Initial results for urban metal distributions in house dusts of syracuse, New York, USA

Heavy metals in indoor dust: Spatial distribution, influencing factors, and potential health risks

Given the large proportion of time that people spend indoors, the potential health risks posed by heavy metals in the indoor environment deserve greater attention. A global-scale assessment of heavy metal contamination in indoor dust was conducted in this study based on >127 articles published between 1985 and 2019. The pollution levels, spatio-temporal variations, sources, bioaccessibilities, influencing factors, and health risks of heavy metals associated with indoor dust were analyzed. Children's blood lead levels (BLLs) were also estimated using the integrated exposure uptake biokinetic model. The results indicated that the median concentrations of Cu and Zn in 71.9% and 71.0% of the study sites surpassed the corresponding permissible limits, 100 and 300 mg/kg, respectively; thus, their control should be given priority. Heavy metal concentrations in indoor dust from different areas of the world varied greatly, which was closely associated with the type of local human activities, such as mining, melting, e-waste recycling and Pb-related industries. The bioaccessibilities of some key elements, e.g., Pb, Cd, Cu, and Zn, in household dust were high. The levels of heavy metals in indoor dust were mainly affected by a combination of outdoor and indoor sources and related critical factors, and future studies should focus on quantifying the contributions of different sources. Based on the health risk assessment, dust Pb exposure is a major health concern in e-waste recycling areas, which warrants greater attention. 49.8%, 36.8% and 14.4% of study sites showed BLLs exceeding 35 μg/L (threshold limit in Germany), 50 μg/L (threshold limit in the USA), or 100 μg/L (threshold limit in China), respectively. Finally, Pb exposure from indoor dust represents a major contributor to children's blood Pb poisoning in many developing countries. This study details the overall heavy metal contamination status of indoor dust and provides insights for policymakers with respect to pollution prevention measures.

Geographic patterns of non-carpeted floor dust loading in Syracuse, New York (USA) homes

Residential floor dust loading was measured on the smooth floor surface of 488 houses in Syracuse, New York, during the summers of 2003 and 2004. Using U.S. Environmental Protection Agency (EPA) wipe methods, pre-weighed Ghost Wipes, Lead Wipes, or Whatman Filters were employed to collect duplicate samples from (predominantly) kitchens. The collection efficiency of the various media was determined from multiple wipe tests and side-by-side comparisons. The results were normalized and aggregated at the census tract level to determine whether spatial patterns of dust loading could be observed. Loading was found to be log-normally distributed, with a geometric mean value of 0.311 g m(-2) (29 mg of dust per square foot of floor); 95% of the observations fell in the range of 0.042-2.330 g m(-2) (4-216 mg foot(-2)). The sampling for floor dust loading shows some bias for day of the week in which visits to the residential properties were made. After a first-order correction for this effect, results were aggregated by census tract and mapped in a geographic information system (GIS); strong spatial patterns can be identified in an inverse distance weighted mapping. The geographic patterns exhibit a strong correlation with socio-economic/demographic covariates extracted from the 2000 census summaries. Dust mass on the floors is positively correlated with renter-occupied properties and family size; it is negatively correlated with measures of household income.

Risk remaining from fine particle contaminants after vacuum cleaning of hard floor surfaces

In the indoor environment, settled surface dust often functions as a reservoir of hazardous particulate contaminants. In many circumstances, a major contributing source to the dust pool is exterior soil. Young children are particularly susceptible to exposure to both outdoor derived soil and indoor derived dust present in the indoor dust pool. This is because early in life the exploratory activities of the infant are dominated by touching and mouthing behavior. Inadvertent exposure to dust through mouth contact and hand-to-mouth activity is an inevitable consequence of infant development. Clean-up of indoor dust is, in many circumstances, critically important in efforts to minimize pediatric exposure. In this study, we examine the efficiency of vacuum cleaner removal of footwear-deposited soil on vinyl floor tiles. The study utilized a 5 x 10 foot (c. 152.5 x 305 cm) test surface composed of 1-foot-square (c. 30.5 x 30.5 cm) vinyl floor tiles. A composite test soil with moderately elevated levels of certain elements (e.g., Pb) was repeatedly introduced onto the floor surface by footwear track-on. The deposited soil was subsequently periodically removed from randomly selected tiles using a domestic vacuum cleaner. The mass and loading of soil elements on the tiles following vacuuming were determined both by wet wipe collection and by subsequent chemical analysis. It was found that vacuum cleaner removal eliminated much of the soil mass from the floor tiles. However, a small percentage of the mass was not removed and a portion of this residual mass could be picked up by moistened hand-lifts. Furthermore, although the post-vacuuming tile soil mass was sizably reduced, for some elements (notably Pb) the concentration in the residual soil was increased. We interpret this increased metal concentration to be a particle size effect with smaller particles (with a proportionately higher metal content) remaining in situ after vacuuming.

A first generation dynamic ingress, redistribution and transport model of soil track-in: DIRT

This work introduces a spatially resolved quantitative model, based on conservation of mass and first order transfer kinetics, for following the transport and redistribution of outdoor soil to, and within, the indoor environment by track-in on footwear. Implementations of the DIRT model examined the influence of room size, rug area and location, shoe size, and mass transfer coefficients for smooth and carpeted floor surfaces using the ratio of mass loading on carpeted to smooth floor surfaces as a performance metric. Results showed that in the limit for large numbers of random steps the dual aspects of deposition to and track-off from the carpets govern this ratio. Using recently obtained experimental measurements, historic transport and distribution parameters, cleaning efficiencies for the different floor surfaces, and indoor dust deposition rates to provide model boundary conditions, DIRT predicts realistic floor surface loadings. The spatio-temporal variability in model predictions agrees with field observations and suggests that floor surface dust loadings are constantly in flux; steady state distributions are hardly, if ever, achieved.

Geographic sampling of urban soils for contaminant mapping: how many samples and from where

Properly sampling soils and mapping soil contamination in urban environments requires that impacts of spatial autocorrelation be taken into account. As spatial autocorrelation increases in an urban landscape, the amount of duplicate information contained in georeferenced data also increases, whether an entire population or some type of random sample drawn from that population is being analyzed, resulting in conventional power and sample size calculation formulae yielding incorrect sample size numbers vis-à-vis model-based inference. Griffith (in Annals, Association of American Geographers, 95, 740-760, 2005) exploits spatial statistical model specifications to formulate equations for estimating the necessary sample size needed to obtain some predetermined level of precision for an analysis of georeferenced data when implementing a tessellation stratified random sampling design, labeling this approach model-informed, since a model of latent spatial autocorrelation is required. This paper addresses issues of efficiency associated with these model-based results. It summarizes findings from a data collection exercise (soil samples collected from across Syracuse, NY), as well as from a set of resampling and from a set of simulation experiments following experimental design principles spelled out by Overton and Stehman (in Communications in Statistics: Theory and Methods, 22, 2641-2660). Guidelines are suggested concerning appropriate sample size (i.e., how many) and sampling network (i.e., where).