Application of sensing techniques for quantifying CO2 flux and dynamics in environments affected by the fundão dam collapse, mariana, Brazil

Abstract

The District of Mariana in Minas Gerais, Brazil, witnessed one of the largest natural disasters in history. It involved the deposition of thousands of cubic meters of mining waste on soils near tributaries of the Doce River, resulting in degraded areas and a new environmental landscape. Numerous studies have focused on changes in soil microbiota and physical-chemical attributes. However, studies quantifying and analyzing carbon flux (FCO2) dynamics in the field using proximal sensors under varying temperatures, humidity conditions, and vegetation cover are scarce and nearly nonexistent. These studies could aid in monitoring the recovery of degraded areas and in understanding FCO2 dynamics in such environments. The aim of this study was to quantify and assess FCO2 dynamics using combined sensing techniques in diverse mining-affected areas, comparing different stages of recovery and their vegetation impact, and linking the results to pedoenvironmental factors. To achieve this, four areas were carefully selected and evaluated: affected pasture (AP), pasture (P) (non-affected), mix (MIX), and native trees (NT). In each area, readings were taken using an Infrared gas analyzer (IRGA) sensor, and temperature and soil moisture were measured. Additionally, soil samples were collected for chemical, physical, and biological characterization. Normalized Difference Vegetation Index (NDVI) assessments were conducted using satellites to evaluate vegetation cover. Non-parametric tests (Kruskal Wallis and Principal Component Analyses - PCA) were employed to evaluate differences between the areas and to assess the correlation among the environmental variables. The P area had the highest FCO2 and total organic carbon (TOC), differing significantly from the other site areas. Conversely, AP had the lowest FCO2 and was distinct from other sites. The MIX and NT areas had intermediate FCO2 and TOC values, which were statistically similar to each other. PCA identified distinct patterns in FCO2, soil temperature, and moisture. FCO2 was positively correlated with soil temperature and negatively correlated with moisture. There was a relationship between the biological variables microbial quotient (qMic), metabolic quotient (qCO2), COT, and FCO2. qMic reached the highest values in the MIX area, decreasing linearly from NT to P. Conversely, AP had the lowest qMic. qCO2 had the highest value in AP and NT. The proximal IRGA sensor effectively quantified FCO2 and differentiated mining tailing-affected areas. This assessment incorporated soil temperature, moisture sensors, vegetation index, and soil attribute data. The FCO2 levels were higher in P areas and lower in AP areas. Elevated FCO2 levels were correlated with a high soil temperature and low humidity. In AP, qMic was low, and qCO2 was high, indicating lower FCO2 levels. Conversely, P exhibited a higher FCO2. Higher NDVI values were correlated with elevated FCO2 in areas with specific vegetation cover during environmental recovery, while lower NDVI areas had lower FCO2 levels.