Comparing ordinary kriging and inverse distance weighting for soil as pollution in Beijing

Qiao, Pengwei; Lei, Mei; Yang, Shanshan; Yang, Jun; Guo, Guijie; Zhou, Xiaoyong

doi:10.1007/s11356-018-1552-y

Cited by 90 publications

(42 citation statements)

References 40 publications

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“…In sampling region I, based on the statistical characteristics, the EFs of Zn, Cu, and Cd exceeding 1.5 accounted for 10.3%, 10.3%, and 34.5% of the total 29 soil profiles, respectively (Figure 4). Spatial variation in the EF of potentially toxic elements can be interpolated by inverse distance weighting [72]. According to the spatial distribution of the EFs of the potentially toxic elements, the EFs of Zn that were higher than one accounted for 97.62% of the total area, those exceeding 1.5 accounted for 1.16% of the total area, and there were no areas with EFs greater than two.…”

Section: Resultsmentioning

confidence: 99%

Spatial and Vertical Variations and Heavy Metal Enrichments in Irrigated Soils of the Syr Darya River Watershed, Aral Sea Basin, Kazakhstan

Abuduwaili

Smanov

et al. 2019

IJERPH

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In the Syr Darya River watershed, 225 samples from three different layers in 75 soil profiles were collected from irrigated areas in three different spatial regions (I: n = 29; II: n = 17; III: n = 29), and the spatial and vertical variation characteristics of potentially toxic elements (Cd, Co, Cu, Ni, and Zn) and a metallic element (Mn) were studied. The human health risks and enrichment factors were also evaluated in the Syr Darya River watershed of the Aral Sea Basin in Kazakhstan. There were significant differences in the contents of heavy metals in the different soil layers in the different sampling regions. Based on element variation similarity revealed by hierarchical cluster analysis, the elemental groupings were consistent in the different layers only in region I. For regions II and III, the clustered elemental groups were the same between surface layer A and B, but differed from those in the deep layer C. In sampling region I, the heavy metals in surface soils were significantly correlated with the ones in deep layers, reflecting that they were mainly affected by the elemental composition of parent materials. In region II, the significant correlations only existed for Cu, Mn, and Zn between the surface and deep layers. The similar phenomenon with significant correlation was also observed for heavy metals in sampling region III, except for Cd. Finally, enrichment factor was used to study the mobilization and enrichment of potentially toxic elements. The enrichment factors of Zn, Cu, and Cd in surface layer A that were greater than 1.5 accounted for 1.16%, 6.79%, and 24.36% of sampling region I, respectively. In sampling region II, the enrichment factors of Zn, Cu, Cd, and Co that were greater than 1.5 accounted for 0.03%, 4.76%, 0.54%, and 9.03% of the total area, respectively. In sampling region III, only the enrichment factors of Zn, Cu, and Cd that exceeded 1.5 accounted for 0.24%, 4.90%, and 6.89% of the total area, respectively. Although the contents of the heavy metals were not harmful to human health, the effects of human activities on the heavy metals in the irrigated soils revealed by enrichment factors have been shown in this study area.

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Section: Resultsmentioning

confidence: 99%

Spatial and Vertical Variations and Heavy Metal Enrichments in Irrigated Soils of the Syr Darya River Watershed, Aral Sea Basin, Kazakhstan

Abuduwaili

Smanov

et al. 2019

IJERPH

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“…We estimated environmental noise exposure at the geocoded residential address of each individual using spatial interpolation. Spatial interpolation methods such as inverse distance weighted (IDW), and kriging are commonly used in epidemiological studies to assess environmental exposure [16,17]. Environmental noise in particular can be estimated using kriging which has been proposed as a suitable spatial interpolation method in producing noise maps covering the full temporal and spatial extent of the study area [18,19].…”

Section: Environmental Noisementioning

confidence: 99%

Nighttime environmental noise and semen quality: A single fertility center cohort study

Choe

Kim

et al. 2020

PLoS ONE

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With increased population and urban development, there are growing concerns regarding health impacts of environmental noise. We assessed the relationship between nighttime environmental noise and semen quality of men who visited for fertility evaluation. This is a retrospective cohort study of 1,972 male patient who had undertaken semen analysis between 2016-2018 at a single fertility center of Seoul, South Korea. We used environmental noise data of National Noise Information System (NNIS), Korea. Using semiannual nighttime noise measurement closest to the time of semen sampling, individual noise exposures at each patient's geocoded address were estimated with empirical Bayesian kriging method. We explored the association between environmental noise and semen quality indicators (volume, concentration, % of progressive motility, vitality, normal morphology, total motile sperm count, oligozoospermia, asthenozoospermia, and severe teratozoospermia) using multivariable regression and generalized additive models. Estimated exposure to nighttime environmental noise level in the study population was 58.3±2.2 L eq. Prevalence of oligozoospermia, asthenozoospermia, and severe teratozoospermia were 3.3%, 14.0%, and 10.1%. Highest quartile nighttime noise was associated with 3.5 times higher odds of oligozoospermia (95% CI: 1.18, 10.17) compared to lowest quartile. In men whose noise exposure is in 3 rd quartile, odds ratio (OR) of severe teratozoospermia was 0.57 (95% CI: 0.33, 0.98). The OR for 4 th quartile noise were toward null. In generalized additive model, the risk of oligozoospermia increases when the nighttime noise is 55 Leq dB or higher. Our study adds an evidence of potential impact of environmental noise on semen quality in men living in Seoul. Additional studies with more refined noise measurement will confirm the finding.

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“…The Inverse Distance Weight Interpolation (IDW) is a weighted average interpolation method [63]. In this study, the IDW was used to determine the spatial distribution of soil risk elements (Eq.…”

Section: Spatial Distribution Of Risk Elementsmentioning

confidence: 99%

Contamination, Distribution and Health Risk Assessment of Risk Elements in Topsoil for Amusement Parks in Xi’an, China

Guo¹,

Su²,

Lin³

et al. 2020

Pol. J. Environ. Stud.

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The index of geo-accumulation (I geo) and the pollution load index (PLI) were used to assess the pollution level and the U.S. Environmental Protection Agency (EPA) health risk model was implemented to determine the health risk for children. Besides, a geographic information system (GIS) was utilized to map the distribution characteristics of risk elements contents. The results showed that: (1) the mean content of Mn was the highest in each park. Besides, the mean content of all risk elements except As exceeded the background value of Shaanxi in each park. (2) The I geo of Co was obviously higher than other elements in each park and the highest (4.07) content occurred in the Xing Qing Park (XQP). The pollution condition of the Urban Sports Park (USP) was the most serious among the four parks. (3) The children were more susceptible to risk elements pollution through oral intake. (4) The hazard indices (HI) of non-carcinogenic risk (NCR) were within the safe range (HI = 1) and the carcinogenic risks (CR) of Cr, Ni, As and Co for children were within the receivable range in each park.

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Comparing ordinary kriging and inverse distance weighting for soil as pollution in Beijing

Cited by 90 publications

References 40 publications

Spatial and Vertical Variations and Heavy Metal Enrichments in Irrigated Soils of the Syr Darya River Watershed, Aral Sea Basin, Kazakhstan

Spatial and Vertical Variations and Heavy Metal Enrichments in Irrigated Soils of the Syr Darya River Watershed, Aral Sea Basin, Kazakhstan

Nighttime environmental noise and semen quality: A single fertility center cohort study

Contamination, Distribution and Health Risk Assessment of Risk Elements in Topsoil for Amusement Parks in Xi’an, China

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