Invisible and at risk: identification and measurement of population impacts of the Brumadinho dam collapse in Brazil

The dam collapse in the iron ore mine “Mina Córrego do Feijão”, located in the municipality of Brumadinho, is one of the worst technological disasters in Brazilian history, causing the death of 270 people and significant socioenvironmental impact alongside the Paraopeba River basin. We discuss results of a large research project developed by UFMG that addressed the consequences of the disaster, including the identification and measurement of the intensity of the impacts over heterogeneous population subgroups – urban, rural, riverine, indigenous and other traditional populations, and with distinct socioeconomic and cultural attributes - that remain invisible to official statistics. We define two objectives in this paper. First, we discuss a conceptual framework and methodological strategy to identify the impacted population and to characterize the types and intensities of damages caused by the disaster. We present a conceptual framework and a typology of vulnerability and damages involving ten dimensions (socioeconomic, environmental, sanitation, health, education, urban infrastructure, culture, riverine livelihoods, urban livelihoods, public safety). We also present a novel approach to assess populations impacted by disasters using a multiphasic mixed-method approach that integrate four stages of qualitative and quantitative data collection, with deductive and inductive approaches: qualitative exploratory (type instrumentbuilding model), quantitative explanatory (pre-test), qualitative confirmatory and quantitative explanatory. This last stage includes the description of quantitative data collection methods and instruments using a combination of census and sampling strategies for four distinct population subgroups. The second objective is to present and discuss some qualitative as well as sampling and census results and how they allow us to discuss compensation as well as adaptation policies for populations impacted by large-scale mining disasters.