Studies on the Potential Ecological Risk and Homology Correlation of Heavy Metal in the Surface Soil

Qiu, Haiyuan

doi:10.5539/jas.v2n2p194

Cited by 32 publications

(25 citation statements)

References 8 publications

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“…The PERI method took into the consideration the toxicology of heavy metals and illustrated the potential ecological risk caused by overall levels of contamination [22]. PERI can be obtained using the following equation:…”

Section: Assessment Methodsmentioning

confidence: 99%

Distribution and Risk Assessment of Heavy Metals in Soils from a Typical Pb-Zn Mining Area

Huang¹,

Yuan²,

Li³

et al. 2017

Pol. J. Environ. Stud.

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Section: Assessment Methodsmentioning

confidence: 99%

Distribution and Risk Assessment of Heavy Metals in Soils from a Typical Pb-Zn Mining Area

Huang¹,

Yuan²,

Li³

et al. 2017

Pol. J. Environ. Stud.

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“…From all pollution load indices consider, the study showed that cassava mill effluents in receiving soil are contributing to slight heavy metals pollution. According to Qiu (2010), heavy metals pollution from industrial setting typically originates from three sources including exhaust, human activities and secondary pollution. Based on the various pollution indices under study, the heavy metals resulting from cassava mill effluent is leading to low/slightly polluted to moderate pollution.…”

Section: Resultsmentioning

confidence: 99%

Assessment of Pollution Load Indices of Heavy Metals in Cassava Mill Effluents Contaminated Soil: a Case Study of Small-scale Processors in a Rural Community in the Niger Delta, Nigeria

Izah¹,

Bassey²,

Ohimain³

2017

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Cassava mill effluents are discharged into the environment by smallholder cassava processor in rural communities in the Niger Delta region of Nigeria. Cassava mill effluents are known to induce toxicity in some biodiversity such as livestock (sheep, goat), vegetation, microorganisms and fisheries. This study evaluated the pollution load indices of heavy metals in cassava mill effluents contaminated soil in rural community in the Niger Delta region of Nigeria. Secondary data from cassava mill effluents soil were used for the study. The data were classified based on seasons. The pollution load was calculated following standard protocol. Nine pollution indices were considered including Contamination factor (CF), Degree of contamination (CD), Pollution load index (PLI), Pollution index (PI), Sum of pollution index (SPI), Pollution index/ Contamination Index (PI/CI), Metal pollution Index (MPI), Average Pollution Index (API) and Nemerow integrated pollution index (NIPI). In few instance that some heavy metals was not detected, 50% of mean detected individual metals were considered for the location that the metals were not detected. Geometric (BGM) and median mean (BMM) were considered for the background scenarios except for API and PI/CI in which median mean was used. The pollution load resulting from these heavy metals viz: Fe, Cr, Zn, Cu, Co, Ni, Mn, Pb and Cd revealed that CF and CD had low to moderate contamination level in both seasons apart from Pb that had considerable pollution in one of the locations for wet season, PLI were within no pollution to moderate pollution, PI were also within no pollution to low pollution level and NIPI were within warning line of pollution to low level of pollution for dry season, and warning line of pollution to high pollution in wet season. MPI, PI/CI and API showed slight pollution. The findings of this study also showed that cassava processing by smallholder in rural communities in the Niger Delta is slightly contributing to heavy metals pollution is receiving soil which varies according to seasons. Furthermore, age and heavy metal content in the cassava tuber and quantity of cassava processed in each mill and other anthropogenic activities could account for difference in pollution among the various locations, while runoff resulting from rainfall could account for the seasonal influence.

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“…Previous work has found that variation in microbial biomass can be partially explained by soil type and drainage class [39,40], soil texture [37,41], organic matter content, and pH [34,42]. The conventional plow depth is 20 cm [43,44], but roots reach from 0-30 cm [45,46], and microbes are usually present in the topsoil (0-7.5 cm) [47][48][49].…”

Section: Introductionmentioning

confidence: 99%

Investigation of the spatial heterogeneity of soil microbial biomass carbon and nitrogen under long-term fertilizations in fluvo-aquic soil

2018

Preprint

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Soils are heterogeneous and microbial spatial distribution can clearly indicate the spatial characteristics of the soil carbon and nitrogen cycle. However, it is not clear how long-term fertilization affects the spatial distribution of microbial biomass in fluvo-aquic soil. We collected fluvo-aquic soil samples (topsoil 0-7.5 cm and sub-topsoil 7.5-20 cm) using a spatially-explicit design within three 40.5 m 2 plots in each of four fertilization treatments. Fertilization treatments were: cropping without fertilizer inputs (CK); chemical nitrogen, phosphorus, and potassium fertilizer (NPK); chemical fertilizer with straw return (NPKS); and chemical fertilizer with animal manure (NPKM). Variables included soil microbial biomass carbon (MBC) and nitrogen (MBN), and MBC/MBN. For both soil layers, we hypothesized that: microbial biomass was lowest in CK but with the largest spatial heterogeneity; and microbial biomass was highest in NPKM and NPKS but with the lowest spatial heterogeneity. Results showed that: (1) Fertilization significantly increased MBC and MBN more in topsoil than sub-topsoil but had no MBC/MBN changes.(2) The coefficient of variation (CV) and Cochran's C showed that variation was largest in CK in topsoil and NPK in sub-topsoil and that variation of topsoil was generally lower than in sub-topsoil. The sample size of the three variables was largest in CK in topsoil but had little variation among the other treatments. (3) The trend-surface model showed that within-plot heterogeneity varied substantially with fertilization (NPKM = NPK > NPKS > CK), but Moran's I and the interpolation map showed that spatial variability with fertilization followed the order NPK > NPKS > CK = NPKM at a fine scale in topsoil. In sub-topsoil, the trend-surface model showed that within-plot heterogeneity followed the order NPKM = CK > NPK > NPKS and that the fine-scale pattern was NPKM>NPK=NPKS>CK.MBC had the highest spatial heterogeneity among the three variables in both soil layers. Our results indicate that the application of organic fertilizer (straw or manure) reduced the variation of MBC and MBN but increased the spatial variability of MBC and MBN. The spatial variation of the three variables was MBC > MBN > MBC/MBN regardless of whether variation was considered at the plot-scale or the fine-scale in both layers.

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Studies on the Potential Ecological Risk and Homology Correlation of Heavy Metal in the Surface Soil

Cited by 32 publications

References 8 publications

Distribution and Risk Assessment of Heavy Metals in Soils from a Typical Pb-Zn Mining Area

Distribution and Risk Assessment of Heavy Metals in Soils from a Typical Pb-Zn Mining Area

Assessment of Pollution Load Indices of Heavy Metals in Cassava Mill Effluents Contaminated Soil: a Case Study of Small-scale Processors in a Rural Community in the Niger Delta, Nigeria

Investigation of the spatial heterogeneity of soil microbial biomass carbon and nitrogen under long-term fertilizations in fluvo-aquic soil

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