Scholarlink

<p>Nitrogen dioxide (NO₂) is a critical air pollutant and key indicator for air quality. Due to limited monitoring, we leveraged TROPOMI NO₂ and NASA POWER meteorological datasets to evaluate the meteorological drivers on NO₂ tropospheric column concentrations and to develop predictive models for NO₂ levels over Ghana. Employing an 8:2 ratio for model training and testing, NO₂ and meteorology relationships were assessed by seasonality indices and correlation analyses. Results indicate marked seasonal variability in NO₂ columns, prominent during the dry season. Wind speed, relative humidity, and precipitation significantly reduce NO₂, whereas temperature correlated positively in the southern forested zone. Predictive models demonstrate varying efficacy across climatic zones, with mean percentage differences ranging 9.87 to 37.76% and agreement index up to 0.96. The Random Forest and XGBoost models showed outstanding performance, with correlation reaching 0.92. This results presents a scalable methodology for NO₂ monitoring providing insights for air quality management.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Nitrogen dioxide (NO2) Meteorology and predictability for air quality management using TROPOMI

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月12日

查看全文
  • Prince Junior Asilevi,
  • Enock Nyameasem Dzidzorm,
  • Patrick Boakye,
  • Emmanuel Quansah

摘要

Nitrogen dioxide (NO₂) is a critical air pollutant and key indicator for air quality. Due to limited monitoring, we leveraged TROPOMI NO₂ and NASA POWER meteorological datasets to evaluate the meteorological drivers on NO₂ tropospheric column concentrations and to develop predictive models for NO₂ levels over Ghana. Employing an 8:2 ratio for model training and testing, NO₂ and meteorology relationships were assessed by seasonality indices and correlation analyses. Results indicate marked seasonal variability in NO₂ columns, prominent during the dry season. Wind speed, relative humidity, and precipitation significantly reduce NO₂, whereas temperature correlated positively in the southern forested zone. Predictive models demonstrate varying efficacy across climatic zones, with mean percentage differences ranging 9.87 to 37.76% and agreement index up to 0.96. The Random Forest and XGBoost models showed outstanding performance, with correlation reaching 0.92. This results presents a scalable methodology for NO₂ monitoring providing insights for air quality management.