| Literature DB >> 27529268 |
Jin Ah Kim1, Jin Hee Park2, Won Ju Hwang3.
Abstract
Street dust is a hazard for workers in traditional markets. Exposure time is longer than for other people, making them vulnerable to heavy metals in street dust. This study investigated heavy metal concentrations in street dust samples collected from different types of markets. It compared the results with heavy metal concentrations in heavy traffic and rural areas. Street dust was significantly enriched with most heavy metals in a heavy traffic area while street dust from a fish market was contaminated with cupper (Cu), lead (Pb) and zinc (Zn). Street dust from medicinal herb and fruit markets, and rural areas were not contaminated. Principal component and cluster analyses indicated heavy metals in heavy traffic road and fish market dust had different sources. Relatively high heavy metal concentration in street dust from the fish market may negatively affect worker's mental health, as depression levels were higher compared with workers in other markets. Therefore, intensive investigation of the relationship between heavy metal concentrations in street dust and worker's health in traditional marketplaces should be conducted to elucidate the effect of heavy metals on psychological health in humans.Entities:
Keywords: depression; dust; health; heavy metal; marketplace; street
Mesh:
Substances:
Year: 2016 PMID: 27529268 PMCID: PMC4997506 DOI: 10.3390/ijerph13080820
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 3.390
Figure 1Sampling locations: a medicinal herb market, a fish market, two fruit markets, and a heavy traffic area in Seoul, and rural areas in Jeongeup, South Korea.
Total elemental concentrations in street dust samples collected from a heavy traffic road, marketplaces and rural areas.
| Samples | Al | Na | Mg | K | Ca | Fe | Mn | Cr | Ni | Cu | Zn | Pb |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| (wt %, Mean ± SD) | (mg/kg, Mean ± SD) | |||||||||||
| Heavy traffic road | 4.13 ± 0.23 | 6.38 ± 0.47 | 1.39 ± 0.12 | 1.88 ± 0.17 | 6.85 ± 0.49 | 3.79 ± 0.22 | 769 ± 2 | 794 ± 75 | 245 ± 47 | 353 ± 3 | 1188 ± 18 | 128 ± 1 |
| Medicinal herb market | 6.99 ± 0.66 | 1.44 ± 0.12 | 0.96 ± 0.08 | 3.01 ± 0.29 | 1.99 ± 0.14 | 3.58 ± 0.30 | 641 ± 4 | 146 ± 11 | 50 ± 3 | 111 ± 2 | 544 ± 52 | 49 ± 2 |
| Fruit market-1 | 7.20 ± 0.01 | 1.55 ± 0.12 | 0.92 ± 0.00 | 2.77 ± 0.25 | 2.49 ± 0.64 | 3.80 ± 0.01 | 772 ± 27 | 105 ± 0 | 31 ± 4 | 61 ± 2 | 697 ± 34 | 63 ± 6 |
| Fruit market-2 | 9.28 ± 0.02 | 1.92 ± 0.08 | 0.71 ± 0.01 | 3.68 ± 0.11 | 1.75 ± 0.08 | 3.73 ± 0.04 | 581 ± 65 | 51 ± 4 | 24 ± 0 | 44 ± 2 | 372 ± 25 | 68 ± 0 |
| Fish market | 8.18 ± 0.91 | 1.87 ± 0.19 | 0.88 ± 0.10 | 2.62 ± 0.25 | 4.75 ± 0.49 | 5.46 ± 0.63 | 723 ± 11 | 140 ± 5 | 52 ± 3 | 344 ± 32 | 2425 ± 6 | 241 ± 4 |
| Rural area-1 | 7.39 ± 0.30 | 2.02 ± 0.02 | 0.58 ± 0.03 | 3.62 ± 0.03 | 2.79 ± 0.02 | 2.14 ± 0.11 | 368 ± 21 | 22 ± 1 | 6 ± 0 | 30 ± 1 | 288 ± 17 | 49 ± 4 |
| Rural area-2 | 8.25 ± 0.64 | 2.44 ± 0.13 | 0.48 ± 0.03 | 4.31 ± 0.25 | 1.97 ± 0.10 | 2.01 ± 0.14 | 258 ± 10 | 42 ± 14 | 8 ± 0 | 25 ± 2 | 216 ± 5 | 43 ± 1 |
| Rural area-3 | 8.64 ± 0.06 | 2.25 ± 0.03 | 0.66 ± 0.01 | 4.03 ± 0.07 | 2.36 ± 0.07 | 2.68 ± 0.03 | 394 ± 9 | 40 ± 0 | 12 ± 0 | 43 ± 5 | 380 ± 18 | 61 ± 1 |
Figure 2Enrichment factors of heavy metals in street dust samples.
Correlation matrix for the metal concentrations in street dust samples.
| Elements | Al | Na | Mg | K | Ca | Mn | Cr | Fe | Ni | Cu | Zn |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Na | −0.79 * | ||||||||||
| Mg | −0.83 * | 0.67 | |||||||||
| K | 0.77 | −0.53 | −0.94 * | ||||||||
| Ca | −0.76 | 0.82 * | 0.77 | −0.80 | |||||||
| Mn | −0.48 | 0.25 | 0.84 * | −0.90 * | 0.54 | ||||||
| Cr | −0.90 * | 0.93 * | 0.89 * | −0.78 | 0.88 * | 0.55 | |||||
| Fe | −0.10 | 0.02 | 0.57 | −0.70 | 0.46 | 0.84 * | 0.28 | ||||
| Ni | −0.90 * | 0.94 | 0.87 * | −0.77 | 0.88 * | 0.52 | 1.00 * | 0.26 | |||
| Cu | −0.58 | 0.59 | 0.76 | −0.82 * | 0.91 * | 0.66 | 0.74 | 0.73 | 0.74 | ||
| Zn | −0.21 | 0.18 | 0.48 | −0.66 | 0.67 | 0.62 | 0.35 | 0.87 * | 0.35 | 0.88 * | |
| Pb | −0.20 | 0.23 | 0.44 | −0.61 | 0.71 | 0.54 | 0.36 | 0.81 * | 0.36 | 0.88 * | 0.99 * |
* p < 0.01 correlation is significant.
Figure 3Plot of loadings of the first two principal components in principal component analysis.
Figure 4Principal component score plot of the street dust in the projection of principal components 1 and 2.
Figure 5Dendrogram showing the clustering of the street dust samples. Similarities have been calculated from Euclidean distance.
General characteristics and depression level of workers in the traditional marketplace.
| Variables | Total ( | Fish Market ( | Fruits Market ( | Medicinal Herb Market ( | F or χ2 | |
|---|---|---|---|---|---|---|
| Mean ± SD | ||||||
| Age (years) | 59.43 ± 9.35 | 60.58 ± 9.26 a | 59.71 ± 8.22 a | 59.31 ± 10.85 a | 0.691 | 0.503 |
| Working hours (hour/day) | 11.44 ± 2.21 | 11.75 ± 2.05 a | 11.35 ± 2.53 a | 11.36 ± 1.81 a | 0.471 | 0.625 |
| Work experience (years) | 21.61 ± 11.83 | 21.89 ± 9.36 a | 21.14 ± 11.05 a | 22.08 ± 14.32 a | 0.109 | 0.897 |
| Street venders (%) | 34.4 | 28.6 | 43.8 | 14.3 | 12.22 | 0.002 |
| Depression level (CES-D) | 16.77 ± 8.78 | 21.13 ± 9.08 a | 14.34 ± 8.56 b | 16.98 ± 7.61 b | 8.190 | <0.001 |
Means with the same letter (a or b) are not significantly different at p < 0.005.