Unprecedentedly High Dust Ingestion Estimates for the General Population in a Mining District of DR Congo

Abstract

The mining of metals in low income countries is often associated with high exposure to dust that contributes to metal exposure. Here, dust ingestion estimates were made from fecal excretion of inert tracers with corrections for dietary contribution. The study took place in the cobalt mining area of Lubumbashi (DR Congo) and involved 120 non-occupationally exposed participants in the dry season, with 51 of these being repeated in the rainy season. For each participant, duplicate meals (0-96 h), feces (24-120 h) and indoor/outdoor dust (<250 µm) were collected. The dust ingestion estimates (g day -1 ) were derived from Nb, Ti and V as best tracers and were 0.28 (geometric mean), 3.3 (mean) and 13 (P95); these values are almost a factor ten above currently accepted estimates for the general population in high income countries. Mean dust ingestion in the dry season was twice that of the rainy season and the P95s were significantly higher in children (3-15 years) than in male adults and toddlers, geophagy (>40 g day -1 ) was suspected in three individuals. These data explain the previously reported extreme cobalt exposures in children and support the need to manage dust in the metal mining operations.

Metal mining and birth defects: a case-control study in Lubumbashi, Democratic Republic of the Congo

“…Another limitation of our study is that the control group was also highly exposed to trace metals. This high background exposure is not surprising in view of our previous findings 4,7 and the fact that, by design, controls were recruited from the same maternity ward (and, therefore, the same community) as cases. We chose this procedure for obtaining controls, because a case-control design requires that cases and controls are recruited from the same source population, and we reasoned that recruiting controls from another town or from more affluent areas with less mining would be inappropriate.…”

“…In about half of the participants, home visits were done (by DKwK) within a few weeks after their inclusion. During these visits, surface dust was collected, as in our previous surveys, 7 with a dustpan (by sweeping a surface of approximately 1 m²) from the yard in front of the house (outdoor dust), and also from inside the house (usually the living room) if the participants consented. Samples of drinking water were obtained as indicated by the participants (communal tap water or wells).…”

“…Our group has previously shown that people living near mining or metallurgical activities in south Katanga are highly exposed to Co, arsenic (As), and U. [4][5][6][7] In a case study 5 among mineworkers and residents of an urban neighbourhood that had been transformed into an artisanal cobalt mine, we documented very high concentrations of Co, U and manganese (Mn) in urine and blood, especially among children, in whom there was also evidence of exposure-related oxidative DNA damage.…”

A Systematic Framework for Collecting Site-Specific Sampling and Survey Data to Support Analyses of Health Impacts from Land-Based Pollution in Low- and Middle-Income Countries

“…Characterizing exposures and intake requires information on chemical concentration, frequency and duration of exposure, and various exposure factors such as behavior, time and activity patterns, and contact or intake rates [ 18 , 19 ]. Standardized exposure factors have not been derived for application in LMICs as they have in other countries [ 15 , 16 , 18 , 20 , 21 , 22 ]. Differing dietary habits, earthen floor housing, climate and time spent outdoors, local environments, dusty conditions during particular times of the year and unpaved roads, and other factors and behaviors all imply that exposure factors, while unknown, are likely to differ from data obtained from high-income countries [ 23 , 24 ].…”

“…Moreover, while standardized exposure factors (e.g., intake rates, consumption patterns, individual behaviors, lifestyle factors, etc.) are fairly well-described in a few high-income countries, there is a lack of such standardized data from LMICs [ 15 , 16 ].…”