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

Spatiotemporal discrepancies between satellite- and inventory-derived estimates of global ammonia emissions

Bottom-up inventories and satellite-constrained estimates of ammonia emissions often diverge, but the conditions driving these discrepancies remain unclear. Here we compare monthly global estimates at 0.1° resolution from 2008 to 2016 using a normalised discrepancy index. Satellite-constrained emissions are higher on average, with a land-mean index of 0.077. The discrepancy strengthens during warm months and in agricultural areas, reaching 0.229 in May, and increases with near-surface air and dew-point temperatures. Differences are larger in subsistence, low-input and rainfed systems than in high-input or irrigated systems, whereas associations with soil properties, precipitation and evaporation are weak. These patterns indicate that current inventories do not fully resolve the effects of local weather, land use and management timing. This study provides insights into bridging the top-down and bottom-up estimates into closer agreement. Satellite-derived ammonia emissions exceed inventory estimates most strongly in warm-season croplands or low-input and rainfed systems, revealing inventories’ limited sensitivity to weather and management, based on monthly global comparisons using a Normalised Discrepancy Index from 2008–2016

Assessing the Representativeness of Surface Atmospheric Ammonia Observations for Satellite Data Interpretation

Enhancing Prediction of Satellite-Based NO2 Concentration in ASEAN Using Random Forest Models with Spatial Coordinates and Temporal Lag Features

Nitrogen dioxide (NO2) is a critical indicator of anthropogenic emissions, making its monitoring essential for environmental management in the rapidly urbanizing ASEAN region. While satellite imagery provides the necessary high spatial coverage to overcome the limitations of sparse ground-based stations, traditional machine learning models applied to these datasets often overlook the inherent spatial heterogeneity and temporal persistence of air pollutants. This study aims to quantify the marginal contributions of geographic coordinates and temporal lag features to the prediction accuracy of satellite-derived NO2 concentrations using Random Forest (RF) models. By evaluating four progressive RF configurations across 1,500 locations from July 2018 to December 2024, the study identifies the optimal feature combination for regional air quality modeling. The results demonstrated that the RF model incorporating both geographic coordinates and a 12-month lag variable achieved the best performance, yielding an  of 0.832 and an RMSE of 5.42 . Feature importance analysis revealed that the 12-month lag of NO2, nighttime lights, and location were the most influential predictors, highlighting the strong annual seasonality and the impact of economic activities on pollution levels. These findings provide a robust, data-driven framework for regional air quality monitoring and policy formulation in developing tropical regions.

Model Estimation of Ground-based PM2.5 Concentration Over Nigeria and Its Assessment Using MERRA-2 Reanalysis

Accurate estimation of fine particulate matter (PM2.5) remains a major challenge in data-sparse regions such as West Africa because of limited ground-based monitoring networks and highly variable atmospheric conditions. This study evaluated statistical and machine-learning models for predicting ground-level PM2.5 concentrations across seven monitoring stations representing diverse ecological zones in Nigeria. Predictor variables included satellite-derived aerosol optical depth (AOD), meteorological parameters, gaseous pollutants, and temporal features. Ordinary Least Squares (OLS), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), and stacked ensemble models were developed and evaluated using a consistent time-based training and testing strategy. Model performance was assessed using the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (r), bias, normalized mean bias (NMB), and the Seasonal Stability Index (SSI). The results revealed substantial spatial and seasonal variability in model performance across Nigeria. Random Forest consistently achieved the highest predictive accuracy, producing the lowest regional mean RMSE (10.56 µg m⁻3) and ranking as the best-performing model at four of the seven monitoring stations. OLS demonstrated competitive performance in several locations, indicating that linear relationships remained important under certain environmental conditions, whereas XGBoost and LSTM generally exhibited lower predictive performance. In contrast, the MERRA-2 reanalysis dataset showed considerably larger prediction errors than the developed models. Seasonal analysis further demonstrated that model performance varied across ecological zones, with greater instability observed in the Sahel and more consistent predictions in the Guinea Coast. Overall, the findings demonstrated that ensemble tree-based machine learning models provided robust and reliable PM2.5 predictions in Nigeria and outperformed conventional statistical models and coarse-resolution reanalysis products. The proposed framework provides a practical approach for improving air quality assessment, exposure estimation, and evidence-based air pollution management in Nigeria and other data-sparse regions.

