Keli Zhao

Heavy metal sources identification and sampling uncertainty analysis in a field-scale vegetable soil of Hangzhou, China

At a field-scale (6.7ha), 100 surface soil samples were collected from a vegetable field to determine total concentrations of Cd, Co, Cu, Hg, Mn, Ni and Zn. To identify possible sources of these metals and characterize their spatial variation, classic statistic and geostatistic techniques were applied. Through correlation and geostatistical analysis, it was found that the primary inputs of Co, Mn and Ni were due to pedogenic sources, whereas the sources of Hg and Cd were mainly due to human activities. Because of their different sources, their variations followed: Hg>Cd approximately Cu>Zn approximately Co approximately Mn approximately Ni. Based on their relationships with other soil properties, co-kriging was used to minimize sampling density. Sampling numbers for Cd, Cu, Zn, Mn, Co and Ni can be reduced from 100 to 90, 80, 70, 60, 60 and 60, respectively, without losing accuracy relative to ordinary kriging.

Contamination and Spatial Variation of Heavy Metals in the Soil-Rice System in Nanxun County, Southeastern China

There is an increasing concern about heavy metal contamination in farmland in China and worldwide. In order to reveal the spatial features of heavy metals in the soil-rice system, soil and rice samples were collected from Nanxun, Southeastern China. Compared with the guideline values, elevated concentrations of heavy metals in soils were observed, while heavy metals in rice still remained at a safe level. Heavy metals in soils and rice had moderate to strong spatial dependence (nugget/sill ratios: 13.2% to 49.9%). The spatial distribution of copper (Cu), nickel (Ni), lead (Pb) and zinc (Zn) in soils illustrated that their high concentrations were located in the southeast part. The high concentrations of cadmium (Cd) in soils were observed in the northeast part. The accumulation of all the studied metals is related to the long-term application of agrochemicals and industrial activities. Heavy metals in rice showed different spatial distribution patterns. Cross-correlograms were produced to quantitatively determine the spatial correlation between soil properties and heavy metals composition in rice. The pH and soil organic matter had significant spatial correlations with the concentration of heavy metals in rice. Most of the selected variables had clear spatial correlation ranges for heavy metals in rice, which could be further applied to divide agricultural management zones.

Using GIS and Geostatistics to Optimize Soil Phosphorus and Magnesium Sampling in Temperate Grassland

Abstract Soil sampling design is an important issue for agricultural management and environmental monitoring. In this study, a total of 537 soil samples were collected based on a 30 × 30–m grid from a permanent dairy farm in southeastern Ireland. Five different subsample experiments at lower densities based on the original data set were performed to study the optimal soil sampling design for soil P and Mg using geostatistics and a GIS (geographical information system). Soil P ranged from 1.3 to 35.7 mg L−1, with a CV value of 0.68. Soil Mg ranged from 134.7 to 685.2 mg L−1, with a small CV value of 0.28. Soil P followed neither a normal nor a lognormal distribution. Box-Cox transformation was applied to achieve normality. On the other hand, soil Mg followed a normal distribution, as did its subdata. For soil P, an omnidirectional spherical model was used to describe the spatial autocorrelation. For soil Mg, a nested model (an exponential model combined with a Gaussian model) was used to fit the variograms. Further soil P interpolated maps revealed that soil grid sampling interval could increase to 90 m without a significant loss of spatial information, whereas soil Mg sampling interval could increase to 120 m, confirming that soil Mg had much stronger spatial structure than soil P. According to this study, a grid of 90 × 90 m was recommended for soil sampling, which was confirmed in other practical grassland farms. The spatial structure information was very useful to optimize soil sampling design for practical grassland management.

Field-scale variability of soil test phosphorus and other nutrients in grasslands under long-term agricultural managements

Field-scale variation of soil nutrients in grassland is becoming important because of the use of soil-nutrient information as a basis for policies such as the recently introduced EU Nitrates Directive. This study investigates the field-scale variability of soil-test phosphorus (STP) and other nutrients in two grasslands with a long-term history of poultry litter application. Two fields (field 1 for silage and field 2 for grazing pasture) were selected, and soil samples were collected based on 12 m by 12 m (field 1) and 15 m by 15 m (field 2) grids. Data were analysed using conventional statistics, geostatistics, and a geographic information system (GIS). In field 1, STP values ranged from 12.4 to 90 mg L–1 (average 38.5 mg L–1). In field 2, STP values ranged from 4.3 to 130.0 mg L–1 (average 21.4 mg L–1). Attention should be paid to long-term poultry application, as the average STP values in both fields were much greater than the recommended agronomic optimum STP status in Ireland of 8 mg L–1. Coefficient of variation values of soil nutrients in field 2 were much higher than those in field 1. Log-transformation and Box–Cox transformation were applied to achieve normality. Statistically significant (P < 0.01), positive correlations between P and other nutrients were found in both fields. Exponential and spherical models were fitted to the experimental variograms of STP in fields 1 and 2, respectively. Compared with the counterparts in field 1, soil nutrients in field 2 had larger ‘nugget-to-sill’ values, revealing that sheep grazing could weaken the spatial auto-correlation of soil nutrients. A grid of 60 m by 60 m was recommended for soil sampling in grassland, based on this study. High STP concentrations in field 1 were in the north-eastern side, which was related to uneven poultry litter application. Strong spatial similarity of low STP, magnesium, and pH values in their spatial distribution were found in field 2, confirming their strong statistical correlation.

