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Nitrogen (N) fertilization is known to have a substantial effect on heavy metal uptake in plants. However, the impact of N fertilization on plant growth and heavy metal uptake of hyperaccumulator plants remains unclear. This study examined the effect of N fertilization on growth and uptake of zinc (Zn) and cadmium (Cd) in Zn/Cd-hyperaccumulating plants species, Sedum alfredii (S. alfredii) Hance (Crassulaceae). Plants of S. alfredii were grown for 60 days in nutrient solution with 100 μmol Cd L−1 and 0–10 mmol N L−1, and in a Cd-contaminated soil receiving N fertilizer and composted pig manure amendment. Biomass production, nutrient uptake, and concentrations and accumulation of Zn and Cd in plant parts were measured. In the hydroponic experiment, both low ( 5 mmol L−1) N supply decreased the growth and Zn and Cd accumulation in the whole plants. The 2.5 mmol N L−1 is an optimal N dosage for shoot biomass production and Zn accumulation in shoots, while the 1.0 mmol N L−1 is an optimal N dosage for Cd accumulation in shoots, which was 68.1% higher than the control. The N doses of 1 to 2.5 mmol L−1 N represented optimal conditions for Zn and Cd accumulation in the shoots of S. alfredii seedlings. In the soil pot experiment, shoot dry weight decreased with increasing N fertilization rate, while composted pig manure decreased biomass production. Both Zn and Cd accumulation in the shoots of S. alfredii decreased along with the addition of higher N fertilization rate. However, the composted pig manure amendment increased the accumulation of Zn, but not Cd in the shoots. The application of N at appropriate amount enhanced the phytoremediation efficiency by S. alfredii in Zn/Cd-polluted fields, but the effectiveness of phytoextraction technology needs to be validated in the field trials.

In this study, we investigated the effect of forest types changes (from coniferous and broadleaf mixed forest (CBMF) to plantation forests of bamboo (Phyllostachys pubescens forest, MBF) and hickory (Carya cathayensis forest, CHF)) combined with intensive management on soil organic carbon (SOC) and microbial community structure, using the 13C-nuclear magnetic resonance (NMR) and phospholipid fatty acid (PLFA). The results indicated that soil organic carbon significantly decreased by 30.7 and 28.5% in MBF and CHF, respectively. The aromatic C and aromaticity also significantly decreased in MBF and CHF (P 0.05). Significant changes of the soil microbial community were found after the forest type changed from CBMF to MBF and CHF. Total soil microbial PLFAs, soil bacteria PLFAs, fungus PLFAs, actinobacteria PLFAs, arbuscular mycorrhizal fungi PLFAs and protozoan PLFAs ranked as follows: CBMF > CHF > MBF (P CHF (0.66) > CBMF (0.49) (P CHF > MBF, P

Outliers are classified as global outliers and spatial outliers. Up to now, there is little information about the outliers especially the spatial outliers and their influence on the spatial structure modeling of soil properties. A total of 537 soil samples were collected based on a 30 × 30 m grid in a permanent dairy farm in southeast Ireland. Graphic methods of histogram and box plot combined with Moran’s I were applied to detect the outliers of soil phosphorus (P). Sixteen outliers (5 global outliers and 11 spatial outliers) of soil P were found in the study area. Compared to the raw data, the data with global outliers excluded always had the larger global Moran’s I value indicating a stronger spatial autocorrelation. Clear spatial clusters (High–High and Low–Low clusters) were observed based on local Moran’s I. The High–High spatial clusters were located around the main farm yard and near the traffic route due to more intensive management by farmers. The Low–Low spatial clusters were mainly close to the river. For these areas, P fertilizer or slurry should be applied for healthy grass growth. The dataset with outliers excluded had a reliable semi-variogram model with a low nugget/sill ratio (32.4 %), which was closed to its corresponding transformed data (30.5 %). The cross-validation results revealed that the dataset without outliers had the strongest linear regression model (r = 0.768), indicating that the outliers played an important role in the spatial structure modeling.

