Evaluating the suitability of sentinel-5P satellite data for air quality monitoring in Tehran.

Satellite Image-Based Spatial Analysis of Urban Air Quality Index

Abstract. Accurate air quality assessments are essential for understanding exposure to pollution and supporting environmental policy decisions. This study presents a comprehensive methodology for estimating air quality index (AQI) using satellite data and Google Earth Engine (GEE). The research process begins with identifying shortcomings in current approaches to air quality monitoring and reviewing relevant literature on satellite-based pollutant estimation and AQI calculation. To simplify the analysis, a dedicated application has been developed in GEE to enable dynamic selection of study areas and integration of satellite column density data. To estimate surface concentrations at ground level, the pollutant columns extracted from the satellite are processed through molecular weight conversion and atmospheric scaling. The concentration is then standardised from mol/m² to μg/m³ to facilitate the calculation of the AQI. For key air pollutants, individual air quality indicators are generated to provide insight into spatial patterns of air quality in the study area. This methodology demonstrates a scalable and reproducible approach to satellite-based air quality monitoring and provides the basis for future improvements, including higher-resolution datasets, predictive modelling, and integration with terrestrial measurements.

Integrated assessment of air pollution dynamics and health impacts in Myanmar: Evidence from ground monitoring, satellite observations, and modeling.

Least Developed Countries (LDCs), such as Myanmar, experience disproportionately high air pollution burdens due to limited regulatory capacity, inadequate monitoring, and political instability. This study provides a multi-source assessment of air pollution dynamics and potential health impacts in Myanmar, combining ground-based monitoring, satellite observations, and statistical modeling to inform evidence-based policymaking. Ground-based data (2019-2024) from two monitoring stations in Yangon revealed an average PM2.5 concentration of 24.4 μg/m3 (SD = 18.6), with the highest seasonal mean in winter (39.4 μg/m3; November-February). Analysis of particulate matter (PM) components showed high contributions of sulfate (SO42-) in both winter and summer, followed by nitrate (NO3-) in winter and Ca2+ in summer. A health impact assessment estimated that compliance with the WHO PM2.5 guideline (5 μg/m3) could have prevented 2,106 (95% confidence interval 345-3,818) premature deaths (aged ≥ 30) annually in Yangon during 2020-2021. A generalized additive model revealed that relative humidity and wind speed were significantly associated with PM2.5, whereas the daily maximum 8 h average (MDA8) O3 showed a limited meteorological association with relative humidity. National daily means of TROPOMI HCHO and NO2 were strongly correlated (Pearson's r = 0.754; p = 0.001), and HCHO/NO2 ratios indicated predominantly NOx-sensitive regimes across Myanmar. These findings suggest that O3 air quality can be substantially improved by reducing NOx emissions. Despite Myanmar's limited data, this study provides a comprehensive spatiotemporal assessment of air pollution hotspots and their associated health burdens, thereby informing targeted emission control strategies.

Assessing the spatiotemporal characteristics of urban air pollution in Ado-Ekiti and its environs, southwest Nigeria

Air pollution poses significant environmental and public health challenges in rapidly urbanising regions of sub-Saharan Africa, where ground-based monitoring infrastructure remains limited. This study examined the spatiotemporal distribution of key air pollutants in Ado-Ekiti and its environs, Nigeria, from 2019 to 2024. Columnar concentrations of carbon monoxide (CO) and formaldehyde (HCHO) were retrieved from Sentinel-5P, while MODIS aerosol optical depth data was used to estimate particulate matter (PM2.5 and PM10). Meteorological variables (rainfall, wind speed, and wind direction components u and v) were derived from the Weather Research and Forecasting model, and one week of ground-based measurements of pollutants were collected to enable correlation analysis with satellite-derived estimates. All datasets were aggregated to monthly and annual timescales, resampled to a 1 km spatial resolution and re-projected to UTM Zone 31 N. Results indicated that particulate matter dominated the pollutant profile, with annual mean concentrations of 59.37–65.29 µg/m³ for PM2.5 and 89.35–99.51 µg/m³ for PM10, exceeding WHO guideline limits. Peak concentrations occurred during the Harmattan season, with PM₂.₅ > 100 µg/m³ and PM₁₀ > 200 µg/m³, driven primarily by Saharan dust transport and local activities. Mann-Kendall trend analysis revealed significant increasing trends in HCHO, PM2.5, and PM10, whereas CO showed no significant trend. Satellite-derived particulate estimates showed positive but weak correlations with ground-based particulate concentrations (r < 0.20), whereas rainfall significantly reduced particulate levels, and meridional winds (v) facilitated long-range PM₁₀ transport. This study provides a significant integrated satellite model for air quality assessment in data-scarce urban environments while providing evidence to support targeted emission control strategies.

