Air pollution monitoring in ungauged zones presents unique challenges yet remains critical for understanding environmental health impacts and socioeconomic dynamics in the Eastern Mediterranean region. This study investigates air pollution patterns in northwestern Syria during 2019–2024, analyzing NO₂ and CO concentrations using Sentinel-5P TROPOMI satellite data processed through Google Earth Engine. Monthly concentration averages were examined across eight key locations using linear regression analysis to determine temporal trends, with Spearman’s rank correlation coefficients calculated between pollutant levels and five meteorological parameters (temperature, humidity, wind speed, atmospheric pressure, and precipitation) to determine the influence of political governance, economic conditions, and environmental sustainability factors on pollution dynamics. Quality assurance filtering retained only measurements with values ≥ 0.75, and statistical significance was assessed at a p < 0.05 level. The findings reveal distinctive spatiotemporal patterns that reflect the region’s complex political-economic landscape. NO₂ concentrations exhibited clear political signatures, with opposition-controlled territories showing upward trends (Al-Rai: 6.18 × 10⁻⁸ mol/m²) and weak correlations with climatic variables (<0.20), indicating consistent industrial operations. In contrast, government-controlled areas demonstrated significant downward trends (Hessia: −2.6 × 10⁻⁷ mol/m²) with stronger climate–pollutant correlations (0.30–0.45), reflecting the impact of economic sanctions on industrial activities. CO concentrations showed uniform downward trends across all locations regardless of political control. This study contributes significantly to multiple Sustainable Development Goals (SDGs), providing critical baseline data for SDG 3 (Health and Well-being), mapping urban pollution hotspots for SDG 11 (Sustainable Cities), demonstrating climate–pollution correlations for SDG 13 (Climate Action), revealing governance impacts on environmental patterns for SDG 16 (Peace and Justice), and developing transferable methodologies for SDG 17 (Partnerships). These findings underscore the importance of incorporating environmental safeguards into post-conflict reconstruction planning to ensure sustainable development.

Air pollution represents a critical environmental challenge in stressed riverine cities, particularly in regions experiencing rapid urbanization and inadequate emission management infrastructure. This study investigates the spatio-temporal dynamics of atmospheric pollution in Baghdad, Iraq, during 2012–2023, analyzing seven key pollutants (CO, CO₂, SO₂, SO₄, O₃, CH₄, and AOD) using NASA’s Giovanni platform coupled with Google Earth Engine analytics. Monthly time-series data were processed through advanced statistical techniques, including Seasonal Autoregressive Integrated Moving Average (SARIMA) modeling and correlation analysis with meteorological parameters, to identify temporal trends, seasonal variations, and driving mechanisms. The analysis revealed three distinct pollutant trajectory categories reflecting complex emission–atmosphere interactions. Carbon monoxide exhibited dramatic decline (60–70% reduction from 2021), attributed to COVID-19 pandemic restrictions and demonstrating rapid responsiveness to activity modifications. Conversely, greenhouse gases showed persistent accumulation, with CO₂ increasing from 400.5 to 417.5 ppm and CH₄ rising 5.9% over the study period, indicating insufficient mitigation efforts. Sulfur compounds and ozone displayed stable concentrations with pronounced seasonal oscillations (winter peaks 2–3 times summer levels), while aerosol optical depth showed high temporal variability linked to dust storm events. Spatial analysis identified pronounced urban–rural concentration gradients, with central Baghdad CO levels exceeding 0.40 ppm compared to peripheral regions below 0.20 ppm. Linear concentration patterns along transportation corridors and industrial zones confirmed anthropogenic source dominance. Correlation analysis revealed strong relationships between meteorological factors and pollutant concentrations (atmospheric pressure: r = 0.62–0.70 with NO₂), providing insights for integrated climate–air quality management strategies. The study demonstrates substantial contributions to Sustainable Development Goals across four dimensions (Environmental Health 30%, Sustainable Cities and Climate Action 25%, Economic Development 25%, and Institutional Development 20%) while providing transferable methodological frameworks for evidence-based policy interventions and environmental monitoring in similar stressed urban environments globally.