Copper (Cu) is one of the essential elements for plant growth, while excessive Cu in soils has potential environmental risks. There is little information on spatial variation of Cu in practical paddy fields. This is now important for appropriate agricultural management. The spatial patterns of Cu, its fractions in soils, and its concentrations in rice were investigated in a typical rice production region-Wenling of southeastern China. A total of 96 pairs of rice grain and soil samples (0-15 cm) were collected. The total concentration of Cu and its fractions were very variable, with large skewness, kurtosis, and coefficient of variation (CV) values. Compared to the guideline value (50 mg kg(-1)), Cu pollution in paddy fields was observed in the study area. All the measured Cu concentrations in rice were lower than 10 mg kg(-1), suggesting that they remained at a safe level. Spatial analyses including Moran's I index and geostatistics results indicated that high-high spatial patterns for both Cu in soils and rice were found in the northwest part, which was mainly related to industrial and E-waste dismantling activities. The low-low spatial patterns of Cu in the soil-rice system were located in the south part of study area. The cross-correlogram results indicated that Cu concentration in rice was significantly spatially correlated with total Cu in soils, its fractions, and soil organic matter (SOM), but significantly negatively correlated with pH and electrical conductivity (EC). Most of the selected variables had a clear spatial correlation range with Cu in rice. The ranges of significant spatial correlation (p 0.05) could be obtained and further used for dividing agricultural management zones.

Forest ecosystems play an important role in sequestering carbon in their biomass and soils. Moso bamboo plantations, as a special forest type, are mainly distributed in southern China. There is little information about the carbon storage in moso bamboo plantations, it is now important to better understand the moso bamboo’s carbon sequestration. The main objectives of this study were to investigate the spatial variation of aboveground biomass carbon (AGC) and soil organic carbon (SOC) in moso bamboo plantations and to compare carbon storage in moso bamboo plantations under different management options. A total of 73 moso bamboo plots were selected in Anji County, which is a famous “bamboo town” in northwest Zhejiang province, China. The diameter at breast height and the age of each moso bamboo in the selected plots were measured in order to calculate the AGC. SOC was analyzed using sulfuric acid–potassium dichromate solution. One-way ANOVA was applied to analyze the significant difference of AGC and SOC under different management options. Geostatistics and geographical information were used to study the spatial dependence characteristics of AGC and SOC. The AGC values were very variable, ranging from 9.92 to 38.70 Mg ha−1, with an average of 20.85 Mg ha−1. The SOC values were from 34.8 to 176.17 Mg ha−1. Both the AGC and SOC values were followed normal distributions. Moso bamboo plantations contributed about 16.5 % of total forest biomass carbon in Zhejiang Province, indicating its important influence on regional carbon budget. Geostatistical analysis revealed that the AGC had moderate spatial autocorrelation. A nested model (a spherical model with a Gaussian model) was chosen to describe the variogram. Spatial patterns for AGC were found in Anji County, with relatively high AGC values were found in the southwestern part of Anji County, and low values were located in the eastern and central parts of the county. While no clear spatial autocorrelation trend was observed in the semivariogram of SOC, indicating a random distribution pattern for SOC in the study area. Meanwhile, the Pearson’s correlation between AGC and SOC in bamboo plantation was weak (r = 0.064, p = 0.496), due to moso bamboo’s special growth process and different management options by human beings. In this study, moderate spatial dependency was found for AGC, while the spatial autocorrelation of SOC was poor. In moso bamboo forest ecosystem, SOC was mainly stored at the top 40 cm layer. Management options were proved to be an important factor for carbon sequestration. Extensive management is an efficient way to increase carbon stock, compared to moderate and intensive management. With the rapid increase of plantation area, moso bamboo ecosystem will continue to play an important role in regional carbon budget.

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.

