Multi-scale spatial structure of heavy metals in agricultural soils in Beijing

To effectively investigate the spatial variability of heavy metals in soil, produce a higher quality spatial distribution map, and identify the potential pollution sources of heavy metals, geostatistics was employed to evaluate the effect of scale on spatial variability of heavy metals in Beijing agricultural soils. The results revealed that spatial variability of Cr, Ni, Zn, and Hg was dependent on scale. Validation of the optimality of theoretical semivariance and comparative analysis of the estimation accuracy demonstrated that the multi-scale nested model can reveal the spatial structure of heavy metals effectively and improve the estimation accuracy better than the single-scale method, thereby enabling production a higher quality spatial interpolation map. Thus, the multi-scale kriging nested model is a useful tool for revealing spatial variability of heavy metals in soils, while the spatial distribution maps allow the identification of hot spots with high concentrations of heavy metals.     

Diversity of epiphytic lichens and metal contents of Parmelia caperata thalli as monitors of air pollution in the town of Pistoia (c Italy)

The results of a biomonitoring survey carried out in the town of Pistoia (central Italy) using the biodiversity of epiphytic lichens and the accumulation of heavy metals in thalli of Parmelia caperata as indicators of air pollution are reported. Compared to previous surveys, the overall situation generally improved, with higher lichen diversity at most stations and lower metal concentrations in P. caperata thalli. However, the general picture according to a calibrated scale of environmental naturality/alteration was substantially negative, with about 87% of the study area classified as `altered' (including the lichen desert) or `semi-altered'. To explain this apparent contradiction, it has been suggested that lichen colonization is determined by declining SO₂ concentrations, while major injuries to lichen communities are caused by the constantly high levels of NO_X. In spite of the low levels of Pb measured in P. caperata thalli, vehicular traffic was excluded as the main source of atmospheric pollution. Domestic heating seems to be the main cause of changes in the diversity of epiphytic lichens in the study area.     

A necessary distinction between spatial representativeness of an air quality monitoring station and the delimitation of exceedance areas

The European legislation on ambient air quality introduces the concepts of spatial representativeness of a monitoring station and spatial extent of an exceedance zone. Spatial representativeness is an essential macro-scale siting criterion which should be evaluated before the setting-up and during the life of a monitoring point. As for the exceedance area, it has to be defined each time an environmental objective is exceeded in an assessment zone. No specific approach is prescribed to delimit such areas. A probabilistic methodology is presented, based on a preliminary kriging estimation of atmospheric concentrations at each point of the domain. It is applied to NO₂ pollution on the urban scale. In the proposed approach, a point belongs to the area of representativeness of a station if its concentration differs from the station measurement by less than a given threshold. To take the estimation uncertainty into account, the standard deviation of the kriging error is used in a probabilistic framework. The choice of the criteria used to deal with overlapping areas is first tested on NO₂ annual mean concentration maps of France, built by combining surface monitoring observations and outputs from the CHIMERE chemistry transport model. At the local scale, data from passive sampling surveys and high -resolution auxiliary variables are used to provide a more precise estimation of the background pollution in different French cities. The traffic-related pollution can also be accounted for in the map by additional predictors such as distance to the road, and traffic-related NO_x emissions. Similarly, the proposed approach is implemented to identify the points, at a given statistical risk, where the NO₂ concentration is above the annual limit value.     

Staggered nested designs to assess scales of variability: the advantages of a spatially explicit analysis

A staggered nested sampling design was used to identify spatial scales of variation in the abundance of an intertidal clam Austrovenus stutchburyi. A georeferenced sampling design permitted assessment of abundance at spatial lags between 0.1 and 87 m. An analysis of variance approach produced imprecise estimates of variability, whereas spatially explicit analyses improved the resolution greatly. A geostatistical model identified the spatial scale of residual variance as 13 m and that of the asymptote of spatial dependence as 17 m. It also permitted mapping of bivalve abundance. Staggered nested designs are highly efficient for comparing hierarchies of scale, but in this study analysis of detailed positional information was required to tease out useful spatial information.     

Can Basin Land Use Effects on Physical Characteristics of Streams Be Determined at Broad Geographic Scales?

