Bayesian material flow analysis for systems with multiple levels of disaggregation and high dimensional data.

Material flow analysis (MFA) is used to quantify and understand the life cycles of materials from production to end of use, which enables environmental, social, and economic impacts and interventions. MFA is challenging as available data are often limited and uncertain, leading to an under-determined system with an infinite number of possible stocks and flows values. Bayesian statistics is an effective way to address these challenges by principally incorporating domain knowledge, quantifying uncertainty in the data, and providing probabilities associated with model solutions.

Artisanal gold mining in Kakamega and Vihiga counties, Kenya: potential human exposure and health risk.

Artisanal and small-scale gold mining (ASGM) represents 20% of gold supply and 90% of gold mining workforce globally, which operates in highly informal setups. Pollutants from mined ores and chemicals introduced during gold processing pose occupational and inadvertent health risks to the extent that has not been well elucidated in Africa. Trace and major elements were analysed using inductively coupled plasma mass spectrometry in soil, sediment and water samples from 19 ASGM villages in Kakamega and Vihiga counties.