Machine Learning-Based Estimation of Surface PM2.5 in Nigeria Using Integration of Satellite, Ground Observations and Reanalysis Data

Accurate estimation of surface PM2.5 across Nigeria remains challenging because satellite AOD and global reanalysis products alone cannot adequately represent near-surface particulate concentrations owing to complex aerosol-meteorology interactions, regional transport, and limited ground observations for calibration. This study addresses these limitations by developing a multi-source data fusion framework based on the Random Forest (RF) algorithm that integrates low-cost sensor measurements, satellite observations, atmospheric composition, meteorological variables, and reanalysis data to improve PM2.5 estimation. Ground-based observations from Purple Air and Clarity sensors were combined with satellite-derived aerosol optical depth, atmospheric trace gases, meteorological variables, and MERRA-2 reanalysis products. The RF model was trained and evaluated using a spatial cross-validation (leave location out) framework and assessed using RMSE, MAE, coefficient of determination (R²), Index of Agreement (IOA), and correlation coefficient. The RF model consistently outperformed MERRA-2 across all monitoring stations, yielding substantially lower prediction errors (RMSE: 14-35 µg m⁻³ versus 30-126 µg m⁻³; MAE: 9-31 µg m⁻³ versus 18-89 µg m⁻³) and stronger agreement with observations (IOA up to 0.68). Whereas MERRA-2 produced large negative R² values at several locations, the RF model achieved improved predictive performance, including a positive R² of 0.29 in Lagos and higher correlation coefficients across most stations. Feature importance analysis identified relative humidity as the dominant predictor, followed by MERRA-2 PM2.5, O3, NO2, and AOD, highlighting the combined influence of aerosol hygroscopic growth, atmospheric chemistry, and regional transport. These findings demonstrate that multi-source machine learning data fusion substantially improves surface PM2.5 estimation over Nigeria with scalable framework...

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

This study evaluates the applicability of Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) satellite observations for air quality monitoring in Tehran, a megacity characterized by complex topography and persistent air pollution challenges. A comprehensive comparison was conducted between ground-based measurements from 22 air quality monitoring stations and Sentinel-5P Level-2 products, including carbon monoxide (CO), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), and the Ultraviolet Aerosol Index (UVAI) over the period 2019-2024. Data preprocessing included outlier removal using the Interquartile Range method, Min-Max normalization, and Air Quality Index analysis. Agreement between satellite and ground observations was evaluated using Pearson and Spearman correlation coefficients and the Kling-Gupta Efficiency metric. In addition, spatiotemporal pollutant trajectories derived from both datasets were compared. The results revealed substantial differences between satellite-derived and ground-based observations. Among the investigated pollutants, NO₂ exhibited the strongest agreement, whereas CO, SO₂, and O₃ showed generally weak correspondence. Spatiotemporal trajectory analyses also demonstrated notable discrepancies between satellite-derived and ground-based pollutant distributions, reflecting differences in spatial representativeness and measurement characteristics. Comparisons involving particulate matter should be interpreted cautiously because UVAI represents atmospheric aerosol loading rather than direct surface particulate matter concentrations. The observed discrepancies are primarily attributed to the spatial resolution of Sentinel-5P, temporal sampling limitations, and the complex meteorological and topographical conditions of Tehran, which generate highly localized pollution patterns. The findings indicate that Sentinel-5P has limited standalone capability for near-surface urban air quality assessments in complex environments, but remains valuable for identifying regional pollution gradients, long-term trends, and broader atmospheric patterns. The results support the development of hybrid monitoring frameworks that combine satellite observations with ground-based measurements, meteorological information, and advanced data-fusion techniques. More broadly, this study provides guidance for improving satellite-based air quality assessment and supports the design of more effective monitoring systems for rapidly urbanizing regions worldwide.