Geospatial assessment of environmental quality indicators in Annaba Port using sentinel-5P and sentinel-2 data

Satellite-based estimation of ambient ammonia reveals meteorological influences and emission inventory uncertainties in South Korea.

Atmospheric ammonia (NH3) is an important precursor gas of secondary PM2.5; however, sparse ground-based NH3 monitoring limits the characterization of its spatiotemporal distribution and provides insufficient observational evidence for evaluating emission inventories. To address these gaps, this study developed a spatiotemporal model to estimate monthly ground-level NH3 concentrations at 15 km resolution across South Korea for 2013-2017. A two-stage framework combining a linear mixed-effects model (LMM) and a generalized additive model (GAM) was applied to refine NH3 information from Cross-track Infrared Sounder satellite observations. The LMM incorporated meteorological variables and the Clean Air Policy Support System emission inventory, while the GAM characterized residual spatial patterns not fully represented by these predictors. The model demonstrated robust performance, with cross-validation R2 = 0.73, mean absolute error = 0.15, and root mean squared error = 0.21. Estimated NH3 concentrations were highest in agricultural areas, increasing from March to June, then declined. In the LMM, all meteorological factors were significantly associated with monthly NH3 concentrations. Temperature showed the strongest association with NH3 from March to June, peaking in June (+18.9% per +1 °C), while relative humidity and wind speed had their largest effects in March (+2.1% per +1% RH and -18.7% per +1 m/s). The GAM captured month-specific LMM residual patterns and identified agricultural NH3 hotspots that may reflect emission inventory gaps. These findings improve understanding of meteorological and emission-related controls on NH3 concentrations and support refinement of emission inventories, agricultural hotspots identification, and improved future PM2.5 air pollution assessment under changing environmental conditions.

Satellite-Based Atmospheric Gas Monitoring in Maritime Chokepoints: Integration of Sentinel-5P TROPOMI and AIS Data for Emission Control in the Istanbul Strait

Anthropogenic greenhouse gases (GHGs) and emissions from maritime transport represent a significant challenge for atmospheric monitoring and control. The Istanbul Strait, characterized by its narrow, winding geography and high traffic density, presents a unique chokepoint where these emissions directly impact local air quality. This study proposes a gas-focused integrated framework that combines Sentinel-5 Precursor (Sentinel-5P) TROPOspheric Monitoring Instrument (TROPOMI) satellite observations with Automatic Identification System (AIS) data to analyze atmospheric trace pollutant time series in the Istanbul Strait during 2025. A bottom-up emission methodology based on the IMO 4th GHG Study was employed, yielding annual gaseous pollutant totals of 213,678 tons of carbon dioxide (CO2), 5970 tons of nitrogen oxides (NOx), and 686 tons of sulfur oxides (SOx). Time-series and cross-correlation analyses demonstrated a quantifiable relationship between AIS-derived NOx estimates and TROPOMI NO2 tropospheric column densities (r = 0.76, p < 0.05, n = 12), validating the use of satellite sensors for marine atmospheric monitoring. A decision support system (DSS) proof of concept (PoC) was developed to evaluate emission control scenarios through speed optimization. The results indicate that implementing a 10% speed reduction strategy could reduce CO2 emissions by 18% (38,462 tons) and generate net economic savings of EUR 3.07 million under the European Union Emissions Trading System (EU ETS) carbon pricing framework. Furthermore, a scenario with a 20% speed reduction resulted in a 35% decrease in CO2 emissions. The findings underscore the potential of integrating satellite-based gas remote sensing with AIS data, thereby facilitating real-time atmospheric monitoring and strengthening emission control policy enforcement in maritime chokepoints.