The environmental setting (e.g., climate, topography, geology) and land use affect stream physical characteristics singly and cumulatively. At broad geographic scales, we determined the importance of environmental setting and land use in explaining variation in stream physical characteristics. We hypothesized that as the spatial scale decreased from national to regional, land use would explain more of the variation in stream physical characteristics because environmental settings become more homogeneous. At a national scale, stepwise linear regression indicated that environmental setting was more important in explaining variability in stream physical characteristics. Although statistically discernible, the amount of variation explained by land use was not remarkable due to low partial correlations. At level II ecoregion spatial scales (southeastern USA plains, central USA plains, and a combination of the western Cordillera and the western interior basins and ranges), environmental setting variables were again more important predictors of stream physical characteristics, however, as the spatial scale decreased from national to regional, the portion of variability in stream physical characteristics explained by basin land use increased. Development of stream habitat indicators of land use will depend upon an understanding of relations between stream physical characteristics and environmental factors at multiple spatial scales. Smaller spatial scales will be necessary to reduce the confounding effects of variable environmental settings before the effects of land use can be reliably assessed.     

Multiscale Spatial and Small-Scale Temporal Variation in the Composition of Riverine Fish Communities

We studied the multiscale (sites, river reaches and rivers) and short-term temporal (monthly) variability in a freshwater fish assemblage. We found that small-scale spatial variation and short-term temporal variability significantly influenced fish community structure in the Macquarie and Namoi Rivers. However, larger scale spatial differences between rivers were the largest source of variation in the data. The interaction between temporal change and spatial variation in fish community structure, whilst statistically significant, was smaller than the variation between rivers. This suggests that although the fish communities within each river changed between sampling occasions, the underlying differences between rivers were maintained. In contrast, the strongest interaction between temporal and spatial effects occurred at the smallest spatial scale, at the level of individual sites. This means whilst the composition of the fish assemblage at a given site may fluctuate, the magnitude of these changes is unlikely to affect larger scale differences between reaches within rivers or between rivers. These results suggest that sampling at any time within a single season will be sufficient to show spatial differences that occur over large spatial scales, such as comparisons between rivers or between biogeographical regions.     

Determining the size of a complete disturbance landscape: multi-scale,continental analysis of forest change

The scale of investigation for disturbance-influenced processes plays a critical role in theoretical assumptions about stability, variance, and equilibrium, as well as conservation reserve and long-term monitoring program design. Critical consideration of scale is required for robust planning designs, especially when anticipating future disturbances whose exact locations are unknown. This research quantified disturbance proportion and pattern (as contagion) at multiple scales across North America. This pattern of scale-associated variability can guide selection of study and management extents, for example, to minimize variance (measured as standard deviation) between any landscapes within an ecoregion. We identified the proportion and pattern of forest disturbance (30 m grain size) across multiple landscape extents up to 180 km². We explored the variance in proportion of disturbed area and the pattern of that disturbance between landscapes (within an ecoregion) as a function of the landscape extent. In many ecoregions, variance between landscapes within an ecoregion was minimal at broad landscape extents (low standard deviation). Gap-dominated regions showed the least variance, while fire-dominated showed the largest. Intensively managed ecoregions displayed unique patterns. A majority of the ecoregions showed low variance between landscapes at some scale, indicating an appropriate extent for incorporating natural regimes and unknown future disturbances was identified. The quantification of the scales of disturbance at the ecoregion level provides guidance for individuals interested in anticipating future disturbances which will occur in unknown spatial locations. Information on the extents required to incorporate disturbance patterns into planning is crucial for that process.     

The Spatial-Scale Effect of an Atmospheric Environmental Impact Assessment in Regional Strategic Environmental Assessment (R-SEA)

When evaluating the atmospheric environment in regional strategic environment assessment (R-SEA), the variation and choice of the spatial scale have a substantial influence on the conclusions of the assessment. In this study, we used numerical simulation to investigate the spatial-scale effect. Two varying spatial extents and two varying spatial details of pollutant emission data (emission inventories in this case) were provided for numerical modeling, and output distributions of atmospheric pollutants at different air pollution levels were compared. The results show that the resolution and spatial range of data collection do indeed influence the atmospheric prediction and assessment results in R-SEA. The spatial-scale effect is more significant under the air pollution condition than under excellent and good air quality conditions. A comparison of varying spatial extents of emission inventory shows that narrowing the prediction area to a local scale is more conducive to identifying the impact of local pollution sources. A comparison of varying spatial details of emission inventory indicates that a higher resolution is favorable for identifying local high concentrations of pollutants and their locations.     