Although the bioavailability of heavy metals has been widely investigated, little information is available on the spatial correlations of heavy metals in soil–rice systems at a regional scale. A study of heavy metals in soil–rice systems at a present rice production area could provide valuable information on the safety of rice production and provide guidelines beneficial to agriculture management and strategic sustainable agriculture in China and other rapidly developing regions in the world. The overall goals of this study were to identify the characteristics of metal fractions and their bioavailability to rice plants in the paddy fields of a present rice production region. In the rice harvest season (October 2006), 96 pairs of rice grain and rooted soil samples were collected from rice production area of Wenling in southeast Zhejiang province, China, which is one of the well-known electronic and electric waste (E-waste) recycling centers. Soil samples were analyzed for total heavy metal concentrations, metal fraction concentrations, and soil properties. Soil properties analyzed in this study included soil pH, electrical conductivity, organic matter, Fe oxides, and soil particle size distribution. Rice grain samples were analyzed for heavy metal concentrations. Multivariate statistical and geostatistical methods were applied to study the spatial dependence characteristics of metal fractions and their spatial correlation with uptake by rice plants in the rice production area and to identify the bioavailability of metal fractions to rice plants. The paddy soils of the studied area showed Cd contamination and some paddy soils presented a potential Cu, Ni, and Zn risk. The elevated levels of Cd were predominantly in non-residual (extractable) fractions. The percentage of Cd in fractions decreased in the order of exchangeable > Fe–Mn oxide bound > residual > organic bound fraction. In contrast, soil Cu, Ni, Pb, and Zn were mainly in the residual (non-extractable) fractions. The fractions of Ni, Pb, and Zn followed the order of residual > Fe–Mn oxide bound > organic bound > exchangeable fraction; the fractions of Cu decreased in the order of residual > organic bound > Fe–Mn oxide bound fraction. The spatial distribution patterns of non-residual fractions exhibited similarities with the highest metal concentrations in the northwest area owing to the industries and E-waste recycling activities. Most metals in rice grain were the strongest spatially correlated with the exchangeable fraction, followed by the organic bound fraction, indicating that exchangeable and organic bound fractions had the highest bioavailability. Rice Cd and Zn were strong spatially correlated with exchangeable, Fe–Mn oxide, and organic bound fractions; rice Ni and Cu were strongly spatially correlated with the exchangeable and organic bound fractions, respectively. The principal component analysis results also confirmed that exchangeable, Fe–Mn oxide, and organic bound soil fractions can be considered as bioavailable fractions to rice for Cd and Zn, while exchangeable and organic fractions were more important sinks for Ni and Cu, respectively. Due to a comparatively high input of Cd in the paddy soils, soil Cd was predominantly associated with non-residual fractions, especially with the exchangeable fraction. The soil Cu, Ni, Pb, and Zn were largely associated with the residual fraction while little associated with the exchangeable fraction. The bioavailability of the fractions to rice varied with metal fractions. In general, the exchangeable fraction had the highest bioavailability to rice plants, followed by the organic bound fraction. The bioavailability of the fractions to rice also varied with heavy metals. The exchangeable, Fe–Mn oxide, and organic bound fractions had high bioavailability to rice for Cd and Zn; the exchangeable and organic bound fractions had highest bioavailability for Ni and Cu, respectively.

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.

The present study examines the spatial dependency of soil organic matter and nutrients in paddy fields at three different scales using geostatistics and geographic information system techniques (GIS). The spatial variability of soil organic matter (SOM), total nitrogen (TN) and available phosphorus (AP) has been characterized using a total of 460, 131 and 64 samples that were, respectively, collected from the Hangzhou–Jiaxing–Huzhou (HJH) Plain (10 km), Pinghu county (1,000 m) and a test plot area (100 m) within the Pinghu county, Zhejiang province of the southeast China. Semivariograms showed that the SOM and TN had moderate spatial dependency on the large scale of HJH plain and moderate scale of Pinghu county with long spatial correlation distances. At the moderate scale of Pinghu county and the small scale of a test plot area, the AP data did not show any spatial correlation, but had moderate spatial dependency in HJH plain. Spherical and exponential variogram models were best fitted to all these soil properties. Maps of SOM and TN were generated through interpolation of measured values by ordinary kriging, and AP by lognormal kriging. This study suggests that precision management of SOM and TN is feasible at all scales, and precision management of AP is feasible at large scales.