Soil organic carbon (SOC) is a crucial component of the planet and is essential for agriculture development. Our region is known for its livestock and agricultural activities. Hence, understanding the spatial distribution of SOC is crucial for sustainable land management of soils in the mountain ecosystems in the Andes. The methodology consisted of obtaining 53 soil samples from depths of 0 to 105 cm, which were analyzed to obtain SOC (Kg/m²) and organic matter (OM) (%). Ordinary kriging, a geostatistical method, was used to determine SOC. Pearson’s statistical method was applied to determine the association between SOC and precipitation, temperature, altitude, and organic matter and gave acceptable correlations of 0.38, −0.32, 0.40, and 0.59. These results were used to apply weighting criteria for climatological and environmental variables. The weighted overlay tool was used for modeling and mapping the spatial variability of SOC. The estimated spatial distribution of SOC in the micro-watershed reveals an increasing trend from south to north, specifically within the 0–20 cm depth profile. The study confirmed through the spatial analysis that regions with intensive agriculture have low reserves (<3 Kg/m²) of SOC, and areas without agricultural activity but with grazing have average resources of 3 Kg/m² to 5 Kg/m² of SOC. Finally, in the upper micro-watersheds where there is no agricultural activity, the reserves are high (5 Kg/m² to 6.8 Kg/m²). Accordingly, we can promote sustainable and responsible land use practices that support long-term productivity, environmental protection, and societal well-being by prioritizing efficient land utilization, soil conservation, biodiversity conservation, land restoration, and informed land use planning in the high Andean ecosystem of Puno–Peru.

The retreats of the planet’s tropical glaciers are natural indicators of the variation of precipitation, temperature, and other variables. The glaciers of the Alto Santa sub-basin are sources of freshwater storage for consumptive and non-consumptive use for different sectors. As a result of climatic variations, it is essential to analyze the dynamics of the snow cover area (SCA). The methodology consisted of processing 6578 MODIS Snow Cover MOD10A1 product images and generating 18-year time series using the Platform Google Earth Engine (GEE). Normalized Difference Snow Index (NDSI) was used to estimate the extent of snow cover, and to validate the MODIS snow cover product, we used the same overlapping date of Landsat 5 and 8 Surface Reflectance Tier 1, to examine the relationships between daily precipitation and temperature. The standardized correlation results gave good results with stations over 4500 m.a.s.l., such as Artesonraju AP2 (4828 m.a.s.l.) of −0.84 and −0.74, precipitation, and temperature. These results show coherent behaviors of the retreat due to the variation of the climatological variables. In some years, there were anomalies in the conduct of the three variables, but these originated from events of natural weather phenomena. Regarding the dynamics of the SCA in 18 years, it decreased from 649 km² to 311.6. km² between 2000 and 2017, representing a retreat of 41%; we can conclude and confirm that the glacier retreat is imminent due to the consequences of climate change, which would affect the security of freshwater from the tropical glaciers of the Peruvian Andes.

Atmospheric pollution associated with carbon monoxide (CO), nitrogen dioxide (NO ₂ ), and sulfur dioxide (SO ₂ ) represents an important environmental and public health concern in Andean regions influenced by urban growth, transportation, agricultural burning, mining-related activities, and complex topography. This study analyzed the spatiotemporal variability of CO, NO ₂ , and SO ₂ in the Apurimac region, Peru, during 2020–2023 using Sentinel-5P/TROPOMI satellite products processed in Google Earth Engine. The original column-density data, expressed in mol/m ² , were converted into column-derived estimated concentrations using a simplified effective lower-atmospheric layer height of 2,000 m. These values were used only as relative indicators and were not interpreted as direct ground-level air quality measurements. The results showed heterogeneous spatial patterns across Apurimac. CO presented relatively higher values mainly in Abancay, Chincheros, and Andahuaylas, while NO ₂ showed higher relative values in Cotabambas, Grau, Abancay, and Chincheros. SO ₂ exhibited a more irregular and uncertain behavior, with localized positive values, negative retrievals, and high variability, indicating greater sensitivity to satellite retrieval noise. Therefore, SO ₂ was interpreted as an exploratory indicator rather than robust evidence of province-level pollution hotspots. Temporal analysis showed seasonal fluctuations and isolated peaks, especially during dry-season months. Trend detection was performed using the Mann-Kendall test and Sen’s slope estimator. CO showed a statistically significant decreasing trend, NO ₂ showed a weak but statistically significant increasing trend, and SO ₂ did not show a statistically significant trend. Overall, Sentinel-5P/TROPOMI and Google Earth Engine proved useful for identifying relative pollutant patterns in a data-sparse Andean region. Future studies should integrate satellite observations with in situ measurements, meteorological data, boundary-layer information, emission inventories, and independent datasets to improve validation, source attribution, and regional air quality assessment.

Atmospheric pollution, produced by CO, NO₂, and SO₂, represents a significant risk to health and the environment globally. In regions such as Apurimac, Peru, this problem is aggravated by industrial and vehicular emissions. The study aimed to analyze the spatiotemporal trends of these pollutants between 2020 and 2023 using Sentinel-5P satellite data, to identify critical areas and seasonal variations. Data from the TROPOMI instrument on board Sentinel-5P, processed using Google Earth Engine (GEE), were used to map pollutant concentrations. The analyses were performed with JavaScript algorithms. The study focused on urban, industrial, and mining areas. The results showed elevated CO levels in urban areas, such as Abancay, with peaks reaching up to 150 µg/m³, associated with vehicular traffic and agricultural burning. Critical NO₂ points were located near mining operations with maximum values of 0.375 µg/m³, while SO₂ peaks up to 9.8 µg/m³ coincided with industrial activities. Seasonal increases were observed during the months of pasture burning (August to October). Correlations between pollutants were weak (r = 0.1) for CO, and a coefficient of determination for NO2 (R2 = 0.004), reflecting diverse emission sources. Although concentrations generally remained below World Health Agency (WHO) limits, localized pollution hotspots persist, driven by anthropogenic activities. The study demonstrates the usefulness of GEE for regional air quality monitoring and highlights the need for policies aimed at reducing emissions, especially in mining and urban areas.