Spatiotemporal analysis of atmospheric pollutant trends near the Sigus Cement Plant region: a GEE-based and remote sensing approach
Nassima Khenchouche, Mohamed Amine Khenchouche, Karima Benhalilou, Dalal Farid, Mostefa Hani, Almustafa Abdelkader Ayek, Abdelali Benchenna, Moufida Bouguebrine, Yazid ChetbaniPurpose
This study aims to focus on the evaluation of the spatiotemporal variability of methane (CH4), carbon monoxide CO, sulfur dioxide (SO2) nitrogen dioxide (NO2) and formaldehyde (HCHO) over Sigus, northeastern Algeria, during 2019–2025 using Sentinel-5P/TROPOMI data processed in Google Earth Engine, with particular emphasis on the environmental influence of emissions associated with the Sigus Cement Plant.
Design/methodology/approach
Sentinel-2-derived Normalized Difference Vegetation Index (NDVI), temperature from NASA POWER and wind data from the Copernicus Climate Data Store were integrated to interpret seasonal pollutant variations and assess environmental impacts through a comprehensive satellite-based monitoring framework.
Findings
The results showed distinct temporal patterns among the investigated pollutants. CH4 concentrations increased progressively during 2019–2025, while CO, SO2, NO2 and HCHO exhibited moderate interannual fluctuations with strong seasonal variability. NO2 and SO2 levels were higher during colder periods, while HCHO peaked in summer due to enhanced photochemical reactions at elevated temperatures. Wind speeds (3.5–18.1 m s−¹) contributed to pollutant dispersion and transport, particularly for short-lived atmospheric species. Pearson correlation analysis revealed a moderate negative correlation between NDVI and NO2 (r = −0.566), indicating that reduced vegetation cover was generally associated with increased NO2 levels. Conversely, NDVI showed a moderate positive relationship with HCHO (r = 0.510). Weak correlations with CO, SO2, CH4 and O3 suggest that atmospheric processes and meteorological factors mainly controlled their variability.
Research limitations/implications
Given the limited seven-year dataset, these correlations represent indicative trends rather than conclusive statistical relationships.
Originality/value
This study highlights the integration of Sentinel-5P, Sentinel-2 and Google Earth Engine as a reproducible framework for long-term pollution monitoring, supporting environmental management and sustainable industrial development in data-scarce regions.