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Environmental pollution caused by electrical waste (E-waste) dismantling has attracted much attention from environmental researchers in China and worldwide. A total of 90 topsoil samples, 10 soil profile samples and 14 groundwater samples, were collected from a typical E-waste dismantling area in southeastern China. The spatial and vertical variations of potentially hazardous metals (PHM) in paddy soils and their potentially ecological risks were studied. The results showed that the mean total concentrations of five PHM (Cd, Cu, Pb, Zn and Ni) in paddy soils were 0.38, 35.13, 35.40, 121.38 and 28.13 mg kg−1, respectively. The total Cd, and Pb concentrations in paddy soils had strong variability coefficients of 121.05% and 109.38%, respectively. Compared with the background values in Zhejiang province, the PHM were enriched in paddy soils. Part of the study area was seriously contaminated by these metals. The soil was contaminated with Cd, with high Pi Ave value (>1). The shallow groundwater was also contaminated by Cd in the E-waste dismantling area. Results of vertical variations of PHM in soils revealed significant accumulation of PHM in the topsoil (0–20 cm, 20–40 cm). Soil pH and organic matter (SOM) was significantly correlated with most of the total PHM in soils. Kriging interpolation and Moran's I were used to identify the contaminated hotspots of these PHM. It was found that high soil Cd, Cu, Ni, Zn were located in Da Xi and Zhe Guo towns, which was attributed to E-waste dismantling and other anthropogenic activities. Soil Ni was mainly influenced by the parent material. The PHM in soils may pose a potential threat to local ecosystem and human health.

Chinese hickory (Carya cathayensis Sarg.) is a famous woody nut in China. Currently, there is an increasing concern related to the soil heavy metal pollution in Chinese hickory plantations. In this study, a total of 188 soil samples were collected from Lin’an city, a typical Chinese hickory production area. The results showed that the average background concentrations of Cd, Cu, Zn, Pb, Ni and Cr in soils were 0.37, 40.76, 87.61, 30.10, 28.33 and 56.57 mg kg−1, respectively. The Cd and Pb concentrations in Chinese hickory plantation soils had strong variability coefficients of 186.49% and 95.42%, respectively. Compared with the background values in Zhejiang province, the heavy metals were enriched in the plantation soils. The accumulation ratio followed the order of Cu > Cd > Pb > Ni > Zn > Cr. Part of the study area was seriously contaminated by Cd and Cu, as 31.38% of soil Cd and Cu samples exceeded the second grade standardized value of Environmental Quality Standards of Soils (EQSS). The soils in our study area generally reached a moderate ecological risk, as its average RI value reached 103. Moran’s I and Kriging interpolation results revealed that all the studied heavy metals in soils had clear spatial distribution patterns. The high values of soil heavy metals (except Pb) were mainly distributed in the localities closest to the mine area. The apportionment of heavy metal pollution sources showed that the Cu, Ni and Cr in soils were mainly related to mining activities, Pb was closely related to fertilizer application, Cd and Zn were related to both. The heavy metals in soils may pose a potential threat to local ecosystem and human health. Our results implied that soil heavy metals should be tested, and risk-based management should be performed in the economic plantations in China. These maps could provide useful information for forestry and environmental management.

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 treatmen...

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, with a CV value of 0.68. Soil Mg ranged from 134.7 to 685.2 mg L, 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.

Miscible displacement column experiments were performed to investigate the mobility of Zn in two different soils, Windsor (mixed, mesic Typic Udipsamment) and Webster (fine-loamy, mixed, superactive, mesic Typic Endoaquoll). In the Webster soil, strong Zn retention was observed due to a higher percentage of clay, organic matter, amorphous Fe and Al, and the presence of carbonates compared with Windsor soil. Observed Zn breakthrough curves (BTCs) were asymmetric and exhibited extensive retardation and slow desorption. The Webster soil exhibited limited Zn mobility, with only 14% recovery of Zn in the effluent solution, whereas considerable mobility was exhibited by Windsor, with 59% recovery. The Zn distribution with depth in the soil column showed that, for Windsor, Zn was retained within the surface layer and decreased with depth, whereas for Webster, a concentration maximum was observed below the soil surface. The presence of P in the Zn pulse solution increased the amount of Zn sorbed for the Windsor soil, whereas the opposite was observed for the Webster soil. Measured BTCs for P indicated higher P sorption for Windsor, with P recovery of 67%, than for Webster, with recovery of 85%. Simulations using a linear model with a first-order irreversible reaction provided inadequate predictions of Zn BTCs. Therefore, the linear model is not recommended. Improved Zn predictions of the BTC were obtained when a multireaction and transport model (MRTM) was used. Nevertheless, the MRTM model, which accounted for nonlinear kinetic reversible and irreversible retention reactions, provided inadequate Zn BTC predictions for the Webster soil. There is a need for an improved MRTM model that accounts for the various retention mechanisms governing adsorption–desorption reactions during transport in soils.