Sampling Scale Effects in Random Fields and Implications for Environmental Monitoring

The concept of a sampling scale triplet of spacing, extent and support is used to define the spatial dimensions of a monitoring network or a field study. The spacing is the average distance between samples, the extent is the size of the domain sampled and the support is the averaging area of one sample. The aim of this paper is to examine what is the bias and the random error (uncertainty) introduced by the sampling scale triplet into estimates of the mean, the spatial variance and the integral scale of a variable in a landscape. The integral scale is a measure of the average distance over which a variable is correlated in space. A large number of two dimensional random fields are generated from which hypothetical samples, conforming to a certain sampling scale triplet, are drawn which in turn are used to estimate the sample mean, spatial variance and integral scale. The results indicate that the biases can be up to two orders of magnitude. The bias of the integral scale is positively related to the magnitude of any of the components of the scale triplet while the bias of the spatial variance is different for different components of the scale triplet. All sampling scale effects are relative to the underlying correlation length of the variable of interest which is closely related to the integral scale. The integral scale can hence be used for sampling design and data interpretation. Suggestions are given on how to adjust a monitoring network to the scales of the variables of interest and how to interpret sampling scale effects in environmental data.     

Identifying the origin of atmospheric inputs of trace elements in the Prades Mountains (Catalonia) with bryophytes, lichens, and soil monitoring

The biomonitors Hypnum cupressiforme and Xanthoria parietina were used to assess the deposition of trace elements and their possible origin in the Prades Mountains, a protected Mediterranean forest area of NE Spain with several pollution sources nearby. Al, As, Cd, Co, Cu, Cr, Ni, Pb, Sb, Ti, V, and Zn were determined in 16 locations within this protected area. Soil trace element concentrations were also ascertained to calculate enrichment factors (EF) and use them to distinguish airborne from soilborne trace element inputs. In addition, lichen richness was measured to further assess atmospheric pollution. EF demonstrated to be useful not only for the moss but also for the lichen. Cd, Cr, Cu, Ni, and Zn presented values higher than three in both biomonitors. These trace elements were also the main ones emitted by the potential sources of pollutants. The distance between sampling locations and potential pollution sources was correlated with the concentrations of Cu, Sb, and Zn in the moss and with Cr, Ni, and Sb in the lichen. Lichen richness was negatively correlated with lichen Cu, Pb, and V concentrations on dry weight basis. The study reflected the remarkable influence that the pollution sources have on the presence of trace elements and on lichen species community composition in this natural area. The study highlights the value of combining the use of biomonitors, enrichment factors, and lichen diversity for pollution assessment to reach a better overview of both trace elements’ impact and the localization of their sources.     

Predicting coral bleaching in response to environmental stressors using 8 years of global-scale data

Coral reefs have experienced extensive mortality over the past few decades as a result of temperature-induced mass bleaching events. There is an increasing realization that other environmental factors, including water mixing, solar radiation, water depth, and water clarity, interact with temperature to either exacerbate bleaching or protect coral from mass bleaching. The relative contribution of these factors to variability in mass bleaching at a global scale has not been quantified, but can provide insights when making large-scale predictions of mass bleaching events. Using data from 708 bleaching surveys across the globe, a framework was developed to predict the probability of moderate or severe bleaching as a function of key environmental variables derived from global-scale remote-sensing data. The ability of models to explain spatial and temporal variability in mass bleaching events was quantified. Results indicated approximately 20% improved accuracy of predictions of bleaching when solar radiation and water mixing, in addition to elevated temperature, were incorporated into models, but predictive accuracy was variable among regions. Results provide insights into the effects of environmental parameters on bleaching at a global scale.     

Should foliar cadmium concentrations be expressed on a dry weight or dry ash weight basis?

Foliar analysis is a valuable tool for evaluating the pollution status of forests. However, the use of foliar diagnosis in large-scale surveys is a complicated process owing to the high variability within the crown. The method used to express foliar concentrations has often been found to diminish the variability. The effect of the method used to express element concentrations on the spatial variability of cadmium (Cd) in the leaves of crack willow (Salix fragilis L.) was investigated by sampling the leaves of one willow at 292 locations in the crown, each sampling location having a volume of 0.027 m3 (0.3 m x 0.3 m x 0.3 m). Cadmium showed a distinct spatial trend in the crown of the tree. Concentrations as low as 2.4 mg kg(-1) dry weight (DW) or 23.1 mg kg(-1) dry ash weight (DAW) were obtained in the top of the crown, and 10.6 mg kg(-1) DW or 73.0 mg kg(-1) DAW in the bottom of the crown. The lower relative standard deviation and weaker correlation with the sampling height support the use of DAW in large-scale surveys especially. The lower variability of the DAW Cd concentration makes this variable less sensitive to fluctuations caused by differences in growing conditions and sampling methodology. However, the majority of publications in this field report metal concentrations on a DW basis. Therefore, the restrictions set on the use of results expressed on a DAW basis in large-scale surveys of foliar metal concentrations have to be offset against the advantages offered by a reduction of the variability in metal concentrations.     