Variation of soil P and other nutrients in a long-term grazed grassland P experiment field

Field-scale variation of soil phosphorus (P) information is very important for P fertilizer application and its soil sampling design in grassland. A total of 108 soil samples were collected from a long-term (41 years) grazed grassland P experiment field at Teagasc, Johnstown Castle, Wexford, Ireland, in March 2009. There were six P treatments (P0-0, P0-30, P15-15, P15-5, P30-30, and P30-0) since 1968, with changes since 1999. Each treatment had 6 replicate plots (a total of 36 plots, 3 soil samples per plot). The samples were analyzed for available (Morgan’s) P, potassium (K), magnesium (Mg), lime requirement (LR), and pH. The highest mean available P concentration was found in the P30-30 (30 kg P ha−1 pre- and post-1998) plots, and the lowest mean available P concentration was found in the P0-0 (no P fertilizer since 1968) plots. Significant differences of mean P, Mg, LR, and pH values in different treatments were observed. There was a positive proportional effect for both the 36 plots and the 6 treatments for the P data: the local standard deviation increased with the increase of local mean. The proportional effect should be considered in order to optimize sampling design. Fewer samples can reflect soil P status in fields with low soil P levels, while more attention should be paid to the fields with high P levels in order to reduce environmental consequences of uniform applications.

Ten-year regional monitoring of soil-rice grain contamination by heavy metals with implications for target remediation and food safety

Farmland soil heavy metal contamination could pose potential risks to ecosystems, food safety and human health ultimately. Regional researches on the long-term monitoring of heavy metals in a soil-rice grain system, changed with environmental policy adjustment, have been hindered by limited detailed data. In this study, we collected 169 paired paddy rice grain and corresponding soil samples from a former intensive electronic-waste dismantling region to survey the current status of heavy metal contamination, and to reveal the temporal trends over the past decade based on the previous data obtained in 2006 and 2011. Moderate contaminations of Cd, Cu, Zn and Ni were observed in soil currently. Furthermore, 20.7% of rice grain samples exceeded the Cd threshold value. Cd, Cu, Zn and Pb shared the similar spatial distribution pattern with higher concentrations in northwest, which were contrary to Cr, Ni and As. Risk assessment indicated that much attention is required for the carcinogenic risk of Cr, Cd and As and non-carcinogen risk of Cr. Combining the spatial distribution of heavy metals in soil and rice grains, and the potential ecological risks, with the human health risks, the middle-west rice paddies were identified and proposed as priority areas. Percentage of soil Pb, Cd and Zn decreased in most area and slightly increased in northwest and east. Cu decreased in southwest and increased in central part, while Ni slightly increased in the whole region between 2006 and 2016. With the scrutiny of strict environmental policy, Cd still remained relatively constant levels in soil and rice grains during the last decade, which confirmed that the heavy metals were persisted over the long duration. Target sustainable and ongoing green remediation methods should be adopted urgently in specific area to guarantee food safety and human health for local residents.

Using ArcGIS and Geostatistics to Study Spatial Pattern of Forest Litter Carbon Density in Zhejiang Province, China

A total of 840 forest litter samples were collected based on a 4-km (south–north)×6-km (east–west)-grid system in Zhejiang province. Traditional statistics and geostatistics were applied to study the spatial pattern of forest litter carbon density in an ArcGIS environment. Forest litter carbon density values were very variable, ranging from 10.2 to 4,289.8kghm−2, with an average of 1,627.1kghm−2. Spherical model was chosen to fit the experimental semi-variogram. The high nugget/sill (0.6275) value revealed that random variation played a key role in spatial heterogeneity of forest litter carbon density; human activities and land management further weaken its spatial correlation. Forest litter carbon density in Zhejiang province was above the average level of forest in China. High forest litter carbon values were mainly distributed in hilly region in Zhejiang, while HangJiaHu Plain, NingShao Plain, and coastal areas had low forest litter carbon density due to low forest coverage.

Fractions of Heave Metals in Paddy Fields and Their Spatial Relationship to Rice Plant

To access the distribution in fractions of heavy metals in paddy fields in Wenling city in Zhejiang Province, China, extensive sampling was carried out to identify spatial correlation and bioavailability to rice grown on these soils at the regional scale. The results showed that Cd in soil were predominantly association with exchangeable and Fe-Mn oxide fraction; while Cu, Ni, Pb and Zn with residual fraction. Most heavy metals studied in rice were significantly (P<0.01) correlated with exchangeable fraction, which is considered as readily bioavailability to rice.