Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

Modeling tropospheric ozone and particulate matter in Tunis, Tunisia using generalized additive model

Domain:

environment and energyclimate

Record type:

paper
Creator:
HamHedBlo
Editor:
LabLab
Publisher:
CCSDNat
Host:avatar
International audience The main purpose of this paper is to analyze the sensitivity of tropospheric ozone and particulate matter concentrations to changes in local scale meteorology with the aid of meteorological variables (wind speed, wind direction, relative humidity, solar radiation and temperature) and intensity of traffic using hourly concentration of NOX, which are measured in three different locations in Tunis, (i.e. Gazela, Mannouba and Bab Aliwa). In order to quantify the impact of meteorological conditions and precursor concentrations on air pollution, a general model was developed where the logarithm of the hourly concentrations of O3 and PM10 were modeled as a sum of non-linear functions using the framework of Generalized Additive Models (GAMs). Partial effects of each predictor are presented. We obtain a good fit with R² = 85% for the response variable O3 at Bab Aliwa station. Results show the aggregate impact of meteorological variables in the models explained 29% of the variance in PM10 and 41% in O3. This indicates that local meteorological condition is an active driver of air quality in Tunis. The time variables (hour of the day, day of the week and month) also have an effect. This is especially true for the time variable “month” that contributes significantly to the description of the study area.

Visit

hal.science

Tags

Air pollutionParticulate MatterTropospheric OzoneGAMmeteorologytraffic[SDE]Environmental Sciences[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere[SHS.GEO]Humanities and Social Sciences/Geography

Licenses

https://about.hal.science/hal-authorisation-v1/info:eu-repo/semantics/OpenAccess

Similar

Spatial modeling of HIV prevalence in Malawi using generalized additive modelsModelling Spatial and Non-Linear Trends in Climate Data Using Gaussian Process Regression and Generalized Additive ModelPrediction of Particulate Matter (PM<sub>10</sub>) during High Particulate Event in Peninsular Malaysia using Novel Hybrid ModelShort-Term and Medium-Term Drought Forecasting Using Generalized Additive Modelsanasshoudo/U-Net-Model-for-PM10: Predicting particulate matter (PM10) values using the U-Net modelRoadside vehicle particulate matter concentration estimation using artificial neural network model in Addis Ababa, Ethiopia

Spatial modeling of HIV prevalence in Malawi using generalized additive models

Introduction Malawi has made substantial progress in HIV prevention and treatm

Modelling Spatial and Non-Linear Trends in Climate Data Using Gaussian Process Regression and Generalized Additive Model

International audience Accurate modeling of climate variability is critical for under

Prediction of Particulate Matter (PM<sub>10</sub>) during High Particulate Event in Peninsular Malaysia using Novel Hybrid Model

High Particulate Events (HPE) contributes to the deterioration of air quality, as the fine particles

Short-Term and Medium-Term Drought Forecasting Using Generalized Additive Models

Forecasting extreme hydrological events is critical for drought risk and efficient water resource ma

anasshoudo/U-Net-Model-for-PM10: Predicting particulate matter (PM10) values using the U-Net model

This notebook presents the implementation of the U-Net architecture employed in (Houdou et al., 2025

Roadside vehicle particulate matter concentration estimation using artificial neural network model in Addis Ababa, Ethiopia

International audience