Spatial variability of soil salinity in coastal saline soil at different scales in the Yellow River Delta,China

The objectives of this study were to explore the spatial variability of soil salinity in coastal saline soil at macro, meso and micro scales in the Yellow River delta, China. Soil electrical conductivities (ECs) were measured at 0–15, 15–30, 30–45 and 45–60 cm soil depths at 49 sampling sites during November 9 to 11, 2013. Soil salinity was converted from soil ECs based on laboratory analyses. Our results indicated that at the macro scale, soil salinity was high with strong variability in each soil layer, and the content increased and the variability weakened with increasing soil depth. From east to west in the region, the farther away from the sea, the lower the soil salinity was. The degrees of soil salinization in three deeper soil layers are 1.14, 1.24 and 1.40 times higher than that in the surface soil. At the meso scale, the sequence of soil salinity in different topographies, soil texture and vegetation decreased, respectively, as follows: depression >flatland >hillock >batture; sandy loam >light loam >medium loam >heavy loam >clay; bare land >suaeda salsa >reed >cogongrass >cotton >paddy >winter wheat. At the micro scale, soil salinity changed with elevation in natural micro-topography and with anthropogenic activities in cultivated land. As the study area narrowed down to different scales, the spatial variability of soil salinity weakened gradually in cultivated land and salt wasteland except the bare land.     

Seasonal Variation of Toxic Benzene Emissions in Petroleum Refinery

Petroleum refineries are largest chemical industries that are responsible for the emission of several pollutants into the atmosphere. Benzene is among the most important air pollutants that are emitted by petroleum refineries, since they are involved in almost every refinery process. Volatile organic compounds (VOCs) are a major group of air pollutants, which play a critical role in atmospheric chemistry. These contribute to toxic oxidants, which are harmful to ecosystem, human health and atmosphere. The variability of pollutants is an important factor in determining human exposure to these chemicals. The ambient air concentrations of benzene were measured in several sites around the Digboi petroleum refinery, near the city of Gowahati in northeast India, during winter and summer 2004. The seasonal and spatial variations of the ambient air concentrations of this benzene were investigated and analyzed. An estimation of the contribution of the refinery to the measured atmospheric levels of benzene was also performed. The ambient air mixing ratios of benzene in a large area outside the refinery was generally low, in ppbv range, much lower than the ambient air quality standards. This article presents the temporal and spatial variation of air pollution in and around petroleum refinery and showed that no health risk due to benzene is present in the areas adjacent to the refinery.     

Assessing Spatial, Temporal, and Analytical Variation of Groundwater Chemistry in a Large Nuclear Complex, USA

Statistical analyses were applied at the Hanford Site, USA, to assess groundwater contamination problems that included (1) determining local backgrounds to ascertain whether a facility is affecting the groundwater quality and (2) determining a ‘pre-Hanford' groundwater background to allow formulation of background-based cleanup standards. The primary purpose of this paper is to extend the random effects models for (1) assessing the spatial, temporal, and analytical variability of groundwater background measurements; (2) demonstrating that the usual variance estimate s ², which ignores the variance components, is a biased estimator; (3) providing formulas for calculating the amount of bias; and (4) recommending monitoring strategies to reduce the uncertainty in estimating the average background concentrations. A case study is provided. Results indicate that (1) without considering spatial and temporal variability, there is a high probability of false positives, resulting in unnecessary remediation and/or monitoring expenses; (2) the most effective way to reduce the uncertainty in estimating the average background, and enhance the power of the statistical tests in general, is to increase the number of background wells; and (3) background for a specific constituent should be considered as a statistical distribution, not as a single value or threshold. The methods and the related analysis of variance tables discussed in this paper can be used as diagnostic tools in documenting the extent of inherent spatial and/or temporal variation and to help select an appropriate statistical method for testing purposes.     