This study investigates the spatial and temporal dynamics of key air pollutants—nitrogen dioxide (NO2), carbon monoxide (CO), methane (CH4), formaldehyde (HCHO), and the ultraviolet aerosol index (UVAI)—over the period 2019–2024. Utilizing high-resolution remote sensing data from the Sentinel-5 Precursor satellite and its TROPOspheric Monitoring Instrument (TROPOMI) processed via Google Earth Engine (GEE), pollutant concentrations were analyzed, with spatial visualizations produced using ArcGIS Pro. The results reveal that urban and industrial hotspots—notably in Damascus, Aleppo, Homs, and Hama—exhibit elevated NO2 and CO levels, strongly correlated with population density, traffic, and industrial emissions. Temporal trends indicate significant pollutant fluctuations linked to external factors such as economic activities and regulatory measures. Methane concentrations have shown a steady increase, driven by intensified oil refining, biomass burning, and agricultural practices. Formaldehyde levels initially declined—due to reduced industrial activity—before experiencing a moderate rebound, though remaining below 2019 levels overall. The UV aerosol index demonstrated marked variability, predominantly influenced by wind-driven dust transport, desert dust storms, and localized anthropogenic emissions. These findings underscore the intricate interplay between economic dynamics and environmental processes, highlighting the critical need for robust air quality management strategies.

The precise derivation of spectral indices, including the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI), plays a crucial role in ensuring reliable environmental assessments through the application of remote sensing methodologies. This research examines the manner in which the transformation of Landsat 8–9 Level 2 digital numbers (DN) into surface reflectance (SR) influences the precision and clarity of NDVI and NDWI measurements in the ecologically significant Sabkhat Al-Jabbul area of Syria. For that purpose, a comprehensive analysis was conducted utilizing Google Earth Engine and ArcGIS Pro, focusing on 307 cloud-filtered Landsat images collected between 2014 and 2024. The analysis employed time series, spatial maps, and statistical comparisons, including t-tests, RMSE, and KDE plots, to evaluate the distinctions between DN- and SR-derived indices. The findings demonstrate that SR conversion markedly improves the value range of the both indices and the identification of land cover types, particularly in heterogeneous areas characterized by seasonal water bodies and irrigated agricultural fields. It is significant that the indices derived from SR-images provide a comprehensive insight into the dynamics of water and the health status of vegetation. This study emphasizes the effectiveness of data derived from Landsat and its significance in the precise monitoring of environmental issues. It particularly underscores the necessity of converting Digital Numbers to Surface Reflectance, with a focus on research conducted in arid and semi-arid regions. Consequently, it is advisable to incorporate the DN to SR conversion process during the pre-processing phase of remote sensing data, thereby enhancing the precision of environmental assessments.

Land Surface Temperature (LST) is essential for climate monitoring, drought assessment, and urban heat analysis. Despite its importance, the Landsat-8 Collection 2 Level-2 (C2L2) LST product has not been rigorously validated using ground measurements—a critical gap this study addresses. We present the first comprehensive accuracy assessment using 382 coincident satellite–ground observations collected from seven Surface Radiation Budget Network (SURFRAD) stations distributed across diverse climatic regions of the United States during the period 2023–2025. The validation results indicate strong overall agreement between satellite-derived and ground-measured temperatures, yielding an RMSE of 4.20 °C, a coefficient of determination (R²) of 0.91, and a Pearson correlation coefficient (r) of 0.98. These statistics demonstrate the high reliability of the C2L2 LST product across a wide range of environmental conditions. Nevertheless, a systematic warm bias of 1.75 °C was observed, indicating a tendency toward temperature overestimation. Model performance exhibited pronounced seasonal variability. The highest accuracy was achieved during winter conditions (RMSE = 2.17 °C; r = 0.99), whereas performance declined considerably during summer months (RMSE = 5.84 °C; r = 0.91). Analysis of atmospheric water vapor content revealed significant associations with retrieval errors at high-elevation and arid locations, particularly at FPK (r = 0.78) and DRA (r = 0.75), based on 106 matched observations. These relationships provide important insight into the atmospheric factors contributing to seasonal variations in retrieval accuracy. Temperature-dependent analyses further demonstrated that retrieval uncertainty increases with surface temperature. Performance progressively deteriorated from cooler to warmer thermal regimes, with RMSE values increasing from approximately 2.05 °C for temperatures below 20 °C to 5.71 °C for temperatures exceeding 40 °C. Spatial evaluation also revealed substantial differences among stations. Relatively homogeneous, low-elevation sites exhibited superior performance (GWN: RMSE = 2.60 °C; SXF: RMSE = 2.55 °C), whereas stations located in mountainous or topographically complex environments showed reduced accuracy (TBL: RMSE = 5.14 °C; FPK: RMSE = 5.62 °C). These outcomes emphasize the influence of terrain complexity and atmospheric heterogeneity on LST retrieval performance. Overall, this study establishes the first comprehensive benchmark for evaluating the reliability of Landsat-8 C2L2 LST products. The results provide valuable guidance for their application in climate research, precision agriculture, hydrological modeling, and environmental monitoring. Furthermore, the findings identify specific environmental conditions requiring enhanced validation efforts and suggest opportunities for future algorithm refinement through improved atmospheric correction procedures and more accurate surface emissivity characterization.