receptor model Cities 0105 earth and related environmental sciences Air Pollutants lcsh:R Public Health Environmental and Occupational Health source apportionment Metropolitan area Term (time) residential wood burning Environmental science Particulate Matter Ambient data Environmental Monitoring cluster analysis |
| Zdroj: | International Journal of Environmental Research and Public Health, Vol 17, Iss 8455, p 8455 (2020) International Journal of Environmental Research and Public Health Volume 17 Issue 22 |
| ISSN: | 1661-7827 1660-4601 |
| Popis: | Air pollution regulation requires knowing major sources on any given zone, setting specific controls, and assessing how health risks evolve in response to those controls. Receptor models (RM) can identify major sources: transport, industry, residential, etc. However, RM results are typically available for short term periods, and there is a paucity of RM results for developing countries. We propose to combine a cluster analysis (CA) of air pollution and meteorological measurements with a short-term RM analysis to estimate a long-term, hourly source apportionment of ambient PM2.5 and PM10. We have developed a proof of the concept for this proposed methodology in three case studies: a large metropolitan zone, a city with dominant residential wood burning (RWB) emissions, and a city in the middle of a desert region. We have found it feasible to identify the major sources in the CA results and obtain hourly time series of their contributions, effectively extending short-term RM results to the whole ambient monitoring period. This methodology adds value to existing ambient data. The hourly time series results would allow researchers to apportion health benefits associated with specific air pollution regulations, estimate source-specific trends, improve emission inventories, and conduct environmental justice studies, among several potential applications. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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