Application of the probability-based Maryland Biological Stream Survey to the state’s assessment of water quality standards

The Clean Water Act presents a daunting task for states by requiring them to assess and restore all their waters. Traditional monitoring has led to two beliefs: (1) ad hoc sampling (i.e., non-random) is adequate if enough sites are sampled and (2) more intensive sampling (e.g., collecting more organisms) at each site is always better. We analyzed the 1,500 Maryland Biological Stream Survey (MBSS) random sites sampled in 2000–2004 to describe the variability of Index of Biotic Integrity (IBI) scores at the site, reach, and watershed scales. Average variability for fish and benthic IBI scores increased with increasing spatial scale, demonstrating that single site IBI scores are not representative at watershed scales and therefore at best 25% of a state’s stream length can be representatively sampled with non-random designs. We evaluated the effects on total taxa captured and IBI precision of sampling for twice as many benthic macroinvertebrates at 73 MBSS sites with replicate samples. When sampling costs were fixed, the precision of the IBI decreased as the number of sites had to be reduced by 15%. Only 1% more taxa were found overall when the 73 sites where combined. We concluded that (1) comprehensive assessment of a state’s waters should be done using probability-based sampling that allows the condition across all reaches to be inferred statistically and (2) additional site sampling effort should not be incorporated into state biomonitoring when it will reduce the number of sites sampled to the point where overall assessment precision is lower.     

佳木斯大气污染物扩散影响因素及区域探究

对影响佳木斯市大气污染物扩散的因素进行了分析。结果表明,大气稳定度、垂直和水平温度梯度、低空风场引起的平流动力输送、地面粗糙度、湍流运动、雨雾等都不同程度地影响大气污染物的扩散,以一定的传输和扩散规律决定城市大气污染水平的高低,并由此使局部区域污染程度各不相同。城市热岛效应和温度层结是影响大气污染物扩散的重要因素。影响区域主要为城市主导风向下风向的城市东部地区。     

Lichen biomonitoring of trace metals in the Pistoia area (central northern Italy)

The epiphytic lichen Parmelia caperata (L.) Ach. was used as bioaccumulator of the heavy metals Cd, Cr, Cu, Hg, Ni, Pb and Zn in the environs of the town of Pistoia (central northern Italy). The concentrations of Cd, Cr, Ni, Hg and Pb were comparable with those found in areas not subject to atmospheric pollution. Copper and especially Zn were found in rather high concentrations. Fertilizers and pesticides were the main source of atmospheric contamination.     

Soil Contribution to the Elemental Composition of Epiphytic Lichens (Tuscany, Central Italy)

Total concentrations of Al, Ba, Cd, Co, Cr, Cu, Fe, Mn, Mo, Pb, Sr, Ti, V and Zn in the epiphytic lichen Parmelia sulcata and superficial soils from 60 remote sampling sites in Tuscany (central Italy) were determined to evaluate the contribution of soil to the elemental composition of the lichen. The results showed that in the Mediterranean environment, the trace element content of unwashed lichen samples is greatly affected by soil contamination. However, despite the strong correlations between the concentrations of lithogene elements such as Al, Fe and Ti in P. sulcata, lichen levels of these elements were not at all linearly correlated with their concentrations in the soil, suggesting that dust contamination is highly variable and probably dependent on local site characteristics. All methods evaluated to minimize soil contamination indicated Cu, Pb and Zn as elements of atmospheric origin. However, while levels of Pb were similar to those reported for background areas, moderate pollution by Cu and Zn, probably from fertilizers used in agriculture, was revealed. For elements such as Cd and Mo, identified as atmophile, some uncertainty exists due to the fact that they are essential for lichen metabolism and accumulate intracellularly in lichens; they may therefore occur in soluble form in the lichen thallus.     

Spatial variability of salinity and alkalinity of a field having salination risk in semi-arid climate in northern Turkey

Spatial variability of salinity and alkalinity is important for site-specific management since they are the most important factors influencing soil quality and agricultural production. The objectives of this study were to analyze spatial variability in salinity and alkalinity and some soil properties affecting salinity and alkalinity, using classical statistics and geostatistical methods, in an irrigated field with low-quality irrigation water diverted from drainage canals. A field of 5 da was divided into 10 m x 10 m grids (5 lines in the east-west direction and 10 lines in the north-south direction). The soil samples were collected from three depths (0-30, 30-60 and 60-90 cm) at each grid corner. The variation coefficients of OM and sand contents were higher than other soil properties. OM had the maximum variability, with a mean of 1.63% at 0-30 cm depth and 0.71% at 30-60 cm depth. Significant correlations occurred between ESP, EC and each of Ca, Mg, K and CaCO(3) contents of the soils (p<0.01). Experimental semivariograms were fitted to spherical and gaussian models. All geostatistical range values were greater than 36 m. The soil properties had spatial variability at small distances at 60-90 cm depth. EC was variable within short distances at 30-60 cm depth. The nugget effect of ESP increased with soil depth. Kriged contour maps revealed that soils had a salinisation and alkalisation tendency at 60-90 cm depth based on spatial variance structure of the EC and ESP values. Spatial variability in EC and ESP can depend on ground water level, quality of irrigation water, and textural differences.