Suspended sediment concentration (SSC) is an important parameter for monitoring coastal water quality. Suspended particles are also the main optically active substances for ocean color remote sensing. It is important to study the surface reflectance spectra features of coastal turbid water, as it can be the basis for establishing more accurate remote-sensing inversion models. In this study, Hangzhou Bay, China, was selected as the study area. Two in situ measurement and sampling stations in the estuary of the Qiantang River which flows into Hangzhou Bay were set up separately. Above-water spectrum observation method, which the NASA recommended, was adopted to measure the reflection spectrum of turbid waters. Surface water samples were simultaneously collected to obtain the corresponding SSC data. The results showed that the total suspended particle concentrations in the Hangzhou Bay were typically high, and the inorganic suspended particle concentrations were far greater than the phytoplankton concentrations, which averages 705 mg/L and 1.16 mg/m3. The SSC at two sampling stations both showed significant temporal variability, particularly appearing short-period rapid fluctuations accompanying the tidal cycle. The measured surface water reflectance spectra all showed typical curve characteristics of high turbid water, and as the SSC increased, the corresponding reflectivity of surface water also increased. The increments at different wavelengths were variational, with two reflectance peaks appearing at 650–700-nm and near the 800-nm wavelength of spectral curves, respectively. The first derivative of spectral curves showed that the first reflectance peak location appeared to be a “red shift” phenomenon with the SSC increasing. The correlation coefficients between the SSC of surface water and the remote-sensing reflectance according to moderate resolution imaging spectra-radiometer (MODIS) channels’ central wavelength were different significantly, which were larger at MODIS long-wavelength channels (>650 nm) and smaller at MODIS short-wavelength channels (400–550 nm). The value of determination coefficient R 2 was 0.82 when the reflectance ratio of MODIS band 2 to band 1 was selected as the SSC sensitive bands combination and exponential regression analysis was employed. Therefore, the reflectance ratio of MODIS band 2 to band 1 can be adopted as the main band combination for establishing surface water SSC remote-sensing inversion model in the Hangzhou Bay.

The spatial variation of soil nutrients especially the soil test phosphorus (STP) in grassland soils is becoming important because of the use of soil-nutrients information as a basis for policies such as the recently EU-introduced Nitrates Directive. Up to now, the small-scale spatial variation of soil nutrients in grassland has not been studied. The main aim of this study was to investigate the spatial patterns of soil nutrients in two grazed grassland plots with a long-term (38 y) P-application experiment, in order to better understand the spatial variation of soil nutrients and the correlation among soil nutrients in grasslands. Two small areas (one from a high-P background and the other from a medium-P background) were selected. Soil samples (304 per study area) were collected based on a 1 m × 1 m grid system. The samples were analyzed for STP, Mg, K, pH, and lime requirement (LR). The results were analyzed using conventional statistics, Moran's I, geostatistics, and a GIS. Based on the global Moran's I values, significant positive spatial autocorrelations were found for STP, Mg, pH, and LR in both study areas. Spatial clusters and spatial outliers were detected using the local Moran's I index. Clear linear-shaped high-high or low-low value clusters of the studied variables except K were observed in the study areas due to long-term usage of machine spreader or other agricultural-management methods in the past. The corresponding linear patterns were further found in the spatial-distribution maps. Small spatial patches were found for soil K revealing that it had a random spatial distribution related to the relatively uniform K fertilizer in the study areas. The spatial clusters revealed by local Moran's I were in line with the spatial patterns in the distribution maps.

As a source of nutrient supplements, the deficiency or excess of micronutrients in soil is directly connected to the plant uptake and, thereby, status of micronutrients in the human population. Proper management of micronutrients requires an understanding of the variations of soil micronutrients across the fields. This study is to investigate the spatial patterns of soil available Zn and Cu in paddy rice fields. Four hundred and sixty three soil samples were taken in Hangzhou–Jiaxing–Huzhou (HJH) watershed in Zhejiang Province, China, and available Zn and Cu were analyzed using an atomic adsorption spectrometer. Geostatistical semivariograms analysis indicated that the available Zn and Cu were best fitted to a spherical model with a range of 40.5 and 210.4 km, respectively. There were moderate spatial dependences for Zn and Cu over a long distance and the dependence were attributed to soil types and anthropogenic activities. The overlay analysis of spatial patterns and soil types gave us greater understanding about how intrinsic factors affect the spatial variation of available micronutrients. Based on the above, macroscopically regionalized management of soil available micronutrients and the implications to potential risk were discussed.

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: HgCd approximately CuZn 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.

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