Spatiotemporal of Particulate Matter (PM2.5) and Ozone (O3) in Eastern Northeast Brazil


de Souza A., ÖZONUR D., de Medeiros E. S., Pobocikova I., de Oliveira-Júnior J. F., Zenteno Jimenez J. r., ...Daha Fazla

Ozone: Science and Engineering, 2024 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1080/01919512.2024.2388597
  • Dergi Adı: Ozone: Science and Engineering
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, Chemical Abstracts Core, Communication Abstracts, Compendex, Environment Index, INSPEC, Metadex, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Anahtar Kelimeler: Modeling, ozone, particulate matter, pollution, urban area
  • Gazi Üniversitesi Adresli: Evet

Özet

This research examines particulate matter with a diameter of 2.5 micrometers (PM2.5) and ozone (O3) variation across the climatic mesoregions of Alagoas. Mean PM2.5 and O3 concentrations across three mesoregions were as follows: East (5.91; 44.22 μg.m−3), Hinterland (6.90; 44.18 μg.m−3), and Arid (7.03; 44.23 μg.m−3). Spatial and temporal variations were observed, with PM2.5 concentrations highest in the northwest (NW) and O3 concentrations in the east (E), influenced by local sources, weather, and transportation. Differences between rainy and dry years were noted, attributed to biomass burning and dust particle transport. This study establishes a baseline for understanding air quality, lacking monitoring stations, aiding policymaking. It provides insights into PM2.5 and O3 dynamics, with PM2.5 concentrations highest in the NW and lower O3 concentrations in the E. Temporal variations emphasize the need for dynamic monitoring. Positive correlations between PM2.5 and O3 highlight complex relationships. Practical implications include proactive policymaking and the necessity for monitoring stations. Rigorous statistical methodologies inform model selection, enhancing environmental data understanding. Despite changes in land use, studies on extreme probability distributions are limited, emphasizing the need for robust methodologies for environmental data analysis to address air pollution challenges effectively in Alagoas.